Optimization of sewage treatment processes: Process control based on artificial intelligence

Xu Liu

doi:10.54254/2755-2721/93/20240981

Research Article

Open access

Published on 8 November 2024

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Liu,X. (2024). Optimization of sewage treatment processes: Process control based on artificial intelligence. Applied and Computational Engineering,93,185-190.

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Optimization of sewage treatment processes: Process control based on artificial intelligence

Xu Liu *^,1,

¹ Harbin Engineering University, Heilongjiang, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/93/20240981

Abstract

The optimization of sewage treatment processes is critical for improving efficiency and reducing energy consumption. This paper explores the application of machine learning and artificial intelligence algorithms in optimizing key processes such as aeration, sedimentation, and filtration. By leveraging real-time monitoring and adaptive control, these algorithms can dynamically adjust operational parameters to enhance treatment efficiency and minimize energy usage. This study provides detailed insights into the implementation and benefits of AI-driven process control in sewage treatment, supported by case studies and data analysis. The findings indicate significant improvements in treatment performance, showcasing the transformative potential of AI in environmental engineering.

Keywords

Artificial Intelligence, Sewage Treatment, Process Optimization, Machine Learning, Aeration.

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References

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Cite this article

Liu,X. (2024). Optimization of sewage treatment processes: Process control based on artificial intelligence. Applied and Computational Engineering,93,185-190.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation

Conference website: https://2024.confmla.org/

ISBN：978-1-83558-627-3(Print) / 978-1-83558-628-0(Online)

Conference date: 21 November 2024

Editor：Mustafa ISTANBULLU, Xinqing Xiao

Series: Applied and Computational Engineering

Volume number: Vol.93

ISSN：2755-2721(Print) / 2755-273X(Online)

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