Sintering is an important production process in iron and steel metallurgy. Carbon fuel consumption accounts for about 80% of the total energy consumption in the sintering process. To enhance the efficiency of carbon fuel consumption, we need to determine the factors affecting carbon efficiency and build a model of it. In this paper, the CO/CO₂ is taken to be a measure of carbon efficiency, and a cascade predictive model is built to predict it. This model has two parts: the key state parameter submodel and the CO/CO₂ submodel. The submodels are built using particle swarm optimization-based back propagation neural networks (PSO-BPNNs). Based on the mechanism analysis, spearman’s rank correlation coefficient (SRCC) and stepwise regression analysis (SRA) are used to determine the relationship between the process parameters, in order to determine the inputs of each submodel. Finally, the results of a simulation show the feasibility of the cascade model, which will serve as the basic model for the optimization and control of the carbon efficiency of the sintering process.

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