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Behera SK, Karthika S, Mahanty B, Meher SK, Zafar M, Baskaran D, Rajamanickam R, Das R, Pakshirajan K, Bilyaminu AM, Rene ER. Application of artificial intelligence tools in wastewater and waste gas treatment systems: Recent advances and prospects. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 370:122386. [PMID: 39260284 DOI: 10.1016/j.jenvman.2024.122386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 08/17/2024] [Accepted: 08/31/2024] [Indexed: 09/13/2024]
Abstract
The non-linear complex relationships among the process variables in wastewater and waste gas treatment systems possess a significant challenge for real-time systems modelling. Data driven artificial intelligence (AI) tools are increasingly being adopted to predict the process performance, cost-effective process monitoring, and the control of different waste treatment systems, including those involving resource recovery. This review presents an in-depth analysis of the applications of emerging AI tools in physico-chemical and biological processes for the treatment of air pollutants, water and wastewater, and resource recovery processes. Additionally, the successful implementation of AI-controlled wastewater and waste gas treatment systems, along with real-time monitoring at the industrial scale are discussed.
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Affiliation(s)
- Shishir Kumar Behera
- Process Simulation Research Group, School of Chemical Engineering, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India.
| | - S Karthika
- Department of Chemical Engineering, Alagappa College of Technology, Anna University, Chennai, 600 025, Tamil Nadu, India
| | - Biswanath Mahanty
- Division of Biotechnology, Karunya Institute of Technology & Sciences, Coimbatore, 641 114, Tamil Nadu, India
| | - Saroj K Meher
- Systems Science and Informatics Unit, Indian Statistical Institute, Bangalore, 560059, India
| | - Mohd Zafar
- Department of Applied Biotechnology, College of Applied Sciences & Pharmacy, University of Technology and Applied Sciences - Sur, P.O. Box: 484, Zip Code: 411, Sur, Oman
| | - Divya Baskaran
- Department of Chemical and Biomolecular Engineering, Chonnam National University, Yeosu, Jeonnam, 59626, South Korea; Department of Biomaterials, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Chennai, 600 077, Tamil Nadu, India
| | - Ravi Rajamanickam
- Department of Chemical Engineering, Annamalai University, Chidambaram, 608002, Tamil Nadu, India
| | - Raja Das
- Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India
| | - Kannan Pakshirajan
- Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, 781 039, Assam, India
| | - Abubakar M Bilyaminu
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, P. O. Box 3015, 2601, DA Delft, the Netherlands
| | - Eldon R Rene
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, P. O. Box 3015, 2601, DA Delft, the Netherlands
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Qin Q, Yang G, Li J, Sun M, Jia H, Wang J. A review of flow field characteristics in submerged hollow fiber membrane bioreactor: Micro-interface, module and reactor. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 365:121525. [PMID: 38897085 DOI: 10.1016/j.jenvman.2024.121525] [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: 03/18/2024] [Revised: 05/27/2024] [Accepted: 06/16/2024] [Indexed: 06/21/2024]
Abstract
As an important part of the membrane field, hollow fiber membranes (HFM) have been widely concerned by scholars. HFM fouling in the industrial application results in a reduction in its lifespan and an increase in cost. In recent years, various explorations on the HFM fouling control strategies have been carried out. In the current work, we critically review the influence of flow field characteristics in HFM-based bioreactor on membrane fouling control. The flow field characteristics mainly refer to the spatial and temporal variation of the related physical parameters. In the HFM field, the physical parameter mainly refers to the variation characteristics of the shear force, flow velocity and turbulence caused by hydraulics. The factors affecting the flow field characteristics will be discussed from three levels: the micro-flow field near the interface of membrane (micro-interface), the flow field around the membrane module and the reactor design related to flow field, which involves surface morphology, crossflow, aeration, fiber packing density, membrane vibration, structural design and other related parameters. The study of flow field characteristics and influencing factors in the HFM separation process will help to improve the performance of HFM in full-scale water treatment plants.
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Affiliation(s)
- Qingwen Qin
- School of Environmental Engineering, Henan University of Technology, Zhengzhou, 450001, China
| | - Guang Yang
- College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao, 266590, China
| | - Juan Li
- 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
| | - Min Sun
- Centre for Complexity Science, Henan University of Technology, Zhengzhou, 450001, 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.
| | - 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; Cangzhou Institute of Tiangong University, Cangzhou, 061000, China.
