A (semi-)closed greenhouse is a novel greenhouse with an active cooling system and temporary heat storage in an aquifer. Air is cooled, heated and dehumidified by air treatment units. Climate in (semi-)closed greenhouses differs from that of conventional open greenhouses. The aims of our research were first, to analyze the effect of active cooling on greenhouse climate, in terms of stability, gradient and average levels; second, to determine crop growth and production in closed and semi-closed greenhouses. An experiment with tomato crop was conducted from December 2007 until November 2008 in a closed greenhouse with 700 W m-2 cooling capacity, two semi-closed greenhouses with 350 and 150 W m-2 cooling capacity, respectively, and an open greenhouse. The higher the cooling capacity, the more independent the greenhouse climate was of the outside climate. As the cooling ducts were placed underneath the plants, cooling led to a remarkable vertical temperature gradient. Under sunny conditions temperature could be 5°C higher at the top than at the bottom of the canopy in the closed greenhouse. Cumulative production in the semi-closed greenhouses with 350 and 150 W m-2 cooling capacity were 10% (61 kg m-2) and 6% (59 kg m-2) higher than that in the open greenhouse (55 kg m-2), respectively. Cumulative production in the closed greenhouse was 14% higher than in the open greenhouse in week 29 after planting but at the end of the experiment the cumulative increase was only 4% due to botrytis. Model calculations showed that the production increase in the closed and semi-closed greenhouses was explained by higher CO2 concentration.
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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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Semi-closed greenhouses have been developed in which window ventilation is minimized due to active cooling, enabling enhanced CO2 concentrations at high irradiance. Cooled and dehumidified air is blown into the greenhouse from below or above the canopy. Cooling below the canopy may induce vertical temperature gradients along the length of the plants. Our first aim was to analyze the effect of the positioning of the inlet of cooled and dehumidified air on the magnitudes of vertical temperature and VPD gradients in the semi-closed greenhouses. The second aim was to investigate the effects of vertical temperature gradients on assimilate production, partitioning, and fruit growth. Tomato crops were grown year-round in four semiclosed greenhouses with cooled and dehumidified air blown into the greenhouses from below or above the crop. Cooling below the canopy induced vertical temperature and VPD gradients. The temperature at the top of the canopy was over 5°C higher than at the bottom, when outside solar radiation was high (solar radiation >250 J cm-2 h-1). Total dry matter production was not affected by the location of the cooling (4.64 and 4.80 kg m-2 with cooling from above and from below, respectively). Percentage dry matter partitioning to the fruits was 74% in both treatments. Average over the whole growing season the fresh fruit weight of the harvested fruits was not affected by the location of cooling (118 vs 112 g fruit-1). However, during summer period the average fresh fruit weight of the harvested fruits in the greenhouse with cooling from below was higher than in the greenhouse with cooling from above (124 vs 115 g fruit-1).
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Development of novel testing strategies to detect adverse human health effects is of interest to replace in vivo-based drug and chemical safety testing. The aim of the present study was to investigate whether physiologically based kinetic (PBK) modeling-facilitated conversion of in vitro toxicity data is an adequate approach to predict in vivo cardiotoxicity in humans. To enable evaluation of predictions made, methadone was selected as the model compound, being a compound for which data on both kinetics and cardiotoxicity in humans are available. A PBK model for methadone in humans was developed and evaluated against available kinetic data presenting an adequate match. Use of the developed PBK model to convert concentration–response curves for the effect of methadone on human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) in the so-called multi electrode array (MEA) assay resulted in predictions for in vivo dose–response curves for methadone-induced cardiotoxicity that matched the available in vivo data. The results also revealed differences in protein plasma binding of methadone to be a potential factor underlying variation between individuals with respect to sensitivity towards the cardiotoxic effects of methadone. The present study provides a proof-of-principle of using PBK modeling-based reverse dosimetry of in vitro data for the prediction of cardiotoxicity in humans, providing a novel testing strategy in cardiac safety studies.
