Data‐driven recursive subspace identification based online modelling for prediction and control of molten iron quality in blast furnace ironmaking

Zhou, Ping; Dai, Peng; Song, Heda; Chai, Tianyou

doi:10.1049/iet-cta.2016.1474

Cited by 37 publications

(28 citation statements)

References 25 publications

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“…1, the primary task of BF ironmaking process is to continuously produce liquid molten iron of high quality with low production cost [4]- [6]. Therefore, the control of molten iron quality (MIQ) is of great significance [5]- [ 9]. Generally, the MIQ is characterized by the Si content ([Si]) and the molten iron temperature (MIT) [5].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the control of molten iron quality (MIQ) is of great significance [5]- [ 9]. Generally, the MIQ is characterized by the Si content ([Si]) and the molten iron temperature (MIT) [5]. The Si content is an important index indicating the chemical heat of molten iron and the MIT reflects the physical thermal state of hot metal.…”

Section: Introductionmentioning

confidence: 99%

“…However, this model cannot be updated online to capture the time-varying dynamics of ironmaking process, thus cannot always achieve satisfactory performances of modeling and control. Recently, Zhou et al [5] proposed a data-driven online modeling method for prediction and control of MIQ indices. However, due to the nonlinear nature of the ironmaking BF, the linear model developed in [5] cannot capture the nonlinear dynamics of BF process well.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Zhou et al [5] proposed a data-driven online modeling method for prediction and control of MIQ indices. However, due to the nonlinear nature of the ironmaking BF, the linear model developed in [5] cannot capture the nonlinear dynamics of BF process well. Thus it is limited that the modeling precision and control effect of MIQ indices when the method in [5] is applied to industrial practice.…”

Section: Introductionmentioning

confidence: 99%

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Recursive Learning-Based Bilinear Subspace Identification for Online Modeling and Predictive Control of a Complicated Industrial Process

2020

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In this paper, a recursive learning based bilinear subspace identification (R-B-SI) algorithm is proposed for online modeling and data-driven predictive control of blast furnace (BF) ironmaking process with strong nonlinear time-varying dynamics. Different from the existing linear SI algorithms, the R-B-SI algorithm can make full use of the process data information by adding the Kronecker product term of input data and the Kronecker product term between input and output data into the data block Hankel matrices. Moreover, the parameters of the R-B-SI model can be updated online with the latest data using recursive learning. In order to adjust the sensitivity of the algorithm to the data at different sampling times, the forgetting factor is introduced into the involved recursive learning as well. Therefore, the nonlinear time-varying dynamics of process can be well captured and described by the R-B-SI model. As a result, the data-driven predictive controller designed by the R-B-SI prediction model with parameter adaption is always capable of achieving satisfactory control performances regardless of the variation in various process operating conditions. Data experiments using actual industrial data have verified the practicability and superiority of the proposed methods. INDEX TERMS Recursive bilinear subspace identification, online modeling, recursive learning, data-driven predictive control, blast furnace ironmaking process, nonlinear time-varying dynamics.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recursive Learning-Based Bilinear Subspace Identification for Online Modeling and Predictive Control of a Complicated Industrial Process

2020

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“…Because there are highly complicated chemistry processes and the transport phenomena in blast furnaces, the data-driven models 27) with the assistance of data mining are of vital importance for throwing light on the complex interrelations among variables in ironmaking process. 28,29) The work concentrates on the development of the support vector regression (SVR) model based on data mining to predict and optimize FR of the BF with the optimal feature set including barely five parameters. The quantitative control of FR can been realized by adjusting the parameters.…”

Section: Introductionmentioning

confidence: 99%

Fuel Ratio Optimization of Blast Furnace Based on Data Mining

Zhai

Chen

2020

ISIJ Int.

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Despite the age of the process, the blast furnace (BF) ironmaking is still crucial to iron and steel industry. To improve the competitiveness of enterprises, fuel ratio (FR) in BF ironmaking process needs to be kept its lowest level possibly. In the work, a prediction model was established to predict FR of the BF by using feature selection and support vector regression (SVR). GA-SVR (genetic algorithm-SVR) method was employed to select the most informative five features from the candidate features.The experimental results indicated that the SVR model brought high learning precision and excellent prediction generalization ability. To explore and discover the laws of BF production, the influences of the five features on FR were discussed by simulation analysis of the model. All the calculations were performed on the computational platform of data mining developed by us. The work can provide guides for the operators on modulating input parameters in advance. The methods outlined here can provide valuable hints into revealing mechanisms of BF ironmaking process and realizing controlled production of BF with guidance of quantitative analysis methods.

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Optimal tracking control for blast furnace molten iron quality based on subspace identification and Krotov's method

Liu

Zhou

2023

Optim Control Appl Methods

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SummaryBlast furnace ironmaking is a complex nonlinear dynamic process, which has strict requirements on the quality of final molten iron. To improve the control effect of molten iron quality, this paper proposes an optimal tracking control method for molten iron quality based on the bilinear subspace identification and Krotov's method. First, a state space model between key process variables and blast furnace molten iron quality, using the bilinear subspace identification method, is established based on actual industrial data. Then, an augmented system is established combining the state vector and the reference trajectory, and the improving feedback controller is designed by solving a set of Riccati equations, which are established based on Krotov's method. In combination with the designed controller and the established system, the specific implementation steps are proposed. Finally, the effectiveness and advancement of the proposed method are verified based on actual industrial data of a large blast furnace.

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Data‐driven recursive subspace identification based online modelling for prediction and control of molten iron quality in blast furnace ironmaking

Cited by 37 publications

References 25 publications

Recursive Learning-Based Bilinear Subspace Identification for Online Modeling and Predictive Control of a Complicated Industrial Process

Recursive Learning-Based Bilinear Subspace Identification for Online Modeling and Predictive Control of a Complicated Industrial Process

Fuel Ratio Optimization of Blast Furnace Based on Data Mining

Optimal tracking control for blast furnace molten iron quality based on subspace identification and Krotov's method

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