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Ibrahim S, Abdul Wahab N. Optimizing neural network algorithms for submerged membrane bioreactor: A comparative study of OVAT and RSM hyperparameter optimization techniques. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2024; 89:1701-1724. [PMID: 38619898 DOI: 10.2166/wst.2024.099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 03/10/2024] [Indexed: 04/17/2024]
Abstract
Hyperparameter tuning is an important process to maximize the performance of any neural network model. This present study proposed the factorial design of experiment for screening and response surface methodology to optimize the hyperparameter of two artificial neural network algorithms. Feed-forward neural network (FFNN) and radial basis function neural network (RBFNN) are applied to predict the permeate flux of palm oil mill effluent. Permeate pump and transmembrane pressure of the submerge membrane bioreactor system are the input variables. Six hyperparameters of the FFNN model including four numerical factors (neuron numbers, learning rate, momentum, and epoch numbers) and two categorical factors (training and activation function) are used in hyperparameter optimization. RBFNN includes two numerical factors such as a number of neurons and spreads. The conventional method (one-variable-at-a-time) is compared in terms of optimization processing time and the accuracy of the model. The result indicates that the optimal hyperparameters obtained by the proposed approach produce good accuracy with a smaller generalization error. The simulation results show an improvement of more than 65% of training performance, with less repetition and processing time. This proposed methodology can be utilized for any type of neural network application to find the optimum levels of different parameters.
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Affiliation(s)
- Syahira Ibrahim
- 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 E-mail:
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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: 4] [Impact Index Per Article: 4.0] [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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Jadhav AR, Pathak PD, Raut RY. Water and wastewater quality prediction: current trends and challenges in the implementation of artificial neural network. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:321. [PMID: 36689041 DOI: 10.1007/s10661-022-10904-0] [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] [Received: 05/13/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Traditional freshwater supplies have been over-abstracted in the current global problem of water scarcity. Through the analysis of complex experimental and real-time data, to improve the activity of water and wastewater treatment (WWT) systems, an artificial neural network (ANN), a computational model inspired by the human brain, and its variants were created. This review paper focuses on recent trends and advances in modeling and simulating different water and wastewater systems using ANN. This study uses ANN in watershed management, impurity removal from wastewater, and wastewater treatment plants. According to the literature review, ANN can predict nonlinear, linear, and complex systems with high accuracy and well control. Finally, the limitations and future scope of ANNs were discussed. ANN proved itself in the prediction of various water and WWT processes. Still, implementation has practical challenges, which include a lack of data availability, poorly built models, timely updates in developed models, and low repeatability. The use of a proper toolbox, faster computing power, and proper domain knowledge makes the practical implementation of ANN successful. As a result, ANN can build a solid foundation for attracting and motivating investigators to work in this region in the forthcoming.
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Affiliation(s)
| | - Pranav D Pathak
- MIT School of Bioengineering Sciences & Research, MIT-Art, Design and Technology University, Pune, Maharashtra, India.
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Khan N, Ammar Taqvi SA. Machine Learning an Intelligent Approach in Process Industries: A Perspective and Overview. CHEMBIOENG REVIEWS 2022. [DOI: 10.1002/cben.202200030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Nadia Khan
- NED University of Engineering & Technology Polymer and Petrochemical Engineering Department Karachi Pakistan
| | - Syed Ali Ammar Taqvi
- NED University of Engineering & Technology Chemical Engineering Department Karachi Pakistan
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Niu C, Li X, Dai R, Wang Z. Artificial intelligence-incorporated membrane fouling prediction for membrane-based processes in the past 20 years: A critical review. WATER RESEARCH 2022; 216:118299. [PMID: 35325824 DOI: 10.1016/j.watres.2022.118299] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 02/11/2022] [Accepted: 03/13/2022] [Indexed: 05/26/2023]
Abstract
Membrane fouling is one of major obstacles in the application of membrane technologies. Accurately predicting or simulating membrane fouling behaviours is of great significance to elucidate the fouling mechanisms and develop effective measures to control fouling. Although mechanistic/mathematical models have been widely used for predicting membrane fouling, they still suffer from low accuracy and poor sensitivity. To overcome the limitations of conventional mathematical models, artificial intelligence (AI)-based techniques have been proposed as powerful approaches to predict membrane filtration performance and fouling behaviour. This work aims to present a state-of-the-art review on the advances in AI algorithms (e.g., artificial neural networks, fuzzy logic, genetic programming, support vector machines and search algorithms) for prediction of membrane fouling. The working principles of different AI techniques and their applications for prediction of membrane fouling in different membrane-based processes are discussed in detail. Furthermore, comparisons of the inputs, outputs, and accuracy of different AI approaches for membrane fouling prediction have been conducted based on the literature database. Future research efforts are further highlighted for AI-based techniques aiming for a more accurate prediction of membrane fouling and the optimization of the operation in membrane-based processes.
