Hybrid LSTM–DNN Architecture with Low-Discrepancy Hypercube Sampling for Adaptive Forecasting and Data Reliability Control in Metallurgical Information-Control Systems

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