Study programme 2019-2020 | Français | ||
Biological System Modelling and Software Sensor Design | |||
Programme component of Master's in Electrical Engineering : Specialist Focus on Signals, Systems and BioEngineering à la Faculty of Engineering |
Students are asked to consult the ECTS course descriptions for each learning activity (AA) to know what assessment methods are planned for the end of Q3 |
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Code | Type | Head of UE | Department’s contact details | Teacher(s) |
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UI-M2-IRELBS-003-M | Compulsory UE | VANDE WOUWER Alain | F107 - Automatique |
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Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
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| Anglais | 36 | 24 | 0 | 0 | 0 | 5 | 5.00 | 1st term |
AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
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I-AUTO-108 | Biological System Modelling and Software Sensor Design | 36 | 24 | 0 | 0 | 0 | Q1 | 100.00% |
Programme component | ||
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UI-M2-IRELBS-002-M Optimal Control and Estimation |
Objectives of Programme's Learning Outcomes
Learning Outcomes of UE
discover population models in ecology;
establish a link with some models commonly used to describe enzymatic and biochemical reaction systems;
study macroscopic modeling of cultures of micro-organisms (yeasts, bacteria, ...) in bioreactors; implement numerical simulators of dynamic systems;
review system modeling and parameter identification techniques, with the concern of taking several sources of uncertainties into account;
design several state estimators dedicated to bioprocess monitoring, as well as a few control strategies;
use neural networks to build black-box models of biological systems.
Content of UE
introduction to population models; macroscopic models of bioprocesses; numerical simulation of dynamic systems; parameter identification (linear and nonlinear problems, least squares and maximum likelihood); introduction to neural networks for modeling and monitoring; state estimation and design of software sensors (asymptotic observers, receding-horizon observers, ...); introduction to control of bioreactors (linearizing feedback control, adaptive RST control); exercises.
Prior Experience
state space equations, observers
Type of Assessment for UE in Q1
Q1 UE Assessment Comments
oral exam
Type of Assessment for UE in Q3
Q3 UE Assessment Comments
oral exam
Type of Resit Assessment for UE in Q1 (BAB1)
Q1 UE Resit Assessment Comments (BAB1)
Not applicable
Type of Teaching Activity/Activities
AA | Type of Teaching Activity/Activities |
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I-AUTO-108 |
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Mode of delivery
AA | Mode of delivery |
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I-AUTO-108 |
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Required Reading
AA | |
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I-AUTO-108 |
Required Learning Resources/Tools
AA | Required Learning Resources/Tools |
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I-AUTO-108 | Not applicable |
Recommended Reading
AA | |
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I-AUTO-108 |
Recommended Learning Resources/Tools
AA | Recommended Learning Resources/Tools |
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I-AUTO-108 | Not applicable |
Other Recommended Reading
AA | Other Recommended Reading |
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I-AUTO-108 | G. Bastin, D. Dochain, "On-line Estimation and Adaptive Control of Bioreactors", Elsevier, 1990 |
Grade Deferrals of AAs from one year to the next
AA | Grade Deferrals of AAs from one year to the next |
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I-AUTO-108 | Authorized |