SCORN: Sinter Composition Optimization with Regressive Convolutional Neural Network

Abstract

Sinter composition optimization is an important process of iron and steel companies. To increase companies’ profits, they often rely on innovative technology or the workers’ operating experience to improve final productions. However, the former is costly because of patents, and the latter is error-prone. In addition, traditional linear programming optimization methods of sinter compositions are inefficient in the face of large-scale problems and complex nonlinear problems. In this paper, we are the first to propose a regressive convolutional neural network (RCNN) approach for the sinter composition optimization (SCORN). Our SCORN is a single input and multiple outputs regression model. Sinter plant production is used as the input of the SCORN model, and the outputs are the optimized sintering compositions. The SCORN model can predict the optimal sintering compositions to reduce the input of raw materials consumption to save costs and increase profits. By constructing a new neural network structure, the RCNN model is trained to increase its feature extraction capability for sintering production. The SCORN model has a better performance compared with several regressive approaches. The practical application of this predictive model can not only formulate corresponding production plans without feeding materials but also give better input parameters of sintered raw materials during the sintering process.