The ultimate goal of these Master Theses are database containing geometric models for gas/liquid (G/L) and liquid/liquid (L/L) reactor mixing elements. These geometric models should be characterized via CFD simulations, such that their selection according to predefined synthesis conditions is possible. Additionally, an approximate model that describes the flow (e.g., the mean speed in the device) in these mixing elements should be established.
Based on the results of the CFD simulation studies, advanced G/L and L/L reactor elements will be prepared, and evaluated in the laboratory (this experimental work is not part of the theses). Based on the outcome of these evaluations, additional simulations should be performed, and results should be compared with experimental findings.
The objectives of this work are:
Start: May 2018
Contact: Stefan Radl (radlnoSpam@tugraz.at, 0316 873 30412)
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.
Dr. Christian Witz
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:
Start: March 2018
Contact: Bianca Grabner (b.grabnernoSpam@tugraz.at, 0316 873 30409)
Current trends in paper and pulp production aim on product diversification covering new markets, i.e. fibre-plastic compounds. Separating fibres and fines (i.e., particles smaller than 200μm) may become a crucial process step in future. Answering to this future need, we developed a novel fractionation device in a collaborative project with industry.
First studies revealed a dependence of the separation performance on key flow parameters, and was investigated by means of high-speed imaging (Figure 1, right panel).
Figure 1: Illustration of the fractionation device. (© Jakob D. Redlinger-Pohn, IPPT, TUG)
Bachelor projects will aim on detail investigations of how the fibre network formation affects separation performance. The bachelor students will receive training in the handling of the fractionator, image recording and post-processing with our existing high-speed camera.
Stefan Radl, radl(at)tugraz.at; 0316 873 30412
The bachelor thesis projects can be started earliest in summer 2018
Current trends in paper and pulp production aim on product diversification covering new markets, e.g., fibre-plastic compounds. Separating fibres by length may become a crucial process step in future. Answering to this future need, we developed a novel fractionation device in a collaborative project with industry.
The master student will prepare construction drawings using CAD, preferably SolidWorks. Prior skills from a technical high-school (HTL) are of advantage, but not required. The master student will receive training in the handling of the fractionator, image recording and post-processing with our existing high-speed camera.
· high industrial and scientific relevance (i.e., a novel separation process which will be applied in “real-world” trials at a paper mill)
· bleeding edge high-speed camera equipment and image post-processing routines
· support from the project team at IPPT and IPZ
· desk and office space
· Remuneration: 6 months á 440€.
For details CLICK HERE.
Swallowing issues of standard tablets and capsules is an increasing issue in delivering especially higher dosed medicines to patients. One of the most promising approaches is the use of small multiparticulate systems that can be dispersed in food or beverages for administration. In order to achieve a precise dose of the medicine, a precise dose of multiparticulates is filled into two piece capsules, which are opened before the administration.
This thesis will focus on the engineering concept development of a capsule opening device by simple manual opening mechanism. This master thesis will include a variety of different research tools from literature research to engineering concept development and preliminary functional assessment. The master student will be supervised by myself and supported by PhD students.
· Understanding in mechanical systems and engineering
· Motivation and creativity towards problem solving
· Interest in working on medical device development and human factored design
· A project that matters the patient and is highly relevant for the pharmaceutical industry
· A thesis in the fast evolving field of patient centric drug products
· Coaching and career development support
· Financial support during the thesis work
Univ.-Prof. Dr. Sven Stegemann
TU Graz - IPPT
Phone: +43 316 873 0422
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.