LrgpV1, Istex, Corpus, bibRecord, 000E23

CVP-based optimal control of an industrial depropanizer column

Identifieur interne : 000E23 ( Istex/Corpus ); précédent : 000E22; suivant : 000E24

CVP-based optimal control of an industrial depropanizer column

Auteurs : M. Fikar ; M. A. Latifi ; J. P. Corriou ; Y. Creff

Source :

Computers and Chemical Engineering [ 0098-1354 ] ; 2000.

RBID : ISTEX:C545BD57EEF0639C52F55F352A07CC708487C468

English descriptors

KwdEn :
- Algebraic equations, Chemical engineering, Column model, Computers chemical engineering, Condenser, Constant control, Constant pressure drop, Constraint, Continuous derivatives, Control segments, Control trajectories, Control trajectory, Control variables, Cost function, Depropanizer, Derivative, Differential equations, Distillation column, Disturbance rejection, Dynamic optimization, Elsevier science, Energy balances, Equal length, Feed characteristics, Feed composition, Fikar, Final derivatives, Final time, Final times, First half, First phase, Hydrodynamic models, Impurity, Industrial depropanizer column, Internal energy, Kmol, Last control segment, Molar, Molar volumes, Negligible differences, Optimal control problems, Optimization, Optimization method, Optimization problem, Optimized, Optimized control segments, Original model, Original problem, Other hand, Output variables, Partial flows, Partial molar flows, Reboiler, Reboiler duty, Reflux flow rate, Setpoint change, Simulation, State variables, Steady state, Time derivatives, Time formulation, Time problem, Trajectory, Typical situations, Value unit temperature pressure, Vapor molar flow, Vapor molar flowrate, Vapor phases.
Teeft :
- Algebraic equations, Chemical engineering, Column model, Computers chemical engineering, Condenser, Constant control, Constant pressure drop, Constraint, Continuous derivatives, Control segments, Control trajectories, Control trajectory, Control variables, Cost function, Depropanizer, Derivative, Differential equations, Distillation column, Disturbance rejection, Dynamic optimization, Elsevier science, Energy balances, Equal length, Feed characteristics, Feed composition, Fikar, Final derivatives, Final time, Final times, First half, First phase, Hydrodynamic models, Impurity, Industrial depropanizer column, Internal energy, Kmol, Last control segment, Molar, Molar volumes, Negligible differences, Optimal control problems, Optimization, Optimization method, Optimization problem, Optimized, Optimized control segments, Original model, Original problem, Other hand, Output variables, Partial flows, Partial molar flows, Reboiler, Reboiler duty, Reflux flow rate, Setpoint change, Simulation, State variables, Steady state, Time derivatives, Time formulation, Time problem, Trajectory, Typical situations, Value unit temperature pressure, Vapor molar flow, Vapor molar flowrate, Vapor phases.

Abstract

Abstract: In this contribution we analyze an industrial multi-component depropanizer model and show how to rewrite the original model to index-one formulation that is well adapted to dynamic optimization. The optimal control problems are then studied with the aim of determination of the optimal output trajectories for minimum changeover time requirements and disturbance rejection. The problems studied are typical situations that frequently occur in the industrial plant. The optimization method used is the control vector parametrization (CVP) which consists in converting the original problem into a non-linear programming (NLP) problem which was solved by a successive quadratic programming (SQP) method. The resulting profiles can be utilized as setpoints for the existing real plant control.

Url:

https://api.istex.fr/document/C545BD57EEF0639C52F55F352A07CC708487C468/fulltext/pdf

DOI: 10.1016/S0098-1354(00)00355-0

Links to Exploration step

ISTEX:C545BD57EEF0639C52F55F352A07CC708487C468

Le document en format XML

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<ce:keyword><ce:text>Changeover problems</ce:text>
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<abstract lang="en">Abstract: In this contribution we analyze an industrial multi-component depropanizer model and show how to rewrite the original model to index-one formulation that is well adapted to dynamic optimization. The optimal control problems are then studied with the aim of determination of the optimal output trajectories for minimum changeover time requirements and disturbance rejection. The problems studied are typical situations that frequently occur in the industrial plant. The optimization method used is the control vector parametrization (CVP) which consists in converting the original problem into a non-linear programming (NLP) problem which was solved by a successive quadratic programming (SQP) method. The resulting profiles can be utilized as setpoints for the existing real plant control.</abstract>
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CVP-based optimal control of an industrial depropanizer column

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