A comprehensive literature review (LR) on optimization algorithms of Sewage Water Treatment Processes

TLDR

This paper examines how machine learning which is associated with artificial intelligence (AI) can be applied to fine-tune critical stages like aeration, sedimentation, as well asfiltration in wastewater treatment through real-time data as well as adaptive control strategies.

Abstract

Optimizing sewage treatment processes is essential for boosting efficiency and cutting down on energy utilize. This paper examines how machine learning which is associated with artificial intelligence (AI) can be applied to fine-tune critical stages like aeration, sedimentation, as well as filtration. Furthermore, via utilising real-time data as well as adaptive control strategies, these intelligent systems can continuously adjust operational settings to improve treatment outcomes while reducing energy demands. Through real-world case studies and in-depth data analysis, the research highlights how AI-driven control systems can be effectively implemented in wastewater facilities. The results demonstrate notable gains in performance, underscoring AI’s potential to revolutionize environmental engineering and address long-standing challenges in wastewater treatment.