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- Analysis and Control of Nonlinear Process Systems | Katalin M. Hangos | Springer

Hillestad, G. Extraction of operating signatures by episodic representation T. Fujiwara et al.

## Publications

Monitoring chemical reaction systems using incremental target factor analysis O. Prinz, D. Sequential control issues in the plant-wide control system D. Hiranaka, H. Comparison of advanced distillation control techniques V. Gokhale et al. Statistical process control of multivariate processes J. Statistical Control Techniques I. Predictive maintenance using PCA D.

Lewin, Y. Autoassociative neural networks in bioprocess condition monitoring J. Glassey et al. Modeling and Simulation IV. Ill-conditionedness and process directionality - the use of condition numbers in process control J. Waller et al. Nonlinear Control and Optimization IV. Elementary nonlinear decoupling control of composition in binary distillation columns J. Balchen, B. External model control of a peristaltic pump A.

Medvedev, G. Statistical Control Techniques II. MacGregor et al. Bias detection and estimation in dynamic data reconciliation K. McBrayer, T. Author Index.

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This publication brings together the latest research findings in the key area of chemical process control; including dynamic modelling and simulation - modelling and model validation for application in linear and nonlinear model-based control: nonlinear model-based predictive control and optimization - to facilitate constrained real-time optimization of chemical processes; statistical control techniques - major developments in the statistical interpretation of measured data to guide future research; knowledge-based v model-based control - the integration of theoretical aspects of control and optimization theory with more recent developments in artificial intelligence and computer science.

For process control engineers, academics specializing in the control aspects of process engineering, regulatory bodies, government laboratories. We are always looking for ways to improve customer experience on Elsevier. We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit. Horizon mnimum zyxwvutsrqponm controlledby manipulating the inlet and coolant flow rates. Manipulated variable constraints are handled by changing systems with a relative order of 1. The RSS framework the desired state reponse dynamics when a constraint is appears more intuitive to the nonspecialist than the dif- encountered.

An absorption column control problem is ferential-geometric-based approaches. The reviews and presented as a second example. Hutchinson and McAvoy tutorials by McLellan et al. The nonlinear techniques.

The differential geometric techniques are control system was compared with time-optimal control. Bhat et al.

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Knowledge of heat-transfer coefficients and areas are required. Two tuning parameters are A,, the desired first-order response time and k, a proportional gain to force the error between the reference model and the process output to vanish. Temperature and the time derivative of temperature are necessary for this control strategy, so their technique is sensitive to large measurement noise. Jayadeva et al. The restrictive assumptions are that the nonlinearity lies in a conic sector and that the states are bounded.

## Analysis and Control of Nonlinear Process Systems | Katalin M. Hangos | Springer

Linear controllers are designed to function over desired windows of operation in the phase plane. Schaper et al. Bequette domain. They consider concentration control of a CSTR has developed a single-step-ahead control law for a CSTR subject to feed temperature disturbances, using coolant that also does not require knowledge of reaction kinetics.

All unknown parameters are lumped into a single param- Khambanonda et al. A "filtered" bound approach to test for the stability of nonlinear nonlinear discrete deadbeat controller is then formulated.

They use the Krener trans- Good results are obtained for an open-loop unstable pro- formation to decompose a nonlinear operator in a linear cess with noisy temperature measurements. The accuracy Summary of Reference System Synthesis. Many of of their stability test is shown in the heat-exchanger ex- the RSS techniques can be considered to be a t the "art" ample of Alsop and Edgar Khambanonda et al. The desired response trajectory is not limited to a linear system; Bartusiak et al. Predictive Control Approaches zyxw nonlinear responses. Like other model inverse approaches, During the past decade there has been an increasing use RSS produces an unstable controller when the plant has of linear model predictive control LMPC techniques.

A right-half-plane zeros.

Another disadvantage is that the survey of model predictive control, including applied and time derivative of the output must be directly coupled to theoretical papers, has been performed by Garcia et al. This problem DMC by Bartusiak et al. Model uncertainty and process disturbances are handled by calculating an additive dis- There are a number of important issues that must be addressed in the solution of 26a-i. The choice of con- zyxwv turbance as the difference between the process measure- strained optimizationtechnique ia one of the first decisions ment and the model prediction at the current time step.

Another major issue is how to solve It is assumed that the future disturbances are equal to the the dynamic model constraints 26b. Since many of the current disturbance, and a new trajectory is calculated. The model feedback provided by the disturbance estimate which also outputs y, are a function of the state variables ; compensates for model uncertainty.

Quadratic dynamic matric control QDMC state variables 26i can be obtained from the solution of Garcia and Morshedi, was developed to explicitly the model over the previous time step, based on the ma- incorporate process constraints, on both the manipulated nipulated action actually implemented on the plant this variables and the process outputs. Ricker discusses is equivalent to using an open-loop observer. The DMC an approach similar to QDMC but presents the material characterization of the future additive disturbances as the zy zy in a more generic fashion and develops the idea of ma- difference between the model output and the plant output nipulated variable blocking to reduce the size of the op- at the current time step can be used to predict the output timization problem.

A direct extension of the linear model fication procedure should normally be used either ex- zyxwvu predictive control methods results when a nonlinear dy- plicitly or implicitly , so that the model state variables do namic process model is used, rather than a linear convo- not significantly diverge from the actual state variables. For linear open-loop unstable systems with an unstable The objective of nonlinear predictive control NLPC model , a state-variable estimation strategy must be used is to select a set of future control moves control horizon to satisfy internal stability requirements for a more com- in order to minimize a function based on a desired output plete discussion of internal stability see Morari and Za- trajectory over a prediction horizon, as shown is Figure 5.

Sistu and Bequette b have shown that this is not the case for systems with parameter or model structure uncertainty. Solution of the Dynamic Model Equations. The optimization decision variables are the control prediction horizon or by a linearizationat a number of time actions L time steps into the future; after the Lth time step steps in the prediction horizon. I feel that the approach used for the solution of the Notice that absolute 26d and velocity 26e constraints ODEs is not nearly as important as the other issues in- on the manipulated variables are explicitly included in this volved, such as selection of the initial conditions for the formulation.

A only the fmt control action is implemented. After the first classical optimal control formulation is to use a two-stage control action is implemented, plant output measurements approach where an optimization routine serves as an outer are obtained. The objective function gradients can be determined by a finite differences based on small changes in the manipulated Ind.

An intermediate approach is zyxwvu Jang et al.

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Using a requires the integration of fewer ODEs, at the expense of performing the integration more often. Joseph and co-workers integrate the model ODE'S and sensitivity equations for the optimal solution. They use a first-order filter for the setpoints, which elim- inates the deadbeat type of control that normally results when the prediction horizon and control horizon are the same. Jang et al. This intermediate approach is used by Bequette , and Sistu and Bequette a,b. Bequette shows the effect of manipulated-variable velocity constraints on the control of a biochemical reactor.

Bequette has found that, for SISO systems, the control horizon can generally be set to one-time step with lated variable constraints. Modeling Maurath et al. A number of researchers are included. Brengel and universal dynamic matrix control UDMC. The modeling Seider use analytical derivatives to integrate the equations are integrated by using any nonlinear ODE plant equations into the future. The model is linearized solver.