1
|
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.
Collapse
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.
| |
Collapse
|
2
|
Hooda S, Mondal P. Predictive modeling of plastic pyrolysis process for the evaluation of activation energy: Explainable artificial intelligence based comprehensive insights. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 360:121189. [PMID: 38759553 DOI: 10.1016/j.jenvman.2024.121189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/30/2024] [Accepted: 05/13/2024] [Indexed: 05/19/2024]
Abstract
Pyrolysis, a thermochemical conversion approach of transforming plastic waste to energy has tremendous potential to manage the exponentially increasing plastic waste. However, understanding the process kinetics is fundamental to engineering a sustainable process. Conventional analysis techniques do not provide insights into the influence of characteristics of feedstock on the process kinetics. Present study exemplifies the efficacy of using machine learning for predictive modeling of pyrolysis of waste plastics to understand the complexities of the interrelations of predictor variables and their influence on activation energy. The activation energy for pyrolysis of waste plastics was evaluated using machine learning models namely Random Forest, XGBoost, CatBoost, and AdaBoost regression models. Feature selection based on the multicollinearity of data and hyperparameter tuning of the models utilizing RandomizedSearchCV was conducted. Random forest model outperformed the other models with coefficient of determination (R2) value of 0.941, root mean square error (RMSE) value of 14.69 and mean absolute error (MAE) value of 8.66 for the testing dataset. The explainable artificial intelligence-based feature importance plot and the summary plot of the shapely additive explanations projected fixed carbon content, ash content, conversion value, and carbon content as significant parameters of the model in the order; fixed carbon > carbon > ash content > degree of conversion. Present study highlighted the potential of machine learning as a powerful tool to understand the influence of the characteristics of plastic waste and the degree of conversion on the activation energy of a process that is essential for designing the large-scale operations and future scale-up of the process.
Collapse
Affiliation(s)
- Sanjeevani Hooda
- Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India
| | - Prasenjit Mondal
- Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India.
| |
Collapse
|
3
|
Kane S, Miller SA. Predicting biochar properties and pyrolysis life-cycle inventories with compositional modeling. BIORESOURCE TECHNOLOGY 2024; 399:130551. [PMID: 38458265 DOI: 10.1016/j.biortech.2024.130551] [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: 11/28/2023] [Revised: 03/05/2024] [Accepted: 03/06/2024] [Indexed: 03/10/2024]
Abstract
Biochar, formed through slow pyrolysis of biomass, has garnered attention as a pathway to bind atmospheric carbon in products. However, life cycle assessment data for biomass pyrolysis have limitations in data quality, particularly for novel processes. Here, a compositional, predictive model of slow pyrolysis is developed, with a focus on CO2 fluxes and energy products, reflecting mass-weighted cellulose, hemicellulose, and lignin pyrolysis products for a given pyrolysis temperature. This model accurately predicts biochar yields and composition within 5 % of experimental values but shows broader distributions for bio-oil and syngas (typically within 20 %). This model is demonstrated on common feedstocks to quantify biochar yield, energy, and CO2 emissions as a function of temperature and produce key life cycle inventory flows (e.g., 0.73 kg CO2/kg poplar biochar bound carbon at 500 °C). This model can be adapted to any lignocellulosic biomass to inform development of pyrolysis processes that maximize carbon sequestration.
Collapse
Affiliation(s)
- Seth Kane
- Department of Civil and Environmental Engineering, University of California, Davis, United States.
| | - Sabbie A Miller
- Department of Civil and Environmental Engineering, University of California, Davis, United States
| |
Collapse
|
4
|
Wang W, Chang JS, Lee DJ. Machine learning applications for biochar studies: A mini-review. BIORESOURCE TECHNOLOGY 2024; 394:130291. [PMID: 38184089 DOI: 10.1016/j.biortech.2023.130291] [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: 10/29/2023] [Revised: 12/20/2023] [Accepted: 12/31/2023] [Indexed: 01/08/2024]
Abstract
Biochar is a promising carbon sink whose application can assist in reducing carbon emissions. Development of this technology currently relies on experimental trials, which are time-consuming and labor-intensive. Machine learning (ML) technology presents a potential solution for streamlining this process. This review summarizes the current research on ML's applications in biochar production, characterization, and applications. It briefly explains commonly used machine learning algorithms and discusses prospects and challenges. A hybrid model that combines ML with mechanism-based analysis could be a future trend, addressing the ML's black-box nature. While biochar studies have adopted ML technology, current works mostly use lab-scale data for model training. Further work is needed to develop ML models based on pilot or industrial-scale data to realize the use of ML techniques for the field application of biochar.
