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Yu Y, Jia H, Gao F, Zhu H, Zhang L, Wang J. Spectral fusion-based machine learning classifiers for discriminating membrane breakage in multiple scenarios. WATER RESEARCH 2024; 257:121714. [PMID: 38723357 DOI: 10.1016/j.watres.2024.121714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 04/26/2024] [Accepted: 05/01/2024] [Indexed: 05/29/2024]
Abstract
Membrane breakage can lead to filtration failure, which allows harmful substances to enter the effluent, posing potential hazards to human health and the environment. This study is an innovative combination of fluorescence and ultraviolet-visible (UV-Vis) spectroscopy to identify membrane breakage. It aims to unravel more comprehensive information, improve detection sensitivity and selectivity, and enable real-time monitoring capabilities. Fluorescence and UV-Vis data are extracted through variance partitioning analysis (VPA) and integrated through a decision tree algorithm to form a superior system with enhanced discrimination capabilities. VPA improves discrimination efficiency by extracting key information from spectral data and eliminating redundancy. The decision tree algorithm, on the other hand, can process large amounts of data simultaneously. In addition, the method has a wide range of applications and can be used in various scenarios accurately. The scenarios include domestic sewage, micropollutant water, aquaculture wastewater, and secondary treated sewage. The experimental results validate the application of machine learning classifiers in membrane breakage detection with an accuracy rate of 96.8 % to 97.4 %.
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Affiliation(s)
- Yang Yu
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, China; School of Environmental Science and Engineering, Tiangong University, Tianjin 300387, China
| | - Hui Jia
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, China; School of Environmental Science and Engineering, Tiangong University, Tianjin 300387, China
| | - Fei Gao
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, China; School of Environmental Science and Engineering, Tiangong University, Tianjin 300387, China
| | - Haifeng Zhu
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, China; School of Environmental Science and Engineering, Tiangong University, Tianjin 300387, China
| | - Lei Zhang
- Shenyang Academy of Environmental Sciences, Shenyang 110167, China
| | - Jie Wang
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, China; School of Environmental Science and Engineering, Tiangong University, Tianjin 300387, China; Hebei Industrial Technology Research Institute of Membranes, Cangzhou Institute of Tiangong University, Cangzhou 061000, China.
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Elsayed A, Ghaith M, Yosri A, Li Z, El-Dakhakhni W. Genetic programming expressions for effluent quality prediction: Towards AI-driven monitoring and management of wastewater treatment plants. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 356:120510. [PMID: 38490009 DOI: 10.1016/j.jenvman.2024.120510] [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: 10/27/2023] [Revised: 02/21/2024] [Accepted: 02/26/2024] [Indexed: 03/17/2024]
Abstract
Continuous effluent quality prediction in wastewater treatment processes is crucial to proactively reduce the risks to the environment and human health. However, wastewater treatment is an extremely complex process controlled by several uncertain, interdependent, and sometimes poorly characterized physico-chemical-biological process parameters. In addition, there are substantial spatiotemporal variations, uncertainties, and high non-linear interactions among the water quality parameters and process variables involved in the treatment process. Such complexities hinder efficient monitoring, operation, and management of wastewater treatment plants under normal and abnormal conditions. Typical mathematical and statistical tools most often fail to capture such complex interrelationships, and therefore data-driven techniques offer an attractive solution to effectively quantify the performance of wastewater treatment plants. Although several previous studies focused on applying regression-based data-driven models (e.g., artificial neural network) to predict some wastewater treatment effluent parameters, most of these studies employed a limited number of input variables to predict only one or two parameters characterizing the effluent quality (e.g., chemical oxygen demand (COD) and/or suspended solids (SS)). Harnessing the power of Artificial Intelligence (AI), the current study proposes multi-gene genetic programming (MGGP)-based models, using a dataset obtained from an operational wastewater treatment plant, deploying membrane aerated biofilm reactor, to predict the filtrated COD, ammonia (NH4), and SS concentrations along with the carbon-to-nitrogen ratio (C/N) within the effluent. Input features included a set of process variables characterizing the influent quality (e.g., filtered COD, NH4, and SS concentrations), water physics and chemistry parameters (e.g., temperature and pH), and operation conditions (e.g., applied air pressure). The developed MGGP-based models accurately reproduced the observations of the four output variables with correlation coefficient values that ranged between 0.98 and 0.99 during training and between 0.96 and 0.99 during testing, reflecting the power of the developed models in predicting the quality of the effluent from the treatment system. Interpretability analyses were subsequently deployed to confirm the intuitive understanding of input-output interrelations and to identify the governing parameters of the treatment process. The developed MGGP-based models can facilitate the AI-driven monitoring and management of wastewater treatment plants through devising optimal rapid operation and control schemes and assisting the plants' operators in maintaining proper performance of the plants under various normal and disruptive operational conditions.
