Energy Performance Assessment of Innovative Building Solutions Coming from Construction and Demolition Waste Materials

Prefabricated solutions incorporating thermal insulation are increasingly adopted as an energy conservation measure for building renovation. The InnoWEE European project developed three technologies from Construction and Demolition Waste (CDW) materials through a manufacturing process that supports the circular economy strategy of the European Union. Two of them consisted of geopolymer panels incorporated into an External Thermal Insulation Composite System (ETICS) and a ventilated façade. This study evaluates their thermal performance by means of monitoring data from three pilot case studies in Greece, Italy, and Romania, and calibrated building simulation models enabling the reliable prediction of energy savings in different climates and use scenarios. Results showed a reduction in energy demand for all demo buildings, with annual energy savings up to 25% after placing the novel insulation solutions. However, savings are highly dependent on weather conditions since the panels affect cooling and heating loads differently. Finally, a parametric assessment is performed to assess the impact of insulation thickness through an energy performance prediction and a cash flow analysis.This research was funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 723916 (Project H2020-EEB-2016 InnoWEE, G.A. 723916). This study reflects only the authors’ views and the Commission is not responsible for any use that may be made of the information contained therei

Thermodynamic Analysis for the Selection of low GWP Refrigerants in Ground Source Heat Pumps

One of the main objectives of the European Commission in the buildings sector, responsible for approximately 40% of total energy consumption and 36% of greenhouse gas emissions, is to identify technological solutions capable of reducing energy consumption and at the same time greenhouse gas emissions. For this purpose, ground source heat pump system (GSHPs) is a technology of particular interest that promises to considerably reduce greenhouse gas emissions of HVAC systems (up to 44% compared to air source heat pumps). In order to develop and test innovative GSHPs to be used for heating and cooling in the various European climatic zones, EU has funded the GEO4CIVHIC project, which will have a duration of 4 years and will end in 2022. As part of the project, the problem of identifying new generation low environmental impact refrigerants to be used in innovative GSHPs is tackled. In this article, we report the results of an energetic and exergetic analysis of the performance of heat pumps based on simulations carried out both on simple reverse cycles and on more complex cycles. Low pressure alternative fluids have been considered as an alternative to R134a and high pressure fluids as an alternative to R410A. The simulations were conducted at various heat sink and heat source temperature conditions, in order to evaluate the GSHPs performance in the whole range of real conditions that can be found in Europe. Particular attention was paid to the compression phase, with the aim to simulate the compressor performance in a more realistic way than simply assuming constant isentropic efficiency

EU project "Cheap-GSHPs": the geoexchange field laboratory

Abstract The Molinella test site is the open-air laboratory of the EU project entitled "Cheap-GSHPs: Cheap and Efficient Application of Reliable Ground Source Heat Exchangers and Pumps". Here, innovative helical heat baskets and steel coaxial probes are installed next to the traditional double-U. The tests involve the probes design as well as materials and drilling techniques and machines, therefore the newly developed GSHEs can be directly compared with the traditional ones with respect to technical issues and energetic performances. The Molinella test site therefore represents a very extraordinary possibility to improve the knowledge of heat transfer processes in shallow geo-exchange systems

A European Database of Building Energy Profiles to Support the Design of Ground Source Heat Pumps

