Document Detail

Enhancing dissolved oxygen control using an on-line hybrid fuzzy-neural soft-sensing model-based control system in an anaerobic/anoxic/oxic process.
MedLine Citation:
PMID:  24052227     Owner:  NLM     Status:  Publisher    
An on-line hybrid fuzzy-neural soft-sensing model-based control system was developed to optimize dissolved oxygen concentration in a bench-scale anaerobic/anoxic/oxic (A(2)/O) process. In order to improve the performance of the control system, a self-adapted fuzzy c-means clustering algorithm and adaptive network-based fuzzy inference system (ANFIS) models were employed. The proposed control system permits the on-line implementation of every operating strategy of the experimental system. A set of experiments involving variable hydraulic retention time (HRT), influent pH (pH), dissolved oxygen in the aerobic reactor (DO), and mixed-liquid return ratio (r) was carried out. Using the proposed system, the amount of COD in the effluent stabilized at the set-point and below. The improvement was achieved with optimum dissolved oxygen concentration because the performance of the treatment process was optimized using operating rules implemented in real time. The system allows various expert operational approaches to be deployed with the goal of minimizing organic substances in the outlet while using the minimum amount of energy.
Mingzhi Huang; Jinquan Wan; Kang Hu; Yongwen Ma; Yan Wang
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-9-20
Journal Detail:
Title:  Journal of industrial microbiology & biotechnology     Volume:  -     ISSN:  1476-5535     ISO Abbreviation:  J. Ind. Microbiol. Biotechnol.     Publication Date:  2013 Sep 
Date Detail:
Created Date:  2013-9-20     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9705544     Medline TA:  J Ind Microbiol Biotechnol     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
College of Environment and Energy, South China University of Technology, Guangzhou, 510640, P.R. China,
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