| Type: | Open University
Language: English |
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No credit points with: C3633, C3635, 36231, 36435
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Prerequisite: 36230 Regulering af kemiske procesanlæg 36230 Chemical Process Control
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Desirable: Mathematical and Process technology background
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Recommended semester: 7th - 9th semester
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Scope and form: Lectures and group exercises. The exercises can focus upon either standard chemical engineering or biotechnological processes at the choice of the participants.
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Examination: Approval of coursework and evaluation of report Approval of obligatory exercises and exam report (13-scale)
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Participant limitation: max. 24
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Remarks: This course is included into the ph.d. program for the KTB sector.
Interim arrangement: During year 2000 the old courses 36231 and 36435 may be completed in connection with course 36232.
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| Contact person: | Sten Bay Jørgensen, Building 227, Tel. +45 4525 2872, email sbj@kt.dtu.dk |
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Department: Department of Chemical Engineering
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Aim: To enable design of experiments for obtaining a model suitable for multivariable process control design. The participants will learn how to evaluate performance of the controlled plant. Through combination of model development, control system design and complete system evaluation the participants are introduced to the modelling cycle for operation of chemical process plants. The methodology provided during this course enable operational optimization of chemical process plants.
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Contents: "Modelling and analysis": The course introduces linear and non-linear dynamical models for continuous and batch operated chemical plants. Linear state space models for chemical plants in continuous and discrete time and frequency function models and their interrelations are given. The models are analysed via linear and non-linear system analysis. Stability concepts (Lyapunov) and fundamental concepts from real analysis are given.
"Process identification": Includes nonparamentrical representation of data; parameter estimation in linear and non-linear multivariable state space models and methods for cross validation of models.
"state estimation and optimal control": Include dynamic programming and discrete-time optimal control for design of the central elements of the control system: The state estimator and controller. Linear quadratic control and Kalman filtering are introduced both from a deterministic and a stochastic point of view. Limitations in actoators and states are motivated from an industrial perspective which leads to control systems based upon model predictive control "MPC) and mowing horizon estimators (MHE).
"Simulation": Simulation design and analysis are carried out in Matlab. The acheivable performance with model predictive control towards a given set of disturbances is simulated.
The subjects in the modelling cycle are introduced upon a theoretical basis, but emphasis is given to practical application aspects. A large scale example is given for the application of the introduced methods. Exam problems may be offered both within chemical and biotechnologi processes.
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