Syllabus academic year 2009/2010
(Created 2009-08-11.)

Higher education credits: 7,5. Grading scale: TH. Level: G2 (First level). Language of instruction: The course will be given in English on demand. KTE190 overlap following cours/es: KTE160, KTE180, KTE160, KTE180, KTE160, KTE180, KTE160 och KTE180. Optional for: RH4, W4, W4ve. Course coordinator: Mattias Alveteg,, Inst för kemiteknik. The course might be cancelled if the numer of applicants is less than 10. Assessment: Fulfilled assignment tasks and active participation in presentation of assignments needed to pass the course. Voluntary oral examination for higher grades. Home page:

The mathematical tools and models that engineers has long used e.g. for designing factories is today also used to describe natural systems and how human activities affect these systems. Mathematical models, e.g. of the climate regulation mechanisms of our earth, currently has a great impact on what we conceive to be politically, economically and socially feasible.

The aim with this course is that the student should learn to identify as well as orally, graphically and mathematically describe feedbacks in biogeochemical systems, thereby achieving a deep understanding for the limitations of the mathematical models that politicians and scientists refer to.

Knowledge and understanding
For a passing grade the student must

Skills and abilities
For a passing grade the student must

Judgement and approach
For a passing grade the student must

The course is built upon a number of simulation tasks. The simulation tasks can concern e.g. soil acidification/recovery, eutrophication, the global carbon cycle, etc. and cover chemical and physical processes as well as ecological processes and population dynamics. Each simulation task is supported by an overview of relevant theory. The course also cover more general theoretic aspects including

Systems Analysis: Causal Loop Diagrams (CLD), reinforcing and balancing feedback loops, Reference Behaviour Patterns (RBP).

The fundamentals of: Model robustness and deterministic chaos. Uncertainty/sensitivity analysis. Monte Carlo simulation and sampling strategies. Spatial and temporal variability. Classification of sources of uncertinaty. Population dynamics: Intensity based models, individually based models and cohort models.

Reference literature, simulation task specific compendiums available as handouts or as PDF-files.