Type-2 Fuzzy Broad Echo State Learning System for Carbon Efficiency Prediction in Iron Ore Sintering Process

Accurately predicting carbon efficiency is crucial for optimizing sintering operations in iron and steel production. This process is highly energy-intensive and has significant environmental and economic implications. However, the sintering process exhibits complex characteristics, including strong nonlinearity, time-varying dynamics, and operational uncertainties. These characteristics severely limit the performance of conventional modeling approaches. Existing data-driven methods often fail to capture temporal dependencies and handle uncertainty simultaneously, resulting in inaccurate and unreliable predictions. To address these challenges, this article introduces a novel type-2 fuzzy broad echo state learning system (T2FBESLS). This system integrates an interval type-2 fuzzy neural network into the feature layer of a broad learning system to improve uncertainty modeling. The enhancement nodes are replaced with echo state reservoirs that effectively encode the temporal dynamics of the sintering process. When evaluated using real-world industrial sintering data, the T2FBESLS achieved a reduction in root-mean-square error of at least about 15% compared to several state-of-the-art models. This demonstrates its superior prediction accuracy and stability. The key innovation lies in the fusion of type-2 fuzzy logic for handling uncertainty, broad learning for efficient structure expansion, and an echo state network for temporal modeling, offering a new approach to intelligent modeling in complex metallurgical processes.