Information concerning time and location of lectures and seminars in addition to current examination dates is available on TUMonline.
| Lecturer: | Dr. techn. Daniel Schiochet Nasato, M. Sc. Vicente Arevalo Taibo, M. Sc. Lukas Maier |
| Contact: | M. Sc. Vicente Arevalo Taibo, M. Sc. Lukas Maier |
Description: Matlab is a widely used tool for data analysis and simulation tasks in both industry and research. This course offers an introduction to the fundamental usage of Matlab, with a strong emphasis on practical exercises, enabling students to immediately apply their learning on the computer. After gaining basic programming skills, the course will cover data analysis and parameter estimation methods. Additionally, students will explore numerical solution techniques for algebraic equations, as well as ordinary and partial differential equations. The final section of the course focuses on image processing and analysis. | |
| Lecturer: | Prof. Dr.-Ing. Heiko Briesen |
| Contact: | Dr.-Ing. Yuan Tan |
Description:
This course covers the fundamentals of kinematics and kinetics. Kinematics describes the relationships between position, time, velocity and acceleration – in other words, where a body is at any given time, and what its velocity and acceleration are. Kinetics relates kinematics to forces. The course begins with a comprehensive introduction to the kinematics of point masses, which forms the basis for the kinetics of point masses, as defined by Newton’s second law. The principles covered are then extended from individual point masses to systems of point masses and finally to extended rigid bodies.
Prerequisites:
Proficiency in the mathematical tools used in the lecture (algebraic manipulations, differentiation, integration, vector algebra – scalar products, cross products).
Up-to-date information about the course (venue, time, exam dates, etc.) can be found on Moodle.
| Lecturer: | Dr. -Ing. Yuan Tan, Prof. Dr.-Ing. Heiko Briesen | |
| Contact: | Dr. techn. Daniel Schiochet Nasato | |
Description: Models and simulation techniques get more and more important in the field of disperse process engineering because they enable predictions for different process conditions. The module contains systems such as the formation of filter cakes, the mixing of powders in pharmaceutical processes, milling processes in food industry and the growth of filamentous fungi as producers of biotechnologically important compounds. To simulate these and other tasks concerning process engineering, the frequently used techniques discrete element method (DEM) and population balance modeling (PBM) will be applied. Besides the theory of these methods, the implementation in suitable softwares will also be teached. For further information we refer to: campus.tum. | ||
| Lecturer: | Prof. Dr.-Ing. Heiko Briesen |
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| Contact: | M. Sc. Barbara Forster |
Description: This is a mandatory subject for the degree courses in Food Technology, Brewing and Beverage Technology and Pharmaceutical Bioprocess Technology. The lecture is accompanied by an exercise and concludes with a revision course before the examination. The examination is a module examination which includes thermal process engineering (held by the Chair of Biothermodynamics). Important fundamentals and operations of process engineering of disperse systems are taught in this course: Characterization of particles, representation of particle size distributions, conversion of particle size distributions, forces on particles in the flow field, interparticle forces, sedimentation analysis, characteristics of a separation, filtration, fluidized bed. These basic principles are intended to provide students with an introduction to the process engineering treatment of the diverse issues of disperse systems.
Current information on the course (location, time, examination dates, etc.) can be found in Moodle.
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| Lecturer and contact: | Dr.-Ing. Yuan Tan |
Description: The content of the seminar is determined individually for each student and deals exclusively with topics relating to food technology and food science. The topic is selected in advance from a list provided by the participating departments within a specified timeframe. Students will explore their chosen topic exclusively at a theoretical level and in consultation with their supervisor. No practical experiments are to be carried out. Prerequisites: Bachelor’s thesis Up-to-date information on the course (venue, time, examination dates, etc.) can be found on Moodle. | |
| Lecturer and contact: | Dr. techn. Daniel Schiochet Nasato |
Description:
Apparatus engineering deals with the conceptualisation, design and dimensioning of apparatus and plant. Closely linked to the process engineering procedures that take place within the relevant apparatus, the course ‘Fundamentals of Engineering in Apparatus Engineering’ covers the following topics: Materials engineering (particularly metals), strength of materials (types of loading, component failure, safety factors, …), fasteners (bolts, welds, seals, …), transport elements (pipes, pumps, conveyor belts, , etc.) as well as apparatus for material storage and conversion (silos, vessels, boilers, reactors, etc.). The central objectives of the course are to provide students with a repertoire of possible apparatus and plant components, as well as the associated calculation methods, which are essential for the qualified selection and design of such systems.
