Optimized convolutional neural networks for fault diagnosis in wastewater treatment processes-Reference-Cited by-同舟云学术

Optimized convolutional neural networks for fault diagnosis in wastewater treatment processes

Published:2024 Issue:2 Volume:10 Page:364-375
ISSN:2053-1400
Container-title:Environmental Science: Water Research & Technology
language:en
Short-container-title:Environ. Sci.: Water Res. Technol.

Author:

Hu Tong¹²,Zhang Yuchen¹²,Wang Xinyuan¹²,Sha Jiulong¹,Dai Hongqi²^ORCID,Xiong Zhixin²,Wang Dongsheng³,Zhang Fengshan⁴,Liu Hongbin¹²⁴^ORCID

Affiliation:

1. Guangxi Key Laboratory of Clean Pulp & Papermaking and Pollution Control, College of Light Industry and Food Engineering, Guangxi University, Nanning 530004, China

2. Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China

3. College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

4. Laboratory for Comprehensive Utilization of Paper Waste of Shandong Province, Shandong Huatai Paper Co. Ltd., Dongying 257335, China

Abstract

An optimized deep learning model with high classification performance was proposed for fault diagnosis in wastewater treatment processes.

Funder

Guangxi Key Laboratory of Clean Pulp and Papermaking and Pollution Control

Natural Science Foundation of Shandong Province

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Publisher

Royal Society of Chemistry (RSC)

Subject

Water Science and Technology,Environmental Engineering

Link

http://pubs.rsc.org/en/content/articlepdf/2024/EW/D3EW00619K

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