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The optimization of biodiesel production from transesterification of sesame oil via applying ultrasound-assisted techniques: comparison of RSM and ANN–PSO hybrid model

  • Hadi Soltani , Asadollah Karimi ORCID logo EMAIL logo and Sahar Falahatpisheh
Published/Copyright: September 28, 2020
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Chemical Product and Process Modeling
From the journal Chemical Product and Process Modeling Volume 17 Issue 1

Abstract

Due to the finite source of fossil fuels and their high emissions, it is remarkable to recognize appropriate ways to produce alternative fuels with less pollution. In this paper, the production of biodiesel (fatty acid methyl ester) from transesterification of methanol with sesame oil under ultrasound-assisted waves (using a homogeneous sodium hydroxide catalyst) was investigated. In addition, the optimization and prediction of biodiesel production was studied and compared with the two methods of response surface methodology (RSM) and the combined model of artificial neural network (ANN) – particle swarm algorithm (PSO). The central composite design (CCD) was used to investigate the effect of independent variables (methanol/oil molar ratio, catalyst percentage, reaction time and temperature) on the yield of biodiesel in Expert Design software. Analysis of experimental results was performed using RSM and ANN–PSO hybrid methods and also the optimal conditions for maximizing the yield were calculated. The highest yield of biodiesel predicted by RSM and ANN–PSO were 87.4 and 90.58%, respectively. RSM and ANN–PSO hybrid models were compared based on least squared errors statistically. The correlation coefficients in the RSM and ANN–PSO hybrid models were 0.959 and 0.999 respectively. While both models demonstrated a good agreement with actual results, but the ANN–PSO hybrid model had a powerful prediction for the optimal points over the RSM.

Keywords: artificial neural networks (ANN); optimization; particle swarm optimization (PSO); response surface methodology (RSM); ultrasound
Corresponding author: Asadollah Karimi, Department of Chemical Engineering, Faculty of Engineering, University of Maragheh, Maragheh, Islamic Republic of Iran, E-mail:

Acknowledgment

The authors would like to thank Dr. Amir Heidari for their cooperation.

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Received: 2020-07-28
Accepted: 2020-09-09
Published Online: 2020-09-28

© 2020 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Optimal tuning of PID controller in time delay system: a review on various optimization techniques
  4. Pareto domain: an invaluable source of process information
  5. The optimization of biodiesel production from transesterification of sesame oil via applying ultrasound-assisted techniques: comparison of RSM and ANN–PSO hybrid model
  6. Process simulation for the production of methanol via CO2 reforming of methane route
  7. Nitrate removal studies on polyurea membrane using nanofiltration system – membrane characterization and model development
https://doi.org/10.1515/cppm-2020-0076
Keywords for this article
artificial neural networks (ANN); optimization; particle swarm optimization (PSO); response surface methodology (RSM); ultrasound

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Optimal tuning of PID controller in time delay system: a review on various optimization techniques
  4. Pareto domain: an invaluable source of process information
  5. The optimization of biodiesel production from transesterification of sesame oil via applying ultrasound-assisted techniques: comparison of RSM and ANN–PSO hybrid model
  6. Process simulation for the production of methanol via CO2 reforming of methane route
  7. Nitrate removal studies on polyurea membrane using nanofiltration system – membrane characterization and model development
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