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.

Sparked interest?

Contact: Daniela Fiedler,
daniela.fiedlernoSpam@tugraz.at, +43 316 873 30418

Start: as soon as possible

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

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.

Qualification

• Interest in Computational Fluid Dynamics (CFD), Discrete Element Method (DEM)
• Basics programming skills (Matlab/octave, Python, C/C++ or other), and interest to refine your programming skills during the Master Thesis project

We offer

• Extremely high scientific and industrial relevance
• Introduction to the leading open-source gas-particle simulation tools OpenFOAM® and CFDEM®. Support with the AI platform, as well as with respect to programming.
• Computer power, desk and office space
• Possibility to publish the results and findings in a scientific journal

Contact

Josef Tausendschön: josef.tausendschoennoSpam@tugraz.at, Stefan Radl: radlnoSpam@tugraz.at