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9th IFAC International symposium on dynamics and control of process systems (DYCOPS)/11th IFAC International symposium on Computer Applications in Biotechnology (CAB)

作者： Hoang, H. Couenne, F. Jallut, C. Le Gorrec, Y. ENSMM FEMTO ST AS2M F-25000 Besancon France Univ Lyon LAGEP F-69622 Lyon France Univ Lyon 1 F-69622 Villeurbanne France

This paper proposes a thermodynamical pseudo-Hamiltonian formulation of Continuous Stirred Tank Reactor model in which takes place some chemical reaction. This is done both in the isothermal and non isothermal cases. It is shown that the Gibbs free energy and the opposite of entropy can be chosen as Hamiltonian function respectively. For the non isothermal case, the so-called Interconnection and Damping Assignment Passivity Based control method is applied to stabilize the system at a desired state. For this general reaction scheme, the control problem is shown to be easy to solve as soon as the closed loop Hamiltonian function is chosen to be proportional to the so-called thermodynamic availability function. Simulation results based on a simple first order reaction and operating conditions leading to multiple steady states of the CSTR are given to validate the proposed control design procedure. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Port Hamiltonian systems Lyapunov stability Thermodynamics IDA-PBC control Chemical reactors

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Correlation-based spectral clustering for flexible process monitoring

Correlation-based spectral clustering for flexible process m...

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9th IFAC International symposium on dynamics and control of process systems (DYCOPS)/11th IFAC International symposium on Computer Applications in Biotechnology (CAB)

作者： Fujiwara, Koichi Kano, Manabu Hasebe, Shinji Kyoto Univ Dept Chem Engn Nishikyo Ku Kyoto 6158510 Japan

The individuality of production devices should be taken into account when statistical models are designed for parallelized devices. In the present work, a new clustering method, referred to as NC-spectral clustering, is proposed for discriminating the individuality of production devices. The key idea is to classify samples according to the differences of the correlation among measured variables, since the individuality of production devices is expressed by the correlation. In the proposed NC-spectral clustering, the nearest correlation (NC) method and spectral clustering are integrated. The NC method generates the weighted graph that expresses the correlation-based similarities between samples, and the constructed graph is partitioned by spectral clustering. A new statistical process monitoring method and a new soft-sensor design method are proposed on the basis of NC-spectral clustering. The usefulness of the proposed methods is demonstrated through a numerical example and a case study of parallelized batch processes. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Soft-sensor Statistical process monitoring Spectral clustering Batch process Nearest correlation method Fault detection Estimation

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Analysis of heterogeneous cell populations: A density-based modeling and identification framework

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JOURNAL OF process control 2011年第10期21卷 1417-1425页

作者： Hasenauer, Jan Waldherr, Steffen Doszczak, Malgorzata Scheurich, Peter Radde, Nicole Allgoewer, Frank Univ Stuttgart Inst Syst Theory & Automat Control Stuttgart Germany Univ Stuttgart Inst Cell Biol & Immunol Stuttgart Germany

In many biological processes heterogeneity within clonal cell populations is an important issue. One of the most striking examples is a population of cancer cells in which after a common, identical death signal some cells die whereas others survive. The reason for this heterogeneity is intrinsic and extrinsic noise. In this paper we present a mechanistic multi-scale modeling framework for cell populations, in which the dynamics of every individual cell is captured by a parameter dependent stochastic differential equation (SDE). Heterogeneity among individual cells is accounted for by differences in parameter values, modeling extrinsic influences. Based on the statistical properties of the extrinsic noise and the SDE model for the individual cell, a partial differential equation (PDE) model is derived. This PDE describes the evolution of the population density. To determine the statistics of the extrinsic noise from experimental population data, a density-based statistical data model of the noise-corrupted data is derived. Employing this data model we show that the statistics of the extrinsic can be computed using a convex optimization. This efficient way of assessing the parameters allows for a so far infeasible uncertainty analysis via bootstrapping. To evaluate the proposed method, a model for the caspase activation cascade is considered. It is shown that for known noise properties the unknown parameter densities in this model are well estimated by the proposed method. 2011 Elsevier Ltd. All rights reserved.

