Title :
Soft Sensing and Optimization of Pesticide Waste Incinerator
Author :
Yan Zhengbing ; Liu Xinggao
Author_Institution :
Dept. of Control Sci. & Eng., Zhejiang Univ., Hangzhou, China
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;
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
