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Cairone S, Hasan SW, Choo KH, Li CW, Zarra T, Belgiorno V, Naddeo V. Integrating artificial intelligence modeling and membrane technologies for advanced wastewater treatment: Research progress and future perspectives. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 944:173999. [PMID: 38879019 DOI: 10.1016/j.scitotenv.2024.173999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/28/2024] [Accepted: 06/12/2024] [Indexed: 06/20/2024]
Abstract
Membrane technologies have become proficient alternatives for advanced wastewater treatment, ensuring high contaminant removal and sustainable resource recovery. Despite significant progress, ongoing research efforts aim to further optimize treatment performance. Among the challenges faced, membrane fouling persists as a relevant obstacle in membrane technologies, necessitating the development of more effective mitigation strategies. Mathematical models, widely employed for predicting treatment performance, generally exhibit low accuracy and suffer from uncertainties due to the complex and variable nature of wastewater. To overcome these limitations, numerous studies have proposed artificial intelligence (AI) modeling to accurately predict membrane technologies' performance and fouling mechanisms. This approach aims to provide advanced simulations and predictions, thereby enhancing process control, optimization, and intensification. This literature review explores recent advancements in modeling membrane-based wastewater treatment processes through AI models. The analysis highlights the enormous potential of this research field in enhancing the efficiency of membrane technologies. The role of AI modeling in defining optimal operating conditions, developing effective strategies for membrane fouling mitigation, enhancing the performance of novel membrane-based technologies, and improving membrane fabrication techniques is discussed. These enhanced process optimization and control strategies driven by AI modeling ensure improved effluent quality, optimized resource consumption, and minimized operating costs. The potential contribution of this cutting-edge approach to a paradigm shift toward sustainable wastewater treatment is examined. Finally, this review outlines future perspectives, emphasizing the research challenges that require attention to overcome the current limitations hindering the integration of AI modeling in wastewater treatment plants.
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Affiliation(s)
- Stefano Cairone
- Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II #132, 84084 Fisciano, SA, Italy
| | - Shadi W Hasan
- Center for Membranes and Advanced Water Technology (CMAT), Department of Chemical and Petroleum Engineering, Khalifa University of Science and Technology, PO, Box 127788, Abu Dhabi, United Arab Emirates
| | - Kwang-Ho Choo
- Department of Environmental Engineering, Kyungpook National University (KNU), 80 Daehak-ro, Bukgu, Daegu 41566, Republic of Korea
| | - Chi-Wang Li
- Department of Water Resources and Environmental Engineering, Tamkang University, 151 Yingzhuan Road Tamsui District, New Taipei City 25137, Taiwan
| | - Tiziano Zarra
- Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II #132, 84084 Fisciano, SA, Italy
| | - Vincenzo Belgiorno
- Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II #132, 84084 Fisciano, SA, Italy
| | - Vincenzo Naddeo
- Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II #132, 84084 Fisciano, SA, Italy.
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Baig U, Usman J, Abba SI, Yogarathinam LT, Waheed A, Bafaqeer A, Aljundi IH. Insight into soft chemometric computational learning for modelling oily-wastewater separation efficiency and permeate flux of polypyrrole-decorated ceramic-polymeric membranes. J Chromatogr A 2024; 1725:464897. [PMID: 38678694 DOI: 10.1016/j.chroma.2024.464897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 03/27/2024] [Accepted: 04/09/2024] [Indexed: 05/01/2024]
Abstract
Reliable modeling of oily wastewater emphasizes the paramount importance of sustainable and health-conscious wastewater management practices, which directly aligns with the Sustainable Development Goals (SDG) while also meeting the guidelines of the World Health Organization (WHO). This research explores the efficiency of utilizing polypyrrole-coated ceramic-polymeric membranes to model oily wastewater separation efficiency (SE) and permeate flux (PF) based on established experimental procedures. In this area, computational simulation still needs to be explored. The study developed predictive regression models, including robust linear regression (RLR), stepwise linear regression (SWR) and linear regression (LR) for the ceramic-polymeric porous membrane, aiming to interpret its complex performance across diverse conditions and, thus, develop its utility in oily wastewater treatment applications. Subsequently, a novel, simple average ensemble paradigm was explored to reduce errors and improve prediction skills. Prior to the development of the model, stability and reliability analysis of the data was conducted based on Philip Perron tests with the Bartlett kernel estimation method. The accuracy of the SE exhibited a high consistency, averaging 99.92% with minimal variability (standard deviation of 0.026%), potentially simplifying its prediction compared to PF. The modes were validated and evaluated using metrics like MAE, RMSE, Speed, and MSE, in addition to 2D graphical and cumulative distribution function graphs. The LR model emerged as the best with the lowest RMSE =0.21951, indicating superior prediction accuracy, followed closely by RLR with an RMSE = 0.22359. SWLR, while having the highest RMSE = 0.34573, marked its dominance in prediction speed with 110 observations per second. Notably, the RLR model justified a reduction in error by approximately 35.29% compared to SWLR. Moreover, the training efficiency of the LR model exceeded, demanding a mere 2.9252 s, marking a reduction of about 32.54% compared to SWLR. The improved simple ensemble learning proved merit over the three models regarding error accuracy. This study emphasizes the essential role of soft-computing learning in optimizing the design and performance of ceramic-polymeric membranes.
