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Sheik AG, Krishna SBN, Patnaik R, Ambati SR, Bux F, Kumari S. Digitalization of phosphorous removal process in biological wastewater treatment systems: Challenges, and way forward. ENVIRONMENTAL RESEARCH 2024; 252:119133. [PMID: 38735379 DOI: 10.1016/j.envres.2024.119133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 03/22/2024] [Accepted: 05/10/2024] [Indexed: 05/14/2024]
Abstract
Phosphorus in wastewater poses a significant environmental threat, leading to water pollution and eutrophication. However, it plays a crucial role in the water-energy-resource recovery-environment (WERE) nexus. Recovering Phosphorus from wastewater can close the phosphorus loop, supporting circular economy principles by reusing it as fertilizer or in industrial applications. Despite the recognized importance of phosphorus recovery, there is a lack of analysis of the cyber-physical framework concerning the WERE nexus. Advanced methods like automatic control, optimal process technologies, artificial intelligence (AI), and life cycle assessment (LCA) have emerged to enhance wastewater treatment plants (WWTPs) operations focusing on improving effluent quality, energy efficiency, resource recovery, and reducing greenhouse gas (GHG) emissions. Providing insights into implementing modeling and simulation platforms, control, and optimization systems for Phosphorus recovery in WERE (P-WERE) in WWTPs is extremely important in WWTPs. This review highlights the valuable applications of AI algorithms, such as machine learning, deep learning, and explainable AI, for predicting phosphorus (P) dynamics in WWTPs. It emphasizes the importance of using AI to analyze microbial communities and optimize WWTPs for different various objectives. Additionally, it discusses the benefits of integrating mechanistic and data-driven models into plant-wide frameworks, which can enhance GHG simulation and enable simultaneous nitrogen (N) and Phosphorus (P) removal. The review underscores the significance of prioritizing recovery actions to redirect Phosphorus from effluent to reusable products for future considerations.
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Affiliation(s)
- Abdul Gaffar Sheik
- Institute for Water and Wastewater Technology, Durban University of Technology, Durban, 4001, South Africa.
| | - Suresh Babu Naidu Krishna
- Institute for Water and Wastewater Technology, Durban University of Technology, Durban, 4001, South Africa
| | - Reeza Patnaik
- Institute for Water and Wastewater Technology, Durban University of Technology, Durban, 4001, South Africa
| | - Seshagiri Rao Ambati
- Department of Chemical Engineering, Indian Institute of Petroleum and Energy, Visakhapatnam, 530003, Andhra Pradesh, India
| | - Faizal Bux
- Institute for Water and Wastewater Technology, Durban University of Technology, Durban, 4001, South Africa
| | - Sheena Kumari
- Institute for Water and Wastewater Technology, Durban University of Technology, Durban, 4001, South Africa.
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2
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Wang R, He Z, Chen H, Guo S, Zhang S, Wang K, Wang M, Ho SH. Enhancing biomass conversion to bioenergy with machine learning: Gains and problems. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172310. [PMID: 38599406 DOI: 10.1016/j.scitotenv.2024.172310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/20/2024] [Accepted: 04/06/2024] [Indexed: 04/12/2024]
Abstract
The growing concerns about environmental sustainability and energy security, such as exhaustion of traditional fossil fuels and global carbon footprint growth have led to an increasing interest in alternative energy sources, especially bioenergy. Recently, numerous scenarios have been proposed regarding the use of bioenergy from different sources in the future energy systems. In this regard, one of the biggest challenges for scientists is managing, modeling, decision-making, and future forecasting of bioenergy systems. The development of machine learning (ML) techniques can provide new opportunities for modeling, optimizing and managing the production, consumption and environmental effects of bioenergy. However, researchers in bioenergy fields have not widely utilized the ML concepts and practices. Therefore, a comparative review of the current ML techniques used for bioenergy productions is presented in this paper. This review summarizes the common issues and difficulties existing in integrating ML with bioenergy studies, and discusses and proposes the possible solutions. Additionally, a detailed discussion of the appropriate ML application scenarios is also conducted in every sector of the entire bioenergy chain. This indicates the modernized conversion processes supported by ML techniques are imperative to accurately capture process-level subtleties, and thus improving techno-economic resilience and socio-ecological integrity of bioenergy production. All the efforts are believed to help in sustainable bioenergy production with ML technologies for the future.
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Affiliation(s)
- Rupeng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Zixiang He
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Honglin Chen
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Silin Guo
- School of Medicine and Health, Harbin Institute of Technology, Harbin 150040, PR China
| | - Shiyu Zhang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Ke Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Meng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Shih-Hsin Ho
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China.
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Kovačić Đ, Radočaj D, Jurišić M. Ensemble machine learning prediction of anaerobic co-digestion of manure and thermally pretreated harvest residues. BIORESOURCE TECHNOLOGY 2024; 402:130793. [PMID: 38703965 DOI: 10.1016/j.biortech.2024.130793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 04/30/2024] [Accepted: 05/01/2024] [Indexed: 05/06/2024]
Abstract
This study aimed to clarify the statistical accuracy assessment approaches used in recent biogas prediction studies using state-of-the-art ensemble machine learning approach according to 10-fold cross-validation in 100 repetitions. Three thermally pretreated harvest residue types (maize stover, sunflower stalk and soybean straw) and manure were anaerobically co-digested, measuring biogas and methane yield alongside eight thermal preprocessing and biomass covariates. These were the inputs to an ensemble machine learning approach for biogas and methane yield prediction, employing three feature selection approaches. The Support Vector Machine prediction with the Recursive Feature Elimination resulted in the highest prediction accuracy, achieving the coefficient of determination of 0.820 and 0.823 for biogas and methane yield prediction, respectively. This study demonstrated an extreme dependency of prediction accuracy to input dataset properties, which could only be mitigated with ensemble machine learning and strongly suggested that the split-sample approach, often used in previous studies, should be avoided.
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Affiliation(s)
- Đurđica Kovačić
- Josip Juraj Strossmayer University of Osijek, Faculty of Agrobiotechnical Sciences Osijek, Chair of Geoinformation Technology and GIS, Vladimira Preloga 1, 31000 Osijek, Croatia
| | - Dorijan Radočaj
- Josip Juraj Strossmayer University of Osijek, Faculty of Agrobiotechnical Sciences Osijek, Chair of Geoinformation Technology and GIS, Vladimira Preloga 1, 31000 Osijek, Croatia.
| | - Mladen Jurišić
- Josip Juraj Strossmayer University of Osijek, Faculty of Agrobiotechnical Sciences Osijek, Chair of Geoinformation Technology and GIS, Vladimira Preloga 1, 31000 Osijek, Croatia
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4
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An T, Feng K, Cheng P, Li R, Zhao Z, Xu X, Zhu L. Adaptive prediction for effluent quality of wastewater treatment plant: Improvement with a dual-stage attention-based LSTM network. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 359:120887. [PMID: 38678908 DOI: 10.1016/j.jenvman.2024.120887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 04/04/2024] [Accepted: 04/10/2024] [Indexed: 05/01/2024]
Abstract
The accurate effluent prediction plays a crucial role in providing early warning for abnormal effluent and achieving the adjustment of feedforward control parameters during wastewater treatment. This study applied a dual-staged attention mechanism based on long short-term memory network (DA-LSTM) to improve the accuracy of effluent quality prediction. The results showed that input attention (IA) and temporal attention (TA) significantly enhanced the prediction performance of LSTM. Specially, IA could adaptively adjust feature weights to enhance the robustness against input noise, with R2 increased by 13.18%. To promote its long-term memory ability, TA was used to increase the memory span from 96 h to 168 h. Compared to a single LSTM model, the DA-LSTM model showed an improvement in prediction accuracy by 5.10%, 2.11%, 14.47% for COD, TP, and TN. Additionally, DA-LSTM demonstrated excellent generalization performance in new scenarios, with the R2 values for COD, TP, and TN increasing by 22.67%, 20.06%, and 17.14% respectively, while the MAPE values decreased by 56.46%, 63.08%, and 42.79%. In conclusion, the DA-LSTM model demonstrated excellent prediction performance and generalization ability due to its advantages of feature-adaptive weighting and long-term memory focusing. This has forward-looking significance for achieving efficient early warning of abnormal operating conditions and timely management of control parameters.
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Affiliation(s)
- Tong An
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Kuanliang Feng
- Zhejiang Supcon Information Technology Co., Ltd, Hangzhou, 310052, China
| | - Peijin Cheng
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Ruojia Li
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zihao Zhao
- Shanghai Municipal Engineering Design Institute (group) Co., Ltd, Shanghai, 200092, China
| | - Xiangyang Xu
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Zhejiang Provincial Engineering Laboratory of Water Pollution Control, Hangzhou, 310058, China
| | - Liang Zhu
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Innovation Center of Yangtze River Delta, Zhejiang University, Jiashan, 314100, China; Zhejiang Provincial Engineering Laboratory of Water Pollution Control, Hangzhou, 310058, China.
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Ganeshan P, Bose A, Lee J, Barathi S, Rajendran K. Machine learning for high solid anaerobic digestion: Performance prediction and optimization. BIORESOURCE TECHNOLOGY 2024; 400:130665. [PMID: 38582235 DOI: 10.1016/j.biortech.2024.130665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 04/02/2024] [Accepted: 04/04/2024] [Indexed: 04/08/2024]
Abstract
Biogas production through anaerobic digestion (AD) is one of the complex non-linear biological processes, wherein understanding its dynamics plays a crucial role towards process control and optimization. In this work, a machine learning based biogas predictive model was developed for high solid systems using algorithms, including SVM, ET, DT, GPR, and KNN and two different datasets (Dataset-1:10, Dataset-2:5 inputs). Support Vector Machine had the highest accuracy (R2) of all the algorithms at 91 % (Dataset-1) and 87 % (Dataset-2), respectively. The statistical analysis showed that there was no significant difference (p = 0.377) across the datasets, wherein with less inputs, accurate results could be predicted. In case of biogas yield, the critical factors which affect the model predictions include loading rate and retention time. The developed high solid machine learning model shows the possibility of integrating Artificial Intelligence to optimize and control AD process, thus contributing to a generic model for enhancing the overall performance of the biogas plant.
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Affiliation(s)
- Prabakaran Ganeshan
- Department of Environmental Science and Engineering, School of Engineering and Sciences, SRM University-AP, Amaravati, Andhra Pradesh 522240, India
| | - Archishman Bose
- Process and Chemical Engineering, School of Engineering and Architecture, University College Cork, Cork, Ireland; Environmental Research Institute, MaREI Centre, University College Cork, Cork, Ireland
| | - Jintae Lee
- School of Chemical Engineering, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Republic of Korea
| | - Selvaraj Barathi
- School of Chemical Engineering, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Republic of Korea.
| | - Karthik Rajendran
- Department of Environmental Science and Engineering, School of Engineering and Sciences, SRM University-AP, Amaravati, Andhra Pradesh 522240, India.