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Affiliation(s)
- Ahmed Elsayed
- Department of Civil Engineering, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L7, Canada; Department of Irrigation and Hydraulic Engineering, Faculty of Engineering, Cairo University, 1 Gamaa Street, Giza 12613, Egypt.
| | - Maysara Ghaith
- Department of Civil Engineering, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L7, Canada; Department of Irrigation and Hydraulic Engineering, Faculty of Engineering, Cairo University, 1 Gamaa Street, Giza 12613, Egypt
| | - Ahmed Yosri
- Department of Civil Engineering, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L7, Canada; Department of Irrigation and Hydraulic Engineering, Faculty of Engineering, Cairo University, 1 Gamaa Street, Giza 12613, Egypt
| | - Zhong Li
- Department of Civil Engineering, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L7, Canada
| | - Wael El-Dakhakhni
- Department of Civil Engineering, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L7, Canada; School of Computational Science and Engineering, McMaster University, 1280 Main Street West, Hamilton, ON L8S4K1, Canada
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Wang T, Li YY. Predictive modeling based on artificial neural networks for membrane fouling in a large pilot-scale anaerobic membrane bioreactor for treating real municipal wastewater. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169164. [PMID: 38081428 DOI: 10.1016/j.scitotenv.2023.169164] [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: 07/01/2023] [Revised: 11/25/2023] [Accepted: 12/05/2023] [Indexed: 12/17/2023]
Abstract
Membrane fouling is the primary obstacle to applying anaerobic membrane bioreactors (AnMBRs) in municipal wastewater treatment. This issue holds critical significance as efficient wastewater treatment serves as a cornerstone for achieving environmental sustainability. This study uses machine learning to predict membrane fouling, taking advantage of rapid computational and algorithmic advances. Based on the 525-day operation data of a large pilot-scale AnMBR for treating real municipal wastewater, regression prediction was realized using multilayer perceptron (MLP) and long short-term memory (LSTM) artificial neural networks under substantial variations in operating conditions. The models involved employing the organic loading rate, suspended solids concentration, protein concentration in extracellular polymeric substance (EPSp), polysaccharide concentration in EPS (EPSc), reactor temperature, and flux as input features, and transmembrane pressure as the prediction target output. Hyperparameter optimization enhanced the regression prediction accuracies, and the rationality and utility of the MLP model for predicting large-scale AnMBR membrane fouling were confirmed at global and local levels of interpretability analysis. This work not only provides a methodological advance but also underscores the importance of merging environmental engineering with computational advancements to address pressing environmental challenges.
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Affiliation(s)
- Tianjie Wang
- Laboratory of Environmental Protection Engineering, Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University, 6-6-06 Aza-Aoba, Aramaki, Aoba Ward, Sendai, Miyagi 980-8579, Japan
| | - Yu-You Li
- Laboratory of Environmental Protection Engineering, Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University, 6-6-06 Aza-Aoba, Aramaki, Aoba Ward, Sendai, Miyagi 980-8579, Japan.
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Abuwatfa WH, AlSawaftah N, Darwish N, Pitt WG, Husseini GA. A Review on Membrane Fouling Prediction Using Artificial Neural Networks (ANNs). MEMBRANES 2023; 13:685. [PMID: 37505052 PMCID: PMC10383311 DOI: 10.3390/membranes13070685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/29/2023] [Accepted: 07/11/2023] [Indexed: 07/29/2023]
Abstract
Membrane fouling is a major hurdle to effective pressure-driven membrane processes, such as microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), and reverse osmosis (RO). Fouling refers to the accumulation of particles, organic and inorganic matter, and microbial cells on the membrane's external and internal surface, which reduces the permeate flux and increases the needed transmembrane pressure. Various factors affect membrane fouling, including feed water quality, membrane characteristics, operating conditions, and cleaning protocols. Several models have been developed to predict membrane fouling in pressure-driven processes. These models can be divided into traditional empirical, mechanistic, and artificial intelligence (AI)-based models. Artificial neural networks (ANNs) are powerful tools for nonlinear mapping and prediction, and they can capture complex relationships between input and output variables. In membrane fouling prediction, ANNs can be trained using historical data to predict the fouling rate or other fouling-related parameters based on the process parameters. This review addresses the pertinent literature about using ANNs for membrane fouling prediction. Specifically, complementing other existing reviews that focus on mathematical models or broad AI-based simulations, the present review focuses on the use of AI-based fouling prediction models, namely, artificial neural networks (ANNs) and their derivatives, to provide deeper insights into the strengths, weaknesses, potential, and areas of improvement associated with such models for membrane fouling prediction.
