The publication presents the prognostic modeling of steel production in Poland by 2020. Based on the general framework predicting, an attempt was made to adjust the model to empirical data, which were related to the size of steel production in Poland. Two prognostic approaches were used. The first approach included determination of prognoses for total steel production on the basis of empirical data for the 1990-2014 period. The second approach used empirical data for the volume of steel production according to production technologies for the 2000-2015 period. Prognoses until 2020 were determined in both cases. Thus obtained prognoses were related to changes observed on the global steel market

B. Gajdzik

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241METALURGIJA 56 (2017) 1-2, 241-244B. GAJDZIKPROGNOSTIC MODELING OF TOTAL STEEL PRODUCTION AND ACCORDING TO PRODUCTION TECHNOLOGY IN POLANDReceived – Primljeno: 2016-10-06Accepted – Prihvaćeno: 2016-11-25Preliminary Note – Prethodno priopćenjeB. Gajdzik, The Silesian University of Technology, Faculty of Materials Science and Metallurgy, Katowice, PolandThe publication presents the prognostic modeling of steel production in Poland by 2020. Based on the general framework predicting, an attempt was made to adjust the model to empirical data, which were related to the size of steel production in Poland. Two prognostic approaches were used. The first approach included determination of prognoses for total steel production on the basis of empirical data for the 1990-2014 period. The second approach used empirical data for the volume of steel production according to production technologies for the 2000-2015 period. Prognoses until 2020 were determined in both cases. Thus obtained prognoses were related to changes observed on the global steel market.Key words: steel production, basic oxygen furnace (BOF), electric arc furnace (EAF), prognostic model, Polish steel industry INTRODUCTIONMetallurgical market in Poland has undergone a rad-ical transformation in recent decades. From subsidized state-owned enterprises to private companies operating at their own risk. Privatized state-owned enterprises as a part of their rehabilitation programs adapted each business area to the conditions of market economy. One of these areas was the volume of production. Volumes of production were adapted to the needs of the market, eliminating unused production capacity and outdated technology [1]. In this publication, an attempt was made to predict changes in the volume of steel production for the next years based on changes in the volume of steel production in Poland in recent years. The construction of predictions for previous periods (the prediction ex-ante), had a recurring nature and consisted of the use of calculations of prognosis from previous periods. The size of steel production in Poland /M mt and the share of BF/BOF steel (steel from integrated processes) and the share of EAF steel (steel produced in electrical process)/M mt were the basis for predictions made.PREDICTING THE VOLUME OF CRUDE STEEL PRODUCTIONEmpirical data: steel production in Poland in years 1990-2014 (Table 1). Prognosis method: One of the adaptive prediction models is the autocorrelation and au-toregression (AR) model. This modeling is based on minimizing the value [2] of: the relative average error of expired prognosis root mean squared error of expired prognosis RMSE*. ISSN 0543-5846METABK 56(1-2) 241-244 (2017)UDC – UDK 669.14-338.455:658.2:621.72:001.19:438=111Table 1 Crude steel production in Poland [3]Year Crude steel/ M mt Year Crude steel/ M mt1990 13,5 2002 8,41991 10,3 2003 9,11992 9,8 2004 10,51993 8,9 2005 8,31994 11,0 2006 10,01995 11,7 2007 10,71996 10,4 2008 9,71997 11,6 2009 7,11998 9,9 2010 8,01999 8,8 2011 8,82000 10,5 2012 8,22001 8,8 2013 8,02014 8,6In the first stage, a prognosis is built based on the lin-ear model (Figure 1). A thorough analysis of the resulting linear regression model, however, precludes its use for accurate predictions of steel production in future periods. Firstly, the obtained coefficient of determination R2 was equal to 0,4444 - only approx. 