Improving the energy efficiency of chemical industries and increasing their environmental
friendliness requires an assessment of the parameters of consumption and losses of energy resources.
The aim of the study is to develop and test a method for solving the problem of optimizing the
use of energy resources in chemical production based on the methodology of descriptive statistics
and training of neural networks. Research methods: graphic and tabular tools for descriptive data
analysis to study the dynamics of the structure of energy carriers and determine possible reserves
for reducing their consumption; correlation analysis with the construction of scatter diagrams to
identify the dependences of the range of limit values of electricity consumption on the average
rate of energy consumption; a method for training neural networks to predict the optimal values
of energy consumption; methods of mathematical optimization and standardization. The authors
analyzed the trends in the energy intensity of chemical industries 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; determined the dynamics of energy losses at Russian
industrial enterprises; established the correlation dependence of the range of limiting values of power
consumption on the average rate of power consumption; determined the optimal limiting limits of
the norms for the loss of electrical energy by the example of rubbers of solution polymerization. The
results of the study can be used in the development of software complexes for intelligent energy
systems that allow tracking the dynamics of consumption and losses of energy resources. Using the
results allows you to determine the optimal parameters of energy consumption and identify reserves
for improving energy efficiency.