NEURAL NETWORK MODELING AND OPTIMISING OF THE AGGLOMERATION PROCESS OF SULPHIDE POLYMETALLIC ORES

During the operation of the lead-zinc production while processing of polymetallic ores, problems arose related to the quality of products and the efficient use of equipment – agglomeration furnace and crushing apparatus. Previously, such issues were resolved due to the experiences and based on mathe...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	G. Abitova, V. Nikulin, T. Zadenova
Formato:	article
Lenguaje:	EN
Publicado:	Astana IT University 2021
Materias:	neural network technology, modeling of technological processes, optimizing the mode, agglomeration furnace, control system and industrial automation Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/de3bb557bcb244feb522895f28ebb16f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:de3bb557bcb244feb522895f28ebb16f
record_format	dspace
spelling	oai:doaj.org-article:de3bb557bcb244feb522895f28ebb16f2021-12-02T13:00:20ZNEURAL NETWORK MODELING AND OPTIMISING OF THE AGGLOMERATION PROCESS OF SULPHIDE POLYMETALLIC ORES2707-90312707-904Xhttps://doaj.org/article/de3bb557bcb244feb522895f28ebb16f2021-05-01T00:00:00Zhttp://ojs.astanait.edu.kz/index.php/sjaitu/article/view/56https://doaj.org/toc/2707-9031https://doaj.org/toc/2707-904XDuring the operation of the lead-zinc production while processing of polymetallic ores, problems arose related to the quality of products and the efficient use of equipment – agglomeration furnace and crushing apparatus. Previously, such issues were resolved due to the experiences and based on mathematical modeling of processes. The mathematical model for optimizing unnecessary such operating mode is a difficult program. Performing calculations is required a fairly large investment of time and resources. Therefore, the program of the mathematical model for optimizing the operating mode of the agglomeration furnace and the crushing device for sinter firing was replaced with a neural network by implementing the process of training the network based on the results of calculations on a mathematical model. The results obtained showed that neural network models were more accurate than mathematical models, which made it possible to solve production optimization problems of great complexity. The use of neural networks for modeling technological processes has made it possible to increase the efficiency of product quality control systems and automatic control systems for the roasting of sulfide polymetallic ores.G. AbitovaV. NikulinT. ZadenovaAstana IT Universityarticleneural network technology, modeling of technological processes, optimizing the mode, agglomeration furnace, control system and industrial automationInformation technologyT58.5-58.64ENScientific Journal of Astana IT University, Vol 6, Iss 6, Pp 4-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	neural network technology, modeling of technological processes, optimizing the mode, agglomeration furnace, control system and industrial automation Information technology T58.5-58.64
spellingShingle	neural network technology, modeling of technological processes, optimizing the mode, agglomeration furnace, control system and industrial automation Information technology T58.5-58.64 G. Abitova V. Nikulin T. Zadenova NEURAL NETWORK MODELING AND OPTIMISING OF THE AGGLOMERATION PROCESS OF SULPHIDE POLYMETALLIC ORES
description	During the operation of the lead-zinc production while processing of polymetallic ores, problems arose related to the quality of products and the efficient use of equipment – agglomeration furnace and crushing apparatus. Previously, such issues were resolved due to the experiences and based on mathematical modeling of processes. The mathematical model for optimizing unnecessary such operating mode is a difficult program. Performing calculations is required a fairly large investment of time and resources. Therefore, the program of the mathematical model for optimizing the operating mode of the agglomeration furnace and the crushing device for sinter firing was replaced with a neural network by implementing the process of training the network based on the results of calculations on a mathematical model. The results obtained showed that neural network models were more accurate than mathematical models, which made it possible to solve production optimization problems of great complexity. The use of neural networks for modeling technological processes has made it possible to increase the efficiency of product quality control systems and automatic control systems for the roasting of sulfide polymetallic ores.
format	article
author	G. Abitova V. Nikulin T. Zadenova
author_facet	G. Abitova V. Nikulin T. Zadenova
author_sort	G. Abitova
title	NEURAL NETWORK MODELING AND OPTIMISING OF THE AGGLOMERATION PROCESS OF SULPHIDE POLYMETALLIC ORES
title_short	NEURAL NETWORK MODELING AND OPTIMISING OF THE AGGLOMERATION PROCESS OF SULPHIDE POLYMETALLIC ORES
title_full	NEURAL NETWORK MODELING AND OPTIMISING OF THE AGGLOMERATION PROCESS OF SULPHIDE POLYMETALLIC ORES
title_fullStr	NEURAL NETWORK MODELING AND OPTIMISING OF THE AGGLOMERATION PROCESS OF SULPHIDE POLYMETALLIC ORES
title_full_unstemmed	NEURAL NETWORK MODELING AND OPTIMISING OF THE AGGLOMERATION PROCESS OF SULPHIDE POLYMETALLIC ORES
title_sort	neural network modeling and optimising of the agglomeration process of sulphide polymetallic ores
publisher	Astana IT University
publishDate	2021
url	https://doaj.org/article/de3bb557bcb244feb522895f28ebb16f
work_keys_str_mv	AT gabitova neuralnetworkmodelingandoptimisingoftheagglomerationprocessofsulphidepolymetallicores AT vnikulin neuralnetworkmodelingandoptimisingoftheagglomerationprocessofsulphidepolymetallicores AT tzadenova neuralnetworkmodelingandoptimisingoftheagglomerationprocessofsulphidepolymetallicores
_version_	1718393535338643456

NEURAL NETWORK MODELING AND OPTIMISING OF THE AGGLOMERATION PROCESS OF SULPHIDE POLYMETALLIC ORES

Ejemplares similares