Intelligent Techniques for Wastewater Treatment : A Technical Review
Keywords:
wastewater treatment, machine learning, algorithm, modeling, optimization
Abstract
Industrial wastewater treatment is a crucial but challenging task. The perpetual chemical and bio-chemical reactions impart a great deal of complexity to the composition of industrial wastewaters. While conventional modeling approaches can handle linear processes, complex systems exhibiting non-stationary behavior can prove challenging. Machine learning techniques based on variants of Artificial Neural Networks, Bayesian approaches and Genetic Algorithms have proven promising for outlier detection, model generation and prediction in the field of wastewater treatment. In this context, intelligent techniques enable both feature extraction and application of suitable algorithms to datasets to obtain precise results. Inference mechanisms that support decision-making combined with visualization render machine learning algorithms as the most dependable techniques for analyzing various factors affecting wastewater treatment systems. Machine learning approaches are useful for data processing, real-time modeling and actionable inference for compliance with government norms for wastewater treatment. Moreover, machine learning algorithms have also been applied in wastewater treatment to optimize efficiency parameters.
This paper reviews the application of machine learning algorithms for data processing, modeling, parameter optimization, prediction, and decision-making for efficient management of wastewater treatment processes. The challenges and limitations of these approaches are also discussed.
References
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2.Akratos CS, Papaspyros INE, Tsihrintzis VA (2009) Artificial neural network use in ortho-phosphate and total phosphorus removal prediction in horizontal subsurface flow constructed wetlands. Biosyst Eng 102:190-201
3.Arismendy L, Cardenas C, Gomez D, Maturana A, Mejia R, Quintero MCG (2020) Intelligent system for the predictive analysis of an industrial wastewater treatment process. Sustainability 12
4.Bahramian M, Dereli RK, Zhao W, Giberti M, Casey E (2023) Data to intelligence: The role of data-driven models in wastewater treatment. Expert Syst Appl 217:119453
5.Bakht A, Nawaz A, Lee M, Lee H (2022) Ingredient analysis of biological wastewater using hybrid multi-stream deep learning framework. Comput Chem Eng 168: 108038
6.Bellamoli F, Di Lorio M, Vian M, Melgani F (2023) Machine learning methods for anomaly classification in wastewater treatment plants. J Environ Manage 344: 118594
7.Candelieri A, Perego R, Archetti F (2018) Bayesian optimization of pump operations in water distribution systems. J Global Optim 71: 213
8.Chang P, Bao X, Meng FC, Lu RW (2023a) Multi-objective Pigeon-inspired Optimized feature enhancement soft-sensing model of Wastewater Treatment Process. Expert Syst Appl 215:119193
9.Chang P, Xu Y, Meng FC (2023b) Efficient fault monitoring in wastewater treatment processes with time stacked broad learning network. Expert Syst Appl 233:120958
10.Cheng T, Harrou F, Kadri F, Sun Y, Leiknes T (2020) Forecasting of Wastewater Treatment Plant Key Features Using Deep Learning-Based Models: A Case Study. IEEE Access 8:184475
11.Curteanu S, Godini K, Piuleac CG, Azarian G, Rahmani AR, Butnariu C (2014) Electro-oxidation method applied for activated sludge treatment: experiment and simulation based on supervised machine learning methods. Ind Eng Chem Res 53:4902
12.Fang X, Zhai Z, Xiong R, Zhang Li, Gao B (2022) LSTM-based Modelling for Coagulant Dosage Prediction in Wastewater Treatment Plant. 3rd International Conference on Artificial Intelligence in Electronics Engineering (AIEE 2022) Bangkok, Thailand, ACM, New York, NY, USA.
