1
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Kumari S, Chowdhry J, Chandra Garg M. AI-enhanced adsorption modeling: Challenges, applications, and bibliographic analysis. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 351:119968. [PMID: 38171130 DOI: 10.1016/j.jenvman.2023.119968] [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: 09/27/2023] [Revised: 12/24/2023] [Accepted: 12/24/2023] [Indexed: 01/05/2024]
Abstract
Inorganic and organic contaminants, such as fertilisers, heavy metals, and dyes, are the primary causes of water pollution. The field of artificial intelligence (AI) has received significant interest due to its capacity to address challenges across various fields. The use of AI techniques in water treatment and desalination has recently shown useful for optimising processes and dealing with the challenges of water pollution and scarcity. The utilization of AI in the water treatment industry is anticipated to result in a reduction in operational expenditures through the lowering of procedure costs and the optimisation of chemical utilization. The predictive capabilities of artificial intelligence models have accurately assessed the efficacy of different adsorbents in removing contaminants from wastewater. This article provides an overview of the various AI techniques and how they can be used in the adsorption of contaminants during the water treatment process. The reviewed publications were analysed for their diversity in journal type, publication year, research methodology, and initial study context. Citation network analysis, an objective method, and tools like VOSviewer are used to find these groups. The primary issues that need to be addressed include the availability and selection of data, low reproducibility, and little proof of uses in real water treatment. The provision of challenges is essential to ensure the prospective success of AI associated with technologies. The brief overview holds importance to everyone involved in the field of water, encompassing scientists, engineers, students, and stakeholders.
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Affiliation(s)
- Sheetal Kumari
- Amity Institute of Environmental Science (AIES), Amity University Uttar Pradesh, Sector-125, Noida, 201313, Gautam Budh Nagar, India
| | | | - Manoj Chandra Garg
- Amity Institute of Environmental Science (AIES), Amity University Uttar Pradesh, Sector-125, Noida, 201313, Gautam Budh Nagar, India.
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2
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Medl M, Leisch F, Dürauer A, Scharl T. Explainable deep learning enhances robust and reliable real-time monitoring of a chromatographic protein A capture step. Biotechnol J 2024; 19:e2300554. [PMID: 38385524 DOI: 10.1002/biot.202300554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/07/2023] [Accepted: 12/12/2023] [Indexed: 02/23/2024]
Abstract
The application of model-based real-time monitoring in biopharmaceutical production is a major step toward quality-by-design and the fundament for model predictive control. Data-driven models have proven to be a viable option to model bioprocesses. In the high stakes setting of biopharmaceutical manufacturing it is essential to ensure high model accuracy, robustness, and reliability. That is only possible when (i) the data used for modeling is of high quality and sufficient size, (ii) state-of-the-art modeling algorithms are employed, and (iii) the input-output mapping of the model has been characterized. In this study, we evaluate the accuracy of multiple data-driven models in predicting the monoclonal antibody (mAb) concentration, double stranded DNA concentration, host cell protein concentration, and high molecular weight impurity content during elution from a protein A chromatography capture step. The models achieved high-quality predictions with a normalized root mean squared error of <4% for the mAb concentration and of ≈10% for the other process variables. Furthermore, we demonstrate how permutation/occlusion-based methods can be used to gain an understanding of dependencies learned by one of the most complex data-driven models, convolutional neural network ensembles. We observed that the models generally exhibited dependencies on correlations that agreed with first principles knowledge, thereby bolstering confidence in model reliability. Finally, we present a workflow to assess the model behavior in case of systematic measurement errors that may result from sensor fouling or failure. This study represents a major step toward improved viability of data-driven models in biopharmaceutical manufacturing.
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Affiliation(s)
- Matthias Medl
- Institute of Statistics, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Friedrich Leisch
- Institute of Statistics, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Astrid Dürauer
- Institute of Bioprocess Science and Engineering, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Theresa Scharl
- Institute of Statistics, University of Natural Resources and Life Sciences, Vienna, Austria
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3
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Helleckes LM, Hemmerich J, Wiechert W, von Lieres E, Grünberger A. Machine learning in bioprocess development: from promise to practice. Trends Biotechnol 2023; 41:817-835. [PMID: 36456404 DOI: 10.1016/j.tibtech.2022.10.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/20/2022] [Accepted: 10/27/2022] [Indexed: 11/30/2022]
Abstract
Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess development provides large amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have great potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. Herein we demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring, and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges, and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.
