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2008 American control Conference (ACC), vol.4

作者： Pradeep Y. Tiwari Mayuresh V. Kothare Chemical Process Modeling and Control Center Department of Chemical Engineering Lehigh University Bethlehem PA USA

Repetitive systems can be characterized by two time variables, namely, the finite time within each repeating cycle and the cycle index, each embodying a distinct connotation of time. Conventional optimal control theory does not explicitly account for this two dimensional (2D) description of repetitive systems. We propose a new formulation for control of repetitive systems using Model Predictive control (MPC) that explicitly incorporates a 2D representation of the system. The proposed formulation uses a 2D Lyapunov function and the stability requirements are established along each time dimension of the system. The resulting controller synthesis problem is expressed in convex form using Linear Matrix Inequalities (LMIs). The approach allows explicit incorporation of input/output constraints in the controller design. Two examples illustrate the applicability of the proposed approach.

关键词： Predictive models Predictive control Linear matrix inequalities Radio control control systems control system synthesis Stability Economic forecasting chemical processes chemical industry

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Plant friendly input design for system identification in closed loop

Plant friendly input design for system identification in clo...

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Universite Libre de Bruxelles

作者： Narasimhan, Sridharakumar Bombois, Xavier Dept. of Chemical Engineering IIT Madras Chennai India Delft Center for Systems and Control Delft University of Technology Netherlands

ISBN: (纸本)9783902823069

Optimal experiment or input design is the scientific exercise of designing informative excitation signals for the identification of a real-life dynamic system. In the least costly input design framework, the input is designed such that the identification cost is minimized while meeting desired specifications on the quality of the identified model. Identification of real-life processes require that the identification be "plant-friendly". These are typically imposed as constraints on experiment time, input and output amplitudes or input move sizes. This work focusses on an LMI based plant friendly input design in the least costly framework. © 2012 IFAC.

关键词： Convex optimization

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Optimal scheduling with energy constraints

Optimal scheduling with energy constraints

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2008 AIChE Annual Meeting, AIChE 100

作者： Castro, Pedro Harjunkoski, Iiro Grossmann, Ignacio E. Department of Process Modeling and Simulation INETI Lisbon Portugal Corporate Research ABB Ladenburg Germany Dept. of Chemical Engineering Center for Advanced Process Decision-making Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA 15213 United States

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An open software architecture for batch process simulation

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COMPUTERS & chemical engineering 1997年第Sup期21卷 S769-S773页

作者： Fritz, M Engell, S Process Control Group Dept. of Chemical Engineering University of Dortmund D-44221 Dortmund Germany

Simulation of recipe driven batch processes is a complex demanding task. It can be performed either with general purpose simulators, causing a high modelling effort, or with specific tools which often lack flexibility. Neither of these approaches is appropriate in all cases. In this contribution, we outline a software architecture for batch process simulation which is flexible enough to be adapted to meet the particular requirements of the simulation problem at hand. It consists mainly of the elements model creation, output clients, simulator, and user interface. All these components are exchangeable to offer the software solution which is best suited for the specific problem. The software concepts are presented in terms of design patterns.

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The Value of Simple Multivariable and Nonlinear Models in process control

The Value of Simple Multivariable and Nonlinear Models in Pr...

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American control Conference (ACC)

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

Simple conceptual or mathematical models have always been used in design of linear SISO process controllers. On the other hand the design of nonlinear or MIMO controllers has always been perceived as requiring a detailed dynamic model. This communication aims to show that, the use of simple models, can drastically affect the design of such controllers by suggesting a design framework that centers around the extensive variable of the process instead of the intensive one that are usually measured.

关键词： process control Mathematical model Inductors chemical processes Communication system control Polyethylene chemical reactors Polymers Tellurium chemical engineering

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Nonlinear model predictive control of end-use properties in batch reactors under uncertainty

Nonlinear model predictive control of end-use properties in ...

