Optimization Method for Carbon Efficiency in the Green Manufacturing of Sinter Ore and its Application

Sintering process is the second most energy-consuming process in steel making and the main energy consumption of the process is the combustion of carbon. Under the background of the transformation of the world's major economies to the low carbon economy, to improve the carbon efficiency for saving energy and reducing undesired emissions is of great signification. In this paper, the comprehensive coke ratio (CCR) is taken as the index of the carbon efficiency. Mechanism analysis was carried out to analyze the influences of the CCR and a predictive model of the CCR based on back-propagation neural network (BPNN) is built that contains state parameters predictive model and the CCR predictive model. Then the optimization method for the CCR is formulated, which aims to minimize the CCR by optimizing the operating parameters. Finally, the method was implemented in an intelligent optimization and control system for carbon efficiency (IOCSCE) in an iron and steel plant. The running results show that the method can reduce the CCR by an average of 1.98 kg/t and effectively reduce the energy consumption of the sintering process. Thus, it can provide guidance for the operators of the sintering process and improve the carbon efficiency.