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Hmaissia A, Bareha Y, Vaneeckhaute C. Correlations and impact of anaerobic digestion operating parameters on the start-up duration: Database construction for robust start-up guidelines. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 359:121068. [PMID: 38728989 DOI: 10.1016/j.jenvman.2024.121068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/17/2024] [Accepted: 04/29/2024] [Indexed: 05/12/2024]
Abstract
Anaerobic digestion (AD) has become a popular technique for organic waste management while offering economic and environmental advantages. As AD becomes increasingly prevalent worldwide, research efforts are primarily focused on optimizing its processes. During the operation of AD systems, the occurrence of unstable events is inevitable. So far, numerous conclusions have been drawn from full and lab-scale studies regarding the driving factors of start-up perturbations. However, the lack of standardized practices reported in start-up studies raises concerns about the comparability and reliability of obtained data. This study aims to develop a knowledge database and investigate the possibility of applying machine learning techniques on experimentation-extracted data to assist start-up planning and monitoring. Thus, a standardized database referencing 75 cases of start-up of one-stage wet continuously-stirred tank reactors (CSTR) processing agricultural, industrial, or municipal organic effluent in mono-digestion from 31 studies was constructed. 10 % of the total observations included in this database concern failed start-up experiments. Then, correlations between the parameters and their impacts on the start-up duration were studied using multivariate analysis and a model-based ranking methodology. Insights into trends of choices were highlighted through the correlation analysis of the database. As such, scenarios favoring short start-up duration were found to involve relatively low retention times (average initial and final hydraulic retention times, (HRTi) and (HRTf) of 26.25 and 20.6 days, respectively), high mean organic loading rates (average OLRmean of 5.24 g VS·d-1·L -1) and the processing of highly fermentable substrates (average feed volatile solids (VSfeed) of 81.35 g L-1). The model-based ranking of AD parameters demonstrated that the HRTf, the VSfeed, and the target temperature (Tf) have the strongest impact on the start-up duration, receiving the highest relative scores among the evaluated AD parameters. The database could serve as a reference for comparison purposes of future start-up studies allowing the identification of factors that should be closely controlled.
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Affiliation(s)
- Amal Hmaissia
- BioEngine Research Team on Green Process Engineering and Biorefineries, Chemical Engineering Department, Université Laval, Pavillon Adrien-Pouliot 1065, av. de la Médecine, Québec, QC, Canada; CentrEau, Centre de Recherche sur l'eau, Université Laval, 1065 Avenue de la Médecine, Québec, QC, G1V 0A6, Canada.
| | - Younes Bareha
- BioEngine Research Team on Green Process Engineering and Biorefineries, Chemical Engineering Department, Université Laval, Pavillon Adrien-Pouliot 1065, av. de la Médecine, Québec, QC, Canada; CentrEau, Centre de Recherche sur l'eau, Université Laval, 1065 Avenue de la Médecine, Québec, QC, G1V 0A6, Canada.
| | - Céline Vaneeckhaute
- BioEngine Research Team on Green Process Engineering and Biorefineries, Chemical Engineering Department, Université Laval, Pavillon Adrien-Pouliot 1065, av. de la Médecine, Québec, QC, Canada; CentrEau, Centre de Recherche sur l'eau, Université Laval, 1065 Avenue de la Médecine, Québec, QC, G1V 0A6, Canada.
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7
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Su H, Zhu T, Lv J, Wang H, Zhao J, Xu J. Leveraging machine learning for prediction of antibiotic resistance genes post thermal hydrolysis-anaerobic digestion in dairy waste. BIORESOURCE TECHNOLOGY 2024; 399:130536. [PMID: 38452951 DOI: 10.1016/j.biortech.2024.130536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 02/17/2024] [Accepted: 03/04/2024] [Indexed: 03/09/2024]
Abstract
Anaerobic digestion holds promise as a method for removing antibiotic resistance genes (ARGs) from dairy waste. However, accurately predicting the efficiency of ARG removal remains a challenge. This study introduces a novel appproach utilizing machine learning to forecast changes in ARG abundances following thermal hydrolysis-anaerobic digestion (TH-AD) treatment. Through network analysis and redundancy analyses, key determinants of affect ARG fluctuations were identified, facilitating the development of machine learning models capable of accurately predicting ARG changes during TH-AD processes. The decision tree model demonstrated impressive predictive power, achieving an impessive R2 value of 87% against validation data. Feature analysis revealed that the genes intI2 and intI1 had a critical impact on the absolute abundance of ARGs. The predictive model developed in this study offers valuable insights for improving operational and managerial practices in dairy waste treatment facilities, with the ultimate goal of mitigating the spread of antibiotic resistance.
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Affiliation(s)
- Haiyan Su
- School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
| | - Tianjiao Zhu
- School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
| | - Jiaqiang Lv
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Shenzhen, Shenzhen, 518055, China
| | - Hongcheng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Shenzhen, Shenzhen, 518055, China
| | - Ji Zhao
- School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China; Inner Mongolia Key Laboratory of Environmental Pollution Prevention and Waste Resource Recycle, Inner Mongolia University, Hohhot 010021, China
| | - Jifei Xu
- School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China; Inner Mongolia Key Laboratory of Environmental Pollution Prevention and Waste Resource Recycle, Inner Mongolia University, Hohhot 010021, China.
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8
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Zhao B, Yu Z, Wang H, Shuai C, Qu S, Xu M. Data Science Applications in Circular Economy: Trends, Status, and Future. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:6457-6474. [PMID: 38568682 DOI: 10.1021/acs.est.3c08331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
The circular economy (CE) aims to decouple the growth of the economy from the consumption of finite resources through strategies, such as eliminating waste, circulating materials in use, and regenerating natural systems. Due to the rapid development of data science (DS), promising progress has been made in the transition toward CE in the past decade. DS offers various methods to achieve accurate predictions, accelerate product sustainable design, prolong asset life, optimize the infrastructure needed to circulate materials, and provide evidence-based insights. Despite the exciting scientific advances in this field, there still lacks a comprehensive review on this topic to summarize past achievements, synthesize knowledge gained, and navigate future research directions. In this paper, we try to summarize how DS accelerated the transition to CE. We conducted a critical review of where and how DS has helped the CE transition with a focus on four areas including (1) characterizing socioeconomic metabolism, (2) reducing unnecessary waste generation by enhancing material efficiency and optimizing product design, (3) extending product lifetime through repair, and (4) facilitating waste reuse and recycling. We also introduced the limitations and challenges in the current applications and discussed opportunities to provide a clear roadmap for future research in this field.
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Affiliation(s)
- Bu Zhao
- School for Environment and Sustainability, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Zongqi Yu
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Hongze Wang
- School of Professional Studies, Columbia University, New York, New York 10027, United States
| | - Chenyang Shuai
- School of Management Science and Real Estate, Chongqing University, Chongqing, 40004, China
| | - Shen Qu
- School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China
- Center for Energy & Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, China
| | - Ming Xu
- School of Environment, Tsinghua University, Beijing, 100084, China
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Zhai S, Chen K, Yang L, Li Z, Yu T, Chen L, Zhu H. Applying machine learning to anaerobic fermentation of waste sludge using two targeted modeling strategies. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 916:170232. [PMID: 38278257 DOI: 10.1016/j.scitotenv.2024.170232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 01/13/2024] [Accepted: 01/15/2024] [Indexed: 01/28/2024]
Abstract
Anaerobic fermentation is an effective method to harvest volatile fatty acids (VFAs) from waste activated sludge (WAS). Accurately predicting and optimizing VFAs production is crucial for anaerobic fermentation engineering. In this study, we developed machine learning models using two innovative strategies to precisely predict the daily yield of VFAs in a laboratory anaerobic fermenter. Strategy-1 focuses on model interpretability to comprehend the influence of variables of interest on VFAs production, while Strategy-2 takes into account the cost of variable acquisition, making it more suitable for practical applications in prediction and optimization. The results showed that Support Vector Regression emerged as the most effective model in this study, with testing R2 values of 0.949 and 0.939 for the two strategies, respectively. We conducted feature importance analysis to identify the critical factors that influence VFAs production. Detailed explanations were provided using partial dependence plots and Shepley Additive Explanations analyses. To optimize VFAs production, we integrated the developed model with optimization algorithms, resulting in a maximum yield of 2997.282 mg/L. This value was 45.2 % higher than the average VFAs level in the operated fermenter. Our study offers valuable insights for predicting and optimizing VFAs production in sludge anaerobic fermentation, and it facilitates engineering practice in VFAs harvesting from WAS.
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Affiliation(s)
- Shixin Zhai
- Beijing Key Lab for Source Control Technology of Water Pollution, Beijing Forestry University, Beijing 100083, China
| | - Kai Chen
- Beijing Key Lab for Source Control Technology of Water Pollution, Beijing Forestry University, Beijing 100083, China
| | - Lisha Yang
- Beijing Key Lab for Source Control Technology of Water Pollution, Beijing Forestry University, Beijing 100083, China
| | - Zhuo Li
- Beijing Key Lab for Source Control Technology of Water Pollution, Beijing Forestry University, Beijing 100083, China
| | - Tong Yu
- Beijing Key Lab for Source Control Technology of Water Pollution, Beijing Forestry University, Beijing 100083, China
| | - Long Chen
- Beijing Key Lab for Source Control Technology of Water Pollution, Beijing Forestry University, Beijing 100083, China
| | - Hongtao Zhu
- Beijing Key Lab for Source Control Technology of Water Pollution, Beijing Forestry University, Beijing 100083, China.
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Wu X, Gong J, Ren S, Tan F, Wang Y, Zhao H. A machine learning-based QSAR model reveals important molecular features for understanding the potential inhibition mechanism of ionic liquids to acetylcholinesterase. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 915:169974. [PMID: 38199350 DOI: 10.1016/j.scitotenv.2024.169974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 01/02/2024] [Accepted: 01/04/2024] [Indexed: 01/12/2024]
Abstract
The broad application of ionic liquids (ILs) has been hindered by uncertainties surrounding their ecotoxicity. In this work, a Quantitative Structure-Activity Relationship (QSAR) model was devised to predict the inhibition of ILs towards the activity of AChE, employing both Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) machine learning approaches. Fourteen kings of essential molecular feature descriptors were screened from an initial roster of 244 descriptors through the application of a feature importance index and they showed a significant impact on the activity of AChE activity. The two models based solely on the 14 most critical molecular descriptors could maintain model's robustness and reliability. The correlation analysis between these 14 descriptors and the inhibition of AChE activity revealed the potential impact of the molecular characteristics on ILs toxicity. The results underscored the main influence of cations in ILs on the inhibitory activity towards the AChE enzyme. Specifically, cations exhibiting hydrophobicity properties were found to exert more potent inhibitory effects on the AChE enzyme. In addition, some other properties of the cations, such as the degree of branching, atomic weight and partial charge also modulated their inhibition potential. This study enhances the comprehension of the structure-activity relationship between ILs and AChE inhibition, providing a reference for designing safer and greener ILs.
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Affiliation(s)
- Xuri Wu
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Jixiang Gong
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Suyu Ren
- School of Environmental and Material Engineering, Yantai University, Yantai 264005, China
| | - Feng Tan
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China.
| | - Yan Wang
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Hongxia Zhao
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
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Salamattalab MM, Hasani Zonoozi M, Molavi-Arabshahi M. Innovative approach for predicting biogas production from large-scale anaerobic digester using long-short term memory (LSTM) coupled with genetic algorithm (GA). WASTE MANAGEMENT (NEW YORK, N.Y.) 2024; 175:30-41. [PMID: 38154165 DOI: 10.1016/j.wasman.2023.12.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 12/21/2023] [Accepted: 12/23/2023] [Indexed: 12/30/2023]
Abstract
An artificial neural network (ANN) model called long-short term memory (LSTM), coupled with a genetic algorithm (GA) for feature selection, was used to predict biogas production of large-scale anaerobic digesters (ADs) of Tehran South Wastewater Treatment Plant (Iran), with a biogas production of approximately 30,000 Nm3/d. In order to employ the real conditions, the hydraulic retention time (HRT) of the ADs (21 days) was considered as the LSTM look-back window. To evaluate the model predictions, three different scenarios were defined. In the first scenario, the model predicted the produced biogas by using raw wastewater characteristics and reached the coefficient of determination of R2 = 0.84. The GA selected four out of eleven parameters of raw wastewater, including loads of BOD5, COD, TSS, and TN (kg/d), as the most informative data for the model. In the second scenario, the model predicted the produced biogas by employing the data of the thickened sludge streams entering the ADs and yielded a higher accuracy (R2 = 0.89). In this scenario, GA selected two out of six parameters of the sludge streams, including total flow rate (m3/d) and average solids content (w/w%). Finally, in the third scenario, by putting the parameters of the two previous scenarios together, the model's prediction accuracy increased slightly (R2 = 0.90). The results demonstrated that the GA-LSTM modeling technique could achieve reliable performance in predicting biogas production of large-scale ADs by including HRT in modeling procedure. It was also found that the raw wastewater characteristics severely affect AD behavior and can be successfully used as the input data of the AD models.
