Schedule: 10.09.2019 - 25.10.2019
Teacher in charge (valid 01.08.2018-31.07.2020):
Teaching Period (valid 01.08.2018-31.07.2020):
Learning Outcomes (valid 01.08.2018-31.07.2020):
After the course the student
*Understands the information flows in industrial plants and enterprises
*Knows the systems involved in the information handling: automation systems, production and resource planning and controlling systems (MES, ERP, APS)
*Understands the most basic functions of an automation system;
*Knows functions and tuning methods of a basic controller types: PID, feed-forward, cascade, ratio controllers
*Knows how to analyze process dynamics and the dynamics of a system with a controller
*Knows the fundamentals of experimental modelling of chemical processes
Content (valid 01.08.2018-31.07.2020):
Automation systems, MES, ERP, APS. Process dynamics, process modelling and identification, classical control theory, single-loop control and controller design.
Modeling of heat exchanger
First principle modeling and model linearization of the 3-tank system
Feed-forward control of tanks in series
Details on the course content (applies in this implementation):
Actual course content (2019)
The course is an introduction to modern process control. We study the mathematical principles and the basic computational tools of state-feedback and optimal control theory to manipulate the dynamic behaviour of process systems. The course aims at bringing understanding of feeback control in process systems while at the same time showing how this approach can be used in general application domains in chemical and bio-chemical engineering.
- - Introduction to process systems automation (systems analysis, model types and properties, model representations);
- - Mathematical modelling of dynamic process systems with inputs and outputs using ordinary differential equations. State-space representation. Dynamics and stability of linear time-invariant systems. Linearisation of nonlinear systems around a fixed point;
- - Synthesis of state-feedback controllers. Controllability and reacheability. Controllability tests. Eigenvalue placement. Optimal control and the linear quadratic regulator;
- - Full-state estimation from sensor data. Observability and detectability. Observability tests. Design of statistical state estimators. Optimal state estimation and the Kalman filter;
- - Optimal estimation and optimal control with the linear quadratic Gaussian regulator.
Assessment Methods and Criteria (valid 01.08.2018-31.07.2020):
Independent study and exam
Elaboration of the evaluation criteria and methods, and acquainting students with the evaluation (applies in this implementation):
To pass the course the participant must pass 1) the examination and 2) the course project. The final assessment (the grade) is given by the (rounded) weighted sum of the examination's assessment (weight 60%, the examination grade gets multiplied by 0.6) and the assessment of the project (weight 40%, the project grade gets multiplied by 0.4).
The course examination is a standard 'pen-and-paper' examination (see Aalto's guidelines) and the course project consists of the collection of assignments returned during the course. Within the project, course assignments are equally weighted and unreturned assignments will receive a grade equal to zero.
Workload (valid 01.08.2018-31.07.2020):
Lectures 24 h
Exercises 24 h
Independent studying, homeworks / preparing for exam 80 h
Exam 4 h
Study Material (valid 01.08.2018-31.07.2020):
To be announced later.
Course Homepage (valid 01.08.2018-31.07.2020):
Grading Scale (valid 01.08.2018-31.07.2020):
Fail, 1 - 5
Registration for Courses (valid 01.08.2018-31.07.2020):
WebOodi. A maximum number of 65 students can be admitted to the course. Priority is given to the degree students in Chemical and Process Engineering major. If there is space, other students (Aalto degree students and exchange students) can be admitted to the course in the order of registration.
- Teacher: Francesco Corona