Collapse
Affiliation(s)
- Wei Wang
- Department of Chemical Engineering, National Taiwan University, Taipei 106, Taiwan
| | - Jo-Shu Chang
- Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407, Taiwan; Department of Chemical Engineering, National Cheng Kung University, Tainan, Taiwan; Department of Chemical and Materials Engineering, Tunghai University, Taichung 407, Taiwan
| | - Duu-Jong Lee
- Department of Chemical Engineering, National Taiwan University, Taipei 106, Taiwan; Department of Mechanical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong.
| |
Collapse
|
5
|
Ma J, Zhang S, Liu X, Wang J. Machine learning prediction of biochar yield based on biomass characteristics. BIORESOURCE TECHNOLOGY 2023; 389:129820. [PMID: 37805089 DOI: 10.1016/j.biortech.2023.129820] [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: 08/12/2023] [Revised: 10/01/2023] [Accepted: 10/01/2023] [Indexed: 10/09/2023]
Abstract
Slow pyrolysis is a widely used thermochemical pathway that can convert organic waste into biochar. We employed six machine learning models to predictively model 13 selected variables using pearson feature selection. Additionally, partial dependence analysis is used to reveal the deep relationship between feature variables. Both the gradient boosting decision tree and the Levenberg-Marquardt backpropagation neural network achieved training set R2 > 0.9 and testing set R2 > 0.8. But the other models displayed lower performance on the testing set, with R2 < 0.8. The partial dependence plot demonstrates that pyrolysis conditions have greater impact on biochar yield than biomass composition. Furthermore, the highest treatment temperature, being the sole consistently changing feature, can serve as a guiding factor for regulating biochar yield. This study highlights the immense potential of machine learning in experimental prediction, providing a scientific reference for reducing time and economic costs in pyrolysis experiments and process development.
Collapse
Affiliation(s)
- Jingjing Ma
- School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, 710049, China
| | - Shuai Zhang
- School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, 710049, China
| | - Xiangjun Liu
- School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, 710049, China
| | - Junqi Wang
- School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, 710049, China.
| |
Collapse
|
6
|
Gupta D, Das A, Mitra S. Role of modeling and artificial intelligence in process parameter optimization of biochar: A review. BIORESOURCE TECHNOLOGY 2023; 390:129792. [PMID: 37820969 DOI: 10.1016/j.biortech.2023.129792] [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: 08/10/2023] [Accepted: 09/17/2023] [Indexed: 10/13/2023]
Abstract
Enhancement of crop yield, conservation and quality upgradation of soil, and efficient water management are the main objectives of sustainable agriculture and mitigating climate change's impact on agriculture. In recent days, biochar, obtained via thermochemical alteration of biomass is becoming a powerful agent for soil and water quality improvement, carbon sequestration, greenhouse gas emission reduction, and heavy metal adsorption. The present study predominantly focuses on various process parameters related to biochar preparation through pyrolysis, their impact on biochar production as well as physicochemical characteristics, and the optimization of such process parameters. Different designs of the experiment (DOE) and optimization techniques including traditional and non-traditional optimizations are discussed in the current review, along with their applicability and shortcomings. Since the biochar preparation process is tedious and energy-consuming, the present review will help to understand the importance of optimization in preparing biochar, thereby leading to a better way to prepare biochar.
Collapse
Affiliation(s)
- Debaditya Gupta
- Agro-ecotechnology Laboratory, School of Agro & Rural Technology, Indian Institute of Technology Guwahati, Assam 781039, India
| | - Ashmita Das
- Agro-ecotechnology Laboratory, School of Agro & Rural Technology, Indian Institute of Technology Guwahati, Assam 781039, India
| | - Sudip Mitra
- Agro-ecotechnology Laboratory, School of Agro & Rural Technology, Indian Institute of Technology Guwahati, Assam 781039, India.
| |
Collapse
|
7
|
Zhang X, Bhattacharya T, Wang C, Kumar A, Nidheesh PV. Straw-derived biochar for the removal of antibiotics from water: Adsorption and degradation mechanisms, recent advancements and challenges. ENVIRONMENTAL RESEARCH 2023; 237:116998. [PMID: 37634688 DOI: 10.1016/j.envres.2023.116998] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/21/2023] [Accepted: 08/25/2023] [Indexed: 08/29/2023]
Abstract
Antibiotics, a kind of containments with the properties of widely distributed and difficult to degrade, has aroused extensive attention in the world. As a prevalent agricultural waste, straws can be utilized to prepare biochar (straw-derived biochar, SBC) to remove antibiotics from aquatic environment. To date, although a number of review papers have summarized and discussed research on biochar application in wastewater treatment and soil remediation, there are few reviews on SBC for antibiotic removal. Due to the limitations of poor adsorption and degradation performance of the pristine SBC, it is necessary to modify SBC to improve its applications for antibiotics removal. The maximum antibiotic removal capacity of modified SBC could reach 1346.55 mg/g. Moreover, the adsorption mechanisms between modified SBC and antibiotics mainly involve π-π interactions, electrostatic interactions, hydrophobic interactions, and charge dipole interactions. In addition, the modified SBC could completely degrade antibiotics within 6 min by activating oxidants, such as PS, PDS, H2O2, and O3. The mechanisms of antibiotic degradation by SBC activated oxidants mainly include free radicals (including SO4•-, •OH, and O2•-) and non-free radical pathway (such as, 1O2, electrons transfer, and surface-confined reaction). Although SBC and modified SBC have demonstrated excellent performance in removing antibiotics, they still face some challenges in practical applications, such as poor stability, high cost, and difficulties in recycling. Therefore, the further research directions and trends for the development of SBC and biochar-based materials should be taken into consideration.
