22MMKP - Modeling and data correlation in petrochemical processes
| Course specification | ||||
|---|---|---|---|---|
| Course title | Modeling and data correlation in petrochemical processes | |||
| Acronym | 22MMKP | |||
| Study programme | ||||
| Module | ||||
| Lecturer (for classes) | ||||
| Lecturer/Associate (for practice) | ||||
| Lecturer/Associate (for OTC) | ||||
| ESPB | 4.0 | Status | ||
| Condition | Облик условљености | |||
| The goal | Knowledge attainment in the fields of petrochemical processes modeling and application of regression and chemical engineering correlations to experimental and literature data. | |||
| The outcome | On the basis of the knowledge gained in this course, students are capable of: modeling of simpler petrochemical separation and reaction processes, analyzing of experimental data using regression models and applying correlations from chemical engineering. | |||
| Contents | ||||
| Contents of lectures | Linear and multiple regression. Designing engineering experiments with one or several factors. Regression and data correlation examples: Estimation of Antoine equation parameters using multiple regression; Antoine equation parameters for various hydrocarbons; Correlation of thermodynamic and physical properties of n-propane; Correlation of binary activity coefficients using Margules equations for system benzene - n-heptane; Heat transfer correlations from dimensional analysis; Rate data analysis for a catalytic reforming reaction; Regression of rate data - Checking dependency among variables; Regression of heterogeneous catalytic rate data. Process modeling examples: Flash evaporation of various hydrocarbon mixtures; Three stage flash evaporator for recovering hexane from octane; Calculation of dew point, bubble point and the composition of the phases for non ideal mixtures; Fenske-Underwood-Gilliland correlations for separation towers; Rigorous distillation calculations for simpler separation towers. | |||
| Contents of exercises | Practical part consists of process modeling, data analysis and correlation using POLYMATH, Excel and MATLAB software. | |||
| Literature | ||||
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| Number of hours per week during the semester/trimester/year | ||||
| Lectures | Exercises | OTC | Study and Research | Other classes |
| 2 | 1 | |||
| Methods of teaching | Lectures and practical part. Consultations. | |||
| Knowledge score (maximum points 100) | ||||
| Pre obligations | Points | Final exam | Points | |
| Activites during lectures | Test paper | 40 | ||
| Practical lessons | 10 | Oral examination | ||
| Projects | ||||
| Colloquia | 30 | |||
| Seminars | ||||