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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Pressure on natural resources, unsustainable production and consumption, inequality and a growing global population lie at the base of the big challenges that people face. This chapter investigates how businesses can take responsibility in dealing with these challenges by means of frugal business model innovation. The notion of ‘frugal innovation’ was first introduced in the context of emerging markets, giving non-affluent customers opportunities to consume affordable products and services suited to their needs. Business modelling with a frugal mindset opens up a path that provides significant value while minimizing the use of resources such as energy, capital and time. Business models require intentional design if they are to deliver aspired sustainability impacts. Diminish or simplify resources can be described as the means to remove or reduce features, resources, required activities and/or waste streams. Decompose can be described as the removal of resources from the commercial value proposition and replacing them with resources the user/consumer already can access or uses. This is an Accepted Manuscript of a book chapter published by Routledge/CRC Press in Circular Economy : Challenges and Opportunities for Ethical and Sustainable Business on 2021, available online: https://doi.org/10.4324/9780367816650
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At this moment, no method is available to objectively estimate the temperature to which skeletal remains have been exposed during a fire. Estimating this temperature can provide crucial information in a legal investigation. Exposure of bone to heat results in observable and measurable changes, including a change in colour. To determine the exposure temperature of experimental bone samples, heat related changes in colour were systemically studied by means of image analysis. In total 1138 samples of fresh human long bone diaphysis and epiphysis, varying in size, were subjected to heat ranging from room temperature to 900 °C for various durations and in different media. The samples were scanned with a calibrated flatbed scanner and photographed with a Digital Single Lens Reflex camera. Red, Green, Blue values and Lightness, A-, and B-coordinates were collected for statistical analysis. Cluster analysis showed that discriminating thresholds for Lightness and B-coordinate could be defined and used to construct a model of decision rules. This model enables the user to differentiate between seven different temperature clusters with relatively high precision and accuracy. The proposed decision model provides an objective, robust and non-destructive method for estimating the exposure temperature of heated bone samples.
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Fouling plays a major role in the Dairy industry. Five criteria: defined flow, no circulation, real factory product, defined product temperature and defined wall temperature, are used to review articles on this topic published between 2003 and 2020. To show the effect of those criteria in experiments, a simulation model is developed. For a good experimental design to measure fouling, the use of a dairy product in a tubular heater with a known developed flow is advised. The temperature-time history of the product and the wall temperature of the heater should be recorded. Circulation of a product will increase the fouling and decrease the flow. Although none of the reviewed articles complied to all criteria, 71% of the reviewed articles met at least two criteria. If not all criteria are met, the results are of less use for the application for process lines on industrial scale. A simulated computer model can be helpful.
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In practice, faults in building installations are seldom noticed because automated systems to diagnose such faults are not common use, despite many proposed methods: they are cumbersome to apply and not matching the way of thinking of HVAC engineers. Additionally, fault diagnosis and energy performance diagnosis are seldom combined, while energy wastage is mostly a consequence of component, sensors or control faults. In this paper new advances on the 4S3F diagnose framework for automated diagnostic of energy waste in HVAC systems are presented. The architecture of HVAC systems can be derived from a process and instrumentation diagram (P&ID) usually set up by HVAC designers. The paper demonstrates how all possible faults and symptoms can be extracted on a very structured way from the P&ID, and classified in 4 types of symptoms (deviations from balance equations, operational states, energy performances or additional information) and 3 types of faults (component, control and model faults). Symptoms and faults are related to each other through Diagnostic Bayesian Networks (DBNs) which work as an expert system. During operation of the HVAC system the data from the BMS is converted to symptoms, which are fed to the DBN. The DBN analyses the symptoms and determines the probability of faults. Generic indicators are proposed for the 4 types of symptoms. Standard DBN models for common components, controls and models are developed and it is demonstrated how to combine them in order to represent the complete HVAC system. Both the symptom and the fault identification parts are tested on historical BMS data of an ATES system including heat pump, boiler, solar panels, and hydronic systems. The energy savings resulting from fault corrections are estimated and amount 25%. Finally, the 4S3F method is extended to hard and soft sensor faults. Sensors are the core of any FDD system and any control system. Automated diagnostic of sensor faults is therefore essential. By considering hard sensors as components and soft sensors as models, they can be integrated into the 4S3F method.
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The traditional energy industry is transitioning from a centralised fossil fuel based industry to a decentralised renewable energy industry for several reasons including climate change, policy, and changing customer needs. Furthermore, renewable sources, such as wind and solar, are intermittent and unpredictable. This has implications for the business models of energy producers, such as increased mismatch between demand and supply, increased price volatility, shift in drivers of value creation. Due to the low marginal cost of production and the intermittent nature of renewables, the price volatility on the electricity markets, in particular the imbalance market, are expected to increase. However, there is potential for market parties operating in the electricity sector to profit from this development by providing flexibility to balance electricity supply and demand. Therefore, new business models are needed that can harness and exploit flexibility in a viable manner. In these business models, flexibility becomes the key driver of value creation.
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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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Almere is a green city where the greenery extends into the centre through a framework of nature, forests, parks and canals. With this green environment, Almere fulfils an important condition for a liveable city, where it is pleasant to live and work. An important goal for the municipality is to challenge its residents to develop a healthy lifestyle by using that green framework.But what really motivates Almeerders to go outside to exercise, enjoy the surroundings and meet each other? Are there sufficient green meeting or sports facilities nearby? Could the routes that connect the living and working environment with the larger parks or forests be better designed? And can those routes simultaneously contribute to climate adaptation?With the Green Escape Challenge, we invited students and young professionals to work on these assignments together.
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There are three volumes in this body of work. In volume one, we lay the foundation for a general theory of organizing. We propose that organizing is a continuous process of ongoing mutual or reciprocal influence between objects (e.g., human actors) in a field, whereby a field is infinite and connects all the objects in it much like electromagnetic fields influence atomic and molecular charged objects or gravity fields influence inanimate objects with mass such as planets and stars. We use field theory to build what we now call the Network Field Model. In this model, human actors are modeled as pointlike objects in the field. Influence between and investments in these point-like human objects are explained as energy exchanges (potential and kinetic) which can be described in terms of three different types of capital: financial (assets), human capital (the individual) and social (two or more humans in a network). This model is predicated on a field theoretical understanding about the world we live in. We use historical and contemporaneous examples of human activity and describe them in terms of the model. In volume two, we demonstrate how to apply the model. In volume 3, we use experimental data to prove the reliability of the model. These three volumes will persistently challenge the reader’s understanding of time, position and what it means to be part of an infinite field. http://dx.doi.org/10.5772/intechopen.99709
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