Optimization Process of Glycerol Esterification Using Real-Time Adaptive Control

Abstract

The synthesis reaction used in esterification needs high energy consumption and a precise processing time to get the best yield of target. In this study, a model was formulated to optimize glycerol esterification process by minimizing the time needed for the process and maximizing the yield of monoglycerides. This optimization has gained importance for boosting the esterification industry and improving the production efficiency. Optimization through adaptive monitoring and control has provided significant advances in the process efficiency. Optimization through adaptive monitoring and control has provided significant advances in the process efficiency, a lower energy consumption and a better product quality. This paper presents the optimization with a computational algorithm in real time and adaptive control (RTAC), as compared to the conventional (traditional) methods to monitor and control of glycerol esterification processes. The identification of esterification status based on temperature and time are evaluated to strengthen the optimization. An adaptive method as feature selection to select wavelength IR sensors at specified intervals was carried out with Relief algorithm and Adaptive pillar K-means clustering method to set the parameter control was proposed in this paper. Many combinations were evaluated from real time condition process, to achieve the best optimization results. The experimental results demonstrate that real time adaptive control can be achieved by using three clusters, which are heating up, stabilizing and finishing. In RTAC, each cluster has it’s own parameter to set the control point by the servo motor that was attached to magnetic stirrer-heater By using optimization parameter for each cluster, esterification process time can be shortened by 15-20 minutes with a higher yield (7% or more), lower range stireer rotation (300rpm-450rpm) and a lower final temperature of 200C – 210C.