Year: 2025
Type: conference-paper
Abstract: Accurate prediction of temperature and energy consumption in process industries faces challenges from noisy sensor data and physical model parameter sensitivity. This paper proposes a hybrid model combining Long Short-Term Memory (LSTM) networks with the heat transfer equation to balance data-driven flexibility and physical interpretability. The LSTM captures nonlinear temporal features from multi-sensor data (temperature, flow rate, pressure), while the heat transfer equation provides physical constraints through discretized thermal diffusion simulations. An attention mechanism dynamically weights the outputs of both channels, and a joint loss function enforces both data accuracy and physical consistency. Experiments on a real-world dataset from a magnesia production plant (10, 000+ time steps) demonstrate that the hybrid model reduces RMSE by 32% (from 2.17°C to 1.48°C) compared to pure LSTM and 61% compared to the pure physical model. The results highlight the model’s ability to handle sensor noise while adhering to thermodynamic laws.
Authors: Xin Li, Baichuan Lin, Hongchen Liu, Miao Yu, Sai Luo, Di Fang
Institutions: Shenyang Institute of Automation, Chinese Academy of Sciences
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4413154258