Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems

Nitin Kumar Singh, Manish Yadav, Vijai Singh, Hirendrasinh Padhiyar, Vinod Kumar, Shashi Kant Bhatia, Pau Loke Show

Research output: Contribution to journalReview articlepeer-review

49 Scopus citations

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.

Original languageBritish English
Article number128486
JournalBioresource Technology
Volume369
DOIs
StatePublished - Feb 2023

Keywords

  • Artificial intelligence
  • Biological wastewater treatment
  • Machine learning
  • Model functions
  • Model predictive control
  • Performance indicators

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