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Zeng Y, Wang H, Liang D, Yuan W, Li S, Xu H, Chen J. Navigating the difference of riverine microplastic movement footprint into the sea: Particle properties influence. JOURNAL OF HAZARDOUS MATERIALS 2024; 476:134888. [PMID: 38897117 DOI: 10.1016/j.jhazmat.2024.134888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 06/01/2024] [Accepted: 06/10/2024] [Indexed: 06/21/2024]
Abstract
As a critical source of marine microplastics (MPs), estuarine MPs community varied in movement due to particle diversity, while tide and runoff further complicated their transport. In this study, a particle mass gradient that represents MPs in the surface layer of the Yangtze River estuary was established. This was done by calculating the masses of 16 particle types using the particle size probability density function (PDF), with typical shapes and polymers as classifiers. Further, Aschenbrenner shape factor and polymer density were embedded into drag coefficients to categorically trace MP movement footprints. Results revealed that the MPs in North Branch moved northward and the MPs in South Branch moved southeastward in a spiral oscillation until they left the model boundary under Changjiang Diluted Water front and the northward coastal currents. Low-density fibrous MPs are more likely to move into the open ocean and oscillate more than films, with a single PE fiber trajectory that reached a maximum oscillatory width of 16.7 km. Over 95 % of the PVC fiber particles settled in nearshore waters west of 122.5°E. Elucidating the aggregation and retention of different MPs types can provide more accurate environmental baseline reference for more precise MP exposure levels and risk dose of ingestion for marine organisms.
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Affiliation(s)
- Yichuan Zeng
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lake of Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China; College of Environment, Hohai University, Nanjing 210098, China
| | - Hua Wang
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lake of Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China; College of Environment, Hohai University, Nanjing 210098, China.
| | - Dongfang Liang
- Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK
| | - Weihao Yuan
- Nanjing Institute of Environmental Science, Ministry of Ecology and Environment, Nanjing 210042, China
| | - Siqiong Li
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lake of Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China; College of Environment, Hohai University, Nanjing 210098, China
| | - Haosen Xu
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lake of Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China; College of Environment, Hohai University, Nanjing 210098, China
| | - Jingwei Chen
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lake of Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China; College of Environment, Hohai University, Nanjing 210098, China
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Yogarathinam LT, Abba SI, Usman J, Lawal DU, Aljundi IH. Predicting micropollutant removal through nanopore-sized membranes using several machine-learning approaches based on feature engineering. RSC Adv 2024; 14:19331-19348. [PMID: 38887641 PMCID: PMC11181297 DOI: 10.1039/d4ra02475c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 06/05/2024] [Indexed: 06/20/2024] Open
Abstract
Predicting the efficacy of micropollutant separation through functionalized membranes is an arduous endeavor. The challenge stems from the complex interactions between the physicochemical properties of the micropollutants and the basic principles underlying membrane filtration. This study aimed to compare the effectiveness of a modest dataset on various machine learning tools (ML) tools in predicting micropollutant removal efficiency for functionalized reverse osmosis (RO) and nanofiltration (NF) membranes. The inherent attributes of both the micropollutants and the membranes are utilized as input factors. The chosen ML tools are supervised algorithm (adaptive network-based fuzzy inference system (NF), linear regression framework (linear regression (LR)), stepwise linear regression (SLR) and multivariate linear regression (MVR)), and unsupervised algorithm (support vector machine (SVM) and ensemble boosted tree (BT)). The feature engineering and parametric dependency analysis revealed that characteristics of micropollutants, such as maximum projection diameter (MaxP), minimal projection diameter (MinP), molecular weight (MW), and compound size (CS), exhibited a notably positive impact on the correlation with removal efficiency. Model combination with key variables demonstrated high prediction accuracy in both supervised and unsupervised ML for micropollutant removal efficiency. An NF-grid partitioning (NF-GP) model achieved the highest accuracy with an R 2 value of 0.965, accompanied by low error metrics, specifically an RMSE and MAE of 3.65. It is owed to the handling of the complex spatial and temporal aspects of micropollutant data through division into consistent subsets facilitating improved identification of rejection efficiency and relationships. The inclusion of inputs with both negative and positive correlations introduces variability, amplifies the system responsiveness, and impedes the precision of predictive models. This study identified key micropollutant properties, including MaxP, MinP, MW, and CS, as crucial factors for efficient micropollutant rejection during real-time filtration applications. It also allowed the design of pore size of self-prepared membranes for the enhanced separation of micropollutants from wastewater.
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Affiliation(s)
- Lukka Thuyavan Yogarathinam
- Interdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia
| | - Sani I Abba
- Interdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia
| | - Jamilu Usman
- Interdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia
| | - Dahiru U Lawal
- Interdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia
- Department of Mechanical Engineering, King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia
| | - Isam H Aljundi
- Interdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia
- Department of Chemical Engineering, King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia
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3
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Yang M, Zhu JJ, McGaughey AL, Priestley RD, Hoek EMV, Jassby D, Ren ZJ. Machine Learning for Polymer Design to Enhance Pervaporation-Based Organic Recovery. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:10128-10139. [PMID: 38743597 DOI: 10.1021/acs.est.4c00060] [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: 05/16/2024]
Abstract
Pervaporation (PV) is an effective membrane separation process for organic dehydration, recovery, and upgrading. However, it is crucial to improve membrane materials beyond the current permeability-selectivity trade-off. In this research, we introduce machine learning (ML) models to identify high-potential polymers, greatly improving the efficiency and reducing cost compared to conventional trial-and-error approach. We utilized the largest PV data set to date and incorporated polymer fingerprints and features, including membrane structure, operating conditions, and solute properties. Dimensionality reduction, missing data treatment, seed randomness, and data leakage management were employed to ensure model robustness. The optimized LightGBM models achieved RMSE of 0.447 and 0.360 for separation factor and total flux, respectively (logarithmic scale). Screening approximately 1 million hypothetical polymers with ML models resulted in identifying polymers with a predicted permeation separation index >30 and synthetic accessibility score <3.7 for acetic acid extraction. This study demonstrates the promise of ML to accelerate tailored membrane designs.
