Selection of optimal coal blends in terms of ash fusion temperatures using Support Vector Machine (SVM) classifier - a case study for Polish coals

• Autorzy: Żogała Alina , Rzychoń Maciej , Łączny Jacek M. , Róg Leokadia

Identyfikatory

DOI

10.5277/ppmp19059

• Języki publikacji: EN

Abstrakty

EN

One of the most important criteria for selecting coal for a given technology are the ash Fusion temperatures (AFTs). An effective way to regulate the AFTs so that they meet the criteria for a given industrial application is to form blends of different coals. The values of the AFTs in the blends are nonadditive, therefore they can't be calculated using the weighted average of the blend components. On the other hand, direct determination of ATFs values requires many additional time-consuming and expensive laboratory tests. Therefore, it is important to develop a solution that, in addition to the effective prediction of the values of AFTs, will also enable optimal selection of components of the blend in terms of its key parameters. The aim of the work was to develop an algorithm for the selection of the optimal coal blends in terms of AFTs for given industrial applications. This algorithm uses nonlinear classifying model which was built using machine learning method, support vector machine (SVM). To carry out the training samples of Polish hard coals from different mines of the Upper Silesian Coal Basin were used. The accuracy of the developed model is 92.3%. The results indicate the effectiveness of the proposed solution, which can find practical application in the form of an expert system used in the coal industry. The paper presents the concept of developed IT tool which has been tested for a selected case.

Słowa kluczowe

EN

coal blends; ash fusion temperature; support vector machine; principal component analysis; machine learning

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Physicochemical Problems of Mineral Processing
• Rocznik: 2019
• Tom: Vol. 55, iss. 5
• Strony: 1311--1322
• Opis fizyczny: Bibliogr. 33 poz., rys., tab., wykr., wz.

Twórcy

autor

Żogała Alina

• Central Mining Institute, plac Gwarków 1, 40-166 Katowice, Poland

autor

Rzychoń Maciej

• Central Mining Institute, plac Gwarków 1, 40-166 Katowice, Poland

autor

Łączny Jacek M.

• Central Mining Institute, plac Gwarków 1, 40-166 Katowice, Poland

autor

Róg Leokadia

• Central Mining Institute, plac Gwarków 1, 40-166 Katowice, Poland

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).