Optimizing Rubber Seed Oil Extraction for Biodiesel Production Using Machine Learning Tools: A Comparative Study of Response Surface Methodology and Artificial Neural Networks
نویسندگان
1 Department of Chemical Engineering, Federal University of Technology, Owerri, Nigeria
2 Department of Geology ,Voronezh State University, Russia
3 People’s Friendship University Of Russia Oil & Gas Engineering, Russia
4 Department of Mathematics, Emory University, Atlanta, United States
5 Department of Chemical Engineering, Federal University of Technology, Minna, Nigeria
6 Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD 20742, USA
7 Department of Electrical and Electronic engineering. Federal University of Technology, Akure, Nigeria
8 Department of Oil and Gas Transport and Refinery Operation Engineering, Kazan National Research Technological University, Russia
9 Department of Electrical Electronics Engineering, Kent State University, USA
10 Department of Mechatronics Engineering and Robotics, MIREA Russian Technological University, Moscow, Russia
11 Department of Data Science, National University of Science and Technology, Russia
12 Department of Anatomy, Nnamdi Azikiwe University, Nigeria
13 Department of biochemistry, University of Nigeria, Nsukka, Nigeria
14 Department of Chemical Engineering, Federal University of Technology, Owerri, Nigeria
15 Department of Mechanical Engineering, Federal University of Petroleum Resources Effurun, Delta State, Nigeria
16 Department of Mathematics, Federal University of Technology, Minna, Nigeria
doi
10.48309/pcbr.2025.510277.1399چکیده
The efficiency of extracting oil from oil-bearing seeds is significantly affected by various process conditions, making optimization essential. This study utilizes the Box-Behnken Design (BBD) to examine how solvent volume, sample weight, and particle size influence rubber seed oil yield during batch-mode solvent extraction using n-hexane. The optimization process was carried out using both Response Surface Methodology (RSM) and an Artificial Neural Network (ANN). A quadratic model developed through RSM estimated the oil yield based on these key factors. For ANN modeling, the optimal structure was identified as a Multilayer Full Feed Forward (MFFF) network trained using the Quick Propagation (QP) learning algorithm. The hyperbolic tangent (Tanh) function served as the best activation function for both hidden and output layers. The ANN architecture included three input neurons, three hidden neurons, and one output neuron. According to the RSM model, the highest predicted oil yield was 56.57% under the conditions of 294.47 ml solvent volume, 10 g sample weight, and 1 mm particle size. Meanwhile, the ANN model estimated a maximum yield of 55.46% with a solvent volume of 300 ml under similar conditions. A comparative assessment revealed that ANN performed better than RSM, achieving a higher coefficient of determination (R² = 0.9998) and a lower Root Mean Square Error (RMSE = 0.3050), whereas RSM resulted in R² = 0.9789 and RMSE = 0.7035. These findings indicate that ANN provides superior accuracy and reliability in modeling and optimizing the impact of process parameters on rubber seed oil yield.
