Modelling of fermentative bioethanol production from indigenous Ulva prolifera biomass by Saccharomyces cerevisiae NFCCI1248 using an integrated ANN-GA approach

Dave, Niyam and Thivaharan, Varadavenkatesan and Selvaraj, Raja and Vinayagam, Ramesh (2021) Modelling of fermentative bioethanol production from indigenous Ulva prolifera biomass by Saccharomyces cerevisiae NFCCI1248 using an integrated ANN-GA approach. Science of The Total Environment, 791. ISSN 0048-9697

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Abstract

Third generation biomass (marine macroalgae) has been projected as a promising alternative energy resource for bioethanol production due to its high carbon and no lignin composition. However, the major challenge in the technologies of production lies in the fermentative bioconversion process. Therefore, in the present study the predictive ability of an integrated artificial neural network with genetic algorithm (ANN-GA) in the modelling of bioethanol production was investigated for an indigenous marine macroalgal biomass (Ulva prolifera) by a novel yeast strain, Saccharomyces cerevisiae NFCCI1248 using six fermentative parameters, viz., substrate concen�tration, fermentation time, inoculum size, temperature, agitation speed and pH. The experimental model was de�veloped using one-variable-at-a-time (OVAT) method to analyze the effects of the fermentative parameters on bioethanol production and the obtained regression equation was used as a fitness function for the ANN-GA modelling. The ANN-GA model predicted a maximum bioethanol production at 30 g/L substrate, 48 h fermenta�tion time, 10% (v/v) inoculum, 30 °C temperature, 50 rpm agitation speed and pH 6. The maximum experimental bioethanol yield obtained after applying ANN-GA was 0.242 ± 0.002 g/g RS, which was in close proximity with the predicted value (0.239 g/g RS). Hence, the developed ANN-GA model can be applied as an efficient approach for predicting the fermentative bioethanol production from macroalgal biomass.

Item Type: Article
Uncontrolled Keywords: Bioethanol Artificial neural network Genetic algorithm Ulva prolifera Saccharomyces cerevisiae
Subjects: Engineering > MIT Manipal > Biotechnology
Engineering > MIT Manipal > Chemical
Depositing User: MIT Library
Date Deposited: 23 Dec 2021 09:54
Last Modified: 23 Dec 2021 09:54
URI: http://eprints.manipal.edu/id/eprint/157892

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