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|Title:||Neural network modeling to predict the total glucose yield after enzymatic saccharification of H2SO4-catalyzed hydrothermally pretreated sugarcane bagasse|
|Author:||Ccopa Rivera, E.|
Plazas Tovar, L.
Wolf Maciel, M. R.
Maciel Filho, R.
|Abstract:||In this work, sugarcane bagasse was processed by H2SO4-catalyzed hydrothermal pretreatment (CHP). CHP was performed under various solid loadings (SP) 10, 20 and 25 %, acid concentrations (A) 1.0, 2.0 and 3.0 % w/v and reaction times (tP) 30, 60, 90 and 150 min. The results showed that more than 60 % of the hemicellulose was removed. Cellulignin material was processed by enzymatic hydrolysis using a solid loading of 3.0 % w/v (on a dry basis) and by the action of cellulase from T. reesei and cellobiase from Aspergillus niger. Based on this experimental dataset, three Multilayer Perceptron Neural Network-MLPN models were developed (one for each level of acid concentration (A) in CHP) for predicting total glucose yield (GYield) as a function of solids loading (SP), pre-treatment reaction time (tP) and enzymatic hydrolysis time (t). The prediction by the model using the test dataset gave acceptable performance measures (mean absolute error, MAE, and residual standard deviation, RSD), equivalent to those obtained for the validation and training datasets. The dynamic models developed can be used to predict GYield during enzymatic hydrolysis of H2SO4- catalyzed hydrothermally (CHP) pre-treated sugarcane bagasse, which promote a successful hydrolysis process control|
|Subject:||Bagaço de cana|
|Editor:||Associazione Italiana di Ingegneria Chimica|
|Appears in Collections:||FEQ - Artigos e Outros Documentos|
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