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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

Full text
Author(s):
Tovar, Laura Plazas [1, 2] ; Rivera, Elmer Ccopa [2, 3] ; Mariano, Adriano Pinto [2] ; Wolf Maciel, Maria Regina [2] ; Maciel Filho, Rubens [2]
Total Authors: 5
Affiliation:
[1] Univ Fed Santa Maria, Dept Chem Engn, BR-97105900 Santa Maria, RS - Brazil
[2] Univ Estadual Campinas, Sch Chem Engn, Campinas, SP - Brazil
[3] Ohio State Univ, Dept Anim Sci, Wooster, OH - USA
Total Affiliations: 3
Document type: Journal article
Source: JOURNAL OF CHEMICAL TECHNOLOGY AND BIOTECHNOLOGY; v. 93, n. 4, p. 1031-1043, APR 2018.
Web of Science Citations: 1
Abstract

BACKGROUND: In this work a single artificial neural network (ANN) was used to model the overall yield of glucose (Y-GLC) as a function of a wide range of operating conditions of both pretreatment and enzymatic hydrolysis. RESULTS: The model was validated experimentally and presented good predictions of Y-GLC. 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 g(WIS) enzyme concentration. CONCLUSION: This study demonstrated how pretreatment and enzymatic hydrolysis data can be used to parameterize a single ANN model. Acceptable predictions of Y-GLC are achieved in terms of RSD, MSE and R-2. Supported by the model, this study provided a good insight for process development. (C) 2017 Society of Chemical Industry. (AU)

FAPESP's process: 16/01785-0 - Butanol production from eucalyptus wood: effect of glycerol on in situ detoxification of lignocellulose-derived microbial inhibitors and in silico analysis of the metabolism of Clostridium beijerinckii NCIMB 8052
Grantee:Elmer Alberto Ccopa Rivera
Support type: Scholarships abroad - Research
FAPESP's process: 12/10857-3 - Modeling and evaluation of different operating process for pre-treatment, hydrolysis and fermentation for 2G ethanol production
Grantee:Laura Plazas Tovar
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 08/57873-8 - An integrated process for total bioethanol production and zero CO2 emission
Grantee:Rubens Maciel Filho
Support type: Program for Research on Bioenergy (BIOEN) - Thematic Grants