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A local operating theater ventilation device to specifically ventilate the wound area has been developed and investigated. The ventilation device is combined with a blanket which lies over the patient during the operation. Two configurations were studied: Configuration 1 where HEPA-filtered air was supplied around and parallel to the wound area and Configuration 2 where HEPA-filtered air was supplied from the top surface of the blanket, perpendicular to the wound area. A similar approach is investigated in parallel for an instrument table. The objective of the study was to verify the effectiveness of the local device. Prototype solutions developed were studied experimentally (laboratory) and numerically (CFD) in a simplified setup, followed by experimental assessment in a full scale mock-up. Isothermal as well as non-isothermal conditions were analyzed. Particle concentrations obtained in proposed solutions were compared to the concentration without local ventilation. The analysis procedure followed current national guidelines for the assessment of operating theater ventilation systems, which focus on small particles (<10 mm). The results show that the local system can provide better air quality conditions near the wound area compared to a theoretical mixing situation (proof-of-principle). It cannot yet replace the standard unidirectional downflow systems as found for ultraclean operating theater conditions. It does, however, show potential for application in temporary and emergency operating theaters
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In this article a generic fault detection and diagnosis (FDD) method for demand controlled ventilation (DCV) systems is presented. By automated fault detection both indoor air quality (IAQ) and energy performance are strongly increased. This method is derived from a reference architecture based on a network with 3 generic types of faults (component, control and model faults) and 4 generic types of symptoms (balance, energy performance, operational state and additional symptoms). This 4S3F architecture, originally set up for energy performance diagnosis of thermal energy plants is applied on the control of IAQ by variable air volume (VAV) systems. The proposed method, using diagnosis Bayesian networks (DBNs), overcomes problems encountered in current FDD methods for VAV systems, problems which inhibits in practice their wide application. Unambiguous fault diagnosis stays difficult, most methods are very system specific, and finally, methods are implemented at a very late stage, while an implementation during the design of the HVAC system and its control is needed. The IAQ 4S3F method, which solves these problems, is demonstrated for a common VAV system with demand controlled ventilation in an office with the use of a whole year hourly historic Building Management System (BMS) data and showed it applicability successfully. Next to this, the influence of prior and conditional probabilities on the diagnosis is studied. Link to the formal publication via its DOI https://doi.org/10.1016/j.buildenv.2019.106632
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This study explores if multiple alterations of the classrooms' indoor environmental conditions, which lead to environmental conditions meeting quality class A of Dutch guidelines, result in a positive effect on students' perceptions and performance. A field study, with a between-group experimental design, was conducted during the academic course in 2020–2021. First, the reverberation time (RT) was lowered in the intervention condition to 0.4 s (control condition 0.6 s). Next, the horizontal illuminance (HI) level was raised in the intervention condition to 750 lx (control condition 500 lx). Finally, the indoor air quality (IAQ) in both conditions was improved by increasing the ventilation rate, resulting in a reduction of carbon dioxide concentrations, as a proxy for IAQ, from ~1100 to <800 ppm. During seven campaigns, students' perceptions of indoor environmental quality, health, emotional status, cognitive performance, and quality of learning were measured at the end of each lecture using questionnaires. Furthermore, students' objective cognitive responses were measured with psychometric tests of neurobehavioural functions. Students' short-term academic performance was evaluated with a content-related test. From 201 students, 527 responses were collected. The results showed that the reduction of the RT positively influenced students' perceived cognitive performance. A reduced RT in combination with raised HI improved students' perceptions of the lighting environment, internal responses, and quality of learning. However, this experimental condition negatively influenced students' ability to solve problems, while students' content-related test scores were not influenced. This shows that although quality class A conditions for RT and HI improved students' perceptions, it did not influence their short-term academic performance. Furthermore, the benefits of reduced RT in combination with raised HI were not observed in improved IAQ conditions. Whether the sequential order of the experimental conditions is relevant in inducing these effects and/or whether improving two parameters is already beneficial, is unknown.
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A replacement of cars with conventional internal combustion engines (ICEs) by electric vehicles (EVs) is seen by many as a means to improve local air quality, reduce dependence on fossil fuels and CO2 emissions. The market for EV is slowly developing with a growing number of (subsidized) manufacturers offering EV models in different market segments to (subsidized) car owners. The number of EVs is still small in most countries, but policymakers and manufacturers see partial or even full replacement of ICEs by EVs as realistic in the coming decade. EV engines are powered by rechargeable lithium-ion batteries. Li-ion is produced from precursors, either liquid (brine metal salt) or solid (hard rocks). Lithium mining is still concentrated in a few countries. Lithium is used for batteries, ceramics, grease and medicine. This reliance comes at a cost, as conventional lithium mining creates several externalities. The following main question will be addressed: How to source a required volume of lithium in a way that reduces the environmental and social-economic impact of mining this resource? To address this question, we will use a combination of relevant literature and a local case study supported by a model-based estimation. The focus is on the Netherlands, an EV user country, but the approach is generic.
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Introducing a hyperbolic vortex into a showerhead is a possibility to achieve higher spray velocities for a given discharge without reducing the nozzle diameter. Due to the introduction of air bubbles into the water by the vortex, the spray is pushed from a transition (dripping faucet) regime into a jetting regime, which results in higher droplet and jet velocities using the same nozzle diameter and throughput. The same droplet and jet diameters were realized compared to a showerhead without a vortex. Assuming that the satisfaction of a shower experience is largely dependent on the droplet size and velocity, the implementation of a vortex in the showerhead could provide the same shower experience with 14% less water consumption compared to the normal showerhead. A full optical and physical analysis was presented, and the important chemical parameters were investigated.
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Sensors in offices mainly measure environmental data, missing qualitative insights into office workers’ perceptions. This opens the opportunity for active individual participation in data collection. To promote reflection on office well-being while overcoming experience sampling challenges in terms of privacy, notification, and display overload, and in-the-moment data collection, we developed Click-IO. Click-IO is a tangible, privacy-sensitive, mobile experience sampling tool that collects contextual information. We evaluated Click-IO for 20-days. The system enabled real-time reflections for office workers, promoting self-awareness of their environment and well-being. Its non-digital design ensured privacy-sensitive feedback collection, while its mobility facilitated in-the-moment feedback. Based on our findings, we identify design recommendations for the evelopment of mobile experience sampling tools. Moreover, the integration of contextual data with environmental sensor data presented a more comprehensive understanding of individuals’ experiences. This research contributes to the development of experience sampling tools and sensor integration for understanding office well-being.
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