The Institute of Process and Particle Engineering is a world leader in the development of simulation tools for industrial-scale bioprocessing units, funded by the Spin-Off Fellowship Program of the FFG. For example, our current code can model processes in large-scale bioreactors, up to 200m3 . We are therefore offering a student job with the possibility to do a master thesis with the goal of creating a comparison algorithm for bioreactors. The objective is to find the influencing factors that determine the productivity difference between reactors. This should be done by comparing reactors of different scales and for reactors at the same scale but different geometry and should aid scale up or process transfer processes in the industry.
What we offer:
Start: Fall 2020
Contact Dr. Christian Witz 0316 873 30416 christian.witznoSpam@tugraz.at
Biopharmaceuticals steadily increase in market share on the pharmaceutical market. New and innovative formulations for biopharmaceuticals such as protein-based drugs are of high interest for the future. Currently biopharmaceutical drugs are either formulated liquid based or freeze-dried.
Our research group focuses on alternatives to freeze drying such es e.g. spray drying (SD, Figure 1 right). The drying of a droplet to a solid particle encompasses three main steps; i) atomization of the feed formulation into micron sized droplets, ii) drying in several drying stages to a solid particle and iii) collection of the produced protein powder. The process parameters (e.g. inlet temperature, volumetric drying air rate, spray rate) and process configuration (e.g. type of nozzle, residence time) strongly interplay with the formulation properties such as viscosity, surface tension or protein concentration (Figure 1). In depth knowledge and sound understanding of the process and formulation correlations with regard to the product should be developed throughout this study.
This work includes literature research on excipient candidates and design of experiments (DoE), followed by setting up a DoE and the classification of excipient properties and performing of drying experiments. We offer you high scientific and industrial relevant work in the emerging field of drying biopharmaceuticals. You will be supported from the IPPE project team and obtain desk and office space. In case of a Masters’ thesis monetary compensation is possible.
Figure 1: Left: protein dispersed in excipient matrix (e.g. polymer); right: schematic representation of a spray dryer. Interplay between formulation properties and process settings on protein and powder characteristics should be evaluated.
Contact: Daniela Fiedler, daniela.fiedlernoSpam@tugraz.at, +43 316 873 30418
Start: as soon as possible
The current COVID-19 crisis highlights the need to treat inside air, as inside-air viral transmission is considered an important route of infection. Interestingly, UV-based irradiation of virus particles is known to inactivate the virus. Thus, the goal of the thesis is to develop a low-cost UV-based air flow-through device (UV decontamination reactor) that can be integrated in typical air handling systems in restaurants, movie theatres, concert halls, trains, etc.
Start: Summer/Fall 2020
Assoc. Prof. Dr. Stefan Radl Univ.-Prof. Dr. Johannes Khinast (radlnoSpam@tugraz.at, khinastnoSpam@tugraz.at)
For this construction thesis we are looking for a student to design a water bath made of stainless steel for continuous cooling crystallization in a tubular reactor (plug flow crystallizer).
Using such a tubular crystallizer the continuous cooling crystallization process should be carried out in two water baths of different temperature. Each water bath has separate glass inserts which enable the visual observation of the crystallization process within the tubes via a high speed camera. To achieve high resolution images a backlight source e.g. a LED panel in addition to the camera is necessary. Due to the reflection of the water inside the bath the distance between the backlight and the camera should be as short as possible. To keep the tube cycles uniformly immersed and to guide them special tube mountings are also required.
What we offer
Dipl.-Ing. Alexander Meister, BSc Inffeldgasse 13 / III, 8010 Graz alexander.meisternoSpam@tugraz.at
The fact that human beings differ from each other, regarding their physiology, pathology and environment suggests, that not everyone can be treated using the same medication. Therefore, a trend towards personalized medicine is observable. This trend brings up some challenges, as it comes to the production of tablets. The dose of the Active Pharmaceutical Ingredient (API) as well as the amount of any excipient needs to be dosed accurately, for each individual tablet.
The development of a device being capable to do so, is the goal of this thesis. As the dosing shall be done gravimetrically, the dosing unit will contain a feeder and a weighing cell. A scheme of the set-up and its features can be seen below.