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Affiliation(s)
- Meiqi Yang
- Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, United States
- Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States
| | - Jun-Jie Zhu
- Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, United States
- Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States
| | - Allyson L McGaughey
- Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, United States
- Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, United States
| | - Rodney D Priestley
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, United States
| | - Eric M V Hoek
- Department of Civil & Environmental Engineering, University of California Los Angeles, Los Angeles, California 90095, United States
| | - David Jassby
- Department of Civil & Environmental Engineering, University of California Los Angeles, Los Angeles, California 90095, United States
| | - Zhiyong Jason Ren
- Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, United States
- Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States
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Chen K, Guo C, Wang C, Zhao S, Xiong B, Lu G, Reinfelder JR, Dang Z. Prediction of Cr(VI) and As(V) adsorption on goethite using hybrid surface complexation-machine learning model. WATER RESEARCH 2024; 256:121580. [PMID: 38614029 DOI: 10.1016/j.watres.2024.121580] [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: 10/04/2023] [Revised: 04/02/2024] [Accepted: 04/05/2024] [Indexed: 04/15/2024]
Abstract
This study aimed to develop surface complexation modeling-machine learning (SCM-ML) hybrid model for chromate and arsenate adsorption on goethite. The feasibility of two SCM-ML hybrid modeling approaches was investigated. Firstly, we attempted to utilize ML algorithms and establish the parameter model, to link factors influencing the adsorption amount of oxyanions with optimized surface complexation constants. However, the results revealed the optimized chromate or arsenate surface complexation constants might fall into local extrema, making it unable to establish a reasonable mapping relationship between adsorption conditions and surface complexation constants by ML algorithms. In contrast, species-informed models were successfully obtained, by incorporating the surface species information calculated from the unoptimized SCM with the adsorption condition as input features. Compared with the optimized SCM, the species-informed model could make more accurate predictions on pH edges, isotherms, and kinetic data for various input conditions (for chromate: root mean square error (RMSE) on test set = 5.90 %; for arsenate: RMSE on test set = 4.84 %). Furthermore, the utilization of the interpretable formula based on Local Interpretable Model-Agnostic Explanations (LIME) enabled the species-informed model to provide surface species information like SCM. The species-informed SCM-ML hybrid modeling method proposed in this study has great practicality and application potential, and is expected to become a new paradigm in surface adsorption model.
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Affiliation(s)
- Kai Chen
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, PR China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou 510006, PR China
| | - Chuling Guo
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, PR China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou 510006, PR China.
| | - Chaoping Wang
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, PR China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou 510006, PR China
| | - Shoushi Zhao
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, PR China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou 510006, PR China
| | - Beiyi Xiong
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, PR China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou 510006, PR China
| | - Guining Lu
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, PR China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou 510006, PR China
| | - John R Reinfelder
- Department of Environmental Sciences, Rutgers University, New Brunswick, NJ 08901, USA
| | - Zhi Dang
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, PR China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou 510006, PR China; Guangdong Provincial Key Lab of Solid Wastes Pollution Control and Recycling, South China University of Technology, Guangzhou 510006, China
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Chen J, Wang T, Dai R, Wu Z, Wang Z. Trade-off between Endocrine-Disrupting Compound Removal and Water Permeance of the Polyamide Nanofiltration Membrane: Phenomenon and Molecular Insights. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:9416-9426. [PMID: 38662937 DOI: 10.1021/acs.est.4c01383] [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: 05/29/2024]
Abstract
The polyamide (PA) nanofiltration (NF) membrane has the potential to remove endocrine-disrupting compounds (EDCs) from water and wastewater to prevent risks to both the aquatic ecosystem and human health. However, our understanding of the EDC removal-water permeance trade-off by the PA NF membrane is still limited, although the salt selectivity-water permeance trade-off has been well illustrated. This constrains the precise design of a high-performance membrane for removing EDCs. In this study, we manipulated the PA nanostructures of NF membranes by altering piperazine (PIP) monomer concentrations during the interfacial polymerization (IP) process. The upper bound coefficient for EDC selectivity-water permeance was demonstrated to be more than two magnitudes lower than that for salt selectivity-water permeance. Such variations were derived from the different membrane-solute interactions, in which the water/EDC selectivity was determined by the combined effects of steric exclusion and the hydrophobic interaction, while the electrostatic interaction and steric exclusion played crucial roles in water/salt selectivity. We further highlighted the role of the pore number and residual groups during the transport of EDC molecules across the PA membrane via molecular dynamics (MD) simulations. Fewer pores decreased the transport channels, and the existence of residual groups might cause steric hindrance and dynamic disturbance to EDC transport inside the membrane. This study elucidated the trade-off phenomenon and mechanisms between EDC selectivity and water permeance, providing a theoretical reference for the precise design of PA NF membranes for effective removal of EDCs in water reuse.