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Affiliation(s)
- Mohammad Milad Salamattalab
- Department of Civil Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran.
| | - Maryam Hasani Zonoozi
- Department of Civil Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran.
| | - Mahboubeh Molavi-Arabshahi
- Department of Mathematics, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran.
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12
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Qian S, Qiao X, Zhang W, Yu Z, Dong S, Feng J. Machine learning-based prediction for settling velocity of microplastics with various shapes. WATER RESEARCH 2024; 249:121001. [PMID: 38113602 DOI: 10.1016/j.watres.2023.121001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/22/2023] [Accepted: 12/07/2023] [Indexed: 12/21/2023]
Abstract
Microplastics can easily enter the aquatic environment and be transported between water bodies. The terminal settling velocity of microplastics, which affects their transport and distribution in the aquatic environment, is mainly influenced by their size, density, and shape. Due to the difficulty in accurately predicting the terminal settling velocity of microplastics with various shapes, this study focuses on establishing high-performance prediction models and understanding the importance and effect of each feature parameter using machine learning. Based on the number of principal dimensions, the shapes of microplastics are classified into fiber, film, and fragment, and their thresholds are identified. The microplastics of different shape categories have different optimal shape parameters for predicting the terminal settling velocity: Corey shape factor, flatness, elongation, and sphericity for the fragment, film, fiber, and mixed-shape MPs, respectively. By including the dimensionless diameter, relative density and optimal shape parameter in the input parameter combination, the machine learning models can well predict the terminal settling velocity for the microplastics of different shape categories and mixed-shape with R2 > 0.867, achieving significantly higher performance than the existing theoretical and regression models. The interpretable analysis of machine learning reveals the highest importance of the microplastic size and its marginal effect when the dimensionless diameter D* = dn(g/v2)1/3 > 80, where dn is the equivalent diameter, g is the gravitational acceleration, and ν is the fluid kinematic viscosity. The effect of shape is weak for small microplastics and becomes significant when D* exceeds 65.
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Affiliation(s)
- Shangtuo Qian
- National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu 210024, China; College of Agricultural Science and Engineering, Hohai University, Nanjing 211100, China
| | - Xuyang Qiao
- National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu 210024, China; College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
| | - Wenming Zhang
- Department of Civil and Environmental Engineering, University of Alberta, Edmonton AB T6G 1H9, Canada
| | - Zijian Yu
- Department of Civil and Environmental Engineering, University of Alberta, Edmonton AB T6G 1H9, Canada
| | - Shunan Dong
- College of Agricultural Science and Engineering, Hohai University, Nanjing 211100, China
| | - Jiangang Feng
- National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu 210024, China; College of Agricultural Science and Engineering, Hohai University, Nanjing 211100, China.
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13
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Sharma S, Gupta V, Mudgal D. Response surface methodology and machine learning based tensile strength prediction in ultrasonic assisted coating of poly lactic acid bone plates manufactured using fused deposition modeling. ULTRASONICS 2024; 137:107204. [PMID: 37979518 DOI: 10.1016/j.ultras.2023.107204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 11/10/2023] [Accepted: 11/11/2023] [Indexed: 11/20/2023]
Abstract
Poly Lactic Acid (PLA) based bone plates fabricated using Fused Deposition Modeling have poor mechanical strength which can be improved by biocompatible polydopamine (PDM) coating. However, PDM particles, being heavy in nature, settle at the container bottom with increase in coating solution concentration at the time of bone plate coating using dip coating technique. Thus, the present work aims to witness the effect of ultrasonic assisted coating parameters on tensile strength of coated bone plates. The coating parameters involving power of ultrasonic vibrations, coating solution concentration and immersion time were varied. The standard Response Surface Methodology (RSM) was applied and experimental trials were performed for obtaining tensile strength of bone plates under varied coating parameters. The objective of the present study was to compare the values of tensile strength predicted using RSM and machine learning (ML) models. Based on the obtained experimental values, gradient boosting regression (GBReg), linear regression (LReg) and random forest regression (RFReg) were trained and tested for predicting tensile strength of bone plates. The accuracy and prediction errors corresponding to RSM and ML based models were compared with respect to R2, Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The findings revealed that GBReg exhibited R2, MSE, RMSE and MAE values as 0.9312, 1.7142, 1.2877 and 1.0861 respectively, while RSM showed R2, MSE, RMSE and MAE values as 0.882, 2.13, 1.4595 and 1.258 respectively. RSM model has shown minimum accuracy with high prediction errors amongst the four models. GBReg has outperformed other ML models in terms of their accuracy and error metrics. The present study therefore suggests the application of GBReg based ML model for predicting tensile strength of PDM coated bone plates in response to its accurate and robust prediction performance.
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Affiliation(s)
- Shrutika Sharma
- Mechanical Engineering Department, Thapar Institute of Engineering and Technology, Patiala 147004, Punjab, India
| | - Vishal Gupta
- Mechanical Engineering Department, Thapar Institute of Engineering and Technology, Patiala 147004, Punjab, India.
| | - Deepa Mudgal
- Mechanical Engineering Department, Thapar Institute of Engineering and Technology, Patiala 147004, Punjab, India
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14
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Ma Z, Wang R, Song G, Zhang K, Zhao Z, Wang J. Interpretable ensemble prediction for anaerobic digestion performance of hydrothermal carbonization wastewater. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 908:168279. [PMID: 37926246 DOI: 10.1016/j.scitotenv.2023.168279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 10/12/2023] [Accepted: 10/31/2023] [Indexed: 11/07/2023]
Abstract
Hydrothermal carbonization (HTC) is a method to improve fuel quality that can directly treat wet solid waste, but the treatment produces large amounts of wastewater. Hydrothermal carbonation wastewater treatment for methane production by anaerobic digestion can lead to waste utilization and energy saving. However, anaerobic digestion performance prediction of HTC wastewater is challenging due to the complexity of influencing factors. This study applies interpretable machine learning combined with ensemble learning to construct ensemble prediction models for the biogas yield and CH4 concentration. The machine learning ensemble model can integrate the advantages of single models and effectively improve the prediction accuracy of the anaerobic digestion performance of HTC wastewater, with the best R2 reaching 0.836 and 0.820, respectively, which is better than 0.780 and 0.802 of the best single models. The SHapley Additive exPlanations theory is combined with the ensemble models to show that anaerobic digestion reacted time with HTC temperature, pH, and COD has a coupling effect on daily biogas yield and CH4 concentration.
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Affiliation(s)
- Zherui Ma
- Hebei Key Laboratory of Low Carbon and High Efficiency Power Generation Technology, North China Electric Power University, Baoding 071003, Hebei, China; Baoding Key Laboratory of Low Carbon and High Efficiency Power Generation Technology, North China Electric Power University, Baoding 071003, Hebei, China
| | - Ruikun Wang
- Hebei Key Laboratory of Low Carbon and High Efficiency Power Generation Technology, North China Electric Power University, Baoding 071003, Hebei, China; Baoding Key Laboratory of Low Carbon and High Efficiency Power Generation Technology, North China Electric Power University, Baoding 071003, Hebei, China.
| | - Gaoke Song
- Hebei Key Laboratory of Low Carbon and High Efficiency Power Generation Technology, North China Electric Power University, Baoding 071003, Hebei, China; Baoding Key Laboratory of Low Carbon and High Efficiency Power Generation Technology, North China Electric Power University, Baoding 071003, Hebei, China
| | - Kai Zhang
- Hebei Key Laboratory of Low Carbon and High Efficiency Power Generation Technology, North China Electric Power University, Baoding 071003, Hebei, China; Baoding Key Laboratory of Low Carbon and High Efficiency Power Generation Technology, North China Electric Power University, Baoding 071003, Hebei, China
| | - Zhenghui Zhao
- Hebei Key Laboratory of Low Carbon and High Efficiency Power Generation Technology, North China Electric Power University, Baoding 071003, Hebei, China; Baoding Key Laboratory of Low Carbon and High Efficiency Power Generation Technology, North China Electric Power University, Baoding 071003, Hebei, China
| | - Jiangjiang Wang
- Hebei Key Laboratory of Low Carbon and High Efficiency Power Generation Technology, North China Electric Power University, Baoding 071003, Hebei, China
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15
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Piadeh F, Offie I, Behzadian K, Bywater A, Campos LC. Real-time operation of municipal anaerobic digestion using an ensemble data mining framework. BIORESOURCE TECHNOLOGY 2024; 392:130017. [PMID: 37967795 DOI: 10.1016/j.biortech.2023.130017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/05/2023] [Accepted: 11/10/2023] [Indexed: 11/17/2023]
Abstract
This study presents a novel approach for real-time operation of anaerobic digestion using an ensemble decision-making framework composed of weak learner data mining models. The framework utilises simple but practical features such as waste composition, added water and feeding volume to predict biogas yield and to generate an optimised weekly operation pattern to maximise biogas production and minimise operational costs. The effectiveness of this framework is validated through a real-world case study conducted in the UK. Comparative analysis with benchmark models demonstrates a significant improvement in prediction accuracy, increasing from the range of 50-80% with benchmark models to 91% with the proposed framework. The results also show the efficacy of the weekly operation pattern, which leads to a substantial 78% increase in biogas generation during the testing period. Moreover, the pattern contributes to a reduction of 71% in total days required for feeding and 30% in total days required for pre-feeding.
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Affiliation(s)
- Farzad Piadeh
- School of Computing and Engineering, University of West London, London W5 5RF, United Kingdom; School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, United Kingdom
| | - Ikechukwu Offie
- School of Computing and Engineering, University of West London, London W5 5RF, United Kingdom
| | - Kourosh Behzadian
- School of Computing and Engineering, University of West London, London W5 5RF, United Kingdom; Centre for Urban Sustainability and Resilience, Department of Civil, Environmental and Geomatic Engineering, University College London, London WC1E6BT, United Kingdom.
| | - Angela Bywater
- Water and Environmental Engineering Group, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK
| | - Luiza C Campos
- Centre for Urban Sustainability and Resilience, Department of Civil, Environmental and Geomatic Engineering, University College London, London WC1E6BT, United Kingdom
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16
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Ullah H, Chen B, Rashid A, Zhao R, Shahab A, Yu G, Wong MH, Khan S. A critical review on selenium removal capacity from water using emerging non-conventional biosorbents. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 339:122644. [PMID: 37827352 DOI: 10.1016/j.envpol.2023.122644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 08/27/2023] [Accepted: 09/27/2023] [Indexed: 10/14/2023]
Abstract
Anthropogenic-driven selenium (Se) contamination of natural waters has emerged as severe health and environmental concern. Lowering Se levels to safe limits of 40 μg-L-1 (recommended by WHO) presents a critical challenge for the scientific community, necessitating reliable and effective methods for Se removal. The primary obectives of this review are to evaluate the efficiency of different biosorbents in removing Se, understand the mechanism of adsorption, and identify the factors influencing the biosorption process. A comprehensive literature review is conducted to analyze various studies that have explored the use of modified biochars, iron oxides, and other non-conventional biosorbents for selenium removal. The assessed biosorbents include biomass, microalgae-based, alginate compounds, peats, chitosan, and biochar/modified biochar-based adsorbents. Quantitative data from the selected studies analyzed Se adsorption capacities of biosorbents, were collected considering pH, temperature, and environmental conditions, while highlighting advantages and limitations. The role of iron impregnation in enhancing the biosorption efficiency is investigated, and the mechanisms of Se adsorption on these biosorbents at different pH levels are discussed. A critical literature assessment reveals a robust understanding of the current state of Se biosorption and the effectiveness of non-conventional biosorbents for Se removal, providing crucial information for further research and practical applications in water treatment processes. By understanding the strengths and limitations of various biosorbents, this review is expected to scale-up targeted research on Se removal, promoting the development of innovative and cost-effective adsorbents, efficient and sustainable approaches for Se removal from water.
