초록
<P><B>Abstract</B></P><P><B>BACKGROUND</B></P><P>In this work a single artificial neural network (ANN) was used to model the overall yield of glucose (<I>Y<SUB>GLC</SUB></I>) as a function of a wide range of operating conditions of both pretreatment and enzymatic hydrolysis.</P><P><B>RESULTS</B></P><P>The model was validated experimentally and presented good predictions of <I>Y<SUB>GLC</SUB></I>. Sensitivity analysis using the ANN model indicated that most of the operating parameters, except for pretreatment time, were statistically significant (<I>P</I>‐value <0.05). Experiments showed that the processing of sugarcane bagasse (<I>in natura</I>) 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 g<SUB>WIS</SUB> enzyme concentration.</P><P><B>CONCLUSION</B></P><P>This study demonstrated how pretreatment and enzymatic hydrolysis data can be used to parameterize a single ANN model. Acceptable predictions of <I>Y<SUB>GLC</SUB></I> are achieved in terms of RSD, MSE and R<SUP>2</SUP>. Supported by the model, this study provided a good insight for process development. © 2017 Society of Chemical Industry</P>