Adaptive Weighted Broad Echo State Learning System-Based Dynamic Modeling of Carbon Consumption in Sintering Process

Carbon consumption dynamic modeling is essential for energy saving, emission reduction, and green manufacturing of iron ore sintering process. This article proposes a novel adaptive weighted broad echo state learning system (AWBESLS) for carbon consumption dynamic prediction in the sintering process by integrating adaptive weights and a reservoir with echo state characteristics. Different from previous studies, the AWBESLS adaptively matches a weight to each production data to overcome the effects of anomalous data in production data and utilizes an echo state network (ESN) for catching the dynamic state in sintering process. Carbon consumption experiments using actual production data reveal the effectiveness of the AWBESLS and compare it with some state-of-the-art methods. The results show that the AWBESLS is superior to other methods in improving the prediction performance with lowest prediction error. In summary, the AWBESLS is an effective and applicable technique for dynamic modeling of the sintering process that is easily applicable for the modeling of other manufacturing processes.