Prediction of overall glucose yield in hydrolysis of pretreated sugarcane bagasse using a single artificial neural network: good insight for process development

Abstract

AbstractBACKGROUNDIn this work a single artificial neural network (ANN) was used to model the overall yield of glucose (YGLC) as a function of a wide range of operating conditions of both pretreatment and enzymatic hydrolysis.RESULTSThe model was validated experimentally and presented good predictions of YGLC. Sensitivity analysis using the ANN model indicated that most of the operating parameters, except for pretreatment time, were statistically significant (P‐value <0.05). Experiments showed that the processing of sugarcane bagasse (in natura) results in a satisfactory glucose yield of 69.34% when pretreated for 60 min with low initial biomass concentration and acid concentration (10% and 1.0% w/v), and followed by enzymatic hydrolysis for 72 h with 3.0% w/v substrate loading and 60 FPU per gWIS enzyme concentration.CONCLUSIONThis study demonstrated how pretreatment and enzymatic hydrolysis data can be used to parameterize a single ANN model. Acceptable predictions of YGLC are achieved in terms of RSD, MSE and R2. Supported by the model, this study provided a good insight for process development. © 2017 Society of Chemical Industry