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Affiliation(s)
- Habib Ullah
- Innovation Center of Yangtze River Delta, Zhejiang University, Hangzhou, Zhejiang, China; Department of Environmental Science, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Baoliang Chen
- Innovation Center of Yangtze River Delta, Zhejiang University, Hangzhou, Zhejiang, China; Department of Environmental Science, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Audil Rashid
- Faculty of Sciences, Department of Botany, University of Gujrat, Gujrat-50700, Pakistan
| | - Ruohan Zhao
- Department of Environmental Science, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Asfandyar Shahab
- College of Environmental Science and Engineering, Guilin University of Technology, Guilin, China.
| | - Guo Yu
- College of Environmental Science and Engineering, Guilin University of Technology, Guilin, China.
| | - Ming Hung Wong
- Consortium on Health, Environment, Education, and Research (CHEER), and Department of Science and Environmental Studies, The Education University of Hong Kong, Hong Kong, China.
| | - Sangar Khan
- Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China.
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17
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Wang C, Zhang X, Zhao G, Chen Y. Mechanisms, methods and applications of machine learning in bio-alcohol production and utilization: A review. CHEMOSPHERE 2023; 342:140191. [PMID: 37716556 DOI: 10.1016/j.chemosphere.2023.140191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 09/13/2023] [Accepted: 09/14/2023] [Indexed: 09/18/2023]
Abstract
Bio-alcohols have been proven promising alternatives to fossil fuels. Machine learning (ML), as an analytical tool for uncovering intrinsic correlations and mining data connotations, is also becoming widely used in the field of bio-alcohols. This article reviews the mechanisms, methods, and applications of ML in the bio-alcohols field. In terms of mechanisms, we describe the workflow of ML applications, emphasizing the importance of a well-defined research problem and complete feature engineering for a robust model. Prediction and optimization are the main application scenarios. In terms of methods, we illustrate the characteristics of different ML models and analyze their applicability in the bio-alcohol field. The role of ML in the production of bio-methanol by pyrolysis and gasification, as well as in the three stages of fermentation for bioethanol production are highlighted. In terms of utilization, ML is used to optimize engine performance and reduce emissions. This review provides guidance on how to use novel ML methods in the bio-alcohol field, showing the potential of ML to streamline work in the whole biofuel field.
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Affiliation(s)
- Chen Wang
- State Key Laboratory of Pollution Control and Resources Reuse, School of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China
| | - Xuemeng Zhang
- State Key Laboratory of Pollution Control and Resources Reuse, School of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China
| | - Guohua Zhao
- School of Chemical Science and Engineering, Tongji University, Shanghai, 200092, China
| | - Yinguang Chen
- State Key Laboratory of Pollution Control and Resources Reuse, School of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China.
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18
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Luisa de Castro E Silva H, Sigurnjak I, Robles-Aguilar A, Adriaens A, Meers E. Development of conversion factors to estimate the concentrations of heavy metals in manure-derived digestates. WASTE MANAGEMENT (NEW YORK, N.Y.) 2023; 168:334-343. [PMID: 37336141 DOI: 10.1016/j.wasman.2023.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 05/16/2023] [Accepted: 06/07/2023] [Indexed: 06/21/2023]
Abstract
During biogas production, a residual by-product rich in organic matter, nutrients, and trace elements - called digestate - is generated. Due to the nature of the anaerobic digestion process (i.e., conversion of organic matter into biogas) and the non-digestibility of trace elements, metal concentrations are higher in digestate than initially in the treated feedstock, resulting in a detrimental effect on the environment when directly applied as fertiliser on the soil. This study aims to predict the concentration of heavy metals in digestate through four different process parameters (Biogas yield - M1, Biodegradable fraction - M2, Dry matter - M3 and Power generation - M4) in full-scale biogas plants. For the validation of the process parameters, the predictions were compared against laboratory analyses of feedstocks and digestates samples from mono- and co-digestion processes. The convergence between the conversion factors based on laboratory data and process parameters (CLD and CFA, respectively) ranged in the following order: M3 > M2 > M1 > M4. Based on laboratory analyses, better predictions were obtained for Al, Cr, Cu, Fe, Mn, and Zn employing M3. Moreover, a robust convergence was achieved between the CLD and CFA conversion factors for the mono-digestion process. Further assessment of a diverse range of feedstocks is needed to increase the convergence between the conversion factors based on process parameters and laboratory data, specifically for the co-digestion process M3. The concentrations of Cd, Co, Ni, and Pb elements were below the detection limits, whereas Cr, Cu, and Zn did not exceed the legal threshold limits of the legislations.
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Affiliation(s)
- Hellen Luisa de Castro E Silva
- Faculty of Bioscience Engineering - Department of Green Chemistry and Technology, Ghent University, Coupure Links 653, 9000 Gent, Belgium.
| | - Ivona Sigurnjak
- Faculty of Bioscience Engineering - Department of Green Chemistry and Technology, Ghent University, Coupure Links 653, 9000 Gent, Belgium
| | - Ana Robles-Aguilar
- Faculty of Bioscience Engineering - Department of Green Chemistry and Technology, Ghent University, Coupure Links 653, 9000 Gent, Belgium
| | - Anne Adriaens
- Faculty of Bioscience Engineering - Department of Green Chemistry and Technology, Ghent University, Coupure Links 653, 9000 Gent, Belgium
| | - Erik Meers
- Faculty of Bioscience Engineering - Department of Green Chemistry and Technology, Ghent University, Coupure Links 653, 9000 Gent, Belgium
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19
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Said Z, Sharma P, Thi Bich Nhuong Q, Bora BJ, Lichtfouse E, Khalid HM, Luque R, Nguyen XP, Hoang AT. Intelligent approaches for sustainable management and valorisation of food waste. BIORESOURCE TECHNOLOGY 2023; 377:128952. [PMID: 36965587 DOI: 10.1016/j.biortech.2023.128952] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/18/2023] [Accepted: 03/21/2023] [Indexed: 06/18/2023]
Abstract
Food waste (FW) is a severe environmental and social concern that today's civilization is facing. Therefore, it is necessary to have an efficient and sustainable solution for managing FW bioprocessing. Emerging technologies like the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) are critical to achieving this, in which IoT sensors' data is analyzed using AI and ML techniques, enabling real-time decision-making and process optimization. This work describes recent developments in valorizing FW using novel tactics such as the IoT, AI, and ML. It could be concluded that combining IoT, AI, and ML approaches could enhance bioprocess monitoring and management for generating value-added products and chemicals from FW, contributing to improving environmental sustainability and food security. Generally, a comprehensive strategy of applying intelligent techniques in conjunction with government backing can minimize FW and maximize the role of FW in the circular economy toward a more sustainable future.
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Affiliation(s)
- Zafar Said
- Department of Sustainable and Renewable Energy Engineering, University of Sharjah, Sharjah, P. O. Box 27272, United Arab Emirates; U.S.-Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad, Pakistan; Department of Industrial and Mechanical Engineering, Lebanese American University (LAU), Byblos, Lebanon
| | - Prabhakar Sharma
- Mechanical Engineering Department, Delhi Skill and Entrepreneurship University, Delhi-110089, India
| | | | - Bhaskor J Bora
- Energy Institute Bengaluru, Centre of Rajiv Gandhi Institute of Petroleum Technology, Karnataka-560064, India
| | - Eric Lichtfouse
- State Key Laboratory of Multiphase Flow in Power Engineering, Xi'an Jiaotong University, Xi'an Shaanxi 710049 PR China
| | - Haris M Khalid
- Department of Electrical and Electronics Engineering, Higher Colleges of Technology, Sharjah 7947, United Arab Emirates; Department of Electrical and Electronic Engineering Science, University of Johannesburg, Auckland Park 2006, South Africa; Department of Electrical Engineering, University of Santiago, Avenida Libertador 3363, Santiago, RM, Chile
| | - Rafael Luque
- Peoples Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Str., 117198 Moscow, Russian Federation; Universidad ECOTEC, Km. 13.5 Samborondón, Samborondón, EC092302, Ecuador
| | - Xuan Phuong Nguyen
- PATET Research Group, Ho Chi Minh City University of Transport, Ho Chi Minh City, Vietnam
| | - Anh Tuan Hoang
- Institute of Engineering, HUTECH University, Ho Chi Minh City, Vietnam.
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20
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Sonwai A, Pholchan P, Tippayawong N. Machine Learning Approach for Determining and Optimizing Influential Factors of Biogas Production from Lignocellulosic Biomass. BIORESOURCE TECHNOLOGY 2023; 383:129235. [PMID: 37244314 DOI: 10.1016/j.biortech.2023.129235] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 05/19/2023] [Accepted: 05/21/2023] [Indexed: 05/29/2023]
Abstract
Machine learning (ML) was used to predict specific methane yields (SMY) with a dataset of 14 features from lignocellulosic biomass (LB) characteristics and operating conditions of completely mixed reactors under continuous feeding mode. The random forest (RF) model was best suited for predicting SMY with a coefficient of determination (R2) of 0.85 and root mean square error (RMSE) of 0.06. Biomass compositions greatly influenced SMYs from LB, and cellulose prevailed over lignin and biomass ratio as the most important feature. Impact of LB to manure ratio was assessed to optimize biogas production with the RF model. Under typical organic loading rates (OLR), optimum LB to manure ratio of 1:1 was identified. Experimental results confirmed influential factors revealed by the RF model and provided the highest SMY of 79.2% of the predicted value. Successful applications of ML for anaerobic digestion modelling and optimization specifically for LB were revealed in this work.
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Affiliation(s)
- Anuchit Sonwai
- Department of Environmental Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Patiroop Pholchan
- Department of Environmental Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand.
| | - Nakorn Tippayawong
- Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand
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21
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Yildirim O, Ozkaya B. Prediction of biogas production of industrial scale anaerobic digestion plant by machine learning algorithms. CHEMOSPHERE 2023:138976. [PMID: 37230302 DOI: 10.1016/j.chemosphere.2023.138976] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 04/25/2023] [Accepted: 05/16/2023] [Indexed: 05/27/2023]
Abstract
In the anaerobic digestion (AD) process there are some difficulties in maintaining process stability due to the complexity of the system. The variability of the raw material coming to the facility, temperature fluctuations and pH changes as a result of microbial processes cause process instability and require continuous monitoring and control. Increasing continuous monitoring, and internet of things applications within the scope of Industry 4.0 in AD facilities can provide process stability control and early intervention. In this study, five different machine learning (ML) algorithms (RF, ANN, KNN, SVR, and XGBoost) were used to describe and predict the correlation between operational parameters and biogas production quantities collected from a real-scale anaerobic digestion plant. The KNN algorithm had the lowest accuracy in predicting total biogas production over time, while the RF model had the highest prediction accuracy of all prediction models. The RF method produced the best prediction, with an R2 of 0.9242, and it was followed by XGBoost, ANN, SVR, and KNN (with R2 values of 0.8960, 0.8703, 0.8655, 0.8326, respectively). Real-time process control will be provided and process stability will be maintained by preventing low-efficiency biogas production with the integration of ML applications into AD facilities.
