Optimized conversion of waste vegetable oil to biofuel with Meta heuristic methods and design of experiments

Authors

  • Van Huong Dong Institute of Mechanical Engineering, Ho Chi Minh University of Transport, Ho Chi Minh City, Vietnam
  • Prabhakar Sharma Department of Mechanical Engineering, Delhi Skill and Entrepreneurship University, Delhi 110089, India

DOI:

https://doi.org/10.61435/jese.2023.4

Abstract

Biodiesel generated from waste cooking oil (WCO) shows enormous potential for accomplishing SDGs and embracing circular economy principles. This strategy coincides with SDGs 7 and 12, which promote clean energy along with ethical consumerism, by converting waste cooking oil into biofuel. It reduces dependency on fossil fuels, reduces emissions, and promotes sustainable energy sources. Furthermore, using WCO biodiesel adheres to the circular economy concept, reducing waste and pollution while conserving resources (SDGs 12, 14, and 15). To optimize this process, a hybrid technique comprising RSM, ANOVA, and particle swarm optimization is being explored. Researchers achieved 90% biodiesel production employing this technology, encouraging both eco-friendly energy and resource-efficient practices. The optimized parameters produced remarkable results: 82.98% biodiesel generation with a reaction time of 101 minutes, 2% catalyst, and a methanol-to-oil ratio of 20%, demonstrating the potential of this integrated strategy. 

Keywords:

Biofuels, sustainability, meta-heuristic optimization, alternative fuel

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

Azad, A. S., A. Rahaman, M. S., Watada, J., Vasant, P., & Vintaned, J. A. G. (2020). Optimization of the hydropower energy generation using Meta-Heuristic approaches: A review. Energy Reports, 6, 2230–2248. https://doi.org/10.1016/j.egyr.2020.08.009

Bahiraei, M., Nazari, S., & Safarzadeh, H. (2021). Modeling of energy efficiency for a solar still fitted with thermoelectric modules by ANFIS and PSO-enhanced neural network: A nanofluid application. Powder Technology, 385, 185–198. https://doi.org/10.1016/j.powtec.2021.03.001

Beccarello, M., & Di Foggia, G. (2022). Sustainable Development Goals Data-Driven Local Policy: Focus on SDG 11 and SDG 12. Administrative Sciences, 12(4), 167. https://doi.org/10.3390/admsci12040167

Chen, L., Duan, L., Shi, Y., & Du, C. (2020). PSO_LSSVM Prediction Model and Its MATLAB Implementation. IOP Conference Series: Earth and Environmental Science, 428(1), 012089. https://doi.org/10.1088/1755-1315/428/1/012089

Cozzi, L., Ferroukhi, R., Souza, L., Portale, E., & Adair-Rohani, H. (2022). Tracking SDG7: The energy progress report 2022. https://iea.blob.core.windows.net/assets/37fb9f89-71de-407f-8ff4-12f46ec20a16/TrackingSDG7TheEnergyProgressReport2022.pdf

Degfie, T. A., Mamo, T. T., & Mekonnen, Y. S. (2019). Optimized Biodiesel Production from Waste Cooking Oil (WCO) using Calcium Oxide (CaO) Nano-catalyst. Scientific Reports, 9(1), 18982. https://doi.org/10.1038/s41598-019-55403-4

Dimitriou, P., Peng, Z., Lemon, D., Gao, B., & Soumelidis, M. (2013, September 8). Diesel Engine Combustion Optimization for Bio-Diesel Blends Using Taguchi and ANOVA Statistical Methods. https://doi.org/10.4271/2013-24-0011

El-Gendy, N. S., Ali, B. A., Abu Amr, S. S., Aziz, H. A., & Mohamed, A. S. (2016). Application of D-optimal design and RSM to optimize the transesterification of waste cooking oil using natural and chemical heterogeneous catalyst. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 38(13), 1852–1866. https://doi.org/10.1080/15567036.2014.967417

Falowo, O. A., Apanisile, O. E., Aladelusi, A. O., Adeleke, A. E., Oke, M. A., Enamhanye, A., Latinwo, L. M., & Betiku, E. (2021). Influence of nature of catalyst on biodiesel synthesis via irradiation-aided transesterification of waste cooking oil-honne seed oil blend: Modeling and optimization by Taguchi design method. Energy Conversion and Management: X, 12, 100119. https://doi.org/10.1016/j.ecmx.2021.100119

Hariram, V., C, D., K, E. M., K, A., A, M. F., Seralathan, S., K. L., V., & Micha Premkumar, T. (2021). Performance assessment of artificial neural network on the prediction of Calophyllum inophyllum biodiesel through two-stage transesterification. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 43(9), 1060–1072. https://doi.org/10.1080/15567036.2019.1634164