关键词： Cell population Parameter estimation Density estimation Convex optimization Bootstrapping Apoptotic signaling

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Partial enumeration MPC: Robust stability results and application to an unstable CSTR

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JOURNAL OF process control 2011年第10期21卷 1459-1466页

作者： Pannocchia, Gabriele Wright, Stephen J. Rawlings, James B. Univ Pisa Dept Chem Engn DICCISM Pisa Italy Univ Wisconsin Dept Comp Sci Madison WI 53706 USA Univ Wisconsin Chem & Biol Engn Dept Madison WI USA

We describe a partial enumeration (PE) method for fast computation of a suboptimal solution to linear MPC problems [1] with robust stability properties. Given that the suboptimal PE-based control law is non-unique (that is, a set-valued map) and (possibly) discontinuous, we treat the closed-loop system, appropriately augmented, as a difference inclusion. We derive novel robust exponential stability results for difference inclusions of this type. In particular we show that Strong Robust Exponential Stability (SRES) holds, for any sufficiently small but otherwise arbitrary perturbation. Such approach allows us to show SRES of the closed-loop system under PE-based MPC. Application to a simulated open-loop unstable CSTR with separation unit and recycle is presented to show performance and timing results for PE-based MPC, as well as to highlight its robustness to process/model mismatch, disturbances and measurement noise. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Partial enumeration MPC Explicit control laws Inherent robust stability Lyapunov functions

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Experiences in batch trajectory alignment for pharmaceutical process improvement through multivariate latent variable modelling

Experiences in batch trajectory alignment for pharmaceutical...

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9th IFAC International symposium on dynamics and control of process systems (DYCOPS)/11th IFAC International symposium on Computer Applications in Biotechnology (CAB)

作者： Garcia-Munoz, Salvador Polizzi, Mark Prpich, Andrew Strain, Cathal Lalonde, Adam Negron, Vilmary Pfizer Global Res & Dev Groton CT 06340 USA Pfizer Global Mfg Groton CT 06340 USA Pfizer Global Mfg Barceloneta PR USA

The primary objective of batch data as trajectory alignment (or synchronization) is to standardize the data sampling per batch according to the evolution of the process, and secondarily to homogenize the samples per run. The use of an indicator variable performs both objectives well. Two examples from the pharmaceutical sector are discussed to illustrate the different ways to deal with uneven samples across batches and across variables in the same batch. Since trajectory alignment requires large time investment, a simple triage approach is proposed to assess the need to analyze the dynamics of a given process and hence perform alignment. The presented examples are representative of a broad variety of batch processes that are operated by recipe in the pharmaceutical sector. In our experience, the variables associated with the automation triggers in these recipes are the best indicator variables to use for alignment. This is due to (i) the fact that the trigger variables are easy to identify from the automation of the recipe, (ii) operators are familiar with these, (iii) the target values for triggers are known a priori and hence the resulting alignment scheme can be performed in real-time for monitoring applications and (iv) it makes the monitoring scheme easy to understand and justify around the design-space since the design-space may originally be defined in terms of the trigger variables for each phase of the batch. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Batch process Multivariate monitoring Multi-way PCA Multi-way PLS Alignment

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process analytical technologies and real time process control a review of some spectroscopic issues and challenges

Process analytical technologies and real time process contro...

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9th IFAC International symposium on dynamics and control of process systems (DYCOPS)/11th IFAC International symposium on Computer Applications in Biotechnology (CAB)

作者： Chen, Zengping Lovett, David Morris, Julian Newcastle Univ Sch Chem Engn & Adv Mat Ctr Proc Analyt & Control Technol Newcastle Upon Tyne NE1 7RU Tyne & Wear England Hunan Univ Coll Chem & Chem Engn State Key Lab Chemo Biosensing & Chemometr Changsha 410082 Hunan Peoples R China Daresbury Innovat Ctr Daresbury Cheshire England

process analytical technologies (PAT) are increasingly being explored and adopted by pharmaceutical and industrial biotechnology companies for enhanced process understanding, Quality by Design (QbD) and Real Time Release (RTR). To achieve these aspirations there is a critical need to extract the most information, and hence understanding, from complex and often 'messy' spectroscopic data. This contribution reviews a number of new approaches that have been shown to overcome the limitations of existing calibration/modelling methodologies and describes a practical system which would enhance robustness of the closed loop process control system and overall 'control strategy'. Application studies are described of the use of on-line spectroscopy for the monitoring and control of a downstream solvent recovery column, batch cooling crystallization and pharmaceutical fermentation. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.

关键词： process analytical technologies Spectroscopic data Chemometrics Calibration process control

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On the Consideration of Model Uncertainties in Model Updating of Dynamic systems

On the Consideration of Model Uncertainties in Model Updatin...

