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Unformatted text preview: 1 28150. Introduction to proces control 2. First principles process modelling Krist V. Gernaey 31 August 2009 28150 Learning objectives At the end of this lesson you should be able to: Create, in a structured stepwise fashion, a firstprinciples dynamic model for a process Implement and simulate the model (exercises, next week!) 31 August 2009 28150 Outline Introduction Model development principles Degrees of freedom analysis A systematic model development approach Modelling examples 31 August 2009 28150 Outline Introduction Model development principles Degrees of freedom analysis A systematic model development approach Modelling examples 2 31 August 2009 28150 What is a model? A model (M) for a system (S) and an experiment (E) is anything to which E can be applied in order to answer questions (P) about S (Minsky, 1965) S P M E 31 August 2009 28150 Different models Physical model of a system Pilot plant Mathematical model of a system Equations Graphs Linguistic model of a system Example: If substrate is available, dissolved oxygen consumption of the biomass will increase 31 August 2009 28150 What is a first principles model? A model developed using the principles of chemistry, physics and biology (conservation laws) first engineering principles model, theoretical model, deterministic model, white box model Steadystate model versus unsteadystate model ( dynamic model ) 31 August 2009 28150 Model classification First engineering principles models Empirical (black Box )models Semi empirical (grey box) models Process data Process knowledge Neural network, ARX, ARMAX, etc. 3 31 August 2009 28150 Properties of first principles models Model complexity must be determined (assumptions), depends on model purpose Can be computationally expensive (not realtime) May be expensive/timeconsuming to obtain Good for extrapolation, scaleup Does not require experimental data to obtain (data required for validation and fitting) 31 August 2009 28150 Properties of blackbox models Large number of unknown parameters Can be obtained quickly (e.g., linear regression) Model structure is subjective Dangerous to extrapolate 31 August 2009 28150 Properties of greybox models Compromise of first two approaches Model structure may be simpler Good versatility, can be extrapolated Can be run in realtime 31 August 2009 28150 The use of a model Real world problem Mathematical problem Mathematical solution Interpretation of solution 4 31 August 2009 28150 Outline Introduction Model development principles Degrees of freedom analysis A systematic model development approach Modelling examples 31 August 2009 28150 Model development principles Conservation of mass Conservation of component i { } { } out mass of rate in mass of rate mulation mass of accu rate =...
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This note was uploaded on 11/20/2009 for the course CHME DTUabroad taught by Professor Rafiqulgani during the Fall '09 term at Rensselaer Polytechnic Institute.
 Fall '09
 RafiqulGani

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