A Framework for Time-Series Dynamic Modeling of Carbon Consumption in Sintering Process
针对钢铁烧结过程中碳消耗的时间序列动态预测问题，提出一种基于改进即时学习和门控循环单元级联宽度学习系统的建模方法，实验证明其优于现有方法。
It becomes apparent that time-series dynamic prediction for carbon consumption in sintering production process holds immense significance in the steel industry, as it plays a pivotal role in determining the efficiency and environmental impact of the operation. Given the complexities of the sintering process, encompassing multiple operating conditions, numerous parameters, nonlinearities, etc., this article proposes a time-series dynamic modeling method for carbon consumption based on an improved just-in-time learning (JITL) and a gated recurrent unit-based temporal cascade broad learning system (GRU-TCBLS). First, the data correlation analysis method is employed to determine the process parameters affecting carbon consumption. Further, an improved JITL method incorporating moving window and JITL is developed to obtain relevant training data in real-time for model training. Finally, based on these relevant training data, the GRU-TCBLS is formulated to construct a carbon consumption prediction model. Experiments based on actual production data demonstrate the superiority of the proposed method with respect to some state-of-the-art modeling methods.