ORIGINAL PAPER
A study on the influence of particle size on the identification accuracy of coal and gangue
1
School of Mechanical Engineering, Anhui University of Science and Technology
Submission date: 2022-11-04
Final revision date: 2022-12-12
Acceptance date: 2023-01-25
Publication date: 2023-03-22
Corresponding author
Gospodarka Surowcami Mineralnymi – Mineral Resources Management 2023;39(1):109-129
KEYWORDS
TOPICS
Optimization of resources and reserves managementAccompanying minerals utilizationPreparation and processing of mineral raw materialsUtilization of recycled mineral raw materials and substitutes
ABSTRACT
In order to explore the impact of coal and gangue particle size changes on recognition accuracy and to improve the single particle size of coal and gangue identification accuracy of sorting equipment, this study established a database of different particle sizes of coal and gangue through image gray and texture feature extraction, using a relief feature selection algorithm to compare different particle size of coal and gangue optimal features of the combination, and to identify the points and particle size of coal and gangue. The results show that the optimal features and number of coal and gangue are different with different particle sizes. Based on visible-light coal and gangue separation technology, the change of coal and gangue particle size cause fluctuations in the recognition accuracy, and the fluctuation of recognition accuracy will gradually decrease with increases in the number of features. In the process of particle size classification, if the training model has a single particle size range, the recognition accuracy of each particle size range is low, with the highest recognition accuracy being 98% and the average recognition rate being only 97.2%. The method proposed in this paper can effectively improve the recognition accuracy of each particle size range. The maximum recognition accuracy is 100%, the maximum increase is 4%, and the average recognition accuracy is 99.2%. Therefore, this method has a high practical application value for the separation of coal and gangue with single particle size.
ACKNOWLEDGEMENTS
This work is supported in part by the National Natural Science Foundation of China under Grant (No. 52274152), in part by the Collaborative Innovation Project of Universities in Anhui Province under Grant (No. GXXT-2020-054), and in part by the Collaborative Innovation Project of Universities in Anhui Province under Grant (No. GXXT-2020-060).
METADATA IN OTHER LANGUAGES:
Polish
Badanie wpływu wielkości cząstek na dokładność identyfikacji węgla i skały płonnej
wielkość cząstek, cecha szarości, cecha tekstury, maszyna wektorów pomocniczych, identyfikacja węgla i skały płonnej
W celu zbadania wpływu zmian wielkości cząstek węgla i skały płonnej na dokładność rozpoznawania oraz poprawienia dokładności identyfikacji pojedynczych cząstek węgla i skały płonnej przez urządzenia sortujące, w ramach tej pracy utworzono bazę danych różnych rozmiarów cząstek węgla i skały płonnej za pomocą obrazów szarych i ekstrakcję cech tekstury przy użyciu algorytmu wyboru cech reliefowych w celu porównania różnych rozmiarów cząstek węgla i skały płonnej przy optymalnych cechach kombinacji oraz identyfikacji punktów i wielkości cząstek węgla i skały płonnej. Wyniki pokazują, że optymalne liczby cech węgla i skały płonnej są różne dla różnych rozmiarów cząstek. W oparciu o technologię separacji węgla i skały płonnej w świetle widzialnym, zmiana wielkości cząstek węgla i skały płonnej powoduje fluktuacje dokładności rozpoznawania, a te z kolei będą stopniowo zmniejszać się wraz ze wzrostem liczby cech. W procesie klasyfikacji wielkości cząstek, jeśli model uczący ma jeden zakres wielkości cząstek, dokładność rozpoznawania każdego zakresu wielkości cząstek jest niska, przy czym najwyższa dokładność rozpoznawania wynosi 98%, a średni wskaźnik rozpoznawania wynosi tylko 97,2%. Metoda zaproponowana w tym artykule może skutecznie poprawić dokładność rozpoznawania każdego zakresu wielkości cząstek. Maksymalna dokładność rozpoznawania wynosi 100%, maksymalny wzrost to 4%, a średnia dokładność rozpoznawania to 99,2%. Dlatego ta metoda ma dużą praktyczną wartość użytkową do oddzielania węgla i skały płonnej według rozmiaru pojedynczej cząstki.
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