Response Surface Methodology and Artificial Neural Networks-Based Yield Optimization of Biodiesel Sourced from Mixture of Palm and Cotton Seed Oil

Razzaq, L; Abbas, MM; Miran, S; Asghar, S; Nawaz, S; Soudagar, MEM; Shaukat, N; Veza, I; Khalil, S; Abdelrahman, A; Kalam, MA

Response Surface Methodology and Artificial Neural Networks-Based Yield Optimization of Biodiesel Sourced from Mixture of Palm and Cotton Seed Oil

Razzaq, L Abbas, MM Miran, S Asghar, S Nawaz, S Soudagar, MEM Shaukat, N Veza, I Khalil, S Abdelrahman, A Kalam, MA

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Sustainability (Switzerland), 2022, 14, (10)
Issue Date:: 2022-05-01

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Field	Value	Language
dc.contributor.author	Razzaq, L
dc.contributor.author	Abbas, MM
dc.contributor.author	Miran, S
dc.contributor.author	Asghar, S
dc.contributor.author	Nawaz, S
dc.contributor.author	Soudagar, MEM
dc.contributor.author	Shaukat, N
dc.contributor.author	Veza, I
dc.contributor.author	Khalil, S
dc.contributor.author	Abdelrahman, A
dc.contributor.author	Kalam, MA
dc.date.accessioned	2023-04-11T04:04:30Z
dc.date.available	2023-04-11T04:04:30Z
dc.date.issued	2022-05-01
dc.identifier.citation	Sustainability (Switzerland), 2022, 14, (10)
dc.identifier.issn	2071-1050
dc.identifier.issn	2071-1050
dc.identifier.uri	http://hdl.handle.net/10453/169569
dc.description.abstract	In this present study, cold flow properties of biodiesel produced from palm oil were improved by adding cotton seed oil into palm oil. Three different mixtures of palm and cotton oil were prepared as P50C50, P60C40, and P70C30. Among three oil mixtures, P60C40 was selected for biodiesel production via ultrasound assisted transesterification process. Physiochemical characteristics—including density, viscosity, calorific value, acid value, and oxidation stability—were measured and the free fatty acid composition was determined via GCMS. Response surface methodology (RSM) and artificial neural network (ANN) techniques were utilized for the sake of relation development among operating parameters (reaction time, methanol-to-oil ratio, and catalyst concentration) ultimately optimizing yield of palm–cotton oil sourced biodiesel. Maximum yield of P60C40 biodiesel estimated via RSM and ANN was 96.41% and 96.67% respectively, under operating parameters of reaction time (35 min), M:O molar ratio (47.5 v/v %), and catalyst oncentration (1 wt%), but the actual biodiesel yield obtained experimentally was observed 96.32%. The quality of the RSM model was examined by analysis of variance (ANOVA). ANN model statistics exhibit contented values of mean square error (MSE) of 0.0001, mean absolute error (MAE) of 2.1374, and mean absolute deviation (MAD) of 2.5088. RSM and ANN models provided a coefficient of determination (R2) of 0.9560 and a correlation coefficient (R) of 0.9777 respectively.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Sustainability (Switzerland)
dc.relation.isbasedon	10.3390/su14106130
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	12 Built Environment and Design
dc.title	Response Surface Methodology and Artificial Neural Networks-Based Yield Optimization of Biodiesel Sourced from Mixture of Palm and Cotton Seed Oil
dc.type	Journal Article
utslib.citation.volume	14
utslib.for	12 Built Environment and Design
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
pubs.organisational-group	/University of Technology Sydney/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment
utslib.copyright.status	open_access	*
dc.date.updated	2023-04-11T04:04:28Z
pubs.issue	10
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	10

Abstract:

In this present study, cold flow properties of biodiesel produced from palm oil were improved by adding cotton seed oil into palm oil. Three different mixtures of palm and cotton oil were prepared as P50C50, P60C40, and P70C30. Among three oil mixtures, P60C40 was selected for biodiesel production via ultrasound assisted transesterification process. Physiochemical characteristics—including density, viscosity, calorific value, acid value, and oxidation stability—were measured and the free fatty acid composition was determined via GCMS. Response surface methodology (RSM) and artificial neural network (ANN) techniques were utilized for the sake of relation development among operating parameters (reaction time, methanol-to-oil ratio, and catalyst concentration) ultimately optimizing yield of palm–cotton oil sourced biodiesel. Maximum yield of P60C40 biodiesel estimated via RSM and ANN was 96.41% and 96.67% respectively, under operating parameters of reaction time (35 min), M:O molar ratio (47.5 v/v %), and catalyst oncentration (1 wt%), but the actual biodiesel yield obtained experimentally was observed 96.32%. The quality of the RSM model was examined by analysis of variance (ANOVA). ANN model statistics exhibit contented values of mean square error (MSE) of 0.0001, mean absolute error (MAE) of 2.1374, and mean absolute deviation (MAD) of 2.5088. RSM and ANN models provided a coefficient of determination (R2) of 0.9560 and a correlation coefficient (R) of 0.9777 respectively.

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http://hdl.handle.net/10453/169569