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Affiliation(s)
- Umair Baig
- Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Jamil Usman
- Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Sani I Abba
- Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.
| | - Lukka Thuyavan Yogarathinam
- Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Abdul Waheed
- Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Abdullah Bafaqeer
- Interdisciplinary Research Center for Refining & Advanced Chemicals, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Isam H Aljundi
- Interdisciplinary Research Center for Membrane 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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Agbasi JC, Egbueri JC. Prediction of potentially toxic elements in water resources using MLP-NN, RBF-NN, and ANFIS: a comprehensive review. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:30370-30398. [PMID: 38641692 DOI: 10.1007/s11356-024-33350-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 04/12/2024] [Indexed: 04/21/2024]
Abstract
Water resources are constantly threatened by pollution of potentially toxic elements (PTEs). In efforts to monitor and mitigate PTEs pollution in water resources, machine learning (ML) algorithms have been utilized to predict them. However, review studies have not paid attention to the suitability of input variables utilized for PTE prediction. Therefore, the present review analyzed studies that employed three ML algorithms: MLP-NN (multilayer perceptron neural network), RBF-NN (radial basis function neural network), and ANFIS (adaptive neuro-fuzzy inference system) to predict PTEs in water. A total of 139 models were analyzed to ascertain the input variables utilized, the suitability of the input variables, the trends of the ML model applications, and the comparison of their performances. The present study identified seven groups of input variables commonly used to predict PTEs in water. Group 1 comprised of physical parameters (P), chemical parameters (C), and metals (M). Group 2 contains only P and C; Group 3 contains only P and M; Group 4 contains only C and M; Group 5 contains only P; Group 6 contains only C; and Group 7 contains only M. Studies that employed the three algorithms proved that Groups 1, 2, 3, 5, and 7 parameters are suitable input variables for forecasting PTEs in water. The parameters of Groups 4 and 6 also proved to be suitable for the MLP-NN algorithm. However, their suitability with respect to the RBF-NN and ANFIS algorithms could not be ascertained. The most commonly predicted PTEs using the MLP-NN algorithm were Fe, Zn, and As. For the RBF-NN algorithm, they were NO3, Zn, and Pb, and for the ANFIS, they were NO3, Fe, and Mn. Based on correlation and determination coefficients (R, R2), the overall order of performance of the three ML algorithms was ANFIS > RBF-NN > MLP-NN, even though MLP-NN was the most commonly used algorithm.
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Affiliation(s)
- Johnson C Agbasi
- Department of Geology, Chukwuemeka Odumegwu Ojukwu University, Uli, Nigeria
| | - Johnbosco C Egbueri
- Department of Geology, Chukwuemeka Odumegwu Ojukwu University, Uli, Nigeria.
- Research Management Office (RMO), Chukwuemeka Odumegwu Ojukwu University, Anambra State, Nigeria.
