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American control Conference (ACC)

作者： Qiang Zhan V.G. Grassi Chemical Process Modeling and Control Center Department of Chemical Engineering Lehigh University Bethlehem PA USA Chemical Process Modeling Center Air Products and Chemicals Inc. Allentown PA USA

Model predictive control technology has been successfully applied within the industry for more than 20 years. Most of the applications appear in the refining and petrochemicals industry, with few in specialty chemical plant. In this paper DMC+ commercial multivariable control software is evaluated for a specialty chemical process using offline simulation. The relative benefit that can be obtained from DMC+ above and beyond advanced regulatory plantwide control is carefully studied. In this study, DMC+ is built as a supervisory controller on the decentralized SISO plantwide control structure. DMC+ uses the setpoints of PI controllers as the manipulated variables. In this paper, different plantwide control structures are used and the performance of DMC+ on top of all these different plantwide control structures are compared. It will be shown that a well designed regulatory plantwide control structure is necessary to stabilize and linearize the process around some operating point. Good plantwide control design will give a better performance for DMC+.

关键词： chemical industry Predictive models Predictive control chemical technology Industrial control Application software Refining Petrochemicals chemical processes control design

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Nonlinear reduced order models for separation processes via augmentation of linear subspace models

Nonlinear reduced order models for separation processes via ...

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1998 American control Conference, ACC 1998

作者： Docter, William A. Georgakis, Christos Chemical Process Modeling and Control Research Center Department of Chemical Engineering Lehigh University 111 Research Drive Bethlehem PA 18015 United States

ISBN: (纸本)0780345304

This paper presents a general methodology for developing Nonlinear Low Order Model (NLLOM) from data collected from large detailed nonlinear models. This methodology is divided into two tasks: development of an Average Linear Low Order Model (ALLOM) and augmentation of the ALLOM with selected nonlinear terms to form the NLLOM. The tools required for the augmentation step that is the focus of this paper include stepwise regression and nonlinear optimization. Results will be presented for the application of these techniques in the development of an NLLOM from a detailed high purity air separation distillation column model supplied by Praxair, Inc. © 1998 AACC.

关键词： Nonlinear programming

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Nonlinear Term Selection and Parameter Estimation in the Identification of Nonlinear Reduced Order State Space Models

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IFAC Proceedings Volumes 1998年第11期31卷 335-340页

作者： William Docter Christos Georgakis Chemical Process Modeling and Control Research Center and Department of Chemical Engineering Lehigh University Bethlehem Pennsylvania USA

This paper presents a general methodology for the development of Nonlinear Low Order Models (NLLOM) from data collected from detailed nonlinear simulation models. This methodology is divided into two tasks: development of a Average Linear Low Order Model (ALLOM) and augmentation of the ALLOM to form a NLLOM, the latter task being the focus of this paper. The tools examined for the nonlinear augmentation of the ALLOM include stepwise regression and nonlinear optimization. Results will be presented for the application of these techniques towards the development of an NLLOM from a detailed high purity distillation simulation.

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A New Measure of process Output controllability

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IFAC Proceedings Volumes 1998年第11期31卷 663-672页

作者： David R. Vinson Christos Georgakis Chemical Process Modeling and Control Research Center and Department of Chemical Engineering Lehigh University Bethlehem PA 18015

A steady state, multivariable, and nonlinear measure is presented for assessing the input-output, open-loop controllability of a process. This measure is ascertaining the inherent controllability of the process, as it is calculated in the absence of any regulatory control structure. It is also independent of the inventory control structure that might be assumed present in order to keep the inventory levels constant. This measure evaluates the ability of a design to reach all points of the desired output space and to reject the expected disturbances utilizing input action not exceeding the available input space. Besides being applicable to a SISO case, its multivariable character is shown to be more accurate than existing measures such as RGA, minimum singular value, and condition number.

关键词： control theory controllability Distillation columns Inputs Nonlinear gain Output Polygons

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Application of Nonlinear State Estimation to Batch Polymerization Reactors

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IFAC Proceedings Volumes 1998年第11期31卷 645-650页

作者： G. Mourikas A.J. Morris C. Kiparissides Center of Process Analysis Chemometrics and Control Department of Chemical and Process Engineering University of Newcastle upon Tyne Newcastle UK Department of Chemical Engineering and Chemical Process Engineering Research Institute Aristotle University of Thessaloniki Thessaloniki Greece

Two estimation methods, namely the Extended Kalman Filter and the Receding Horizon State Estimator, were applied to a simulated batch solution polymerization reactor model. Parameter adaptive filters were found to be extremely successful in tracking time-varying model parameters (e.g., reactor fouling and time-varying kinetic rate constants). Finally, the problem of estimating the initial process states was considered, and two estimation algorithms (e.g., the reiterative Kalman Filter and a non-linear optimization based estimator) were implemented to track the initial initiator concentration and the initial value of the overall heat-transfer coefficient in a batch polymerization reactor.

关键词： Nonlinear estimation multirate EKF polymerization reactors time-varying parameters

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Nonlinear reduced order models for separation processes via augmentation of linear subspace models

Nonlinear reduced order models for separation processes via ...

