Comparative Analysis of Nature Inspired Optimization Algorithms for Biodiesel Yield Prediction using Extreme Learning Machine

This study aims to enhance biodiesel yield prediction's precision by applying the advanced Extreme Learning Machine (ELM) model. The optimisation algorithms include Cuckoo Search, Dragonfly Optimization, Firefly Optimization, and Fruitfly Optimization to improve the predictive efficiency of the ELM model. Two datasets are investigated: biodiesel from Ceiba pentandra oil and the bio-oil yield using a Box-Behnken method for the experiment design. Optimisation approaches find the optimal hidden size of the ELM model. The results obtained from the first dataset reveal that Dragonfly Optimization was found to be the most effective algorithm, exhibiting the lowest Root Mean Square Error (RMSE) of 0.098 for Dataset I and 0.000306 for Dataset II and a strong relationship between predicted and actual values with a high coefficient of determination (R-squared) of 0.99 in both datasets. Firefly Optimization achieved exceptional accuracy for the second dataset with a hidden size of 28. High coefficient of determination values, low RMSE, and Mean Square Error (MSE) values highlight the predictive precision of Firefly Optimization. The comparative analysis underscores the potential of optimisation methods to significantly enhance biodiesel yield estimates, with Firefly Optimization and Dragonfly Optimization emerging as standout performers for their respective datasets. This study contributes valuable insights into biodiesel production by showcasing the efficiency of optimised ELM models in predicting biodiesel and bio-oil outputs.