Modeling and Control of CSTR using Model based Neural Network Predictive Control

TL;DR

This paper develops a neural network-based predictive control method for a highly nonlinear Continuous Stirred Tank Reactor (CSTR), demonstrating its effectiveness through simulation results.

Contribution

It introduces a neural network predictive control approach tailored for CSTR, addressing nonlinear modeling challenges with AI techniques.

Findings

Neural network model accurately predicts CSTR behavior.

NNMPC effectively controls product concentration.

Simulation confirms feasibility of the proposed method.

Abstract

This paper presents a predictive control strategy based on neural network model of the plant is applied to Continuous Stirred Tank Reactor (CSTR). This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neural network predictive control, can be a better match to govern the system dynamics. In the paper, the NN model and the way in which it can be used to predict the behavior of the CSTR process over a certain prediction horizon are described, and some comments about the optimization procedure are made. Predictive control algorithm is applied to control the concentration in a continuous stirred tank reactor (CSTR), whose parameters are optimally determined by solving quadratic performance index using the optimization algorithm. An efficient control of the product concentration in cstr can be achieved only through accurate model. Here an attempt is made to alleviate the modeling difficulties using Artificial Intelligent technique such as Neural Network. Simulation results demonstrate the feasibility and effectiveness of the NNMPC technique.