Temperature control for chemical reactor using adaptive neural network control strategy

Abstract

A pilot scaled chemical reactor ['I is constructed and commissioned to study various conventional and advanced control strategies. One of the researches to be due is regarding the use of neural network inverse model based controller to control the temperature of the chemical reactor. Neural network control was chosen due to its capabilities to overcome the hassle in periodically tuning the conventional controller in obtaining good process response for certain set point. Tests in form of load disturbance and set point tracking are carried out to evaluate the neural network controller. The neural network controller exhibits satisfactory performance. Simulation work prior to the planned online implementation is vital and crucial to predict the control strategy performance and behavior. With the expected performance in hand, we have the prior knowledge of the control strategy.

Comparative Analysis of Various Control Strategies for a Nonlinear CSTR System

“…The neural network predictive control for CSTR process for reference point tracking of temperature [19] and level [20] shows reasonable performance. The artificial neural network model [21] for CSTR process and its temperature control [22] is detailed reasonably. The response of the CSTR model with NARMA based PID controller is shown in Figure 11.…”