Your work will include the following points: • Picking a development environment (LabView, Arduino/Raspberry Pi, …) • Picking a weighing cell with sufficient accuracy and fitting dynamic properties • Integration of weighing cell and feeder in an environment for control and data acquisition • Investigation of the feeding/dosing characteristics of the set-up • Development of a self-adjusting dose algorithm
We offer • High scientific and industrial relevance (the approach involving this dosing process could speed up the development in pharmaceutical industry and allow to produce personalized medicine) • Support from the IPPE project team and Prof. Horn from IRT • Desk and office space
Contact: Andreas Kottlan, andreas.kottlannoSpam@tugraz.at, 0316-873-30419 Starting date: as soon as possible 2020
Sonic mixing is a relatively new approach for mixing various systems. The process is used to some extend in pharmaceutical industry to produce powder mixtures and pastes. To achieve a satisfying mixing quality, the system is exposed to vibration with high acceleration and high amplitudes in means of travel. This is commercially realized by using a system working at resonance frequency to minimize the power input.
We aim for a much smaller system size, i.e. the mass of single tablet. This allows us to get rid of the need for a system operated at resonance. Therefore, the frequency can be varied to achieve the most effective mixing process. A schematic sketch of the setup can be seen in figure 1.
This thesis shall bring insight to some specific points: • How do the operating parameters, i.e. frequency and amplitude affect the flow pattern within the mixing vessel? • How does the “flow regime” within the mixing chamber affect the mixing performance? • How do different powders react to different operating parameters? • Is it possible to find a robust mode of operation which provides satisfying results for various powder systems? • Can coating or granulation processes be done using this setup?
The investigation on these topics shall be done using an existing experimental setup, which can be modified to meet arising requirements. The student shall elaborate a design of experiments which covers the relevant operating range of the used vibration generator and accounts for different powder blends. Once defined, the mixing experiments are to be carried out. The blends, produced in this way, need to be analysed. The search for meaningful analysis methods is part of this work.
We offer • High scientific and industrial relevance (the approach involving this mixing process could speed up the development in pharmaceutical industry and allow to produce personalized medicine) • Support from the IPPE project team • Desk and office space
Contact: Andreas Kottlan, andreas.kottlannoSpam@tugraz.at, 0316-873-30419 Starting date: as soon as possible
To dedicated students who are interested in the pharmaceutical field (i.e. students of chemical engineering, pharmaceutical engineering, biomedical engineering, pharmacy, or related disciplines), we offer an opportunity to write a paid Master’s thesis.
OBJECTIVE: Powder processing steps, such as feeding and mixing, are critical in many industries, including the pharmaceutical industry. For example, within a continuous tablet manufacturing environment, powder feeding impacts functionality and quality of the final product. For the rational design of such operations the powder properties need to be known, including the particle size distribution, bulk (poured) and tapped density, flowability, compressibility, electrostatic chargeability and tendency to segregate.
Thus, the development of solid dosage forms and the associated manufacturing processes requires a good understanding of the relationship between powder composition and the properties of the powder.
WITHIN THE FRAMEWORK OF THIS MASTER’S THESIS WE OFFER THE FOLLOWING:
FINANCING: Compensation on the basis of a service contract
If you are interested in writing your thesis at the process and particle engineering institute of TUGraz, please contact us indicating the reference number. Candidates will be selected on a competitive basis and will be selected without regard to sex, race or nationality.
Contact: Sara Fathollahi (sara.fathollahinoSpam@tugraz.at, 0316 873 30938)
Current trends in advanced multiphase flow prediction aim at the usage of machine learning algorithms and deep neural networks to improve the accuracy and to speed-up numerical simulations. Overall, it can be expected that these tools (and artificial intelligence, AI, in a wider context) will become an important part of numerical modelling used in chemical engineering applications.
The overarching goal of this Master Thesis project is to increase the speed of gas-particle flow simulations (see the right panel in the Figure below) by using an AI-powered prediction algorithm.
Your tasks will include (i) a literature review on the usage of deep learning in numerical simulations, (ii) an investigation related to key parameters of a neural net structure to speed up a widely-used gas-particle flow simulator, and (iii) application of the improved simulator to a use case. The machine learning and deep neural nets will be based on existing open source AI platforms (e.g., Tensorflow), for which expert knowledge is already available at our institute.
Josef Tausendschön: josef.tausendschoennoSpam@tugraz.at, Stefan Radl: radlnoSpam@tugraz.at
Institute of Process and Particle Engineering