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Affiliation(s)
- Chengxin Niu
- 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
| | - Xuesong Li
- 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
| | - 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; Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 201210, China.
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Pathak M, Pokhriyal P, Gandhi I, Khambhampaty S. Implementation of chemometrics, design of experiments and neural network analysis for prior process knowledge assessment (PPKA), failure modes and effect analysis (FMEA), scale-down model development (SDM) and process characterization for a chromatographic purification of Teriparatide. Biotechnol Prog 2022; 38:e3252. [PMID: 35340128 DOI: 10.1002/btpr.3252] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 03/24/2022] [Accepted: 03/25/2022] [Indexed: 11/10/2022]
Abstract
Process understanding and characterization forms the foundation, ensuring consistent and robust biologics manufacturing process. Using appropriate modelling tools and machine learning approaches, the process data can be monitored in real time to avoid manufacturing risks. In this article, we have outlined an approach towards implementation of chemometrics and machine learning tools (neural network analysis) to model and predict the behaviour of a mixed-mode chromatography step for a biosimilar (Teriparatide) as a case study. The process development data and process knowledge was assimilated into a prior process knowledge assessment using chemometrics tools to derive important parameters critical to performance indicators (i.e. potential quality and process attributes) and to establish the severity ranking for the FMEA analysis. The characterization data of the chromatographic operation are presented alongwith the determination of the critical, key and non- key process parameters, set points, operating, process acceptance and characterized ranges. The scale-down model establishment was assessed using traditional approaches and novel approaches like batch evolution model and neural network analysis. The batch evolution model was further used to demonstrate batch monitoring through direct chromatographic data, thus demonstrating its application for continuos process verification. Assimilation of process knowledge through a structured data acquisition approach, built-in from process development to continuous process verification was demonstrated to result in a data analytics driven model that can be coupled with machine learning tools for real time process monitoring. We recommend application of these approaches with the FDA guidance on stage wise process development and validation to reduce manufacturing risks. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Mili Pathak
- R&D, Intas Pharmaceuticals Ltd. (Biopharma Division), Ahmedabad, Gujrat, India
| | - Prashant Pokhriyal
- R&D, Intas Pharmaceuticals Ltd. (Biopharma Division), Ahmedabad, Gujrat, India
| | - Irshad Gandhi
- R&D, Intas Pharmaceuticals Ltd. (Biopharma Division), Ahmedabad, Gujrat, India
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Enhancing Real-Time Prediction of Effluent Water Quality of Wastewater Treatment Plant Based on Improved Feedforward Neural Network Coupled with Optimization Algorithm. WATER 2022. [DOI: 10.3390/w14071053] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
To provide real-time prediction of wastewater treatment plant (WWTP) effluent water quality, a machine learning (ML) model was developed by combining an improved feedforward neural network (IFFNN) with an optimization algorithm. Data used as input variables of the IFFNN included hourly influent water quality parameters, influent flow rate and WWTP process monitoring and operational parameters. Additionally, input variables included historical effluent water quality parameters for future prediction. The model was demonstrated in a WWTP in Jiangsu Province, China, where prediction of effluent chemical oxygen demand (COD) and total nitrogen (TN) with large variations were tested. Relative to the traditional feedforward neural network (FFNN) model without considering historical effluent water quality parameter input, the IFFNN enhanced prediction performance by 52.3% (COD) and 72.6% (TN) based on the mean absolute percentage errors of test datasets, after its model structure was optimized with a genetic algorithm (GA). The problem of over-fitting could also be overcome through the use of the IFFNN, with the determination of coefficient increased from 0.20 to 0.76 for test datasets of effluent COD. The GA-IFFNN model, which was efficient in capturing complex non-linear relationships and extrapolation, could be a useful tool for real-time direction of regulatory changes in WWTP operations.
