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SMART MINING: JOINT MODEL FOR PARAMETRIZATION OF COAL EXCAVATION PROCESS BASED ON ARTIFICIAL NEURAL NETWORKS

By
Trivan Jelena ,
Trivan Jelena
Contact Trivan Jelena

Faculty of Mining Prijedor, University of Banja Luka, Banja Luka, Bosnia and Herzegovina

Srđan Kostić
Srđan Kostić

Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia

Abstract

In the present paper we propose a new artificial neural network model for the estimation of coal cutting resistance and excavator performance as a nonlinear relationship between the examined input (excavator movement angle in the left and right direction, slice height and thickness, coal unit weight, compressive and shear strength) and output factors (excavator effective capacity, maximum current/power/force/energy consumption, linear and areal cutting resistance). We analyze the dataset collected from three open-pit coal mines in Serbia: Field D, Tamnava Eastern Field and Tamnava Western Field (all part of the Kolubara coal basin). The model is developed using a multilayer feed-forward neural network, with a Levenberg-Marquardt learning algorithm. Results of the preformed analysis indicate satisfying statistical accuracyof the developed model (R>0.9). Additionally, we analyze the individual effects of input factors on the properties of coal cutting resistance and performance of the excavator, by invokling the multiple linear regression. As a result, we single out the statististically significant and physically possible interactions between the individual controlling factors

References

[{"id":5224,"citationNumber":null,"type":null,"title":"","DOI":null,"author":[],"issued":{"date-parts":[null]},"container-title":null,"volume":null,"issue":null,"page":null}]

[{"id":5225,"citationNumber":null,"type":null,"title":"","DOI":null,"author":[],"issued":{"date-parts":[null]},"container-title":null,"volume":null,"issue":null,"page":null}]

[{"id":5226,"citationNumber":null,"type":null,"title":"A Review of Artificial Intelligence Applications in Mining and Geological Engineering","DOI":null,"author":[{"given":"X.","family":"Bui"},{"given":"H.","family":"Bui"},{"given":"H.","family":"Nguyen"}],"issued":{"date-parts":["2020"]},"container-title":"Proceedings of the International Conference on Innovations for Sustainable and Responsible Mining. Part of the Lecture Notes in Civil Engineering book series (LNCE)","volume":"109","issue":null,"page":null}]

[{"id":5227,"citationNumber":null,"type":null,"title":"Use of Artificial Intelligence in Mining: An Indian Overview","DOI":null,"author":[{"given":"Shrivastava","family":"P."},{"given":"G.K.","family":"Pradhan"}],"issued":{"date-parts":["2022"]},"container-title":"International Congress and Workshop on Industrial AI 2021. Part of the Lecture Notes in Mechanical Engineering book series (LNME)","volume":null,"issue":null,"page":null}]

[{"id":5228,"citationNumber":null,"type":"article-journal","title":"Research and practice of intelligent coal mine technology systems in China","DOI":null,"author":[{"given":"G.","family":"Wang"},{"given":"H.","family":"Ren"},{"given":"G.","family":"Zhao"},{"given":"D.","family":"Zhang"},{"given":"Z.","family":"Wen"},{"given":"L.","family":"Meng"},{"given":"S.","family":"Gong"}],"issued":{"date-parts":["2022"]},"container-title":"International Journal of Coal Science & Technology","volume":"9","issue":"24","page":null}]

[{"id":5229,"citationNumber":null,"type":"article-journal","title":"Deep learning implementations in mining applications: a compact critical review","DOI":"10.1007\/s10462-02310500-9","author":[{"given":"F.","family":"Azhari"},{"given":"C.","family":"Sennersten"},{"given":"C.","family":"Lindley"},{"given":"E.","family":"Sellers"}],"issued":{"date-parts":["2023"]},"container-title":"Artifcial Intelligence Review","volume":null,"issue":null,"page":null}]

