Browsing by Author "Kariwala, Vinay"

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    Bidirectional branch and bound for controlled variable selection. Part I: principles and minimum singular value criterion.
    (Elsevier Science B.V., Amsterdam., 2008-10-17T00:00:00Z) Cao, Yi; Kariwala, Vinay
    The minimum singular value (MSV) rule is a useful tool for selecting controlled variables (CVs) from the available measurements. However, the application of the MSV rule to large-scale problems is difficult, as all feasible measurement subsets need to be evaluated to find the optimal solution. In this paper, a new and efficient branch and bound (BAB) method for selection of CVs using the MSV rule is proposed by posing the problem as a subset selection problem. In traditional BAB algorithms for subset selection problems, pruning is performed downwards (gradually decreasing subset size). In this work, the branch pruning is considered in both upward (gradually increasing subset size) and downward directions simultaneously so that the total number of subsets evaluated is reduced dramatically. Furthermore, a novel bidirectional branching strategy to dynamically branch solution trees for subset selection problems is also proposed, which maximizes the number of nodes associated with the branches to be pruned. Finally, by replacing time-consuming MSV calculations with novel determinant based conditions, the efficiency of the bidirectional BAB algorithm is increased further. Numerical examples show that with these new approaches, the CV selection problem can be solved incredibly fast.
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    Bidirectional branch and bound for controlled variable selection. Part II: exact local method for self-optimizing control
    (Elsevier Science B.V., Amsterdam., 2009-08-12T00:00:00Z) Kariwala, Vinay; Cao, Yi
    The selection of controlled variables (CVs) from available measurements through enumeration of all possible alternatives is computationally forbidding for large-dimensional problems. In Part I of this work [Cao, Y., & Kariwala, V. (2008). Bidirectional branch and bound for controlled variable selection: Part I. Principles and minimum singular value criterion. Comput. Chem. Eng., 32 (10),2306-2319], we proposed a bidirectional branch and bound (BAB) approach for subset selection problems and demonstrated its efficiency using the minimum singular value criterion. In this paper, the BAB approach is extended for CV selection using the exact local method for self-optimizing control. By redefining the loss expression, we show that the CV selection criterion for exact local method is bidirectionally monotonic. A number of novel determinant based criteria are proposed for fast pruning and branching purposes resulting in a computationally inexpensive BAB approach. We also establish a link between the problems of selecting a subset and combinations of measurements as CVs and present a partially bidirectional BAB method for selection of measurements, whose combinations can be used as CVs. Numerical tests using randomly generated matrices and binary distillation column case study demonstrate the computational efficiency of the proposed methods. (C) 2009 Elsevier Ltd. All rights reserved.
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    Bidirectional branch and bound for controlled variable selection. Part III: local average loss minimization
    (IEEE, 2010-02-05T00:00:00Z) Kariwala, Vinay; Cao, Yi
    The selection of controlled variables (CVs) from available measurements through exhaustive search is computationally forbidding for large-scale processes. We have recently proposed novel bidirectional branch and bound (B-3) approaches for CV selection using the minimum singular value (MSV) rule and the local worst- case loss criterion in the framework of self-optimizing control. However, the MSV rule is approximate and worst-case scenario may not occur frequently in practice. Thus, CV selection by minimizing local average loss can be deemed as most reliable. In this work, the B-3 approach is extended to CV selection based on local average loss metric. Lower bounds on local average loss and, fast pruning and branching algorithms are derived for the efficient B-3 algorithm. Random matrices and binary distillation column case study are used to demonstrate the computational efficiency of the proposed method.
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    A branch and bound method for isolation of faulty variables through missing variable analysis
    (Elsevier Science B.V., Amsterdam., 2010-12-31T00:00:00Z) Kariwala, Vinay; Odiowei, P. E.; Cao, Yi; Chen, T.
