Model construction and optimization for raising the concentration of industrial bioethanol production by using a data-driven ANN model

Abstract

This work aims to model and predict the bioethanol produced from a conventional fermentation process. An industrial dataset was obtained from a sugar mill in India. These datasets comprised a total of 1300 experimental values acquired from a total 100 days of production of the sugar mill in the year 2021. Using this data, a framework based on deep learning Artificial Neural Network (ANN) technique model was developed and validated with the test data. Specifically, the normalized dataset was passed through an ANN model consisting of one input layer, two hidden layers and one output layer, and the percent model error calculated by using average absolute deviation metric and was found to be 4.48 and 1.99% for training and testing data, respectively. The optimization of the process variables was performed for the first time using a data synthesis technique in which the normalized dataset was first synthesized and then passed through an ANN model to get an optimized input variable set for an increase in bioethanol concentration (BEC) in the final product by 1°GL. The operating parameters which significantly influenced the BEC are concentration of cell in pre-fermentation (PFM), water fermentation pH (WFMpH), and water fermentation hardness (WFH). An increase of 8.45% in BEC was obtained for Sugar mill, India.