Development of an algorithm for optimizing energy consumption in chemical-technological systems based on statistical training

The purpose of the research. The aim of the study is to develop approaches to solving the problems of optimizing the energy resources of chemical-technological systems based on statistical training. As the main research methods, the article uses graphical and tabular tools for descriptive data analysis to study the dynamics of the structure of energy carriers and determine possible reserves for reducing consumption; method of training neural networks to predict optimal values of energy consumption. Results. The article analyzes the current trends in the energy intensity of the cost of chemical production with an assessment of the degree of transformation of the structure of the energy portfolio and possible reserves for reducing the specific weight of electrical and thermal energy. The method of training neural articles using a regression predictive model was used to determine the minimum possible values of the parameter of energy resources consumption at the upper limit of the range, taking into account the limitations of the technological regulations for the production of chemicals and chemical products. The results of the study are applicable in the development of software complexes for intelligent energy systems, in the process of determining the cause-and-effect relationships of deviations in resource consumption from a given trajectory and the optimal vector of sustainable energy consumption.

optimization algorithm, descriptive analytics, statistical learning, energy resources, specific resource consumption