14MHOH10 - Modeling and data correlation in petrochemical processes

Course specification
Course titleModeling and data correlation in petrochemical processes
Study programmeChemical engineering
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
    ConditionОблик условљености
    The goalKnowledge attainment in the fields of petrochemical processes modeling and application of regression and chemical engineering correlations to experimental and literature data.
    The outcomeOn 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 of lecturesLinear 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 exercisesPractical part consists of process modeling, data analysis and correlation using POLYMATH, Excel and MATLAB software.
    1. Script: Modeling and data correlation in petrochemical processes. Irena Zizovic
    2. Problem Solving in Chemical and Biochemical Engineering with POLYMATH, Excel and MATLAB, M.B. Cutlip and M. Shacham, 2nd Ed., Prentice Hall, 2007.
    3. Probability & Statistics for Engineers & Scientists, 7th Ed. R.E.Walpole, R.H. Myers, S.L. Myers, K. Ye, Pearson, 1995.
    Number of hours per week during the semester/trimester/year
    LecturesExercisesOTCStudy and ResearchOther classes
    Methods of teachingLectures and practical part. Consultations.
    Knowledge score (maximum points 100)
    Pre obligationsPointsFinal examPoints
    Activites during lecturesTest paper40
    Practical lessons10Oral examination