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Single-Stage Microwave-Assisted Coconut-Shell-Based Activated Carbon for Removal of Dichlorodiphenyltrichloroethane (DDT) from Aqueous Solution: Optimization and Batch Studies. INTERNATIONAL JOURNAL OF CHEMICAL ENGINEERING 2021. [DOI: 10.1155/2021/9331386] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This research aims to optimize preparation conditions of coconut-shell-based activated carbon (CSAC) and to evaluate its adsorption performance in removing POP of dichlorodiphenyltrichloroethane (DDT). The CSAC was prepared by activating the coconut shell via single-stage microwave heating under carbon dioxide, CO2 flow. The total pore volume, BET surface area, and average pore diameter of CSAC were 0.420 cm3/g, 625.61 m2/g, and 4.55 nm, respectively. The surface of CSAC was negatively charged shown by the zeta potential study. Response surface methodology (RSM) revealed that the optimum preparation conditions in preparing CSAC were 502 W and 6 min for radiation power and radiation time, respectively, which corresponded to 84.83% of DDT removal and 37.91% of CSAC’s yield. Adsorption uptakes of DDT were found to increase with an increase in their initial concentration. Isotherm study revealed that DDT-CSAC adsorption system was best described by the Langmuir model with monolayer adsorption capacity, Qm of 14.51 mg/g. The kinetic study confirmed that the pseudo-second-order model fitted well with this adsorption system. In regeneration studies, the adsorption efficiency had slightly dropped from 100% to 83% after 5 cycles. CSAC was found to be economically feasible for commercialization owing to its low production cost and high adsorption capacity.
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Javadian H, Asadollahpour S, Ruiz M, Sastre AM, Ghasemi M, Asl SMH, Masomi M. Using fuzzy inference system to predict Pb (II) removal from aqueous solutions by magnetic Fe3O4/H2SO4-activated Myrtus Communis leaves carbon nanocomposite. J Taiwan Inst Chem Eng 2018. [DOI: 10.1016/j.jtice.2018.06.021] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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The Effect of Ca and Mg Ions on the Filtration Profile of Sodium Alginate Solution in a Polyethersulfone-2-(methacryloyloxy) Ethyl Phosphorylchloline Membrane. WATER 2018. [DOI: 10.3390/w10091207] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
The efforts to improve the stability of membrane filtration in applications for wastewater treatment or the purification of drinking water still dominate the research in the field of membrane technology. Various factors that cause membrane fouling have been explored to find the solution for improving the stability of the filtration and prolong membrane lifetime. The present work explains the filtration performance of a hollow fiber membrane that is fabricated from polyethersulfone-2-(methacryloyloxy) ethyl phosphorylchloline while using a sodium alginate (SA) feed solution. The filtration process is designed in a pressure driven cross-flow module using a single piece hollow fiber membrane in a flow of outside-inside We investigate the effect of Ca and Mg ions in SA solution on the relative permeability, membrane resistance, cake resistance, and cake formation on the membrane surface. Furthermore, the performance of membrane filtration is predicted while using mathematical models that were developed based on Darcy’s law. Results show that the presence of Ca ions in SA solution has the most prominent effect on the formation of a cake layer. The formed cake layer has a significant effect in lowering relative permeability. The developed models have a good fit with the experimental data for pure water filtration with R2 values between 0.9200 and 0.9999. When treating SA solutions, the developed models fit well with experimental with the best model (Model I) shows R2 of 0.9998, 0.9999, and 0.9994 for SA, SA + Ca, and SA + Mg feeds, respectively.
