This paper focuses on utilizing the Celciushouse as an escape room in energy education. In a broader context, it also addresses the incorporation of serious gaming in education. The project is part of COVE SEED. SEED - Sustainable Energy Education, aims to develop innovative vocational education and training, working with experts from five different European regions to phase out fossil fuels and contributing to Europe becoming a fossil free energy continent. SEED is a CoVE (Centres of Vocational Excellence) programme. CoVE’s are part of the Erasmus+ program aiming to establish transnational platforms on, among others, regional development, innovation and inclusion. SEED combines education on various international levels including level 2,3,4, and 6. At this moment, the project ESCAPEROOM IN ENERGY EDUCATION is still in its initial phase. With this paper and the accompanying workshop, we aim to gather insights from other international regions involved in the SEED project collaboration. The acceleration of technological developments means that what is learned today may be outdated tomorrow. Therefore, it is essential for educational institutions to focus on developing general skills such as critical thinking, problem-solving, and the ability to quickly absorb new information. The market demands professionals with modern knowledge and skills. Techniques taught to students today may become outdated tomorrow. Therefore, the ability to learn how to learn is becoming increasingly crucial. Analytical and research skills are therefore gaining importance. It is also essential for students to utilize various learning methods. Not just learning from books but particularly learning from practical experience. Practice-oriented learning, where students gain direct experience in real situations, not only reinforces theoretical knowledge but also develops practical skills that are valuable in the job market. To tackle these problems, serious gaming or the establishment of escape rooms can be a solution.
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We get into an Uber car, and the driver passes by the Kremlin walls, guided by GPS. At the end of the ride, the bill turns out to be three times as expensive than usual. What is the matter? We check the route, and the screen shows that we travelled to an airport outside of Moscow. Impossible. We look again: the moment we approached the Kremlin, our location automatically jumped to Vnukovo. As we learned later, this was caused by a GPS fence set up to confuse and disorient aerial sensors, preventing unwanted drone flyovers.
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In this proposal, a consortium of knowledge institutes (wo, hbo) and industry aims to carry out the chemical re/upcycling of polyamides and polyurethanes by means of an ammonolysis, a depolymerisation reaction using ammonia (NH3). The products obtained are then purified from impurities and by-products, and in the case of polyurethanes, the amines obtained are reused for resynthesis of the polymer. In the depolymerisation of polyamides, the purified amides are converted to the corresponding amines by (in situ) hydrogenation or a Hofmann rearrangement, thereby forming new sources of amine. Alternatively, the amides are hydrolysed toward the corresponding carboxylic acids and reused in the repolymerisation towards polyamides. The above cycles are particularly suitable for end-of-life plastic streams from sorting installations that are not suitable for mechanical/chemical recycling. Any loss of material is compensated for by synthesis of amines from (mixtures of) end-of-life plastics and biomass (organic waste streams) and from end-of-life polyesters (ammonolysis). The ammonia required for depolymerisation can be synthesised from green hydrogen (Haber-Bosch process).By closing carbon cycles (high carbon efficiency) and supplementing the amines needed for the chain from biomass and end-of-life plastics, a significant CO2 saving is achieved as well as reduction in material input and waste. The research will focus on a number of specific industrially relevant cases/chains and will result in economically, ecologically (including safety) and socially acceptable routes for recycling polyamides and polyurethanes. Commercialisation of the results obtained are foreseen by the companies involved (a.o. Teijin and Covestro). Furthermore, as our project will result in a wide variety of new and drop-in (di)amines from sustainable sources, it will increase the attractiveness to use these sustainable monomers for currently prepared and new polyamides and polyurethanes. Also other market applications (pharma, fine chemicals, coatings, electronics, etc.) are foreseen for the sustainable amines synthesized within our proposition.
The increasing amount of electronic waste (e-waste) urgently requires the use of innovative solutions within the circular economy models in this industry. Sorting of e-waste in a proper manner are essential for the recovery of valuable materials and minimizing environmental problems. The conventional e-waste sorting models are time-consuming processes, which involve laborious manual classification of complex and diverse electronic components. Moreover, the sector is lacking in skilled labor, thus making automation in sorting procedures is an urgent necessity. The project “AdapSort: Adaptive AI for Sorting E-Waste” aims to develop an adaptable AI-based system for optimal and efficient e-waste sorting. The project combines deep learning object detection algorithms with open-world vision-language models to enable adaptive AI models that incorporate operator feedback as part of a continuous learning process. The project initiates with problem analysis, including use case definition, requirement specification, and collection of labeled image data. AI models will be trained and deployed on edge devices for real-time sorting and scalability. Then, the feasibility of developing adaptive AI models that capture the state-of-the-art open-world vision-language models will be investigated. The human-in-the-loop learning is an important feature of this phase, wherein the user is enabled to provide ongoing feedback about how to refine the model further. An interface will be constructed to enable human intervention to facilitate real-time improvement of classification accuracy and sorting of different items. Finally, the project will deliver a proof of concept for the AI-based sorter, validated through selected use cases in collaboration with industrial partners. By integrating AI with human feedback, this project aims to facilitate e-waste management and serve as a foundation for larger projects.
Currently, many novel innovative materials and manufacturing methods are developed in order to help businesses for improving their performance, developing new products, and also implement more sustainability into their current processes. For this purpose, additive manufacturing (AM) technology has been very successful in the fabrication of complex shape products, that cannot be manufactured by conventional approaches, and also using novel high-performance materials with more sustainable aspects. The application of bioplastics and biopolymers is growing fast in the 3D printing industry. Since they are good alternatives to petrochemical products that have negative impacts on environments, therefore, many research studies have been exploring and developing new biopolymers and 3D printing techniques for the fabrication of fully biobased products. In particular, 3D printing of smart biopolymers has attracted much attention due to the specific functionalities of the fabricated products. They have a unique ability to recover their original shape from a significant plastic deformation when a particular stimulus, like temperature, is applied. Therefore, the application of smart biopolymers in the 3D printing process gives an additional dimension (time) to this technology, called four-dimensional (4D) printing, and it highlights the promise for further development of 4D printing in the design and fabrication of smart structures and products. This performance in combination with specific complex designs, such as sandwich structures, allows the production of for example impact-resistant, stress-absorber panels, lightweight products for sporting goods, automotive, or many other applications. In this study, an experimental approach will be applied to fabricate a suitable biopolymer with a shape memory behavior and also investigate the impact of design and operational parameters on the functionality of 4D printed sandwich structures, especially, stress absorption rate and shape recovery behavior.
