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.

The study focuses on the design of an intelligent information-control system (ICS) for metallurgical production, aimed at robust forecasting of technological parameters and automatic self-adaptation under noise, anomalies, and data drift. The proposed architecture integrates a hybrid LSTM–DNN model with low-discrepancy hypercube sampling using Sobol and Halton sequences to ensure uniform coverage of operating conditions and the hyperparameter space. The processing pipeline includes preprocessing and temporal synchronization of measurements, a parameter identification module, anomaly detection and correction using an ε-threshold scheme, and a decision-making and control loop. In simulation scenarios modeling the dynamics of temperature, pressure, level, and flow (1 min sampling interval, injected anomalies, and measurement noise), the hybrid model outperformed GRU and CNN architectures: a determination coefficient of R² > 0.92 was achieved for key indicators, MAE and RMSE improved by 7–15%, and the proportion of unreliable measurements after correction decreased to <2% (compared with 8–12% without correction). The experiments also demonstrated accelerated adaptation during regime changes. The scientific novelty lies in combining recurrent memory and deep nonlinear approximation with deterministic experimental design in the hypercube of states and hyperparameters, enabling reproducible self-adaptation of the ICS and increased noise robustness without upgrading the measurement hardware. Modern metallurgical information-control systems operate under non-stationary regimes and limited measurement reliability, which reduces the robustness of conventional forecasting and decision-support approaches. To address this issue, a hybrid LSTM–DNN architecture combined with low-discrepancy hypercube probing and anomaly-aware data correction is proposed. The proposed approach is distinguished by the integration of hybrid neural forecasting, deterministic hypercube-based adaptation, and anomaly-aware data correction within a unified information-control loop for non-stationary industrial processes.

The proposed structure of an automated system of operational dispatch control of a complex technological complex using the criterion of noise immunity for decision-making. On the basis of the proposed noise immunity criterion for the operational dispatch control system of a complex technological complex, tasks are formulated that ensure forecasting the possibility of performing planned tasks, determining the control actions necessary to ensure the possibility of performing planned tasks with a given permissible noise immunity assessment, maximizing output while maintaining the permissible value of noise immunity.

The scheme of interaction of intellectual support tools in the systems of automated control and management of the structural system of intellectual control in mining and metallurgical complexesis disclosed. Practical implementation of quality management of technological parameters in complex thermal technological processes on the basis of modern cloud technologies implies a detailed knowledge base of the principles of construction and operation of the system, the specifics of using different algorithms, the features of actuators and the controlled object. The expediency and ways of solving the problem of filtering, distorted by disturbances and noise of the measuring signal on the basis of methods of the theory of fuzzy sets are substantiated. The algorithm of control of reliability of the measuring information in thermal technological processes of mining and metallurgical productions which is based on existence in control system of certain information redundancy of the data and consisting of separate interacting with each other blocks: "Preliminary control", "Transformation of equations", "Equations" and "Calculation xi " of corrected values xi = xi* + vi which satisfy to the initial equations and are most reliable" is offered.

The paper reveals the ultrahigh-frequency (UHF) method of measuring the moisture content of bulk materials in the form of charge in complex metallurgical processes, which is part of the non-destructive method of control. Theoretical and experimental studies of physical properties of materials in the form of blend containing enriched ore raw materials in metallurgical processes are presented. The mechanism of microwave electromagnetic wave action to study the optimum moisture content in the charge, in which the electromagnetic wave emitted by the directional transmitting antenna falls on the controlled non-conducting material with a thickness of d, is considered. It is substantiated that as the mass ratio of moisture increases, the active component σ and grows, increasing microwave energy losses. An analytical expression to estimate this perturbing factor of the dielectric properties of the dispersed medium is given. The implementation of simulation modeling of information-measuring signals (IMS), allowing to build models in complex metallurgical processes, is presented.

Туйчибоев Х.Х., Саиднумонова С.А., Маманазаров У.Б. РАЗРАБОТКА ИНТЕЛЛЕКТУАЛЬНОЙ СИСТЕМЫ УПРАВЛЕНИЯ ТЕПЛОВЫМ РЕЖИМОМ ПЕЧИ ВАНЮКОВА МЕДЕПЛАВИЛЬНОГО ПРОИЗВОДСТВА АО «АЛМАЛЫКСКИЙ ГМК» НА ОСНОВЕ ИСКУССТВЕННЫХ НЕЙРОННЫХ СЕТЕЙ // Universum: технические науки : электрон. научн. журн. 2026. 7(148). URL: https://7universum.com/ru/tech/archive/item/23162