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Chen H, Cao Y, Qin W, Lin K, Yang Y, Liu C, Ji H. Machine learning models for predicting thermal desorption remediation of soils contaminated with polycyclic aromatic hydrocarbons. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172173. [PMID: 38575004 DOI: 10.1016/j.scitotenv.2024.172173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/17/2024] [Accepted: 04/01/2024] [Indexed: 04/06/2024]
Abstract
Among various remediation methods for organic-contaminated soil, thermal desorption stands out due to its broad treatment range and high efficiency. Nonetheless, analyzing the contribution of factors in complex soil remediation systems and deducing the results under multiple conditions are challenging, given the complexities arising from diverse soil properties, heating conditions, and contaminant types. Machine learning (ML) methods serve as a powerful analytical tool that can extract meaningful insights from datasets and reveal hidden relationships. Due to insufficient research on soil thermal desorption for remediation of organic sites using ML methods, this study took organic pollutants represented by polycyclic aromatic hydrocarbons (PAHs) as the research object and sorted out a comprehensive data set containing >700 data points on the thermal desorption of soil contaminated with PAHs from published literature. Several ML models, including artificial neural network (ANN), random forest (RF), and support vector regression (SVR), were applied. Model optimization and regression fitting centered on soil remediation efficiency, with feature importance analysis conducted on soil and contaminant properties and heating conditions. This approach enabled the quantitative evaluation and prediction of thermal desorption remediation effects on soil contaminated with PAHs. Results indicated that ML models, particularly the RF model (R2 = 0.90), exhibited high accuracy in predicting remediation efficiency. The hierarchical significance of the features within the RF model is elucidated as follows: heating conditions account for 52 %, contaminant properties for 28 %, and soil properties for 20 % of the model's predictive power. A comprehensive analysis suggests that practical applications should emphasize heating conditions for efficient soil remediation. This research provides a crucial reference for optimizing and implementing thermal desorption in the quest for more efficient and reliable soil remediation strategies.
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Affiliation(s)
- Haojia Chen
- School of Chemistry and Chemical Engineering, Guangxi Key Laboratory of Petrochemical Resource Processing and Process Intensification Technology, Guangxi University, Nanning 530004, China; School of Chemical Engineering and Light Industry, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China; Synergy Innovation Institute of Guangdong University of Technology, Shantou 515041, China
| | - Yudong Cao
- School of Chemical Engineering and Light Industry, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China; Synergy Innovation Institute of Guangdong University of Technology, Shantou 515041, China
| | - Wei Qin
- School of Chemical Engineering and Light Industry, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China; Synergy Innovation Institute of Guangdong University of Technology, Shantou 515041, China
| | - Kunsen Lin
- Engineering Research Center of Polymer Green Recycling of Ministry of Education, College of Environmental Science and Engineering, Fujian Normal University, Fuzhou 350007, China.
| | - Yan Yang
- School of Chemical Engineering and Light Industry, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China; Synergy Innovation Institute of Guangdong University of Technology, Shantou 515041, China.
| | - Changqing Liu
- Engineering Research Center of Polymer Green Recycling of Ministry of Education, College of Environmental Science and Engineering, Fujian Normal University, Fuzhou 350007, China
| | - Hongbing Ji
- School of Chemistry and Chemical Engineering, Guangxi Key Laboratory of Petrochemical Resource Processing and Process Intensification Technology, Guangxi University, Nanning 530004, China; School of Chemical Engineering and Light Industry, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China; Synergy Innovation Institute of Guangdong University of Technology, Shantou 515041, China
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2
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Mohit A, Remya N. Exploring effects of carbon, nitrogen, and phosphorus on greywater treatment by polyculture microalgae using response surface methodology and machine learning. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 356:120728. [PMID: 38531138 DOI: 10.1016/j.jenvman.2024.120728] [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: 12/14/2023] [Revised: 02/20/2024] [Accepted: 03/19/2024] [Indexed: 03/28/2024]
Abstract
The microalgae-based wastewater treatment is a promising technique that contribute to achieving sustainable development goals (SDGs), such as SDG-6, "Clean Water and Sanitation". However, it is strongly influenced by the initial composition of wastewater. In this study, the impact of initial organics and nutrient concentration on the removal of total organic carbon (TOC), total carbon (TC), ammonium (NH4+), total nitrogen (TN), and phosphate (PO43-) from greywater using native polyculture microalgae was explored. Response surface methodology was employed along with two machine learning approaches, AdaBoost and XGBoost, to evaluate the interactions among three main factors: TOC, NH4+, and PO43-, and their effects on treatment efficiency. The C/N ratios for achieving maximum TOC and TC removal efficiency of 99.2% and 97.7% were determined to be 10.3, and 65.4-73.6, respectively. Notably, the N/P ratio did not significantly affect their removal. The highest NH4+ removal efficiency, reaching 96.2%, was attained at C/N ratios of 4.3, 24.0, 38.2, and 212.9, coupled with N/P ratios of 0.3, 2.6, and 23.4. Highest TN removal efficiency of 77.2% was achieved at C/N and N/P ratios of 12.2 and 2.0, respectively. Highest PO43- removal of 78.8% was obtained at N/P ratio 12.8. However, C/N ratio did not affect the removal efficiency. Maintaining these specified C/N and N/P ratios in the influent greywater would ensure that the treated greywater meets the required standards for various reuse applications, including flushing, groundwater recharge, and surface water discharge. The integration of RSM with AdaBoost and XGBoost provided accurate predictions of removal efficiencies. For all the models, XGBoost had the highest R2, and lowest MAE and MSE values. The cross validation of RSM models with AdaBoost and XGBoost further reinforced the reliability of these models in predicting treatment outcomes.
