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
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While the optimal mean annual temperature for people and nations is said to be between 13 °C and 18 °C, many people live productive lives in regions or countries that commonly exceed this temperature range. One such country is Australia. We carried out an Australia-wide online survey using a structured questionnaire to investigate what temperature people in Australia prefer, both in terms of the local climate and within their homes. More than half of the 1665 respondents (58%) lived in their preferred climatic zone with 60% of respondents preferring a warm climate. Those living in Australia's cool climate zones least preferred that climate. A large majority (83%) were able to reach a comfortable temperature at home with 85% using air-conditioning for cooling. The preferred temperature setting for the air-conditioning devices was 21.7 °C (SD: 2.6 °C). Higher temperature set-points were associated with age, heat tolerance and location. The frequency of air-conditioning use did not depend on the location but rather on a range of other socio-economic factors including having children in the household, the building type, heat stress and heat tolerance. We discuss the role of heat acclimatisation and impacts of increasing air-conditioning use on energy consumption.
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In this article a generic fault detection and diagnosis (FDD) method for demand controlled ventilation (DCV) systems is presented. By automated fault detection both indoor air quality (IAQ) and energy performance are strongly increased. This method is derived from a reference architecture based on a network with 3 generic types of faults (component, control and model faults) and 4 generic types of symptoms (balance, energy performance, operational state and additional symptoms). This 4S3F architecture, originally set up for energy performance diagnosis of thermal energy plants is applied on the control of IAQ by variable air volume (VAV) systems. The proposed method, using diagnosis Bayesian networks (DBNs), overcomes problems encountered in current FDD methods for VAV systems, problems which inhibits in practice their wide application. Unambiguous fault diagnosis stays difficult, most methods are very system specific, and finally, methods are implemented at a very late stage, while an implementation during the design of the HVAC system and its control is needed. The IAQ 4S3F method, which solves these problems, is demonstrated for a common VAV system with demand controlled ventilation in an office with the use of a whole year hourly historic Building Management System (BMS) data and showed it applicability successfully. Next to this, the influence of prior and conditional probabilities on the diagnosis is studied. Link to the formal publication via its DOI https://doi.org/10.1016/j.buildenv.2019.106632
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Current methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not consistent with process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to very limited application of energy performance diagnosis in practice. In a previous paper, a generic reference architecture – hereafter referred to as the 4S3F (four symptoms and three faults) framework – was developed. Because it is closely related to the way HVAC experts diagnose problems in HVAC installations, 4S3F largely overcomes the problem of limited application. The present article addresses the fault diagnosis process using automated fault identification (AFI) based on symptoms detected with a diagnostic Bayesian network (DBN). It demonstrates that possible faults can be extracted from P&IDs at different levels and that P&IDs form the basis for setting up effective DBNs. The process was applied to real sensor data for a whole year. In a case study for a thermal energy plant, control faults were successfully isolated using balance, energy performance and operational state symptoms. Correction of the isolated faults led to annual primary energy savings of 25%. An analysis showed that the values of set probabilities in the DBN model are not outcome-sensitive. Link to the formal publication via its DOI https://doi.org/10.1016/j.enbuild.2020.110289
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Not much is known about the favourable indoor air quality in long term care facilities (LTCFs), where older adults suffering from dementia live. Older adults, especially those who suffer from dementia, are more sensible to the indoor environment. However, no special requirements for the indoor air in long term care facilities exist. Due to the decrease in cognition function, it is hard to evaluate comfort and health in this group. Nevertheless, infectious diseases are a persistent problem. Based on literature an assessment methodology has been developed to analyse LTCFs to determine if differences in building characteristics and Heating, Ventilation and Air Conditioning (HVAC) systems influence the spread of airborne infectious diseases. The developed methodology is applied in seven long term care facilities in the Netherlands. After that, the methodology has been evaluated and its feasibility and applicability are discussed. From this study, it can be concluded that this method has potential to evaluate, compare LTCFs, and develop design guidelines for these buildings. However, some adjustments to the methodology are necessary to achieve this objective. Therefore, the relation between the indoor environment and infection risk is not yet analysed, but a consistent procedure to analyse this link is provided.
