A Data-driven Approach for Selecting Controlled Variables

Ye, Lingjian; Cao, Yi; Li, Yingdao; Song, Zhihuan

doi:10.3182/20120710-4-sg-2026.00062

Cited by 12 publications

(10 citation statements)

References 15 publications

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“…To derive globally optimal solution CV s, it is assumed that the NCO is approximated by CV s and the CV s are perfectly controlled at zero. Then, the loss, L (d) for a particular disturbances d, due to the approximation error, E(d) can be expressed as [1], [9]: (12) where H (d) is the reduced Hessian of the cost function evaluated at point where the CV, c ( d) is perfectly controlled corresponding to particular disturbance, d,…”

Section: Globally Optimal Methodsmentioning

confidence: 99%

“…Lwor st (H) 0.50-2 (J�L2(HGY)-IHY) (8) L (H) 1 II JI/2(HGY)-IHy I1 2 (9) average 6(ny + nd) uu F where 0-and II . IIF denote the maximum singular value and Frobenius norm, respectively, and Y (10)…”

Section: Worst-case Lossmentioning

confidence: 99%

“…Recently, a globally optimal CV selection framework has been proposed by Ye et at. [1], [9]. In this framework, the CV synthesis problem is converted into a regression problem using CV s as measure ment combinations to approximate the Necessary Conditions of Optimality (NCO) globally over the entire operation region.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Branch and bound method for globally optimal controlled variable selection

Kariwala

Cao

2012

Proceedings of 2012 UKACC International Conference on Control

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For selection of controlled variables (CVs) in self optimizing control, various criteria have been proposed in the literature. These criteria are derived based on local linearization of the process model and the necessary conditions of optimality (NCO) at a nominally optimal operating point. Recently, a novel CV selection framework has been proposed by Ye et al.[1] by converting the CV selection problem into a regression problem to approximate the NCO globally over the entire operation region. In this approach, linear combinations of a subset of available measurements are used as CV s. The subset selection problem is combinatorial in nature redering the application of the globally optimal CV selection method to large-scale processes difficult.In this work, an efficient branch and bound (BAB) algorithm is developed to handle the computational complexity associated with the selection of globally optimal CVs. The proposed BAB algorithm identifies the best measurement subset such that the regression error in approximating NCO is minimized. This algorithm is applicable to the general regression problem. The efficiency and effectiveness of the proposed BAB algorithm is demonstrated through a binary disdillation column case study.

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Section: Globally Optimal Methodsmentioning

confidence: 99%

Section: Worst-case Lossmentioning

confidence: 99%

See 1 more Smart Citation

Branch and bound method for globally optimal controlled variable selection

Kariwala

Cao

2012

Proceedings of 2012 UKACC International Conference on Control

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Globally Optimal Methodsmentioning

confidence: 99%

“…The available CV selection criteria are derived based on local linearization of the process model. Recently, a globally optimal CV selection framework has been proposed by Ye et al [2012Ye et al [ , 2013. In this framework, the CV synthesis problem is converted into a regression problem using CVs as measurement combinations to approximate the Necessary Conditions of Optimality (NCO) globally over the entire operation region.…”

Section: Introductionmentioning

confidence: 99%

Branch and bound method for regression-based controlled variable selection

Kariwala

Cao

2013

Computers & Chemical Engineering

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Highlights• A new Branch and Bound algorithm for regression based globally optimal controlled variable selection is proposed.• Local shortcoming of existing self-optimizing control approach is overcome.• New pruning algorithm makes the Branch and Bound much more efficient.• Efficiency and effectiveness of the algorithm are demonstrated though numerical examples. AbstractSelf-optimizing control is a promising method for selection of controlled variables (CVs) from available measurements. Recently, Ye et al. [2012] have proposed a globally optimal method for selection of self-optimizing CVs by converting the CV selection problem into a regression problem. In this approach, the necessary conditions of optimality (NCO) are approximated with linear combinations of available measurements over the entire operation region. In practice, it is desired that a subset of available measurements be combined as CVs to obtain a good trade-off between the economic performance and the complexity of control system. The subset selection problem, however, is combinatorial in nature, which makes the application of the globally optimal CV selection method to large-scale processes difficult. In this work, an efficient branch and bound (BAB) algorithm is developed to handle the computational complexity associated with the selection of globally optimal CVs. The proposed BAB algorithm identifies the best measurement subset such that the regression error in approximating NCO is minimized and is also applicable to the general regression problem. Numerical tests using randomly generated matrices and a binary distillation column case study demonstrate the computational efficiency of the proposed BAB algorithm.

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Identification of sparse nonlinear controlled variables for near‐optimal operation of chemical processes

Ma,

Guan,

2024

Can J Chem Eng

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For optimal operation of chemical processes, the selection of controlled variables plays an important role. A previous proposal is to approximate the necessary conditions of optimality (NCO) as the controlled variables, such that process optimality is automatically maintained by tracking constant zero setpoints. In this paper, we extend the NCO approximation method by identifying sparse nonlinear controlled variables, motivated by the fact that simplicity is always favoured for practical implementations. To this end, the ‐regularization is employed to approximate the NCO, such that the controlled variables are maintained simple, even they are specified as nonlinear functions. The sparse controlled variables are solved using the proximal gradient method, implemented within a tailored Adam algorithm. Two case studies are provided to illustrate the proposed approach.

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A Data-driven Approach for Selecting Controlled Variables

Cited by 12 publications

References 15 publications

Branch and bound method for globally optimal controlled variable selection

Branch and bound method for globally optimal controlled variable selection

Branch and bound method for regression-based controlled variable selection

Identification of sparse nonlinear controlled variables for near‐optimal operation of chemical processes

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