Hybrid fuzzy clustering and temporal deep learning framework for multi-parameter forecasting in industrial thermal processes.

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 (