Feed-forward neural network modeling and optimization using genetic algorithm: Enzymatic hydrolysis of xylose production

Abstract

Demand on modeling an accurate process model as well as optimization of biochemical process has increased as it is vital for sustainable development in bioprocess industries. Therefore, this paper is concerned about the empirical modeling of enzymatic hydrolysis using xylanase for the production of xylose from rice straw. The parameters investigated in this research were temperature, agitation speed of incubator shaker and xylanase concentration, to obtain the production of xylose. Feed-forward neural network (FANN) was employed to describe the relationship of the input and output of the process. Then the genetic algorithm (GA) method was applied to optimize the process condition. The initial data is split into training and validation before re-sampling the data with bootstrap re-sampling method. The training data again was then split into training and testing data.The neural network model was developed with one hidden layer and 6 number of hidden nodes. The correlation coefficient of training and testing set was found to be 0.9970 and 0.9975 respectively, though the correlation coefficient of validation was obtained as 0.8501. The optimization of the parameters namely temperature, agitation speed and xylanase concentration of the xylose production using the GA method was found to be 50.3111°C, 153.5140 rpm and 1.6944 g/l with the optimum xylose production predicted is 0.1845 g/l.