An Online Sintering Batching System Based on Machine Learning and Intelligent Algorithm

Abstract

Aiming at the problem that the accuracy and economy of the traditional off-line batching method are not high, the online batching system (BSMLIA) based on machine learning and intelligent algorithms was put forward from three aspects: real-time, technical requirements and economic benefits. The accurate solution and on-line fast calculation of sintering raw material ratio under the influence of multiple factors are solved. Specifically, a BSMLIA architecture with three levels of data communication layer (DCL), parameter prediction and batching optimization layer (PPBOL), and diagnostic decision layer (DDL) was first designed to realize online monitoring and abnormal diagnosis of sinter performance. Then, the sintering batching adjustment and optimization module (SBAOM) was elaborated. The mixture performance prediction model was developed by MLR and LightGBM algorithm, the model can be based on sinter composition and quality index requirements and current sintering production process parameters to calculate the appropriate mixture performance. In addition, the pre-batching model and the sintering batching model were established to achieve the solution of the lowest raw material cost ratio for a given mixture performance. Finally, the actual production data was used to verify the SBAOM. The results proved that the online batching system can not only quickly calculate the batching plan that meets the requirements, but also reduce the batching cost by RMB 29.54/ton.