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).
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•Biodiesel production optimization from algal oil.•Development of novel optimization method for green renewable energy.•Determination of effects of process parameters on the biofuel yield.