44 % of the empirical re-sults can be explained by the linear model (Table 2). Sec-ondly − after analyzing a simple match, one can notice that the assumption of a random distribution of residuals (i.e. their mutual independence) adopted in this regres-sion model is not correct. The prognosis of steel produc-tion for the years 2015-2020 obtained using a linear mod-el is pointing downward. The yearly decline of produc-tion is about 100 thous. mt. In 2020, the predicted volume of crude steel production will amount to 7.24 M mt (Fig-ure 1). Already in 2015 the volume of production fell to 7,9 M mt of crude steel. In reality, in 2015 Poland pro-duced 9,2 M mt of crude steel, i.e. 1,3 M mt more than in the prognosis. The next step was to analyze the correla-tion modeling of time series with itself - autocorrelation (AR). Construction of models is based upon the premise that there is an autocorrelation between the predicted val-242B. GAJDZIK: PROGNOSTIC MODELING OF TOTAL STEEL PRODUCTION AND ACCORDING TO PRODUCTION... METALURGIJA 56 (2017) 1-2, 241-244industry will produce 9,04 M mt, about 440 thousand tons more than in 2014. All results of the prognosis are pre-sented in Table 2. Difference between prognosis of steel production volume and real data in 2015 is smaller in the AR model (1). According to the statistical data in 2015, Poland produced 9,2 M mt of crude steel [3].PREDICTING THE STEEL PRODUCTION PATTERN BY PROCESSESThe analysis of steel production volume according to production forms was started at 2000, when steel was ues of the variable, and the values of the delay time. Au-tocorrelation coefficients rk for shifting about k compo-nents are calculated using the following relationship:  (1)where:    (2)is the average of data that makes up the original time series. Considering the results obtained as a function whose arguments are the k numbers, a graph called auto-correllogram may be plotted. The obtained calculation results are an array of autocorrelation coefficients, which facilitates selection of the most optimal AR model. My study to predict the volume of steel production used both the linear autoregressive model with one explanatory variable BACK 1 - an AR model (1) and the linear au-toregressive model with two explanatory variables: BACK1 and BACK4 - that is AR model (1,4). Parame-ters of these models were evaluated with the smallest sum of squared residuals [2]. The obtained model AR (1): Volumes of production of steel = 5,936 + 0,363 * BACK1.The modeling is based on minimization of the value (obtained results) of:o  the relative average error of expired prognosis = 0,0954 o  the root mean squared error of expired prognosis RMSE * = 0,934.The obtained model AR (1,4):Volumes of production of steel = 3,80 + 0,369 * BACK 1 + 0.,17 * BACK 4 The modeling is based on the minimization of the value (obtained results) of:o  the relative average error of expired prognosis = 1,108 o  the root mean squared error of expired prognosis RMSE*= 0,768.Based on the performed AR (1) modeling, the size of predicted of steel production has a growing trend (Figure 1). In 2020, Polish steel industry will produce 9,32 M mt, about 729 tons more than in 2014. Based on the performed AR (1,4) modeling, the size of predicted of steel produc-tion has a growing trend (Figure 2). In 2020, Polish steel Figure 1  Prognosis of crude steel production based on the AR (1) model [4]Figure 2  Prognosis of crude steel production based on the AR model (1,4) [4]Table 2 Results of prognosis of crude steel production [4]Year Linear modelCrude steel/M mtAR(1) modelCrude steel/M mtAR(1,4) modelCrude steel/M mt1990 11,171991 11,04 10,841992 10,91 9,681993 10,78 9,501994 10,65 9,17 10,011995 10,52 9,93 10,091996 10,39 10,19 10,241997 10,26 9,71 9,571998 10,12 10,15 10,461999 9,99 9,53 9,992000 9,86 9,13 9,302001 9,73 9,75 10,192002 