Hosseinzadeh-Bandbafha, H., Nizami, A.-S., Kalogirou, S. A., Gupta, V. K., Park, Y.-K., Fallahi, A., Sulaiman, A., Ranjbari, M., Rahnama, H., Aghbashlo, M., Peng, W., & Tabatabaei, M. (2022). Environmental life cycle assessment of biodiesel production from waste cooking oil: A systematic review. Renewable and Sustainable Energy Reviews, 161, 112411. https://doi.org/10.1016/j.rser.2022.112411

Houssein, E. H., Elaziz, M. A., Oliva, D., & Abualigah, L. (2022). Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems. Springer.

Jayaraman, J., Dawn, S. S., Appavu, P., Mariadhas, A., Joy, N., Alshgari, R. A., Karami, A. M., Huong, P. T., Rajasimmam, M., & Kumar, J. A. (2022). Production of biodiesel from waste cooking oil utilizing zinc oxide nanoparticles combined with tungsto phosphoric acid as a catalyst and its performance on a CI engine. Fuel, 329, 125411. https://doi.org/10.1016/j.fuel.2022.125411

Jume, B. H., Gabris, M. A., Rashidi Nodeh, H., Rezania, S., & Cho, J. (2020). Biodiesel production from waste cooking oil using a novel heterogeneous catalyst based on graphene oxide doped metal oxide nanoparticles. Renewable Energy, 162, 2182–2189. https://doi.org/10.1016/j.renene.2020.10.046

Mahmood Khan, H., Iqbal, T., Haider Ali, C., Javaid, A., & Iqbal Cheema, I. (2020). Sustainable biodiesel production from waste cooking oil utilizing waste ostrich (Struthio camelus) bones derived heterogeneous catalyst. Fuel, 277, 118091. https://doi.org/10.1016/j.fuel.2020.118091

Moyo, L. B., Iyuke, S. E., Muvhiiwa, R. F., Simate, G. S., & Hlabangana, N. (2021). Application of response surface methodology for optimization of biodiesel production parameters from waste cooking oil using a membrane reactor. South African Journal of Chemical Engineering, 35, 1–7. https://doi.org/10.1016/j.sajce.2020.10.002

Mulligan, M., van Soesbergen, A., Hole, D. G., Brooks, T. M., Burke, S., & Hutton, J. (2020). Mapping nature’s contribution to SDG 6 and implications for other SDGs at policy relevant scales. Remote Sensing of Environment, 239, 111671. https://doi.org/10.1016/j.rse.2020.111671

Ramachander, J., Gugulothu, S. K., Sastry, G. R. K., Kumar Panda, J., & Surya, M. S. (2021). Performance and emission predictions of a CRDI engine powered with diesel fuel: A combined study of injection parameters variation and Box-Behnken response surface methodology based optimization. Fuel, 290, 120069. https://doi.org/10.1016/j.fuel.2020.120069

Sulaiman, N. F., Ramly, N. I., Abd Mubin, M. H., & Lee, S. L. (2021). Transition metal oxide (NiO, CuO, ZnO)-doped calcium oxide catalysts derived from eggshells for the transesterification of refined waste cooking oil. RSC Advances, 11(35), 21781–21795. https://doi.org/10.1039/D1RA02076E

Wong, S. F., Tiong, A. N. T., & Chin, Y. H. (2023). Pre-treatment of waste cooking oil by combined activated carbon adsorption and acid esterification for biodiesel synthesis via two-stage transesterification. Biofuels, 1–11. https://doi.org/10.1080/17597269.2023.2196804

Yu, M., Kubiczek, J., Ding, K., Jahanzeb, A., & Iqbal, N. (2022). Revisiting SDG-7 under energy efficiency vision 2050: the role of new economic models and mass digitalization in OECD. Energy Efficiency, 15(1), 2. https://doi.org/10.1007/s12053-021-10010-z

Zhao, Y., Wang, C., Zhang, L., Chang, Y., & Hao, Y. (2021). Converting waste cooking oil to biodiesel in China: Environmental impacts and economic feasibility. Renewable and Sustainable Energy Reviews, 140, 110661. https://doi.org/10.1016/j.rser.2020.11066

Downloads

Published

2023-09-02

How to Cite

Dong, V. H., & Sharma, P. (2023). Optimized conversion of waste vegetable oil to biofuel with Meta heuristic methods and design of experiments . Journal of Emerging Science and Engineering, 1(1), 22–28. https://doi.org/10.61435/jese.2023.4

Issue

Section

Articles

Most read articles by the same author(s)