TY - JOUR
T1 - A novel performance assessment method of the carbon efficiency for iron ore sintering process
AU - Zhou, Kailong
AU - Chen, Xin
AU - Wu, Min
AU - Nakanishi, Yosuke
AU - Cao, Weihua
AU - Hu, Jie
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China under Grant 61210011 , the Hubei Provincial Natural Science Foundation of China under Grant 2015CFA010 , the 111 project under Grant B17040 .
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/10
Y1 - 2021/10
N2 - 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.
AB - 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.
KW - Carbon efficiency
KW - Fuzzy synthetic evaluation
KW - Performance assessment
KW - Sintering process
KW - Two-level combination
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U2 - 10.1016/j.jprocont.2021.08.011
DO - 10.1016/j.jprocont.2021.08.011
M3 - Article
AN - SCOPUS:85118784948
SN - 0959-1524
VL - 106
SP - 44
EP - 53
JO - Journal of Process Control
JF - Journal of Process Control
ER -