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Affiliation(s)
- Waad H Abuwatfa
- Materials Science and Engineering Ph.D. Program, College of Arts and Sciences, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
- Department of Chemical and Biological Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
| | - Nour AlSawaftah
- Materials Science and Engineering Ph.D. Program, College of Arts and Sciences, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
- Department of Chemical and Biological Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
| | - Naif Darwish
- Department of Chemical and Biological Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
| | - William G Pitt
- Chemical Engineering Department, Brigham Young University, Provo, UT 84602, USA
| | - Ghaleb A Husseini
- Materials Science and Engineering Ph.D. Program, College of Arts and Sciences, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
- Department of Chemical and Biological Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
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Yuan S, Ajam H, Sinnah ZAB, Altalbawy FMA, Abdul Ameer SA, Husain A, Al Mashhadani ZI, Alkhayyat A, Alsalamy A, Zubaid RA, Cao Y. The roles of artificial intelligence techniques for increasing the prediction performance of important parameters and their optimization in membrane processes: A systematic review. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 260:115066. [PMID: 37262969 DOI: 10.1016/j.ecoenv.2023.115066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/13/2023] [Accepted: 05/22/2023] [Indexed: 06/03/2023]
Abstract
Membrane-based separation processes has been recently of significant global interest compared to other conventional separation approaches due to possessing undeniable advantages like superior performance, environmentally-benign nature and simplicity of application. Computational simulation of fluids has shown its undeniable role in modeling and simulation of numerous physical/chemical phenomena including chemical engineering, chemical reaction, aerodynamics, drug delivery and plasma physics. Definition of fluids can be occurred using the Navier-Stokes equations, but solving the equations remains an important challenge. In membrane-based separation processes, true perception of fluid's manner through disparate membrane modules is an important concern, which has been significantly limited applying numerical/computational procedures such s computational fluid dynamics (CFD). Despite this noteworthy advantage, the optimization of membrane processes using CFD is time-consuming and expensive. Therefore, combination of artificial intelligence (AI) and CFD can result in the creation of a promising hybrid model to accurately predict the model results and appropriately optimize membrane processes and phase separation. This paper aims to provide a comprehensive overview about the advantages of commonly-employed ML-based techniques in combination with the CFD to intelligently increase the optimization accuracy and predict mass transfer and the unfavorable events (i.e., fouling) in various membrane processes. To reach this objective, four principal strategies of AI including SL, USL, SSL and ANN were explained and their advantages/disadvantages were discussed. Then after, prevalent ML-based algorithm for membrane-based separation processes. Finally, the application potential of AI techniques in different membrane processes (i.e., fouling control, desalination and wastewater treatment) were presented.
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Affiliation(s)
- Shuai Yuan
- Information Engineering College, Yantai Institute of Technology, Yantai, Shandong 264005, China.
