Valid from: Autumn 2014
Decided by: FN2/Eva Nordberg Karlsson
Date of establishment: 2014-03-10
Division: Chemical Engineering
Course type: Third-cycle course
Teaching language: English
To give insight into how mathematical models of process systems can be calibratied and validated against experimental data and how experiments can be planned to give good model calibration.
Knowledge and Understanding
For a passing grade the doctoral student must know the basic methods for linear and nonlinear regression and corresponding regression analysis
Competences and Skills
For a passing grade the doctoral student must use the basic methods for parameter estimation, particular the ones in MATLAB, use LabView for data logging
Judgement and Approach
For a passing grade the doctoral student must understand how quality in data and design of experiment influence model calibration and value properties in methods for parameter estimation and how these influence model calibration
The course presents methods and their behaviour for parameter estimation and model calibration. The first part focus on fundamental methods for linear and nonlinear regression together with model calibration. The second part discuss in more detail the tre basic aspects on calibration; experiment planning, model structure and calibration purpose. Hand-in exercises and a model calibration project using LabView.
Englezos, P. & Kalogerakis, N.: Applied parameter estimation for chemical engineers. Dekker media, 2001.
Types of instruction: Lectures, seminars, exercises, project
Examination formats: Written report, written assignments
Grading scale: Failed, pass
Admission requirements: Fundamental Courses in transport phenomena, reaction engineering and separation processes or corresponding education.
Assumed prior knowledge: KETN01 Process simulation
Web page: http://staff.chemeng.lth.se/~BerntN/Courses/ModCal.html