14MZM5 - Mathematical Modeling and Process Optimization
| Course specification | ||||
|---|---|---|---|---|
| Course title | Mathematical Modeling and Process Optimization | |||
| Acronym | 14MZM5 | |||
| Study programme | Chemical engineering | |||
| Module | ||||
| Lecturer (for classes) | ||||
| Lecturer/Associate (for practice) | ||||
| Lecturer/Associate (for OTC) | ||||
| ESPB | 5.0 | Status | ||
| Condition | None | Облик условљености | ||
| The goal | The goal of the advanced course in mathematical modeling is to learn students to set-up, solve and analyze complex models of chemical engineering processes and systems. In this course, students also acquire essential knowledge of process optimization - the structure, methods and applications. Theoretical basis of modeling and optimization will be demonstrated through examples from various fields of chemical engineering, with the use of modern approaches and software packages. | |||
| The outcome | After completing and passing the exam, students will be able to: ; 1. Construct mathematical models for more complex problems in chemical engineering. ; 2. Choose an approach, method and software package for solving a model. ; 3. Set an optimization problem and optimize a system using software. ; 4. Perform model sensitivity and uncertainty analysis. ; | |||
| Contents | ||||
| Contents of lectures | 1. Overview of approaches, numerical methods and software packages for solving complex chemical engineering problems. ; 2. Computational Fluid Dynamics (CFD) and Discrete Particle Modeling (DPM) - differences in the approaches and applications. ; 3. Neural networks models - the structure, adequacy and examples in chemical engineering. ; 4. Model sensitivity and uncertainty analysis, model order reduction. ; 5. Process optimization - the purpose, structure, applications in chemical engineering. ; 6. Optimization methods - linear (LP) and non-linear programming (NLP), stochastic programming, dynamic optimization. ; | |||
| Contents of exercises | Practical lessons are carried out in the computer lab. In the first part students learn to solve more complex examples from chemical engineering, using MATLAB software and the CFD simulator. Some of the examples include optimization (solved in MATLAB), demonstrating its capabilities and applications. In the second part students work independently on the project assignment. | |||
| Literature | ||||
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| Number of hours per week during the semester/trimester/year | ||||
| Lectures | Exercises | OTC | Study and Research | Other classes |
| 1 | 3 | |||
| Methods of teaching | Lectures, practical classes in the computer laboratory, consultations | |||
| Knowledge score (maximum points 100) | ||||
| Pre obligations | Points | Final exam | Points | |
| Activites during lectures | Test paper | |||
| Practical lessons | Oral examination | |||
| Projects | 50 | |||
| Colloquia | ||||
| Seminars | 20 | |||