Soft Sensing and Optimization of Pesticide Waste Incinerator

Author

Yan Zhengbing ; Liu Xinggao

Author_Institution

Dept. of Control Sci. & Eng., Zhejiang Univ., Hangzhou, China

fYear

2011

fDate

23-26 May 2011

Firstpage

281

Lastpage

286

Abstract

Three soft sensor models (RBF, SVM, ICA-SVM) are proposed to infer the Chemical Oxygen Demand (COD) of quench water produced from pesticide waste incinerator respectively. An optimization model of COD is further proposed based on the above soft sensor models. Furthermore, chaos genetic algorithm is introduced to solve the optimization model. The procedure is demonstrated and discussed for practical industrial cases, where the mean relative error of the proposed ICA-SVM model is 0.16% for COD prediction, and mean of COD can decrease from 1140 to 393, by 65.53%, with the proposed optimal soft sensing approach.

Keywords

agrochemicals; combustion equipment; genetic algorithms; incineration; independent component analysis; industrial waste; production engineering computing; radial basis function networks; support vector machines; wastewater treatment; ICA-SVM model; RBF; chaos genetic algorithm; chemical oxygen demand; optimization model; pesticide waste incinerator; quench water; soft sensing; soft sensor models; Combustion; Data models; Incineration; Optimization; Predictive models; Support vector machines; Temperature measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on

Conference_Location

Hangzhou

Print_ISBN

978-1-4244-7460-8

Electronic_ISBN

978-988-17255-0-9

Type

conf

Filename

5930439