Artificial neural network-based model for microwave pretreatment of sugarcane bagasse

Pretreatment of lignocellulosic materials is an essential step to improve the susceptibility of this material to enzymatic hydrolysis and to enhance the efficiency of enzymatic hydrolysis. Pretreatment is a highly nonlinear process, making it difficult to set a theoretical model with confident prediction ability. Artificial neural networks (ANNs) are very effective in developing predictive models for processes where the mechanism is not described clearly compared to more traditional deterministic approaches. In this work, model predicting reducing sugar as a function of sugarcane bagasse pretreatment conditions was developed using artificial neural network. The inputs of the model were three parameters of pretreatment ( microwave assisted pretreatment type , power of microwave irradiation, and microwave treatment time), while the reducing sugar concentration was the output. The effect of the number of hidden processing elements on the error in prediction was studied

Artificial neural network-based model for microwave pretreatment of sugarcane bagasse Keywords:

Microwave pretreatment , artificial neural network , sugarcane bagasse , enzymatic hydrolysis