Details for the Course Syllabus for Course KET121F valid from Autumn 2019

General

Aim

Give insight into how mathematical models of process systems can be calibrated and validated against experimental data and how experiments should be planned for good model calibration.

Contents

The course presents methods and their properties for parameter estimation and model calibration. The first part focuses on basic methods for linear and nonlinear regression along with model calibration. The second introduces experimental planning (DoE) and multivariate statistics, and machine learning. Some assignments and a model calibration project are also included in the course.

Knowledge and Understanding

For a passing grade the doctoral student must
know the basic methods for linear and nonlinear regression as well as corresponding regression analysis.

Competences and Skills

For a passing grade the doctoral student must
use the basic methods in parameter estimation, model calibration and machine learning.

Judgement and Approach

For a passing grade the doctoral student must
understand how the quality of data and design of experiments affect model calibration and evaluate properties of methods for parameter estimation and how these influence model calibration.

Types of Instruction

Examination Formats

Admission Requirements

Basic courses in transport phenomena, reaction engineering and separtion engineering or equivalent courses

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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