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A local operating theater ventilation device to specifically ventilate the wound area has been developed and investigated. The ventilation device is combined with a blanket which lies over the patient during the operation. Two configurations were studied: Configuration 1 where HEPA-filtered air was supplied around and parallel to the wound area and Configuration 2 where HEPA-filtered air was supplied from the top surface of the blanket, perpendicular to the wound area. A similar approach is investigated in parallel for an instrument table. The objective of the study was to verify the effectiveness of the local device. Prototype solutions developed were studied experimentally (laboratory) and numerically (CFD) in a simplified setup, followed by experimental assessment in a full scale mock-up. Isothermal as well as non-isothermal conditions were analyzed. Particle concentrations obtained in proposed solutions were compared to the concentration without local ventilation. The analysis procedure followed current national guidelines for the assessment of operating theater ventilation systems, which focus on small particles (<10 mm). The results show that the local system can provide better air quality conditions near the wound area compared to a theoretical mixing situation (proof-of-principle). It cannot yet replace the standard unidirectional downflow systems as found for ultraclean operating theater conditions. It does, however, show potential for application in temporary and emergency operating theaters
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Thermal comfort in operating theatres is a less addressed research component of the in-door environment in operating theatres. The air quality naturally gets most attention when considering the risk of surgical site infections. However, the importance of thermal comfort must not be underestimated. In this research, the current thermal comfort situation of staff members is investigated. Results show that the thermal comfort for the members of a surgical team is perceived as not optimal. Application of the PMV and DR models needs further attention when applied for operating theatres. For the investigated ventilation systems, the differences in thermal comfort outcomes are small.
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The use of cleanrooms is increasing and the expectation is that this growth will continue in the coming decade. When compared to an average office building, cleanrooms consume large amounts of energy due to their high Air Change Rates (ACRs) and strict air conditioning requirements. Application of Demand Controlled Filtration (DCF) is a means to reduce the (fan) energy demand. The question is whether the air quality is compromised at reduced ACR and overpressure conditions in the non-operational hours of a cleanroom. In a cleanroom mock-up, experiments have been performed to investigate the particle concentration build-up for different cases with DCF, including an extreme case with zero ACR and zero pressure difference. For the DCF conditions and the specific case study, conditions for particle concentration outside the cleanroom, that may still provide high-quality Good Manufacturing Practices (GMP) conditions in the cleanroom, are derived from the results. Furthermore, it assumes DCF application via occupancy sensing, i.e. starting DCF 30 min after the last person left the cleanroom. When applying DCF for a normal workweek (production 08:00–17:00), fan energy savings higher than 70% can be obtained without compromising the air quality requirements under normal circumstances. DCF, in combination with a reduced pressure difference, therefore is regarded as a feasible solution to reduce the energy demand of cleanrooms when the personnel in the cleanroom are the main source of contamination. These results are obtained for the presented case study. Though assuming a conservative approach, confirmation of these outcomes for other cleanrooms is recommended.
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Thermal comfort is determined by the combined effect of the six thermal comfort parameters: temperature, air moisture content, thermal radiation, air relative velocity, personal activity and clothing level as formulated by Fanger through his double heat balance equations. In conventional air conditioning systems, air temperature is the parameter that is normally controlled whilst others are assumed to have values within the specified ranges at the design stage. In Fanger’s double heat balance equation, thermal radiation factor appears as the mean radiant temperature (MRT), however, its impact on thermal comfort is often ignored. This paper discusses the impacts of the thermal radiation field which takes the forms of mean radiant temperature and radiation asymmetry on thermal comfort, building energy consumption and air-conditioning control. Several conditions and applications in which the effects of mean radiant temperature and radiation asymmetry cannot be ignored are discussed. Several misinterpretations that arise from the formula relating mean radiant temperature and the operative temperature are highlighted, coupled with a discussion on the lack of reliable and affordable devices that measure this parameter. The usefulness of the concept of the operative temperature as a measure of combined effect of mean radiant and air temperatures on occupant’s thermal comfort is critically questioned, especially in relation to the control strategy based on this derived parameter. Examples of systems which deliver comfort using thermal radiation are presented. Finally, the paper presents various options that need to be considered in the efforts to mitigate the impacts of the thermal radiant field on the occupants’ thermal comfort and building energy consumption.
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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.
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