Power-to-Methanol (P2Me) technology is emerging as a scalable solution for energy storage during the transition to net zero. However, P2Me's efficiency and cost competitiveness are hindered by the intermittent nature of wind and solar energy, and the stable energy demand required for continuous methanol production. This leads to higher production costs for e-methanol compared to fossil-based alternatives. Introducing process flexibility offers a promising strategy to lower these costs by approximately 20%. Flexibility allows the P2Me process to adapt to the fluctuating availability of renewable energy, incorporating demand-side management to better align power generation with methanol production needs. Despite this potential, no specific flexible process design has been proposed, nor have comprehensive evaluations of the benefits and trade-offs of flexible P2Me processes been explored from a multi-dimensional perspective. The FlexP2Me project aims to revolutionize the P2Me process by integrating flexibility into its multi-level design, optimization, and assessment. This project will pursue two goals with high techno-economic and social impacts: (a) proposing an innovative flexible P2Me process featuring load-adaptable reactor and distillation configurations, and (b) optimizing and evaluating the P2Me process across various dimensions—efficiency, cost, environmental impact, and safety. To achieve this, FlexP2Me will develop a novel multi-stage reactor and a flash vapor circulation-based distillation system, coupled with thermal storage, to dynamically adapt to the fluctuations in renewable energy inputs. This unique approach, absent in current processes, is expected to enhance efficiency by at least 15% and lower e-methanol production costs to within 20% of fossil-based methanol. Additionally, FlexP2Me aims to reduce CO2 emissions by 20% through complete process electrification of P2Me and ensure operational risks are tolerable to society via risk-informed design.

Battery Management System (BMS) plays a pivotal role in monitoring, control, and protecting the Electric Vehicle (EV) Lithium-ion battery packs. In vehicular applications, batteries are usually subjected to harsh operating cycles and varying environmental conditions leading to very complicated interactions of different aging factors and unforeseeable modeling uncertainties. Therefore, the classical model-based techniques cannot completely handle the foregoing factors, which always leave an unwanted state estimation error in the BMS. This project intends to apply a multidisciplinary approach by combining the advantages of deep reinforcement learning and classical model-based techniques to improve the BMS functionality in EVs. Specifically, DeepBMS aims to: 1-Develop efficient deep reinforcement learning-based algorithms which are able to capture the convoluted time-varying behavior of battery and can gradually improve themselves by learning in real-time 2- Combine the beneficial features of model-based and data-driven techniques to improve the state estimation accuracy in a wide temperature range and over the full life span of the batteries, thereby increasing the reliability and extending the battery lifetime. The interdisciplinary nature of DeepBMS is very strong, involving a combination of control and state estimation theory, power electronics, battery storage systems, and machine learning. The supervisor and candidate have excellent complemental research experiences in these fields providing the necessary competencies to bring the project to successful completion. The project ensures two-way transfer of knowledge including training of the candidate in cutting-edge advanced techniques in a state-of-the-art laboratory, which improves his future career prospects. Likewise, DeepBMS is in line with the EU strategic action plan on batteries and its results have a great potential to be further developed at the fundamental and applied levels through follow-up research.

To address the challenges of climate change and energy crisis, the European Union (EU) is accelerating the transition to carbon neutrality, using wind power as the key driver. To fully exploit EU’s offshore wind resources, the strategy of constructing wind power hub has been devised recently and will be implemented in the North Sea. However, integration of numerous power converters in wind power hub forms a sophisticated system, making analysis difficult. This project (PhyDAWN) will tackle these challenges by developing physics-informed data-driven modelling and stability assessment methodologies for wind power hub. Firstly, effective impedance model of voltage source converters for varying operation points will be developed based on physics-informed machine learning. Next, a scalable aggregated model for wind power hub will be proposed using transfer learning, improving modelling effectiveness under insufficient data. Then, the stability of wind power hub will be assessed in a probabilistic manner, realizing intensive stability analysis for internal converters. A software toolbox will also be developed for fully exploitation of the research results in the industry. The applicant has expertise in data-driven grid analysis. During the project, she will collaborate with the supervisor at Aalborg University, who has rich research experience on power converter modelling and analysis. Besides, she will collaborate with the secondment supervisor at KTH Royal Institute of Technology, who is an expert of control theory. Through these collaborations, PhyDAWN will create novel knowledge for wind power hubs, and also strengthen the applicant’s career prospects. The research results will be disseminated by publications in top journals and conferences, producing a positive impact on raising the Europe knowledge base. Moreover, communication activities are planned to expand the impact of this project to the general public, committing to EU’s carbon neutrality ambition.

MicMicrobial communities play a vital role in most processes in the biosphere and are essential for solving present and future environmental challenges. Examples include the impact of the human microbiome on health and disease, the discovery of new antibiotics, and turning waste products into valuables. In the past 10 years, new DNA sequencing-based methods have revolutionized our access to the genomes of microbial communities and have sparked an explosion of new fundamental discoveries based on genomic evidence. However, despite the fundamental discoveries enabled by new methods in the past decade, we are far from having a meaningful genomic representation of the tree of life - and we are even further away from understanding how microbes realize their genomic potential in complex environments. This is underlined by the fact that the current microbial genome databases contain genomic information on 47,894 prokaryotic species, while the most conservative analysis estimates millions of different species in nature. The NanoEat project will enable the next generation of large-scale studies in microbial communities to answer the fundamental questions of who is there and what do they eat. In nature, most microbes modify their DNA in highly specific combinations, either as a defense system against viruses or to regulate activity. In NanoEat we will exploit this feature using the raw Nanopore sequencing signal that, in principle, enables discovery of any type of modified DNA. By developing new machine learning frameworks that can identify these species-specific modification patterns we can utilize this novel feature to supercharge recovery of individual microbial genomes from complex communities. Furthermore, by supplying synthetic nucleotides, that can be detected by Nanopore sequencing, we hypothesize that it is possible to estimate how microbes grow, by using the incorporation rate of these synthetic nucleotides to estimate replication in complex communities at scale.

Existing CO2 capture technologies, such as amine-based absorption and cryogenic distillation, face challenges and problems including high energy requirements, large infrastructure needs, and high costs. These technologies often require significant retrofitting or integration into existing industrial processes, limiting their scalability and commercial viability. In contrast, the utilization of MOFs (Metal-Organic Frameworks) for CO2 capture has garnered significant interest due to the numerous advantages they offer compared to other materials such as high adsorption capacity, selectivity, tunability, regenerability, and potential for direct utilization, making MOFs a promising solution for efficient and effective CO2 capture. However, it is challenging to design MOF materials with extremely high CO2 capture capacity, gas selectivity, and water stability along with moderate regeneration energy as water dissociation causes hydroxyl-poisoning that impairs CO2 sorption by both high temperature and moisture exposure. Additionally, the high energy consumption during blowdown and evacuation steps for the process cycle of MOFs need to be improved to ensure long-term performance. The novelty of this work lies in its ability to strengthen the interaction between CO2 molecules and the MOF structure through an innovative dual activation method, utilizing both N and S atoms. This approach surpasses conventional single-atom activation in MOFs, resulting in enhanced binding. Furthermore, the incorporation of Lewis Base Sites (LBSs) has become increasingly popular for reducing the energy needed for MOF regeneration, consequently improving CO2 binding affinity, selectivity, and reversibility. Advanced thermal modeling will be employed to analyze the dynamic processes of CO2 adsorption/desorption, sweep gas, and dry-out sequences. This modeling considers both the mechanical and chemical properties of the synthesized MOF.