Decoupling Control Method With Fuzzy Theory for Top Pressure of Blast Furnace

An, Jianqi; Yang, Jun-Yu; Wu, Min; She, Jinhua; Terano, Takao

doi:10.1109/tcst.2018.2862859

Cited by 31 publications

(12 citation statements)

References 16 publications

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“…The radial distribution of the charged solid raw materials, i.e., burden surface (see the right of Fig. 1), influences the pressure loss and local mass flows of solid and gas inside the furnace and further affects the indirect reduction degree of the ore [24], [25].…”

Section: A Actual Adjustment Status Description Of Burden Surfacementioning

confidence: 99%

Data-Driven Multiobjective Optimization for Burden Surface in Blast Furnace With Feedback Compensation

Zhang

et al. 2020

IEEE Trans. Ind. Inf.

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In this paper, an intelligent data-driven optimization scheme is proposed for finding the proper burden surface distribution, which exerts large influences on keeping blast furnace running smoothly in energy-efficient state. In the proposed scheme, production indicators prediction models are firstly developed using kernel extreme learning machine algorithm. To heel, burden surface decision is presented as a multi-objective optimization problem for the first time and solved by a modified two-stage intelligent optimization strategy to generate the initial setting values of burden surface. Furthermore, considering the existence of approximation error of the created prediction models, feedback compensation is implemented to enhance the reliability of the results, in which, an improved association rules mining method is developed to find the corrected values to compensate the initial setting values. Finally, we apply the proposed optimization scheme to determine the setting values of burden surface using actual data, and experimental results illustrate its effectiveness and feasibility.

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Section: A Actual Adjustment Status Description Of Burden Surfacementioning

confidence: 99%

Data-Driven Multiobjective Optimization for Burden Surface in Blast Furnace With Feedback Compensation

Zhang

et al. 2020

IEEE Trans. Ind. Inf.

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“…At the same time, there are many problems in blast furnace ironmaking process, such as highly coupled parameters and restricted variable detection, which makes it impossible to obtain the detection information of each molten iron quality index in time. 5,6 Therefore, traditional model-based modeling and control methods are difficult to be applied here. In this case, the data-driven modeling and control approaches highlight their superiority in that it does not rely on process mechanism and operational knowledge.…”

Section: Introductionmentioning

confidence: 99%

“…The modeling and control of blast furnace molten iron quality has always been a hot and difficult research topic in the realization of blast furnace automation, which is due to the fact that blast furnace ironmaking is an extremely complex nonlinear dynamic system, and the furnace is accompanied by harsh smelting environment such as high temperature, high pressure, and multi‐phase multi‐field coupling coexistence. At the same time, there are many problems in blast furnace ironmaking process, such as highly coupled parameters and restricted variable detection, which makes it impossible to obtain the detection information of each molten iron quality index in time 5,6 . Therefore, traditional model‐based modeling and control methods are difficult to be applied here.…”

Section: Introductionmentioning

confidence: 99%

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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“…An et al 9 introduce a fuzzy decoupling control (FDC) system in order to control the BF’s top pressure in another study. A dynamic type mathematical model, that is based on wet type top gas cleaning and recovery system, for the furnace top pressure is carried out.…”

Section: Introductionmentioning

confidence: 99%

An experimental modelling and performance validation study: Top gas pressure tracking system in a blast furnace using soft computing methods

Tunckaya¹

2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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The blast furnace is a master iron-producing plant of iron and steel factories and affected by several process parameters as well as top gas pressure , which is a key process control phenomenon to maintain stability and operational productivity in such plants. Blast furnace operation is not tolerant to any interruption, unbalanced operations, momentary disturbances or loss of control due to its nature of intensive chemical reactions and heat balance requirements. Consequently, it is crucial to monitor and control top gas system components of the furnace with instrumentation measurements to maintain stable, efficient operation and system safety ongoing. In this study, a novel top gas pressure tracking system is developed using the chronologically obtained live process data of Erdemir BF#2 in Turkey. Eight process parameters are considered as input parameters as per the plant maintenance team's recommendations and soft computing methods, artificial neural networks and adaptive neuro fuzzy inference system are employed and a statistical regression tool, autoregressive integrated moving average, is also applied for comparison. Performance and success ratio analysis is carried out using coefficient of determination ( R2), mean absolute percentage error and root mean squared error terms. The best performing model output for the adaptive neuro fuzzy inference system is found to be 0.95, 1.21 and 0.023, and slightly lower performance is obtained for the artificial neural network model with the output values of 0.94, 0.029 and 1.32 against R2, mean absolute percentage error and root mean squared error terms, respectively. The maximum prediction error is found to be 9.85% and 10.2%, and the average prediction error is found to be 1.19% and 1.29% for adaptive neuro fuzzy inference system and ANN models, respectively, for optimum simulations. The proposed neuro-fuzzy-driven top gas pressure prediction system is unique in the literature and should be integrated into existing control systems to improve operational awareness and sustainability or can be used as input guidance for a possible future top gas recovery system.

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Decoupling Control Method With Fuzzy Theory for Top Pressure of Blast Furnace

Cited by 31 publications

References 16 publications

Data-Driven Multiobjective Optimization for Burden Surface in Blast Furnace With Feedback Compensation

Data-Driven Multiobjective Optimization for Burden Surface in Blast Furnace With Feedback Compensation

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

An experimental modelling and performance validation study: Top gas pressure tracking system in a blast furnace using soft computing methods

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