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American control Conference (ACC)

作者： W.A. Docter C. Georgakis Chemical Process Modeling and Control Research Center and Department of Chemical Engine Lehigh University Bethlehem PA USA D311 Iacocca Hal Bethlehem PA

This paper presents a general methodology for developing nonlinear low-order model (NLLOM) from data collected from large detailed nonlinear models. This methodology is divided into two tasks: development of an average linear low-order model (ALLOM) and augmentation of the ALLOM with selected nonlinear terms to form the NLLOM. The tools required for the augmentation step that is the focus of this paper include stepwise regression and nonlinear optimization. Results will be presented for the application of these techniques in the development of an NLLOM from a detailed high purity air separation distillation column model supplied by Praxair, Inc.

关键词： Separation processes Predictive models Solid modeling Integrated circuit modeling Discrete Fourier transforms chemical processes process control chemical engineering Distillation equipment Thermodynamics

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The Effect of controllers in State Estimation Schemes

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IFAC Proceedings Volumes 1998年第11期31卷 577-582页

作者： Guillermo E. Eliçabe Christos Georgakis Departamento de Ingeniería Química/INTEMA Facultad de Ingeniería Universidad Nacional de Mar del Plata (7600) Mar del Plata Argentina Chemical Process Modeling and Control Research Center Lehigh University Iacocca Hall #111 Bethlehem PA 18015 USA

It is common practice in state estimation of chemical systems to include augmented states modeled as random constant or random walk processes. When in these systems, process controllers with integral term are present, undesirable interaction effects may occur between the augmented states and the controllers. It was noted in practice that if no attention is paid to this interaction the resulting estimator may diverge. In this work the interaction between controller and augmented states is analyzed. Using linear systems theory, it is shown that the unwanted interaction and final divergence are caused by lack of observability at steady state and consequently a test to check observability at steady state is developed. In many cases the test can be performed by simple inspection. When this is not the case, results can be obtained after a few manipulations. A series of examples are given where the concept of observability at steady state is applied to help detecting and preventing the negative interaction between controllers and augmented states. Finally, a discussion is presented comparing the results of the standard observability test, applied to a real problem, with those obtained with the test developed here and with the behavior of a real estimator for the same problem. It is concluded that the standard observability test is not able to discriminate between different estimator designs and consequently to produce practical results as those obtained with the alternative test, i.e. the detection of unfeasible estimator designs.

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Identification of reduced order average linear models from nonlinear dynamic simulations

Identification of reduced order average linear models from n...

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American control Conference (ACC)

作者： W.A. Docter C. Georgakis Chemical Process Modeling and Control Research Center Department of Chemical Engineering Lehigh University Bethlehem PA USA

Presents a general methodology for the identification of average linear low order models (ALLOM) from data collected from detailed nonlinear models. While there are many methods available in the literature for identifying linear models, these methods tend to produce inaccurate and ill-conditioned models when used on nonlinear data sets. The method in this paper differs from traditional linearization methods in that it better approximates the dynamic characteristics over a wider area around the reference steady state.

关键词： Signal processing Predictive models Open loop systems chemical processes process control chemical engineering Steady-state Separation processes Thermodynamics State estimation

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Closed-Loop Identification of State Space Models using Subspace Techniques

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IFAC Proceedings Volumes 1997年第9期30卷 37-42页

作者： Hemant Jha Christos Georgakis Chemical Process Modeling and Control Research Center and Department of Chemical Engineering Lehigh University Bethlehem Pennsylvania 18015 Phone: (610)-758-5432 Fax:(610)-758-5297

The paper presents a subspace algorithm which identifies the process model from closed-loop data. The algorithm is based on the use of a closed-loop oblique projection for the direct identification of the plant dynamics. This closed-loop oblique projection is particularly useful because it eliminates the use of a minimal realization step after the identification has been performed. Similar to the open-loop algorithm, the proposed closed-loop algorithm is ideally suited for MIMO problems.

关键词： Closed-loop Identification Subspace Methods State Space Models Multivariable Systems Matrix Projection

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Real-time estimation and control of particle size in semi-batch emulsion polymerization

Real-time estimation and control of particle size in semi-ba...

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American control Conference (ACC)

作者： V. Liotta C. Georgakis M.S. El-Aasser Air Products and Chemicals Inc. Allentown PA USA Chemical Process Modeling and Control Research Center and Emulsion Polymers Institute Lehigh University Bethlehem PA USA

The control of the relative particle growth of a bidisperse emulsion polymerization is investigated in an automated reactor control facility capable of online density and online particle size measurements. Final diameter ratio values are controlled by manipulating the monomer feed rate to the reactor using a control scheme comprised of a parameter adaptive Kalman filter with steady state gains and a nonlinear model predictive controller. One of the main features of the implemented controller is the connection of the prediction horizon to the criterion that the overall conversion equal 90% at the end of the batch. Feedback is incorporated into the algorithm at each sampling time by starting the model prediction calculation at the current estimated values of the model states. The controller performs well to meet a challenging diameter ratio set point at the end of the batch. The robustness of the controller to reject an induced initial initiator concentration disturbance is demonstrated.

关键词： Size control Automatic control Predictive models Inductors Polymers Size measurement Feeds Programmable control Adaptive control Steady-state

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