Applied Engineering

2025

Attend a PhD defence or find the archive of concluded doctoral research

2025

Attend a PhD defence or find the archive of concluded doctoral research

'Electrochemical Reduction of Carbon Dioxide with Flue Gas Impurities: Turning Fumes into fuels' (08/05/2025)

Sam Van Daele

Abstract

Carbon dioxide (CO2) emissions drive climate change and require urgent abatement. The electrochemical reduction of CO2, powered by renewable energy, presents great prospects to turn this waste product into value-added products, yet the high costs of CO2 capture and purification hampers its profitability. One solution is to directly utilize waste CO2 gases, for example from an industrial flue gas exhaust stream as a feedstock to produce value-added chemical products. However, these waste gases typically contain CO2 in low concentrations together with impurities like O2, SO2 and NO that could interfere with the electrochemical process. This PhD thesis investigated the influence of gaseous impurities during the electrochemical CO2 conversion to chemical products.

'Concept and control optimisation for collective heating and cooling in apartment buildings' (30/04/2025)

Stef Jacobs

Abstract

How can apartment buildings be heated and cooled sustainably and efficiently? Collective heating and cooling systems play a crucial role in the energy transition. However, they can often be designed and controlled more intelligently. This doctoral research presents concrete solutions to improve the efficiency of mini heating networks in apartment buildings without compromising comfort.

This was achieved through three approaches:
1. Smarter supply temperature control in central change-over systems
A newly proposed control strategy groups heating demands based on temperature needs. This allows the central distribution temperature to be dynamically adjusted. As a result, energy use is reduced by 36%, and energy costs decrease by 32%. If slight temperature fluctuations in domestic hot water are permitted, the potential savings can be even greater. Because the distribution temperature is centrally adjusted and influences the entire system, this is referred to as a “central change-over system”.

2. Artificial intelligence for system control
The research also explored ways to train artificial intelligence to better understand the dynamics of these slow-reacting networks. By improving the learning strategy of a reinforcement learning algorithm, learning performance increased by 3% to 15% compared to conventional learning schemes. This is a step towards more advanced and better-regulated systems.

3. A new method for improved design choices
Finally, a new assessment method was introduced to provide clearer insights into which systems are best suited for different types of buildings and residents. The study demonstrated that occupant behavior significantly impacts which central change-over system should be installed. This assessment method enables more objective and efficient design decisions, ultimately leading to improved energy performance in buildings.

Thanks to these insights, collective energy systems in apartment buildings can become more sustainable and efficient. Moreover, this doctoral research paves the way for further innovations in smart and sustainable heating networks.

'Quality of Service Compliance Verification Using Federated Learning Empowered by Blockchain' (26/03/2025)

João de Brito Gonçalves

  • 26 March 2025
  • 7 p.m.
  • ​Online​
  • Promotors: prof. dr. Johann Marquez-Barja & prof. dr. Rodolfo da Silva Villaça  
  • Faculty of Applied Engineering

Abstract

Edge computing brings a new paradigm in which computing, storage, and bandwidth resources are shared as closely as possible to mobile devices or sensors, generating a large amount of data, but the sensitive nature of the data means that there are risks and responsibilities to storing them in a centralized location. Moreover, to address the data privacy required for some data in these devices, we propose the use of Federated Learning (FL) so that specific data on services performed by clients do not leave the source machines. FL is a new sub-field of Machine Learning (ML) that allows training models without collecting the data itself. Instead of sharing data, users collaboratively train a model by only sending weight updates to a server. However, the naive use of FL in the scenarios aforementioned exposes it to a risk of corruption, whether intentional or not, during the training phase. This is due to the lack of training monitoring and the difficulty in verifying the quality of training datasets. To improve the security of FL systems, we propose a Blockchain-based framework in an edge computing scenario. Blockchain, with its immutability and traceability, can be an effective tool to prevent malicious attacks in FL. More specifically, the immediate updates made by each participant to its local model can be chained together on the distributed ledger offered by a blockchain such that those model updates are audited, and malicious trainers can be removed of the system. We also apply blockchain to create a reward mechanism in FL to enable an incentive strategy for trainers.

'Impact of rational catalyst design on stability for electrochemical ammonia synthesis revealed by electron microscopy' (21/01/2025)

Saskia Hoekx

Abstract

Ammonia is a high commodity chemical that is predominantly used as a fertilizer in the agricultural industry. Over 180 million metric tons are produced annually, and the demand is expected to increase by 3-5% each year. Currently, this is synthesized using the energy-intensive and polluting Haber-Bosch process, which produces over 1% of the world’s total yearly greenhouse gas emissions, and 15% of the total carbon dioxide emitted by the chemical industry. One promising alternative is the electrochemical nitrate reduction reaction, which can convert polluting nitrates in agricultural wastewater back into useful ammonia under ambient conditions, with no carbon footprint. However, in order to be industrially applicable, this reaction requires an efficient and stable catalyst. In this work, characterization by electron microscopy and electrochemical tests are combined to approach catalyst optimization rationally. This resulted in a more stable catalyst that can effectively catalyze the nitrate reduction reaction for at least 24 hours without a significant loss in electrochemical performance.

'Development of a reversibly immobilised cell reactor for the valorisation of lignocellulosic wastewaters to microbial oil' (14/01/2025)

Waut Broos

  • 14 January 2025
  • 4 p.m.
  • ​Campus Drie Eiken- Building O, room d.O.01
  • Promotor: prof. dr. prof. dr. Iris Cornet, prof. dr. Siegfried E. Vlaeminck & prof. dr. Jan Dries ​
  • Faculty of Applied Engineering

Abstract

Lignocellulose-based biorefineries produce a significant amount of wastewater containing several phenolic compounds. Today, these wastewaters are mostly considered a burden. However, some microorganisms, e.g., Rhodotorula kratochvilovae and Cutaneotrichosporon oleaginosum can convert phenolics into valuable intracellular components, namely single-cell oil (SCO). This turns the waste stream into a raw material and an economic opportunity. However, valorising wastewater to SCO is challenging due to its low substrate concentration. Therefore, supplementation with a high-value carbon source is used in literature to obtain appreciable SCO titters. To date, the production of SCO from high-value carbon sources has proven to be economically unfeasible. As a result, this study explores the use of a repeated batch fermentation strategy to overcome the low substrate concentration. In a repeated batch, the cells are recycled for use in the next batch. Cell recycling and repeated feeding allow intracellular SCO to accumulate in the cells. This allows high SCO concentrations to be achieved from substrate-poor wastewaters. Three technologies can be used for cell recycling: centrifugation, membrane technology and immobilisation. However, each of these technologies has its drawbacks: centrifugation is energy-consuming and requires a significant investment, membrane technology is prone to fouling, and conventional immobilisation technologies make recovery of the intracellular product problematic.
Our hypothesis is that the design of a new reactor type, namely a reversible immobilised cell reactor (RICR), offers a possible solution. In this reactor, the cells are first immobilised on a suitable support, repeated batch fermentation occurs, and finally remobilised to recover the intracellular components. As a case study, the wastewater obtained from thermochemical pre-treatment of lignocellulose is investigated as a substrate for microbial oil production. This study aimed to design an economically feasible process for valorising this lignocellulosic wastewater.