[EN] The design of ground source heat pumps is a fundamental step to ensure the high energy efficiency of heat pump systems throughout their operating years. To enhance the diffusion of ground source heat pump systems, two different tools are developed in the H2020 research project named, Cheap GSHPs: A design tool and a decision support system. In both cases, the energy demand of the buildings may not be calculated by the user. The main input data, to evaluate the size of the borehole heat exchangers, is the building energy demand. This paper presents a methodology to correlate energy demand, building typologies, and climatic conditions for different types of residential buildings. Rather than envelope properties, three insulation levels have been considered in different climatic conditions to set up a database of energy profiles. Analyzing European climatic test reference years, 23 locations have been considered. For each location, the overall energy and the mean hourly monthly energy profiles for heating and cooling have been calculated. Pre-calculated profiles are needed to size generation systems and, in particular, ground source heat pumps. For this reason, correlations based on the degree days for heating and cooling demand have been found in order to generalize the results for different buildings. These correlations depend on the Koppen-Geiger climate scale.This work received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 657982.Carnieletto, L.; Badenes Badenes, B.; Belliardi, M.; Bernardi, A.; Graci, S.; Emmi, G.; Urchueguía Schölzel, JF.... (2019). A European Database of Building Energy Profiles to Support the Design of Ground Source Heat Pumps. Energies. 12(13):1-23. https://doi.org/10.3390/en12132496S1231213De Carli, M., Tonon, M., Zarrella, A., & Zecchin, R. (2010). A computational capacity resistance model (CaRM) for vertical ground-coupled heat exchangers. Renewable Energy, 35(7), 1537-1550. doi:10.1016/j.renene.2009.11.034Grossi, I., Dongellini, M., Piazzi, A., & Morini, G. L. (2018). Dynamic modelling and energy performance analysis of an innovative dual-source heat pump system. Applied Thermal Engineering, 142, 745-759. doi:10.1016/j.applthermaleng.2018.07.022Engineering Reference Manual. In EnergyPlus V8.5https://energyplus.net/Sandberg, N. H., Bergsdal, H., & Brattebø, H. (2011). Historical energy analysis of the Norwegian dwelling stock. Building Research & Information, 39(1), 1-15. doi:10.1080/09613218.2010.528186Application of Energy Performance Indicators for Residential Building Stocks Experiences of the EPISCOPE Projecthttp://episcope.eu/fileadmin/episcope/public/docs/reports/EPISCOPE_Indicators_ConceptAndExperiences.pdfGustafsson, M., Dipasquale, C., Poppi, S., Bellini, A., Fedrizzi, R., Bales, C., … Holmberg, S. (2017). Economic and environmental analysis of energy renovation packages for European office buildings. Energy and Buildings, 148, 155-165. doi:10.1016/j.enbuild.2017.04.079De Carli, M., Bernardi, A., Cultrera, M., Dalla Santa, G., Di Bella, A., Emmi, G., … Zarrella, A. (2018). A Database for Climatic Conditions around Europe for Promoting GSHP Solutions. Geosciences, 8(2), 71. doi:10.3390/geosciences8020071Cartalis, C., Synodinou, A., Proedrou, M., Tsangrassoulis, A., & Santamouris, M. (2001). Modifications in energy demand in urban areas as a result of climate changes: an assessment for the southeast Mediterranean region. Energy Conversion and Management, 42(14), 1647-1656. doi:10.1016/s0196-8904(00)00156-4Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World Map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift, 15(3), 259-263. doi:10.1127/0941-2948/2006/0130Herrera, M., Natarajan, S., Coley, D. A., Kershaw, T., Ramallo-González, A. P., Eames, M., … Wood, M. (2017). A review of current and future weather data for building simulation. Building Services Engineering Research and Technology, 38(5), 602-627. doi:10.1177/0143624417705937Peel, M. C., Finlayson, B. L., & McMahon, T. A. (2007). Updated world map of the Köppen-Geiger climate classification. Hydrology and Earth System Sciences, 11(5), 1633-1644. doi:10.5194/hess-11-1633-2007D’Amico, A., Ciulla, G., Panno, D., & Ferrari, S. (2019). Building energy demand assessment through heating degree days: The importance of a climatic dataset. Applied Energy, 242, 1285-1306. doi:10.1016/j.apenergy.2019.03.167Al-Hadhrami, L. M. (2013). Comprehensive review of cooling and heating degree days characteristics over Kingdom of Saudi Arabia. Renewable and Sustainable Energy Reviews, 27, 305-314. doi:10.1016/j.rser.2013.04.034Degree Days.net-Custom Degree Day Datahttp://www.degreedays.netAnnunziata, E., Frey, M., & Rizzi, F. (2013). Towards nearly zero-energy buildings: The state-of-art of national regulations in Europe. Energy, 57, 125-133. doi:10.1016/j.energy.2012.11.049Principle for Nearly Zero-Energy Buildings, Ecofys Germany GmbHhttp://bpie.eu/documents/BPIE/publications/LR_nZEB%20study.pdfAhern, C., Griffiths, P., & O’Flaherty, M. (2013). State of the Irish housing stock—Modelling the heat losses of Ireland’s existing detached rural housing stock & estimating the benefit of thermal retrofit measures on this stock. Energy Policy, 55, 139-151. doi:10.1016/j.enpol.2012.11.039Kaklauskas, A., Zavadskas, E. K., Raslanas, S., Ginevicius, R., Komka, A., & Malinauskas, P. (2006). Selection of low-e windows in retrofit of public buildings by applying multiple criteria method COPRAS: A Lithuanian case. Energy and Buildings, 38(5), 454-462. doi:10.1016/j.enbuild.2005.08.005Zavadskas, E., Raslanas, S., & Kaklauskas, A. (2008). The selection of effective retrofit scenarios for panel houses in urban neighborhoods based on expected energy savings and increase in market value: The Vilnius case. Energy and Buildings, 40(4), 573-587. doi:10.1016/j.enbuild.2007.04.015Aerts, D., Minnen, J., Glorieux, I., Wouters, I., & Descamps, F. (2014). A method for the identification and modelling of realistic domestic occupancy sequences for building energy demand simulations and peer comparison. Building and Environment, 75, 67-78. doi:10.1016/j.buildenv.2014.01.021Yang, Z., & Becerik-Gerber, B. (2014). The coupled effects of personalized occupancy profile based HVAC schedules and room reassignment on building energy use. Energy and Buildings, 78, 113-122. doi:10.1016/j.enbuild.2014.04.002Richardson, I., Thomson, M., & Infield, D. (2008). A high-resolution domestic building occupancy model for energy demand simulations. Energy and Buildings, 40(8), 1560-1566. doi:10.1016/j.enbuild.2008.02.006Villi, G., Peretti, C., Graci, S., & De Carli, M. (2013). Building leakage analysis and infiltration modelling for an Italian multi-family building. Journal of Building Performance Simulation, 6(2), 98-118. doi:10.1080/19401493.2012.69998

