Jaarlijks worden ongeveer 80.000 patiënten behandeld op de ruim 80 intensive care-afdelingen in Nederland.Op een intensive careafdeling worden vitale functies bewaakt en meestal zelfs overgenomen. Bij de meeste patiënten is voor kortere of langere tijd kunstmatige beademing noodzakelijk. Kunstmatige beademing is effectief en soms zelfs levens reddend maar is in het geheel niet zonder risico’s. Lector Critical Care Frederique Paulus gaat in haar rede in op de uitdagingen die de interprofessionele teams op de Intensive Care hebben ten aanzien van de luchtweg- en beademingszorg. Zij zal proberen te schetsen wat ‘Wij gaan goed voor u zorgen’ op een Intensive Care betekent. Het bijzonder lectoraat CriticalCare is ingesteld in samenwerking methet Amsterdam UMC locatie AMC.
Current symptom detection methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not standardised and not consistent with HVAC process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to a very limited application of energy performance diagnosis systems in practice. This paper proposes detection methods to overcome these issues, based on the 4S3F (four types of symptom and three types of faults) framework. A set of generic symptoms divided into three categories (balance, energy performance and operational state symptoms) is discussed and related performance indicators are developed, using efficiencies, seasonal performance factors, capacities, and control and design-based operational indicators. The symptom detection method was applied successfully to the HVAC system of the building of The Hague University of Applied Sciences. Detection results on an annual, monthly and daily basis are discussed and compared. Link to the formail publication via its DOI https://doi.org/10.1016/j.autcon.2020.103344
The Heating Ventilation and Air Conditioning (HVAC) sector is responsible for a large part of the total worldwide energy consumption, a significant part of which is caused by incorrect operation of controls and maintenance. HVAC systems are becoming increasingly complex, especially due to multi-commodity energy sources, and as a result, the chance of failures in systems and controls will increase. Therefore, systems that diagnose energy performance are of paramount importance. However, despite much research on Fault Detection and Diagnosis (FDD) methods for HVAC systems, they are rarely applied. One major reason is that proposed methods are different from the approaches taken by HVAC designers who employ process and instrumentation diagrams (P&IDs). This led to the following main research question: Which FDD architecture is suitable for HVAC systems in general to support the set up and implementation of FDD methods, including energy performance diagnosis? First, an energy performance FDD architecture based on information embedded in P&IDs was elaborated. The new FDD method, called the 4S3F method, combines systems theory with data analysis. In the 4S3F method, the detection and diagnosis phases are separated. The symptoms and faults are classified into 4 types of symptoms (deviations from balance equations, operating states (OS) and energy performance (EP), and additional information) and 3 types of faults (component, control and model faults). Second, the 4S3F method has been tested in four case studies. In the first case study, the symptom detection part was tested using historical Building Management System (BMS) data for a whole year: the combined heat and power plant of the THUAS (The Hague University of Applied Sciences) building in Delft, including an aquifer thermal energy storage (ATES) system, a heat pump, a gas boiler and hot and cold water hydronic systems. This case study showed that balance, EP and OS symptoms can be extracted from the P&ID and the presence of symptoms detected. In the second case study, a proof of principle of the fault diagnosis part of the 4S3F method was successfully performed on the same HVAC system extracting possible component and control faults from the P&ID. A Bayesian Network diagnostic, which mimics the way of diagnosis by HVAC engineers, was applied to identify the probability of all possible faults by interpreting the symptoms. The diagnostic Bayesian network (DBN) was set up in accordance with the P&ID, i.e., with the same structure. Energy savings from fault corrections were estimated to be up to 25% of the primary energy consumption, while the HVAC system was initially considered to have an excellent performance. In the third case study, a demand-driven ventilation system (DCV) was analysed. The analysis showed that the 4S3F method works also to identify faults on an air ventilation system.
