Research On The Relationship Between Electricity Consumption Data And Emissions In Typical Air Pollution Industries And Its Application

Currently,China is in a critical period of development during the"14th Five-Year Plan",which has put forward new requirements for pollution prevention and control,emphasizing precision,scientific and lawful pollution control.Electric power is a strategic resource and core production factor for enterprises.Electricity consumption data can reflect the economic operation and production status of enterprises,and has significant value in data mining.Its application prospect in atmospheric environmental regulation and pollution prevention is broad.This paper takes Weifang City as the research area and conducts the following three aspects of research work:(1)During the 2022 Beijing Winter Olympics,combine the enterprise electricity consumption data,pollutant online discharge data and emergency control measures to construct a The response time and response depth evaluation indicators of control measures,and to evaluate the control measures and emission reduction effects of different industries in Weifang City during the Beijing Winter Olympics.And according to the dynamic changes in electricity consumption and pollutant emissions of enterprises,a mathematical relationship model between electricity consumption data of typical industries and mathematical emissions of air pollutants was initially established;(2)The whole year of 2021 was selected as the research period to explore and verify typical Mathematical relational model of industry(steel,foundry,cement,etc.)power consumption and air pollutant emission data;(3)Apply the established mathematical relational model to optimize the time distribution coefficient of pollutant emission based on electric power data,and analyze industrial enterprises The trend of pollution emissions over time.The main conclusions of the paper are as follows:(a)The research results of the evaluation of the control effect of industrial pollution sources during the Winter Olympics in Weifang City based on electricity consumption data show that:(1)from January 30 to February 20,2022,during the air quality assurance period of the Beijing Winter Olympics,industrial enterprises adopt emission reduction measures to stop production and limit production.The average electricity consumption of 1163 Weifang electricity monitoring enterprises decreased by 57.62%compared to before the control,which indirectly reflects the control effect of emission reduction measures;(2)Enterprises in various industries that have taken measures to shut down production during the control period have seen a 80%to 100%decrease in electricity consumption;(3)The trend of electricity consumption reduction in various stages of industries such as steel,coal based nitrogen fertilizer,and thermal power generation is roughly the same as the trend of pollutant emissions reduction,with flue gas emissions highly positively correlated with electricity consumption(r>0.7).(b)The research results on the relationship between electricity consumption and air pollutant emissions in industrial enterprises indicate that:(1)typical air pollution industries such as steel,casting,and cement have a stronger correlation between enterprise electricity consumption and production process flue gas emissions compared to enterprise electricity consumption and various pollutant emissions.The reason may be that the smoke emissions from production facilities are more representative of changes in product output,while the concentration of atmospheric pollutant emissions is mainly affected by the removal efficiency of pollution control measures;(2)The mathematical model of flue gas emissions and production electricity consumption of cupola foundry enterprises in the foundry industry presents significantly different characteristics during the day and at night.Under two production conditions,the cubic curve regression model with the highest fitting degree(R~2>0.6)shows the variation of flue gas emissions with production electricity consumption,but the mathematical model parameters are significantly different,which is related to the yield changes under different production conditions;In addition,under the same production conditions,due to the difference of cupola models,the mathematical model parameters of the relationship between enterprise flue gas emissions and production power consumption are also different;(3)In various production processes of electric furnace casting enterprises,the electricity consumption for electric furnace melting accounts for over 80%.The correlation between flue gas emissions and production electricity consumption is strong,and the quadratic curve regression model with changes in production electricity consumption has the highest fitting degree(R2>0.7);(4)Among the main polluting processes of steel industry enterprises,the correlation between smoke emissions from sintering and self-owned power plant processes and production electricity consumption is strong,and the clustering characteristics are obvious.The quadratic curve regression model of smoke emissions from sintering and self-owned power plant processes with changes in production electricity consumption has the highest fitting degree(R~2>0.8),and its air pollutant emissions can be evaluated through its electricity data;(5)The fitting degree of the quadratic curve regression model of the change of flue gas emissions with the production power consumption is the highest(R~2>0.6)for the kiln head and kiln tail of the main pollutant discharge process of clinker industry enterprises.(c)The research results on the temporal distribution of air pollutant emissions based on electricity data indicate that:(1)Based on enterprise electricity consumption data,combined with the mathematical relationship model established by various industries between enterprise electricity consumption and flue gas emissions,the predicted flue gas emissions of enterprises in various industries can be calculated.The error range between the predicted and actual flue gas emissions of enterprises in various industries is between-15.24%and 16.28%,with relatively small prediction errors,indicating the reliability of the mathematical relationship model between enterprise electricity consumption and flue gas emissions.(2)Based on enterprise power data,the smoke emissions of various industry enterprises are gradually allocated to monthly and daily time scales,and the monthly and daily time distribution coefficients obtained for each industry enterprise can better reflect the different smoke emission characteristics of industrial enterprises in different seasons,months,working days,and rest days.(3)By combining online monitoring data of enterprise air pollutant emissions and using the concentration of various air pollutants as weight coefficients,the monthly and daily time spectra of various pollutant emissions can be further optimized,and the temporal trend of various pollutants can be more intuitively characterized.