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Affiliation(s)
- Jiansuxuan Chen
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, Tongji Advanced Membrane Technology Center, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Tianlin Wang
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, Tongji Advanced Membrane Technology Center, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Ruobin Dai
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, Tongji Advanced Membrane Technology Center, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Zhichao Wu
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, Tongji Advanced Membrane Technology Center, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Zhiwei Wang
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, Tongji Advanced Membrane Technology Center, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
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6
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Zhu H, Szymczyk A, Ghoufi A. Multiscale modelling of transport in polymer-based reverse-osmosis/nanofiltration membranes: present and future. DISCOVER NANO 2024; 19:91. [PMID: 38771417 PMCID: PMC11109084 DOI: 10.1186/s11671-024-04020-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 04/22/2024] [Indexed: 05/22/2024]
Abstract
Nanofiltration (NF) and reverse osmosis (RO) processes are physical separation technologies used to remove contaminants from liquid streams by employing dense polymer-based membranes with nanometric voids that confine fluids at the nanoscale. At this level, physical properties such as solvent and solute permeabilities are intricately linked to molecular interactions. Initially, numerous studies focused on developing macroscopic transport models to gain insights into separation properties at the nanometer scale. However, continuum-based models have limitations in nanoconfined situations that can be overcome by force field molecular simulations. Continuum-based models heavily rely on bulk properties, often neglecting critical factors like liquid structuring, pore geometry, and molecular/chemical specifics. Molecular/mesoscale simulations, while encompassing these details, often face limitations in time and spatial scales. Therefore, achieving a comprehensive understanding of transport requires a synergistic integration of both approaches through a multiscale approach that effectively combines and merges both scales. This review aims to provide a comprehensive overview of the state-of-the-art in multiscale modeling of transport through NF/RO membranes, spanning from the nanoscale to continuum media.
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Affiliation(s)
- Haochen Zhu
- State Key Laboratory of Pollution Control and Resources Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, 1239 Siping Rd., Shanghai, 200092, China.
| | - Anthony Szymczyk
- CNRS, ISCR (Institut des Sciences Chimiques de Rennes) - UMR 6226, Univ Rennes, 35000, Rennes, France.
| | - Aziz Ghoufi
- CNRS, ICMPE (Institut de Chimie et des Matériaux Paris-Est) - UMR 7182, Univ Paris-East Creteil, 94320, Thiais, France.
- CNRS, IPR (Institut de Physique de Rennes) - UMR 6251, Univ Rennes, 35000, Rennes, France.
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7
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Sikder R, Zhang H, Gao P, Ye T. Machine learning framework for predicting cytotoxicity and identifying toxicity drivers of disinfection byproducts. JOURNAL OF HAZARDOUS MATERIALS 2024; 469:133989. [PMID: 38461660 DOI: 10.1016/j.jhazmat.2024.133989] [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: 12/25/2023] [Revised: 03/06/2024] [Accepted: 03/06/2024] [Indexed: 03/12/2024]
Abstract
Drinking water disinfection can result in the formation disinfection byproducts (DBPs, > 700 have been identified to date), many of them are reportedly cytotoxic, genotoxic, or developmentally toxic. Analyzing the toxicity levels of these contaminants experimentally is challenging, however, a predictive model could rapidly and effectively assess their toxicity. In this study, machine learning models were developed to predict DBP cytotoxicity based on their chemical information and exposure experiments. The Random Forest model achieved the best performance (coefficient of determination of 0.62 and root mean square error of 0.63) among all the algorithms screened. Also, the results of a probabilistic model demonstrated reliable model predictions. According to the model interpretation, halogen atoms are the most prominent features for DBP cytotoxicity compared to other chemical substructures. The presence of iodine and bromine is associated with increased cytotoxicity levels, while the presence of chlorine is linked to a reduction in cytotoxicity levels. Other factors including chemical substructures (CC, N, CN, and 6-member ring), cell line, and exposure duration can significantly affect the cytotoxicity of DBPs. The similarity calculation indicated that the model has a large applicability domain and can provide reliable predictions for DBPs with unknown cytotoxicity. Finally, this study showed the effectiveness of data augmentation in the scenario of data scarcity.
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Affiliation(s)
- Rabbi Sikder
- Department of Civil and Environmental Engineering, South Dakota School of Mines and Technology, Rapid City, SD 57701, United States
| | - Huichun Zhang
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Peng Gao
- Department of Environmental and Occupational Health, and Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA 15261, United States; UPMC Hillman Cancer Center, Pittsburgh, PA 15232, United States
| | - Tao Ye
- Department of Civil and Environmental Engineering, South Dakota School of Mines and Technology, Rapid City, SD 57701, United States.
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Kuznetsova V, Coogan Á, Botov D, Gromova Y, Ushakova EV, Gun'ko YK. Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2308912. [PMID: 38241607 PMCID: PMC11167410 DOI: 10.1002/adma.202308912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 01/10/2024] [Indexed: 01/21/2024]
Abstract
Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.
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Affiliation(s)
- Vera Kuznetsova
- School of Chemistry, CRANN and AMBER Research Centres, Trinity College Dublin, College Green, Dublin, D02 PN40, Ireland
| | - Áine Coogan
- School of Chemistry, CRANN and AMBER Research Centres, Trinity College Dublin, College Green, Dublin, D02 PN40, Ireland
| | - Dmitry Botov
- Everypixel Media Innovation Group, 021 Fillmore St., PMB 15, San Francisco, CA, 94115, USA
- Neapolis University Pafos, 2 Danais Avenue, Pafos, 8042, Cyprus
| | - Yulia Gromova
- Department of Molecular and Cellular Biology, Harvard University, 52 Oxford St., Cambridge, MA, 02138, USA
| | - Elena V Ushakova
- Department of Materials Science and Engineering, and Centre for Functional Photonics (CFP), City University of Hong Kong, Hong Kong SAR, 999077, P. R. China
| | - Yurii K Gun'ko
- School of Chemistry, CRANN and AMBER Research Centres, Trinity College Dublin, College Green, Dublin, D02 PN40, Ireland
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Wang H, Zeng J, Dai R, Wang Z. Understanding Rejection Mechanisms of Trace Organic Contaminants by Polyamide Membranes via Data-Knowledge Codriven Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:5878-5888. [PMID: 38498471 DOI: 10.1021/acs.est.3c08523] [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: 03/20/2024]
Abstract
Data-driven machine learning (ML) provides a promising approach to understanding and predicting the rejection of trace organic contaminants (TrOCs) by polyamide (PA). However, various confounding variables, coupled with data scarcity, restrict the direct application of data-driven ML. In this study, we developed a data-knowledge codriven ML model via domain-knowledge embedding and explored its application in comprehending TrOC rejection by PA membranes. Domain-knowledge embedding enhanced both the predictive performance and the interpretability of the ML model. The contribution of key mechanisms, including size exclusion, charge effect, hydrophobic interaction, etc., that dominate the rejections of the three TrOC categories (neutral hydrophilic, neutral hydrophobic, and charged TrOCs) was quantified. Log D and molecular charge emerge as key factors contributing to the discernible variations in the rejection among the three TrOC categories. Furthermore, we quantitatively compared the TrOC rejection mechanisms between nanofiltration (NF) and reverse osmosis (RO) PA membranes. The charge effect and hydrophobic interactions possessed higher weights for NF to reject TrOCs, while the size exclusion in RO played a more important role. This study demonstrated the effectiveness of the data-knowledge codriven ML method in understanding TrOC rejection by PA membranes, providing a methodology to formulate a strategy for targeted TrOC removal.
