Study on Soft Sensor Modeling and Optimization Control of Biological Fluidized Bed Swage Disposal-Reference-Cited by-同舟云学术

Study on Soft Sensor Modeling and Optimization Control of Biological Fluidized Bed Swage Disposal

Published:2011-10 Issue: Volume:128-129 Page:1059-1064
ISSN:1662-7482
Container-title:Applied Mechanics and Materials
language:
Short-container-title:AMM

Author:

Wang Xiao Yi¹,Liu Meng¹,Liu Zai Wen¹,Su Zhen¹,Lian Xiao Feng¹,An Xiao Dong²

Affiliation:

1. Beijing Technology and Business University

2. Taiyuan YiTaiHao Technology LTD

Abstract

To solve the problem that outlet water index Biochemistry Oxygen Demand (BOD) of the Biological Fluidized Bed (BFB) sewage treatment process is difficult to online measure and optimization control of sewage treatment process, Water quality index neural network soft measurement method and the fuzzy control strategy was put forward in this paper. Considering the sewage treatment process exists nonlinear and time-varying characteristic, the effluent water BOD soft sensor model was established employing the process neural network. On the base of this, the optimization control was realized adopting Dissolved Oxygen (DO) for fuzzy control variable Through the soft measurement and fuzzy control model’s simulation training, Shows that the effectiveness and feasibility of these models, an effective soft measurement and optimization control way for BFB sewage treatment have been provided.

Publisher

Trans Tech Publications, Ltd.

Link

https://www.scientific.net/AMM.128-129.1059.pdf

Reference9 articles.

1. Massimo C. DI, Saunders A. C. G, in: Nonlinear estimation and control of mycelia fermentations. Pittsburg, USA: ACC, 1989, 1994~(1999).

2. Mcacvoy T. J, in: Contemplative stance for chemical process control. Automatica, 1992, 28(2): 441-442.

3. Qi guoqiang, Liu zaiwen, Cui lifeng, in: Sewage treatment soft-measuring method based on RBF artificial neural network, 2004(3): 36-38.

4. Peng yongzhen, Wang zhihui, Wang shuying, in: Research of A / O nitrogen removal system pluing Simulation of carbon based on BP Neural Networks [J]. Chemical Journal. 2005, 56(2): 296-300.

5. He xinggui, Liang jiuzhen, in: Some theoretical issues on procedure neural networks[J]. Engineering Science, 2000, 2(12): 40-44.