BTLW001998 Fundamentals of modelling for bioprocess engineering

Type
course with continuous assessment
Semester hours
2
Lecturer (assistant)
Teles Barbosa, Felipe , Beck, Jürgen
Organisation
Bioprocess Science and Engineering
Offered in
Sommersemester 2026
Languages of instruction
Englisch
Content

1. Fundamentals of modelling: 1.1 Visualizing linear, nonlinear, static and dynamic behaviours; 1.2 Identifying linear, nonlinear and differential models; 1.3 Drafting models for experimental data - mechanistic and empirical models.
2. Working with linear models: 2.1 Model parametrisation and estimation principles; 2.2 Linear model diagnostics; 2.3 Nested models, inclusion of new variables and comparison of experimental conditions; 2.4 Model outputs: parameter estimates, confidence intervals, confidence and prediction bands.
3. Working with nonlinear models: 3.1 Review of numerical calculus: exact and numerical methods, implicit and explicit solutions; 3.2 Structuring a computational procedure: initial solution, iterations, step size, numerical error and precision; 3.3 discretization methods for differential equations - Runge-Kutta methods; 3.4 Linearisation of nonlinear models; 3.5 Nonlinear Least Squares and Gradient-based methods for parameter estimation; 3.6 Nonlinear model diagnostics; 3.7 Model outputs: parameter estimates, confidence intervals, confidence and prediction bands.
4. Application of fitted models: 4.1 Model-based predictions, simulation of experimental conditions; 4.2 Asymptotic behaviours and stability of model’s solutions - how to use this information in controlling and automation.
Previous knowledge expected

Basic programming knowledge.
Numerical calculus and inferential statistics are desirable, but not required.
Objective (expected results of study and acquired competences)

Upon completion of this course, students will be able to identify, implement and simulate appropriate models related to their area of research with the aid of different numerical and statistical methods. It is also expected that students will develop critical statistical thinking skills, be able to evaluate is the adequacy of a model in describing their experimental data by analysing numerical and graphical outputs, be able to draw inferences from fitted models, and implement different computational procedures, such as optimization methods for parameter estimation and iterative methods to solve differential equations and nonlinear equations related to the bioprocess engineering field.
You can find more details like the schedule or information about exams on the course-page in BOKUonline.