A weighted local steady‐state determination approach based on the globally optimal economic steady‐states

Liu, Jian-Bang; Sun, Haojie; Lu, Yunsong; Hu, Jingtao; Zou, Tao

doi:10.1002/cjce.23946

Cited by 4 publications

(4 citation statements)

References 21 publications

(42 reference statements)

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“…The steady-state optimization layer has two functions: one is feasibility determination and soft constraint adjustment, and the other is steady-state target optimization. [15,16] In the first part, if the optimization problem has no feasible region, the soft constraints of the CVs need to be changed to hard engineering constraints. The second part calculates the steady-state target for the feasible region and the steadystate model.…”

Section: Double-layer Model Predictive Controlmentioning

confidence: 99%

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A multi‐priority hierarchical optimization method for double‐layer model predictive control

et al. 2022

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Considering the demand for the sequential regulation of manipulated variables in actual industrial process control, the conventional solution of double‐layer model predictive control faces the problem that the weight coefficients are difficult to tune. This paper proposes an improved hierarchical optimization method for manipulated variables in the steady‐state optimization layer of double‐layer model predictive control. The proposed method can adjust the manipulated variables sequentially without an accurate weight coefficient to avoid difficulty in tuning the weight coefficients. The relation between the optimal solution and the feasible region of the steady‐state optimization layer is analysed to describe the reoptimization of the key manipulated variables. The impact of the economic cost coefficient on the optimal solution with the sensitivity analysis method is studied, and the complexity of using the weight coefficient to solve the priority optimization problem of the manipulated variables is assessed. The steady‐state optimization solution procedure is improved based on the theory of the multiobjective complete hierarchical method. The hierarchical and sequential optimization of the manipulated variables results in expanding the space and freedom of the key manipulated variables, increasing efficiency, reducing consumption, and improving economic performance. The improved hierarchical optimization method is direct and simple in achieving optimization sequentially and satisfies the need for adjusting the manipulated variables according to human intentions.

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Section: Double-layer Model Predictive Controlmentioning

confidence: 99%

“…In solving an economic optimization problem with the simplex method, the discriminant number σ is used to determine whether to enter the new basis or leave the old basis. σ is calculated by Equation (16).…”

Section: Weight Coefficient and Optimal Solutionmentioning

confidence: 99%

A multi‐priority hierarchical optimization method for double‐layer model predictive control

et al. 2022

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“…MPC with steady-state optimization layer and dynamic control layer is called double-layer MPC (DLMPC), domestic and foreign scholars have rich research works on its theory. 8–13…”

Section: Introductionmentioning

confidence: 99%

“…MPC with steady-state optimization layer and dynamic control layer is called double-layer MPC (DLMPC), domestic and foreign scholars have rich research works on its theory. [8][9][10][11][12][13] The valve position control (VPC) system is a constant value control method proposed by Shinskey to reduce energy consumption in distillation columns. 14 The set value of the master valve in the VPC system functions as the secondary valve measurement signal, enabling the system with quick responsiveness via the primary manipulated variable (MV) and with economy via the secondary MV.…”

Section: Introductionmentioning

confidence: 99%

A double-layer model predictive control algorithm for valve position control systems

Wang

Zou

Zheng

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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Valve position control systems have the characteristics of combining static and dynamic, as well as fast and slow, which enables the system’s quick response while maintaining some economic performance. However, there is further potential to optimize the economic performance of the valve position control system under conventional control algorithms. This article proposes an improved double-layer model predictive control algorithm for the valve position control system to exploit the potential economic performance while satisfying control goals. In the steady-state optimization layer of the improved double-layer structural model predictive control, the ideal residence point of the key manipulated variable is calculated from the set point of the controlled variable and the steady-state model to characterize the coupling relationship between the primary and secondary manipulated variables in the valve position control system, which realizes the successive actuation feature between the manipulated variables. In order to improve the response speed of the system, the dynamic control layer adopts a two-mode switching strategy, where the mode switching symbol is the time at which the controlled variable first approaches a given steady-state threshold value. Based on the feedback from the dynamic control layer, the steady-state optimization layer optimizes set points of controlled and manipulated variables that can handle disturbances entered into the process at any time to improve the economic performance of valve position control systems. This article implements classical control methods via advanced process strategies to consider the optimal control of processes from a superior perspective.

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Steady-state sequence optimization with incremental input constraints in two-layer model predictive control

et al. 2022

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A weighted local steady‐state determination approach based on the globally optimal economic steady‐states

Cited by 4 publications

References 21 publications

A multi‐priority hierarchical optimization method for double‐layer model predictive control

A multi‐priority hierarchical optimization method for double‐layer model predictive control

A double-layer model predictive control algorithm for valve position control systems

Steady-state sequence optimization with incremental input constraints in two-layer model predictive control

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