Multimodal data-driven intelligent prediction of sinter ore FeO content: A KanFuzz-Trans model approach

Abstract

One of the hottest topics in current research is the use of different kinds of deep learning models for the identification and forecasting of important indicators in the sintering process. The model's poor prediction accuracy results from the fact that current research approaches mostly focus on chemical composition analysis or single sintering picture data analysis, making it challenging to properly investigate the complementary information between several forms of heterogeneous data. Using a convolutional neural network (CNN) framework, this study builds a Fuzzy Kolmogorov-Arnold Networks (KAN) + Transformer (KanFuzz-Trans) model that offers a precise and effective way to estimate the FeO concentration of sintered ore intelligently. To accurately depict the complex non-linear relationship between FeO content and various influencing factors, the model fully utilises the spatial feature extraction capability of CNN, the network adaptive feature optimisation capability of fuzzy KAN, and the multi-attention mechanism of Transformer. These models can capture the global feature dependency relationship, connection between several influencing elements and FeO content. The prediction accuracy is greatly increased by the model's ability to fully take into account numerous feature information by combining multimodal input, such as text and images, throughout the sintering process. Furthermore, by combining characteristics from several sources of heterogeneous data, this model significantly improves prediction accuracy, as evidenced by its improved performance metrics (R² = 0.93, mean squared error = 0.046, root mean squared error = 0.068, mean absolute error = 0.054). The new model presented in this article overcomes the main drawbacks of multimodal information utilisation and prediction accuracy when compared to previous methods. It also offers a fresh concept for achieving intelligent prediction and online detection of FeO content in sintered ores.