Title :
Optimization control system for nitrifying process
Author_Institution :
China Jiliang Univ., Hangzhou, China
Abstract :
The nitrifying process is an important step in dinitrochlorobenzene production. This paper presented an optimization control system to implement the modeling, optimization, and control of the nitrifying process. Models for predicting the quality of nitrifying process are derived and implemented using improved back-propagation neural networks, and an algorithm combining c-means clustering, genetic, and chaos approaches for the optimization of the operating parameters of the nitrifying process is presented. The results of actual runs demonstrate the validity of the system.
Keywords :
backpropagation; chemical technology; genetic algorithms; neural nets; pattern clustering; process control; production control; c-means clustering; chaos approaches; dinitrochlorobenzene production; genetic algorithms; improved backpropagation neural networks; nitrifying process; operating parameters; optimization control system; Artificial neural networks; Chaos; Cooling; Neurons; Optimization; Process control;
Conference_Titel :
Modelling, Identification and Control (ICMIC), Proceedings of 2011 International Conference on
Conference_Location :
Shanghai
DOI :
10.1109/ICMIC.2011.5973735