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Affiliation(s)
- Aggarwal Mohit
- School of Infrastructure, Indian Institute of Technology Bhubaneswar, Odisha, 752050, India
| | - Neelancherry Remya
- School of Infrastructure, Indian Institute of Technology Bhubaneswar, Odisha, 752050, India.
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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.
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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.
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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.
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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.
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Lin SL, Zhang H, Chen WH, Song M, Kwon EE. Low-temperature biochar production from torrefaction for wastewater treatment: A review. BIORESOURCE TECHNOLOGY 2023; 387:129588. [PMID: 37558107 DOI: 10.1016/j.biortech.2023.129588] [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: 06/13/2023] [Revised: 07/25/2023] [Accepted: 07/27/2023] [Indexed: 08/11/2023]
Abstract
Biochar, a carbon-rich and por ous material derived from waste biomass resources, has demonstrated tremendous potential in wastewater treatment. Torrefaction technology offers a favorable low-temperature biochar production method, and torrefied biochar can be used not only as a solid biofuel but also as a pollutant adsorbent. This review compares torrefaction technology with other thermochemical processes and discusses recent advancements in torrefaction techniques. Additionally, the applications of torrefied biochar in wastewater treatment (dyes, oil spills, heavy metals, and emerging pollutants) are comprehensively explored. Many studies have shown that high productivity, high survival of oxygen-containing functional groups, low temperature, and low energy consumption of dried biochar production make it attractive as an adsorbent for wastewater treatment. Moreover, used biochar's treatment, reuse, and safe disposal are introduced, providing valuable insights and contributions to developing sustainable environmental remediation strategies by biochar.
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Affiliation(s)
- Sheng-Lun Lin
- Department of Environmental Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - Hongjie Zhang
- School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Wei-Hsin Chen
- Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan 70101, Taiwan; Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407, Taiwan; Department of Mechanical Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan.
| | - Mengjie Song
- School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Eilhann E Kwon
- Department of Earth Resources and Environmental Engineering, Hanyang University, Seoul 04763, Republic of Korea
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Zaki M, Rowles LS, Adjeroh DA, Orner KD. A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste. ACS ES&T ENGINEERING 2023; 3:1424-1467. [PMID: 37854077 PMCID: PMC10580293 DOI: 10.1021/acsestengg.3c00043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 09/11/2023] [Accepted: 09/11/2023] [Indexed: 10/20/2023]
Abstract
Municipal and agricultural organic waste can be treated to recover energy, nutrients, and carbon through resource recovery and carbon capture (RRCC) technologies such as anaerobic digestion, struvite precipitation, and pyrolysis. Data science could benefit such technologies by improving their efficiency through data-driven process modeling along with reducing environmental and economic burdens via life cycle assessment (LCA) and techno-economic analysis (TEA), respectively. We critically reviewed 616 peer-reviewed articles on the use of data science in RRCC published during 2002-2022. Although applications of machine learning (ML) methods have drastically increased over time for modeling RRCC technologies, the reviewed studies exhibited significant knowledge gaps at various model development stages. In terms of sustainability, an increasing number of studies included LCA with TEA to quantify both environmental and economic impacts of RRCC. Integration of ML methods with LCA and TEA has the potential to cost-effectively investigate the trade-off between efficiency and sustainability of RRCC, although the literature lacked such integration of techniques. Therefore, we propose an integrated data science framework to inform efficient and sustainable RRCC from organic waste based on the review. Overall, the findings from this review can inform practitioners about the effective utilization of various data science methods for real-world implementation of RRCC technologies.
