Modeling and Optimisation of Xylose Production by Enzymatic Hydrolysis using Neural Network and Particle Swarm Optimization

Abstract

Abstract Particle swarm optimization (PSO) method is used for the optimization of an enzymatic hydrolysis process for the production of xylose from rice straw. The enzymatic hydrolysis process conditions such as temperature, agitation speed and concentration of enzyme were optimized by using PSO to obtain the optimum yield of xylose. Data collected from an experimental design using response surface methodology were necessitated to develop the neural network modeling. The neural network model is used as a model in objective function of PSO to predict the optimum conditions, which involved in the enzymatic hydrolysis process. The optimum value is obtained from the performance of the best particle swarm among the optimum conditions in PSO. The predicted optimum values were validated through the experiment of the enzymatic hydrolysis process. The optimum temperature, agitation speed and xylanase concentration is observed to be 50.3°C, 132 rpm and 1.6474 mg/ml, respectively. The optimal yield of xylose is predicted as 0.1845 mg/ml using PSO.