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

Zhou, Ping; Guo, Dongwei; Wang, Hong; Chai, Tianyou

doi:10.1109/TNNLS.2017.2749412

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

Abstract

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 optimizemore »« less

Authors:

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

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

Publication Date:: 2017-09-29

Research Org.:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Org.:: USDOE; National Natural Science Foundation of China (NSFC)

OSTI Identifier:: 1413455

Report Number(s):: PNNL-SA-127751
Journal ID: ISSN 2162-237X; TRN: US1800429

Grant/Contract Number:: AC05-76RL01830; 61473064; 61290323; 61621004; 61333007; N160805001; N160801001

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Neural Networks and Learning Systems

Additional Journal Information:: Journal Volume: 29; Journal Issue: 9; Journal ID: ISSN 2162-237X

Publisher:: IEEE Computational Intelligence Society

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; 42 ENGINEERING; 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

Citation Formats


                    Zhou, Ping, Guo, Dongwei, Wang, Hong, and Chai, Tianyou. Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking.  United States: N. p., 2017. 
Web.  doi:10.1109/TNNLS.2017.2749412.

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                    Zhou, Ping, Guo, Dongwei, Wang, Hong, & Chai, Tianyou. Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking.  United States.  https://doi.org/10.1109/TNNLS.2017.2749412

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                    Zhou, Ping, Guo, Dongwei, Wang, Hong, and Chai, Tianyou. Fri .  
"Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking".  United States.  https://doi.org/10.1109/TNNLS.2017.2749412.  https://www.osti.gov/servlets/purl/1413455.

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@article{osti_1413455,

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

  author       = {Zhou, Ping and Guo, Dongwei and Wang, Hong and Chai, Tianyou},

  abstractNote = {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.},

  doi          = {10.1109/TNNLS.2017.2749412},

  journal      = {IEEE Transactions on Neural Networks and Learning Systems},

  number       = 9,

  volume       = 29,

  place        = {United States},

  year         = {2017},

  month        = {9}

}

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