Control of a hydrolyzer in an oleochemical plant using neural network based controllers
Hydrolyzer is a commonly found unit operation in the splitting of crude palm oil into fatty acids and glycerol in the oleochemical industry of Malaysia. The control of this hydrolyzer has to be done carefully since efficiency in the control of this unit will affect the further yield of the process. At present conventional controllers such as the PID controller have been used to control the unit especially during startup and shutdown of the plant and under presence of disturbances. However experience shows that these PID controllers cannot efficiently handle random disturbance entering the plant. In this study, neural network have been applied as an alternative to cope with the nonlinear dynamics of the hydrolyzer. A mathematical model had been developed and used to simulate the dynamic responses of the temperatures when the controllers were applied into the system. Two types of control strategies namely, direct inverse controller (DIC) and internal model controller (IMC) were implemented, in simulation with actual industrial data, within the control system. The controllers were evaluated on the ability to track set-point and the ability to control the temperature when disturbances and noise appeared in the system. Based on the results, IMC was found to perform very well in the temperature control of the hydrolyzer during set-point tracking and disturbance tests. The responses generated by the IMC was much more stable as compared to the conventional controllers and when noise disturbance was taken into consideration, the IMC also performs better than the DIC controller.
hydrolyzer oleochemical neural network direct inverse controller internal model controller