Hierarchical nonlinear optimization-based controller of a continuous strip annealing furnace

Strommer, Stephan; Niederer, Martin; Steinboeck, Andreas; Kugi, Andreas

doi:10.1016/j.conengprac.2017.12.005

Cited by 23 publications

(10 citation statements)

References 28 publications

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“…In section II, the nonlinear model was shown to be accurate by comparing the nonlinear model with actual operation data. Therefore, we obtained the large number of data set applying numerous inputs to the nonlinear model [5], [12]. y L (t) is obtained by exciting aū L (t) to the nonlinear model simulation and we extract data sets of y L (t) andū L (t).ū L (t) is based on the following rules:…”

Section: B Data Extractionmentioning

confidence: 99%

“…Let us define the discrete-time observer estimation error e a (k) = x a (k) −x a (k). Using (12), the e a dynamics can be represented as:…”

Section: Adaptive Observermentioning

confidence: 99%

“…MPC is widely used in many practical systems [32]- [35]. MPC has been used to control the temperature of the strip in consideration of strip changes with long prediction horizons because the time response of the input is slow [2], [5], [9], [12], [21]. The MPC performance is highly dependent on the accuracy of the predictive model.…”

Section: Introductionmentioning

confidence: 99%

“…MPC, using the predictive model, achieved small temperature tracking error. Moreover, MPC is applied to a hierarchical method to manage the control input restrictions [2], [9], [12].…”

Section: Introductionmentioning

confidence: 99%

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Low-Order Model Identification and Adaptive Observer-Based Predictive Control for Strip Temperature of Heating Section in Annealing Furnace

et al. 2021

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The heating section of an annealing furnace is a plant that raises the temperature of steel strips to the desired target temperature to ensure that the strips achieve the desired material properties. Model predictive control (MPC) has been used to increase the temperature in previously published studies, because it reflects the geometrical and material characteristics of the strip. An accurate temperature predictive model for the annealing furnace is required for the optimization of the MPC. For a large and complex annealing furnace, nonlinear models from existing studies are computationally expensive. Therefore, we propose identification method of a linear low-order model, and then apply it to the MPC. In addition, we introduce parameter estimation and an adaptive observer for the estimation to address difficulties in identifying unknown parameters. To verify the identified model and adaptive observer-based MPC, control results from the tracking of target temperature are shown and analyzed through a simulation with conditions close to the actual operation conditions. Furthermore, the simulation results of the proposed method are compared with those of the PI controller and nonlinear MPC. The proposed method solves the problems caused by the massive computational complexity and absence of sensors. The proposed method allows for the accurate control of the temperature using MPC in various types of annealing furnaces.

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Section: B Data Extractionmentioning

confidence: 99%

“…Let us define the discrete-time observer estimation error e a (k) = x a (k) −x a (k). Using (12), the e a dynamics can be represented as:…”

Section: Adaptive Observermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…MPC, using the predictive model, achieved small temperature tracking error. Moreover, MPC is applied to a hierarchical method to manage the control input restrictions [2], [9], [12].…”

Section: Introductionmentioning

confidence: 99%

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Low-Order Model Identification and Adaptive Observer-Based Predictive Control for Strip Temperature of Heating Section in Annealing Furnace

et al. 2021

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“…The authors of [4,[13][14][15][16][17] considered various approaches to dynamic optimization that can generate the blank heating trajectories to the specified criteria. Possible optimization criteria include fuel efficiency and heating quality.…”

Section: Introductionmentioning

confidence: 99%

System of Energy-Saving Optimal Control of Metal Heating Process in Heat Treatment Furnaces of Rolling Mills

Andreev

2019

Machines

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Heat-treatment furnaces for workpiece pre-rolling heating are heating plants of the transition type where sheet slabs with various characteristics are simultaneously heated. The heat treatment furnace performance is directly connected with the efficient operation of the rolling mill process plants. The irregular operation pace of the rolling mill complicates the implementation of energy-saving workpiece pre-rolling heating modes and increases the risk of delivering an insufficiently heated workpiece. This paper proposes the system of controlling the heat mode of the heat-treatment furnace by the criterion of fuel consumption rate minimization and controlling over a real heated state of a sheet slab located at the workpiece pushing from the heat-treatment furnace to the rolling mill. The author uses a simplified mathematical model of workpiece heating intended for defining the energy-saving heating path accounting for the impact of technological and structural constraints in the workpiece heating process. The calculation is conducted in real time and allows obtaining an optimal heating path of each workpiece at the moment of its loading into the heat-treatment furnace. The paper considers the necessity of interaction and the interaction itself of the subsystems performing the key function in the energy-saving operation mode of the heat-treatment furnace at workpiece heating. The author studied the subsystems of forecasting the workpiece heating time on the state of the rolling mill equipment and the geometrical dimensions of the rolled band, control over gaseous fuel burning in the heat-treatment furnace operation space, system of control over the workpiece heating state before pushing from the heat-treatment furnace and forecasting the roll temperature after the treatment of the rolling mill roughing train.

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Optimal control for spatial-temporal distributed nonlinear autoregressive with exogenous inputs correlation model of steel rolling reheating furnace: a coordinated time-sharing control approach

Bao,

Zhang,

Guo

et al. 2023

Control Theory Technol.

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Hierarchical nonlinear optimization-based controller of a continuous strip annealing furnace

Cited by 23 publications

References 28 publications

Low-Order Model Identification and Adaptive Observer-Based Predictive Control for Strip Temperature of Heating Section in Annealing Furnace

Low-Order Model Identification and Adaptive Observer-Based Predictive Control for Strip Temperature of Heating Section in Annealing Furnace

System of Energy-Saving Optimal Control of Metal Heating Process in Heat Treatment Furnaces of Rolling Mills

Optimal control for spatial-temporal distributed nonlinear autoregressive with exogenous inputs correlation model of steel rolling reheating furnace: a coordinated time-sharing control approach

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