Advanced Modeling and Simulation Techniques

Recommended Prerequisites

Calculus I, II and III; Applied Computing; Transport Phenomena I; Transport Phenomena II; Transport Phenomena III; Chemical Reactors I; Chemical Reactors II; Modeling, Simulation and Optimization; Applied Statistics.

Teaching Methods

Classes are used to present the basic concepts, definitions, formulations and illustrative examples. These concepts are later practiced by the students during the solution of a set of tasks, in groups of 2-3 people.

Learning Outcomes

This curricular unit will help the students in acquiring capabilities of developing mathematical models of physico-chemical systems, of mechanistic and empirical (statistical) nature. Systematic methodologies of model building are addressed, in each of the previous model classes. The students will acquire the capability of evaluating the dominant physical phenomena that occur and simplify the models produced, together with the capability of knowledge integration in the domains of transfer, transformation and separation processes. This course also aims at developing the capabilities of the students in the area of solution, manipulation and analysis of complex models, together with the capabilities of solving problems that require the integration of knowledge from various other curricular units.

Work Placement(s)

No

Syllabus

Mathematical modeling of mechanistic nature. Principle of conservation of extensities. Constitutive relations.

Extensity balances (macro and microscopic). Systems of lumped and distributed parameters. Analogies between different system types. Typical applications in chemical processes.

Dynamic simulation of chemical and biological systems. Numerical solution strategies. Computational fluid dynamics simulators.

Analysis of state-space models.

Analysis of industrial data. Objectives and methodologies.

Principal component analysis. Variable scaling. Tendency and variability analysis.

Multiple linear regression. Parameter estimation and inference. Confidence intervals and forecast. ANOVA tests. Methods of dimensional selection (principal component regression – PCR and partial least squares - PLS).

System identification and parameter estimation.

Head Lecturer(s)

Nuno Manuel Clemente de Oliveira

Assessment Methods

Assessment
Resolution Problems: 30.0%
Exam: 70.0%

Bibliography

1. Aris, R. Vectors, Tensors, and the Basic Equations of Fluid Mechanics, Dover Publications, New York, 1962.

2. Bird, R. B.; Stewart, W. E; Lightfoot, E. N. Transport Phenomena, 2ª ed., John Wiley & Sons, New York, 2002.

3. Hangos, K. M.; Cameron, I. T. Process Modeling and Model Analysis, Academic Press, New York, 2001.

4. Roffel, B.; Betlem, B. Process Dynamics and Control: Modeling for Control and Prediction, John Wiley & Sons, New York, 2006.

5. Himmelblau, D. M.; Bischoff, K. B.Process Analysis and Simulation - Deterministic Systems, John Wiley & Sons, New York, 1967.

6. Fogler, H. S. Elements of Chemical Reaction Engineering, 4ª ed., Prentice-Hall Inc., Englewood Cliffs, 2006.

7. Finlayson, B. A. Introduction to Chemical Engineering Computing, John Wiley & Sons, New York, 2006.

8. Montegomery, D. C.; Peck, E. A.; Vining, G. G. Introduction to Linear Regression Analysis, 4ª ed., John Wiley & Sons, New York, 2006.