Application of multivariate data analysis in the construction of predictive model for the chemical properties of coke

• Autorzy: Sajdak M. , Smędowski Ł.

Identyfikatory

DOI

10.2478/ctg-2014-0010

• Języki publikacji: EN

Abstrakty

EN

The aim of this work was to develop a statistical model which can predict values describing chemical composition of cokes performed in industrial scale. This model was developed on the basis of data that were taken from the production system used in the one of Polish coking plant. Elaborated equation include quality parameters of initial coals that form coal blends as well as contribution of additions such as coke and petrochemical coke. These equations allow to predict chemical composition of coke, e.g. contributions of: sulphur, ash, phosphorus and chlorine within the coke. A model was elaborated with use of STATISTICA 10 program and it is based on factor and multiply regression analyses. These analyses were chosen from among few kinds of regression analyses. They allowed to develop prediction model with the required goodness of fit between calculated and actual values. Goodness of fit was elaborated with: - residuals analyses, - residues normality and predicted normality - mean absolute error - Pearson correlation confidence.

Słowa kluczowe

EN

coke; chemical composition; prediction; mathematical model

PL

koks; skład chemiczny; prognoza; model matematyczny

• Wydawca: De Gruyter
• Czasopismo: Contemporary Trends in Geoscience
• Rocznik: 2013
• Tom: Vol. 2, iss. 1
• Strony: 67--74
• Opis fizyczny: Bibliogr. 14 poz., tab., wykr.

Twórcy

autor

Sajdak M.

• Institute for Chemical Processing of Coal, Zamkowa Str. 1, 41-803 Zabrze

autor

Smędowski Ł.

• Institute for Chemical Processing of Coal, Zamkowa Str. 1, 41-803 Zabrze

Bibliografia

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Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.