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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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Zhao Y, Deng Y, Shen F, Huang J, Yang J, Lu H, Wang J, Liang X, Su G. Characteristics and partitions of traditional and emerging organophosphate esters in soil and groundwater based on machine learning. JOURNAL OF HAZARDOUS MATERIALS 2024; 477:135351. [PMID: 39088951 DOI: 10.1016/j.jhazmat.2024.135351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 07/14/2024] [Accepted: 07/26/2024] [Indexed: 08/03/2024]
Abstract
Organophosphate esters (OPEs) pose hazards to both humans and the environment. This study applied target screening to analyze the concentrations and detection frequencies of OPEs in the soil and groundwater of representative contaminated sites in the Pearl River Delta. The clusters and correlation characteristics of OPEs in soil and groundwater were calculated by self-organizing map (SOM). The risk assessment and partitions of OPEs in industrial park soil and groundwater were conducted. The results revealed that 14 out of 23 types of OPEs were detected. The total concentrations (Σ23OPEs) ranged from 1.931 to 743.571 ng/L in the groundwater, and 0.218 to 79.578 ng/g in the soil, the former showed highly soluble OPEs with high detection frequencies and concentrations, whereas the latter exhibited the opposite trend. SOM analysis revealed that the distribution of OPEs in the soil differed significantly from that in the groundwater. In the industrial park, OPEs posed acceptable risks in both the soil and groundwater. The soil could be categorized into Zone I and II, and the groundwater into Zone I, II, and III, with corresponding management recommendations. Applying SOM to analyze the characteristics and partitions of OPEs may provide references for other new pollutants and contaminated sites.
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Affiliation(s)
- Yanjie Zhao
- Guangdong Key Laboratory of Contaminated Sites Environmental Management and Remediation, Guangdong Laboratory of Soil Pollution Fate and Risk Management in Earth's Critical Zone, Guangdong Provincial Academy of Environmental Science, Guangzhou 510045, China
| | - Yirong Deng
- Guangdong Key Laboratory of Contaminated Sites Environmental Management and Remediation, Guangdong Laboratory of Soil Pollution Fate and Risk Management in Earth's Critical Zone, Guangdong Provincial Academy of Environmental Science, Guangzhou 510045, China.
| | - Fang Shen
- Guangdong Key Laboratory of Contaminated Sites Environmental Management and Remediation, Guangdong Laboratory of Soil Pollution Fate and Risk Management in Earth's Critical Zone, Guangdong Provincial Academy of Environmental Science, Guangzhou 510045, China
| | - Jianan Huang
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Jie Yang
- Guangdong Key Laboratory of Contaminated Sites Environmental Management and Remediation, Guangdong Laboratory of Soil Pollution Fate and Risk Management in Earth's Critical Zone, Guangdong Provincial Academy of Environmental Science, Guangzhou 510045, China
| | - Haijian Lu
- Guangdong Key Laboratory of Contaminated Sites Environmental Management and Remediation, Guangdong Laboratory of Soil Pollution Fate and Risk Management in Earth's Critical Zone, Guangdong Provincial Academy of Environmental Science, Guangzhou 510045, China
| | - Jun Wang
- Guangdong Key Laboratory of Contaminated Sites Environmental Management and Remediation, Guangdong Laboratory of Soil Pollution Fate and Risk Management in Earth's Critical Zone, Guangdong Provincial Academy of Environmental Science, Guangzhou 510045, China
| | - Xiaoyang Liang
- Guangdong Key Laboratory of Contaminated Sites Environmental Management and Remediation, Guangdong Laboratory of Soil Pollution Fate and Risk Management in Earth's Critical Zone, Guangdong Provincial Academy of Environmental Science, Guangzhou 510045, China
| | - Guanyong Su
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
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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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Yang C. Neural networks for predicting air gap membrane distillation performance. J INDIAN CHEM SOC 2023. [DOI: 10.1016/j.jics.2023.100921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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5
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Zhong S, Zhang K, Bagheri M, Burken JG, Gu A, Li B, Ma X, Marrone BL, Ren ZJ, Schrier J, Shi W, Tan H, Wang T, Wang X, Wong BM, Xiao X, Yu X, Zhu JJ, Zhang H. Machine Learning: New Ideas and Tools in Environmental Science and Engineering. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:12741-12754. [PMID: 34403250 DOI: 10.1021/acs.est.1c01339] [Citation(s) in RCA: 98] [Impact Index Per Article: 32.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.
