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Optimization of methyl ester production from Prunus Amygdalus seed oil using response surface methodology and Artificial Neural Networks

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  • Esonye, Chizoo
  • Onukwuli, Okechukwu Dominic
  • Ofoefule, Akuzuo Uwaoma

Abstract

This research work investigated the optimization of biodiesel production from Sweet Almond (Prunus amygdalus) Seed oil (SASO) using Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) models through base (NaOH) transesterification. The Central Composite Design (CCD) optimization conditions were temperature (30 °C to 70 °C), catalyst concentration (0.5%w/w to 2.5% w/w), reaction time (45 min–65 min) and oil/methanol molar ratio (1:3 mol/mol to 1:7 mol/mol). The physico-chemical properties of the seed oil and the methyl ester were carried out using standard methods. The fatty acids were determined using GC-MS and characterized using FT-IR techniques. An optimized biodiesel yield of 94.36% from the RSM and 95.45% from the ANN models respectively were obtained at catalyst concentration of 1.5w/w%, reaction time of 65 min, oil/methanol molar ratio of 1:5 mol/mol and temperature of 50 °C. The quality of the RSM model was analyzed using Analysis of Variance (ANOVA). Model statistics of the ANN showed comfortable values of Mean Squared Error (MSE) of 6.005, Mean Absolute Error (MAE) of 2.786 and Mean Absolute Deviation (MAD) of 1.89306. The RSM and ANN models gave coefficient of determination (R2) of 0.9446 and correlation coefficient (R) of 0.96637 respectively.

Suggested Citation

  • Esonye, Chizoo & Onukwuli, Okechukwu Dominic & Ofoefule, Akuzuo Uwaoma, 2019. "Optimization of methyl ester production from Prunus Amygdalus seed oil using response surface methodology and Artificial Neural Networks," Renewable Energy, Elsevier, vol. 130(C), pages 61-72.
    • Handle: RePEc:eee:renene:v:130:y:2019:i:c:p:61-72
    DOI: 10.1016/j.renene.2018.06.036
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    References listed on IDEAS

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    1. Shahid, Ejaz M. & Jamal, Younis, 2011. "Production of biodiesel: A technical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 4732-4745.
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    3. Yuan, Xingzhong & Liu, Jia & Zeng, Guangming & Shi, Jingang & Tong, Jingyi & Huang, Guohe, 2008. "Optimization of conversion of waste rapeseed oil with high FFA to biodiesel using response surface methodology," Renewable Energy, Elsevier, vol. 33(7), pages 1678-1684.
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    4. Ning, Yilin & Niu, Shengli & Wang, Yongzheng & Zhao, Jianli & Lu, Chunmei, 2021. "Sono-modified halloysite nanotube with NaAlO2 as novel heterogeneous catalyst for biodiesel production: Optimization via GA_BP neural network," Renewable Energy, Elsevier, vol. 175(C), pages 391-404.
    5. Yang, Xuanmin & Kang, Kang & Qiu, Ling & Zhao, Lixin & Sun, Renhua, 2020. "Effects of carbonization conditions on the yield and fixed carbon content of biochar from pruned apple tree branches," Renewable Energy, Elsevier, vol. 146(C), pages 1691-1699.
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    7. Adama, K.K. & Aluyor, E.O. & K, Audu T.O., 2021. "Component distribution associated with phase separation and purification of tropical almond biodiesel at different temperatures," Renewable Energy, Elsevier, vol. 165(P1), pages 67-76.
    8. Farzaneh-Gord, Mahmood & Mohseni-Gharyehsafa, Behnam & Arabkoohsar, Ahmad & Ahmadi, Mohammad Hossein & Sheremet, Mikhail A., 2020. "Precise prediction of biogas thermodynamic properties by using ANN algorithm," Renewable Energy, Elsevier, vol. 147(P1), pages 179-191.
    9. Zhao Xue & Jun Fu & Qiankun Fu & Xiaokang Li & Zhi Chen, 2023. "Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach," Agriculture, MDPI, vol. 13(10), pages 1-16, September.

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