Data-based control of an industrial tubular reactor

Akamatsu, K.; Lakshminarayanan, S.; Manako, H.; Takada, Haruo; Satou, T.; Shah, Sirish L.

doi:10.1016/s0967-0661(00)00039-3

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…LVRM inversion was also proposed for process control for trajectory tracking , and manipulation . Flores-Cerillo and MacGregor presented several process control approaches , based on model inversion to control and ensure the quality of the operation in a batch process.…”

Section: Introductionmentioning

confidence: 99%

General Framework for Latent Variable Model Inversion for the Design and Manufacturing of New Products

Tomba

Barolo

García‐Muñoz

2012

Ind. Eng. Chem. Res.

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Latent variable regression model (LVRM) inversion is a useful tool to support the development of new products and their manufacturing conditions. The objective of the model inversion exercise is that of finding the best combination of regressors (e.g., raw material properties, process parameters) that are needed to obtain a desired response (e.g., product quality) from the model. Each of the published applications where model inversion has been applied utilizes a tailored approach to achieve the inversion, given the specific objectives and needs. These approaches range from the direct inversion of the LVRM to the formulation of an objective function that is optimized using nonlinear programming. In this paper we present a framework that aims to give a holistic view of the optimization formulations that can arise from the need to invert an LVRM. The different sets of equations that become relevant (either as a term within the objective function or as a constraint) are discussed, and an example of these scenarios is also provided. Additional to the formulation of the different scenarios and their objective functions, this work proposes a new metric (the P 2 statistic) to cross-validate the ability of the model to reconstruct the regressor vector (analogous to the Q 2 statistic aimed at the predictability of the response). This new metric comes from the need to not only predict the response from the regressor, but to also reconstruct the regressors from the scores values. In this context, a discussion is provided on the effect of uncertainty in the reconstruction of the regressor (the actual design) as these values are normally given upstream as targets to the supplier of materials, or as set points to the process.

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Section: Introductionmentioning

confidence: 99%

General Framework for Latent Variable Model Inversion for the Design and Manufacturing of New Products

Tomba

Barolo

García‐Muñoz

2012

Ind. Eng. Chem. Res.

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“…This indicates that current process region is similar to nominal operating region at which controller gets designed. To fulfill control objectives in alpha-olefins production plant, canonical variate analysis algorithm was employed by Akamatsu et al (2000) for identification of state space model in subspace and that was used for design of feedback and feed forward controllers [23]. In this work, PCA based temperature controller has been developed that resembles proportional controller.…”

Section: Introductionmentioning

confidence: 99%

Control of Yeast Fermentation Bioreactor in Subspace

Damarla¹,

Kundu²

2013

IJCA

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PCA based temperature controller was used to control Ethanol concentration produced in Yeast fermentation process. The controller was designed at a specific operating point and its disturbance rejection performances were studied. Substrate inlet temperature proved to be the most significant disturbance input from the analysis of open loop responses.-statistic (SPE) of process measurements confirmed that in the face of disturbances and noise the process could be held to the specific operating condition using the controller designed in subspace.

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“…The proposed PCA control formulation, in a reduced control space, is analogous to modal control (also called eigenvalueassignment control, (Feng, 1999;Magni, 2002). In (Akamatsu, et al, 2000), PCA control and PI control using CVA (canonical variate analysis) modeling is tested, and compared, on a chemical reactor. A statistical controller based on PCA was applied to the Tennessee Eastman process in (Chen & McAvoy, 1996).…”

Section: Introductionmentioning

confidence: 99%