Collapse
Affiliation(s)
- Xiuxiu Zhang
- School of Chemical Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Tansuhree Bhattacharya
- Department of Civil and Environmental Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India
| | - Chongqing Wang
- School of Chemical Engineering, Zhengzhou University, Zhengzhou, 450001, China.
| | - Abhishek Kumar
- Department of Civil and Environmental Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India
| | - Puthiya Veetil Nidheesh
- Environmental Impact and Sustainability Division, CSIR - National Environmental Engineering Research Institute, Nagpur, Maharashtra, India.
| |
Collapse
|
8
|
Supraja KV, Kachroo H, Viswanathan G, Verma VK, Behera B, Doddapaneni TRKC, Kaushal P, Ahammad SZ, Singh V, Awasthi MK, Jain R. Biochar production and its environmental applications: Recent developments and machine learning insights. BIORESOURCE TECHNOLOGY 2023; 387:129634. [PMID: 37573981 DOI: 10.1016/j.biortech.2023.129634] [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: 06/30/2023] [Revised: 08/01/2023] [Accepted: 08/03/2023] [Indexed: 08/15/2023]
Abstract
Biochar production through thermochemical processing is a sustainable biomass conversion and waste management approach. However, commercializing biochar faces challenges requiring further research and development to maximize its potential for addressing environmental concerns and promoting sustainable resource management. This comprehensive review presents the state-of-the-art in biochar production, emphasizing quantitative yield and qualitative properties with varying feedstocks. It discusses the technology readiness level and commercialization status of different production strategies, highlighting their environmental and economic impacts. The review focuses on integrating machine learning algorithms for process control and optimization in biochar production, improving efficiency. Additionally, it explores biochar's environmental applications, including soil amendment, carbon sequestration, and wastewater treatment, showcasing recent advancements and case studies. Advances in biochar technologies and their environmental benefits in various sectors are discussed herein.
Collapse
Affiliation(s)
- Kolli Venkata Supraja
- Waste Treatment Laboratory, Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Himanshu Kachroo
- School of Interdisciplinary Research, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Gayatri Viswanathan
- School of Interdisciplinary Research, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Vishal Kumar Verma
- Waste Treatment Laboratory, Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Bunushree Behera
- Bioprocess Laboratory, Department of Biotechnology, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, India
| | - Tharaka Rama Krishna C Doddapaneni
- Chair of Biosystems Engineering, Institute of Forestry and Engineering, Estonian University of Life Sciences, Kreutzwaldi 56, 51014 Tartu, Estonia
| | - Priyanka Kaushal
- Centre for Rural Development and Technology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Sk Ziauddin Ahammad
- Waste Treatment Laboratory, Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Vijai Singh
- Department of Biosciences, School of Science, Indrashil University, Rajpur, Mehsana 382715, India
| | - Mukesh Kumar Awasthi
- College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China
| | - Rohan Jain
- Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Bautzner landstrasse 400, 01328 Dresden, Germany.
| |
Collapse
|
9
|
Manikandan S, Vickram S, Subbaiya R, Karmegam N, Woong Chang S, Ravindran B, Kumar Awasthi M. Comprehensive review on recent production trends and applications of biochar for greener environment. BIORESOURCE TECHNOLOGY 2023; 388:129725. [PMID: 37683709 DOI: 10.1016/j.biortech.2023.129725] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 08/21/2023] [Accepted: 09/05/2023] [Indexed: 09/10/2023]
Abstract
The suitability of biochar as a supplement for environmental restoration varies significantly based on the type of feedstocks used and the parameters of the pyrolysis process. This study comprehensively examines several aspects of biochar's potential benefits, its capacity to enhance crop yields, improve nutrient availability, support the co-composting, water restoration and enhance overall usage efficiency. The supporting mechanistic evidence for these claims is also evaluated. Additionally, the analysis identifies various gaps in research and proposes potential directions for further exploration to enhance the understanding of biochar application. As a mutually advantageous approach, the integration of biochar into agricultural contexts not only contributes to environmental restoration but also advances ecological sustainability. The in-depth review underscores the diverse suitability of biochar as a supplement for environmental restoration, contingent upon the specific feedstock sources and pyrolysis conditions used. However, concerns have been raised regarding potential impacts on human health within agricultural sectors.
