Poster presentation: decentralized gas storage.
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With a market demand for low cost, easy to produce, flexible and portable applications in healthcare, energy, biomedical or electronics markets, large research programs are initiated to develop new technologies to provide this demand with new innovative ideas. One of these fast developing technologies is organic printed electronics. As the term printed electronics implies, functional materials are printed via, e.g. inkjet, flexo or gravure printing techniques, on to a substrate material. Applications are, among others, organic light emitting diodes (OLED), sensors and Lab-on-a-chip devices. For all these applications, in some way, the interaction of fluids with the substrate is of great importance. The most used substrate materials for these low-cost devices are (coated) paper or plastic. Plastic substrates have a relatively low surface energy which frequently leads to poor wetting and/or poor adhesion of the fluids on the substrates during printing and/ or post-processing. Plasma technology has had a long history in treating materials in order to improve wetting or promote adhesion. The µPlasma patterning tool described in this thesis combines a digital inkjet printing platform with an atmospheric dielectric barrier discharge plasma tool. Thus enabling selective and local plasma treatment, at atmospheric pressure, of substrates without the use of any masking materials. In this thesis, we show that dependent on the gas composition the substrate surface can either be functionalized, thus increasing its surface energy, or material can be deposited on the surface, lowering its surface energy. Through XPS and ATR-FTIR analysis of the treated (polymer) substrate surfaces, chemical modification of the surface structure was confirmed. The chemical modification and wetting properties of the treated substrates remained present for at least one month after storage. Localized changes in wettability through µPlasma patterning were obtained with a resolution of 300µm. Next to the control of wettability of an ink on a substrate in printed electronics is the interaction of ink droplets with themselves of importance. In printing applications, coalescence of droplets is standard practice as consecutive droplets are printed onto, or close to each other. Understanding the behaviour of these droplets upon coalescence is therefore important, especially when the ink droplets are of different composition and/or volume. For droplets of equal volume, it was found that dye transport across the coalescence bridge could be fully described by diffusion only. This is as expected, as due to the droplet symmetry on either side of the bridge, the convective flows towards the bridge are of equal size but opposite in direction. For droplets of unequal volume, the symmetry across the bridge is no longer present. Experimental analysis of these merging droplets show that in the early stages of coalescence a convective flow from the small to large droplet is present. Also, a smaller convective flow of shorter duration from the large into the small droplet was identified. The origin of this flow might be due to the presence of vortices along the interface of the bridge, due to the strong transverse flow to open the bridge. To conclude, three potential applications were showcased. In the first application we used µPlasma patterning to create hydrophilic patterns on hydrophobic dodecyl-trichlorosilane (DTS) covered glass. Capillaries for a Lab-on-a-chip device were successfully created by placing two µPlasma patterned glass slides on top of each other separated by scotch tape. In the second application we showcased the production of a RFID tag via inkjet printing. Functional RFID-tags on paper were created via inkjet printing of silver nanoparticle ink connected to an integrated circuit. The optimal operating frequency of the produced tags is in the range of 860-865 MHz, making them usable for the European market, although the small working range of 1 m needs further improvement. Lastly, we showed the production of a chemresistor based gas sensor. In house synthesised polyemeraldine salt (PANi) was coated by hand on top of inkjet printed silver electrodes. The sensor proved to be equally sensitive to ethanol and water vapour, reducing its selectivity in detecting changes in gas composition.
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The Heating Ventilation and Air Conditioning (HVAC) sector is responsible for a large part of the total worldwide energy consumption, a significant part of which is caused by incorrect operation of controls and maintenance. HVAC systems are becoming increasingly complex, especially due to multi-commodity energy sources, and as a result, the chance of failures in systems and controls will increase. Therefore, systems that diagnose energy performance are of paramount importance. However, despite much research on Fault Detection and Diagnosis (FDD) methods for HVAC systems, they are rarely applied. One major reason is that proposed methods are different from the approaches taken by HVAC designers who employ process and instrumentation diagrams (P&IDs). This led to the following main research question: Which FDD architecture is suitable for HVAC systems in general to support the set up and implementation of FDD methods, including energy performance diagnosis? First, an energy performance FDD architecture based on information embedded in P&IDs was elaborated. The new FDD method, called the 4S3F method, combines systems theory with data analysis. In the 4S3F method, the detection and diagnosis phases are separated. The symptoms and faults are classified into 4 types of symptoms (deviations from balance equations, operating states (OS) and energy performance (EP), and additional information) and 3 types of faults (component, control and model faults). Second, the 4S3F method has been tested in four case studies. In the first case study, the symptom detection part was tested using historical Building Management System (BMS) data for a whole year: the combined heat and power plant of the THUAS (The Hague University of Applied Sciences) building in Delft, including an aquifer thermal energy storage (ATES) system, a heat pump, a gas boiler and hot and cold water hydronic systems. This case study showed that balance, EP and OS symptoms can be extracted from the P&ID and the presence of symptoms detected. In the second case study, a proof of principle of the fault diagnosis part of the 4S3F method was successfully performed on the same HVAC system extracting possible component and control faults from the P&ID. A Bayesian Network diagnostic, which mimics the way of diagnosis by HVAC engineers, was applied to identify the probability of all possible faults by interpreting the symptoms. The diagnostic Bayesian network (DBN) was set up in accordance with the P&ID, i.e., with the same structure. Energy savings from fault corrections were estimated to be up to 25% of the primary energy consumption, while the HVAC system was initially considered to have an excellent performance. In the third case study, a demand-driven ventilation system (DCV) was analysed. The analysis showed that the 4S3F method works also to identify faults on an air ventilation system.
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