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Affiliation(s)
- Laura M Helleckes
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Johannes Hemmerich
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Wolfgang Wiechert
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Eric von Lieres
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Alexander Grünberger
- Multiscale Bioengineering, Technical Faculty, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany; Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany; Institute of Process Engineering in Life Sciences, Section III: Microsystems in Bioprocess Engineering, Karlsruhe Institute of Technology, Fritz-Haber-Weg 2, 76131, Karlsruhe, Germany.
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4
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Hua L, Zhang C, Sun W, Li Y, Xiong J, Nazir MS. An evolutionary deep learning soft sensor model based on random forest feature selection technique for penicillin fermentation process. ISA TRANSACTIONS 2023; 136:139-151. [PMID: 36404151 DOI: 10.1016/j.isatra.2022.10.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 10/24/2022] [Accepted: 10/30/2022] [Indexed: 05/16/2023]
Abstract
Accurate and reliable measurement of key biological parameters during penicillin fermentation is of great significance for improving penicillin production. In this research context, a new hybrid soft sensor model method based on RF-IHHO-LSTM (random forest-improved Harris hawks optimization-long short-term memory) is proposed for penicillin fermentation processes. Firstly, random forest (RF) is used for feature selection of the auxiliary variables for penicillin. Next, improvements are made for the Harris hawks optimization (HHO) algorithm, including using elite opposition-based learning strategy (EOBL) in initialization to enhance the population diversity, and using golden sine algorithm (Gold-SA) in the search strategy to make the algorithm accelerate convergence. Then the long short-term memory (LSTM) network is constructed to build a soft sensor model of penicillin fermentation processes. Finally, the hybrid soft sensor model is used to the Pensim platform in simulation experimental research. The simulation test results show that the established soft sensor model, with high accuracy of measurement and good effect, can meet the actual requirements of engineering.
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Affiliation(s)
- Lei Hua
- Faculty of Automation, Huaiyin Institute of Technology, Huai'an 223003, China.
| | - Chu Zhang
- Faculty of Automation, Huaiyin Institute of Technology, Huai'an 223003, China; Jiangsu Permanent Magnet Motor Engineering Research Center, Huaiyin Institute of Technology, Huai'an 223003, China.
| | - Wei Sun
- Faculty of Automation, Huaiyin Institute of Technology, Huai'an 223003, China
| | - Yiman Li
- Faculty of Automation, Huaiyin Institute of Technology, Huai'an 223003, China
| | - Jinlin Xiong
- Faculty of Automation, Huaiyin Institute of Technology, Huai'an 223003, China
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5
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Xie C, Yao R, Zhu L, Gong H, Li H, Chen X. Soft-Sensor Development through Deep Learning with Spatial and Temporal Feature Extraction of Complex Processes. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c03137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Changrui Xie
- State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang310027, China
| | - Runjie Yao
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang310014, China
| | - Lingyu Zhu
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang310014, China
| | - Han Gong
- Zhejiang Amino-Chem Company Limited, Shaoxing, Zhejiang312369, China
| | - Hongyang Li
- Zhejiang Amino-Chem Company Limited, Shaoxing, Zhejiang312369, China
| | - Xi Chen
- State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang310027, China
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6
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Ji C, Ma F, Wang J, Sun W. Profitability Related Industrial-Scale Batch Processes Monitoring via Deep Learning based Soft Sensor Development. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.108125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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7
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Vasilas N, Papadopoulos AI, Papadopoulos L, Salamanis A, Kazepidis P, Soudris D, Kehagias D, Seferlis P. Approximate computing, skeleton programming and run-time scheduling in an algorithm for process design and controllability in distributed and heterogeneous infrastructures. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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8
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Samotylova SA, Torgashov AY. Application of a First Principles Mathematical Model of a Mass-Transfer Technological Process to Improve the Accuracy of the Estimation of the End Product Quality. THEORETICAL FOUNDATIONS OF CHEMICAL ENGINEERING 2022. [DOI: 10.1134/s0040579522020117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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9
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Li F, Vanrolleghem PA. An influent generator for WRRF design and operation based on a recurrent neural network with multi-objective optimization using a genetic algorithm. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2022; 85:1444-1453. [PMID: 35290224 DOI: 10.2166/wst.2022.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Nowadays, modelling, automation and control are widely used for Water Resource Recovery Facilities (WRRF) upgrading and optimization. Influent generator (IG) models are used to provide relevant input time series for dynamic WRRF simulations used in these applications. Current IG models found in literature are calibrated on the basis of a single performance criterion, such as the mean percentage error or the root mean square error. This results in the IG being adequate on average but with a lack of representativeness of, for instance, the observed temporal variability of the dataset. However, adequately capturing influent variability may be important for certain types of WRRF optimization, e.g., reaction to peak loads, control system performance evaluation, etc. Therefore, in this study, a data-driven IG model is developed based on the long short-term memory (LSTM) recurrent neural network and is optimized by a multi-objective genetic algorithm for both mean percentage error and variability. Hence, the influent generator model is able to generate a time series with a probability distribution that better represents reality, thus giving a better influent description for WRRF design and operation. To further increase the variability of the generated time series and in this way approximate the true variability better, the model is extended with a random walk process.