Collapse
Affiliation(s)
- Sivasubramanian Manikandan
- Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai 602 105. Tamil Nadu, India
| | - Sundaram Vickram
- Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai 602 105. Tamil Nadu, India
| | - Ramasamy Subbaiya
- Department of Biological Sciences, School of Mathematics and Natural Sciences, The Copperbelt University, Riverside, Jambo Drive, P O Box 21692 Kitwe, Zambia
| | - Natchimuthu Karmegam
- PG and Research Department of Botany, Government Arts College (Autonomous), Salem 636 007, Tamil Nadu, India
| | - Soon Woong Chang
- Department of Environmental Energy and Engineering, Kyonggi University Yeongtong-Gu, Suwon, Gyeonggi-Do 16227, Republic of Korea
| | - Balasubramani Ravindran
- Department of Medical Biotechnology and Integrative Physiology, Institute of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, 602105, Tamil Nadu, India; Department of Environmental Energy and Engineering, Kyonggi University Yeongtong-Gu, Suwon, Gyeonggi-Do 16227, Republic of Korea
| | - Mukesh Kumar Awasthi
- College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, China.
| |
Collapse
|
10
|
Chen J, Zhang M, Xu Z, Ma R, Shi Q. Machine-learning analysis to predict the fluorescence quantum yield of carbon quantum dots in biochar. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 896:165136. [PMID: 37379935 DOI: 10.1016/j.scitotenv.2023.165136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 06/11/2023] [Accepted: 06/23/2023] [Indexed: 06/30/2023]
Abstract
Biochar nanoparticles have recently attracted attention, owing to their environmental behavior and ecological effects. However, biochar has not been shown to contain carbon quantum dots (< 10 nm) with unique photovoltaic properties. Therefore, this study utilized several characterization techniques to demonstrate the generation of carbon quantum dots in biochar produced from 10 types of farm waste. The generated carbon quantum dots had a quasi-spherical morphology and high-resolution lattice stripes with lattice spacings of 0.20-0.23 nm. Moreover, they contained functional groups with good hydrophilic properties, such as amino and hydroxyl groups, and elemental O, C, and N on the surface. A crucial determinant of the photoluminescence properties of carbon quantum dots is their fluorescence quantum yield. Therefore, the relationship between the biochar preparation parameters and the fluorescence quantum yield was investigated using six machine learning analytical models based on 480 samples. Among the models, the gradient-boosting decision-tree regression model exhibited the best predictive performance (R2 > 0.9, RMSE <0.02, and MAPE <3), and was used for the analysis of feature importance; compared to the properties of the raw material, the production parameters had a greater effect on the fluorescence quantum yield. Additionally, four key features were identified: pyrolysis temperature, residence time, N content, and C/N ratio, which were independent of farm waste type. These features can be used to accurately predict the fluorescence quantum yield of carbon quantum dots in biochar. The relative error range between the predicted and the experimental value of fluorescence quantum yield is 0.00-4.60 %. Thus, the prediction model has the potential to predict the fluorescence quantum yield of carbon quantum dots in other types of farm waste biochar, and provides fundamental information for the study of biochar nanoparticles.
Collapse
Affiliation(s)
- Jiao Chen
- College of Ecology and Environment, Xin Jiang University, Urumqi 830046, PR China
| | - Mengqian Zhang
- China Energy Conservation and Environmental Protection Group, Beijing 100035, PR China
| | - Zijun Xu
- College of Ecology and Environment, Xin Jiang University, Urumqi 830046, PR China..
| | - Ruoxin Ma
- College of Ecology and Environment, Xin Jiang University, Urumqi 830046, PR China
| | - Qingdong Shi
- College of Ecology and Environment, Xin Jiang University, Urumqi 830046, PR China
| |
Collapse
|
11
|
Yuan X, Cao Y, Li J, Patel AK, Dong CD, Jin X, Gu C, Yip ACK, Tsang DCW, Ok YS. Recent advancements and challenges in emerging applications of biochar-based catalysts. Biotechnol Adv 2023; 67:108181. [PMID: 37268152 DOI: 10.1016/j.biotechadv.2023.108181] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 05/21/2023] [Accepted: 05/24/2023] [Indexed: 06/04/2023]
Abstract
The sustainable utilization of biochar produced from biomass waste could substantially promote the development of carbon neutrality and a circular economy. Due to their cost-effectiveness, multiple functionalities, tailorable porous structure, and thermal stability, biochar-based catalysts play a vital role in sustainable biorefineries and environmental protection, contributing to a positive, planet-level impact. This review provides an overview of emerging synthesis routes for multifunctional biochar-based catalysts. It discusses recent advances in biorefinery and pollutant degradation in air, soil, and water, providing deeper and more comprehensive information of the catalysts, such as physicochemical properties and surface chemistry. The catalytic performance and deactivation mechanisms under different catalytic systems were critically reviewed, providing new insights into developing efficient and practical biochar-based catalysts for large-scale use in various applications. Machine learning (ML)-based predictions and inverse design have addressed the innovation of biochar-based catalysts with high-performance applications, as ML efficiently predicts the properties and performance of biochar, interprets the underlying mechanisms and complicated relationships, and guides biochar synthesis. Finally, environmental benefit and economic feasibility assessments are proposed for science-based guidelines for industries and policymakers. With concerted effort, upgrading biomass waste into high-performance catalysts for biorefinery and environmental protection could reduce environmental pollution, increase energy safety, and achieve sustainable biomass management, all of which are beneficial for attaining several of the United Nations Sustainable Development Goals (UN SDGs) and Environmental, Social and Governance (ESG).