    Fault detection and diagnosis is a critical approach to ensure safe and efficient operation of manufacturing and chemical processing plants. Although multivariate statistical process monitoring has received considerable attention, investigation into the diagnosis of the source or cause of the detected process fault has been relatively limited. This is partially due to the difficulty in isolating multiple variables, which jointly contribute to the occurrence of fault, through conventional contribution analysis. In this work, a method based on probabilistic principal component analysis is proposed for fault isolation. Furthermore, a branch and bound method is developed to handle the combinatorial nature of problem involving finding the contributing variables, which are most likely to be responsible for the occurrence of fault. The efficiency of the method proposed is shown through benchmark examples, such as Tennessee Eastman process, and randomly generated cases.
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    Branch and bound method for multiobjective pairing selection
    (Elsevier Science B.V., Amsterdam., 2010-05-31T00:00:00Z) Kariwala, Vinay; Cao, Yi
    Most of the available methods for selection of input-output pairings for decentralized control require evaluation of all alternatives to find the optimal pairings. As the number of alternatives grows rapidly with process dimensions, pairing selection through an exhaustive search can be computationally forbidding for large-scale processes. Furthermore, the different criteria can be conflicting necessitating pairing selection in a multiobjective optimization framework. In this paper, an efficient branch and bound (BAB) method for multiobjective pairing selection is proposed. The proposed BAB method is illustrated through a biobjective pairing problem using selection criteria involving the relative gain array and the mu-interaction measure. The computational efficiency of the proposed method is demonstrated by using randomly generated matrices and the large-scale case study of cross-direction control. (C) 2010 Elsevier Ltd. All rights reserved.
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    Branch and bound method for regression-based controlled variable selection
    (Elsevier, 2013-03-27) Kariwala, Vinay; Ye, Lingjian; Cao, Yi
    Self-optimizing control is a promising method for selection of controlled variables (CVs) from available measurements. Recently, Ye, Cao, Li, and Song (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 by 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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    Incorporating feedforward action into self-optimising control policies
    (Chemical Institute of Canada, 2014-01-31T00:00:00Z) Umar, Lia Maisarah; Cao, Yi; Kariwala, Vinay
    Control structure design traditionally involves two steps of selections, namely the selection of controlled and manipulated variables and the selection of pairings interconnecting these variables. The available criteria for both selections require enumeration of every alternative. Hence, an exhaustive search can be computationally forbidding for large-scale processes. On the other hand, owing to the computational complexity, variables and pairings are often selected sequentially, which may result in suboptimal control structures. In this paper, an efficient branch and bound (BAB) method is proposed to select the variables and pairings together in a multiobjective optimization framework. As an illustration of the proposed multiobjective BAB framework, the minimum singular value rule and the μ-interaction measure are used as the criteria for selection of controlled variables and pairings, respectively. Numerical tests using randomly generated matrices and the large-scale case study of hydrodealkylation of toluene (HDA) process show that the BAB method is able to reduce the solution time by several orders of magnitude in comparison with exhaustive search.
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    Self-optimizing control – A survey
    (Elsevier Science B.V., Amsterdam., 2017-04-04) Jäschke, Johannes; Cao, Yi; Kariwala, Vinay
    Self-optimizing control is a strategy for selecting controlled variables. It is distinguished by the fact that an economic objective function is adopted as a selection criterion. The aim is to systematically select the controlled variables such that by controlling them at constant setpoints, the impact of uncertain and varying disturbances on the economic optimality is minimized. If a selection leads to an acceptable economic loss compared to perfectly optimal operation then the chosen control structure is referred to as “self-optimizing”. In this comprehensive survey on methods for finding self-optimizing controlled variables we summarize the progress made during the last fifteen years. In particular, we present brute-force methods, local methods based on linearization, data and regression based methods, and methods for finding nonlinear controlled variables for polynomial systems. We also discuss important related topics such as handling changing active constraints. Finally, we point out open problems and directions for future research.