9,60 9,13 9,192003 9,47 8,99 8,812004 9,34 9,24 9,432005 9,21 9,75 9,582006 9,08 8,95 8,682007 8,94 9,57 9,462008 8,81 9,82 10,022009 8,68 9,24 8,962010 8,55 8,51 8,592011 8,42 8,84 9,072012 8,29 9,13 9,022013 8,16 8,91 8,362014 8,03 8,84 8,492015 7,90 9,15 8,882016 7,77 9,26 8,852017 7,63 9,30 8,802018 7,50 9,31 8,912019 7,37 9,32 9,012020 7,24 9,32 9,04243B. GAJDZIK: PROGNOSTIC MODELING OF TOTAL STEEL PRODUCTION AND ACCORDING TO PRODUCTION...METALURGIJA 56 (2017) 1-2, 241-244practically no longer produced in open hearth furnaces (grey colour in Tables), 2003 marked the end of open hearth furnace production. The main currently used pro-duction methods include steel from integrated processes (BF/BOF) and steel produced in electrical process (EAF). The prepared prognoses of changes to the vol-ume of steel production according to production forms until 2020 were based on empirical data for the 2000-2015 period (Table 3).Table 3  Production of crude steel, BOF steel and EAF steel [4]Year Crude steel/M mt BOF steel/M mt EAF steel/M mt2000 10,498 6,800 3,2852001 8,809 5,823 2,8092002 8,367 5,799 2,5612003 9,107 6,070 3,0372004 10,578 6,858 3,7212005 8,336 4,893 3,4432006 9,992 5,766 4,2252007 10,631 6,198 4,4332008 9,727 5,225 4,5022009 7,128 3,236 3,8932010 7,993 3,995 3,9982011 8,776 4,424 4,3532012 8,348 4,227 4,1322013 7,950 4,399 3,5512014 8,558 5,067 3,4912015 9,202 5,323 3,879Both adaptation models and econometric models (lin-ear, non-linear and including autocorrelation) were used in order to create a prognosis of the total volume of steel production in Poland, taking into account the production processes used. Tests of accepted methods showed that the best fit was obtained using autoregression (AR) and the crawling trend method – prognosis using the harmon-ic weights method. In the case of the autoregression mod-el, Durbin-Watson statistic was estimated and an autocor-rellogram was created. The obtained autocorrellogram confirmed that offsets may be included in the AR model in the case of BOF BACK 1, EAF BACK 1, 2,6. The fol-lowing assumptions were applied to the crawling model: Segments with k = 4 observations were selected, weights, or factors taking into account information aging were cal-culated using formula 3:  (3)Ex-ante prognoses were calculated using formula 4 (4)Tables 4 and 5 present ex post and ex ante errors calculated for individual prognostic methods. Tables 6 and 7 provide a summary of results obtained for BOF and EAF processes.The following prognostic model was obtained using the AR model (1) with the BOF process:  yT* = 2,426+ 0,5193*BACK1The correlation factor of R=0,3217 indicates rather poor model fitting to empirical points of data.Conclusion from prognosis: according to the mod-els (Table 6) BOF steel production will be on level 5,06 or 5,87 M mt in 2020 in Poland.The following prognostic model was obtained for the electric process:  yT* = 5,893 + 0,178*BACK1 –  – 0,334*BACK2 - 0,338*BACK6whereby the correlation coefficient was R = 0,7340 – indicating good model fitting to empirical data points. Conclusion from prognosis: according to the mod-els (Table 7) EAF steel production will be on level 3,7 or 4,2 M mt in 2020 in Poland.Adding steel production volumes in BOF and EAF processes results in the total steel production for prog-nosed years.Table 4  Ex post and ex ante errors for prognostic methods – BOF process [4]Type of modelEstimationex post ex anteψ *RMSE ψCrawling model (k=4) 0,059 0,379 0,0046Model autoregression AR(1) 0,124 0,772 0,0499Table 5  Ex post and ex ante errors for prognostic methods – EAF process [4]Type of modelEstimationex post ex anteψ *RMSE ψCrawling model (k=4) 0,039 0,171 0,0340Model autoregression AR(1,2,6) 0,041 0,171 0,0615Table 6 Prognosis of BOF steel production [4]Year BOF steel/M mtAR (1) modelCrude steel/M mtCrawling modelCrude steel/ M mt2000 6,800 6,4552001 5,823 5,957 5,9322002 5,799 5,450 6,0582003 6,070 5,438 6,1072004 6,858 5,578 6,1472005 4,893 5,987 5,6652006 5,766 4,967 