Modeling and simulation of bio-systems
Ordinary differential equations are employed to model all kinds of systems. Examples are found in ecology, chemistry, bio-chemistry, process industry and mechanics. In this course, the students learn how to develop these mathematical models, estimate their parameters given experimental data, and how the quality of the parameter estimation can be improved through careful design of the experiment. Also the inherent model properties will be examined through sensitivity and uncertainty analysis. By applying systems theory, real-life interpretation of fixed points and stability becomes apparent.
In this course, students learn the fundaments of automated process control using the proportional-integral-derivative control law (PID control). First, the principle and mathematical framework of feedback control is introduced using both conceptual and applied examples. Next, different control stability criteria and tuning methods are investigated in detail. Practicalities and considerations regarding PID-control in practice are also introduced, e.g. anti-integral wind-up and overshooting prevention. Finally, more advanced control schemes are introduced to deal with specific problems, e.g. feedforward control, dead-time compensation and cascade control.
Modeling and simulation with partial differential equations in practice
This course provides the insights and tools needed for the development and understanding of mathematical models used to describe real-world phenomena. The emphasis of this course is on partial differential equations. In the first part, discretisation schemes and solution techniques are extended for multiple dimensions (x, y, z and time). In the second part, these mathematical techniques are applied for Computational Fluid Dynamics (CFD) modelling. The students will be introduced to the open-source CFD software OpenFOAM.
Modeling and control of waste water treatment plants
This course covers industry relevant modelling and simulation tools for the design and optimisation of wastewater treatment plants. The students are introduced to a stepwise modelling framework covering each unit process of an entire treatment plant. The first part of the course aims for a thorough understanding of model calibration and hands-on simulation experience. This is realised through the use of industry standard models, e.g. Activated Sludge Model No.1 (ASM1), combined with measurement data of a specific waste water treatment plant. The second part of this course deals with process control and the objective evaluation of its economic benefits with respect to nutrient removal.
This course introduces modern control theory, i.e. optimal control using the mathematical state-space framework of systems. Through the combination of a state observer and state feedback control, stabilization and tracking problems are solved in an optimal manner by solving an LQ-problem. Since real-world biological, pharmaceutical and chemical dynamics are often nonlinear, we study how these techniques can be adapted for the nonlinear case. In addition, this course also provides wet lab activities focussing on data acquisition and the application of control theory in practice. The students learn how to control a lab-scale setup by continuously measuring the outputs of the system, processing the measured signal, implementing a controller and returning the desired control action.
Integrated modelling and design and basin management plans
This course focuses on integrating models to evaluate technologies for an improved water quality. Several modelling aspects regarding a river basin management framework are highlighted: wastewater treatment plants modelling, hydrological modelling and river quality modelling. The integrated approach demonstrates the interdependencies between the different subsystems which allows the student to think in river basin scale including all its aspects.