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Affiliation(s)
- Shifa Zhong
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States
| | - Kai Zhang
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States
| | - Majid Bagheri
- Department of Civil, Architectural, and Environmental Engineering, Missouri University of Science and Technology, Rolla, Missouri 65409, United States
| | - Joel G Burken
- Department of Civil, Architectural, and Environmental Engineering, Missouri University of Science and Technology, Rolla, Missouri 65409, United States
| | - April Gu
- Department of Civil and Environmental Engineering, Cornell University, Ithaca, New York 14850, United States
| | - Baikun Li
- Department of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Xingmao Ma
- Department of Civil and Environmental Engineering, Texas A&M University, College Station, Texas, 77843, United States
| | - Babetta L Marrone
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Zhiyong Jason Ren
- Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, United States
| | - Joshua Schrier
- Department of Chemistry, Fordham University, The Bronx, New York 10458 United States
| | - Wei Shi
- School of Environment, Nanjing University, Nanjing, 210093 China
| | - Haoyue Tan
- School of Environment, Nanjing University, Nanjing, 210093 China
| | - Tianbao Wang
- Department of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Xu Wang
- School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Bryan M Wong
- Department of Chemical & Environmental Engineering, Materials Science & Engineering Program, University of California-Riverside, Riverside, California 92521 United States
| | - Xusheng Xiao
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, Ohio 44106, United States
| | - Xiong Yu
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States
| | - Jun-Jie Zhu
- Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, United States
| | - Huichun Zhang
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States
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Performance Assessment for Short-Term Water Demand Forecasting Models on Distinctive Water Uses in Korea. SUSTAINABILITY 2021. [DOI: 10.3390/su13116056] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
It is crucial to forecast the water demand accurately for supplying water efficiently and stably in a water supply system. In particular, accurately forecasting short-term water demand helps in saving energy and reducing operating costs. With the introduction of the Smart Water Grid (SWG) in a water supply system, the amount of water consumption is obtained in real-time through a smart meter, which can be used for forecasting the short-term water demand. The models widely used for water demand forecasting include Autoregressive Integrated Moving Average, Radial Basis Function-Artificial Neural Network, Quantitative Multi-Model Predictor Plus, and Long Short-Term Memory. However, there is a lack of research on assessing the performance of models and forecasting the short-term water demand in the SWG demonstration plant. Therefore, in this study, the short-term water demand was forecasted for each model using the data collected from a smart meter, and the performance of each model was assessed. The Smart Water Grid Research Group installed a smart meter in block 112 located in YeongJong Island, Incheon, and the actual data used for operating the SWG demonstration plant were adopted. The performance of the model was assessed by using the Residual, Root Mean Square Error, Normalized Root Mean Square Error, Nash–Sutcliffe Efficiency, and Pearson Correlation Coefficient as indices. As a result of water demand forecasting, it is difficult to forecast water demand only by time and water consumption. Therefore, as the short-term water demand forecasting models using only time and the amount of water consumption have limitations in reflecting the characteristics of consumers, a water supply system can be managed more precisely if other factors (weather, customer behavior, etc.) influencing the water demand are applied.
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Prediction of BOD Concentration in Wastewater Treatment Process Using a Modular Neural Network in Combination with the Weather Condition. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10217477] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Since weather has a huge impact on the wastewater treatment process (WWTP), the prediction accuracy for the Biochemical Oxygen Demand (BOD) concentration in WWTP would degenerate if using only one single artificial neural network as the model for soft measurement method. Aiming to solve this problem, the present study proposes a novel hybrid scheme using a modular neural network (MNN) combining with the factor of weather condition. First, discriminative features among different weather groups are selected to ensure a high accuracy for sample clustering based on weather conditions. Second, the samples are clustered based on a density-based clustering algorithm using the discriminative features. Third, the clustered samples are input to each module in MNN, with the auxiliary variables correlated with BOD prediction input to the corresponding model. Finally, a constructive radial basis function neural network with the error-correction algorithm is used as the model for each subnetwork to predict BOD concentration. The proposed scheme is evaluated on a standard wastewater treatment platform—Benchmark Simulation Model 1 (BSM1). Experimental results demonstrate the performance improvement of the proposed scheme on the prediction accuracy for BOD concentration in WWTP. Besides, the training time is shortened and the network structure is compact.
