Subspace identification for closed-loop 2-D separable-in-denominator systems

Standard partial least square (PLS) is a useful tool for process monitoring; however, it still encounters some problems for the diagnosis of key performance indicator (KPI) faults. One of its recent modifications, improved PLS (IPLS), decomposes the process measurements into KPI-related and KPI-unrelated parts according to the correlation matrix obtained from the standard PLS. The entire residual space of PLS is categorized as the IPLS's KPI-unrelated part. However, the residual space still involves some information related to KPI, and hence IPLS's decomposition may be inappropriate. In this study, a new modified PLS is proposed, which also decomposes the residual space according to the KPI. The loadings of input data are first decomposed to obtain a projection model. Next, the input data are more appropriately decomposed into KPI-related and KPI-unrelated parts. Correspondingly, two statistic indices can be designed for fault diagnosis. A numerical example and the Tennessee-Eastman (TE) benchmark process are utilized to demonstrate the effectiveness and advantages of the proposed approach.

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“…In the future, the proposed method will be extended to two-dimensional systems. [22][23][24][25][26][27][28]…”

Section: Resultsmentioning

confidence: 99%

Modified partial least square for diagnosing key‐performance‐indicator‐related faults

Wang

Liu

2017

Can J Chem Eng

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“…It is also worth noting that alternative subspace algorithms that can handle closed loop data have been presented and may be applicable. 42 6.4. Model Training and Validation.…”

Section: Simulation Studymentioning

confidence: 99%

“…However, from a practical standpoint, open loop data still provide the richest dynamic information for identification purposes. It is also worth noting that alternative subspace algorithms that can handle closed loop data have been presented and may be applicable …”

Section: Simulation Studymentioning

confidence: 99%

Data-Driven Modeling and Quality Control of Variable Duration Batch Processes with Discrete Inputs

Corbett

Mhaskar

2017

Ind. Eng. Chem. Res.

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Batch process reactors are often used for products where quality is of paramount importance. To this end, this work addresses the problem of direct, data-driven, quality control for batch processes. Specifically, previous results using subspace identification for modeling dynamic evolution and making quality predictions are extended with two key novel contributions: first, a method is proposed to account for midbatch ingredient additions in both the modeling and control stages. Second, a novel model predictive control scheme is proposed that includes batch duration as a decision variable. The efficacy of the proposed modeling and control approaches are demonstrated using a simulation study of a poly(methyl methacrylate) (PMMA) reactor. Closed loop simulation results show that the proposed controller is able to reject disturbances in feed stock and drive the number-average molecular weight, weight-average molecular weight, and conversion to their respective set-points. Specifically, mean absolute percentage errors (MAPE) in these variables are reduced from 8.66%, 7.87%, and 6.13% under traditional PI control to 1.61%, 1.90%, and 1.67%, respectively.

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“…Roesser model system identification has been considered previously, see Cheng et al (2017), Farah et al (2014), Ramos (1994), , , and Ramos andMercère (2016, 2017a, b), and it has been applied in modelling the spatial dynamics of deformable mirrors (see Voorsluys 2015), heat exchangers (see Farah et al 2016), batch processes controlled by iterative learning control (see Wei et al 2015), and in image processing (see Ramos and Mercère 2017b). Our approach to compute an unfalsified model differs fundamentally from previous work.…”

Section: Introduction and Problem Statementmentioning

confidence: 99%

“…Such aspect makes our method conceptually simple, and it helps to reduce the amount of bookkeeping necessary for calculations. Moreover, approaching the problem from a frequency-domain and a duality point of view allows us to avoid imposing restrictive assumptions on the data-generating system, such as the separability-in-the-denominator property required by earlier work on 2D subspace identification such as Cheng et al (2017), Ramos (1994), and . We note that the recent publication Ramos and Mercère (2017b), provides a subspace algorithm for the identification of general, i.e.…”

Section: Introduction and Problem Statementmentioning

confidence: 99%

Discrete Roesser state models from 2D frequency data

Rapisarda

2018

Multidim Syst Sign Process

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We identify a general, i.e. not necessarily denominator-separable Roesser model from 2D discrete vector-geometric trajectories generated by a controllable, quarter-plane causal system. Our procedure consists of two steps: the first one is the computation of state trajectories from the factorization of constant matrices directly constructed from input-output data. The second step is the computation of the state, output, and input matrices of a Roesser model as solutions of a system of linear equations involving the given input-output data and the computed state trajectories.

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Subspace identification for closed-loop 2-D separable-in-denominator systems

Cited by 11 publications

References 37 publications

Modified partial least square for diagnosing key‐performance‐indicator‐related faults

Modified partial least square for diagnosing key‐performance‐indicator‐related faults

Data-Driven Modeling and Quality Control of Variable Duration Batch Processes with Discrete Inputs

Discrete Roesser state models from 2D frequency data

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