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Affiliation(s)
- Feiyi Li
- modelEAU, Université Laval, 1065, Avenue de la Médecine, Québec, QC G1 V 0A6, Canada E-mail: ; CentrEau, Québec Water Research Center, 1065 avenue de la Médecine, Québec, QC G1 V 0A6, Canada
| | - Peter A Vanrolleghem
- modelEAU, Université Laval, 1065, Avenue de la Médecine, Québec, QC G1 V 0A6, Canada E-mail: ; CentrEau, Québec Water Research Center, 1065 avenue de la Médecine, Québec, QC G1 V 0A6, Canada
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10
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Gärtler M, Khaydarov V, Klöpper B, Urbas L. The Machine Learning Life Cycle in Chemical Operations – Status and Open Challenges. CHEM-ING-TECH 2021. [DOI: 10.1002/cite.202100134] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Marco Gärtler
- ABB Corporate Research Center Wallstadter Straße 59 68526 Ladenburg Germany
| | - Valentin Khaydarov
- Technische Universität Dresden Professur für Prozessleittechnik 01062 Dresden Germany
| | - Benjamin Klöpper
- ABB Corporate Research Center Wallstadter Straße 59 68526 Ladenburg Germany
| | - Leon Urbas
- Technische Universität Dresden Professur für Prozessleittechnik 01062 Dresden Germany
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11
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Recent Advances in Dynamic Modeling and Process Control of PVA Degradation by Biological and Advanced Oxidation Processes: A Review on Trends and Advances. ENVIRONMENTS 2021. [DOI: 10.3390/environments8110116] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Polyvinyl alcohol (PVA) is an emerging pollutant commonly found in industrial wastewater, owing to its extensive usage as an additive in the manufacturing industry. PVA’s popularity has made wastewater treatment technologies for PVA degradation a popular research topic in industrial wastewater treatment. Although many PVA degradation technologies are studied in bench-scale processes, recent advancements in process optimization and control of wastewater treatment technologies such as advanced oxidation processes (AOPs) show the feasibility of these processes by monitoring and controlling processes to meet desired regulatory standards. These wastewater treatment technologies exhibit complex reaction mechanisms leading to nonlinear and nonstationary behavior related to variability in operational conditions. Thus, black-box dynamic modeling is a promising tool for designing control schemes since dynamic modeling is more complicated in terms of first principles and reaction mechanisms. This study seeks to provide a survey of process control methods via a comprehensive review focusing on PVA degradation methods, including biological and advanced oxidation processes, along with their reaction mechanisms, control-oriented dynamic modeling (i.e., state-space, transfer function, and artificial neural network modeling), and control strategies (i.e., proportional-integral-derivative control and predictive control) associated with wastewater treatment technologies utilized for PVA degradation.
