Categorize Level of Crystal Sugar Making with Recurrent Neural Network

Abstract

This research presents the study of recurrent neural networks to predict industrial crystal sugar making. The recurrent neural network trains on six parameters consisting of liquid in the pan, Brix levels, vacuum in the pan, liquor temperatures, water steam supplier, and current for mix-motor agitator. The input variables were the trained model to predict by categorizing data in three levels high, middle, and low which the data came from human control the sugar boiler machine. The trained model for the future can be extended to make an experience meter to indicate the ability of workers to control the machine.