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European control Conference (ECC)

作者： Jaleel Valappil Christos Georgakis Chemical Process Modeling and Control Research Center and Department of Chemical Engineering Lehigh Univeristy Bethlehem PA USA

There are significant incentives in controlling the end-use properties in batch reactors to reduce the variability in the final product quality specifications. Here we define an approach for controlling the properties within a desired target region, instead of a set point, with an economic objective and with consideration of the model uncertainty. The approach to handling process-model mismatch is based on determining the uncertainty in the predicted final values of the properties, in the form of elliptical confidence regions. The controller then ensures that the complete confidence region is within the target set. A semi-infinite programming problem is solved to find the input values for the rest of the batch, utilizing techniques that reduce the complexity of the problem. An emulsion polymerization process for styrene is chosen as a case study and the methods developed here are demonstrated on this challenging example process.

关键词： Uncertainty Approximation methods Mathematical model Covariance matrices Equations Programming Eigenvalues and eigenfunctions

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Optimal parameter estimation of the Izhikevich single neuron model using experimental Inter-Spike Interval (ISI) data

Optimal parameter estimation of the Izhikevich single neuron...

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作者： Kumar, Gautam Aggarwal, Vikram Thakor, Nitish V. Schieber, Marc H. Kothare, Mayuresh V. Department of Chemical Engineering Chemical Process Modeling and Control Center Lehigh University Bethlehem PA 18015 United States Department of Biomedical Engineering Johns Hopkins University Baltimore MD 21205 United States Department of Neurobiology and Anatomy University of Rochester Medical Center Rochester NY 14627 United States

ISBN: (纸本)9781424474264

We propose to use the Izhikevich single neuron model to represent a motor cortex neuron for studying a control-theoretic perspective of a neuroprosthetic system. The problem of estimating model parameters is addressed when the only available data from intracortical recordings of a neuron are the Inter-Spike Intervals (ISIs). Non-linear constrained and unconstrained optimization problems are formulated to estimate model parameters as well as synaptic inputs using ISIs data. The primal-dual interior-point method is implemented to solve the constrained optimization problem. Reasonable model parameters are estimated by solving these optimization problems which may serve as a template for studying and developing a model of ensemble cortical neurons for neuroprosthesis applications. © 2010 AACC.

关键词： Neurons

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On-line Estimation of Reaction Rates in Batch Reactors

On-line Estimation of Reaction Rates in Batch Reactors

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American control Conference (ACC)

作者： Guillermo Elicabe Christos Georgakis Chemical Process Modeling and Control Research Center Department of Chemical Engineering Lehigh University Bethlehem PA USA

Very often one needs to know the current values of reaction rates in chemical reactors in order to control them effectively or in order to avoid unsafe operating conditions. ln this work state estimation techniques are used to estimate on-line reaction rates in well mixed isothermal batch reactors. The proposed scheme assumes the availability of on-line measurements of overall concentrations of the reactants, and the lack of knowledge on the reaction rate dependence with those concentrations. The state estimator used in this application is a Kalman filter based estimator. Relationships between the continuous and the discrete versions of the filter as well as a Laplace domain analysis were considered. The estimation scheme is applied to two examples of semi-continuous emulsion co-polymerization.

关键词： Inductors Equations chemical reactors Tellurium State estimation Content addressable storage Isothermal processes Kalman filters Phase estimation Steady-state

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State estimation and nonlinear model predictive control of end-use properties in batch reactors

State estimation and nonlinear model predictive control of e...

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American control Conference (ACC)

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

There are economic incentives in controlling the end-use properties in batch reactors to reduce the variability in the final product quality specifications. Here we define an approach for controlling the properties within a desired target region, instead of a set point, with an economic objective and with consideration of the model uncertainty. The approach to handling process-model mismatch is based on determining the uncertainty in the predicted final values of the properties, in the form of elliptical confidence regions. The controller then ensures that the complete confidence region is within the target region. A semi-infinite programming problem is solved to find the input values for the rest of the batch. The methods developed here are demonstrated on an emulsion polymerization example.

关键词： State estimation Predictive models Predictive control Inductors Uncertainty Economic forecasting Polymers chemical processes process control chemical engineering

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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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