Collapse
Affiliation(s)
- Xiangzhou Yuan
- Ministry of Education of Key Laboratory of Energy Thermal Conversion and Control, School of Energy and Environment, Southeast University, Nanjing 210096, China; Korea Biochar Research Center, APRU Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Yang Cao
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Jie Li
- CAS Key Laboratory of Urban Pollutant Conversion, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Anil Kumar Patel
- Institute of Aquatic Science and Technology, College of Hydrosphere, National Kaohsiung University of Science and Technology, Kaohsiung 81157, Taiwan
| | - Cheng-Di Dong
- Department of Marine Environmental Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 81157, Taiwan
| | - Xin Jin
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Cheng Gu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Alex C K Yip
- Department of Chemical and Process Engineering, University of Canterbury, Christchurch, New Zealand
| | - Daniel C W Tsang
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China; Research Centre for Resources Engineering towards Carbon Neutrality, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China.
| | - Yong Sik Ok
- Korea Biochar Research Center, APRU Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, Republic of Korea.
| |
Collapse
|
12
|
Huang J, Tan X, Ali I, Duan Z, Naz I, Cao J, Ruan Y, Wang Y. More effective application of biochar-based immobilization technology in the environment: Understanding the role of biochar. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 872:162021. [PMID: 36775150 DOI: 10.1016/j.scitotenv.2023.162021] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 01/12/2023] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
In recent years, biochar-based immobilization technology (BIT) has been widely used to treat different environmental issues because of its cost-effectiveness and high removal performance. However, the complexity of the real environment is always ignored, which hinders the transfer of the BIT from lab-scale to commercial applications. Therefore, in this review, the analysis is performed separately on the internal side of the BIT (microbial fixation and growth) and on the external side of the BIT (function) to achieve effective BIT performance. Importantly, the internal two stages of BIT have been discussed concisely. Further, the usage of BIT in different areas is summarized precisely. Notably, the key impacts were systemically analyzed during BIT applications including environmental conditions and biochar types. Finally, the suggestions and perspectives are elucidated to solve current issues regarding BIT.
Collapse
Affiliation(s)
- Jiang Huang
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China
| | - Xiao Tan
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China.
| | - Imran Ali
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China
| | - Zhipeng Duan
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China; College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
| | - Iffat Naz
- Department of Biology, Deanship of Educational Services, Qassim University, Buraidah 51452, Kingdom of Saudi Arabia
| | - Jun Cao
- National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, Nanjing 210098, China
| | - Yinlan Ruan
- Institute for Photonics and Advanced Sensing, The University of Adelaide, SA 5005, Australia
| | - Yimin Wang
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China
| |
Collapse
|
13
|
Prasertpong P, Onsree T, Khuenkaeo N, Tippayawong N, Lauterbach J. Exposing and understanding synergistic effects in co-pyrolysis of biomass and plastic waste via machine learning. BIORESOURCE TECHNOLOGY 2023; 369:128419. [PMID: 36462765 DOI: 10.1016/j.biortech.2022.128419] [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: 10/10/2022] [Revised: 11/20/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
During co-pyrolysis of biomass with plastic waste, bio-oil yields (BOY) could be either induced or reduced significantly via synergistic effects (SE). However, investigating/ interpreting the SE and BOY in multidimensional domains is complicated and limited. This work applied XGBoost machine-learning and Shapley additive explanation (SHAP) to develop interpretable/ explainable models for predicting BOY and SE from co-pyrolysis of biomass and plastic waste using 26 input features. Imbalanced training datasets were improved by synthetic minority over-sampling technique. The prediction accuracy of XGBoost models was nearly 0.90 R2 for BOY while greater than 0.85 R2 for SE. By SHAP, individual impact and interaction of input features on the XGBoost models can be achieved. Although reaction temperature and biomass-to-plastic ratio were the top two important features, overall contributions of feedstock characteristics were more than 60 % in the system of co-pyrolysis. The finding provides a better understanding of co-pyrolysis and a way of further improvements.
