Integrating machine learning algorithm with sewer process model to realize swift prediction and real-time control of H2S pollution in sewer systems-Reference-Cited by-同舟云学术

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Integrating machine learning algorithm with sewer process model to realize swift prediction and real-time control of H2S pollution in sewer systems

Published:2024-05 Issue: Volume:23 Page:100230
ISSN:2589-9147
Container-title:Water Research X
language:en
Short-container-title:Water Research X

Author:

Liang Zhensheng,Xie Wenlang,Li Hao,Li Yu,Jiang Feng

Funder

Guangxi Key Research and Development Program

Science and Technology Planning Project of Guangdong Province

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Publisher

Elsevier BV

Reference28 articles.

1. Real-time model predictive control of a wastewater treatment plant based on machine learning;Bernardelli;Water Sci. Technol.,2020

2. Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach;Canete;Comput. Chem. Eng.,2021

3. Implementation of an evolving fuzzy model (eFuMo) in a monitoring system for a waste-water treatment process;Dovzan;IEEE Trans. Fuzzy Syst.,2015

4. Data analytics in control and operation of municipal wastewater treatment plants: qualitative analysis of needs and barriers;Eerikinen;Water Sci. Technol.,2020

5. Machine learning weather soft-sensor for advanced control of wastewater treatment plants;Félix;Sensors,2019

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