Prerequisites:
Attendance at the Technical Drawing lecture
You can find the latest information about the course (venue, times, exam dates, etc.) on Moodle.
| Lecturer and contact: | Dr.-Ing. Ali Khalifa |
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Description:
The lecture covers the following topics:
- Supervised and unsupervised machine learning,
- Random forest,
- Ridge regression,
- Bayesian classifier,
- k-nearest neighbours,
- neural networks,
- reading, outputting, processing and plotting data,
fitting models to data.
All these topics are covered using Python and machine learning libraries. The basics of Python are taught. Upon successful completion of the module, students will have a grasp of the fundamentals of the Python programming language and will have acquired a good understanding of machine learning methods. This knowledge will enable students to apply various machine learning techniques independently and competently to a range of scientific problems. In particular, students will become familiar with the mathematical background of the most important machine learning techniques, which helps in deciding which technique is best suited to which problem. Learning one of the most powerful yet simplest programming languages of today, Python, combined with knowledge of a groundbreaking technology such as machine learning, makes students more attractive to future employers and expands their academic potential.
Up-to-date information on the course (venue, times, exam dates, etc.) can be found on Moodle.
| Lecturer: | Prof. Dr.-Ing. Heiko Briesen | |
| Contact: | M. Sc. Huitian Yu |
Description:
Compulsory module for the degree programmes in Food Technology and Biotechnology, as well as Brewing and Beverage Technology. The lecture is accompanied by a practical session and concludes with a revision session prior to the examination. The course provides fundamental knowledge of engineering mechanics, specifically tailored to the requirements and conditions of the food and brewing industries. The lecture consists of three main parts: statics, elastostatics and dynamics. The course Technical Mechanics 1 takes place in the summer semester and deals with the fundamentals of statics and elastostatics. Statics deals with mechanical systems of rigid bodies at rest and introduces fundamental elements of technical mechanics (beams, rods, etc.). Elastostatics expands on this by introducing ‘small deformations’. Normal stress, shear stress, as well as strain, bending, torsion and buckling are explained.
Bibliography:
‘Technical Mechanics I–III’, Gross, Hauger, Schnell; Springer-Verlag
‘Technical Mechanics I–III’, Hibberler; Pearson-Verlag
You can find the latest information about the course (venue, times, exam dates, etc.) on Moodle.
| Lecturer and contact: | Dr.-Ing. Ali Khalifa | |
Description: This course forms part of the compulsory module ‘Automation and Control Engineering’ in the Master’s programmes in Pharmaceutical Bioprocess Engineering, Food Technology, and Brewing and Beverage Technology. The weekly course consists of a lecture and an accompanying practical session. The aim is to be able to interpret a given problem in terms of control engineering and to select and apply the appropriate concept for the design of the control system.
Up-to-date information on the course (venue, time, exam date) can be found on TUMonline or Moodle. | ||
| Lecturer and contact: | Dr.-Ing. Yuan Tan | |
Description: The ‘Technical Drawing’ lecture covers the relevant rules and standards. The following topics are covered:
Up-to-date information on the course (venue, time, exam dates, etc.) can be found on Moodle. | ||
| Lecturer: | Prof. Dr.-Ing. Heiko Briesen |
| Contact: | M. Sc. Mahima Bora |
Description:
Process engineering breaks down process steps into unit operations that cannot be further subdivided in a physically meaningful way. During the practical course, students will gain hands-on experience with grinding, classification, mixing and the properties of bulk materials. The raw materials and products are examined and analysed in the laboratory using standard analytical methods for powders, and the results are used to describe the processes. The experimentally determined data thus become parameters for the evaluation and design of process steps. The link between unit operations and food production is illustrated using the example of the manufacture of a nut nougat cream.
Prerequisites:
Participation is only possible if the examination in Process Engineering of Dispersed Systems has been passed.
Up-to-date information on the course (venue, time, exam dates, etc.) can be found on Moodle.
Translated with DeepL.com (free version)
| Lecturer: | Prof. Dr.-Ing. Heiko Briesen | |
| Contact: | M. Sc. Xinyu Pan |
Description:
Compulsory module for the Master’s programmes in Pharmaceutical Bioprocess Engineering, Food Technology and Biotechnology, and Brewing and Beverage Technology. The weekly session consists of one lecture and one practical session. The module covers algorithmic approaches in numerical mathematics.
Upon completion of the module, students will be able to identify problems within their field of study that require numerical solution methods and to apply these methods appropriately.
Up-to-date information on the course (venue, time, exam dates, etc.) can be found on Moodle.