5,7952007 6,198 5,421 5,7112008 5,225 5,645 4,9932009 3,236 5,140 3,9542010 3,995 4,106 3,8552011 4,424 4,501 4,1342012 4,227 4,723 4,3442013 4,399 4,621 4,5352014 5,067 4,711 4,8982015 5,323 5,057 5,3482016 prognosis 5,190 5,4332017 prognosis 5,122 5,5422018 prognosis 5,086 5,6522019 prognosis 5,067 5,7612020 prognosis 5,057 5,871244B. GAJDZIK: PROGNOSTIC MODELING OF TOTAL STEEL PRODUCTION AND ACCORDING TO PRODUCTION... METALURGIJA 56 (2017) 1-2, 241-244CONCLUSION FROM PROGNOSIS FOR STEEL PRODUCTION IN POLANDThe prognosed volume of steel production in Po-land shall slightly exceed 9 M mt per year (optimistic scenario) or oscillate around 9 M mt (pessimistic sce-nario), changes to steel production using the BOF method will be small, converter steel shall comprise over 50 % of the total steel production in Poland, with the remaining share held by steel produced in electric furnaces, the increase of steel production in electric furnaces during the analysed period may result from potential, temporary standstills of blast furnaces be-cause of their upgrades or decreased demand and re-sulting excess of production capacity in Polish metal-work industry. REFERENCE THE RESULTS TO THE GLOBAL PROGNOSIS In 2050, the use of steel will increase. Consumption is projected to be 1,5 times higher than current levels. Opti-mistic predictions are conditioned by the necessity to meet the needs of growing population [5-7]. In Europe according to a British company MEPS this increase can be “slow but steady”. The European Commission plans (“Horizon 2020”) predict that the European Union’s eco-nomic growth will slow in the near future. Public sector spending for the next few years will be very limited, and with it, investments in infrastructure. The private sector will be in turn limited by more restrictive lending policies of banks. The recovery in the consumer goods sector also has to be modest. An opportunity for development of the steel market will create demand (total share of the con-struction and car production in the demand for steel is about 40 % in the EU). It is therefore necessary to realize the link between these sectors and the EU initiative, CARS2020, an initiative to stimulate demand for alterna-tive fuel vehicles and an initiative on sustainable con-struction, including renovation of buildings [8].REFERENCES[1] Gajdzik B.: The road of Polish steelworks towards market success – changes after restructuring process, Metalurgija 52 (2013)3, 421-424.[2] Snarska A.: Statistics, Econometrics, Prognosis. Placet, 2005 (in Polish)[3] Polish steel industry, report, Steel Association in Poland, Katowice 2000-16.[4] Own research on the base of statistic data[5] World steel in figures 2015, World Steel Association 2015: worldsteel.org[6] World Steel Short Range Outlook 2015 – 2016. https://www.worldsteel. org/ media-centre/press-releases/2015/ worldsteel-Short-Range-Outlook-2015---2016.html[7] http://www.salesnews.pl/Article.aspx?id=1303[8] UE, Brussels, 11.06. 2013, European steel industry – good future: http://europa.eu/ rapid/press-release_MEMO-13523_pl.htmNote:  The responsible translator for English language is translate office Niuans, Gliwice, PolandFigure 3 Prognosis of BOF steel production[4] Figure 4 Prognosis of EAF steel production[4]Table 7 Prognosis of EAF steel production [4]Year EAF steel/M mtCrawling model Crude steel/M mtAR (1,2,6) modelCrude steel/M mt2000 3,285 3,072  2001 2,809 2,762  2002 2,561 2,812  2003 3,037 3,026  2004 3,721 3,458  2005 3,443 3,729  2006 4,225 4,146 4,1512007 4,433 4,379 4,5442008 4,502 4,354 4,4042009 3,893 4,093 4,1862010 3,998 4,093 3,8232011 4,353 4,181 4,1392012 4,132 4,028 3,9022013 3,551 3,767 3,6742014 3,491 3,565 3,6222015 3,879 3,641 4,0112016 prognosis 3,850 4,0642017 prognosis 3,820 3,8482018 prognosis 3,790 3,8222019 prognosis 3,761 4,0872020 prognosis 3,731 4,162

Prognostic modeling of total steel production and according to production technology in Poland

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