Implementation of neural network inverse-model-based control (NN-IMBC) strategy in batch reactors - Aziz, N.; Hussain, Mohd Azlan; Mujtaba, I.M. (Computer Aided Chemical Engineering, 2003)
Neural Network Inverse-Model-Based Control (NN-IMBC) strategy is used to track the optimal reactor temperature profiles and its performance is evaluated through a few robustness tests. A complex exothermic batch reaction scheme is used as a case study. The optimal reactor temperature profiles are obtained by solving optimal control problems off-line using Control Vector Parameterisation (CVP) and Successive Quadratic Programming (SQP) techniques. The NN-IMBC strategy is evaluated in tracking both the constant and dynamic optimal set points. Neural Network estimator is embedded to the strategy as the on-line estimator to estimate the amount of heat released by the chemical reaction. The NN-IMBC is found to be well performed in tracking both set points and accommodating changes within its range of training. It also promises robust controller if it is trained with a wide range of the reactor temperature covering all possible conditions of the process and is much easier to implement compared to other typical types of controllers because no tuned parameter is needed. Therefore, it can lead to efficient and profitable operation and provide a better business decision making in setting up new plants or improving the existing operations.