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Affiliation(s)
- Mohammed
T. Zaki
- Wadsworth
Department of Civil and Environmental Engineering, West Virginia University, Morgantown, West Virginia 26505, United States
| | - Lewis S. Rowles
- Department
of Civil Engineering and Construction, Georgia
Southern University, Statesboro, Georgia 30458, United States
| | - Donald A. Adjeroh
- Lane
Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, West Virginia 26505, United States
| | - Kevin D. Orner
- Wadsworth
Department of Civil and Environmental Engineering, West Virginia University, Morgantown, West Virginia 26505, United States
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Sakheta A, Nayak R, O'Hara I, Ramirez J. A review on modelling of thermochemical processing of biomass for biofuels and prospects of artificial intelligence-enhanced approaches. BIORESOURCE TECHNOLOGY 2023; 386:129490. [PMID: 37460019 DOI: 10.1016/j.biortech.2023.129490] [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: 05/31/2023] [Revised: 07/10/2023] [Accepted: 07/12/2023] [Indexed: 07/23/2023]
Abstract
Biofuels from lignocellulosic biomass converted via thermochemical technologies can be renewable and sustainable, which makes them promising as alternatives to conventional fossil fuels. Prior to building industrial-scale thermochemical conversion plants, computational models are used to simulate process flows and conditions, conduct feasibility studies, and analyse process and business risk. This paper aims to provide an overview of the current state of the art in modelling thermochemical conversion of lignocellulosic biomass. Emphasis is given to the recent advances in artificial intelligence (AI)-based modelling that plays an increasingly important role in enhancing the performance of the models. This review shows that AI-based models offer prominent accuracy compared to thermodynamic equilibrium modelling implemented in some models. It is also evident that gasification and pyrolysis models are more matured than thermal liquefaction for lignocelluloses. Additionally, the knowledge gained and future directions in the applications of simulation and AI in process modelling are explored.
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Affiliation(s)
- Aban Sakheta
- Centre for Agriculture and the Bioeconomy, Faculty of Science, Queensland University of Technology, 2 George St, Brisbane City, Queensland 4000, Australia; School of Mechanical, Medical and Process Engineering, Faculty of Engineering, Queensland University of Technology, 2 George St, Brisbane City, Queensland 4000, Australia
| | - Richi Nayak
- School of Computer Science, Faculty of Science, Queensland University of Technology, 2 George St, Brisbane City, Queensland 4000, Australia; Centre for Data Science, Queensland University of Technology, 2 George Street, Brisbane, 4000, QLD, Australia
| | - Ian O'Hara
- Centre for Agriculture and the Bioeconomy, Faculty of Science, Queensland University of Technology, 2 George St, Brisbane City, Queensland 4000, Australia; School of Mechanical, Medical and Process Engineering, Faculty of Engineering, Queensland University of Technology, 2 George St, Brisbane City, Queensland 4000, Australia; ARC Centre of Excellence in Synthetic Biology, Queensland University of Technology (QUT), 2 George Street, Brisbane, Australia
| | - Jerome Ramirez
- Centre for Agriculture and the Bioeconomy, Faculty of Science, Queensland University of Technology, 2 George St, Brisbane City, Queensland 4000, Australia; School of Mechanical, Medical and Process Engineering, Faculty of Engineering, Queensland University of Technology, 2 George St, Brisbane City, Queensland 4000, Australia; ARC Centre of Excellence in Synthetic Biology, Queensland University of Technology (QUT), 2 George Street, Brisbane, Australia.
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Su S, Wang J. Machine learning prediction of contents of oxygenated components in bio-oil using extreme gradient boosting method under different pyrolysis conditions. BIORESOURCE TECHNOLOGY 2023; 379:129040. [PMID: 37037334 DOI: 10.1016/j.biortech.2023.129040] [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: 02/23/2023] [Revised: 04/05/2023] [Accepted: 04/07/2023] [Indexed: 05/03/2023]
Abstract
This work aims to develop a prediction model for the contents of oxygenated components in bio-oil based on machine learning according to different pyrolysis conditions and biomass characteristics. The prediction model was constructed using the extreme gradient boosting (XGB) method, and the prediction accuracy was evaluated using the test dataset. The partial dependence analysis (PDA) method was used to derive the pattern of influence of each input feature individually or in combination on the output variable. The results show that the prediction models constructed from biomass ultimate analysis and pyrolysis conditions can predict the contents of oxygenated components in bio-oil more accurately than the models constructed from biomass proximate analysis. Moderate C and O contents, higher H content of biomass, lower flow rate, and higher pyrolysis temperature can improve bio-oil quality.
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Affiliation(s)
- Sheng Su
- National Key Laboratory of Science and Technology on Aero-Engine Aero-Thermodynamics, School of Energy and Power Engineering, Beihang University, Beijing 100191, China
| | - Juan Wang
- National Key Laboratory of Science and Technology on Aero-Engine Aero-Thermodynamics, School of Energy and Power Engineering, Beihang University, Beijing 100191, China.
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