Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems. (February 2023)

• Record Type: Journal Article
• Authors: Singh, Nitin Kumar; Yadav, Manish; Singh, Vijai; Padhiyar, Hirendrasinh; Kumar, Vinod; Bhatia, Shashi Kant; Show, Pau-Loke
• Is Part Of: Bioresource technology. Volume 369(2023)
• Publication Date: 2023-02
• Subjects: Biological wastewater treatment -- Artificial intelligence -- Machine learning -- Model predictive control -- Performance indicators -- Model functions; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2022.128486
• Languages: English
• ISSNs: 0960-8524

Abstract

Highlights: Performance prediction of WWTS reported through AI and ML-based models. WWTS associated parameters are mainly modelled using ANN, RF, SVM, and RNN. AI and ML – based models also contributed in troubleshooting and fault detection. Various activation and transfer function for WWTS are summarized in this study. Predictive model outcomes are critically analyzed for biological WWTS. Abstract: Artificial intelligence (AI) and machine learning (ML) are currently used in several areas. The applications of AI and ML based models are also reported for monitoring and design of biological wastewater treatment systems (WWTS). The available information is reviewed and presented in terms of bibliometric analysis, model's description, specific applications, and major findings for investigated WWTS. Among the applied models, artificial neural network (ANN), fuzzy logic (FL) algorithms, random forest (RF), and long short-term memory (LSTM) were predominantly used in the biological wastewater treatment. These models are tested by predictive control of effluent parameters such as biological oxygen demand (BOD), chemical oxygen demand (COD), nutrient parameters, solids, and metallic substances. Following model performance indicators were mainly used for the accuracy analysis in most of the studies: root mean squared error (RMSE), mean square error (MSE), and determination coefficient (DC). Besides, outcomes of various models are also summarized in this study.