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Affiliation(s)
- Oznur Yildirim
- Department of Environmental Engineering, Faculty of Civil Engineering, Yildiz Technical University, Davutpasa Campus, 34220, Istanbul, Turkey.
| | - Bestami Ozkaya
- Department of Environmental Engineering, Faculty of Civil Engineering, Yildiz Technical University, Davutpasa Campus, 34220, Istanbul, Turkey
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22
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Moradi S, Omar A, Zhou Z, Agostino A, Gandomkar Z, Bustamante H, Power K, Henderson R, Leslie G. Forecasting and Optimizing Dual Media Filter Performance via Machine Learning. WATER RESEARCH 2023; 235:119874. [PMID: 36947925 DOI: 10.1016/j.watres.2023.119874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 03/06/2023] [Accepted: 03/10/2023] [Indexed: 06/18/2023]
Abstract
Four different machine learning algorithms, including Decision Tree (DT), Random Forest (RF), Multivariable Linear Regression (MLR), Support Vector Regressions (SVR), and Gaussian Process Regressions (GPR), were applied to predict the performance of a multi-media filter operating as a function of raw water quality and plant operating variables. The models were trained using data collected over a seven year period covering water quality and operating variables, including true colour, turbidity, plant flow, and chemical dose for chlorine, KMnO4, FeCl3, and Cationic Polymer (PolyDADMAC). The machine learning algorithms have shown that the best prediction is at a 1-day time lag between input variables and unit filter run volume (UFRV). Furthermore, the RF algorithm with grid search using the input metrics mentioned above with a 1-day time lag has provided the highest reliability in predicting UFRV with a RMSE and R2 of 31.58 and 0.98, respectively. Similarly, RF with grid search has shown the shortest training time, prediction accuracy, and forecasting events using a ROC-AUC curve analysis (AUC over 0.8) in extreme wet weather events. Therefore, Random Forest with grid search and a 1-day time lag is an effective and robust machine learning algorithm that can predict the filter performance to aid water treatment operators in their decision makings by providing real-time warning of the potential turbidity breakthrough from the filters.
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Affiliation(s)
- Sina Moradi
- Algae & Organic Matter Laboratory, School of Chemical Engineering, University of New South Wales, Sydney 2052, Australia; UNESCO Centre for Membrane Science & Technology, School of Chemical Engineering, University of New South Wales, Sydney 2052, Australia
| | - Amr Omar
- School of Chemical Engineering, University of New South Wales, Sydney 2052, Australia
| | - Zhuoyu Zhou
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China
| | - Anthony Agostino
- Algae & Organic Matter Laboratory, School of Chemical Engineering, University of New South Wales, Sydney 2052, Australia
| | - Ziba Gandomkar
- Discipline of Medical Imaging Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2006, Australia
| | | | - Kaye Power
- Sydney WaterCorporation, Sydney, Australia
| | - Rita Henderson
- Algae & Organic Matter Laboratory, School of Chemical Engineering, University of New South Wales, Sydney 2052, Australia; School of Chemical Engineering, University of New South Wales, Sydney 2052, Australia
| | - Greg Leslie
- Algae & Organic Matter Laboratory, School of Chemical Engineering, University of New South Wales, Sydney 2052, Australia; UNESCO Centre for Membrane Science & Technology, School of Chemical Engineering, University of New South Wales, Sydney 2052, Australia; School of Chemical Engineering, University of New South Wales, Sydney 2052, Australia.
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23
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Sappl J, Harders M, Rauch W. Machine learning for quantile regression of biogas production rates in anaerobic digesters. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 872:161923. [PMID: 36764541 DOI: 10.1016/j.scitotenv.2023.161923] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 01/09/2023] [Accepted: 01/27/2023] [Indexed: 06/18/2023]
Abstract
Anaerobic digestion is a well-established tool at wastewater treatment plants for processing raw sludge; it can also be used to generate renewable energy by harvesting biogas in anaerobic digesters. Operational parameters, such as temperature, are usually set by plant operators according to expert knowledge. To completely utilize the potential of operational management, in this study, we calibrated a novel Temporal Fusion Transformer based on six years of life-scale time series data together with categorical features such as public holidays. The model design allows for the interpretability of the output in contrast to traditional data-driven techniques, using multi-head attention. In addition to forecasting the median biogas production rates for the following seven days, our model also yields quantiles, making it less prone to strong fluctuations. We used three well-known statistical techniques as benchmarks. The mean absolute percentage error of our forecasting approach is below 8 %.
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Affiliation(s)
- Johannes Sappl
- Unit of Environmental Engineering, Universität Innsbruck, Technikerstraße 13, 6020 Innsbruck, Austria.
| | - Matthias Harders
- Interactive Graphics and Simulation Group, Universität Innsbruck, Technikerstraße 21 A, 6020 Innsbruck, Austria.
| | - Wolfgang Rauch
- Unit of Environmental Engineering, Universität Innsbruck, Technikerstraße 13, 6020 Innsbruck, Austria.
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24
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Zhang Y, Feng Y, Ren Z, Zuo R, Zhang T, Li Y, Wang Y, Liu Z, Sun Z, Han Y, Feng L, Aghbashlo M, Tabatabaei M, Pan J. Tree-based machine learning model for visualizing complex relationships between biochar properties and anaerobic digestion. BIORESOURCE TECHNOLOGY 2023; 374:128746. [PMID: 36813050 DOI: 10.1016/j.biortech.2023.128746] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 02/09/2023] [Accepted: 02/11/2023] [Indexed: 06/18/2023]
Abstract
The ideal conditions for anaerobic digestion experiments with biochar addition are challenging to thoroughly study due to different experimental purposes. Therefore, three tree-based machine learning models were developed to depict the intricate connection between biochar properties and anaerobic digestion. For the methane yield and maximum methane production rate, the gradient boosting decision tree produced R2 values of 0.84 and 0.69, respectively. According to feature analysis, digestion time and particle size had a substantial impact on the methane yield and production rate, respectively. When particle sizes were in the range of 0.3-0.5 mm and the specific surface area was approximately 290 m2/g, corresponding to a range of O content (>31%) and biochar addition (>20 g/L), the maximum promotion of methane yield and maximum methane production rate were attained. Therefore, this study presents new insights into the effects of biochar on anaerobic digestion through tree-based machine learning.
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Affiliation(s)
- Yi Zhang
- State Key Laboratory of Heavy Oil Processing, Beijing Key Laboratory of Biogas Upgrading Utilization, College of New Energy and Materials, China University of Petroleum Beijing (CUPB), Beijing 102249, PR China
| | - Yijing Feng
- State Key Laboratory of Heavy Oil Processing, Beijing Key Laboratory of Biogas Upgrading Utilization, College of New Energy and Materials, China University of Petroleum Beijing (CUPB), Beijing 102249, PR China
| | - Zhonghao Ren
- State Key Laboratory of Heavy Oil Processing, Beijing Key Laboratory of Biogas Upgrading Utilization, College of New Energy and Materials, China University of Petroleum Beijing (CUPB), Beijing 102249, PR China
| | - Runguo Zuo
- State Key Laboratory of Heavy Oil Processing, Beijing Key Laboratory of Biogas Upgrading Utilization, College of New Energy and Materials, China University of Petroleum Beijing (CUPB), Beijing 102249, PR China
| | - Tianhui Zhang
- State Key Laboratory of Heavy Oil Processing, Beijing Key Laboratory of Biogas Upgrading Utilization, College of New Energy and Materials, China University of Petroleum Beijing (CUPB), Beijing 102249, PR China
| | - Yeqing Li
- State Key Laboratory of Heavy Oil Processing, Beijing Key Laboratory of Biogas Upgrading Utilization, College of New Energy and Materials, China University of Petroleum Beijing (CUPB), Beijing 102249, PR China.
| | - Yajing Wang
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China
| | - Zhiyang Liu
- College of Science, China University of Petroleum Beijing (CUPB), Beijing 102249, PR China
| | - Ziyan Sun
- State Key Laboratory of Heavy Oil Processing, Beijing Key Laboratory of Biogas Upgrading Utilization, College of New Energy and Materials, China University of Petroleum Beijing (CUPB), Beijing 102249, PR China
| | - Yongming Han
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Lu Feng
- NIBIO, Norwegian Institute of Bioeconomy Research, PO Box 115, N-1431 Ås, Norway
| | - Mortaza Aghbashlo
- Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Meisam Tabatabaei
- Higher Institution Centre of Excellence (HICoE), Institute of Tropical Aquaculture and Fisheries (AKUATROP), Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia; Department of Biomaterials, Saveetha Dental College, Saveetha Institute of Medical and Technical Sciences, Chennai 600 077, India
| | - Junting Pan
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China
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25
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Lan DY, He PJ, Qi YP, Wu TW, Xian HY, Wang RH, Lü F, Zhang H. Optimizing the Quality of Machine Learning for Identifying the Share of Biogenic and Fossil Carbon in Solid Waste. Anal Chem 2023; 95:4412-4420. [PMID: 36820858 DOI: 10.1021/acs.analchem.2c04940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Insights into carbon sources (biogenic and fossil carbon) and contents in solid waste are vital for estimating the carbon emissions from incineration plants. However, the traditional methods are time-, labor-, and cost-intensive. Herein, high-quality data sets were established after analyzing the carbon contents and infrared spectra of substantial samples using elemental analysis and attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), respectively. Then, five classification and eight regression machine learning (ML) models were evaluated to recognize the proportion of biogenic and fossil carbon in solid waste. Using the optimized data preprocessing approach, the random forest (RF) classifier with hyperparameter tuning ranked first in classifying the carbon group with a test accuracy of 0.969, and the carbon contents were successfully predicted by the RF regressor with R2 = 0.926 considering performance-interpretability-computation time competition. The above proposed algorithms were further validated with real environmental samples, which exhibited robust performance with an accuracy of 0.898 for carbon group classification and an R2 value of 0.851 for carbon content prediction. The reliable results indicate that ATR-FTIR coupled with ML algorithms is feasible for rapidly identifying both carbon groups and content, facilitating the calculation and assessment of carbon emissions from solid waste incineration.
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Affiliation(s)
- Dong-Ying Lan
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Pin-Jing He
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.,Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China
| | - Ya-Ping Qi
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Ting-Wei Wu
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Hao-Yang Xian
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Rui-Heng Wang
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Fan Lü
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.,Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China
| | - Hua Zhang
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.,Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China
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26
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Meola A, Winkler M, Weinrich S. Metaheuristic optimization of data preparation and machine learning hyperparameters for prediction of dynamic methane production. BIORESOURCE TECHNOLOGY 2023; 372:128604. [PMID: 36634878 DOI: 10.1016/j.biortech.2023.128604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/06/2023] [Accepted: 01/08/2023] [Indexed: 06/17/2023]
Abstract
Machine learning algorithms provide detailed description of the anaerobic digestion process, but the impact of data preparation procedures and hyperparameter optimization has rarely been investigated. A genetic algorithm was developed for optimizing data preparation and model hyperparameters to simulate dynamic methane production from steady-state anaerobic digestion of agricultural residues at full-scale. A long short-term memory neural network was used as prediction model. Results indicate that batch size, learning rate and number of neurons are the most important model parameters for accurate description of methane production rates, whereas combination of hyperparameter and data preparation optimization shows best model efficiencies, with a root mean square scaled error of 76.5 %. Mass of solid feed, time and mass of volatile solids are the most relevant input features. This study provides fundamental steps for optimal prediction of dynamic biomethane production, as a reliable basis for improving bioconversion efficiency during anaerobic digestion of agricultural residues.
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Affiliation(s)
- Alberto Meola
- DBFZ, Deutsches Biomasseforschungszentrum gemeinnützige GmbH, Torgauer Straße 116, Leipzig 04347, Germany; Faculty of Mathematics and Computer Science, Leipzig University, Augustusplatz 10, Leipzig 04109, Germany
| | - Manuel Winkler
- DBFZ, Deutsches Biomasseforschungszentrum gemeinnützige GmbH, Torgauer Straße 116, Leipzig 04347, Germany
| | - Sören Weinrich
- DBFZ, Deutsches Biomasseforschungszentrum gemeinnützige GmbH, Torgauer Straße 116, Leipzig 04347, Germany.
