STROM: A Spatial–Temporal Reduced-Order Model for Zinc Fluidized Bed Roaster Temperature Field Prediction

With the intelligent transformation of process manufacturing, accurate and comprehensive perception information is fundamental for application of artificial intelligence methods. In zinc smelting, the fluidized bed roaster is a key piece of large-scale equipment and plays a critical role in the manufacturing industry; its internal temperature field directly determines the quality of zinc calcine and other related products. However, due to its vast spatial dimensions, the limited observation methods, and the complex multiphase, multi-field coupled reaction atmosphere inside it, accurately and timely perceiving its temperature field remains a significant challenge. To address these challenges, a spatial–temporal reduced-order model (STROM) is proposed, which can realize fast and accurate temperature field perception based on sparse observation data. Specifically, an initial field construction based on data assimilation (IFC-DA) method is proposed to ensure that the initial conditions of the model can be matched with the actual operation state, providing a basis for constructing a high-precision computational fluid dynamics (CFD) model. Additionally, a high uniformity-orthogonal test design (OTD) method with centered L2 deviation is innovatively proposed to ensure high information coverage of the temperature field dataset under typical working conditions.