A hybrid extreme learning machine and deep belief network framework for sludge bulking monitoring in a dynamic wastewater treatment process-Reference-Cited by-同舟云学术

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A hybrid extreme learning machine and deep belief network framework for sludge bulking monitoring in a dynamic wastewater treatment process

Published:2022-04 Issue: Volume:46 Page:102580
ISSN:2214-7144
Container-title:Journal of Water Process Engineering
language:en
Short-container-title:Journal of Water Process Engineering

Author:

Safder Usman,Loy-Benitez Jorge,Nguyen Hai-Tra,Yoo ChangKyoo

Funder

Ministry of SMEs and Startups

Ministry of Science and ICT, South Korea

Ministry of Environment

National Research Foundation of Korea

Publisher

Elsevier BV

Subject

Process Chemistry and Technology,Waste Management and Disposal,Safety, Risk, Reliability and Quality,Biotechnology

Reference46 articles.

1. Water resource systems planning and management: An introduction to methods, models, and applications;Loucks,2017

2. Steady-state analysis of activated sludge processes with a settler model including sludge compression;Diehl;Water Res.,2016

3. An investigation into the effect of MLSS on the effluent quality and sludge settleability in an aerobic-anoxic sequencing batch reactor (AASBR);Alattabi;J. Water Process Eng.,2019

4. An intelligent detection method for bulking sludge of wastewater treatment process;Han;J. Process Control,2018

5. Identifying different types of bulking in an activated sludge system through quantitative image analysis;Mesquita;Chemosphere,2011

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