Hybrid Cloud-Based Information and Control System Using LSTM-DNN Neural Networks for Optimization of Metallurgical Production
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| Publicat a: | Processes vol. 13, no. 7 (2025), p. 2237-2257 |
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| Autor principal: | |
| Altres autors: | , , , , , |
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MDPI AG
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| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2227-9717 | ||
| 024 | 7 | |a 10.3390/pr13072237 |2 doi | |
| 035 | |a 3233242543 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231553 |2 nlm | ||
| 100 | 1 | |a Kuldashbay, Avazov |u Department of Computer Engineering, Gachon University Sujeong-Gu, Seongnam-si 13120, Republic of Korea; kuldoshbay@gachon.ac.kr (K.A.); akmaljon@gachon.ac.kr (A.A.) | |
| 245 | 1 | |a Hybrid Cloud-Based Information and Control System Using LSTM-DNN Neural Networks for Optimization of Metallurgical Production | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a A methodology for detecting systematic errors in sets of equally accurate, uncorrelated, aggregate measurements is proposed and applied within the automatic real-time dispatch control system of a copper concentrator plant (CCP) to refine the technical and economic performance indicators (EPIs) computed by the system. This work addresses and solves the problem of selecting and obtaining reliable measurement data by exploiting the redundant measurements of process streams together with the balance equations linking those streams. This study formulates an approach for integrating cloud technologies, machine learning methods, and forecasting into information control systems (ICSs) via predictive analytics to optimize CCP production processes. A method for combining the hybrid cloud infrastructure with an LSTM-DNN neural network model has been developed, yielding a marked improvement in TEP for copper concentration operations. The forecasting accuracy for the key process parameters rose from 75% to 95%. Predictive control reduced energy consumption by 10% through more efficient resource use, while the copper losses to tailings fell by 15–20% thanks to optimized reagent dosing and the stabilization of the flotation process. Equipment failure prediction cut the amount of unplanned downtime by 30%. As a result, the control system became adaptive, automatically correcting the parameters in real time and lessening the reliance on operator decisions. The architectural model of an ICS for metallurgical production based on the hybrid cloud and the LSTM-DNN model was devised to enhance forecasting accuracy and optimize the EPIs of the CCP. The proposed model was experimentally evaluated against alternative neural network architectures (DNN, GRU, Transformer, and Hybrid_NN_TD_AIST). The results demonstrated the superiority of the LSTM-DNN in forecasting accuracy (92.4%), noise robustness (0.89), and a minimal root-mean-square error (RMSE = 0.079). The model shows a strong capability to handle multidimensional, non-stationary time series and to perform adaptive measurement correction in real time. | |
| 653 | |a Estimates | ||
| 653 | |a Information systems | ||
| 653 | |a Predictive control | ||
| 653 | |a Control systems | ||
| 653 | |a Systems stability | ||
| 653 | |a Flotation | ||
| 653 | |a Machine learning | ||
| 653 | |a Efficiency | ||
| 653 | |a Accuracy | ||
| 653 | |a Systematic errors | ||
| 653 | |a Reagents | ||
| 653 | |a Adaptive systems | ||
| 653 | |a Neural networks | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Digital transformation | ||
| 653 | |a Error correction & detection | ||
| 653 | |a Copper loss | ||
| 653 | |a Root-mean-square errors | ||
| 653 | |a Decision making | ||
| 653 | |a Optimization | ||
| 653 | |a Methods | ||
| 653 | |a Real time | ||
| 653 | |a Energy consumption | ||
| 653 | |a Copper | ||
| 653 | |a Forecasting | ||
| 653 | |a Process parameters | ||
| 653 | |a Creeks & streams | ||
| 700 | 1 | |a Jasur, Sevinov |u Department of Information Processing and Management Systems, Tashkent State Technical University, Tashkent 100095, Uzbekistan; j.sevinov@tdtu.uz | |
| 700 | 1 | |a Barnokhon, Temerbekova |u Department of Information Technologies and Automation of Technological Processes and Production, Almalyk Branch of the National University of Science and Technology MISIS, Almalyk 110100, Uzbekistan; misis_temerbekova@mail.ru (B.T.); m67811@mail.ru (U.M.) | |
| 700 | 1 | |a Gulnora, Bekimbetova |u Department of Economics and Management, Tashkent State University of Economics, Tashkent 100007, Uzbekistan; gmbbkd@gmail.com | |
| 700 | 1 | |a Ulugbek, Mamanazarov |u Department of Information Technologies and Automation of Technological Processes and Production, Almalyk Branch of the National University of Science and Technology MISIS, Almalyk 110100, Uzbekistan; misis_temerbekova@mail.ru (B.T.); m67811@mail.ru (U.M.) | |
| 700 | 1 | |a Akmalbek, Abdusalomov |u Department of Computer Engineering, Gachon University Sujeong-Gu, Seongnam-si 13120, Republic of Korea; kuldoshbay@gachon.ac.kr (K.A.); akmaljon@gachon.ac.kr (A.A.) | |
| 700 | 1 | |a Cho Young Im |u Department of Computer Engineering, Gachon University Sujeong-Gu, Seongnam-si 13120, Republic of Korea; kuldoshbay@gachon.ac.kr (K.A.); akmaljon@gachon.ac.kr (A.A.) | |
| 773 | 0 | |t Processes |g vol. 13, no. 7 (2025), p. 2237-2257 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3233242543/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3233242543/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3233242543/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |