Approximate predictive versus self-tuning adaptive control strategies of biodiesel reactors

Producing biodiesel from palm oil as a raw material involves complex transesterification reactions which add up to the process nonlinearity. In this work, more emphasis will be focused on the reactor nonlinearity and ways of solving its control problem. The reactor nonlinearity is addressed via the application of an instantaneous linearization technique to control the reactor temperature and the triglyceride product concentration. A feedforward neural network with delayed inputs and outputs was trained and validated to capture the dynamics of the biodiesel process. The generated nonlinear model was then utilized in an instantaneous linearization algorithm using two control algorithms adopting the self-tuning adaptive control and an approximate model predictive framework. The two algorithms were compared in terms of set-point tracking capability, efficiency, and stability. The minimum variance control algorithm attained poor performance compared to the poleplacement self-tuning adaptive algorithm. However, the approximate model predictive control strategy was superior to the self-tuning control in terms of its ability for forcing the output to follow the set-point trajectory efficiently with smooth controller moves.