Assessing energy efficiency factors in industrial companies

Table 2. Results of regression analysis on model 2 for the steelmaking of UMMC-Stal LLC in 2019
(calculated by the author based on production reporting) 
Variable 
Model from Equation 2 
Modified Model 
Coeff. 
Standard 
deviation 
t 
Coeff. 
Standard 
deviation 
t 
(Constant) 
0.9184 
0.230 
3.991 5.2603 
0.488 10.781 
Weight of steel output in t 
0.0096 
0.003 
3.678 0.0296 
0.006 
5.308 
Smelting time in minutes 
-0.0106 
0.001 
-12.303 0.0392 
0.002 23.481 
The time that the metal is under current 
0.7054 
0.005 135.623 
- 
- 
- 
The amount of scrap to be loaded. t 
0.0021 
0.002 
1.392 0.0576 
0.003 18.558 
Oxygen consumption for melting, m
3
0.0011 
0.000 
15.288 0.0057 
0.000 41.922 
Carbon consumption for smelting, kg 
-0.0002 
0.000 
-2.864 -0.0014 
0.000 
-9.195 
Natural gas consumption for melting m
3
-0.0007 
0.000 
-3.821 -0.0031 
0.000 
-7.486 
R
2
 adjusted 
0.907
0.573
F-statistic 
7145
1149
The number of observations 
5138
5138
Darbin-Watson test 
0.687
0.891
Estimated regression ratios from table 3 are used to determine the expected energy consumption 
values, which are multiplied by a factor of 0.99 (laying additional savings within 1%). the level of 
savings at which the system is energy efficient. The considered tool, which is used in practice in the 

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MIP: Engineering-2020
IOP Conf. Series: Materials Science and Engineering 862 (2020) 042001
IOP Publishing
doi:10.1088/1757-899X/862/4/042001
5
holding companies, allows to assess the effectiveness of energy-saving measures and to establish the 
degree of achievement of savings at each company of the holding.