G. Abitova, V. Nikulin, T. Zadenova


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.

Ключевые слова

neural network technology, modeling of technological processes, optimizing the mode, agglomeration furnace, control system and industrial automation

Полный текст:

PDF (English)


Abitova, G., Abdrakhmanova, E., Bekish, Z., Zadenova, T., Rzayeva, L., & Kulniyazova, K. (2021, April).

Study and Simulation of Control System of the Process of Roasting in Fluidized Bed Furnaces

of Polymetallic Sulfide Ores under Uncertainty. In 2021 IEEE International Conference on Smart

Information Systems and Technologies (SIST) (pp. 1-6). IEEE.

Vekhnik, V.A. (2002). Thermal Neural Network Modeling Continuous Furnace Operation Metallurgical Heat Engineering. The Proceedings of the National Metallurgical Academy of Ukraine, 8, Publisher:

NMetAU, Dnepropetrovsk, 226.

Andreeva, A.Yu., Romanchuk, V.A. (2015). The use of neurocomputer technologies in methods of

managing complex objects. Modern technology and technology, 4 [Electronic resource]. URL: https:// (date of access: 15.04.2021).

Srinivasan, D., Chang, C.S., & Liew, A.C. (1995). Demand forecasting using fuzzy neural computation,

with special emphasis on weekend and public holiday forecasting. IEEE Transactions on Power

Systems, 10(4), 1897-1903.

Santoso, N.I., & Tan, O.T. (1990). Neural-net based real-time control of capacitors installed on

distribution systems. IEEE Transactions on Power Delivery, 5(1), 266-272.

Caudana, B., Conti, F., Helcke, G., & Pagani, R. (1995). A prototype expert system for large scale

energy auditing in buildings. Pattern recognition, 28(10), 1467-1475.

Hiyama, T., Kouzuma, S., Imakubo, T., & Ortmeyer, T.H. (1995). Evaluation of neural network

based real time maximum power tracking controller for PV system. IEEE transactions on Energy

Conversion, 10(3), 543-548.

Thomas, R.J., Sakk, E., Hashemi, K., Ku, B.Y., & Chiang, H. (1990, May). On-line security classification

using an artificial neural network. In IEEE International Symposium on Circuits and Systems (pp.

-2924). IEEE.

Aggoune, M.E., & Vadari, S.V. (1990, November). Use of artificial neural networks in a dispatcher

training simulator for power system dynamic security assessment. In 1990 IEEE International

Conference on Systems, Man, and Cybernetics Conference Proceedings (pp. 233-238). IEEE.

Michalik-Mielczarska, G. (1992). Dynamilc state estimation of a synchronous generator using neural-networks techniques, (92/15), 21-28.

Gorbunov, V.A. (2011). Using neural network technologies to improve energy efficiency heat technology installations. in Monograph, “Ivanovsky State Power Engineering University named after IN

AND. Lenin”, Ivanovo, 476.

Tomashpolsky V.I. and other. (1992). Heat exchange and thermal modes in industrial furnaces, Minsk:

Higher school, 217.

Sokolov, A.K. (2002). Optimization of operating and design parameters and improvement of calculation methods for gas heating furnaces”, in, 340.

Yu, D., Utigard, T.A., & Barati, M. (2014). Fluidized bed selective oxidation-sulfation roasting of nickel

sulfide concentrate: Part II. Sulfation roasting. Metallurgical and Materials Transactions B, 45(2), 662-

Abitova, G. (2020). Mathematical simulation and study of control stability of the chemicalengineering processes in industry. Scientific Journal of Astana IT University, (4), 4-13



  • Ссылки не определены.

(P): 2707-9031
(E): 2707-904X

Бизнес-центр EXPO, блок C.1.
Казахстан, 010000