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12
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Moreira de Lima JM, Ugulino de Araujo FM. Ensemble deep relevant learning framework for semi-supervised soft sensor modeling of industrial processes. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.07.086] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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13
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He F, Zhao Y. Quality relevant fault detection of batch process via statistical pattern and regression coefficient. CAN J CHEM ENG 2021. [DOI: 10.1002/cjce.24016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Fei He
- Collaborative Innovation Centre of Steel Technology University of Science and Technology Beijing Beijing China
| | - Yanbo Zhao
- Collaborative Innovation Centre of Steel Technology University of Science and Technology Beijing Beijing China
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14
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Matheri AN, Ntuli F, Ngila JC, Seodigeng T, Zvinowanda C. Performance prediction of trace metals and cod in wastewater treatment using artificial neural network. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107308] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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15
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Moreira de Lima JM, Ugulino de Araújo FM. Industrial Semi-Supervised Dynamic Soft-Sensor Modeling Approach Based on Deep Relevant Representation Learning. SENSORS 2021; 21:s21103430. [PMID: 34069123 PMCID: PMC8156853 DOI: 10.3390/s21103430] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 03/30/2021] [Accepted: 04/02/2021] [Indexed: 11/16/2022]
Abstract
Soft sensors based on deep learning have been growing in industrial process applications, inferring hard-to-measure but crucial quality-related variables. However, applications may present strong non-linearity, dynamicity, and a lack of labeled data. To deal with the above-cited problems, the extraction of relevant features is becoming a field of interest in soft-sensing. A novel deep representative learning soft-sensor modeling approach is proposed based on stacked autoencoder (SAE), mutual information (MI), and long-short term memory (LSTM). SAE is trained layer by layer with MI evaluation performed between extracted features and targeted output to evaluate the relevance of learned representation in each layer. This approach highlights relevant information and eliminates irrelevant information from the current layer. Thus, deep output-related representative features are retrieved. In the supervised fine-tuning stage, an LSTM is coupled to the tail of the SAE to address system inherent dynamic behavior. Also, a k-fold cross-validation ensemble strategy is applied to enhance the soft-sensor reliability. Two real-world industrial non-linear processes are employed to evaluate the proposed method performance. The obtained results show improved prediction performance in comparison to other traditional and state-of-art methods. Compared to the other methods, the proposed model can generate more than 38.6% and 39.4% improvement of RMSE for the two analyzed industrial cases.
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16
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Prediction of effluent quality in papermaking wastewater treatment processes using dynamic kernel-based extreme learning machine. Process Biochem 2020. [DOI: 10.1016/j.procbio.2020.06.020] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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17
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Sweeney M, Kabouris J. Modeling, instrumentation, automation, and optimization of water resource recovery facilities (Review of 2018 Literature) DIRECT. WATER ENVIRONMENT RESEARCH : A RESEARCH PUBLICATION OF THE WATER ENVIRONMENT FEDERATION 2020; 92:1618-1624. [PMID: 32706481 DOI: 10.1002/wer.1408] [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: 07/02/2020] [Revised: 07/13/2020] [Accepted: 07/14/2020] [Indexed: 06/11/2023]
Abstract
A review of the literature published in 2018 on topics relating to water resource recovery facilities (WRRF) in the areas of modeling, automation, measurement and sensors and optimization of wastewater treatment (or water resource reclamation) is presented. PRACTITIONER POINTS: Summary of advances in the field of modeling, instrumentation, automation, and optimization in 2018. This review outlines the major contributions of researchers and practitioners that have been published in peer-reviewed journals and conference proceedings. The article is organized into sections for ease of reference, but several reviewed articles are related to more than one section.
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18
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Jia R, Zhang S, You F. Transfer learning for end-product quality prediction of batch processes using domain-adaption joint-Y PLS. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106943] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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19
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Shokry A, Baraldi P, Zio E, Espuña A. Dynamic Surrogate Modeling for Multistep-ahead Prediction of Multivariate Nonlinear Chemical Processes. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c00729] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Ahmed Shokry
- Center for Applied Mathematics, Ecole Polytechnique, Route de Saclay, Palaiseau 91120, France
- Department of Chemical Engineering, Universitat Politècnica de Catalunya, EEBE − Eduard Maristany, 14, Barcelona 08019, Spain
| | - Piero Baraldi
- Energy Department, Politecnico di Milano, Via Lambruschini 4, Milan 20156, Italy
| | - Enrico Zio
- Energy Department, Politecnico di Milano, Via Lambruschini 4, Milan 20156, Italy
