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; Sahar Falahatpisheh

doi:10.1515/cppm-2020-0076

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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 and Sahar Falahatpisheh

Published/Copyright: September 28, 2020

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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: a.karimi@maragheh.ac.ir

Acknowledgment

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

Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

1. Krishnamurthy, KN, Sridhara, SN, Kumar, CA. Synthesis and optimization of Hydnocarpus wightiana and dairy waste scum as feed stock for biodiesel production by using response surface methodology. Energy 2018;153:1073–86. https://doi.org/10.1016/j.energy.2018.04.068.Search in Google Scholar

2. Khan, HM, Ali, CH, Iqbal, T, Yasin, S, Sulaiman, M, Mahmood, H, et al. Current scenario and potential of biodiesel production from waste cooking oil in Pakistan: an overview. Chin J Chem Eng 2019;27:2238–50. https://doi.org/10.1016/j.cjche.2018.12.010.Search in Google Scholar

3. Balamurugan, T, Arun, A, Sathishkumar, GB. Biodiesel derived from corn oil–a fuel substitute for diesel. Renew Sustain Energy Rev 2018;94:772–8. https://doi.org/10.1016/j.rser.2018.06.048.Search in Google Scholar

4. Selvaraj, R, Praveenkumar, R, Moorthy, IG. A comprehensive review of biodiesel production methods from various feedstocks. Biofuels 2019;10:325–33. https://doi.org/10.1080/17597269.2016.1204584.Search in Google Scholar

5. Sørensen, B, Breeze, P, Suppes, GJ, El Bassam, N, Silveira, S, Yang, ST, et al. Renewable energy focus e-Mega handbook. Netherlands: Academic Press; 2008.Search in Google Scholar

6. Deng, X, Han, J, Yin, F. Net energy, CO2 emission and land-based cost-benefit analyses of Jatropha biodiesel: a case study of the Panzhihua region of Sichuan province in China. Energies 2012;5:2150–64. https://doi.org/10.3390/en5072150.Search in Google Scholar

7. Chavan, SB, Yadav, M, Singh, R, Singh, V, Kumbhar, RR, Sharma, YC. Production of biodiesel from three indigenous feedstock: optimization of process parameters and assessment of various fuel properties. Environ Prog Sustain Energy 2017;36:788–95. https://doi.org/10.1002/ep.12606.Search in Google Scholar

8. Sarve, A, Sonawane, SS, Varma, MN. Ultrasound assisted biodiesel production from sesame (Sesamum indicum L.) oil using barium hydroxide as a heterogeneous catalyst: comparative assessment of prediction abilities between response surface methodology (RSM) and artificial neural network (ANN). Ultrason Sonochem 2015;26:218–289. https://doi.org/10.1016/j.ultsonch.2015.01.013.Search in Google Scholar PubMed

9. Raoufi, Z, Gargari, SL. Biodiesel production from microalgae oil by lipase from Pseudomonas aeruginosa displayed on yeast cell surface. Biochem Eng J 2018;140:1–8. https://doi.org/10.1016/j.bej.2018.09.008.Search in Google Scholar

10. Mujtaba, MA, Cho, HM, Masjuki, HH, Kalam, MA, Ong, HC, Gul, M, et al. Critical review on sesame seed oil and its methyl ester on cold flow and oxidation stability. Energy Rep 2020;6:40–54. https://doi.org/10.1016/j.egyr.2019.11.160.Search in Google Scholar

11. Karimi, M, Jenkins, B, Stroeve, P. Multi‐objective optimization of trans esterification in biodiesel production catalyzed by immobilized lipase. Biofuels, Bioprod Biorefining 2016;10:804–18. https://doi.org/10.1002/bbb.1706.Search in Google Scholar

12. Mujtaba, MA, Masjuki, HH, Kalam, MA, Ong, HC, Gul, M, Farooq, M, et al. Ultrasound-assisted process optimization and tribological characteristics of biodiesel from palm-sesame oil via response surface methodology and extreme learning machine-cuckoo search. Renew Energy 2020;158:202–14. https://doi.org/10.1016/j.renene.2020.05.158.Search in Google Scholar

