The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
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In manufacturing of organic electronics, inkjet printing as an alternative technique for depositing materials is becoming increasingly important. Aside to the ink formulations challenges, improving the resolution of the printed patterns is a major goal. In this study we will discuss a newly developed technique to selectively modify the substrate surface energy using plasma treatment as a means to achieve this goal. First, we look at the effects of the μPlasma treatment on the surface energy for a selection of plastic films. Second, we investigated the effects of the μPlasma treatment on the wetting behaviour of inkjet printed droplets to determine the resolution of the μPlasma printing technique. We found that the surface energy for all tested films increased significantly reaching a maximum after 3-5 repetitions. Subsequently the surface energy decreased in the following 8-10 days after treatment, finally stabilizing at a surface energy roughly halfway between the surface energy of the untreated film and the maximum obtained surface energy. When μPlasma printing lines, an improved wetting abillity of inkjet printed materials on the plasma treated areas was found. The minimal achieved μPlasma printed line was found to be 1 mm wide. For future application it is important to increase the resolution of the plasma print process. This is crucial for combining plasma treatment with inkjet print technology as a means to obtain higher print resolutions.
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Airports represent the major bottleneck in the air traffic management system with increasing traffic density. Enhanced levels of automation and coordination of surface operations are imperative to reduce congestion and to improve efficiency. This paper addresses the problem of comparing different control strategies on the airport surface to investigate their impacts and benefits. We propose an optimization approach to solve in a unified manner the coordinated surface operations problem on network models of an actual hub airport. Controlled pushback time, taxi reroutes and controlled holding time (waiting time at runway threshold for departures and time spent in runway crossing queues for arrivals) are considered as decisions to optimize the ground movement problem. Three major aspects are discussed:1) benefits of incorporating taxi reroutes on the airport performance metrics; 2) priority of arrivals and departures in runway crossings; 3) tradeoffs between controlled pushback and controlled holding time for departures. A preliminary study case is conducted in a model based on operations of Paris Charles De-Gaulle airport under the most frequently used configuration. Airport is modeled using a node-link network structure. Alternate taxi routes are constructed based on surface surveillance records with respect to current procedural factors. A representative peak-hour traffic scenario is generated using historical data. The effectiveness of the proposed optimization methods is investigated.
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The textile and clothing sector belongs to the world’s biggest economic activities. Producing textiles is highly energy-, water- and chemical-intensive and consequently the textile industry has a strong impact on environment and is regarded as the second greatest polluter of clean water. The European textile industry has taken significant steps taken in developing sustainable manufacturing processes and materials for example in water treatment and the development of biobased and recycled fibres. However, the large amount of harmful and toxic chemicals necessary, especially the synthetic colourants, i.e. the pigments and dyes used to colour the textile fibres and fabrics remains a serious concern. The limited range of alternative natural colourants that is available often fail the desired intensity and light stability and also are not provided at the affordable cost . The industrial partners and the branch organisations Modint and Contactgroep Textiel are actively searching for sustainable alternatives and have approached Avans to assist in the development of the colourants which led to the project Beauti-Fully Biobased Fibres project proposal. The objective of the Beauti-Fully Biobased Fibres project is to develop sustainable, renewable colourants with improved light fastness and colour intensity for colouration of (biobased) man-made textile fibres Avans University of Applied Science, Zuyd University of Applied Sciences, Wageningen University & Research, Maastricht University and representatives from the textile industry will actively collaborate in the project. Specific approaches have been identified which build on knowledge developed by the knowledge partners in earlier projects. These will now be used for designing sustainable, renewable colourants with the improved quality aspects of light fastness and intensity as required in the textile industry. The selected approaches include refining natural extracts, encapsulation and novel chemical modification of nano-particle surfaces with chromophores.
Synthetic ultra-black (UB) materials, which demonstrate exceptionally high absorbance (>99%) of visible light incident on their surface, are currently used as coatings in photovoltaic cells and numerous other applications. Most commercially available UB coatings are based on an array of carbon nanotubes, which are produced at relatively high temperature and result in numerous by-products. In addition, UB nanotube coatings require harsh application conditions and are very susceptible to abrasion. As a result, these coatings are currently obtained using a manufacturing process with relatively high costs, high energy consumption and low sustainability. Interestingly, an UB coating based on a biologically derived pigment could provide a cheaper and more sustainable alternative. Specifically, GLO Biotics proposes to create UB pigment by taking a bio-mimetic approach and replicate structures found in UB deep-sea fish. A recent study[1] has actually shown that specific fish have melanosomes in their skin with particular dimensions that allow absorption of up to 99.9% of incident light. In addition to this, recent advances in bacterial engineering have demonstrated that it is possible to create bacteria-derived melanin particles with very similar dimensions to the melanosomes in aforementioned fish. During this project, the consortium partners will combine both scientific observations in an attempt to provide the proof-of-concept for developing an ultra-black coating using bacteria-derived melanin particles as bio-based, sustainable pigment. For this, Zuyd University of Applied Sciences (Zuyd) and Maastricht University (UM) collaborate with GLO Biotics in the development of the innovative ‘BLACKTERIA’ UB coating technology. The partners will attempt at engineering an E. coli expression system and adapt its growth in order to produce melanin particles of desired dimensions. In addition, UM will utilize their expertise in industrial coating research to provide input for experimental set-up and the development of a desired UB coating using the bacteria-derived melanin particles as pigment.
Fontys University of Applied Science’s Institute of Engineering, and the Dutch Institute for Fundamental Energy Research (DIFFER) are proposing to set up a professorship to develop novel sensors for fusion reactors. Sensors are a critical component to control and optimise the unstable plasma of Tokamak reactors. However, sensor systems are particularly challenging in fusion-plasma facing components, such as the divertor. The extreme conditions make it impossible to directly incorporate sensors. Furthermore, in advanced reactor concepts, such as DEMO, access to the plasma via ports will be extremely limited. Therefore, indirect or non-contact sensing modalities must be employed. The research group Distributed Sensor Systems (DSS) will develop microwave sensor systems for characterising the plasma in a tokamak’s divertor. DSS will take advantage of recent rapid developments in high frequency integrated circuits, found, for instance, in automotive radar systems, to develop digital reflectometers. Access through the divertor wall will be achieved via surface waveguide structures. The waveguide will be printed using 3D tungsten printing that has improved precision, and reduced roughness. These components will be tested for durability at DIFFER facilities. The performance of the microwave reflectometer, including waveguides, will be tested by using it to analyse the geometry and dynamics of the Magnum PSI plasma beam. The development of sensor-based systems is an important aspect in the integrated research and education program in Electrical Engineering, where DSS is based. The sensing requirements from DIFFER offers an interesting and highly relevant research theme to DSS and exciting projects for engineering students. Hence, this collaboration will strengthen both institutes and the educational offerings at the institute of engineering. Furthermore millimeter wave (mmWave) sensors have a wide range of potential applications, from plasma characterisation (as in this proposal) though to waste separation. Our research will be a step towards realising these broader application areas.
