Temperature control for chemical reactor using adaptive neural network control strategy

A pilot scaled chemical reactor is constructed and commissioned to study various conventional and advanced control strategies. One of the approaches is 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 to obtain a good process response for a certain set point. Tests in a 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 to predict the control strategy performance and behavior. With the expected performance in hand, one has the prior knowledge of the control strategy