Optimization of operating conditions in rice heat blast process for Chinese rice wine production by combinational utilization of neural network and genetic algorithms

Abstract

The rice heat blast process is a novel technique for gelatinizing raw starch. It uses heated air to replace steam for processing rice under high temperature fluidization in a short time period. This is a new technique for making rice wine with the characteristics of easy storage and zero water pollution. This study focused on three major performance indexes (starch gelatinization ratio, total fat content, and amino nitrogen content in the roasted rice), which largely affect the performance of the rice heat blast process and the rice wine quality. The relationship between these performance indexes and the corresponding operation variables were modeled by an artificial neural network (ANN) via learning sets of experimental data. Based on the ANN models obtained, genetic algorithms were used to optimize the operating conditions of the rice heat blast process. The results showed the power in determination of optimal operating conditions by the combinational utilization of artificial neural network and genetic algorithms.