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Affiliation(s)
- Hejia Wang
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Jin Zeng
- School of Software Engineering, Tongji University, Shanghai 201804, China
| | - Ruobin Dai
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Zhiwei Wang
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
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Withana PA, Li J, Senadheera SS, Fan C, Wang Y, Ok YS. Machine learning prediction and interpretation of the impact of microplastics on soil properties. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 341:122833. [PMID: 37931672 DOI: 10.1016/j.envpol.2023.122833] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 09/05/2023] [Accepted: 10/29/2023] [Indexed: 11/08/2023]
Abstract
The annual microplastic (MP) release into soils is 4-23 times higher than that into oceans, significantly impacting soil quality. However, the mechanisms underlying how MPs impact soil properties remain largely unknown. Soil-MP interactions are complex because of soil heterogeneity and varying MP properties. This lack of understanding was exacerbated by the diverse experimental conditions and soil types used in this study. Predicting changes in soil properties in the presence of MPs is challenging, laborious, and time-consuming. To address these issues, machine learning was applied to fit datasets from peer-reviewed publications to predict and interpret how MPs influence soil properties, including pH, dissolved organic carbon (DOC), total P, NO3--N, NH4+-N, and acid phosphatase enzyme activity (acid P). Among the developed models, the gradient boost regression (GBR) model showed the highest R2 (0.86-0.99) compared to the decision tree and random forest models. The GBR model interpretation showed that MP properties contributed more than 50% to altering the acid P and NO3--N concentrations in soils, whereas they had a negligible impact on total P and 10-20% impact on soil pH, DOC, and NH4+-N. Specifically, the size of MPs was the dominant factor influencing acid P (89.3%), pH (71.6%), and DOC (44.5%) in soils. NO3--N was mainly affected by the MP type (52.0%). The NH4+-N was mainly affected by the MP dose (46.8%). The quantitative insights into the impact of MPs on soil properties of this study could aid in understanding the roles of MPs in soil systems.
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Affiliation(s)
- Piumi Amasha Withana
- Korea Biochar Research Center, Association of Pacific Rim Universities (APRU) Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, Seoul, 02841, Republic of Korea; International ESG Association (IESGA), Seoul, 06621, Republic of Korea
| | - Jie Li
- CAS Key Laboratory of Urban Pollutant Conversion, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
| | - Sachini Supunsala Senadheera
- Korea Biochar Research Center, Association of Pacific Rim Universities (APRU) Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, Seoul, 02841, Republic of Korea; International ESG Association (IESGA), Seoul, 06621, Republic of Korea
| | - Chuanfang Fan
- Korea Biochar Research Center, Association of Pacific Rim Universities (APRU) Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, Seoul, 02841, Republic of Korea; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 101408, China
| | - Yin Wang
- CAS Key Laboratory of Urban Pollutant Conversion, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
| | - Yong Sik Ok
- Korea Biochar Research Center, Association of Pacific Rim Universities (APRU) Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, Seoul, 02841, Republic of Korea; International ESG Association (IESGA), Seoul, 06621, Republic of Korea; Institute of Green Manufacturing Technology, College of Engineering, Korea University, Seoul, 02841, Republic of Korea.
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11
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Huang Y, Xie Y, Wu Y, Meng F, He C, Zou H, Wang X, Shui A, Liu S. Modeling Indirect Greenhouse Gas Emissions Sources from Urban Wastewater Treatment Plants: Integrating Machine Learning Models to Compensate for Sparse Parameters with Abundant Observations. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:19860-19870. [PMID: 37976424 DOI: 10.1021/acs.est.3c06482] [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: 11/19/2023]
Abstract
Electricity consumption and sludge yield (SY) are important indirect greenhouse gas (GHG) emission sources in wastewater treatment plants (WWTPs). Predicting these byproducts is crucial for tailoring technology-related policy decisions. However, it challenges balancing mass balance models and mechanistic models that respectively have limited intervariable nexus representation and excessive requirements on operational parameters. Herein, we propose integrating two machine learning models, namely, gradient boosting tree (GBT) and deep learning (DL), to precisely pointwise model electricity consumption intensity (ECI) and SY for WWTPs in China. Results indicate that GBT and DL are capable of mining massive data to compensate for the lack of available parameters, providing a comprehensive modeling focusing on operation conditions and designed parameters, respectively. The proposed model reveals that lower ECI and SY were associated with higher treated wastewater volumes, more lenient effluent standards, and newer equipment. Moreover, ECI and SY showed different patterns when influent biochemical oxygen demand is above or below 100 mg/L in the anaerobic-anoxic-oxic process. Therefore, managing ECI and SY requires quantifying the coupling relationships between biochemical reactions instead of isolating each variable. Furthermore, the proposed models demonstrate potential economic-related inequalities resulting from synergizing water pollution and GHG emissions management.