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27
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Tsui TH, van Loosdrecht MCM, Dai Y, Tong YW. Machine learning and circular bioeconomy: Building new resource efficiency from diverse waste streams. BIORESOURCE TECHNOLOGY 2023; 369:128445. [PMID: 36473583 DOI: 10.1016/j.biortech.2022.128445] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/29/2022] [Accepted: 12/02/2022] [Indexed: 06/17/2023]
Abstract
Biorefinery systems are playing pivotal roles in the technological support of resource efficiency for circular bioeconomy. Meanwhile, artificial intelligence presents great potential in handling scientific tasks of high-dimensional complexity. This review article scrutinizes the status of machine learning (ML) applications in four critical biorefinery systems (i.e. composting, fermentation, anaerobic digestion, and thermochemical conversions) as well as their advancements against traditional modeling techniques of mechanistic approach. The contents cover their algorithm selections, modeling challenges, and prospective improvements. Perspectives are sketched to further inform collective efforts on crucial aspects. The multidisciplinary interchange of modeling knowledge will enable a more progressive digital transformation of sustainability efforts in supporting sustainable development goals.
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Affiliation(s)
- To-Hung Tsui
- Environmental Research Institute, National University of Singapore, 1 Create Way, 138602, Singapore; Energy and Environmental Sustainability for Megacities (E2S2) Phase II, Campus for Research Excellence and Technological Enterprise (CREATE), 1 Create Way, Singapore, 138602, Singapore
| | | | - Yanjun Dai
- School of Mechanical Engineering, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai, 200240, China
| | - Yen Wah Tong
- Environmental Research Institute, National University of Singapore, 1 Create Way, 138602, Singapore; Energy and Environmental Sustainability for Megacities (E2S2) Phase II, Campus for Research Excellence and Technological Enterprise (CREATE), 1 Create Way, Singapore, 138602, Singapore; Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, 117585, Singapore.
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28
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Khan M, Chuenchart W, Surendra KC, Kumar Khanal S. Applications of artificial intelligence in anaerobic co-digestion: Recent advances and prospects. BIORESOURCE TECHNOLOGY 2023; 370:128501. [PMID: 36538958 DOI: 10.1016/j.biortech.2022.128501] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/12/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
Anaerobic co-digestion (AcoD) offers several merits such as better digestibility and process stability while enhancing methane yield due to synergistic effects. Operation of an efficient AcoD system, however, requires full comprehension of important operational parameters, such as co-substrates ratio, their composition, volatile fatty acids/alkalinity ratio, organic loading rate, and solids/hydraulic retention time. AcoD process optimization, prediction and control, and early detection of system instability are often difficult to achieve through tedious manual monitoring processes. Recently, artificial intelligence (AI) has emerged as an innovative approach to computational modeling and optimization of the AcoD process. This review discusses AI applications in AcoD process optimization, control, prediction of unknown input/output parameters, and real-time monitoring. Furthermore, the review also compares standalone and hybrid AI algorithms as applied to AcoD. The review highlights future research directions for data preprocessing, model interpretation and validation, and grey-box modeling in AcoD process.
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Affiliation(s)
- Muzammil Khan
- Department of Molecular Biosciences and Bioengineering, University of Hawai'i at Mānoa, 1955 East-West Road, Honolulu, HI 96822, USA; Department of Civil and Environmental Engineering, University of Hawai'i at Mānoa, 2540 Dole Street, Honolulu, HI 96822, USA
| | - Wachiranon Chuenchart
- Department of Molecular Biosciences and Bioengineering, University of Hawai'i at Mānoa, 1955 East-West Road, Honolulu, HI 96822, USA; Department of Civil and Environmental Engineering, University of Hawai'i at Mānoa, 2540 Dole Street, Honolulu, HI 96822, USA
| | - K C Surendra
- Department of Molecular Biosciences and Bioengineering, University of Hawai'i at Mānoa, 1955 East-West Road, Honolulu, HI 96822, USA; Global Institute for Interdisciplinary Studies, 44600 Kathmandu, Nepal
| | - Samir Kumar Khanal
- Department of Molecular Biosciences and Bioengineering, University of Hawai'i at Mānoa, 1955 East-West Road, Honolulu, HI 96822, USA; Department of Civil and Environmental Engineering, University of Hawai'i at Mānoa, 2540 Dole Street, Honolulu, HI 96822, USA.
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29
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Gupta R, Zhang L, Hou J, Zhang Z, Liu H, You S, Sik Ok Y, Li W. Review of explainable machine learning for anaerobic digestion. BIORESOURCE TECHNOLOGY 2023; 369:128468. [PMID: 36503098 DOI: 10.1016/j.biortech.2022.128468] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/03/2022] [Accepted: 12/06/2022] [Indexed: 06/17/2023]
Abstract
Anaerobic digestion (AD) is a promising technology for recovering value-added resources from organic waste, thus achieving sustainable waste management. The performance of AD is dictated by a variety of factors including system design and operating conditions. This necessitates developing suitable modelling and optimization tools to quantify its off-design performance, where the application of machine learning (ML) and soft computing approaches have received increasing attention. Here, we succinctly reviewed the latest progress in black-box ML approaches for AD modelling with a thrust on global and local model interpretability metrics (e.g., Shapley values, partial dependence analysis, permutation feature importance). Categorical applications of the ML and soft computing approaches such as what-if scenario analysis, fault detection in AD systems, long-term operation prediction, and integration of ML with life cycle assessment are discussed. Finally, the research gaps and scopes for future work are summarized.
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Affiliation(s)
- Rohit Gupta
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK; Nanoengineered Systems Laboratory, UCL Mechanical Engineering, University College London, London WC1E 7JE, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TS, UK
| | - Le Zhang
- Department of Resources and Environment, School of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Jiayi Hou
- Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhikai Zhang
- CAS Key Laboratory of Green Process and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China; School of Water Resources and Environment, Hebei GEO University, Shijiazhuang 050031, Hebei, China
| | - Hongtao Liu
- Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Siming You
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
| | - Yong Sik Ok
- Korea Biochar Research Center, APRU Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, South Korea
| | - Wangliang Li
- CAS Key Laboratory of Green Process and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China.
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30
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Ge H, Zheng J, Xu H. Advances in machine learning for high value-added applications of lignocellulosic biomass. BIORESOURCE TECHNOLOGY 2023; 369:128481. [PMID: 36513310 DOI: 10.1016/j.biortech.2022.128481] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 06/17/2023]
Abstract
Lignocellulose can be converted into biofuel or functional materials to achieve high value-added utilization. Biomass utilization process is complex and multi-dimensional. This paper focuses on the biomass conversion reaction conditions, the preparation of biomass-based functional materials, the combination of biomass conversion and traditional wet chemistry, molecular simulation and process simulation. This paper analyzes the mechanism, advantages and disadvantages of important machine learning (ML) methods. The application examples of ML in different aspects of high value utilization of lignocellulose are summarized in detail. The challenges and future prospects of ML in this field are analyzed.
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Affiliation(s)
- Hanwen Ge
- College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao 266042, PR China
| | - Jun Zheng
- Munich University of Technology, Arcisstraße 21, 80333, München, Germany
| | - Huanfei Xu
- College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao 266042, PR China; Key Laboratory of Pulp and Paper Science & Technology of Ministry of Education, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, PR China; Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, PR China.
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31
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Machine learning approach for predicting anaerobic digestion performance and stability in direct interspecies electron transfer-stimulated environments. Biochem Eng J 2023. [DOI: 10.1016/j.bej.2023.108840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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32
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Vakili AR, Ehtesham S, Danesh-Mesgaran M, Rohani A, Rahimi M. Toward Modeling the In Vitro Gas Production Process by Using Propolis Extract Oil Treatment: Machine Learning and Kinetic Models. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c02318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- Ali Reza Vakili
- Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, 9177948974, Iran
| | - Shahab Ehtesham
- Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, 9177948974, Iran
| | - Mohsen Danesh-Mesgaran
- Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, 9177948974, Iran
| | - Abbas Rohani
- Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, 9177948974, Iran
| | - Mohammad Rahimi
- Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, 9177948974, Iran
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33
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Haffiez N, Chung TH, Zakaria BS, Shahidi M, Mezbahuddin S, Maal-Bared R, Dhar BR. Exploration of machine learning algorithms for predicting the changes in abundance of antibiotic resistance genes in anaerobic digestion. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 839:156211. [PMID: 35623518 DOI: 10.1016/j.scitotenv.2022.156211] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 04/29/2022] [Accepted: 05/20/2022] [Indexed: 06/15/2023]
Abstract
The land application of digestate from anaerobic digestion (AD) is considered a significant route for transmitting antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) to ecosystems. To date, efforts towards understanding complex non-linear interactions between AD operating parameters with ARG/MGE abundances rely on experimental investigations due to a lack of mechanistic models. Herein, three different machine learning (ML) algorithms, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), were compared for their predictive capacities in simulating ARG/MGE abundance changes during AD. The models were trained and cross-validated using experimental data collected from 33 published literature. The comparison of model performance using coefficients of determination (R2) and root mean squared errors (RMSE) indicated that ANN was more reliable than RF and XGBoost. The mode of operation (batch/semi-continuous), co-digestion of food waste and sewage sludge, and residence time were identified as the three most critical features in predicting ARG/MGE abundance changes. Moreover, the trained ANN model could simulate non-linear interactions between operational parameters and ARG/MGE abundance changes that could be interpreted intuitively based on existing knowledge. Overall, this study demonstrates that machine learning can enable a reliable predictive model that can provide a holistic optimization tool for mitigating the ARG/MGE transmission potential of AD.
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Affiliation(s)
- Nervana Haffiez
- Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
| | - Tae Hyun Chung
- Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
| | - Basem S Zakaria
- Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
| | | | | | | | - Bipro Ranjan Dhar
- Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.
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34
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Xu RZ, Cao JS, Ye T, Wang SN, Luo JY, Ni BJ, Fang F. Automated machine learning-based prediction of microplastics induced impacts on methane production in anaerobic digestion. WATER RESEARCH 2022; 223:118975. [PMID: 35987034 DOI: 10.1016/j.watres.2022.118975] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 08/11/2022] [Accepted: 08/12/2022] [Indexed: 06/15/2023]
Abstract
Microplastics as emerging pollutants have been heavily accumulated in the waste activated sludge (WAS) during biological wastewater treatment, which showed significantly diverse impacts on the subsequent anaerobic sludge digestion for methane production. However, a robust modeling approach for predicting and unveiling the complex effects of accumulated microplastics within WAS on methane production is still missing. In this study, four automated machine learning (AutoML) approach was applied to model the effects of microplastics on anaerobic digestion processes, and integrated explainable analysis was explored to reveal the relationships between key variables (e.g., concentration, type, and size of microplastics) and methane production. The results showed that the gradient boosting machine had better prediction performance (mean squared error (MSE) = 17.0) than common neural networks models (MSE = 58.0), demonstrating that the AutoML algorithms succeeded in predicting the methane production and could select the best machine learning model without human intervention. Explainable analysis results indicated that the variable of microplastic types was more important than the variable of microplastic diameter and concentration. The existence of polystyrene was associated with higher methane production, whereas increasing microplastic diameter and concentration both inhibited methane production. This work also provided a novel modeling approach for comprehensively understanding the complex effects of microplastics on methane production, which revealed the dependence relationships between methane production and key variables and may be served as a reference for optimizing operational adjustments in anaerobic digestion processes.
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Affiliation(s)
- Run-Ze Xu
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China
| | - Jia-Shun Cao
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China
| | - Tian Ye
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China
| | - Su-Na Wang
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China
| | - Jing-Yang Luo
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China
| | - Bing-Jie Ni
- Centre for Technology in Water and Wastewater (CTWW), School of Civil and Environmental Engineering, University of Technology Sydney (UTS), Sydney, NSW 2007, Australia
| | - Fang Fang
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China.