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Farhadi M, Pazuki G, Raisi A. Modeling of the pervaporation process for isobutanol purification from aqueous solution using intelligent systems. SEP SCI TECHNOL 2017. [DOI: 10.1080/01496395.2017.1405987] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Mozhdeh Farhadi
- Chemical Engineering Department, Amirkabir University of Technology, Tehran, Iran
| | - Gholamreza Pazuki
- Chemical Engineering Department, Amirkabir University of Technology, Tehran, Iran
| | - Ahmadreza Raisi
- Chemical Engineering Department, Amirkabir University of Technology, Tehran, Iran
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15
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Modeling and optimization of polymer enhanced ultrafiltration using hybrid neural-genetic algorithm based evolutionary approach. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2017.02.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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16
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A practical hybrid modelling approach for the prediction of potential fouling parameters in ultrafiltration membrane water treatment plant. J IND ENG CHEM 2017. [DOI: 10.1016/j.jiec.2016.09.017] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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17
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Agarwal H, Rathore AS, Hadpe SR, Alva SJ. Artificial neural network (ANN)-based prediction of depth filter loading capacity for filter sizing. Biotechnol Prog 2016; 32:1436-1443. [PMID: 27453285 DOI: 10.1002/btpr.2329] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Revised: 07/14/2016] [Indexed: 11/06/2022]
Abstract
This article presents an application of artificial neural network (ANN) modelling towards prediction of depth filter loading capacity for clarification of a monoclonal antibody (mAb) product during commercial manufacturing. The effect of operating parameters on filter loading capacity was evaluated based on the analysis of change in the differential pressure (DP) as a function of time. The proposed ANN model uses inlet stream properties (feed turbidity, feed cell count, feed cell viability), flux, and time to predict the corresponding DP. The ANN contained a single output layer with ten neurons in hidden layer and employed a sigmoidal activation function. This network was trained with 174 training points, 37 validation points, and 37 test points. Further, a pressure cut-off of 1.1 bar was used for sizing the filter area required under each operating condition. The modelling results showed that there was excellent agreement between the predicted and experimental data with a regression coefficient (R2 ) of 0.98. The developed ANN model was used for performing variable depth filter sizing for different clarification lots. Monte-Carlo simulation was performed to estimate the cost savings by using different filter areas for different clarification lots rather than using the same filter area. A 10% saving in cost of goods was obtained for this operation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1436-1443, 2016.
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Affiliation(s)
- Harshit Agarwal
- Dept. of Chemical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi, 110016, India
| | - Anurag S Rathore
- Dept. of Chemical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi, 110016, India
| | | | - Solomon J Alva
- Research and development, Biocon research limited, Bangalore 560100, India
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18
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Pintarič ZN, Škof GP, Kravanja Z. MILP synthesis of separation processes for waste oil-in-water emulsions treatment. Front Chem Sci Eng 2016. [DOI: 10.1007/s11705-016-1559-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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19
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Wu Y, Wang J, Zhang H, Ngo HH, Guo W, Zhang N. The impact of gas slug flow on microfiltration performance in an airlift external loop tubular membrane reactor. RSC Adv 2016. [DOI: 10.1039/c6ra19903h] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Under low gas-velocity, the cake layer gradually formed. Then high gas-velocity scoured the cake layer, which obstructed the cake layer's formation.
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Affiliation(s)
- Yun Wu
- State Key Laboratory of Separation Membranes and Membrane Processes
- Tianjin Polytechnic University
- Tianjin 300387
- China
- School of Environmental and Chemical Engineering
| | - Jie Wang
- State Key Laboratory of Separation Membranes and Membrane Processes
- Tianjin Polytechnic University
- Tianjin 300387
- China
- School of Environmental and Chemical Engineering
| | - Hongwei Zhang
- State Key Laboratory of Separation Membranes and Membrane Processes
- Tianjin Polytechnic University
- Tianjin 300387
- China
- School of Environmental and Chemical Engineering
| | - Huu Hao Ngo
- School of Civil and Environmental Engineering
- University of Technology Sydney
- Australia
| | - Wenshan Guo
- School of Civil and Environmental Engineering
- University of Technology Sydney
- Australia
| | - Nan Zhang
- CNOOC Tianjin Chemical Research and Design Institute
- Tianjin 300387
- China
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Membrane process enhancement of 2-phase and 3-phase olive mill wastewater treatment plants by photocatalysis with magnetic-core titanium dioxide nanoparticles. J IND ENG CHEM 2015. [DOI: 10.1016/j.jiec.2015.05.015] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Jing L, Chen B, Zhang B, Li P. Process simulation and dynamic control for marine oily wastewater treatment using UV irradiation. WATER RESEARCH 2015; 81:101-112. [PMID: 26043376 DOI: 10.1016/j.watres.2015.03.023] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2014] [Revised: 03/23/2015] [Accepted: 03/25/2015] [Indexed: 06/04/2023]
Abstract
UV irradiation and advanced oxidation processes have been recently regarded as promising solutions in removing polycyclic aromatic hydrocarbons (PAHs) from marine oily wastewater. However, such treatment methods are generally not sufficiently understood in terms of reaction mechanisms, process simulation and process control. These deficiencies can drastically hinder their application in shipping and offshore petroleum industries which produce bilge/ballast water and produced water as the main streams of marine oily wastewater. In this study, the factorial design of experiment was carried out to investigate the degradation mechanism of a typical PAH, namely naphthalene, under UV irradiation in seawater. Based on the experimental results, a three-layer feed-forward artificial neural network simulation model was developed to simulate the treatment process and to forecast the removal performance. A simulation-based dynamic mixed integer nonlinear programming (SDMINP) approach was then proposed to intelligently control the treatment process by integrating the developed simulation model, genetic algorithm and multi-stage programming. The applicability and effectiveness of the developed approach were further tested though a case study. The experimental results showed that the influences of fluence rate and temperature on the removal of naphthalene were greater than those of salinity and initial concentration. The developed simulation model could well predict the UV-induced removal process under varying conditions. The case study suggested that the SDMINP approach, with the aid of the multi-stage control strategy, was able to significantly reduce treatment cost when comparing to the traditional single-stage process optimization. The developed approach and its concept/framework have high potential of applicability in other environmental fields where a treatment process is involved and experimentation and modeling are used for process simulation and control.
