Minimising backbreak at the Dewan Cement limestone quarry using an artificial neural network

• Autorzy: Muhammad K. , Shah A.

Identyfikatory

DOI

10.1515/amsc-2017-0055

Warianty tytułu

PL

Minimalizacja zasięgu kruszenia złoża poza obszarem prac strzałowych w kamieniołomie wydobywającym wapień do produkcji cementu w Dewan, przy wykorzystaniu sztucznych sieci neuronowych

• Języki publikacji: EN

Abstrakty

EN

Backbreak, defined as excessive breakage behind the last row of blastholes in blasting operations at a quarry, causes destabilisation of rock slopes, improper fragmentation, minimises drilling efficiency. In this paper an artificial neural network (ANN) is applied to predict backbreak, using 12 input parameters representing various controllable factors, such as the characteristics of explosives and geometrical blast design, at the Dewan Cement limestone quarry in Hattar, Pakistan. This ANN was trained with several model architectures. The 12-2-1 ANN model was selected as the simplest model yielding the best result, with a reported correlation coefficient of 0.98 and 0.97 in the training and validation phases, respectively. Sensitivity analysis of the model suggested that backbreak can be reduced most effectively by reducing powder factor, blasthole inclination, and burden. Field tests were subsequently carried out in which these sensitive parameters were varied accordingly; as a result, backbreak was controlled and reduced from 8 m to less than a metre. The resulting reduction in powder factor (kg of explosives used per m3 of blasted material) also reduced blasting costs.

PL

Kruszenie części złoża poza obszarem prowadzonych prac strzałowych oznacza nadmierne pękanie skał poza ostatnim rzędem otworów strzałowych w trakcie prac w kamieniołomach i prowadzi do destabilizacji górotworu poprzez zmianę nachylenia warstw skalnych, powoduje niepotrzebną fragmentację skał i obniża efektywność prac wiertniczych. W pracy tej wykorzystano sztuczną sieć neuronową (ANN) do przewidywania zasięgu kruszenia dalszej części złoża przy wykorzystaniu 12 parametrów wejściowych. Parametry te opisują różne zmienne czynniki, np. charakterystyka materiału wybuchowego czy przyjęty plan prac strzałowych w kamieniołomie Deewan w regionie Hattar w Pakistanie. Prowadzono proces uczenia sieci dla różnej architektury modelu, wybrano model 12-2-1 ANN, jako model najprostszy, zapewniający najlepszy wynik a współczynniki korelacji uzyskane dla fazy uczenia i walidacji wyniosły odpowiednio 0.98 i 0.97. Przeprowadzona analiza wrażliwości modelu wykazała że zasięg kruszenia dalszych części złoża obniżyć można poprzez zmianę parametrów ładunku strzelniczego, zmianę nachylenia otworów strzałowych oraz zmianę przybitki. Badania terenowe w czasie których ulegały zmianie wartości wyżej wymienionych wrażliwych parametrów wykazały, że zasięg kruszenia złoża poza obszarem prac strzałowych ograniczono z uprzednich 8 m do wielkości poniżej jednego metra. Obniżenie współczynnika charakteryzującego ładunek (kg zastosowanego materiału wybuchowego przypadający na 1 m3 rozkruszonego materiału skalnego) pozwoliło także na obniżenie kosztów prac strzałowych.

Słowa kluczowe

EN

neural network; backbreak; sensitivity analysis; modeling; blast design; quarry

PL

sieci neuronowe; zasięg kruszenia; analiza wrażliwości; modelowanie; projektowanie robót strzałowych; kamieniołom

• Wydawca: Instytut Mechaniki Górotworu PAN
• Czasopismo: Archives of Mining Sciences
• Rocznik: 2017
• Tom: Vol. 62, no. 4
• Strony: 795--806
• Opis fizyczny: Bibliogr. 40 poz., rys., tab., wykr.

Twórcy

autor

Muhammad K.

• Department of Mining Engineering, University of Engineering & Technology, Peshawar, Pakistan

autor

Shah A.

• Dewan Cement Limestone Quarry, Hattar, KP, Pakistan

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018)