- Eminent Scholar, Department of Nuclear Engineering, College of Engineering, Kyung Hee University, Gwangju-si 02447, Republic of Korea
- MINES ParisTech, PSL Research University, CRC, Sophia Antipolis F-06904, France
| | - Antonio Espuña
- Department of Chemical Engineering, Universitat Politècnica de Catalunya, EEBE − Eduard Maristany, 14, Barcelona 08019, Spain
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Katz J, Pappas I, Avraamidou S, Pistikopoulos EN. Integrating deep learning models and multiparametric programming. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106801] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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21
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Ye Z, Yang J, Zhong N, Tu X, Jia J, Wang J. Tackling environmental challenges in pollution controls using artificial intelligence: A review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 699:134279. [PMID: 33736193 DOI: 10.1016/j.scitotenv.2019.134279] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2019] [Revised: 09/02/2019] [Accepted: 09/03/2019] [Indexed: 06/12/2023]
Abstract
This review presents the developments in artificial intelligence technologies for environmental pollution controls. A number of AI approaches, which start with the reliable mapping of nonlinear behavior between inputs and outputs in chemical and biological processes in terms of prediction models to the emerging optimization and control algorithms that study the pollutants removal processes and intelligent control systems, have been developed for environmental clean-ups. The characteristics, advantages and limitations of AI methods, including single and hybrid AI methods, were overviewed. Hybrid AI methods exhibited synergistic effects, but with computational heaviness. The up-to-date review summarizes i) Various artificial neural networks employed in wastewater degradation process for the prediction of removal efficiency of pollutants and the search of optimizing experimental conditions; ii) Evaluation of fuzzy logic used for intelligent control of aerobic stage of wastewater treatment process; iii) AI-aided soft-sensors for precisely on-line/off-line estimation of hard-to-measure parameters in wastewater treatment plants; iv) Single and hybrid AI methods applied to estimate pollutants concentrations and design monitoring and early-warning systems for both aquatic and atmospheric environments; v) AI modelings of short-term, mid-term and long-term solid waste generations, and various ANNs for solid waste recycling and reduction. Finally, the future challenges of AI-based models employed in the environmental fields are discussed and proposed.
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Affiliation(s)
- Zhiping Ye
- College of Environment, Zhejiang University of Technology, Hangzhou 310014, PR China
| | - Jiaqian Yang
- College of Environment, Zhejiang University of Technology, Hangzhou 310014, PR China
| | - Na Zhong
- College of Environment, Zhejiang University of Technology, Hangzhou 310014, PR China
| | - Xin Tu
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, United Kingdom
| | - Jining Jia
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, PR China
| | - Jiade Wang
- College of Environment, Zhejiang University of Technology, Hangzhou 310014, PR China.
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22
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He R, Chen G, Dong C, Sun S, Shen X. Data-driven digital twin technology for optimized control in process systems. ISA TRANSACTIONS 2019; 95:221-234. [PMID: 31109723 DOI: 10.1016/j.isatra.2019.05.011] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 04/22/2019] [Accepted: 05/11/2019] [Indexed: 06/09/2023]
Abstract
Due to the installation of various apparatus in process industries, both factors of complex structures and severe operating conditions could result in higher accident frequencies and maintenance challenges. Given the importance of security in process systems, this paper presents a data-driven digital twin system for automatic process applications by integrating virtual modeling, process monitoring, diagnosis, and optimized control into a cooperative architecture. For unknown model parameters, the adaptive system identification is proposed to model closed-loop virtual systems and residual signals with fault-free case data. Performance indices are improved to make the design of robust monitoring and diagnosis system to identify the apparatus status. Soft-sensor, parameterization control, and model-matching reconfiguration are ameliorated and incorporated into the optimized control configuration to guarantee stable and safe control performance under apparatus faults. The effectiveness and performance of the proposed digital twin system are evaluated by using different simulations on the Tennessee Eastman benchmark process in the presence of realistic fault scenarios.
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Affiliation(s)
- Rui He
- Centre for Offshore Engineering and Safety Technology (COEST), China University of Petroleum (East China), No.66, Changjiang West Road, Qingdao, China.
| | - Guoming Chen
- Centre for Offshore Engineering and Safety Technology (COEST), China University of Petroleum (East China), No.66, Changjiang West Road, Qingdao, China.
| | - Che Dong
- Centre for Offshore Engineering and Safety Technology (COEST), China University of Petroleum (East China), No.66, Changjiang West Road, Qingdao, China.
| | - Shufeng Sun
- Centre for Offshore Engineering and Safety Technology (COEST), China University of Petroleum (East China), No.66, Changjiang West Road, Qingdao, China.
| | - Xiaoyu Shen
- Centre for Offshore Engineering and Safety Technology (COEST), China University of Petroleum (East China), No.66, Changjiang West Road, Qingdao, China.
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