産業用熱プロセスの多パラメータ予測のためのハイブリッドファジークラスタリングと時間的深層学習フレームワーク
Hybrid fuzzy clustering and temporal deep learning framework for multi-parameter forecasting in industrial thermal processes (原題)
V. Vignesh, G. V. Narendran, R. Senthil Kumar, R. Sitharthan
🤖 gxceed AI 要約
日本語
高炉操業の熱・ガスパラメータを同時予測するため、FCMクラスタリングとNARX/RNN/LSTM/GRUなどの時間的深層学習を統合したフレームワークを提案。実稼働高炉の43,396サンプルで評価し、FCM-GRUが最良の性能を示した。CO2予測でR2=0.912を達成し、リアルタイム監視と意思決定支援の可能性を示す。
English
A hybrid framework integrating Fuzzy C-Means clustering with temporal deep learning (NARX, RNN, LSTM, GRU) is proposed for multi-parameter forecasting in blast furnaces. Evaluated on 43,396 industrial DCS samples, FCM-GRU achieved the best performance, with R2 of 0.912 for CO2 prediction, demonstrating feasibility for real-time monitoring and decision support.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の鉄鋼業はカーボンニュートラル目標に向け高炉の省エネ・CO2削減が急務。本手法はAIによる高炉操業最適化でエネルギー効率向上と排出削減に寄与し、SSBJ開示や投資家対応にも有用なデータ基盤となる。
In the global GX context
This work supports global efforts to decarbonize hard-to-abate sectors like steelmaking. AI-driven predictive modeling for blast furnaces can enhance energy efficiency and reduce emissions, aligning with TCFD/ISSB disclosure expectations and transition finance criteria.
👥 読者別の含意
🔬研究者:Provides a novel hybrid approach combining fuzzy clustering with temporal deep learning for multi-parameter forecasting in industrial processes.
🏢実務担当者:Offers a practical framework for real-time monitoring and predictive maintenance in blast furnace operations, potentially reducing energy costs and emissions.
🏛政策担当者:Highlights the role of AI in enabling decarbonization of energy-intensive industries, informing policy on industrial energy efficiency and carbon reduction.
📄 Abstract（原文）
Blast furnace (BF) operation involves strongly coupled thermal and chemical processes governed by nonlinear heat transfer, gas–solid reactions, and dynamic operational control. Accurate real-time prediction of key thermal and gas parameters is essential for maintaining furnace stability, improving energy efficiency, and reducing carbon emissions. However, most existing studies focus on single-parameter forecasting and fail to adequately capture the heterogeneous temporal dynamics and strong process coupling inherent in blast furnace operations. In this paper, a hybrid fuzzy clustering and temporal deep learning framework is proposed for multi-parameter forecasting. This framework integrates Fuzzy C-Means (FCM) clustering with temporal deep learning models, such as Nonlinear Autoregressive with Exogenous Inputs (NARX), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The dataset consists of 43,396 industrial Distributed Control System (DCS) samples collected from an operating BF. FCM is employed to identify different operating regimes and generate fuzzy membership values, which are subsequently utilized as sample weights during model training. The performance of the proposed models is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and coefficient of determination ( R 2 ). Among the developed models, the FCM-GRU model achieved the best overall forecasting performance, attaining R 2 values of 0.602, 0.355, 0.656, and 0.912 for the prediction of Hot Metal Temperature (HMT), silicon content (Si), CO, and CO₂ respectively. The novelty of the proposed work lies in integrating fuzzy membership-based operational-state identification with temporal deep learning architectures for simultaneous forecasting of multiple blast furnace parameters. The obtained results demonstrate the feasibility of the proposed framework for real-time process monitoring and predictive decision support in blast furnace operations.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1826705/pdffirst seen 2026-08-20 05:13:12 · last seen 2026-09-21 05:06:09
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