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Affiliation(s)
- Yujun Huang
- School of Environment, Tsinghua University, 1 Qinghuayuan, Beijing 100084, China
| | - Yifan Xie
- School of Environment, Tsinghua University, 1 Qinghuayuan, Beijing 100084, China
| | - Yipeng Wu
- School of Environment, Tsinghua University, 1 Qinghuayuan, Beijing 100084, China
| | - Fanlin Meng
- School of Environment, Tsinghua University, 1 Qinghuayuan, Beijing 100084, China
| | - Chengyu He
- School of Environment, Tsinghua University, 1 Qinghuayuan, Beijing 100084, China
| | - Hao Zou
- Department of Computer Science and Technology, Tsinghua University, 1 Qinghuayuan, Beijing 100084, China
| | - Xiaoting Wang
- Intelligent Cities Research, JD Technology, 11 Kechuang Street, Beijing 100176, China
| | - Ailun Shui
- School of Environment, Tsinghua University, 1 Qinghuayuan, Beijing 100084, China
| | - Shuming Liu
- School of Environment, Tsinghua University, 1 Qinghuayuan, Beijing 100084, China
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12
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Zhu JJ, Yang M, Ren ZJ. Machine Learning in Environmental Research: Common Pitfalls and Best Practices. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:17671-17689. [PMID: 37384597 DOI: 10.1021/acs.est.3c00026] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
Machine learning (ML) is increasingly used in environmental research to process large data sets and decipher complex relationships between system variables. However, due to the lack of familiarity and methodological rigor, inadequate ML studies may lead to spurious conclusions. In this study, we synthesized literature analysis with our own experience and provided a tutorial-like compilation of common pitfalls along with best practice guidelines for environmental ML research. We identified more than 30 key items and provided evidence-based data analysis based on 148 highly cited research articles to exhibit the misconceptions of terminologies, proper sample size and feature size, data enrichment and feature selection, randomness assessment, data leakage management, data splitting, method selection and comparison, model optimization and evaluation, and model explainability and causality. By analyzing good examples on supervised learning and reference modeling paradigms, we hope to help researchers adopt more rigorous data preprocessing and model development standards for more accurate, robust, and practicable model uses in environmental research and applications.
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Affiliation(s)
- Jun-Jie Zhu
- Department of Civil and Environmental Engineering and Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States
| | - Meiqi Yang
- Department of Civil and Environmental Engineering and Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States
| | - Zhiyong Jason Ren
- Department of Civil and Environmental Engineering and Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States
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13
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Gao H, Zhong S, Dangayach R, Chen Y. Understanding and Designing a High-Performance Ultrafiltration Membrane Using Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:17831-17840. [PMID: 36790106 PMCID: PMC10666290 DOI: 10.1021/acs.est.2c05404] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 02/04/2023] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
Ultrafiltration (UF) as one of the mainstream membrane-based technologies has been widely used in water and wastewater treatment. Increasing demand for clean and safe water requires the rational design of UF membranes with antifouling potential, while maintaining high water permeability and removal efficiency. This work employed a machine learning (ML) method to establish and understand the correlation of five membrane performance indices as well as three major performance-determining membrane properties with membrane fabrication conditions. The loading of additives, specifically nanomaterials (A_wt %), at loading amounts of >1.0 wt % was found to be the most significant feature affecting all of the membrane performance indices. The polymer content (P_wt %), molecular weight of the pore maker (M_Da), and pore maker content (M_wt %) also made considerable contributions to predicting membrane performance. Notably, M_Da was more important than M_wt % for predicting membrane performance. The feature analysis of ML models in terms of membrane properties (i.e., mean pore size, overall porosity, and contact angle) provided an unequivocal explanation of the effects of fabrication conditions on membrane performance. Our approach can provide practical aid in guiding the design of fit-for-purpose separation membranes through data-driven virtual experiments.
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Affiliation(s)
- Haiping Gao
- School
of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
- Shandong
Provincial Key Laboratory of Water Pollution Control and Resource
Reuse, School of Environmental Science and Engineering, Shandong University, Qingdao, Shandong 266237, China
| | - Shifa Zhong
- School
of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
- School
of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China
| | - Raghav Dangayach
- School
of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Yongsheng Chen
- School
of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
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14
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Jeong N, Epsztein R, Wang R, Park S, Lin S, Tong T. Exploring the Knowledge Attained by Machine Learning on Ion Transport across Polyamide Membranes Using Explainable Artificial Intelligence. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:17851-17862. [PMID: 36917705 DOI: 10.1021/acs.est.2c08384] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Recent studies have increasingly applied machine learning (ML) to aid in performance and material design associated with membrane separation. However, whether the knowledge attained by ML with a limited number of available data is enough to capture and validate the fundamental principles of membrane science remains elusive. Herein, we applied explainable artificial intelligence (XAI) to thoroughly investigate the knowledge learned by ML on the mechanisms of ion transport across polyamide reverse osmosis (RO) and nanofiltration (NF) membranes by leveraging 1,585 data from 26 membrane types. The Shapley additive explanation method based on cooperative game theory was used to unveil the influences of various ion and membrane properties on the model predictions. XAI shows that the ML can capture the important roles of size exclusion and electrostatic interaction in regulating membrane separation properly. XAI also identifies that the mechanisms governing ion transport possess different relative importance to cation and anion rejections during RO and NF filtration. Overall, we provide a framework to evaluate the knowledge underlying the ML model prediction and demonstrate that ML is able to learn fundamental mechanisms of ion transport across polyamide membranes, highlighting the importance of elucidating model interpretability for more reliable and explainable ML applications to membrane selection and design.