| | - Hussein Ajam
- Department of Intelligent Medical Systems, Al Mustaqbal University College, Babylon 51001, Iraq
| | - Zainab Ali Bu Sinnah
- Mathematics Department, University Colleges at Nairiyah, University of Hafr Al Batin, Saudi Arabia
| | - Farag M A Altalbawy
- National Institute of Laser Enhanced Sciences (NILES), University of Cairo, Giza 12613, Egypt; Department of Chemistry, University College of Duba, University of Tabuk, Tabuk, Saudi Arabia
| | | | - Ahmed Husain
- Department of Medical Instrumentation, Al-farahidi University, Baghdad, Iraq
| | | | - Ahmed Alkhayyat
- Scientific Research Centre of the Islamic University, The Islamic University, Najaf, Iraq
| | - Ali Alsalamy
- College of Technical Engineering, Imam Ja'afar Al-Sadiq University, Al-Muthanna 66002, Iraq
| | | | - Yan Cao
- School of Computer Science and Engineering, Xi'an Technological University, Xi'an 710021, China
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Deep Study on Fouling Modelling of Ultrafiltration Membranes Used for OMW Treatment: Comparison Between Semi-empirical Models, Response Surface, and Artificial Neural Networks. FOOD BIOPROCESS TECH 2023. [DOI: 10.1007/s11947-023-03033-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
AbstractOlive oil production generates a large amount of wastewater called olive mill wastewater. This paper presents the study of the effect of transmembrane pressure and cross flow velocity on the decrease in permeate flux of different ultrafiltration membranes (material and pore size) when treating a two-phase olive mill wastewater (olive oil washing wastewater). Both semi-empirical models (Hermia models adapted to tangential filtration, combined model, and series resistance model), as well as statistical and machine learning methods (response surface methodology and artificial neural networks), were studied. Regarding the Hermia model, despite the good fit, the main drawback is that it does not consider the possibility that these mechanisms occur simultaneously in the same process. According to the accuracy of the fit of the models, in terms of R2 and SD, both the series resistance model and the combined model were able to represent the experimental data well. This indicates that both cake layer formation and pore blockage contributed to membrane fouling. The inorganic membranes showed a greater tendency to irreversible fouling, with higher values of the Ra/RT (adsorption/total resistance) ratio. Response surface methodology ANOVA showed that both cross flow velocity and transmembrane pressure are significant variables with respect to permeate flux for all membranes studied. Regarding artificial neural networks, the tansig function presented better results than the selu function, all presenting high R2, ranging from 0.96 to 0.99. However, the comparison of all the analyzed models showed that depending on the membrane, one model fits better than the others. Finally, through this work, it was possible to provide a better understanding of the data modelling of different ultrafiltration membranes used for the treatment of olive mill wastewater.
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Razali MC, Wahab NA, Sunar N, Shamsudin NH. Existing Filtration Treatment on Drinking Water Process and Concerns Issues. MEMBRANES 2023; 13:285. [PMID: 36984672 PMCID: PMC10051433 DOI: 10.3390/membranes13030285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 01/27/2023] [Accepted: 02/22/2023] [Indexed: 06/18/2023]
Abstract
Water is one of the main sources of life's survival. It is mandatory to have good-quality water, especially for drinking. Many types of available filtration treatment can produce high-quality drinking water. As a result, it is intriguing to determine which treatment is the best. This paper provides a review of available filtration technology specifically for drinking water treatment, including both conventional and advanced treatments, while focusing on membrane filtration treatment. This review covers the concerns that usually exist in membrane filtration treatment, namely membrane fouling. Here, the parameters that influence fouling are identified. This paper also discusses the different ways to handle fouling, either based on prevention, prediction, or control automation. According to the findings, the most common treatment for fouling was prevention. However, this treatment required the use of chemical agents, which will eventually affect human health. The prediction process was usually used to circumvent the process of fouling development. Based on our reviews up to now, there are a limited number of researchers who study membrane fouling control based on automation. Frequently, the treatment method and control strategy are determined individually.
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Affiliation(s)
- Mashitah Che Razali
- Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka 76100, Malaysia
- Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
| | - Norhaliza Abdul Wahab
- Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
| | - Noorhazirah Sunar
- Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
| | - Nur Hazahsha Shamsudin
- Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka 76100, Malaysia
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Cheng X, Liao Y, Lei Z, Li J, Fan X, Xiao X. Multi-scale design of MOF-based membrane separation for CO2/CH4 mixture via integration of molecular simulation, machine learning and process modeling and simulation. J Memb Sci 2023. [DOI: 10.1016/j.memsci.2023.121430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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Staszak K, Kruszelnicka I, Ginter-Kramarczyk D, Góra W, Baraniak M, Lota G, Regel-Rosocka M. Advances in the Removal of Cr(III) from Spent Industrial Effluents-A Review. MATERIALS (BASEL, SWITZERLAND) 2022; 16:378. [PMID: 36614717 PMCID: PMC9822515 DOI: 10.3390/ma16010378] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 12/23/2022] [Accepted: 12/26/2022] [Indexed: 06/17/2023]
Abstract
The review presents advances in the removal of Cr(III) from the industrial effluents published in the last ten years. Although Cr(III) has low solubility and is less dangerous for the aquatic environment than Cr(VI), it cannot be released into the aquatic environment without limitations and its content in water should be restricted. The development of efficient techniques for the removal of Cr(III) is also a response to the problem of chromium wastewater containing Cr(VI) ions. Very often the first step in dealing with such wastewater is the reduction in chromium content. In some cases, removal of Cr(III) from wastewaters is an important step for pretreatment of solutions to prepare them for subsequent recovery of other metals. In the review, hydrometallurgical operations for Cr(III) removal are presented, including examples of Cr(III) recovery from real industrial effluents with precipitation, adsorption, ion exchange, extraction, membrane techniques, microbial-enhanced techniques, electrochemical methods. The advantages and disadvantages of the operations mentioned are also presented. Finally, perspectives for the future in line with circular economy and low-environmental impact are briefly discussed.