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Usman J, Abba SI, Baig N, Abu-Zahra N, Hasan SW, Aljundi IH. Design and Machine Learning Prediction of In Situ Grown PDA-Stabilized MOF (UiO-66-NH 2) Membrane for Low-Pressure Separation of Emulsified Oily Wastewater. ACS APPLIED MATERIALS & INTERFACES 2024; 16:16271-16289. [PMID: 38514254 DOI: 10.1021/acsami.4c00752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/23/2024]
Abstract
Significant progress has been made in designing advanced membranes; however, persistent challenges remain due to their reduced permeation rates and a propensity for substantial fouling. These factors continue to pose significant barriers to the effective utilization of membranes in the separation of oil-in-water emulsions. Metal-organic frameworks (MOFs) are considered promising materials for such applications; however, they encounter three key challenges when applied to the separation of oil from water: (a) lack of water stability; (b) difficulty in producing defect-free membranes; and (c) unresolved issue of stabilizing the MOF separating layer on the ceramic membrane (CM) support. In this study, a defect-free hydrolytically stable zirconium-based MOF separating layer was formed through a two-step method: first, by in situ growth of UiO-66-NH2 MOF into the voids of polydopamine (PDA)-functionalized CM during the solvothermal process, and then by facilitating the self-assembly of UiO-66-NH2 with PDA using a pressurized dead-end assembly. A stable MOF separating layer was attained by enriching the ceramic support with amines and hydroxyl groups using PDA, which assisted in the assembly and stabilization of UiO-66-NH2. The PDA-s-UiO-66-NH2-CM membrane displayed air superhydrophilicity and underwater superoleophobicity, demonstrating its oil resistance and high antifouling behavior. The PDA-s-UiO-66-NH2-CM membrane has shown exceptionally high permeability and separation capacity for challenging oil-in-water emulsions. This is attributed to numerous nanochannels from the membrane and its high resistance to oil adhesion. The membranes showed excellent stability over 15 continuous test cycles, which indicates that the developed MOFs separating layers have a low tendency to be clogged by oil droplets during separation. Machine learning-based Gaussian process regression (GPR) models as nonparametric kernel-based probabilistic models were employed to predict the performance efficiency of the PDA-s-UiO-66-NH2-CM membrane in oil-in-water separation. The outcomes were compared with the support vector machine (SVM) and decision tree (DT) algorithm. This efficiency includes various metrics related to its separation accuracy, and the models were developed through feature engineering to identify and utilize the most significant factors affecting the membrane's performance. The results proved the reliability of GPR optimization with the highest prediction accuracy in the validation phase. The average percentage increase of the GPR model compared to the SVM and DT model was 6.11 and 42.94%, respectively.
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Affiliation(s)
- Jamilu Usman
- Interdisciplinary Research Centre for Membranes and Water Security (IRC-MWS), King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Sani I Abba
- Interdisciplinary Research Centre for Membranes and Water Security (IRC-MWS), King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Nadeem Baig
- Interdisciplinary Research Centre for Membranes and Water Security (IRC-MWS), King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Nidal Abu-Zahra
- Materials Science and Engineering Department, University of Wisconsin-Milwaukee, 3200 North Cramer Street, Milwaukee, Wisconsin 53201, United States
| | - Shadi W Hasan
- Center for Membranes and Advanced Water Technology (CMAT), Department of Chemical and Petroleum Engineering, Khalifa University of Science and Technology, P.O. Box 127788 Abu Dhabi, United Arab Emirates
| | - Isam H Aljundi
- Interdisciplinary Research Centre for Membranes and Water Security (IRC-MWS), King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
- Chemical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
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Yaseen ZM, Melini Wan Mohtar WH, Homod RZ, Alawi OA, Abba SI, Oudah AY, Togun H, Goliatt L, Ul Hassan Kazmi SS, Tao H. Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic optimization algorithm. CHEMOSPHERE 2024; 352:141329. [PMID: 38296204 DOI: 10.1016/j.chemosphere.2024.141329] [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: 10/26/2023] [Revised: 01/09/2024] [Accepted: 01/28/2024] [Indexed: 02/09/2024]
Abstract
This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies.
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Affiliation(s)
- Zaher Mundher Yaseen
- Civil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia; Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, Saudi Arabia.
| | - Wan Hanna Melini Wan Mohtar
- Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, UKM, Bangi, Selangor, Malaysia; Environmental Management Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.
| | - Raad Z Homod
- Department of Oil and Gas Engineering, Basrah University for Oil and Gas, Basra, Iraq.
| | - Omer A Alawi
- Department of Thermofluids, School of Mechanical Engineering, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor Bahru, Malaysia.
| | - Sani I Abba
- Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, Saudi Arabia.
| | - Atheer Y Oudah
- Department of Computer Sciences, College of Education for Pure Science, University of Thi-Qar, Nasiriyah, 64001, Iraq; Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Nasiriyah, 64001, Iraq.
| | - Hussein Togun
- Department of Mechanical Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq.
| | - Leonardo Goliatt
- Computational and Applied Mechanics Department, Federal University of Juiz de Fora, 36036-900, Brazil.
| | - Syed Shabi Ul Hassan Kazmi
- Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, and Guangdong Provincial Key Laboratory of Marine Biotechnology, Shantou University, Shantou, 515063, China.
| | - Hai Tao
- School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, 558000, Guizhou, China; Institute of Big Data Application and Artificial Intelligence, Qiannan Normal University for Nationalities, Duyun, 558000, Guizhou, China; Faculty of Data Science and Information Technology, INTI International University, 71800, Malaysia.
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