Numerical Approach for the Design of Cost-Effective Renovation of Heating System Control in Buildings

This chapter focuses on advanced tools for transient energy simulation of existing buildings. Budget constraints often hinder the possibility of implementing large-scale retrofit projects. As a consequence, designers must work out low-cost renovation, which asks for a deep knowledge of the current state of the buildings. Furthermore, the performances of heating plants in existing buildings can be enhanced through the improvement of the control of the system. These types of retrofit actions can be carried out with a limited budget, but asks for the availability of very accurate transient energy simulation tools, which can compare the current and the renovated scenarios. On top of them, cost–benefit analyses can be developed. In this chapter, a model of a small hospital is developed in the Dymola/Modelica environment. The high flexibility of the transient simulation model and the very good agreement between numerical estimations and measurements are shown. Then, one scenario regarding enhanced regulation of the heating system by means of a customized ambient temperature control system is developed, and the expected energy savings are estimated

Development and calibration of a model for the dynamic simulation of fans with induction motors

In this paper a model for the dynamic simulation of fans used in mechanical air supply systems is described. Thanks to this model, the behavior of fans subject to control by variable frequency drives (VFD) can be predicted, which includes power absorbed by the fan and expected ventilation rates. Hence, it can help design energy control systems for buildings. The proposed model was based on the Modelica language and was developed from the dynamic phasor domain representation, because this representation is a trade-off between the basic non transient representation, that is computationally efficient but cannot describe fan dynamics, and the dynamic time domain model, that is the most representative one but computationally very demanding. A comparison among these models showed that, within fan frequency variations typical of ventilation systems in buildings, the phasor domain model is as representative as the more complex dynamic time domain model in terms of prediction of the dynamic behavior, that is neglected by the basic non transient model. Moreover, the new phasor domain model was validated against measured data relative to a fan installed in a subway station in Barcelona. Thanks to this model, energy consumption of dynamically driven fans can be estimated at the simulation stage, at the expense of a reasonable computational effort

Bayesian Networks for Supporting Model Based Predictive Control of Smart Buildings

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A Smart Contract-based BPMN Choreography Execution for Management of Construction Processes

Construction management can be grouped into two different levels: strategic early planning, that provides the baseline for project monitoring, and short time initiatives, based on objectives and self-organization from actors who are involved in on-site processes. The latter can be considered as a complex system management issue since it presents emergent behaviors thus it can not be handled in a traditional way. The passage from project scheduling to on site operations management requires a change of perspective. On site short time planning is a process of forecasting future outcomes therefore it deals with uncertainty and indeterminacy. At present this is managed through the representation of many separate orchestrations and this does not allow to eliminate the inefficiencies that arise at the level of synchronization of the individual tasks performed by organizations with contractually separate management. Efficiency in construction management implies to take into consideration choreographies because they better reflect synchronization of different organizations management processes. On the other hand, information processed as a trigger for distributed activities on different management does not guarantee process traceability while smart contracts linked to single task execution assure both promptness and irreversible tracking at single task level. The actual execution of the processes depends both on what happens and on the information that flows between the subjects who actually carry out processes asynchronous to each other, so the only possibility to synchronize them is information. This research aims to describe a framework for applying BPMN choreographies to construction site processes in order to better modeling processes for smart contracts application. The choice of applying BPMN instead of CPM lays in the fact that it allows to model the information flows as well as the preparatory aspects and in addition it allows to represent decision-making moments. Every single activity in the baseline can be modelled as a choreography at a lower level. On the other hand, process performance monitoring can be performed thanks to blockchain tasks notarization. Concrete casting quality assessment process has been chosen as use case. BPMN choreography of this process has been modelled and blockchain application for tasks and information notarization has been development and tested on a construction site

Model predictive energy control of ventilation for underground stations

Smart building systems are opening up new markets, nevertheless the implementation of these novel technologies still lacks suitable and proven whole engineering solutions in complex buildings. This paper presents a detailed approach for the ventilation control of an underground space, as an example of application of the developed solution to a very harsh environment but also highly demanding in terms of energy consumption. The underground spaces are characterized by a particular thermal behavior, because of the continuous and huge thermal exchange they have with the outside, via the openings and the ground surrounding the majority of the building. The main objective of the developed methodology is to reduce energy consumption of ventilation control while maintaining acceptable comfort levels: succeeding in achieving this twofold goal in a real station and the generalization of the approach are the most relevant contributions of the paper. The developed solution is based on a Model-based Predictive Control algorithm used together with a proper monitoring platform. The model predictive control is based on a Bayesian environmental prediction model, which works in cooperation with a weather forecast web service, schedule-based predictions about trains and external fans and an occupancy detection system to appraise the real amount of people. The prediction model develops scenarios useful to allow the controller acting in advance in order to adapt the system to the current and future conditions of use, taking profit of the knowledge of the real ventilation demand. Finally, the proposed control architecture is applied to the Passeig de Gràcia metro station in Barcelona as a case study, validating the usefulness of the proposed approach and obtaining more than 30% of energy savings in the ventilation system, while maintaining the pre-existing comfort levels. The saving percentage values estimated by simulation are confirmed by the direct measures continuously taken on site through energy-meters