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Affiliation(s)
- Katarzyna Staszak
- Institute of Chemical Technology and Engineering, Faculty of Chemical Technology, Poznan University of Technology, ul. Berdychowo 4, 60-965 Poznan, Poland
| | - Izabela Kruszelnicka
- Department of Water Supply and Bioeconomy, Faculty of Environmental Engineering and Energy, Poznan University of Technology, ul. Berdychowo 4, 60-965 Poznan, Poland
| | - Dobrochna Ginter-Kramarczyk
- Department of Water Supply and Bioeconomy, Faculty of Environmental Engineering and Energy, Poznan University of Technology, ul. Berdychowo 4, 60-965 Poznan, Poland
| | - Wojciech Góra
- Department of Water Supply and Bioeconomy, Faculty of Environmental Engineering and Energy, Poznan University of Technology, ul. Berdychowo 4, 60-965 Poznan, Poland
| | - Marek Baraniak
- Institute of Chemistry and Technical Electrochemistry, Faculty of Chemical Technology, Poznan University of Technology, ul. Berdychowo 4, 60-965 Poznan, Poland
| | - Grzegorz Lota
- Institute of Chemistry and Technical Electrochemistry, Faculty of Chemical Technology, Poznan University of Technology, ul. Berdychowo 4, 60-965 Poznan, Poland
| | - Magdalena Regel-Rosocka
- Institute of Chemical Technology and Engineering, Faculty of Chemical Technology, Poznan University of Technology, ul. Berdychowo 4, 60-965 Poznan, Poland
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AlSawaftah N, Abuwatfa W, Darwish N, Husseini GA. A Review on Membrane Biofouling: Prediction, Characterization, and Mitigation. MEMBRANES 2022; 12:membranes12121271. [PMID: 36557178 PMCID: PMC9787789 DOI: 10.3390/membranes12121271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/06/2022] [Accepted: 12/10/2022] [Indexed: 05/12/2023]
Abstract
Water scarcity is an increasing problem on every continent, which instigated the search for novel ways to provide clean water suitable for human use; one such way is desalination. Desalination refers to the process of purifying salts and contaminants to produce water suitable for domestic and industrial applications. Due to the high costs and energy consumption associated with some desalination techniques, membrane-based technologies have emerged as a promising alternative water treatment, due to their high energy efficiency, operational simplicity, and lower cost. However, membrane fouling is a major challenge to membrane-based separation as it has detrimental effects on the membrane's performance and integrity. Based on the type of accumulated foulants, fouling can be classified into particulate, organic, inorganic, and biofouling. Biofouling is considered the most problematic among the four fouling categories. Therefore, proper characterization and prediction of biofouling are essential for creating efficient control and mitigation strategies to minimize the damage associated with biofouling. Moreover, the use of artificial intelligence (AI) in predicting membrane fouling has garnered a great deal of attention due to its adaptive capability and prediction accuracy. This paper presents an overview of the membrane biofouling mechanisms, characterization techniques, and predictive methods with a focus on AI-based techniques, and mitigation strategies.
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Affiliation(s)
- Nour AlSawaftah
- Department of Chemical and Biological Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
- Materials Science and Engineering Program, College of Arts and Sciences, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
| | - Waad Abuwatfa
- Department of Chemical and Biological Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
- Materials Science and Engineering Program, College of Arts and Sciences, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
| | - Naif Darwish
- Department of Chemical and Biological Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
| | - Ghaleb A. Husseini
- Department of Chemical and Biological Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
- Materials Science and Engineering Program, College of Arts and Sciences, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
- Correspondence:
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