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Affiliation(s)
- Nohyeong Jeong
- Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Razi Epsztein
- Department of Civil and Environmental Engineering, Technion - Israel Institute of Technology, Haifa 32000, Israel
| | - Ruoyu Wang
- Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, Tennessee 37235-1831, United States
| | - Shinyun Park
- Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Shihong Lin
- Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, Tennessee 37235-1831, United States
- Department of Chemical and Bimolecular Engineering, Vanderbilt University, Nashville, Tennessee 37235-1831, United States
| | - Tiezheng Tong
- Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, Colorado 80523, United States
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15
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Mousavi SL, Sajjadi SM. Predicting rejection of emerging contaminants through RO membrane filtration based on ANN-QSAR modeling approach: trends in molecular descriptors and structures towards rejections. RSC Adv 2023; 13:23754-23771. [PMID: 37560620 PMCID: PMC10407621 DOI: 10.1039/d3ra03177b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 07/24/2023] [Indexed: 08/11/2023] Open
Abstract
In this work, a quantitative structure-activity relationship (QSAR) study was performed on a set of emerging contaminants (ECs) to predict their rejections by reverse osmosis membrane (RO). A wide range of molecular descriptors was calculated by Dragon software for 72 ECs. The QSAR data was analyzed by an artificial neural network method (ANN), in which four out of 3000 theoretical molecular descriptors were chosen and their significance was computed based on the Garson method. The significance trends of descriptors were as follows in descending order: ESpm14u > R2e > SIC1 > EEig03d. The selected descriptors were ranked based on their importance and then an explorative study was conducted on the QSAR data to show the trends in molecular descriptors and structures toward the rejections values of ECs. The MLR algorithm was used to make a linear model and the results were compared with those of the nonlinear ANN algorithm. The comparison results revealed it is necessary to apply the ANN model to this data with non-linear properties. For the whole dataset, the correlation coefficient (R2) and residual mean squared error (RMSE) of the ANN and MLR methods were 0.9528, 6.4224; and 0.8753, 11.3400, respectively. The comparison results showed the superiority of ANN modeling in the analysis of ECs' QSAR data.
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Affiliation(s)
- Setare Loh Mousavi
- Faculty of Chemistry, Semnan University Semnan Iran +98 23 33384110 +98 23 31533192
| | - S Maryam Sajjadi
- Faculty of Chemistry, Semnan University Semnan Iran +98 23 33384110 +98 23 31533192
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16
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Wang F, Wang W, Wang H, Zhao Z, Zhou T, Jiang C, Li J, Zhang X, Liang T, Dong W. Experiments and machine learning-based modeling for haloacetic acids rejection by nanofiltration: Influence of solute properties and operating conditions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 883:163610. [PMID: 37088392 DOI: 10.1016/j.scitotenv.2023.163610] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 04/13/2023] [Accepted: 04/16/2023] [Indexed: 05/03/2023]
Abstract
Because of potential risks to public health, the presence of haloacetic acids (HAAs) in drinking water is a major concern. Nanofiltration (NF) has shown potential for HAAs rejection, and several factors, namely, membrane properties, solute properties, and operating conditions, have been revealed key roles. However, knowledge of NF separation mechanism by quantifying these factors is limited. This study investigated and modeled NF performance on HAAs rejection. NF performance was experimentally investigated under various transmembrane pressure (TMP), cross-flow velocity (CV), temperature, pH, ionic strength (IS), and HAAs initial feed concentration (Cin). We used machine learning (ML) to understand the mechanism from the perspective of HAAs properties and operating conditions. Multiple linear regression (MLR), support vector machine (SVM), multsilayer perceptron (MLP), extreme gradient boosting (XGBoost), and random forest (RF) models were used. The MLP, XGBoost and RF models achieved significant performance with high R2 (0.970, 0.973, and 0.980) and low RMSE (4.71, 4.41, and 3.84). These three models were analyzed using the Shapley Additive explanation (SHAP) to quantify relative contributions of HAAs properties and operating conditions. XGBoost-SHAP produced the most logical results and was the best-performing model for selecting optimal input variables combinations. The results showed that Stokes radius (rs), logarithmic octanol-water partitioning coefficient (logKow), molecular weight (MW), pH, TMP, and temperature are key variables for interpreting NF process. The effects of HAAs properties were ranked as rs > logKow > MW, suggesting significance of size exclusion and hydrophobic interaction. The impact of the operational conditions followed the order pH > TMP > temperature, illustrating that pH was the major influencing operating condition. This study demonstrated significant capacity of ML, which reduced amount of experimental work. In addition, the main operating conditions can be evaluated in terms of their contributions, making ML an efficient tool for risk management and process optimization.
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Affiliation(s)
- Feifei Wang
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, PR China
| | - Weikang Wang
- Shen Zhen LiYuan Water Design & Consultation CO, LTD, PR China
| | - Hongjie Wang
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, PR China; Shenzhen Key Laboratory of Water Resource Utilization and Environmental Pollution Control, Shenzhen 518055, PR China; State Key Lab of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, PR China.
| | - Zilong Zhao
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, PR China; Shenzhen Key Laboratory of Water Resource Utilization and Environmental Pollution Control, Shenzhen 518055, PR China
| | - Ting Zhou
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, PR China
| | - Chengjun Jiang
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, PR China
| | - Ji Li
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, PR China; Shenzhen Key Laboratory of Water Resource Utilization and Environmental Pollution Control, Shenzhen 518055, PR China; State Key Lab of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, PR China
| | - Xiaolei Zhang
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, PR China
| | - Tianzhe Liang
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, PR China
| | - Wenyi Dong
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, PR China; Shenzhen Key Laboratory of Water Resource Utilization and Environmental Pollution Control, Shenzhen 518055, PR China; State Key Lab of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, PR China
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17
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Yang M, Zhu JJ, McGaughey A, Zheng S, Priestley RD, Ren ZJ. Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:5934-5946. [PMID: 36972410 DOI: 10.1021/acs.est.2c06382] [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] [Indexed: 06/18/2023]
Abstract
The extraction of acetic acid and other carboxylic acids from water is an emerging separation need as they are increasingly produced from waste organics and CO2 during carbon valorization. However, the traditional experimental approach can be slow and expensive, and machine learning (ML) may provide new insights and guidance in membrane development for organic acid extraction. In this study, we collected extensive literature data and developed the first ML models for predicting separation factors between acetic acid and water in pervaporation with polymers' properties, membrane morphology, fabrication parameters, and operating conditions. Importantly, we assessed seed randomness and data leakage problems during model development, which have been overlooked in ML studies but will result in over-optimistic results and misinterpreted variable importance. With proper data leakage management, we established a robust model and achieved a root-mean-square error of 0.515 using the CatBoost regression model. In addition, the prediction model was interpreted to elucidate the variables' importance, where the mass ratio was the topmost significant variable in predicting separation factors. In addition, polymers' concentration and membranes' effective area contributed to information leakage. These results demonstrate ML models' advances in membrane design and fabrication and the importance of vigorous model validation.
