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P&ID-based automated fault identification for energy performance diagnosis in HVAC systems:

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

Agent-based modelling and simulation for circular business model experimentation

The viability of novel network-level circular business models (CBMs) is debated heavily. Many companies are hesitant to implement CBMs in their daily practice, because of the various roles, stakes and opinions and the resulting uncertainties. Testing novel CBMs prior to implementation is needed. Some scholars have used digital simulation models to test elements of business models, but this this has not yet been done systematically for CBMs. To address this knowledge gap, this paper presents a systematic iterative method to explore and improve CBMs prior to actual implementation by means of agent-based modelling and simulation. An agent-based model (ABM) was co-created with case study participants in three Industrial Symbiosis networks. The ABM was used to simulate and explore the viability effects of two CBMs in different scenarios. The simulation results show which CBM in combination with which scenario led to the highest network survival rate and highest value captured. In addition, we were able to explore the influence of design options and establish a design that is correlated to the highest CBM viability. Based on these findings, concrete proposals were made to further improve the CBM design, from company level to network level. This study thus contributes to the development of systematic CBM experimentation methods. The novel approach provided in this work shows that agent-based modelling and simulation is a powerful method to study and improve circular business models prior to implementation.

A new approach to automated energy performance and fault detection and diagnosis of HVAC systems. Development of the 4S3F method

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.

Critical review on an experimental design to measure and model milk fouling in heating equipment

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.

Indoor and outdoor heat stress reduction

The impacts of the thermal radiation field on thermal comfort, energy consumption and control

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.