Applications of Pinch Technology to Total Sites: A Heavy Chemical Industrial Complex and a Steel Plant

This study aims to investigate the air quality in primary school placed in district of Taranto (south of Italy), an area of high environmental risk because of closeness between large industrial complex and urban settlement. The chemical characterization of PM2.5 was performed to identify origin of pollutants detected inside school and the comparison between indoor and outdoor levels of PAHs and metals allowed evaluating intrusion of outdoor pollutants or the existence of specific indoor sources. The results showed that the indoor and outdoor levels of PM2.5, BaP, Cd, Ni, As, and Pb never exceeded the target values issued by World Health Organization (WHO). Nevertheless, high metals and PAHs concentrations were detected especially when school were downwind to the steel plant. TheI/Oratio showed the impact of outdoor pollutants, especially of industrial markers as Fe, Mn, Zn, and Pb, on indoor air quality. This result was confirmed by values of diagnostic ratio as B(a)P/B(g)P, IP/(IP + BgP), BaP/Chry, and BaP/(BaP + Chry), which showed range characteristics of coke and coal combustion. However, Ni and As showedI/Oratio of 2.5 and 1.4, respectively, suggesting the presence of indoor sources.

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Power Flow Prediction for a Steel Plant with a Dynamic Regression Model

IJEEC - INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTING ◽

10.7251/ijeec2101015b ◽

2020 ◽

Vol 5 (1) ◽

Author(s):

Boris Bizjak

Keyword(s):

Linear Regression ◽

Regression Model ◽

Power Flow ◽

Electric Arc Furnace ◽

Electric Arc ◽

Steel Plant ◽

Flow Profile ◽

Industrial Complex ◽

Arc Furnace ◽

Dynamic Regression Model

A power flow forecast it was shown for an industrial complex consisting of more than 20 different companies. The predominant consumer of electricity in the industrial complex is a steelworks company with an electric arc furnace. A steelworks with an electric arc furnace is a very specific example of an energy consumer. Other companies in the industrial complex are not connected to the steel plant technologically, but they are on the same energy connection. They have a weekly power flow profile significantly different from the steel plant. To calculate the forecast model and perform the forecast of power flows we need only two inputs of data: Historical measurements of power flows and the number of loads of the electric arc furnace in the following days. The first showed a prediction with linear regression. The next model to predict was the seasonal ARIMA model with a regressor, also called a dynamic regression model. The dynamic regression model improved the prediction by 15% compared to linear regression, according to the RMSE measure. This was followed by an improvement in the dynamic regression forecasting model by considering the seasonality 7/5 in the time series. We did this with a model with superimposed noise. With this model, we improved the forecasting by 30% to linear regression. Logically, the filter model of the prediction model also improved, gaining more Lag coefficients and losing a constant. Qualitatively, the result is a forecast of power flow for one month with prediction error MAPE 8% and measure R2 is 0.9.

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The export-oriented import substitution potential in the agro-industrial complex of the EAEU

Voprosy Ekonomiki ◽

10.32609/0042-8736-2019-4-74-89 ◽

2019 ◽

pp. 74-89

Author(s):

Boris A. Kheyfets ◽

Veronica Yu. Chernova

Keyword(s):

Global Economy ◽

Import Substitution ◽

Industrial Complex ◽

Important Direction

The paper analyzes the possibilities of improving the Russian policy of import substitution using the potential of the EAEU. A concrete analysis was carried out for the branches of the agro-industrial complex, where the greatest success was achieved in import substitution. There is a need for smart selective import substitution, the most important direction of which is the export-oriented one. This will improve the competitiveness of Russia and the EAEU as a whole in the global economy and will also promote the deepening of mutual ties of the EAEU countries. The main ways of solving this problem are shown.

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