Số phát hành: Số 17 - Tháng 6 - 2026Hóa học - Công nghệ thực phẩm
Optimization of biodiesel production via palmitic acid esterification in excess methanol using a single-hidden-layer artificial neural network
Xuất bản: tháng 6 20, 2026
Tóm tắt
This study optimizes biodiesel production through the esterification of palmitic acid in excess methanol by integrating a single-hidden-layer Artificial Neural Network (ANN) with a Design of Experiments (DOE) approach. The optimal ANN architecture, comprising 29 TanH, 16 Linear, and 50 Gaussian neurons, was developed to model the relationship between three key process variables and fatty acid methyl ester (FAME) content. The ANN model exhibited superior predictive performance compared with conventional experimental methods, achieving a high coefficient of determination (R² = 0.9830) and a low root mean square error (RMSE = 0.2030). The prediction profiler identified a maximum FAME yield of 97.86%, demonstrating that the hybrid ANN–DOE model is a robust and reliable tool for optimizing and predicting process parameters in biodiesel synthesis.
Từ khóa
ANNbiodieselsolid catalystpalmitic acidmethanol
Tài liệu tham khảo
1.
Dang, T. H., & Chen, B. H. (2014). Application of response surface methodology to optimize biodiesel production from the esterification of palmitic acid in excess methanol. Journal of Science and Technology, 53(2), 10.
2.
Ferreira, S. L. C., Bruns, R. E., Ferreira, H. S., Matos, G. D., et al. (2007). Box-Behnken design: An alternative for the optimization of analytical methods. Analytica Chimica Acta, 597(2), 179–186.
3.
Gaurav, N., Sivasankari, S., Kiran, G. S., Ninawe, A., & Selvin, J. (2017). Utilization of bioresources for sustainable biofuels: A review. Renewable and Sustainable Energy Reviews, 73, 205–214.
4.
Hoseini, S. S., Najafi, G., Ghobadian, B., Mamat, R., Sidik, N. A. C., & Azmi, W. H. (2017). The effect of combustion management on diesel engine emissions fueled with biodiesel-diesel blends. Renewable and Sustainable Energy Reviews, 73, 307–331.
Klemeš, J. J., Varbanov, P. S., Pierucci, S., & Huisingh, D. (2010). Minimising emissions and energy wastage by improved industrial processes and integration of renewable energy. Journal of Cleaner Production, 18(9), 843–847.
7.
Mansir, N., Teo, S. H., Rashid, U., Saiman, M. I., Tan, Y. P., Alsultan, G. A., et al. (2018). Modified waste egg shell derived bifunctional catalyst for biodiesel production from high FFA waste cooking oil: A review. Renewable and Sustainable Energy Reviews, 82, 3645–3655.
8.
Melero, J. A., Iglesias, J., & Morales, G. (2009). Heterogeneous acid catalysts for biodiesel production: Current status and future challenges. Green Chemistry, 11(9), 1285–1308.
9.
Montgomery, D. C. (2005). Design and analysis of experiment (6th ed.). John Wiley & Sons.
10.
Prakash, N., Manikandan, S. A., Govindarajan, L., & Vijayagopal, V. (2008). Prediction of biosorption efficiency for the removal of copper(II) using artificial neural networks. Journal of Hazardous Materials, 152(3), 1268–1275.
11.
Sarve, A., Sonawane, S. S., & Varma, M. N. (2015). 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). Ultrasonics Sonochemistry, 26, 218–228.
12.
Shanmugaprakash, M., & Sivakumar, V. (2013). Development of experimental design approach and ANN-based models for determination of Cr(VI) ions uptake rate from aqueous solution onto the solid biodiesel waste residue. Bioresource Technology, 148, 550–559.
13.
Vicente, G., Martínez, M., & Aracil, J. (2007). Optimisation of integrated biodiesel production: Part I. A study of the biodiesel purity and yield. Bioresource Technology, 98(9), 1724–1733.
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