Modeling Simulation and Optimization

Recommended Prerequisites

Linear Algebra and Analytic Geometry, Calculus I, II and III, Applied Computing.

Teaching Methods

Integration of specific chemical engineering applications in the generic modelling/simulation/optimization methodologies. Solution of case studies by computational means within the MATLAB platform.

In the theoretical classes new concepts are introduced supported by real case studies. Slide shows are the main vehicle for presentation and discussion of the contents. Classes in the computer centre are also given for support of the computational assignments required in this course.

Learning Outcomes

With this course is intended to introduce the student to the relevant subjects of modeling simulation and optimization in chemical engineering. Special attention is given to transient state modeling. An integrative approach will be followed. The principal optimization methods will be lectured for multidimensional functions.

The course aims at developing the following skills: Ability in analysis and synthesis;  generalization and abstraction; written communication; computer skills relating to the scope of the study; ability to formulate and solve problems; independent and critical thinking; capacity of autonomous learning; competence in applying theoretical knowledge in practice.

Work Placement(s)

No

Syllabus

Modelling strategies and model classifications. A systematic approach for the construction of process models. Examples of application of models in chemical processes. Numerical solution of ordinary differential equations. Explicit Euler method. Errors definitions. Stability analysis. Euler implicit and modifyed . Runge-Kutta methods: general formulation; Adaptive methods; multistep methods. Numerical solution of systems of ordinary differential equations. Extension of the previous methods to systems. Boundary value problems. Shooting method. Discretization techniques. Finite differences method. Stiff problems. Numerical solution of partial differential  equations. Method of the lines. Finite difference mehods (explicit and implicit). Crank Nicholson method. Matlab programming for differential equations (internal functions). Function optimization in Rn. Analytical and numerical approaches.

Head Lecturer(s)

Pedro Nuno das Neves Lopes Simões

Assessment Methods

Assessment
Synthesis work: 20.0%
Resolution Problems: 20.0%
Exam: 60.0%

Bibliography

Hangos, K., Cameron, I., Process Modelling and Model Analysis, 4th vol. of Process Systems Engineering, Academic Press, San Diego (2001).

Chapra, S., Applied Numerical Methods with MATLAB for Engineers and Scientists, McGraw-Hill, Boston (2005).

Edgar, T.F., Himmelblau, D.M., S.L. Leon, Optimization of Chemical Processes, 2nd Ed., McGraw-Hill, N.Y. (2001).

WEBSITE DO MATLAB http://www.mathworks.com.