Collapse
Affiliation(s)
- Prapaporn Prasertpong
- Department of Mechanical Engineering, Rajamangala University of Technology Thanyaburi 12120, Thailand
| | - Thossaporn Onsree
- Department of Chemical Engineering, University of South Carolina, Columbia, SC, 29201, USA
| | - Nattawut Khuenkaeo
- Graduate Program in Energy Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand; Department of Mechanical 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.
| | - Jochen Lauterbach
- Department of Chemical Engineering, University of South Carolina, Columbia, SC, 29201, USA
| |
Collapse
|
14
|
Lim SJ, Son M, Ki SJ, Suh SI, Chung J. Opportunities and challenges of machine learning in bioprocesses: Categorization from different perspectives and future direction. BIORESOURCE TECHNOLOGY 2023; 370:128518. [PMID: 36565818 DOI: 10.1016/j.biortech.2022.128518] [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: 10/31/2022] [Revised: 12/15/2022] [Accepted: 12/17/2022] [Indexed: 06/17/2023]
Abstract
Recent advances in machine learning (ML) have revolutionized an extensive range of research and industry fields by successfully addressing intricate problems that cannot be resolved with conventional approaches. However, low interpretability and incompatibility make it challenging to apply ML to complicated bioprocesses, which rely on the delicate metabolic interplay among living cells. This overview attempts to delineate ML applications to bioprocess from different perspectives, and their inherent limitations (i.e., uncertainties in prediction) were then discussed with unique attempts to supplement the ML models. A clear classification can be made depending on the purpose of the ML (supervised vs unsupervised) per application, as well as on their system boundaries (engineered vs natural). Although a limited number of hybrid approaches with meaningful outcomes (e.g., improved accuracy) are available, there is still a need to further enhance the interpretability, compatibility, and user-friendliness of ML models.
Collapse
Affiliation(s)
- Seung Ji Lim
- Water Cycle Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
| | - Moon Son
- Water Cycle Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea; Division of Energy and Environmental Technology, KIST School, Korea University of Science and Technology (UST), Seoul 02792, Republic of Korea
| | - Seo Jin Ki
- Department of Environmental Engineering, Gyeongsang National University, Jinju 52725, Republic of Korea
| | - Sang-Ik Suh
- Department of Energy System Engineering, Gyeongsang National University, Jinju 52725, Republic of Korea
| | - Jaeshik Chung
- Water Cycle Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea; Division of Energy and Environmental Technology, KIST School, Korea University of Science and Technology (UST), Seoul 02792, Republic of Korea.
| |
Collapse
|
15
|
Li H, Ai Z, Yang L, Zhang W, Yang Z, Peng H, Leng L. Machine learning assisted predicting and engineering specific surface area and total pore volume of biochar. BIORESOURCE TECHNOLOGY 2023; 369:128417. [PMID: 36462763 DOI: 10.1016/j.biortech.2022.128417] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 11/08/2022] [Accepted: 11/11/2022] [Indexed: 06/17/2023]
Abstract
Biochar produced from pyrolysis of biomass is a platform porous carbon material that have been widely used in many areas. Specific surface area (SSA) and total pore volume (TPV) are decisive to biochar application in hydrogen uptake, CO2 adsorption, and organic pollutant removal, etc. Engineering biochar by traditional experimental methods is time-consuming and laborious. Machine learning (ML) was used to effectively aid the prediction and engineering of biochar properties. The prediction of biochar yield, SSA, and TPV was achieved via random forest (RF) and gradient boosting regression (GBR) with test R2 of 0.89-0.94. ML model interpretation indicates pyrolysis temperature, biomass ash, and volatile matter were the most important features to the three targets. Pyrolysis parameters and biomass mixing ratios for biochar production were optimized via three-target GBR model, and the optimum schemes to obtain high SSA and TPV were experimentally verified, indicating the great potential of ML for biochar engineering.
Collapse
Affiliation(s)
- Hailong Li
- School of Energy Science and Engineering, Central South University, Changsha, Hunan 410083, People's Republic of China
| | - Zejian Ai
- School of Energy Science and Engineering, Central South University, Changsha, Hunan 410083, People's Republic of China
| | - Lihong Yang
- School of Energy Science and Engineering, Central South University, Changsha, Hunan 410083, People's Republic of China
| | - Weijin Zhang
- School of Energy Science and Engineering, Central South University, Changsha, Hunan 410083, People's Republic of China
| | - Zequn Yang
- School of Energy Science and Engineering, Central South University, Changsha, Hunan 410083, People's Republic of China
| | - Haoyi Peng
- School of Energy Science and Engineering, Central South University, Changsha, Hunan 410083, People's Republic of China
| | - Lijian Leng
- School of Energy Science and Engineering, Central South University, Changsha, Hunan 410083, People's Republic of China.
