Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking

Journal Article · Fri Sep 29 00:00:00 EDT 2017 · IEEE Transactions on Neural Networks and Learning Systems

DOI:https://doi.org/10.1109/TNNLS.2017.2749412· OSTI ID:1413455

Zhou, Ping ^[1]; Guo, Dongwei ^[1]; Wang, Hong ^[2]; Chai, Tianyou ^[1]

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1. Northeastern Univ., Shenyang (China). State Key Lab. of Synthetical Automation for Process Industries
2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for the online estimation and control of MIQ indices. First, a nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices to completely capture the nonlinear dynamics of the BF process. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multioutput problem, a multitask transfer learning is proposed to design a novel multioutput LS-SVR (M-LS-SVR) for the learning of the NARX model. Furthermore, a novel M-estimator is proposed to reduce the interference of outliers and improve the robustness of the M-LS-SVR model. Since the weights of different outlier data are properly given by the weight function, their corresponding contributions on modeling can properly be distinguished, thus a robust modeling result can be achieved. Finally, a novel multiobjective evaluation index on the modeling performance is developed by comprehensively considering the root-mean-square error of modeling and the correlation coefficient on trend fitting, based on which the nondominated sorting genetic algorithm II is used to globally optimize the model parameters. Both experiments using industrial data and industrial applications illustrate that the proposed method can eliminate the adverse effect caused by the fluctuation of data in BF process efficiently. In conclusion, this indicates its stronger robustness and higher accuracy. Moreover, control testing shows that the developed model can be well applied to realize data-driven control of the BF process.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: National Natural Science Foundation of China (NNSFC); USDOE
• Grant/Contract Number: AC05-76RL01830
• OSTI ID: 1413455
• Alternate ID(s): OSTI ID: 1510720
• Report Number(s): PNNL-SA--127751
• Journal Information: IEEE Transactions on Neural Networks and Learning Systems, Journal Name: IEEE Transactions on Neural Networks and Learning Systems Journal Issue: 9 Vol. 29; ISSN 2162-237X
• Publisher: IEEE Computational Intelligence Society
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
97 MATHEMATICS AND COMPUTING
Blast furnace (BF)
m-estimator
molten iron quality (MIQ)
multiobjective optimization
multioutput least-squares support vector regression (LS-SVR)
multitask transfer learning (TL)
nonlinear autoregressive exogenous (NARX) model
robust modeling