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Soft Sensor Application in Identification of the Activated Sludge Bulking Considering the Technological and Economical Aspects of Smart Systems Functioning. SENSORS 2020; 20:s20071941. [PMID: 32235669 PMCID: PMC7180765 DOI: 10.3390/s20071941] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 03/24/2020] [Accepted: 03/25/2020] [Indexed: 12/28/2022]
Abstract
The paper presented the methodology for the construction of a soft sensor used for activated sludge bulking identification. Devising such solutions fits within the current trends and development of a smart system and infrastructure within smart cities. In order to optimize the selection of the data-mining method depending on the data collected within a wastewater treatment plant (WWTP), a number of methods were considered, including: artificial neural networks, support vector machines, random forests, boosted trees, and logistic regression. The analysis conducted sought the combinations of independent variables for which the devised soft sensor is characterized with high accuracy and at a relatively low cost of determination. With the measurement results pertaining to the quantity and quality of wastewater as well as the temperature in the activated sludge chambers, a good fit can be achieved with the boosted trees method. In order to simplify the selection of an optimal method for the identification of activated sludge bulking depending on the model requirements and the data collected within the WWTP, an original system of weight estimation was proposed, enabling a reduction in the number of independent variables in a model—quantity and quality of wastewater, operational parameters, and the cost of conducting measurements.
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Cheng D, Ngo HH, Guo W, Liu Y, Chang SW, Nguyen DD, Nghiem LD, Zhou J, Ni B. Anaerobic membrane bioreactors for antibiotic wastewater treatment: Performance and membrane fouling issues. BIORESOURCE TECHNOLOGY 2018; 267:714-724. [PMID: 30082132 DOI: 10.1016/j.biortech.2018.07.133] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 07/24/2018] [Accepted: 07/26/2018] [Indexed: 06/08/2023]
Abstract
Antibiotic wastewater has become a major concern due to the toxicity and recalcitrance of antibiotics. Anaerobic membrane bioreactors (AnMBRs) are considered alternative technology for treating antibiotic wastewater because of their advantages over the conventional anaerobic processes and aerobic MBRs. However, membrane fouling remains the most challenging issue in the AnMBRs' operation and this limits their application. This review critically discusses: (i) antibiotics removal and antibiotic resistance genes (ARGs) in different types of AnMBRs and the impact of antibiotics on membrane fouling and (ii) the integrated AnMBRs systems for fouling control and removal of antibiotics. The presence of antibiotics in AnMBRs could aggravate membrane fouling by influencing fouling-related factors (i.e., sludge particle size, extracellular polymeric substances (EPS), soluble microbial products (SMP), and fouling-related microbial communities). Conclusively, integrated AnMBR systems can be a practical technology for antibiotic wastewater treatment.
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Affiliation(s)
- Dongle Cheng
- Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NWS 2007, Australia
| | - Huu Hao Ngo
- Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NWS 2007, Australia; Joint Research Centre for Protective Infrastructure Technology and Environmental Green Bioprocess, Department of Environmental and Municipal Engineering, Tianjin Chengjian University, Tianjin 300384, China.
| | - Wenshan Guo
- Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NWS 2007, Australia; Joint Research Centre for Protective Infrastructure Technology and Environmental Green Bioprocess, Department of Environmental and Municipal Engineering, Tianjin Chengjian University, Tianjin 300384, China
| | - Yiwen Liu
- Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NWS 2007, Australia
| | - Soon Woong Chang
- Department of Environmental Energy & Engineering, Kyonggi University, 442-760, Republic of Korea
| | - Dinh Duc Nguyen
- Department of Environmental Energy & Engineering, Kyonggi University, 442-760, Republic of Korea; Institution of Research and Development, Duy Tan University, Da Nang, Viet Nam
| | - Long Duc Nghiem
- Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NWS 2007, Australia
| | - Junliang Zhou
- Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NWS 2007, Australia
| | - Bingjie Ni
- Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NWS 2007, Australia
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Treatment of Palm Oil Mill Effluent Using Membrane Bioreactor: Novel Processes and Their Major Drawbacks. WATER 2018. [DOI: 10.3390/w10091165] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Over the years, different types of alternative technologies have been developed and used for palm oil mill effluent (POME) treatment. Specifically, membrane bioreactor (MBR) has been employed to relegate pollutants contained in POME under different operating conditions, and the technology was found to be promising. The major challenge impeding the wider application of this technology is membrane fouling, which usually attracts high operating energy and running cost. In this regard, novel methods of mitigating membrane fouling through the treatment processes have been developed. Therefore, this review article specifically focuses on the recent treatment processes of POME using MBR, with particular emphasis on innovative processes conditions such as aerobic, anaerobic, and hybrid processing as well as their performance in relation to fouling minimization. Furthermore, the effects of sonication and thermophilic and mesophilic conditions on membrane blockage were critically reviewed. The types of foulants and fouling mechanism as influenced by different operating conditions were also analyzed censoriously.
