Formation of a complex method for analyzing multidimensional production data of a processing plant

Economic Annals-ХХI: Volume 194, Issue (11-12), Pages: 36-48

Citation information:
Ivashchuk, O., Ivashchuk, O., Fedorov, V., Rodionov, A., & Shtana, A. (2021). Formation of a complex method for analyzing multidimensional production data of a processing plant. Economic Annals-XXI, 194(11-12), 36-48. doi: https://doi.org/10.21003/ea.V194-05

Olga Ivashchuk
D.Sc. (Engineering),
Professor,
Belgorod State National Research University
85 Pobedy Str., Belgorod, 308015, Russian Federation
ivaschuk@bsu.edu.ru
ORCID ID: https://orcid.org/0000-0002-9383-9141

Orestes Ivashchuk
PhD (Engineering),
Associate Professor,
Belgorod State National Research University
85 Pobedy Str., Belgorod, 308015, Russian Federation
ivaschuk_o@bsu.edu.ru
ORCID ID: https://orcid.org/0000-0002-8261-3702

Vyacheslav Fedorov
PhD (Engineering),
Associate Professor,
Belgorod State National Research University
85 Pobedy Str., Belgorod, 308015, Russian Federation
fedorov_v@bsu.edu.ru
ORCID ID: https://orcid.org/0000-0003-1461-2342

Alexander Rodionov
PhD Student (Engineering),
Belgorod State National Research University
85 Pobedy Str., Belgorod, 308015, Russian Federation
rodionov_y@bsu.edu.ru
ORCID ID: https://orcid.org/0000-0001-6485-8901

Albert Shtana
PhD Student (Engineering),
Belgorod State National Research University
85 Pobedy Str., Belgorod, 308015, Russian Federation
shtana@bsu.edu.ru
ORCID ID: https://orcid.org/0000-0002-0365-323X

Formation of a complex method for analyzing multidimensional production data of a processing plant

Abstract. We discuss the development of a comprehensive data analysis method which allows increasing the accuracy of predicting the performance of roller mill of processing plant (RP) mining and processing plant (GOK) when you change the properties of incoming raw materials for processing. The described method includes primary data processing, determination by statistical analysis methods and data mining algorithms of the most significant factors affecting the resulting parameter, development of mathematical models based on correlation regression and factor analysis, analysis and confirmation of the quality of forecasting by a neural network. The systematization and coordination of production data was carried out, the generated database was processed using statistical and intellectual analysis methods, the physical and chemical parameters of the input raw materials and processed ore that have the greatest impact on the mill productivity were determined, mathematical models were formed to determine the expected productivity, their quality and applicability limits were evaluated, the error in predicting the mill productivity for various mineralogical compositions of the processed ore was determined. The significance of the parameters used in the models is checked by the algorithms of intelligent analysis. The verification of the used models for predicting mill performance by an artificial neural network is carried out by comparing the series of predicted values of the resulting factor obtained by different models.

Keywords: Performance Forecasting; Decision Trees Algorithm; Artificial Neural Network; Processing Plant; Roller Mill

JEL Classifications: Е24; C02; Е64; C20; J31

Acknowledgements and Funding: The authors received no direct funding for this research.

Contribution: The authors contributed equally to this work.

Data Availability Statement: The dataset is available from the authors upon request.

DOI: https://doi.org/10.21003/ea.V194-05

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Received 6.07.2021
Received in revised form 19.08.2021
Accepted 20.09.2021
Available online 27.12.2021