Artificial intelligence as an upcoming technology in wastewater treatment: a comprehensive review

被引：16

作者：

Malviya A. ^{[1
]}

Jaspal D. ^{[2
]}

机构：

[1] Lakshmi Narain College of Technology, Bhopal

[2] Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University), (SIU), Pune

来源：

Environmental Technology Reviews | 2021年 / 10卷 / 01期

关键词：

Artificial intelligence; Artificial neural network; BOD; Technology; Wastewater parameters; Wastewater treatment;

D O I：

10.1080/21622515.2021.1913242

中图分类号：

学科分类号：

摘要：

Artificial intelligence (AI) is nowadays an upcoming technology. It is a practice of simulating human intelligence for varied applications. When compared with the standard practices, AI is developing at a rapid rate. AI has proved its worth in several areas such as agriculture, automobile industry, banking and finance, space exploration, artificial creativity, etc. Owing to the efficiency, speed, and independence from human operations, AI is now entering the wastewater treatment sector. This technology has been used for monitoring the performance of the water treatment plants in terms of efficiency parameters, Biological Oxygen Demand (BOD) and Chemical Oxygen Demand (COD) determination, elimination of nitrogen and sulphur, prediction of turbidity and hardness, uptake of contaminants, etc., in the wastewater sector. Artificial Neural Networks (ANN), Fuzzy Logic Algorithms (FL), and Genetic Algorithms (GA) are the basic three models under AI predominantly used in the wastewater sector. Studies reveal that the determination coefficient values of 0.99 can be attained for COD, BOD, heavy metals and organics removal using ANN and hybrid intelligent systems. This review paper describes research with all the possible models of AI utilized in the water treatment which have enhanced the pollutant removal percentage accuracy of ranging from 84% to 90% and provided viewpoint on future directions of novel research, in the field with due focus on pollution remediation, cost effectiveness, energy economy, and water management. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

引用

页码：177 / 187

页数：10

共 80 条

[1]

Ni J., Wu L., Fan X., Et al., Bioinspired intelligent algorithm and its applications for mobile robot control: a survey, Comput Intell Neurosci, pp. 1-16

[2]

Sengupta S., Basak S., Peters R.A.I., Particle swarm optimization: a survey of historical and recent developments with hybridization perspectives, Mach Learn Knowl Extr, 1, 1, pp. 157-119, (2018)

[3]

Abduljabbar R., Dia H., Liyanage S., Et al., Applications of artificial intelligence in transport: an overview, Sustainability, 11, 1, (2019)

[4]

You Z., Zhu Y., Jang C., Et al., Response surface modeling-based source contribution analysis and VOC emission control policy assessment in a typical ozone-polluted urban Shunde, China, J Environ Sci, 51, pp. 294-304, (2017)

[5]

Kalogirou S.A., Artificial intelligence for the modeling and control of combustion processes: a review, Prog Energy Combust Sci, 29, pp. 515-566, (2003)

[6]

Shi S., Xu G., Novel performance prediction model of a biofilm system treating domestic wastewater based on stacked denoising auto-encoders deep learning network, Chem Eng J, 347, pp. 280-290, (2018)

[7]

Moral H., Aksoy A., Gokcay C.F., Modeling of the activated sludge process by using artificial neural networks with automated architecture screening, Comput Chem Eng, 32, pp. 2471-2478, (2008)

[8]

Emad S.E., Malay C., Mohamed M.E., The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process, J Hazard Mater, 179, 1-3, pp. 127-134, (2010)

[9]

PicosBenitez A.R., Lopez-Hincapie J.D., Chavez-Ramirez A.U., Et al., Artificial intelligence based model for optimization of COD removal efficiency of an up-flow anaerobic sludge blanket reactor in the saline wastewater treatment, Water Sci Technol, 75, pp. 1351-1361, (2017)

[10]

Wan J., Huang M., Ma Y., Et al., Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system, Appl Soft Comput J, 11, pp. 3238-3246, (2011)

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