13. Knothe, G, Krahl, J, Van Gerpen, J, editors. The biodiesel handbook. Elsevier; 2015.Search in Google Scholar

14. López, BC, Cerdán, LE, Medina, AR, López, EN, Valverde, LM, Peña, EH, et al. Production of biodiesel from vegetable oil and microalgae by fatty acid extraction and enzymatic esterification. J Biosci Bioeng 2015;119:706–11. https://doi.org/10.1016/j.jbiosc.2014.11.002.Search in Google Scholar PubMed

15. Xu, H, Zeiger, BW, Suslick, K S. Sonochemical synthesis of nanomaterials. Chem Soc 2013;4:2555–67. https://doi.org/10.1039/c2cs35282f.Search in Google Scholar PubMed

16. Alper Tapan, N, Yıldırım, R, Erdem Günay, M. Analysis of past experimental data in literature to determine conditions for high performance in biodiesel production. Biofuels, Bioprod Biorefining 2016;10:422–34. https://doi.org/10.1002/bbb.1650.Search in Google Scholar

17. Teoh, YP, Don, MM, Ujang, S. Application of box-behnken design to the extraction of flavonoid fraction of Schizophyllum commune and the empirical kinetic study. Chem Prod Process Model 2012;7:1–20 https://doi.org/10.1515/1934-2659.1654.Search in Google Scholar

18. Uzoh, CF, Onukwuli, OD. Application of sinusoidal function and a 25–1 fractional factorial array in the kinetics and optimization study of gmelina seed oil modified alkyd resin synthesis. Chem Prod Process Model 2016;12:1–18 https://doi.org/10.1515/cppm-2016-0003.Search in Google Scholar

19. Kowthaman, CN, Varadappan, AM. Synthesis, characterization, and optimization of Schizochytrium biodiesel production using Na+‐doped nanohydroxyapatite. Int J Energy Res 2019;43:3182–200. https://doi.org/10.1002/er.4387.Search in Google Scholar

20. Whiteman, JK, Kana, EG. Comparative assessment of the artificial neural network and response surface modelling efficiencies for biohydrogen production on sugar cane molasses. Bio Energy Research 2014;7:295–305. https://doi.org/10.1007/s12155-013-9375-7.Search in Google Scholar

21. Māliņš, K, Kampars, V, Brinks, J, Rusakova, T, Ābelniece, Z. The factors affecting the rate of preparation and the content of rapeseed oil methyl esters by using sodium methylate as a catalyst. Rigas Tehniskas Universitates Zinatniskie. Raksti 2012;25:9.Search in Google Scholar

22. Maran, JP, Priya, B. Comparison of response surface methodology and artificial neural network approach towards efficient ultrasound-assisted biodiesel production from muskmelon oil. Ultrason Sonochem 2015;23:192–200. https://doi.org/10.1016/j.ultsonch.2014.10.019.Search in Google Scholar PubMed

23. Gunawan, S, Wasista, HW, Kuswandi, K, Widjaja, A, Ju, YH. The utilization of Xylocarpus moluccensis seed oil as biodiesel feedstock in Indonesia. Ind Crop Prod 2014;52:286–91. https://doi.org/10.1016/j.indcrop.2013.10.039.Search in Google Scholar

24. Avramović, JM, Veličković, AV, Stamenković, OS, Rajković, KM, Milić, PS, Veljković, VB. Optimization of sunflower oil ethanolysis catalyzed by calcium oxide: RSM versus ANN-GA. Energy Convers Manag 2015;105:1149–56. https://doi.org/10.1016/j.enconman.2015.08.072.Search in Google Scholar

25. Gupta, J, Agarwal, M, Dalai, AK. Optimization of biodiesel production from mixture of edible and nonedible vegetable oils. Biocatal Agric Biotechnol 2016;8:112–20. https://doi.org/10.1016/j.bcab.2016.08.014.Search in Google Scholar

