A novel performance assessment method of the carbon efficiency for iron ore sintering process

Improving carbon efficiency is an effective way to save energy and reduce harmful emission for a sintering process. Optimizing carbon efficiency is an effective way to achieve that goal, and its precondition is to assess the performance of the carbon efficiency. However, there is seldom research about how to assess the carbon efficiency whether it needs to be optimized. To address this, this paper introduces a performance assessment method for evaluating the performance of the carbon efficiency. First, the sintering process and the key characteristics are analyzed, and the carbon efficiency indexes are defined. Second, the structure of the assessment method is presented. The method consists of a prediction model based on three NNs, and an assessment method based on the fuzzy synthetic evaluation method. Two-level combination strategy is proposed to improve prediction performance and assessment accuracy, with the using of bootstrap aggregating, linear combination, and majority voting. Finally, verification based on process data shows that the proposed method can assess the performance of the carbon efficiency with high accuracy. More specially, the prediction errors of the combination model for the CCR are basically in the range of [-2.738 kg/t, 3.442 kg/t], and for the CO/CO2 they are basically in the range of [-8.16 × 10−3, 4.828 × 10−3]. The combination models have better prediction performance than single NNs. Moreover, the assessment accuracy of the proposed method is 87%, which has higher accuracy than other models. This model lays the groundwork of improving the carbon efficiency for sintering process. •Prediction models for the carbon efficiency is developed in the forms of three NNs, BPNN, RBF, ELM.•A performance assessment for the carbon efficiency is developed based on comprehensive evaluation method.•Two level combination strategy is proposed to improve the accuracy of the assessment.•The results based on actual run data have verified that the proposed method can meet the needs of the sintering process with higher assessment accuracy than conventional methods.