ENVISION Project Research Articles

Performance of the main technologies demonstrated in the ENVISION project

Within the context of ENVISION project, different technologies have been developed aiming to harvest energy being integrated into the building façade. These technologies have been tested in different demosites with a monitoring phase of around one year in order to assess their performances on-field. The technologies developed within ENVISION and reported in this article are solar façade collectors coupled with Heat Pumps and PV windows. The present paper reports the assessment of the performance of the ENVISION technologies in the different demos. This analysis is carried out through the calculation of normalized KPIs related to four different domains (energy, social, economic, and environmental) and the aggregation of these KPIs in domain scores to understand the strengths and weaknesses of each demo. In all the different demosites, the monitored data have been collected and processed to calculate the most valuable KPIs, which are compared with state-of-the-art values found in the literature to calculate a normalized KPI. Depending on the obtained value of the normalized KPIs a score from 0 (worst performance) to 5 (best performance) is assigned, considering that a score of 3/5 has been assigned to the State-Of-the-Art. Some KPIs are specific for the single demo (e.g. the SCOP of the heat pump), while others are common to all the demos and were used to carry on a direct comparison between the technologies. All the demos showed promising results in the social, energy, and environmental domains. For the last domain, in particular, the best results are obtained, as each technology leads to significant savings in GHG emissions. There is room for improvement on the economic side, which can be filled by progressively lowering the cost of technology over the years.

ENVISION – Improve intensive care of COVID-19 patients with artificial intelligence

The Envision project aims at developing artificial intelligence-based tools for supporting the treatment of critically ill COVID-19 patients in the intensive care unit. Twelve European hospitals participate in the collection of patient data for the development and validation of the artificial intelligence tools. Ten potential use cases have been identified as development targets. Data analysis and results from expert interviews are applied to define the clinically most relevant parameters and functional use cases to be used in providing decision support for the clinicians in the intensive care units for this patient group. The resulting artificial intelligence-based tool may be beneficial in the management of the next similar epidemics, as well.

Control Strategies for solar façade panels coupled with a Heat Pump and interacting with a District Heating Network

This work aims to understand the potential of an innovative technology for solar energy harvesting in a District Heating Network (DHN). The considered technology is aesthetic solar façade thermal panel. In order to guarantee the temperatures required by a 3rd generation DHN (around 75°C), a Heat Pump, using as cold source the heat from the panels, is necessary. It is worth noting that the coupling between façade panels and Heat Pump requires accurate evaluations. The optimum condition for the façade panels is to work at low temperatures (close to ambient or even below), while the Heat Pump reaches high Coefficient Of Performance (COP) when the temperature difference between hot and cold sources is minimized. In the first part of the study, a system model has been built using Matlab SIMULINK using results of tests on the panels already performed inside the H2020 ENVISION project. Different colours are considered. In the second part, a predictive mode-based strategy has been defined and tuned on the system in order to guarantee the best system performances in interaction with the DHN. This work will allow to understand whether this technology is feasible in the presented scenario and this layout can improve local energy exchange.

Techno-economic analysis for the assessment of heat pump integration in a real poly-generative energy district

The present research study aims at analysing technical and economic feasible solutions for heat pumps integration in energy districts for polygeneration purpose and particularly to store excess of electricity via Power-to-heat schemes considering that, from previous researchers’ works, thermal storage has been identified as the most remunerative and easy to handle storage technology to maximise self-consumption in polygeneration grids. This technology is already developed and currently employed for conditioning of residential, commercial and industrial buildings. However, studies regarding the analysis of heat pumps’ integration in energy districts for distributed generation are still limited. The potential advantages of its employment in this context are fuel savings, a lower emission level and the possibility to couple it with local renewable energy sources (i.e. solar panels, wind turbines) and traditional generators (i.e. engines, micro gas turbines) in order to increase flexibility in operational terms. In this paper, a performance analysis of the poly-generation energy district installed at the University of Genoa Campus, located in Savona, is analysed throughout a whole year: the model is implemented using a dedicated software tool, developed by Thermochemical Power Group. Different solutions for the integration of the heat pump, including size optimization, are investigated, considering the real data related to the University of Genoa Campus: the final aim of the analysis is to determinate the best operational strategy, minimizing variable costs (i.e. fuel) and evaluating the economic feasibility of heat pump installation in the energy district. This work has been also redacted as a preliminary analysis for solar-coupled HP integration (from a optimized management point of view) to be performed in the demonstration campaign of H2020 ENVISION Project*, where both RINA Consulting and University of Genova collaborate.

Business Model Innovation: Insights from a Multiple Case Study of Slovenian SMEs

Abstract Background and Purpose: Business model innovation (BMI) has become increasingly important, especially in the fast changing business environment. While large enterprises approach these changes systematically, small and medium sized enterprises (SMEs) are left to their own resourcefulness. For the purpose of developing dedicated methods and tools to support different SMEs in addressing these challenges, we have conducted a multiple case study to gain insights into factors that drive SMEs to innovate their BM, how they approach BMI and what changes they made to their BM. Design/Methodology/Approach: First the framework of analysis was developed based on BMI research frameworks identified in literature review. Then the multiple case studies were conducted following the case study protocol developed by Envision project. Results: Based on the proposed framework the results of four Slovene SME cases revealed differences between enterprises regarding the drivers behind BMI and changes in usage of the different BMI elements. Conclusion: Overall, the results suggest all four SMEs, coming from different sectors, are facing BMI challenges without systematically addressing it and without using any dedicated BM ontologies or tools.

European ENVISION Project for Improving Timing Resolution in PET Systems

European ENVISION Project for Improving Timing Resolution in PET Systems

Modeling social norms and social influence in obesity.

The worldwide increase in obesity has led to changes in what is considered "normal" or desirable weight, especially among populations at higher risk. We show that social norms are key to understanding the obesity epidemic, and that social influence mechanisms provide a necessary linkage between individual obesity-related behaviors and population-level characteristics. Because influence mechanisms cannot be directly observed, we show how three complex systems tools may be used to gain insights into observed epidemiologic patterns: social network analysis, agent-based modeling, and systems dynamics modeling. However, simulation and mathematical modeling approaches raise questions regarding acceptance of findings, especially among policy makers. Nevertheless, we point to modeling successes in obesity and other fields, including the NIH-funded National Collaborative on Childhood Obesity Research (NCCOR) Envison project.

Detector characterization and first coincidence tests of a Compton telescope based on LaBr3 crystals and SiPMs

A Compton telescope for dose monitoring in hadron therapy consisting of several layers of continuous LaBr3 crystals coupled to silicon photomultiplier (SiPM) arrays is under development within the ENVISION project. In order to test the possibility of employing such detectors for the telescope, a detector head consisting of a continuous 16mm×18mm×5mm LaBr3 crystal coupled to a SiPM array has been assembled and characterized, employing the SPIROC1 ASIC as readout electronics. The best energy resolution obtained at 511keV is 6.5% FWHM and the timing resolution is 3.1ns FWHM. A position determination method for continuous crystals is being tested, with promising results. In addition, the detector has been operated in time coincidence with a second detector layer, to determine the coincidence capabilities of the system. The first tests are satisfactory, and encourage the development of larger detectors that will compose the telescope prototype.