13.Guo H, Jeong K. Lim J, Jo J, Kim YM, Park J-p, Kim, JH, Cho KH (2015) Prediction of effluent concentration in a wastewater treatment plant using machine learning models. J Environ Sci 32:90
14.Hamed MM, Khalafallah MG, Hassanien EA (2004) Prediction of wastewater treatment plant performance using artificial neural networks. Environ Model Softw 19:919
15.Han H-G, Zhang L, Liu H-X, Qiao J-F (2018) Multiobjective design of fuzzy neural network controller for wastewater treatment process. Appl Soft Comput 67: 467
16.He Y, Tang C, Zhang D, Liao N (2023) Assessing the effects of the influencing factors on industrial green competitiveness fusing fuzzy C-means, rough set and fuzzy artificial neural network methods. Ecol Indic 147: 109921
17.Jana DK, Bhunia P, Adhikary SD, Bej B (2022,). Optimization of Effluents Using Artificial Neural Network and Support Vector Regression in Detergent Industrial Wastewater Treatment. Clean Chem Eng 3: 100039
18.Kambli A, Modi S (2019) Fuzzy Neuro Approach to Water Management Systems ICMLSC '19: Proceedings of the 3rd International Conference on Machine Learning and Soft Computing. 215
19.Kiiza C, Pan S-q, Bockelmann-Evans B, Babatunde A (2020) Predicting pollutant removal in constructed wetlands using Artificial Neural Networks. Water Sci Eng 13:14
20.Krovvidy S, Wee WG (1990) A knowledge based planning approach for waste water treatment system. 3rd International conference on Industrial and engineering applications of artificial intelligence and expert systems, Volume 2
21.Li D, Yang HZ, Liang XF (2013) Prediction analysis of a wastewater treatment system using a Bayesian network. Environ Model Softw 40:140
22.Liu Y, Li K, Huang D (2021) Prediction of Biochemical Oxygen Demand Based on VIP-PSO-Elman Model in Wastewater Treatment. ACM International Conference on Intelligent Computing and its Emerging Applications (ACM ICEA ’21), Jinan, China. 3506502
23.Manu DS, Thalla AK, (2017) Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl nitrogen from wastewater. Appl water sci 7:3783
24.Matheri AN, Ntuli F, Ngila JC, Seodigeng T, Zvinowanda C (2021) Performance prediction of trace metals and COD in wastewater treatment using artificial neural network. Comput Chem Eng 149:107308
25.Mehrpour P, Mirbagheri SA, Kavianimalayeri M, Sayyahzadeh AH, Ehteshami M (2023) Experimental pH adjustment for different concentrations of industrial wastewater and modeling by Artificial Neural Network. Environ Technol Innov 31:103212
26.Nourani V, Elkiran G, Abba SI (2018) Wastewater treatment plant performance analysis using artificial intelligence - an ensemble approach. Water Sci Technol 78:2064
27.Rai PK, Kant V, Sharma RK, Gupta A (2023). Process optimization for textile industry-based wastewater treatment via ultrasonic-assisted electrochemical processing. Eng Appl Artif Intel 122:106162
28.Ray SS, Verma RK, Singh A, Ganesapillai M, Kwon Y-N (2023) A holistic review on how artificial intelligence has redefined water treatment and seawater desalination processes. Desalination 546:116221
29.R. Vijayabhanu, V. Radha (2012) Statistical Normalization Techniques for the Prediction of COD Level for an Anaerobic Wastewater Treatment Plant. Proceedings of the 2nd International Conference on Computational Science, Engineering and Information Technology (CCSEIT-12), Coimbatore, Tamilnadu, India.
30.Venkatasubramanian N, Davis CA, Eguchi RT (2020) Designing Community Based Intelligent Systems for Water Infrastructure Resilience. 3rd ACM SIGSPATIAL Workshop on Advances in Resilient and Intelligent Cities (ARIC’20), Seattle, WA, USA.
31.Yadav A, Roy SM (2023) An artificial neural network-particle swarm optimization (ANN-PSO) approach to predict the aeration efficiency of venturi aeration system. Smart Agricultural Technology, Volume 4.
32.Mitchell, T. M. (1997) Machine Learning. McGraw-Hill, New York. (ISBN: 0070428077)
33.Jiawei Han and Micheline Kamber (2006). Data Mining: Concepts and Techniques, 2nd edition. Morgan Kaufmann Publishers.
34.MATLAB: https://in.mathworks.com/products/matlab.html
35.TensorFlow: https://www.tensorflow.org/
36.WEKA: https://www.cs.waikato.ac.nz/ml/weka/
Published
2024-07-30
How to Cite
[1]
SHARMA, S. and DALAL, M.K. 2024. Intelligent Techniques for Wastewater Treatment : A Technical Review. IIChE-CHEMCON. (Jul. 2024). DOI:https://doi.org/10.36375/prepare_u.iiche.a414.
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