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Affiliation(s)
- Meiqi Yang
- Department of Civil and Environmental Engineering and Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey08544, United States
| | - Jun-Jie Zhu
- Department of Civil and Environmental Engineering and Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey08544, United States
| | - Allyson McGaughey
- Department of Civil and Environmental Engineering and Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey08544, United States
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey08544, United States
| | - Sunxiang Zheng
- Department of Civil and Environmental Engineering and Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey08544, United States
| | - Rodney D Priestley
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey08544, United States
| | - Zhiyong Jason Ren
- Department of Civil and Environmental Engineering and Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey08544, United States
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18
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Ilyas A, Vankelecom IFJ. Designing sustainable membrane-based water treatment via fouling control through membrane interface engineering and process developments. Adv Colloid Interface Sci 2023; 312:102834. [PMID: 36634445 DOI: 10.1016/j.cis.2023.102834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 12/05/2022] [Accepted: 01/04/2023] [Indexed: 01/09/2023]
Abstract
Membrane-based water treatment processes have been established as a powerful approach for clean water production. However, despite the significant advances made in terms of rejection and flux, provision of sustainable and energy-efficient water production is restricted by the inevitable issue of membrane fouling, known to be the major contributor to the elevated operating costs due to frequent chemical cleaning, increased transmembrane resistance, and deterioration of permeate flux. This review provides an overview of fouling control strategies in different membrane processes, such as microfiltration, ultrafiltration, membrane bioreactors, and desalination via reverse osmosis and forward osmosis. Insights into the recent advancements are discussed and efforts made in terms of membrane development, modules arrangement, process optimization, feed pretreatment, and fouling monitoring are highlighted to evaluate their overall impact in energy- and cost-effective water treatment. Major findings in four key aspects are presented, including membrane surface modification, modules design, process integration, and fouling monitoring. Among the above mentioned anti-fouling strategies, a large part of research has been focused on membrane surface modifications using a number of anti-fouling materials whereas much less research has been devoted to membrane module advancements and in-situ fouling monitoring and control. At the end, a critical analysis is provided for each anti-fouling strategy and a rationale framework is provided for design of efficient membranes and process for water treatment.
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Affiliation(s)
- Ayesha Ilyas
- Membrane Technology Group (MTG), Division cMACS, Faculty of Bioscience Engineering, KU Leuven, Celestijnenlaan 200F, Box 2454, 3001 Leuven, Belgium
| | - Ivo F J Vankelecom
- Membrane Technology Group (MTG), Division cMACS, Faculty of Bioscience Engineering, KU Leuven, Celestijnenlaan 200F, Box 2454, 3001 Leuven, Belgium.
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19
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Machine learning for predicting the dynamic extraction of multiple substances by emulsion liquid membranes. Sep Purif Technol 2023. [DOI: 10.1016/j.seppur.2023.123458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
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20
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Zhu T, Zhang Y, Tao C, Chen W, Cheng H. Prediction of organic contaminant rejection by nanofiltration and reverse osmosis membranes using interpretable machine learning models. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159348. [PMID: 36228787 DOI: 10.1016/j.scitotenv.2022.159348] [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: 07/22/2022] [Revised: 09/21/2022] [Accepted: 10/06/2022] [Indexed: 06/16/2023]
Abstract
Efficiency improvement in contaminant removal by nanofiltration (NF) and reverse osmosis (RO) membranes is a multidimensional process involving membrane material selection and experimental condition optimization. It is unrealistic to explore the contributions of diverse influencing factors to the removal rate by trial-and-error experimentation. However, the advanced machine learning (ML) method is a powerful tool to simulate this complex decision-making process. Here, 4 traditional learning algorithms (MLR, SVM, ANN, kNN) and 4 ensemble learning algorithms (RF, GBDT, XGBoost, LightGBM) were applied to predict the removal efficiency of contaminants. Results reported here demonstrate that ensemble models showed significantly better predictive performance than traditional models. More importantly, this study achieved a compelling tradeoff between accuracy and interpretability for ensemble models with an effective model interpretation approach, which revealed the mutual interaction mechanism between the membrane material, contaminants and experimental conditions in membrane separation. Additionally, feature selection was for the first time achieved based on the aforementioned model interpretation method to determine the most important variable influencing the contaminant removal rate. Ultimately, the four ensemble models retrained by the selected variables achieved distinguished prediction performance (R2adj = 92.4 %-99.5 %). MWCO (membrane molecular weight cut-off), McGowan volume of solute (V) and molecular weight (MW) of the compound were demonstrated to be the most important influencing factors in contaminant removal by the NF and RO processes. Overall, the proposed methods in this study can facilitate versatile complex decision-making processes in the environmental field, particularly in contaminant removal by advanced physicochemical separation processes.
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Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China.
| | - Yu Zhang
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| | - Cuicui Tao
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| | - Wenxuan Chen
- School of Civil Engineering, Southeast University, Nanjing 210096, China
| | - Haomiao Cheng
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
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21
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Zhang W, Huang W, Tan J, Huang D, Ma J, Wu B. Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives. CHEMOSPHERE 2023; 311:137044. [PMID: 36330979 DOI: 10.1016/j.chemosphere.2022.137044] [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: 09/26/2022] [Revised: 10/22/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some cost-effective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.