| |
Collapse
|
16
|
Li Y, Gupta R, Zhang Q, You S. Review of biochar production via crop residue pyrolysis: Development and perspectives. BIORESOURCE TECHNOLOGY 2023; 369:128423. [PMID: 36462767 DOI: 10.1016/j.biortech.2022.128423] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 11/27/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
Worldwide surge in crop residue generation has necessitated developing strategies for their sustainable disposal. Pyrolysis has been widely adopted to convert crop residue into biochar with bio-oil and gas being two co-products. The review adopts a whole system philosophy and systematically summarises up-to-date knowledge of crop residue pyrolysis processes, influential factors, and biochar applications. Essential process design tools for biochar production e.g., cost-benefit analysis, life cycle assessment, and machine learning methods are also reviewed, which has often been overlooked in prior reviews. Important aspects include (a) correlating techno-economics of biochar production with crop residue compositions, (b) process operating conditions and management strategies, (c) biochar applications including soil amendment, fuel displacement, catalytic usage, etc., (d) data-driven modelling techniques, (e) properties of biochar, and (f) climate change mitigation. Overall, the review will support the development of application-oriented process pipelines for crop residue-based biochar.
Collapse
Affiliation(s)
- Yize Li
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
| | - 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 7TY, UK
| | - Qiaozhi Zhang
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Siming You
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.
| |
Collapse
|
17
|
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: 0] [Impact Index Per Article: 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.
Collapse
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.
| |
Collapse
|
18
|
Zhang W, Chen Q, Chen J, Xu D, Zhan H, Peng H, Pan J, Vlaskin M, Leng L, Li H. Machine learning for hydrothermal treatment of biomass: A review. BIORESOURCE TECHNOLOGY 2023; 370:128547. [PMID: 36584720 DOI: 10.1016/j.biortech.2022.128547] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 12/24/2022] [Accepted: 12/26/2022] [Indexed: 06/17/2023]
Abstract
Hydrothermal treatment (HTT) (i.e., hydrothermal carbonization, liquefaction, and gasification) is a promising technology for biomass valorization. However, diverse variables, including biomass compositions and hydrothermal processes parameters, have impeded in-depth mechanistic understanding on the reaction and engineering in HTT. Recently, machine learning (ML) has been widely employed to predict and optimize the production of biofuels, chemicals, and materials from HTT by feeding experimental data. This review comprehensively analyzed the application of ML for HTT of biomass and systematically illustrated basic ML procedure and descriptors for inputs and outputs of ML models (e.g., biomass compositions, operation conditions, yield and physicochemical properties of derived products) that could be applied in HTT. Moreover, this review summarized ML-aided HTT prediction of yield, compositions, and physicochemical properties of HTT hydrochar or biochar, bio-oil, syngas, and aqueous phase. Ultimately, future prospects were proposed to enhance predictive performance, mechanistic interpretation, process optimization, data sharing, and model application during ML-aided HTT.
Collapse
Affiliation(s)
- Weijin Zhang
- School of Energy Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Qingyue Chen
- School of Minerals Processing and Bioengineering, Central South University, Changsha, Hunan 410083, China
| | - Jiefeng Chen
- School of Energy Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Donghai Xu
- Key Laboratory of Thermo-Fluid Science & Engineering, Ministry of Education, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China
| | - Hao Zhan
- School of Energy Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Haoyi Peng
- School of Energy Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Jian Pan
- School of Minerals Processing and Bioengineering, Central South University, Changsha, Hunan 410083, China
| | - Mikhail Vlaskin
- Joint Institute for High Temperatures of the Russian Academy of Sciences, Moscow 125412, Russia
| | - Lijian Leng
- School of Energy Science and Engineering, Central South University, Changsha, Hunan 410083, China.
| | - Hailong Li
- School of Energy Science and Engineering, Central South University, Changsha, Hunan 410083, China
| |
Collapse
|
19
|
Hajabdollahi Ouderji Z, Gupta R, Mckeown A, Yu Z, Smith C, Sloan W, You S. Integration of anaerobic digestion with heat Pump: Machine learning-based technical and environmental assessment. BIORESOURCE TECHNOLOGY 2023; 369:128485. [PMID: 36521822 DOI: 10.1016/j.biortech.2022.128485] [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: 10/30/2022] [Revised: 12/09/2022] [Accepted: 12/11/2022] [Indexed: 06/17/2023]
Abstract
Anaerobic digestion (AD)-based biogas production mitigates the environmental footprint of organic wastes (e.g., food waste and sewage sludge) and facilitates a circular economy. The work proposed an integrated system where the thermal energy demand of an AD is supplied using an air source heat pump (ASHP). The proposed system is compared to a baseline system, where the thermal energy is supplied by a natural gas-based heating system. Several machine learning models are developed for predicting biogas production, among which the Gaussian Process Regression (GPR) showed a superior performance (R2 = 0.84 and RMSE = 0.0755 L gVS-1 day-1). The GPR model further informed a thermodynamic model of the ASHP, which revealed the maximum biogas yield to be approximately 0.585 L.gVS-1.day-1 at an optimal temperature of 55 °C (thermophilic). Subsequently, life cycle assessment showed that ASHP-based AD heating systems achieved 28.1 % (thermophilic) and 36.8 % (mesophilic) carbon abatement than the baseline system.
