Intelligent modeling of batch reactor with partially unmeasurable states
An intelligent modeling approach was developed for batch reactor to solve modeling difficulties of non-linear characteristics,dynamic process and with partially unmeasurable states.Base on separability of reaction,the topology of model was established via structure approaching hybrid neural networks(SAHNN).Compared with normal neural networks,SAHNN has optimized structure and more nodes which represent actual states.This modeling approach utilized virtual supervisor-artificial immune algorithm(VS-AIA)to solve problems of training neural network with unmeasurable states.It firstly initialized information of unmeasurable states during reaction with only a little mechanism information(ascending or descending curve)to make them act as virtual supervisors.Then it improved the precision of modeling by optimizing virtual supervisor and training network weights at the same time.During training,immune population was used to explore new better solutions and avoid wrong direction.Detailed process of modeling and training neural networks were described in dynamical modeling of batch condensation reaction of producing promotor for vulcanizing rubber.The simulation result proved that the approach was effective.