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IUTAM symposium on Nonlinear Stochastic dynamics and control

作者： Schueller, G. I. Goller, B. Univ Innsbruck Inst Engn Mech EU A-6020 Innsbruck Austria

ISBN: (纸本)9789400707313

Model updating procedures are applied in order to improve the match between experimental data and corresponding model output. The updated, i.e. improved, finite element (FE) model can be used for more reliable predictions of the structural performance in the target mechanical environment. The discrepancies between the output of the FE-model and the results of tests are due to the uncertainties that are involved in the modeling process. These uncertainties concern the structural parameters, measurement errors, the incompleteness of the test data and also the FE-model itself. The latter type of uncertainties is often referred to as model uncertainties and is caused by simplifications of the real structure that are made in order to reduce the complexity of reality. Several approaches have been proposed for taking model uncertainties into consideration, where the focus of this manuscript will be set on the updating procedure within the Bayesian statistical framework. A numerical example involving different degrees of non-linearity will be used for demonstrating how this type of uncertainty is considered within the Bayesian updating procedure.

关键词： Bayesian statistics model updating stochastic analysis model uncertainties non-linear dynamics

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Application of an extended IHMPC to an unstable reactor system: Study of feasibility and performance

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JOURNAL OF process control 2011年第10期21卷 1493-1503页

作者： Gonzalez, A. H. Adam, E. J. Marcovecchio, M. G. Odloak, D. UNL CONICET INTEC Inst Desarrollo Tecnol Ind Quim RA-3000 Santa Fe Santa Fe Argentina Consejo Nacl Invest Cient & Tecn INGAR Inst Desarrollo & Diseno RA-3000 Santa Fe Santa Fe Argentina Univ Sao Paulo Dept Chem Engn BR-61548 Sao Paulo Brazil

Almost all the theoretical aspects of model predictive control (MPC), such as stability, recursive feasibility and even the optimality are now well established for both, the nominal and the robust case. The stability and recursive feasibility are usually guaranteed by means of additional terminal constraints, while the optimality is achieved considering closed-loop predictions. However, these significant improvements are not always applicable to real processes. An interesting case is the control of open-loop unstable reactor systems. There, the traditional infinite horizon MPC (IHMPC), which constitutes the simplest strategy ensuring stability, needs to include an additional terminal constraint to cancel the unstable modes, producing in this way feasibility problems. The terminal constraint could be an equality or an inclusion constraint, depending on the local controller assumed for predictions. In both cases, however, a prohibitive length of the control horizon is necessary to produce a reasonable domain of attraction for real applications. In this work, we study the application of an IHMPC formulation that has maximal domain of attraction (i.e., the domain of attraction is determined by the system and the constraints, and not by the controller) to an unstable reactor system. It is shown that the method is suitable for real applications in the sense that it accounts for the case of output tracking and it is offset free if the output target is reachable, and minimizes the offset if some of the constraints become active at steady-state. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Model predictive control Domain of attraction Unstable reactor

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Partial enumeration MPC: Robust stability results and application to an unstable CSTR

Partial enumeration MPC: Robust stability results and applic...

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9th IFAC International symposium on dynamics and control of process systems (DYCOPS)/11th IFAC International symposium on Computer Applications in Biotechnology (CAB)

关键词： Partial enumeration MPC Explicit control laws Inherent robust stability Lyapunov functions

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NCO tracking and self-optimizing control in the context of real-time optimization

NCO tracking and self-optimizing control in the context of r...

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9th IFAC International symposium on dynamics and control of process systems (DYCOPS)/11th IFAC International symposium on Computer Applications in Biotechnology (CAB)

作者： Jaschke, Johannes Skogestad, Sigurd Norwegian Univ Sci & Technol NTNU Dept Chem Engn N-7491 Trondheim Norway

This paper reviews the role of self-optimizing control (SOC) and necessary conditions of optimality tracking (NCO tracking) as presented by [1]. We show that self-optimizing control is not an alternative to NCO tracking for steady state optimization, but is to be seen as complementary. In self-optimizing control, offline calculations are used to determine controlled variables (CVs), which by use of a lower layer feedback controller indirectly keep the process close to the optimum when a disturbance enters the process. Preferably, the setpoints are kept constant, but they may be adjusted by some optimization layer. Good CVs reduce the need for frequent setpoint changes. When selecting self-optimizing CVs, a set of disturbances must be assumed, as unexpected disturbances are not rejected in SOC. On the other hand, the presented NCO tracking procedure adapts the inputs at given sample times without a model or any assumptions on the set of disturbances. However, disturbances with high frequencies or those which do not lead to a steady state are not rejected. By using NCO tracking in the optimization layer and SOC in the lower control layer, we demonstrate that the methods complement each other, with SOC giving fast optimal correction for expected disturbances, while other disturbances are compensated by the model free NCO tracking procedure on a slower time scale. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Self-optimizing control NCO tracking Real-time optimization Optimal operation

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