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Youssef K, Jarenwattananon NN, Archer BJ, Mack J, Iruela-Arispe ML, Bouchard LS. 4-D Flow Control in Porous Scaffolds: Toward a Next Generation of Bioreactors. IEEE Trans Biomed Eng 2016; 64:61-69. [PMID: 26955013 DOI: 10.1109/tbme.2016.2537266] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Tissue engineering (TE) approaches that involve seeding cells into predetermined tissue scaffolds ignore the complex environment where the material properties are spatially inhomogeneous and evolve over time. We present a new approach for controlling mechanical forces inside bioreactors, which enables spatiotemporal control of flow fields in real time. Our adaptive approach offers the flexibility of dialing-in arbitrary shear stress distributions and adjusting flow field patterns in a scaffold over time in response to cell growth without needing to alter scaffold structure. This is achieved with a multi-inlet bioreactor and a control algorithm with learning capabilities to dynamically solve the inverse problem of computing the inlet pressure distribution required over the multiple inlets to obtain a target flow field. The new method constitutes a new platform for studies of cellular responses to mechanical forces in complex environments and opens potentially transformative possibilities for TE.
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Del Moro G, Barca E, De Sanctis M, Mascolo G, Di Iaconi C. Gross parameters prediction of a granular-attached biomass reactor by means of multi-objective genetic-designed artificial neural networks: touristic pressure management case. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2016; 23:5549-5565. [PMID: 26573316 DOI: 10.1007/s11356-015-5729-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2015] [Accepted: 11/03/2015] [Indexed: 06/05/2023]
Abstract
The Artificial Neural Networks by Multi-objective Genetic Algorithms (ANN-MOGA) model has been applied to gross parameters data of a Sequencing Batch Biofilter Granular Reactor (SBBGR) with the aim of providing an effective tool for predicting the fluctuations coming from touristic pressure. Six independent multivariate models, which were able to predict the dynamics of raw chemical oxygen demand (COD), soluble chemical oxygen demand (CODsol), total suspended solid (TSS), total nitrogen (TN), ammoniacal nitrogen (N-NH4 (+)) and total phosphorus (Ptot), were developed. The ANN-MOGA software application has shown to be suitable for addressing the SBBGR reactor modelling. The R (2) found are very good, with values equal to 0.94, 0.92, 0.88, 0.88, 0.98 and 0.91 for COD, CODsol, N-NH4 (+), TN, Ptot and TSS, respectively. A comparison was made between SBBGR and traditional activated sludge treatment plant modelling. The results showed the better performance of the ANN-MOGA application with respect to a wide selection of scientific literature cases.
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Affiliation(s)
- G Del Moro
- Istituto di Ricerca Sulle Acque, Consiglio Nazionale delle Ricerche, Viale F. De Blasio 5, 70132, Bari, Italy
| | - E Barca
- Istituto di Ricerca Sulle Acque, Consiglio Nazionale delle Ricerche, Viale F. De Blasio 5, 70132, Bari, Italy.
| | - M De Sanctis
- Istituto di Ricerca Sulle Acque, Consiglio Nazionale delle Ricerche, Viale F. De Blasio 5, 70132, Bari, Italy
| | - G Mascolo
- Istituto di Ricerca Sulle Acque, Consiglio Nazionale delle Ricerche, Viale F. De Blasio 5, 70132, Bari, Italy
| | - C Di Iaconi
- Istituto di Ricerca Sulle Acque, Consiglio Nazionale delle Ricerche, Viale F. De Blasio 5, 70132, Bari, Italy
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