Hybrid LSTM–DNN Architecture with Low-Discrepancy Hypercube Sampling for Adaptive Forecasting and Data Reliability Control in Metallurgical Information-Control Systems.
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| Title: | Hybrid LSTM–DNN Architecture with Low-Discrepancy Hypercube Sampling for Adaptive Forecasting and Data Reliability Control in Metallurgical Information-Control Systems. |
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| Authors: | Sevinov, Jasur, Temerbekova, Barnokhon, Bekimbetova, Gulnora, Mamanazarov, Ulugbek, Bekimbetov, Bakhodir |
| Source: | Processes; Jan2026, Vol. 14 Issue 1, p147, 29p |
| Subject Terms: | FORECASTING, INTELLIGENT control systems, INDUSTRIAL controls manufacturing, OPTIMIZATION algorithms, OUTLIER detection, NEURAL computers, ROBUST control, DATA integrity |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Processes is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Hybrid LSTM–DNN Architecture with Low-Discrepancy Hypercube Sampling for Adaptive Forecasting and Data Reliability Control in Metallurgical Information-Control Systems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sevinov%2C+Jasur%22">Sevinov, Jasur</searchLink><br /><searchLink fieldCode="AR" term="%22Temerbekova%2C+Barnokhon%22">Temerbekova, Barnokhon</searchLink><br /><searchLink fieldCode="AR" term="%22Bekimbetova%2C+Gulnora%22">Bekimbetova, Gulnora</searchLink><br /><searchLink fieldCode="AR" term="%22Mamanazarov%2C+Ulugbek%22">Mamanazarov, Ulugbek</searchLink><br /><searchLink fieldCode="AR" term="%22Bekimbetov%2C+Bakhodir%22">Bekimbetov, Bakhodir</searchLink> – Name: TitleSource Label: Source Group: Src Data: Processes; Jan2026, Vol. 14 Issue 1, p147, 29p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22FORECASTING%22">FORECASTING</searchLink><br /><searchLink fieldCode="DE" term="%22INTELLIGENT+control+systems%22">INTELLIGENT control systems</searchLink><br /><searchLink fieldCode="DE" term="%22INDUSTRIAL+controls+manufacturing%22">INDUSTRIAL controls manufacturing</searchLink><br /><searchLink fieldCode="DE" term="%22OPTIMIZATION+algorithms%22">OPTIMIZATION algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22OUTLIER+detection%22">OUTLIER detection</searchLink><br /><searchLink fieldCode="DE" term="%22NEURAL+computers%22">NEURAL computers</searchLink><br /><searchLink fieldCode="DE" term="%22ROBUST+control%22">ROBUST control</searchLink><br /><searchLink fieldCode="DE" term="%22DATA+integrity%22">DATA integrity</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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 R<superscript>2</superscript> > 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. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Processes is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/pr14010147 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 147 Subjects: – SubjectFull: FORECASTING Type: general – SubjectFull: INTELLIGENT control systems Type: general – SubjectFull: INDUSTRIAL controls manufacturing Type: general – SubjectFull: OPTIMIZATION algorithms Type: general – SubjectFull: OUTLIER detection Type: general – SubjectFull: NEURAL computers Type: general – SubjectFull: ROBUST control Type: general – SubjectFull: DATA integrity Type: general Titles: – TitleFull: Hybrid LSTM–DNN Architecture with Low-Discrepancy Hypercube Sampling for Adaptive Forecasting and Data Reliability Control in Metallurgical Information-Control Systems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sevinov, Jasur – PersonEntity: Name: NameFull: Temerbekova, Barnokhon – PersonEntity: Name: NameFull: Bekimbetova, Gulnora – PersonEntity: Name: NameFull: Mamanazarov, Ulugbek – PersonEntity: Name: NameFull: Bekimbetov, Bakhodir IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 22279717 Numbering: – Type: volume Value: 14 – Type: issue Value: 1 Titles: – TitleFull: Processes Type: main |
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