[{"id":5230,"citationNumber":null,"type":"article-journal","title":"Prediction of mining subsidence under thin bedrocks and thick unconsolidated layers based on field measurement and artificial neural networks","DOI":null,"author":[{"given":"W.","family":"Yang"},{"given":"X.","family":"Xiaohong"}],"issued":{"date-parts":["2013"]},"container-title":"Computers and Geosciences","volume":"52","issue":null,"page":"199 \u2013 203"}]

[{"id":5231,"citationNumber":null,"type":null,"title":"Assessment of self-heating susceptibility of indian coal seams - A neural network approach","DOI":null,"author":[{"given":"D.C.","family":"Panigrahi"},{"given":"S.K.","family":"Ray"}],"issued":{"date-parts":["2014"]},"container-title":"Archives of Mining Sciences","volume":"59","issue":"4","page":"1061 \u2013 10761"}]

[{"id":5232,"citationNumber":null,"type":"article-journal","title":"The application of artificial intelligence for the identification of the maceral groups and mineral components of coal","DOI":null,"author":[{"given":"M.","family":"Mlynarczuk"},{"given":"M.","family":"Skiba"}],"issued":{"date-parts":["2017"]},"container-title":"Computers and Geosciences","volume":"103","issue":null,"page":"133 \u2013 141"}]

[{"id":5233,"citationNumber":null,"type":"article-journal","title":"Identifying microseismic events in a mining scenario using a convolutional neural network","DOI":null,"author":[{"given":"A.","family":"Wilkins"},{"given":"A.","family":"Strange"},{"given":"Y.","family":"Duan"},{"given":"X.","family":"Luo"}],"issued":{"date-parts":["2020"]},"container-title":"Computers and Geosciences","volume":"137","issue":null,"page":"104418"}]

[{"id":5234,"citationNumber":null,"type":null,"title":"Prediction model with multi-point relationship fusion via graph convolutional network: A case study on mining-induced surface subsidence","DOI":null,"author":[{"given":"B.","family":"Jiang"},{"given":"K.","family":"Zhang"},{"given":"X.","family":"Liu"},{"given":"Y.","family":"Lu"}],"issued":{"date-parts":["2023"]},"container-title":"PLoS ONE","volume":"18","issue":"8","page":"e0289846"}]

[{"id":5235,"citationNumber":null,"type":null,"title":"Spatial prediction of soil organic carbon in coal mining subsidence areas based on RBF neural network","DOI":null,"author":[{"given":"Q.","family":"Qi"},{"given":"X.","family":"Yue"},{"given":"X.","family":"Duo"},{"given":"Z.","family":"Xu"},{"given":"Z.","family":"Li"}],"issued":{"date-parts":["2023"]},"container-title":"International Journal of Coal Science and Technology","volume":"10","issue":"1","page":"30"}]

[{"id":5236,"citationNumber":null,"type":"journal-article","title":"Prediction model with multi-point relationship fusion via graph convolutional network: A case study on mining-induced surface subsidence","DOI":"10.1371\/journal.pone.0289846","author":[{"given":"Baoxing","family":"Jiang"},{"given":"Kun","family":"Zhang"},{"given":"Xiaopeng","family":"Liu"},{"given":"Yuxi","family":"Lu"}],"issued":{"date-parts":[null]},"container-title":"PLOS ONE","volume":"18","issue":"8","page":"e0289846"}]

[{"id":5237,"citationNumber":null,"type":"journal-article","title":"Deep Neural Network Model for Determination of Coal Cutting Resistance and Performance of Bucket-Wheel Excavator Based on the Environmental Properties and Excavation Parameters","DOI":"10.3390\/pr11113067","author":[{"given":"Sr\u0111an","family":"Kosti\u0107"},{"given":"Milan","family":"Stojkovi\u0107"},{"given":"Velibor","family":"Ili\u0107"},{"given":"Jelena","family":"Trivan"}],"issued":{"date-parts":[null]},"container-title":"Processes","volume":"11","issue":"11","page":"3067"}]

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