two software tools for facilitating the choice of ground source heat pumps by stakeholders and designers

For promoting the diffusion of GSHP and making the technology more accessible to the general public, in the H2020 research project "CHeap and Efficient APplication of reliable Ground Source Heat exchangers and PumpS" (acronym Cheap-GSHPs) a tool for sizing these systems has been developed, as well as a Decision Support System (DSS) able to assist the user in the preliminary design of the most suitable configuration. For all these tools a common platform has been carried out considering climatic conditions, energy demand of buildings, ground thermal properties, heat pump solutions repository, as well as renewable energy database to use in synergy with the GSHPs. Since the aims of the tools are different, there are different approaches. The design tool is mainly addressed to designers. The calculation may be done in two ways: with a simplified method based on the ASHRAE approach and with a detailed calculation based on the numerical tool CaRM (Capacity-Resistance method). The DSS final aim is to support decision-making, by providing the stakeholders at all the level with a series of scenario. The Cheap-GSHPs project has developed a DSS tool aimed at accelerating the decision-making process of designers and building owners as well as increasing market share of the Cheap-GSHPs technologies. Hence the DSS generates different possible solutions based on a defined general problem, identifying the optimal solution. Both tools are presented in the paper, showing the potentialities provided by both software

Analisi teorica e sperimentale per la valutazione dei consumi attuali dell'area del CNR di padova e possibili interventi migliorativi

La tesi descrive l’analisi del fabbisogno energetico dell’ADR del CNR di Padova nel quadro di un progetto di riqualificazione energetica su alcuni edifici esistenti in occasione della costruzione di un nuovo edificio che raccoglierà tutti gli edifici. Per i 10 edifici sono stati valutati consumi, comfort, sprechi energetici e fabbisogni validati da un’analisi dinamica con TRNSYS. Sono state fornite ipotesi di intervento per la riqualificazione energetica e una valutazione economic

Weibull Modeling of Controlled Drug Release from Ag-PMA Nanosystems

Traditional pharmacotherapy suffers from multiple drawbacks that hamper patient treatment such as antibiotic resistances or low drug selectivity and toxicity during systemic applications. Some functional hybrid nanomaterials are designed to handle the drug release process under remote-control. More attention has recently been paid to synthetic polyelectrolytes for their intrinsic properties which allow them to rearrange into compact structures, ideal to be used as drug carriers or probes influencing biochemical processes. The presence of Ag nanoparticles (NPs) in the Poly methyl acrylate (PMA) matrix leads to an enhancement of drug release efficiency, even using a low-power laser whose wavelength is far from the Ag Surface Plasmon Resonance (SPR) peak. Further, compared to the colloids, the nanofiber-based drug delivery system has shown shorter response time and more precise control over the release rate. The efficiency and timing of involved drug release mechanisms has been estimated by the Weibull distribution function, whose parameters indicate that the release mechanism of nanofibers obeys Fick’s first law while a non-Fickian character controlled by diffusion and relaxation of polymer chains occurs in the colloidal phase

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy