A transparent and comparable understanding of the energy efficiency, carbon footprint, and environmental impacts of renewable resources are required in the decision making and planning process towards a more sustainable energy system. Therefore, a new approach is proposed for measuring the environmental sustainability of anaerobic digestion green gas production pathways. The approach is based on the industrial metabolism concept, and is expanded with three known methods. First, the Material Flow Analysis method is used to simulate the decentralized energy system. Second, the Material and Energy Flow Analysis method is used to determine the direct energy and material requirements. Finally, Life Cycle Analysis is used to calculate the indirect material and energy requirements, including the embodied energy of the components and required maintenance. Complexity will be handled through a modular approach, which allows for the simplification of the green gas production pathway while also allowing for easy modification in order to determine the environmental impacts for specific conditions and scenarios. Temporal dynamics will be introduced in the approach through the use of hourly intervals and yearly scenarios. The environmental sustainability of green gas production is expressed in (Process) Energy Returned on Energy Invested, Carbon Footprint, and EcoPoints. The proposed approach within this article can be used for generating and identifying sustainable solutions. By demanding a clear and structured Material and Energy Flow Analysis of the production pathway and clear expression for energy efficiency and environmental sustainability the analysis or model can become more transparent and therefore easier to interpret and compare. Hence, a clear ruler and measuring technique can aid in the decision making and planning process towards a more sustainable energy system.
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Annual Review 2014 Renewable Energy in The Netherlands published on EnTranCe website.
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from the article: "Abstract The way in which construction logistics is organised has considerable impact on production flow, transportation efficiency, greenhouse gas emissions and congestion, particularly in urban areas such as city centres. In cities such as London and Amsterdam municipalities have issued new legislation and stricter conditions for vehicles to be able to access cities and city centres in particular. Considerate clients, public as well private, have started developing tender policies to encourage contractors to reduce the environmental impact of construction projects. This paper reports on an ongoing research project applying and assessing developments in the field of construction logistics in the Netherlands. The cases include contractors and third party logistics providers applying consolidation centres and dedicated software solutions to increase transportation efficiency. The case show various results of JIT logistics management applied to urban construction projects leading to higher transportation efficiencies, and reduced environmental impact and increased production efficiency on site. The data collections included to-site en on-site observations, measurement and interviews. The research has shown considerable reductions of vehicles to deliver goods and to transport workers to site. In addition the research has shown increased production flow and less waste such as inventory, waiting and unnecessary motion on site."
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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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This report presents the experimental and numerical work carried out by ECN and Hanze University of Applied Sciences on methane sorption on activated carbon, as part of their activities within the EDGaR Energy Storage project. Eleven different activated carbon types were tested. It was found that MaxSorb MSC-30 offered the highest methane mass storage density (m/m ratio). However, due to the low density of the MaxSorb MSC-30 activated carbon, the highest volumetric methane storage density (V/V ratio) was found for Brightblack. An increase of the packing density and heat conductivity significantly improves the V/V ratio and shortens the time needed to reach thermal equilibrium. In the case of the Brightblack activated carbon, a total V/V ratio of 112 was found at 12 oC and 40 bar, implying an effective storage density that is 3 times higher than for compressed methane. During the adsorption of methane on activated carbon, sorption heat is released and the temperature of the bed is increased, which negatively affects the effective V/V ratio. Temperature rises up to 70 oC were experimentally observed at higher methane inflow rates. For MaxSorb MSC-30 a temperature rise of 25 oC reduced the effective V/V ratio by about 20 %. The temperature rise of the Brightblack bed caused relatively smaller reductions in the volumetric storage density. Calculations with the validated numerical models indicated an even higher temperature increase for the full scale methane storage, reaching bed temperatures up to 137-150 oC in the case of the MaxSorb MSC-30 activated carbon. At this temperature range, the models indicate a V/V ratio fall down to 46. This performance is similar to the one offered by direct methane compression to 40 bar, and is much lower than the V/V ratio of ~ 100 that was found both experimentally and by calculations for the lab scale reactor performance. The calculations showed, that the low bed permeability can limit the gas flow during adsorption and desorption. A high reactor diameter can countervail the effect of permeability, but the higher dimensions impede the heat dissipation and thus decrease the storage efficiency. Efficient temperature control and management are very important to effectively make use of the methane storage capacity through adsorption.
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The design of healthcare facilities is a complex and dynamic process, which involves many stakeholders each with their own set of needs. In the context of healthcare facilities, this complexity exists at the intersection of technology and society because the very design of these buildings forces us to consider the technology–human interface directly in terms of living-space, ethics and social priorities. In order to grasp this complexity, current healthcare design models need mechanisms to help prioritize the needs of the stakeholders. Assistance in this process can be derived by incorporating elements of technology philosophy into existing design models. In this article, we develop and examine the Inclusive and Integrated Health Facilities Design model (In2Health Design model) and its foundations. This model brings together three existing approaches: (i) the International Classification of Functioning, Disability and Health, (ii) the Model of Integrated Building Design, and (iii) the ontology by Dooyeweerd. The model can be used to analyze the needs of the various stakeholders, in relationship to the required performances of a building as delivered by various building systems. The applicability of the In2Health Design model is illustrated by two case studies concerning (i) the evaluation of the indoor environment for older people with dementia and (ii) the design process of the redevelopment of an existing hospital for psychiatric patients.
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Within recent years, Financial Credit Risk Assessment (FCRA) has become an increasingly important issue within the financial industry. Therefore, the search for features that can predict the credit risk of an organization has increased. Using multiple statistical techniques, a variance of features has been proposed. Applying a structured literature review, 258 papers have been selected. From the selected papers, 835 features have been identified. The features have been analyzed with respect to the type of feature, the information sources needed and the type of organization that applies the features. Based on the results of the analysis, the features have been plotted in the FCRA Model. The results show that most features focus on hard information from a transactional source, based on official information with a high latency. In this paper, we readdress and -present our earlier work [1]. We extended the previous research with more detailed descriptions of the related literature, findings, and results, which provides a grounded basis from which further research on FCRA can be conducted.
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From teh UU repository: "Background: Oral immunotherapy (OIT) is a promising therapeutic approach to treat food allergic patients. However, there are some concerns regarding its safety and long-term efficacy. The use of non-digestible oligosaccharides might improve OIT efficacy since they are known to directly modulate intestinal epithelial and immune cells in addition to acting as prebiotics. Aim: To investigate whether a diet supplemented with plant-derived fructo-oligosaccharides (FOS) supports the efficacy of OIT in a murine cow's milk allergy model and to elucidate the potential mechanisms involved. Methods: After oral sensitization to the cow's milk protein whey, female C3H/HeOuJ mice were fed either a control diet or a diet supplemented with FOS (1% w/w) and received OIT (10 mg whey) 5 days a week for 3 weeks by gavage. Intradermal (i.d.) and intragastric (i.g.) challenges were performed to measure acute allergic symptoms and mast cell degranulation. Blood and organs were collected to measure antibody levels and T cell and dendritic cell populations. Spleen-derived T cell fractions (whole spleen-and CD25-depleted) were transferred to naive recipient mice to confirm the involvement of regulatory T cells (Tregs) in allergy protection induced by OIT + FOS. Results: OIT + FOS decreased acute allergic symptoms and mast cell degranulation upon challenge and prevented the challenge-induced increase in whey-specific IgE as observed in sensitized mice. Early induction of Tregs in the mesenteric lymph nodes (MLN) of OIT + FOS mice coincided with reduced T cell responsiveness in splenocyte cultures. CD25 depletion in OIT + FOS-derived splenocyte suspensions prior to transfer abolished protection against signs of anaphylaxis in recipients. OIT + FOS increased serum galectin-9 levels. No differences in short-chain fatty acid (SCFA) levels in the cecum were observed between the treatment groups. Concisely, FOS supplementation significantly improved OIT in the acute allergic skin response, %Foxp3+ Tregs and %LAP+ Th3 cells in MLN, and serum galectin-9 levels. Conclusion: FOS supplementation improved the efficacy of OIT in cow's milk allergic mice. Increased levels of Tregs in the MLN and abolished protection against signs of anaphylaxis upon transfer of CD25-depleted cell fractions, suggest a role for Foxp3+ Tregs in the protective effect of OIT + FOS. "
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Despite the efforts of governments and firms, the construction industry is trailing other industries in labour productivity. Construction companies are interested in increasing their labour productivity, particularly when demand grows and construction firms cope with labour shortages. Off-site construction has proved to be a favourable policy to increase labour productivity. However, a complete understanding of the factors affecting construction labour productivity is lacking, and it is unclear which factors are influenced by off-site construction. This study developed a conceptual model describing how 15 factors influence the construction process and make a difference in labour productivity between off-site and on-site construction. The conceptual model shows that all 15 factors affect labour productivity in three ways: through direct effects, indirect effects and causal loops. The model is a starting point for further research to determine the impact of off-site construction on labour productivity.
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Biogas plays an important role in many future renewable energy scenarios as a source of storable and easily extracted form of renewable energy. However, there remains uncertainty as to which sources of biomass can provide a net energy gain while being harvested in a sustainable, ecologically friendly manner. This study will focus on the utilization of common, naturally occurring grass species which are cut during landscape management and typically treated as a waste stream. This waste grass can be valorized through co-digestion with cow manure in a biogas production process. Through the construction of a biogas production model based on the methodology proposed by (Pierie, Moll, van Gemert, & Benders, 2012), a life cycle analysis (LCA) has been performed which determines the impacts and viability of using common grass in a digester to produce biogas. This model performs a material and energy flow analysis (MEFA) on the biogas production process and tracks several system indicators (or impact factors), including the process energy return on energy investment ((P)EROI), the ecological impact (measured in Eco Points), and the global warming potential (GWP, measured in terms of kg of CO2 equivalent). A case study was performed for the village of Hoogkerk in the north-east Netherlands, to determine the viability of producing a portion of the village’s energy requirements by biogas production using biomass waste streams (i.e. common grass and cow manure in a co-digestion process). This study concludes that biogas production from common grass can be an effective and sustainable source of energy, while reducing greenhouse gas emissions and negative environmental impacts when compared to alternate methods of energy production, such as biogas produced from maize and natural gas production.
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