Modeling and optimization of bioethanol production from breadfruit starch hydrolyzate vis-à-vis response surface methodology and artificial neural network

Eriola Betiku and Abiola Ezekiel Taiwo

Renewable Energy, 2015, vol. 74, issue C, 87-94

Abstract: This study investigated the use of Breadfruit Starch Hydrolysate (BFSH) as the sole carbon source for bioethanol production and the optimization of the fermentation parameters. The results showed that the yeast was able to utilize the BFSH with and without nutrient supplements, with highest bioethanol yield of 3.96 and 3.60% volume fraction, respectively after 24 h of fermentation. A statistically significant quadratic regression model (p < 0.05) was obtained for bioethanol yield prediction. Response Surface Methodology (RSM) optimal condition values established for the bioethanol yield were BFSH concentration of 134.81 g L−1, time of 21.33 h and pH of 5.01 with predicted bioethanol yield of 3.95% volume fraction. Using Artificial Neural Network (ANN), multilayer normal feedforward incremental back propagation with hyperbolic tangent function gave the best performance as a predictive model for bioethanol yield. ANN optimal condition values were BFSH concentration of 120 g L−1, time of 24 h and pH of 4.5 with predicted bioethanol yield of 4.21% volume fraction. The predicted bioethanol yield was validated experimentally as 4.10% volume fraction and 4.22% volume fraction for RSM and ANN, respectively. Coefficient of Determination (R2) and Absolute Average Deviation (AAD) were determined as 1 and 0.09% for ANN and 0.9882 and 1.67% for RSM, respectively. Thus, confirming ANN was better than RSM in both data fittings and estimation capabilities.

Keywords: Breadfruit; Bioethanol; Yeast; Response surface methodology; Artificial neural network; Optimization (search for similar items in EconPapers)
Date: 2015
References: View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:74:y:2015:i:c:p:87-94

DOI: 10.1016/j.renene.2014.07.054

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