Multidimensional Correlation-Regression Analysis of Fuel Chemical Properties

This article is dedicated to exploring methods of applying systems analysis for evaluating the physicochemical composition of fuel produced by mixing several components in appropriate proportions. As part of addressing the problem, a selection of the most influential factors was conducted using correlation-regression analysis. To ensure a mathematical solution to the problem within the program, the Sequential Least Squares Programming (SLSQP) method was utilized, implemented in the «minimize» method of the SciPy library for the Python programming language. The article presents the results of developing an application for calculating the mixing proportions of fuel with specified characteristics, allowing for the optimization of this process.