| Resumo: | Hydrolyzer is a commonly found unit operation in oleochemical industry. Control of hydrolyzer has to be done carefully since efficiency in the control of this unit will affect the yield of the process. At present conventional controllers such as PI and PID have been used to achieve the setpoint especially under presence of disturbances. In this study, neural network have been applied as an alternative to cope with the dynamics behavior of the hydrolyzer. Two types of control strategies namely, direct inverse controller (DIC) and internal model controller (IMC) were implemented in the control system. Two sets of data were used to develop the DIC and IMC. The controllers were evaluated on the ability to track set-points, load disturbance and noise disturbance test and the IMC was found to be the most versatile controller.
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