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35
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Wang L, Guo J, Tian Z, Seery S, Jin Y, Zhang S. Developing a Hybrid Risk Assessment Tool for Familial Hypercholesterolemia: A Machine Learning Study of Chinese Arteriosclerotic Cardiovascular Disease Patients. Front Cardiovasc Med 2022; 9:893986. [PMID: 35990942 PMCID: PMC9381985 DOI: 10.3389/fcvm.2022.893986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 06/22/2022] [Indexed: 11/15/2022] Open
Abstract
Background Familial hypercholesterolemia (FH) is an autosomal-dominant genetic disorder with a high risk of premature arteriosclerotic cardiovascular disease (ASCVD). There are many alternative risk assessment tools, for example, DLCN, although their sensitivity and specificity vary among specific populations. We aimed to assess the risk discovery performance of a hybrid model consisting of existing FH risk assessment tools and machine learning (ML) methods, based on the Chinese patients with ASCVD. Materials and Methods In total, 5,597 primary patients with ASCVD were assessed for FH risk using 11 tools. The three best performing tools were hybridized through a voting strategy. ML models were set according to hybrid results to create a hybrid FH risk assessment tool (HFHRAT). PDP and ICE were adopted to interpret black box features. Results After hybridizing the mDLCN, Taiwan criteria, and DLCN, the HFHRAT was taken as a stacking ensemble method (AUC_class[94.85 ± 0.47], AUC_prob[98.66 ± 0.27]). The interpretation of HFHRAT suggests that patients aged <75 years with LDL-c >4 mmol/L were more likely to be at risk of developing FH. Conclusion The HFHRAT has provided a median of the three tools, which could reduce the false-negative rate associated with existing tools and prevent the development of atherosclerosis. The hybrid tool could satisfy the need for a risk assessment tool for specific populations.
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Affiliation(s)
- Lei Wang
- State Key Laboratory of Complex Severe and Rare Diseases, Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jian Guo
- State Key Laboratory of Complex Severe and Rare Diseases, Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- State Key Laboratory of Complex Severe and Rare Diseases, Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhuang Tian
- State Key Laboratory of Complex Severe and Rare Diseases, Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Samuel Seery
- Department of Humanities and Social Sciences, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ye Jin
- State Key Laboratory of Complex Severe and Rare Diseases, Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shuyang Zhang
- State Key Laboratory of Complex Severe and Rare Diseases, Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Shuyang Zhang,
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Zhang C, Dong H, Geng Y, Liang H, Liu X. Machine learning based prediction for China's municipal solid waste under the shared socioeconomic pathways. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 312:114918. [PMID: 35325735 DOI: 10.1016/j.jenvman.2022.114918] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 02/20/2022] [Accepted: 03/15/2022] [Indexed: 06/14/2023]
Abstract
Reliable forecast of municipal solid waste (MSW) generation is crucial for sustainable and efficient waste management. Big data analysis is a novel method to forecast MSW more accurately. Thus, this study employs five kinds of supervised machine learning approaches including linear regression, polynomial regression, support vector machine, random forest, and extreme gradient boosting (XGBoost) to examine their forecast performances. China's MSW generation from 2020 to 2060 under five shared socioeconomic pathways (SSPs) is further predicted and the mechanisms between MSW generation and socioeconomic features are explored. Results show that population and GDP are two dominant indicators in MSW prediction, and XGBoost model is proved to be effective in MSW forecast. MSW generation of China in 2060 is estimated to be 464-688 megatons under different SSPs scenarios, about four to six times of that in 2000. SSP3 that has the most population, least GDP and the highest climate change challenges is the only scenario showing a potential of MSW peak during the study period. The key for MSW increase is mainly the increase of per capita MSW caused by GDP. Finally, several policy recommendations are raised to reduce the overall MSW generation.
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Affiliation(s)
- Chenyi Zhang
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Huijuan Dong
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Engineering Research Center of Solid Waste Treatment and Resource Recovery, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Yong Geng
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Engineering Research Center of Solid Waste Treatment and Resource Recovery, Shanghai Jiao Tong University, Shanghai, 200240, China; School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Hongda Liang
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xiao Liu
- China Institute for Urban Governance, Shanghai Jiao Tong University, Shanghai, 200030, China
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Huang Y, Wang X, Xiang W, Wang T, Otis C, Sarge L, Lei Y, Li B. Forward-Looking Roadmaps for Long-Term Continuous Water Quality Monitoring: Bottlenecks, Innovations, and Prospects in a Critical Review. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:5334-5354. [PMID: 35442035 PMCID: PMC9063115 DOI: 10.1021/acs.est.1c07857] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 04/05/2022] [Accepted: 04/06/2022] [Indexed: 05/29/2023]
Abstract
Long-term continuous monitoring (LTCM) of water quality can bring far-reaching influences on water ecosystems by providing spatiotemporal data sets of diverse parameters and enabling operation of water and wastewater treatment processes in an energy-saving and cost-effective manner. However, current water monitoring technologies are deficient for long-term accuracy in data collection and processing capability. Inadequate LTCM data impedes water quality assessment and hinders the stakeholders and decision makers from foreseeing emerging problems and executing efficient control methodologies. To tackle this challenge, this review provides a forward-looking roadmap highlighting vital innovations toward LTCM, and elaborates on the impacts of LTCM through a three-hierarchy perspective: data, parameters, and systems. First, we demonstrate the critical needs and challenges of LTCM in natural resource water, drinking water, and wastewater systems, and differentiate LTCM from existing short-term and discrete monitoring techniques. We then elucidate three steps to achieve LTCM in water systems, consisting of data acquisition (water sensors), data processing (machine learning algorithms), and data application (with modeling and process control as two examples). Finally, we explore future opportunities of LTCM in four key domains, water, energy, sensing, and data, and underscore strategies to transfer scientific discoveries to general end-users.
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Affiliation(s)
- Yuankai Huang
- Department
of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Xingyu Wang
- Department
of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Wenjun Xiang
- Department
of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Tianbao Wang
- Department
of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Clifford Otis
- Department
of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Logan Sarge
- Department
of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Yu Lei
- Department
of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Baikun Li
- Department
of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
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38
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Mining Campus Big Data: Prediction of Career Choice Using Interpretable Machine Learning Method. MATHEMATICS 2022. [DOI: 10.3390/math10081289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The issue of students’ career choice is the common concern of students themselves, parents, and educators. However, students’ behavioral data have not been thoroughly studied for understanding their career choice. In this study, we used eXtreme Gradient Boosting (XGBoost), a machine learning (ML) technique, to predict the career choice of college students using a real-world dataset collected in a specific college. Specifically, the data include information on the education and career choice of 18,000 graduates during their college years. In addition, SHAP (Shapley Additive exPlanation) was employed to interpret the results and analyze the importance of individual features. The results show that XGBoost can predict students’ career choice robustly with a precision, recall rate, and an F1 value of 89.1%, 85.4%, and 0.872, respectively. Furthermore, the interaction of features among four different choices of students (i.e., choose to study in China, choose to work, difficulty in finding a job, and choose to study aboard) were also explored. Several educational features, especially differences in grade point average (GPA) during their college studying, are found to have relatively larger impact on the final choice of career. These results can be of help in the planning, design, and implementation of higher educational institutions’ (HEIs) events.
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Andrade Cruz I, Chuenchart W, Long F, Surendra KC, Renata Santos Andrade L, Bilal M, Liu H, Tavares Figueiredo R, Khanal SK, Fernando Romanholo Ferreira L. Application of machine learning in anaerobic digestion: Perspectives and challenges. BIORESOURCE TECHNOLOGY 2022; 345:126433. [PMID: 34848330 DOI: 10.1016/j.biortech.2021.126433] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 11/21/2021] [Accepted: 11/22/2021] [Indexed: 06/13/2023]
Abstract
Anaerobic digestion (AD) is widely adopted for remediating diverse organic wastes with simultaneous production of renewable energy and nutrient-rich digestate. AD process, however, suffers from instability, thereby adversely affecting biogas production. There have been significant efforts in developing strategies to control the AD process to maintain process stability and predict AD performance. Among these strategies, machine learning (ML) has gained significant interest in recent years in AD process optimization, prediction of uncertain parameters, detection of perturbations, and real-time monitoring. ML uses inductive inference to generalize correlations between input and output data, subsequently used to make informed decisions in new circumstances. This review aims to critically examine ML as applied to the AD process and provides an in-depth assessment of important algorithms (ANN, ANFIS, SVM, RF, GA, and PSO) and their applications in AD modeling. The review also outlines some challenges and perspectives of ML, and highlights future research directions.
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Affiliation(s)
- Ianny Andrade Cruz
- Graduate Program in Process Engineering, Tiradentes University, Av. Murilo Dantas, 300, Farolândia, 49032-490 Aracaju, SE, Brazil
| | - Wachiranon Chuenchart
- Department of Civil and Environmental Engineering, University of Hawai'i at Mānoa, 2540 Dole Street, Honolulu, HI 96822, USA; Department of Molecular Biosciences and Bioengineering, University of Hawai'i at Manoa, 1955 East-West Road, Honolulu, HI 96822, USA
| | - Fei Long
- Department of Biological and Ecological Engineering, Oregon State University, Corvallis, OR 97333, USA
| | - K C Surendra
- Department of Molecular Biosciences and Bioengineering, University of Hawai'i at Manoa, 1955 East-West Road, Honolulu, HI 96822, USA; Global Institute for Interdisciplinary Studies, 44600 Kathmandu, Nepal
| | - Larissa Renata Santos Andrade
- Graduate Program in Process Engineering, Tiradentes University, Av. Murilo Dantas, 300, Farolândia, 49032-490 Aracaju, SE, Brazil
| | - Muhammad Bilal
- School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huaian 223003, China
| | - Hong Liu
- Department of Biological and Ecological Engineering, Oregon State University, Corvallis, OR 97333, USA
| | - Renan Tavares Figueiredo
- Graduate Program in Process Engineering, Tiradentes University, Av. Murilo Dantas, 300, Farolândia, 49032-490 Aracaju, SE, Brazil; Institute of Technology and Research, Av. Murilo Dantas, 300, Farolândia, 49032-490 Aracaju, SE, Brazil
| | - Samir Kumar Khanal
- Department of Civil and Environmental Engineering, University of Hawai'i at Mānoa, 2540 Dole Street, Honolulu, HI 96822, USA; Department of Molecular Biosciences and Bioengineering, University of Hawai'i at Manoa, 1955 East-West Road, Honolulu, HI 96822, USA.
| | - Luiz Fernando Romanholo Ferreira
- Graduate Program in Process Engineering, Tiradentes University, Av. Murilo Dantas, 300, Farolândia, 49032-490 Aracaju, SE, Brazil; Institute of Technology and Research, Av. Murilo Dantas, 300, Farolândia, 49032-490 Aracaju, SE, Brazil
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Wang Z, Peng X, Xia A, Shah AA, Huang Y, Zhu X, Zhu X, Liao Q. The role of machine learning to boost the bioenergy and biofuels conversion. BIORESOURCE TECHNOLOGY 2022; 343:126099. [PMID: 34626766 DOI: 10.1016/j.biortech.2021.126099] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/04/2021] [Accepted: 10/05/2021] [Indexed: 06/13/2023]
Abstract
The development and application of bioenergy and biofuels conversion technology can play a significant role for the production of renewable and sustainable energy sources in the future. However, the complexity of bioenergy systems and the limitations of human understanding make it difficult to build models based on experience or theory for accurate predictions. Recent developments in data science and machine learning (ML), can provide new opportunities. Accordingly, this critical review provides a deep insight into the application of ML in the bioenergy context. The latest advances in ML assisted bioenergy technology, including energy utilization of lignocellulosic biomass, microalgae cultivation, biofuels conversion and application, are reviewed in detail. The strengths and limitations of ML in bioenergy systems are comprehensively analysed. Moreover, we highlight the capabilities and potential of advanced ML methods when encountering multifarious tasks in the future prospects to advance a new generation of bioenergy and biofuels conversion technologies.