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Affiliation(s)
- Wentao Zhang
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China
| | - Wenguang Huang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China.
| | - Jie Tan
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China
| | - Dawei Huang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China
| | - Jun Ma
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China
| | - Bingdang Wu
- School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, 215009, People's Republic of China; Key Laboratory of Suzhou Sponge City Technology, Suzhou, 215002, People's Republic of China.
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22
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Wang C, Wang L, Soo A, Bansidhar Pathak N, Kyong Shon H. Machine learning based prediction and optimization of thin film nanocomposite membranes for organic solvent nanofiltration. Sep Purif Technol 2023. [DOI: 10.1016/j.seppur.2022.122328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023]
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23
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Technologies for removing pharmaceuticals and personal care products (PPCPs) from aqueous solutions: Recent advances, performances, challenges and recommendations for improvements. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.121144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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24
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Karbassiyazdi E, Fattahi F, Yousefi N, Tahmassebi A, Taromi AA, Manzari JZ, Gandomi AH, Altaee A, Razmjou A. XGBoost model as an efficient machine learning approach for PFAS removal: Effects of material characteristics and operation conditions. ENVIRONMENTAL RESEARCH 2022; 215:114286. [PMID: 36096170 DOI: 10.1016/j.envres.2022.114286] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 08/19/2022] [Accepted: 09/03/2022] [Indexed: 06/15/2023]
Abstract
Due to the implications of poly- and perfluoroalkyl substances (PFAS) on the environment and public health, great attention has been recently made to finding innovative materials and methods for PFAS removal. In this work, PFAS is considered universal contamination which can be found in many wastewater streams. Conventional materials and processes used to remove and degrade PFAS do not have enough competence to address the issue particularly when it comes to eliminating short-chain PFAS. This is mainly due to the large number of complex parameters that are involved in both material and process designs. Here, we took the advantage of artificial intelligence to introduce a model (XGBoost) in which material and process factors are considered simultaneously. This research applies a machine learning approach using data collected from reported articles to predict the PFAS removal factors. The XGBoost modeling provided accurate adsorption capacity, equilibrium, and removal estimates with the ability to predict the adsorption mechanisms. The performance comparison of adsorbents and the role of AI in one dominant are studied and reviewed for the first time, even though many studies have been carried out to develop PFAS removal through various adsorption methods such as ion exchange, nanofiltration, and activated carbon (AC). The model showed that pH is the most effective parameter to predict PFAS removal. The proposed model in this work can be extended for other micropollutants and can be used as a basic framework for future adsorbent design and process optimization.
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Affiliation(s)
- Elika Karbassiyazdi
- Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Australia
| | - Fatemeh Fattahi
- Department of Chemical Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Negin Yousefi
- Department of Chemical Engineering, Isfahan University of Technology, Isfahan, Iran
| | | | - Arsia Afshar Taromi
- Petrochemicals Department, Iran Polymer and Petrochemical Institute, P.O. Box 14965/115, Tehran, Iran
| | - Javad Zyaie Manzari
- Department of Chemical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Amir H Gandomi
- Faculty of Engineering & Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia.
| | - Ali Altaee
- Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Australia
| | - Amir Razmjou
- School of Engineering, Edith Cowan University, Joondalup, Perth, WA, 6027, Australia; UNESCO Centre for Membrane Science and Technology, School of Chemical Engineering, University of New South Wales, Sydney, NSW, 2052, Australia.
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Deep Learning-Based Predictive Control of Injection Velocity in Injection Molding Machines. ADVANCES IN POLYMER TECHNOLOGY 2022. [DOI: 10.1155/2022/7662264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Rapid and reliable optimal control of injection molding machines (IMMs) is critical for the effective production of injection-molded goods, especially in the situation of restricted computer resources of embedded equipment in IMMs. In this paper, an optimal tracking injection velocity control problem arising in a typical IMM is studied. An effective hybrid intelligent control approach with less computing resources for real-time implementation based on the deep learning (DL) method to mimic the classical model predictive control rule is developed to deal with the tracking control of the injection speed. The proposed method utilizes the gated recurrent unit neural network to learn and predict the optimal time series control process data produced by the traditional model predictive controller. The benefits of this approach over the conventional optimization method are illustrated through simulation results, which show that the convergent DL-based controller can effectively avoid the complex calculation in the control process of IMMs and meet the requirements of more robustness and resist environmental uncertainty to a certain level and can be potentially implemented in embedded hardware much more efficiently and conveniently with a smaller memory footprint and faster computation time.
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26
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Zhu M, Wang J, Yang X, Zhang Y, Zhang L, Ren H, Wu B, Ye L. A review of the application of machine learning in water quality evaluation. ECO-ENVIRONMENT & HEALTH (ONLINE) 2022; 1:107-116. [PMID: 38075524 PMCID: PMC10702893 DOI: 10.1016/j.eehl.2022.06.001] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 05/19/2022] [Accepted: 06/01/2022] [Indexed: 12/31/2023]
Abstract
With the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction. Unlike traditional models used in water-related research, data-driven models based on machine learning can efficiently solve more complex nonlinear problems. In water environment research, models and conclusions derived from machine learning have been applied to the construction, monitoring, simulation, evaluation, and optimization of various water treatment and management systems. Additionally, machine learning can provide solutions for water pollution control, water quality improvement, and watershed ecosystem security management. In this review, we describe the cases in which machine learning algorithms have been applied to evaluate the water quality in different water environments, such as surface water, groundwater, drinking water, sewage, and seawater. Furthermore, we propose possible future applications of machine learning approaches to water environments.
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Affiliation(s)
- Mengyuan Zhu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Jiawei Wang
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Xiao Yang
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Yu Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Linyu Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Hongqiang Ren
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Bing Wu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Lin Ye
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
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27
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Ignacz G, Szekely G. Deep learning meets quantitative structure–activity relationship (QSAR) for leveraging structure-based prediction of solute rejection in organic solvent nanofiltration. J Memb Sci 2022. [DOI: 10.1016/j.memsci.2022.120268] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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