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)
Background The Institute of Process and Particle Engineering is a world leader in the development of simulation tools for industrial-scale bioprocessing units. For example, our current code can model processes in large-scale bioreactors, up to 200m³.
We are therefore looking for a bachelor or master student with engineering background (chemical engineering, bioprocess engineering, physics or similar) interested in extending the simulation tool by adding an adaptive grid refinement algorithm.
The simulation code currently uses a regular grid. For small structures, i.e. small stirrers, the grid around the structure may be too coarse to resolve fine details, e.g. the fluid jet coming from the stirrer. Hence, the objective of this thesis is to employ a new grid addressing scheme and advanced interpolation methods to be able to define areas with a finer grid efficiently within the simulation code.
The coding is done in C++ in combination with the CUDA library on high-performance graphic cards.
Tasks • Find and implement a new grid addressing scheme • Research and choose interpolation methods • Literature study on test cases to validate the program module • Include the validation in the test harness of the code
Requirements • Background in chemical or bioprocess engineering, physics or similar • Basic biotechnological knowledge • Being familiar with thermodynamics, simulation and modeling
What we offer • Integration in an internationally leading team • Opportunity to be part of a commercialization project • Paid master thesis • Start of a future career in software creation
Start: Summer/Fall 2019
Contact Dr. Christian Witz 0316 873 30416 christian.witznoSpam@tugraz.at
Background
The Institute of Process and Particle Engineering is a world leader in the development of simulation tools for industrial-scale bioprocessing units. For example, our current code can model processes in large-scale bioreactors, up to 200m³.
We are therefore looking for a bachelor or master student with engineering background (chemical engineering, bioprocess engineering, physics or similar) interested in extending the simulation tool by adding highly efficient and fast models to calculate the heat transfer from heat jackets, heat exchanger or air bubbles to the fluid in the reactor by employing dimensionless relations like the Nusselt number. An algorithm for the convective heat transport is already included in the code.
Tasks • Implement the respective heat transfer by the heat exchanger, heat jacket or air bubbles to the fluid as sources in the convective heat • Literature study on test cases to validate the program module • Include the validation in the test harness of the code
The Institute of Process and Particle Engineering is a world leader in the development of simulation tools for industrial-scale bioprocessing units. For example, our current code can model processes in large-scale bioreactors, up to 200m3.
We are therefore looking for design exercise (Konstruktionsübung) student with engineering background (chemical engineering, bioprocess engineering, physics or similar) interested in extending the simulation tool by adding algorithms for the automation of the setup and the post processing of the simulation.
The simulation code currently uses a JSON text file as interface to the user to define the simulation parameters. The task of this exercise is to extend a HTML page with Java Script to create this JSON file. A working example already exists.
The second task is to create Python scripts to control the software Paraview, which is currently used to post process the simulation results (e.g. create videos, images and plots automatically).
Tasks • Expand the existing HTML/Java Script page • Write the Python scripts necessary to create videos, images, plots with Paraview automatically
Requirements • Background in chemical or bioprocess engineering, physics or similar • Being familiar with programming, simulation and modeling
What we offer • Integration in an internationally leading team • Opportunity to be part of a commercialization project • Paid design exercise • Start of a future career in software creation
Perfusion reactors are used to host microbial cells, which are able to produce antibiotics, potent drugs substances for cancer therapy or other active pharmaceutical ingredients. During the reactor’s operation, the produced drug molecules have to be extracted continuously. This is currently done via alternating tangential flow filtration (ATF). A part of the solution in the reactor is sucked through a fiber filter element with a diaphragm pump. The concentrated cell solution is pumped back in the reactor by the diaphragm and the cell-free filtrate is pumped to the next stage to extract the active pharmaceutical ingredient.
Tasks
We offer
PDF OF THIS MASTER THESIS SOLICITATION
Contact: Dr. Christian Witz christian.witz@tugraz.at
Statins are the active pharmaceutical ingredient (API) of many cholesterol lowering drugs. Their structure consists of the typical statin side-chain possessing two chiral alcohols linked to a heterocyclic core. This side-chain can be synthesized from simple and inexpensive starting materials via a two-step aldol condensation catalyzed by an enzyme called DERA (2-deoxyribose-5-phosphate aldolase). The side-chain can either be directly built at the core of the molecule or linked to the heterocyclic core subsequently via a C-C coupling reaction catalyzed by Palladium.
The goal of this work is to investigate the biocatalytic step in this synthetic route. A number of substrates, such as acetaldehyde, chloroacetaldehyd, benzaldehyde and cinnamaldehyde, will be testes as acceptors in the aldol condensation. The obtained product will be characterized and evaluated according to their potential for serving as intermediate in the synthetic route of statins. Further the enzyme (enclosed in E. coli cells) will be immobilized in order to apply it in a continuous process.
The results of this thesis will serve in the development of an integrated multistep process for the synthesis of statins consisting of a biocatalytic and a metal-catalyzed step.
The objectives of this work are:
We offer:
Start: March 2018 Contact: Bianca Grabner (b.grabnernoSpam@tugraz.at, 0316 873 30409)
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
Contact
Josef Tausendschön: josef.tausendschoennoSpam@tugraz.at, Stefan Radl: radlnoSpam@tugraz.at
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Institute of Process and Particle Engineering Inffeldgasse 13 8010 Graz