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Affiliation(s)
- Zhengxin Wang
- Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Chongqing University, Ministry of Education, Chongqing 400044, PR China; Institute of Engineering Thermophysics, School of Energy and Power Engineering, Chongqing University, Chongqing 400044, PR China
| | - Xinggan Peng
- School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore
| | - Ao Xia
- Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Chongqing University, Ministry of Education, Chongqing 400044, PR China; Institute of Engineering Thermophysics, School of Energy and Power Engineering, Chongqing University, Chongqing 400044, PR China.
| | - Akeel A Shah
- Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Chongqing University, Ministry of Education, Chongqing 400044, PR China; Institute of Engineering Thermophysics, School of Energy and Power Engineering, Chongqing University, Chongqing 400044, PR China
| | - Yun Huang
- Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Chongqing University, Ministry of Education, Chongqing 400044, PR China; Institute of Engineering Thermophysics, School of Energy and Power Engineering, Chongqing University, Chongqing 400044, PR China
| | - Xianqing Zhu
- Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Chongqing University, Ministry of Education, Chongqing 400044, PR China; Institute of Engineering Thermophysics, School of Energy and Power Engineering, Chongqing University, Chongqing 400044, PR China
| | - Xun Zhu
- Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Chongqing University, Ministry of Education, Chongqing 400044, PR China; Institute of Engineering Thermophysics, School of Energy and Power Engineering, Chongqing University, Chongqing 400044, PR China
| | - Qiang Liao
- Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Chongqing University, Ministry of Education, Chongqing 400044, PR China; Institute of Engineering Thermophysics, School of Energy and Power Engineering, Chongqing University, Chongqing 400044, PR China
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Wang F, Wang Y, Zhang K, Hu M, Weng Q, Zhang H. Spatial heterogeneity modeling of water quality based on random forest regression and model interpretation. ENVIRONMENTAL RESEARCH 2021; 202:111660. [PMID: 34265353 DOI: 10.1016/j.envres.2021.111660] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 06/28/2021] [Accepted: 07/04/2021] [Indexed: 06/13/2023]
Abstract
A systematic understanding of the spatial distribution of water quality is critical for successful watershed management; however, the limited number of physical monitoring stations has restricted the evaluation of spatial water quality distribution and the identification of features impacting the water quality. To fill this gap, we developed a modeling process that employed the random forest regression (RFR) to model the water quality distribution for the Taihu Lake basin in Zhejiang Province, China, and adopted the Shapley Additive exPlanations (SHAP) method to interpret the underlying driving forces. We first used RFR to model three water quality parameters: permanganate index (CODMn), total phosphorus (TP), and total nitrogen (TN), based on 16 watershed features. We then applied the built models to generate water quality distribution maps for the basin, with the CODMn ranging from 1.39 to 6.40 mg/L, TP from 0.02 to 0.23 mg/L, and TN from 1.43 to 4.27 mg/L. These maps showed generally consistent patterns among the CODMn, TN, and TP with minor differences in the spatial distribution. The SHAP analysis showed that the TN was mainly affected by agricultural non-point sources, while the CODMn and TP were affected by agricultural and domestic sources. Due to differences in sewage collection and treatment between urban and rural areas, the water quality in highly populated urban areas was better than that in rural areas, which led to an unexpected positive relationship between water quality and population density. Overall, with the RFR models and SHAP interpretation, we obtained a continuous distribution pattern of the water quality and identified its driving forces in the basin. These findings provided important information to assist water quality restoration projects.
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Affiliation(s)
- Feier Wang
- College of Environmental & Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Yixu Wang
- College of Environmental & Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Kai Zhang
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, OH, 44106, United States
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, 44195, United States
| | - Qin Weng
- College of Environmental & Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Huichun Zhang
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, OH, 44106, United States.
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Jeong K, Abbas A, Shin J, Son M, Kim YM, Cho KH. Prediction of biogas production in anaerobic co-digestion of organic wastes using deep learning models. WATER RESEARCH 2021; 205:117697. [PMID: 34600230 DOI: 10.1016/j.watres.2021.117697] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 08/24/2021] [Accepted: 09/20/2021] [Indexed: 05/25/2023]
Abstract
Interest in anaerobic co-digestion (AcoD) has increased significantly in recent decades owing to enhanced biogas productivity due to the utilization of different organic wastes, such as food waste and sewage sludge. In this study, a robust AcoD model for biogas prediction is developed using deep learning (DL). We propose a hybrid DL architecture, i.e., DA-LSTM-VSN, wherein a dual-stage-attention (DA)-based long short-term memory (LSTM) network is integrated with variable selection networks (VSNs). To enhance the model predictability, we perform hyperparameter optimization. The model accuracy is validated using long-term AcoD monitoring data measured over two years of municipal wastewater treatment plant operation and then compared with those of two other DL-based models (i.e., DA-LSTM and the standard LSTM). In addition, the feature importance (FI) is analyzed to investigate the relative contribution of input variables to biogas production prediction. Finally, we demonstrate the successful application of the validated DL model to the AcoD process optimization. Results show that the model accuracy improved significantly by incorporating DA into LSTM, i.e., the coefficient of determination (R2) increased from 0.38 to 0.68; however, the R2 can be further increased to 0.76 by combining DA-LSTM with a VSN. For the biogas prediction of the AcoD model, the VSN contributes significantly by employing the discontinuous time series of measurement data on biodegradable organic-associated variables during AcoD. In addition, the VSN allows the AcoD model to be interpretable via FI analysis using its weighted input features. The FI results show that the relative importance is vital to variables associated with food waste leachate, whereas it is marginal for those associated with the primary and chemically assisted sedimentation sludges. In conclusion, the AcoD model proposed herein can be utilized in practical applications as a robust tool because it can provide the optimal sludge conditions to improve biogas production. This is because it facilitates the time-series biogas prediction at the full scale using unprocessed datasets with either missing value imputation or outlier removal.
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Affiliation(s)
- Kwanho Jeong
- Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, South Korea
| | - Ather Abbas
- Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, South Korea
| | - Jingyeong Shin
- Department of Civil and Environmental Engineering, Hanyang University, Seongdong-gu, Seoul, 04763, South Korea
| | - Moon Son
- Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, South Korea
| | - Young Mo Kim
- Department of Civil and Environmental Engineering, Hanyang University, Seongdong-gu, Seoul, 04763, South Korea.
| | - Kyung Hwa Cho
- Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, South Korea.
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An emerging machine learning strategy for the assisted‐design of high-performance supercapacitor materials by mining the relationship between capacitance and structural features of porous carbon. J Electroanal Chem (Lausanne) 2021. [DOI: 10.1016/j.jelechem.2021.115684] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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44
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In Situ Monitoring of Nitrate Content in Leafy Vegetables Using Attenuated Total Reflectance − Fourier-Transform Mid-infrared Spectroscopy Coupled with Machine Learning Algorithm. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-021-02048-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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45
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Abstract
PURPOSE OF REVIEW Refinement in machine learning (ML) techniques and approaches has rapidly expanded artificial intelligence applications for the diagnosis and classification of heart failure (HF). This review is designed to provide the clinician with the basics of ML, as well as this technologies future utility in HF diagnosis and the potential impact on patient outcomes. RECENT FINDINGS Recent studies applying ML methods to unique data sets available from electrocardiography, vectorcardiography, echocardiography, and electronic health records show significant promise for improving diagnosis, enhancing detection, and advancing treatment of HF. Innovations in both supervised and unsupervised methods have heightened the diagnostic accuracy of models developed to identify the presence of HF and further augmentation of model capabilities are likely utilizing ensembles of ML algorithms derived from different techniques. SUMMARY This article is an overview of recent applications of ML to achieve improved diagnosis of HF and the resultant implications for patient management.
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Affiliation(s)
- William E Sanders
- University of North Carolina at Chapel Hill, Chapel Hill
- CorVista Health, Inc., Cary, North Carolina, USA
| | - Tim Burton
- CorVista Health, Toronto, Ontario, Canada
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Priami C. Computational approaches to understanding nutrient metabolism and metabolic disorders. Curr Opin Biotechnol 2020; 70:7-14. [PMID: 33038781 DOI: 10.1016/j.copbio.2020.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/01/2020] [Accepted: 09/06/2020] [Indexed: 10/23/2022]
Abstract
Computational methods are becoming more and more essential to elucidate biological systems. Many different approaches exist with pros and cons. This paper reviews the most useful technologies focusing on nutrient metabolism and metabolic disorders. Space limitation prevents from exploring the examples in details, but pointers to the relevant papers are reported.
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Affiliation(s)
- Corrado Priami
- Dipartimento di Informatica, Università di Pisa, Largo Pontecorvo, 56124 Pisa, Italy.
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47
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Not Just Numbers: Mathematical Modelling and Its Contribution to Anaerobic Digestion Processes. Processes (Basel) 2020. [DOI: 10.3390/pr8080888] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Mathematical modelling of bioprocesses has a long and notable history, with eminent contributions from fields including microbiology, ecology, biophysics, chemistry, statistics, control theory and mathematical theory. This richness of ideas and breadth of concepts provide great motivation for inquisitive engineers and intrepid scientists to try their hand at modelling, and this collaboration of disciplines has also delivered significant milestones in the quality and application of models for both theoretical and practical interrogation of engineered biological systems. The focus of this review is the anaerobic digestion process, which, as a technology that has come in and out of fashion, remains a fundamental process for addressing the global climate emergency. Whether with conventional anaerobic digestion systems, biorefineries, or other anaerobic technologies, mathematical models are important tools that are used to design, monitor, control and optimise the process. Both highly structured, mechanistic models and data-driven approaches have been used extensively over half a decade, but recent advances in computational capacity, scientific understanding and diversity and quality of process data, presents an opportunity for the development of new modelling paradigms, augmentation of existing methods, or even incorporation of tools from other disciplines, to ensure that anaerobic digestion research can remain resilient and relevant in the face of emerging and future challenges.
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48
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Machine Learning Based Hybrid System for Imputation and Efficient Energy Demand Forecasting. ENERGIES 2020. [DOI: 10.3390/en13112681] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The ongoing upsurge of deep learning and artificial intelligence methodologies manifest incredible accomplishment in a broad scope of assessing issues in different industries, including the energy sector. In this article, we have presented a hybrid energy forecasting model based on machine learning techniques. It is based on the three machine learning algorithms: extreme gradient boosting, categorical boosting, and random forest method. Usually, machine learning algorithms focus on fine-tuning the hyperparameters, but our proposed hybrid algorithm focuses on the preprocessing using feature engineering to improve forecasting. We also focus on the way to impute a significant data gap and its effect on predicting. The forecasting exactness of the proposed model is evaluated using the regression score, and it depicts that the proposed model, with an R-squared of 0.9212, is more accurate than existing models. For the testing purpose of the proposed energy consumption forecasting model, we have used the actual dataset of South Korea’s hourly energy consumption. The proposed model can be used for any other dataset as well. This research result will provide a scientific premise for the strategy modification of energy supply and demand.
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Abstract
Biogas is a fuel obtained from organic waste fermentation and can be an interesting solution for producing electric energy, heat and fuel. Recently, many European countries have incentivized the production of biomethane to be injected into natural gas grids or compressed and used as biofuel in vehicles. The introduction of an upgrading unit into an existing anaerobic digestion plant to convert biogas to biomethane may have a strong impact on the overall energy balance of the systems. The amount of biomethane produced may be optimized from several points of view (i.e., energy, environmental and economic). In this paper, the mass and energy fluxes of an anaerobic digestion plant were analyzed as a function of the biogas percentage sent to the upgrading system and the amount of biomethane produced. A numerical model of an anaerobic digestion plant was developed by considering an existing case study. The mass and energy balance of the digesters, cogeneration unit, upgrading system and auxiliary boiler were estimated when the amount of produced biomethane was varied. An internal combustion engine was adopted as the cogeneration unit and a CO2 absorption system was assumed for biogas upgrading. Results demonstrated that the energy balance of the plant is strictly dependent on the biomethane production and that an excess of biomethane production makes the plant totally dependent on external energy sources. As for the environmental impact, an optimal level of biomethane production exists that minimizes the emissions of equivalent CO2. However, high biomethane subsides can encourage plant managers to increase biomethane production and thus reduce CO2 savings.
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