Collapse
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
| | - Andrew Mckeown
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
| | - Zhibin Yu
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
| | - Cindy Smith
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
| | - William Sloan
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
| | - Siming You
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.
| |
Collapse
|
20
|
Chen MW, Chang MS, Mao Y, Hu S, Kung CC. Machine learning in the evaluation and prediction models of biochar application: A review. Sci Prog 2023; 106:368504221148842. [PMID: 36628421 PMCID: PMC10450295 DOI: 10.1177/00368504221148842] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
This article reviews recent studies applying machine learning (ML) approaches to biochar applications. We first briefly introduce the general biochar production process. Various aspects are contained, including the biochar application in the elimination of heavy metals and/or organic compounds and the biochar application in environmental and economic scopes, for instance, food security, energy, and carbon emission. The utilization of ML methods, including ANN, RF, and NN, plays a vital role in evaluating and predicting the efficiency of biochar absorption. It has been proved that ML methods can validly predict the adsorption effectiveness of biochar for water heavy metals with higher accuracy. Moreover, the literature proposed a comprehensive data-driven model to forecast biochar yield and compositions under various biomass input feedstock and different pyrolysis criteria. They said a 12.7% improvement in prediction accuracy compared to the existing literature. However, it might need further optimization in this direction. In summary, this review concludes increasing studies that a well-trained ML method can sufficiently reduce the number of experiment trials and working times associated with higher prediction accuracy. Moreover, further studies on ML applications are needed to optimize the trade-off between biochar yield and its composition.
Collapse
Affiliation(s)
- Meng-Wei Chen
- Institute of Economics and Finance, Nanjing Audit University, Nanjing, China
| | | | - Yuehua Mao
- School of International Economics, University of International Business and Economics, Beijing, China
| | - Shuyin Hu
- School of Economics, Jiangxi University of Finance and Economics, Nanchang, China
| | - Chih-Chun Kung
- School of Economics, Jiangxi University of Finance and Economics, Nanchang, China
| |
Collapse
|
21
|
Dong Z, Bai X, Xu D, Li W. Machine learning prediction of pyrolytic products of lignocellulosic biomass based on physicochemical characteristics and pyrolysis conditions. BIORESOURCE TECHNOLOGY 2023; 367:128182. [PMID: 36307026 DOI: 10.1016/j.biortech.2022.128182] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 10/15/2022] [Accepted: 10/18/2022] [Indexed: 06/16/2023]
Abstract
This study predicts pyrolytic product yields via machine learning algorithms from biomass physicochemical characteristics and pyrolysis conditions. Random forest (RF), gradient boosting decision tree (GBDT), eXtreme Gradient Boosting (XGBoost), and Adaptive Boost (Adaboost) algorithms are comparatively analyzed. Among these algorithms, the RF algorithm is the best modeling algorithm and performs best in predicting the bio-oil yield and performs well in predicting biochar and pyrolytic gas yields. The moisture content, carbon content, and final heating temperature are the most important factors in predicting pyrolysis product yields, and biomass characteristics are more important than pyrolysis conditions. Furthermore, the carbon content positively affects the bio-oil yield and negatively affects the biochar yield, and the final temperature positively affects the pyrolytic gas yield and negatively affects the biochar yield. This work provides new insight for controlling the yields of pyrolytic products via the RF algorithm, which can facilitate the process optimization in engineering applications.
Collapse
Affiliation(s)
- Zixun Dong
- School of Technology, Beijing Forestry University, Beijing 100083, China
| | - Xiaopeng Bai
- School of Technology, Beijing Forestry University, Beijing 100083, China; Key Lab of State Forestry Administration on Forestry Equipment and Automation, Beijing 100083, China.
| | - Daochun Xu
- School of Technology, Beijing Forestry University, Beijing 100083, China; Key Lab of State Forestry Administration on Forestry Equipment and Automation, Beijing 100083, China
| | - Wenbin Li
- School of Technology, Beijing Forestry University, Beijing 100083, China; Key Lab of State Forestry Administration on Forestry Equipment and Automation, Beijing 100083, China
| |
Collapse
|