26. Omkaresh, BR, Suresh, R, Yatish, KV. Optimization of Annona squamosa oil biodiesel production by using response surface methodology. Biofuels 2017;8:377–82. https://doi.org/10.1080/17597269.2016.1231957.Search in Google Scholar

27. Quah, RV, Tan, YH, Mubarak, NM, Khalid, M, Abdullah, EC, Nolasco-Hipolito, C. An overview of biodiesel production using recyclable biomass and non-biomass derived magnetic catalysts. J Environ Chem Eng 2019;7:103219. https://doi.org/10.1016/j.jece.2019.103219.Search in Google Scholar

28. Karimi, A, Fatehifar, E, Alizadeh, R, Soltani, H. Kinetic study of the regeneration of spent caustic via the genetic algorithm method. Environ Health Eng Manag J 2018;5:231–9. https://doi.org/10.15171/ehem.2018.31.Search in Google Scholar

29. Karimi, A, Fatehifar, E, Alizadeh, R, Ahadzadeh, I. Regeneration of spent caustic of olefin unit in a bubble column reactor: treatment and recovery optimization. Environ Prog Sustain Energy 2017;36:341–7. https://doi.org/10.1002/ep.12433.Search in Google Scholar

30. Leardi, R. Experimental design in chemistry. a tutorial. Anal Chim Acta 2009;652:161–72. https://doi.org/10.1016/j.aca.2009.06.015.Search in Google Scholar PubMed

31. Montgomery, DC. Statistical quality control. Wiley Global Education; 2012.Search in Google Scholar

32. Köksoy, O, Doganaksoy, N. Joint optimization of mean and standard deviation using response surface methods. J Qual Technol 2003;35:239–52. https://doi.org/10.1080/00224065.2003.11980218.Search in Google Scholar

33. Dai, YM, Kao, IH, Chen, CC. Evaluating the optimum operating parameters of biodiesel production process from soybean oil using the Li2TiO3 catalyst. J Taiwan Inst Chem Eng 2017;70:260–6. https://doi.org/10.1016/j.jtice.2016.11.001.Search in Google Scholar

34. Al‐Sakkari, EG, El‐Sheltawy, ST, Abadir, MF, Attia, NK, El‐Diwani, G. Investigation of cement kiln dust utilization for catalyzing biodiesel production via response surface methodology. Int J Energy Res 2017;41:593–603. https://doi.org/10.1002/er.3635.Search in Google Scholar

35. Rukhaiyar, S, Alam, MN, Samadhiya, NK. A ANN-PSO hybrid model for predicting factor of safety of slope. Int J Geotech Eng 2018;12:556–66. https://doi.org/10.1080/19386362.2017.1305652.Search in Google Scholar

36. Aghaeinejad-Meybodi, A, Ebadi, A, Shafiei, S, Khataee, AR, Rostampour, M. Modeling and optimization of antidepressant drug fluoxetine removal in aqueous media by ozone/H2O2 process: comparison of central composite design and artificial neural network approaches. J Taiwan Inst Chem Eng 2015;48:40–8. https://doi.org/10.1016/j.jtice.2014.10.022.Search in Google Scholar

37. Karimi, A, Soltani, H, Hasanzadeh, A. An analysis of increasing the purity of ethylene production in the ethylene fractionation column by the genetic algorithm. Chem Prod Process Model 2019;15:20190088. https://doi.org/10.1515/cppm-2019-0088.Search in Google Scholar

38. Edgar, TF, Himmelblau, DM, Lasdon, S. Optimization of chemical processes. NewYork: McGraw-Hill; 2001.Search in Google Scholar

39. Bemani, A, Baghban, A, Mohammadi, AH. An insight into the modeling of sulfur content of sour gases in supercritical region. J Petrol Sci Eng 2020;184:106459. https://doi.org/10.1016/j.petrol.2019.106459.Search in Google Scholar

Received: 2020-07-28

Accepted: 2020-09-09

Published Online: 2020-09-28

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Articles in the same Issue

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