Optimization of bioethanol production from sorghum grains using artificial neural networks integrated with ant colony

Sebayang, AH; Masjuki, HH; Ong, HC; Dharma, S; Silitonga, AS; Kusumo, F; Milano, J

Optimization of bioethanol production from sorghum grains using artificial neural networks integrated with ant colony

Sebayang, AH Masjuki, HH Ong, HC Dharma, S Silitonga, AS Kusumo, F Milano, J

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Industrial Crops and Products, 2017, 97, pp. 146-155
Issue Date:: 2017-03-01

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Field	Value	Language
dc.contributor.author	Sebayang, AH
dc.contributor.author	Masjuki, HH
dc.contributor.author	Ong, HC
dc.contributor.author	Dharma, S
dc.contributor.author	Silitonga, AS
dc.contributor.author	Kusumo, F
dc.contributor.author	Milano, J
dc.date.accessioned	2022-09-08T01:35:58Z
dc.date.available	2022-09-08T01:35:58Z
dc.date.issued	2017-03-01
dc.identifier.citation	Industrial Crops and Products, 2017, 97, pp. 146-155
dc.identifier.issn	0926-6690
dc.identifier.issn	1872-633X
dc.identifier.uri	http://hdl.handle.net/10453/161512
dc.description.abstract	In this study, an artificial neural networks (ANN) model is developed to investigate the relationship between bioethanol production and the operating parameters of enzymatic hydrolysis and fermentation processes. The operating parameters of the hydrolysis process which influence the reducing sugar concentration are the substrate loading, α-amylase concentration, amyloglucosidase concentration and strokes speed. The operating parameters of the fermentation process which influence the ethanol concentration are the yeast concentration, reaction temperature and agitation speed. The desirability function of the model is integrated with ant colony optimization (ACO) in order to determine the optimum operating parameters which will maximize reducing sugar and ethanol concentrations. The optimum substrate loading, α-amylase concentration, amyloglucosidase concentration and strokes speed is determined to be 20% (w/v), 109.5 U/g, 36 U/mL and 50 spm, respectively. The reducing sugar obtained at these optimum conditions is 175.94 g/L, which is close to the average value from experiments (174.29 g/L). The optimum yeast concentration, reaction temperature and agitation speed is found to be 1.3 g/L, 35.6 °C and 181 rpm, respectively. The ethanol concentration obtained from the fermentation of sorghum starch by Saccharomyces cerevisiae yeast at these optimum conditions is 82.11 g/L, which is in good agreement with the average value from experiments (81.52 g/L). Based on the results, it can be concluded that the model developed in this study model is an effective method to optimize bioethanol production, and it reduces the cost, time and effort associated with experimental techniques.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Industrial Crops and Products
dc.relation.isbasedon	10.1016/j.indcrop.2016.11.064
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0703 Crop and Pasture Production
dc.subject.classification	Biotechnology
dc.title	Optimization of bioethanol production from sorghum grains using artificial neural networks integrated with ant colony
dc.type	Journal Article
utslib.citation.volume	97
utslib.for	0703 Crop and Pasture Production
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-09-08T01:35:57Z
pubs.publication-status	Published
pubs.volume	97

Abstract:

In this study, an artificial neural networks (ANN) model is developed to investigate the relationship between bioethanol production and the operating parameters of enzymatic hydrolysis and fermentation processes. The operating parameters of the hydrolysis process which influence the reducing sugar concentration are the substrate loading, α-amylase concentration, amyloglucosidase concentration and strokes speed. The operating parameters of the fermentation process which influence the ethanol concentration are the yeast concentration, reaction temperature and agitation speed. The desirability function of the model is integrated with ant colony optimization (ACO) in order to determine the optimum operating parameters which will maximize reducing sugar and ethanol concentrations. The optimum substrate loading, α-amylase concentration, amyloglucosidase concentration and strokes speed is determined to be 20% (w/v), 109.5 U/g, 36 U/mL and 50 spm, respectively. The reducing sugar obtained at these optimum conditions is 175.94 g/L, which is close to the average value from experiments (174.29 g/L). The optimum yeast concentration, reaction temperature and agitation speed is found to be 1.3 g/L, 35.6 °C and 181 rpm, respectively. The ethanol concentration obtained from the fermentation of sorghum starch by Saccharomyces cerevisiae yeast at these optimum conditions is 82.11 g/L, which is in good agreement with the average value from experiments (81.52 g/L). Based on the results, it can be concluded that the model developed in this study model is an effective method to optimize bioethanol production, and it reduces the cost, time and effort associated with experimental techniques.

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http://hdl.handle.net/10453/161512