Cost and Density Evaluation Function Application, for Optimal Biodiesel Mixtures by Genetic Algorithm Implementation

Abstract

The current document presents a fresh method for addressing the optimization challenges concerning fuel mixtures in the production of Biodiesel. Given the rising concerns over diesel emissions and the associated expenses, there's a growing interest in exploring alternative fuel options. Traditional desulphurization methods are time-consuming and require substantial financial investments. Conversely, Biodiesel offers a promising solution as it's derived from renewable resources and is environmentally sustainable. This study introduces an enhanced genetic algorithm that assesses the proportions of components within a fuel mixture blend, aiming to create optimal combinations for Biodiesel production. Apart from cost considerations, the density of the fuel, a key physicochemical characteristic, is pivotal in determining its suitability for widespread use and commercialization. Rigorous experimentation has resulted in highly precise Biodiesel blends, suggesting an optimal fuel solution for each specific set. For instance, in Set 1, Biodiesel was composed of 75.031% diesel and 24.969% biodiesel, with a mixture cost of 1.6975 €/l and a density of 0.8355 g/ml. In Set 2, the fuel mixture consisted of 75.016% diesel and 24.984% biodiesel, with a cost of 1.6977 €/l and a density of 0.8366 g/ml. Notably, the new Biodiesel fuels are significantly cheaper, costing 15.13% less (Set 1) and 15.12% less (Set 2) than diesel (priced at 2.0000 €/l) and are proposed between 1.5 * 109 evaluated biodiesel mixtures.