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Affiliation(s)
- Liang Jing
- Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL A1B 3X5, Canada
| | - Bing Chen
- Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL A1B 3X5, Canada.
| | - Baiyu Zhang
- Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL A1B 3X5, Canada
| | - Pu Li
- Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL A1B 3X5, Canada
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22
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Ghaedi M, Ansari A, Bahari F, Ghaedi AM, Vafaei A. A hybrid artificial neural network and particle swarm optimization for prediction of removal of hazardous dye brilliant green from aqueous solution using zinc sulfide nanoparticle loaded on activated carbon. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2015; 137:1004-1015. [PMID: 25286113 DOI: 10.1016/j.saa.2014.08.011] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2014] [Revised: 07/14/2014] [Accepted: 08/07/2014] [Indexed: 06/03/2023]
Abstract
In the present study, zinc sulfide nanoparticle loaded on activated carbon (ZnS-NP-AC) simply was synthesized in the presence of ultrasound and characterized using different techniques such as SEM and BET analysis. Then, this material was used for brilliant green (BG) removal. To dependency of BG removal percentage toward various parameters including pH, adsorbent dosage, initial dye concentration and contact time were examined and optimized. The mechanism and rate of adsorption was ascertained by analyzing experimental data at various time to conventional kinetic models such as pseudo-first-order and second order, Elovich and intra-particle diffusion models. Comparison according to general criterion such as relative error in adsorption capacity and correlation coefficient confirm the usability of pseudo-second-order kinetic model for explanation of data. The Langmuir models is efficiently can explained the behavior of adsorption system to give full information about interaction of BG with ZnS-NP-AC. A multiple linear regression (MLR) and a hybrid of artificial neural network and partial swarm optimization (ANN-PSO) model were used for prediction of brilliant green adsorption onto ZnS-NP-AC. Comparison of the results obtained using offered models confirm higher ability of ANN model compare to the MLR model for prediction of BG adsorption onto ZnS-NP-AC. Using the optimal ANN-PSO model the coefficient of determination (R(2)) were 0.9610 and 0.9506; mean squared error (MSE) values were 0.0020 and 0.0022 for the training and testing data set, respectively.
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Affiliation(s)
- M Ghaedi
- Chemistry Department, Yasouj University, Yasouj 75918-74831, Iran
| | - A Ansari
- Young Research Club, Fars Science and Research Branch, Islamic Azad University, Fars, Iran
| | - F Bahari
- Department of Chemistry, Science and Research Branch, Islamic Azad University, Fars, Iran
| | - A M Ghaedi
- Department of Chemistry, Gachsaran Branch, Islamic Azad University, P.O. Box 75818-63876, Gachsaran, Iran
| | - A Vafaei
- Department of Chemistry, Gachsaran Branch, Islamic Azad University, P.O. Box 75818-63876, Gachsaran, Iran
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Askari S, Halladj R, Azarhoosh MJ. Modeling and optimization of catalytic performance of SAPO-34 nanocatalysts synthesized sonochemically using a new hybrid of non-dominated sorting genetic algorithm-II based artificial neural networks (NSGA-II-ANNs). RSC Adv 2015. [DOI: 10.1039/c5ra03764f] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
The effects of ultrasound-related variables on the catalytic properties of sonochemically prepared SAPO-34 nanocatalysts in methanol to olefins (MTO) reactions were investigated.
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Affiliation(s)
- Sima Askari
- Faculty of Chemical Engineering
- Amirkabir University of Technology
- Tehran Polytechnic
- Tehran
- Iran
| | - Rouein Halladj
- Faculty of Chemical Engineering
- Amirkabir University of Technology
- Tehran Polytechnic
- Tehran
- Iran
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