Novel and robust machine learning model to optimize biodiesel production from algal oil using CaO and CaO/Al2O3 as catalyst: Sustainable green energy
Yan Liu, Biju Theruvil Sayed, R. Sivaraman, Saad M. Alshahrani, Kumar Venkatesan, Kamal Y. Thajudeen, Mohammed Al-Bahrani, Salema K. Hadrawi, Ghulam Yasin
Abstract
In recent years, significant endeavors have been made to develop environmentally friendly fuels due to the detrimental effects of fossil fuels on ecosystem such as global warming, acid rain and air pollution. Alternative biodiesel transport fuel (ABTF) has shown their noteworthy potential of application due to having significant advantages such as negligible toxicity and excellent biodegradability. In this paper, Neural Network-based approaches were employed to create predictions in this work, including Multilayer Perceptron, Boosted Multilayer Perceptron, and Bagging Multilayer Perceptron. The regression issue has three input features: Reaction duration, catalyst amount, and methanol/oil ratio, and the only output is FAME yield. All three versions of these neural network models were tuned using their critical hyper-parameters and chose the optimal mix. Then, some standard measures are used to evaluate their performance. Multilayer perceptron, Boosted Multilayer perceptron, and Bagging Multilayer perceptron has error rates of 0.998, 0.998, and 0.877, respectively, and have MSE errors of 2.87, 1.19, and 5.57. Additionally, considering the MAPE 1.51E−02, 1.09E−02, and 2.26E−02 values acquired. Finally, the boosted multilayer perceptron is the most general and accurate model. Additionally, the optimal output value is 98.99 when the input vector is (x1 = 158, x2 = 1.25, x3 = 33.75).