Companies in the Brainport region are often characterized as high mix low volume (HMLV) production environments. These companies are distinguished by a wide range of possible products (high product variety), which are produced in low volumes. These are often customer-specific products that are produced once or incidentally. Traditionally, these companies focus on efficient use of resources, where utilisation rate and cost coverage are relevant. The increasing customer demand in the region leads to pressure on production capacity. An initial intuitive response from these companies is to further increase the utilisation rate of machines. To keep costs manageable, the company tries to avoid investing in additional capacity. An undesirable side effect is increasing pressure on timeliness (delivery, such as lead times, delivery reliability, flexibility) and quality. The apparent contradiction between costs and timeliness in these HMLV production environments is a recurring issue in practice-oriented research conducted by Fontys Industrial Engineering and Management students. This results in the following research question: Which sub-aspects may be relevant to the performance regarding Quality, Delivery, and Cost (QDC) of an HMLV production environment?
DOCUMENT
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
DOCUMENT
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.
DOCUMENT
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
DOCUMENT
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.
DOCUMENT
This article describes the relation between mental health and academic performance during the start of college and how AI-enhanced chatbot interventions could prevent both study problems and mental health problems.
DOCUMENT
Innovations are required in urban infrastructures due to the pressing needs for mitigating climate change and prevent resource depletion. In order to address the slow pace of innovation in urban systems, this paper analyses factors involved in attempts to introduce novel sanitary systems. Today new requirements are important: sanitary systems should have an optimal energy/climate performance, with recovery of resources, and with fewer emissions. Anaerobic digestion has been suggested as an alternative to current aerobic waste water treatment processes. This paper presents an overview of attempts to introduce novel anaerobic sanitation systems for domestic sanitation. The paper identifies main factors that contributed to a premature termination of such attempts. Especially smaller scale anaerobic sanitation systems will probably not be able to compete economically with traditional sewage treatment. However, anaerobic treatment has various advantages for mitigating climate change, removing persistent chemicals, and for the transition to a circular economy. The paper concludes that loss avoidance, both in the sewage system and in the waste water treatment plants, should play a key role in determining experiments that could lead to a transition in sanitation. http://dx.doi.org/10.13044/j.sdewes.d6.0214 LinkedIn: https://www.linkedin.com/in/karel-mulder-163aa96/
MULTIFILE
In the wake of neo-liberal informed global trends to set performance standards and intensify accountability, the Dutch government aimed for ‘raising standards for basic skills’. While the implementation of literacy standards was hardly noticed, the introduction of numeracy standards caused a major backlash in secondary schools, which ended in a failed introduction of a high stakes test. How can these major differences be explained? Inspired by Foucault’s governmentality concept a theoretical framework is developed to allow for detailed empirical research on steering processes in complex systems in which many actors are involved in educational decision-making. A mixed-methods multiple embedded case-study was conducted comprising nine school boards and fifteen secondary schools. Analyses unveil processes of responsibilisation, normalisation and emerging dividing practices. Literacy standards reinforced responsibilities of Dutch language teachers; for numeracy, school leadership created entirely new roles and responsibilities for teachers. Literacy standards were incorporated in an already used instrument which made implementation both subtle and inevitable. For numeracy, schools distinguished students by risk of not passing the new test affirming the disciplinary nature of schools in the process. While little changed to address teachers main concerns about students’ literacy skills, the failed introduction of the numeracy test usurped most resources.
DOCUMENT
Athlete impairment level is an important factor in wheelchair mobility performance (WMP) in sports. Classification systems, aimed to compensate impairment level effects on performance, vary between sports. Improved understanding of resemblances and differences in WMP between sports could aid in optimizing the classification methodology. Furthermore, increased performance insight could be applied in training and wheelchair optimization. The wearable sensor-based wheelchair mobility performance monitor (WMPM) was used to measure WMP of wheelchair basketball, rugby and tennis athletes of (inter-)national level during match-play. As hypothesized, wheelchair basketball athletes show the highest average WMP levels and wheelchair rugby the lowest, whereas wheelchair tennis athletes range in between for most outcomes. Based on WMP profiles, wheelchair basketball requires the highest performance intensity, whereas in wheelchair tennis, maneuverability is the key performance factor. In wheelchair rugby, WMP levels show the highest variation comparable to the high variation in athletes’ impairment levels. These insights could be used to direct classification and training guidelines, with more emphasis on intensity for wheelchair basketball, focus on maneuverability for wheelchair tennis and impairment-level based training programs for wheelchair rugby. Wearable technology use seems a prerequisite for further development of wheelchair sports, on the sports level (classification) and on individual level (training and wheelchair configuration).
DOCUMENT
