Details for the Course Syllabus for Course KET055F valid from Autumn 2014

General

Aim

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.

Contents

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.

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

Types of Instruction

Examination Formats

Admission Requirements

Fundamental Courses in transport phenomena, reaction engineering and separation processes or corresponding education.

Assumed Prior Knowledge

Selection Criteria

Literature

Literature:
Englezos, P. & Kalogerakis, N.: Applied parameter estimation for chemical engineers. Dekker media, 2001.

Further Information

Course code

Administrative Information

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