In 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. The model was validated experimentally and presented good predictions of YGLC ....

In 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. The 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. This 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

FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP

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