## 1 Introduction
### Novel Contribution of the Research  
The current research introduces a novel approach for biodiesel mixture optimization, employing an Evolutionary Computation method. The optimal Biodiesel Solution is a result of an adaptive genetic algorithm application.  
It should be noticed that standard diesel emissions are high in sulfur oxides. As a result, desulfurization would have been an ideal process to achieve low sulfur oxide emissions. However, because of the high time and cost required for desulfurization, the approach of using mixtures of standard diesel and biodiesel was proposed. In the Environmental Technology Lab., Chemical Engineering Dept, UOWM, Greece, thorough experiments (approximately 3500) were conducted to develop a novel blend of Diesel and Biodiesel. These experiments yielded valuable insights into the characteristics of both components. Biodiesel, serving as the secondary ingredient, is derived from a combination of animal fat and vegetable sources. Specifically in this work, the key components of the optimal biodiesel blend are diesel and biodiesel, derived from a combination of animal fat (50%) and vegetable sources (50%), as determined by the experimental settings of the Laboratory of Environmental Technology.  
So, the sequence in which cost and density affect the raw material percentages in optimal mixtures is investigated. More specifically, the higher the quality of a product is, the higher the price of the raw material. In parallel, the higher the density of a mixture is, the greater both power output and fuel economy are generated in a diesel engine. Those two properties (cost and density) are used as inputs in the genetic algorithm operation and the relevant experimental simulations. The mixture evaluation is implemented from a new mathematical function that combines the two parameters. The basic operators' Crossover and Mutation contribute to mixture improvement, producing the final bio-diesel fuel. When the Crossover operator creates biodiesel blending within the optimal solution range of the previous iteration, the Mutation process generates biodiesel combinations across the entire spectrum of feasibility, preventing potential entrapment in a localized optimal solution. The effectiveness of this approach revolves around:  
1. implementing innovative modeling techniques, including specific evaluations to enhance modeling.
2. refining and defining the genetic algorithm.  
Moreover, significant findings emerge from the experimental simulations conducted using this approach, including:  
* Reducing Experiment Costs
* Minimizing Experiment Duration
* Improving Cost and Density using Enhanced Evaluation Functions* Developing Environmentally Sustainable Fuels This novel decision-making tool is now accessible to laboratory researchers, promoting the advancement of optimal fuel formulations. The genetic algorithm rapidly suggests the best mixture for experimentation within a vast pool of approximately \(1.5\times 10^{9}\) alternative combinations per experiment set. The benefits of this approach enhance the fuel production process, making the new Biodiesel more appealing compared to other competitive fuels.  
This document is organized in the following parts: the mathematical representation of the Biofuel Blend Issue is presented, and the constraints in relation to ingredient accessibility are discussed in Section 2. Section 3 explores the principal methodological elements of the suggested methodology, aiming to enhance comprehension of the algorithm's fundamental workings. Lastly, the concluding segment provides a recapitulation of significant discoveries and emphasizes notable aspects.