wireless real time monitoring system for the implementation of intelligent control in subways

This chapter looks into the technical features of state-of-the-art wireless sensors networks for environmental monitoring. Technology advances in low-power and wireless devices have made the deployment of those networks more and more affordable. In addition, wireless sensor networks have become more flexible and adaptable to a wide range of situations. Hence, a framework for their correct implementation will be provided. Then, one specific application about real-time environmental monitoring in support of a modelbased predictive control system installed in a metro station will be described. In these applications, filtering, resampling, and post-processing functions must be developed, in order to convert raw data into a dataset arranged in the right format, so that it can inform the algorithms of the control system about the current state of the domain under control. Finally, the whole architecture of the model-based predictive control and its final performances will be reported

Enhancing BIM through Mixed Reality for Facility Management

Implementation of processes in facility management asks for coordination and collaboration among several factors, each implementing its own sub-process. This goal must cope with several challenges caused by the fragmentation of the AEC (architectural, engineering, construction) industry. In the specific case of facility recommissioning, further constraints are determined by limitations posed by the current status of the existing facility whose knowledge is often limited to coarse preliminary surveys. In this chapter, the benefits determined by the integration between BIM and mixed reality will be presented, along with a prototypical platform that realizes an efficient, distributed collaborative workflow enabling asynchronous collaboration among members of the facility management office, the owner, the design team and technical specialists that may be appointed in recommissioning workflows. Technically, this approach provides an immersive mixed-reality environment capable of seamlessly displaying project information, through which specialists can evaluate and refine different recommissioning options. In addition, the platform supports on-site enrichment of BIM models for a facilitated, yet asynchronous, collaboration between remote and on-site users. This technology was validated by means of real-life experiments regarding a hypothetical recommissioning project of the Construction Division of the DICEA Department at Università Politecnica delle Marche (Ancona, Italy)

Early mobilisation in critically ill COVID-19 patients: a subanalysis of the ESICM-initiated UNITE-COVID observational study

Background Early mobilisation (EM) is an intervention that may improve the outcome of critically ill patients. There is limited data on EM in COVID-19 patients and its use during the first pandemic wave. Methods This is a pre-planned subanalysis of the ESICM UNITE-COVID, an international multicenter observational study involving critically ill COVID-19 patients in the ICU between February 15th and May 15th, 2020. We analysed variables associated with the initiation of EM (within 72 h of ICU admission) and explored the impact of EM on mortality, ICU and hospital length of stay, as well as discharge location. Statistical analyses were done using (generalised) linear mixed-effect models and ANOVAs. Results Mobilisation data from 4190 patients from 280 ICUs in 45 countries were analysed. 1114 (26.6%) of these patients received mobilisation within 72 h after ICU admission; 3076 (73.4%) did not. In our analysis of factors associated with EM, mechanical ventilation at admission (OR 0.29; 95% CI 0.25, 0.35; p = 0.001), higher age (OR 0.99; 95% CI 0.98, 1.00; p ≤ 0.001), pre-existing asthma (OR 0.84; 95% CI 0.73, 0.98; p = 0.028), and pre-existing kidney disease (OR 0.84; 95% CI 0.71, 0.99; p = 0.036) were negatively associated with the initiation of EM. EM was associated with a higher chance of being discharged home (OR 1.31; 95% CI 1.08, 1.58; p = 0.007) but was not associated with length of stay in ICU (adj. difference 0.91 days; 95% CI − 0.47, 1.37, p = 0.34) and hospital (adj. difference 1.4 days; 95% CI − 0.62, 2.35, p = 0.24) or mortality (OR 0.88; 95% CI 0.7, 1.09, p = 0.24) when adjusted for covariates. Conclusions Our findings demonstrate that a quarter of COVID-19 patients received EM. There was no association found between EM in COVID-19 patients' ICU and hospital length of stay or mortality. However, EM in COVID-19 patients was associated with increased odds of being discharged home rather than to a care facility. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021)