Comparative Analysis of Machine Learning Models for Predicting and Optimizing Biodiesel Production Yield: A Study of Neural Networks, Random Forest, and Decision Tree Algorithms | ||
| Journal of Data Analytics and Intelligent Decision-making | ||
| مقاله 7، دوره 1، شماره 4، اسفند 2025، صفحه 116-127 اصل مقاله (963.58 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22091/jdaid.2026.14976.1031 | ||
| نویسنده | ||
| Hojjatollah Maghsoodloorad* | ||
| Department of Chemical and Petroleum Engineering, Fouman Faculty of Engineering, College of Engineering, University of Tehran , Tehran, Iran | ||
| چکیده | ||
| This study compares three machine learning algorithms (Multilayer Perceptron Neural Network (MLP), Random Forest (RF), and Decision Tree (DT)) for modeling biodiesel production. For this purpose the synthesis methods (UIMS, MS, FPUI, PUI), the methanol to oil ratio (3:1 to 15:1) and reaction times (5–50 minutes), were considered as input parameters and the percentage of biodiesel production was considered as the output of the model. According to the results, the MLP model demonstrated superior predictive performance, with an R² score of 0.9800, RMSE of 3.28, and MAE of 2.35, significantly outperforming RF (R² = 0.8892) and DT (R² = 0.8500). Also, the neural network model represents that all parameters (reaction time, methanol to oil ratio, and synthesis method) hold nearly equal importance. Based on the neural network model, the optimal synthesis conditions are: the UIMS method, a reaction time of 47 minutes, and a methanol-to-oil ratio of 5.8:1, yielding a predicted conversion of 98%. | ||
| کلیدواژهها | ||
| Biodiesel Production؛ Machine Learning؛ Neural Networks؛ Random Forest؛ Decision Tree؛ Process Optimization | ||
| مراجع | ||
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Arif, M., Alalawy, A. I., Zheng, Y., Koutb, M., Kareri, T., Salama, E.-S., & Li, X. (2025). Artificial intelligence and machine learning models application in biodiesel optimization process and fuel properties prediction. Sustainable Energy Technologies and Assessments, 73, 104097. doi:https://doi.org/10.1016/j.seta.2024.104097 Ayoola, A. A., Hymore, F. K., Omonhinmin, C. A., Olawole, O. C., Fayomi, O. S. I., Babatunde, D., & Fagbiele, O. (2019). Analysis of waste groundnut oil biodiesel production using response surface methodology and artificial neural network. Chemical Data Collections, 22, 100238. doi:https://doi.org/10.1016/j.cdc.2019.100238 Chen, C., Liang, R., Xia, S., Hou, D., Abdoulaye, B., Tao, J., . . . Chen, G. (2023). Fast characterization of biodiesel via a combination of ATR-FTIR and machine learning models. Fuel, 332, 126177. doi:https://doi.org/10.1016/j.fuel.2022.126177 Dobbelaere, M. R., Plehiers, P. P., Van de Vijver, R., Stevens, C. V., & Van Geem, K. M. (2021). Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats. Engineering, 7(9), 1201-1211. doi:https://doi.org/10.1016/j.eng.2021.03.019 Francisco Javier López-Flores, R. O.-B., Alma Yunuen Raya-Tapia, César Ramírez-Márquez, José Maria Ponce-Ortega. (2025). Machine Learning Tools for Chemical Engineering Methodologies and Applications: Elsevier Science. Gao, H., Zhu, L.-T., Luo, Z.-H., Fraga, M. A., & Hsing, I. M. (2022). Machine Learning and Data Science in Chemical Engineering. Industrial & Engineering Chemistry Research, 61(24), 8357-8358. doi:10.1021/acs.iecr.2c01788 Garg, A., & Jain, S. (2020). Process parameter optimization of biodiesel production from algal oil by response surface methodology and artificial neural networks. Fuel, 277, 118254. doi:https://doi.org/10.1016/j.fuel.2020.118254 Gautam, S., Kanakraj, S., & Henry, A. (2022). Computational approach using machine learning modelling for optimization of transesterification process for linseed biodiesel production. AIMS Bioeng, 9(4), 319–336. doi:doi:10.3934/bioeng.2022023 Ma, F., Clements, L. D., & Hanna, M. A. (1999). The effect of mixing on transesterification of beef tallow. Bioresource Technology, 69(3), 289-293. doi:https://doi.org/10.1016/S0960- 8524(98)00184-9 Moayedi, H., Aghel, B., Foong, L. K., & Bui, D. T. (2020). Feature validity during machine learning paradigms for predicting biodiesel purity. Fuel, 262, 116498. doi:https://doi.org/10.1016/j.fuel.2019.116498 Omojola Awogbemi, D. V. V. K. (2023). Application of machine learning technologies in biodiesel production process—A review. Frontiers in Energy Research, 11. doi:https://10.3389/fenrg.2023.1122638 Pawar, C., Shreeprakash, B., Mokshanatha, B., Nikam, K. C., Motgi, N., Jathar, L. D., . . . Abbas, M. (2025). Machine Learning-Based Assessment of the Influence of Nanoparticles on Biodiesel Engine Performance and Emissions: A critical review. Archives of Computational Methods in Engineering, 32(1), 499-533. doi:10.1007/s11831-024-10144- 0 Randell, J. M., Boon, L. M., Nguyen, H. H., Perez, J., Windham, C. D., & Cermak, D. M. (2025). Biodiesel Production, Analysis, and Combustion: An Across-the-Curriculum Laboratory. Journal of Chemical Education, 102(2), 765-775. doi:10.1021/acs.jchemed.4c00868 Selvaraj, R., Moorthy, I. G., Kumar, R. V., & Sivasubramanian, V. (2019). Microwave mediated production of FAME from waste cooking oil: Modelling and optimization of process parameters by RSM and ANN approach. Fuel, 237, 40-49. doi:https://doi.org/10.1016/j.fuel.2018.09.147 Sharma, P., Sahoo, B. B., Said, Z., Hadiyanto, H., Nguyen, X. P., Nižetić, S., . . . Li, C. (2023). Application of machine learning and Box-Behnken design in optimizing engine characteristics operated with a dual-fuel mode of algal biodiesel and waste-derived biogas. International Journal of Hydrogen Energy, 48(18), 6738-6760. doi:https://doi.org/10.1016/j.ijhydene.2022.04.152 Srivastava, G., Paul, A. K., & Goud, V. V. (2018). Optimization of non-catalytic transesterification of microalgae oil to biodiesel under supercritical methanol condition. Energy Conversion and Management, 156, 269-278. doi:https://doi.org/10.1016/j.enconman.2017.10.093 Xing, Y., Zheng, Z., Sun, Y., & Agha Alikhani, M. (2021). A Review on Machine Learning Application in Biodiesel Production Studies. International Journal of Chemical Engineering, 2021(1), 2154258. doi:https://doi.org/10.1155/2021/2154258 Yin, X., Ma, H., You, Q., Wang, Z., & Chang, J. (2012). Comparison of four different enhancing methods for preparing biodiesel through transesterification of sunflower oil. Applied Energy, 91(1), 320-325. doi:https://doi.org/10.1016/j.apenergy.2011.09.016 | ||
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