ORIGINAL PAPER
Prediction of quality parameters of a dry air separation product using machine learning methods
1
Główny Instytut Górnictwa
Submission date: 2018-11-30
Final revision date: 2019-03-19
Acceptance date: 2019-06-28
Publication date: 2019-06-28
Gospodarka Surowcami Mineralnymi – Mineral Resources Management 2019;35(2):119-138
KEYWORDS
TOPICS
ABSTRACT
The purpose of the work was to predict the selected product parameters of the dry separation
process using a pneumatic sorter. From the perspective of application of coal for energy purposes, determination of process parameters of the output as: ash content, moisture content, sulfur content,
calorific value is essential. Prediction was carried out using chosen machine learning algorithms that
proved to be effective in forecasting output of various technological processes in which the relationships
between process parameters are non-linear. The source of data used in the work were experiments
of dry separation of coal samples. Multiple linear regression was used as the baseline predictive
technique. The results showed that in the case of predicting moisture and sulfur content this technique
was sufficient. The more complex machine learning algorithms like support vector machine (SVM)
and multilayer perceptron neural network (MPL) were used and analyzed in the case of ash content
and calorific value. In addition, k-means clustering technique was applied. The role of cluster analysis
was to obtain additional information about coal samples used as feed material. The combination of
techniques such as multilayer perceptron neural network (MPL) or support vector machine (SVM)
with k-means allowed for the development of a hybrid algorithm. This approach has significantly
increased the effectiveness of the predictive models and proved to be a useful tool in the modeling
of the coal enrichment process.
METADATA IN OTHER LANGUAGES:
Polish
Przewidywanie parametrów jakościowych produktu suchej separacji węgla metodami uczenia maszynowego
sztuczne sieci neuronowe, sucha separacja węgla, wielokrotna regresja liniowa, maszyna wektorów nośnych (SVM)
Celem pracy było prognozowanie wybranych parametrów produktu procesu suchej separacji za
pomocą sortera pneumatycznego. Z punktu widzenia zastosowania węgla do celów energetycznych
niezbędne jest określenie parametrów procesowych wydobycia, takich jak: zawartość popiołu, zawartość
wilgoci, zawartość siarki czy wartość kaloryczna. Prognozowanie przeprowadzono przy użyciu
wybranych algorytmów uczenia maszynowego, które okazały się skuteczne w prognozowaniu wyjścia
różnych procesów technologicznych, w których zależności między parametrami procesu są nieliniowe.
Źródłem danych wykorzystanych w pracy były eksperymenty procesu suchej separacji węgla.
Zastosowano wieloraką regresję liniową jako bazową metodę predykcyjną. Wyniki pokazały, że
w przypadku przewidywania zawartości wilgoci i siarki technika ta była wystarczająca. Bardziej
złożone algorytmy uczenia maszynowego, takie jak maszyna wektorów nośnych (SVM) i perceptron
wielowarstwowy (MLP) zostały wykorzystane i przeanalizowane w przypadku zawartości popiołu
i wartości opałowej. Ponadto wdrożono technikę k-średnich. Rolą analizy skupień było uzyskanie dodatkowych
informacji na temat próbek węgla będących wejściem procesu. Połączenie technik, takich
jak perceptron wielowarstwowy (MLP) lub maszyna wektorów nośnych (SVM) z metodą k-średnich
pozwoliło na opracowanie hybrydowego algorytmu. Takie podejście znacznie zwiększyło efektywność
modeli predykcyjnych i okazało się użytecznym narzędziem w modelowaniu procesu wzbogacania
węgla.
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| eISSN: | 2299-2324 |
| ISSN: | 0860-0953 |
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