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.
Snelheid is één van de belangrijkste basisrisicofactoren in het verkeer. Hoe sneller er gereden wordt in een auto hoe groter de kans op (zware) ongevallen en hoe hoger de uitstoot van emissies. Beleid spitst zich daarom in toenemende mate toe op het voorkomen van te hoge snelheden en snelheidsverschillen in het wegverkeer. Het rijhulpsysteem ISA, Intelligente Snelheid Assistent, is één van de technologische oplossingen die hieraan kan bijdragen. ISA kent vele verschijningsvormen, van informerend (via slimme technologie wordt de bestuurder geïnformeerd over de geldende maximumsnelheid) tot dwingend (de auto wordt fysiek beperkt om harder te rijden dan de maximumsnelheid). Inmiddels bestaat voldoende bewijs dat de acceptatiegraad van ISA hoog kan zijn, wanneer het systeem perfect werkt. De praktijk is echter weerbarstig. Het rijden met ISA is afhankelijk van veel systemen en zowel het systeem in de auto als de vele complexe systemen die nodig zijn voor de onderliggende informatievoorziening kunnen falen of incorrecte informatie doorgeven. Ook de actieve beperking van de snelheid door ISA wordt door bestuurders verschillend ervaren en beoordeeld. Dit alles staat de acceptatie van ISA in de weg, waardoor alle positieve effecten niet kunnen worden behaald. In project ACTI-I hebben wij op basis van interviews met experts, potentiële gebruikers en proefpersonen, een literatuurstudie, en onderzoek naar de accuraatheid van digitale kaarten onderzocht welke impact technisch falen heeft op de acceptatie van ISA. In deze rapportage presenteren wij onze methodologie, bevindingen en voornaamste aanbevelingen. Onze belangrijkste conclusie is dat de potentie van ISA voor het verhogen van de verkeersveiligheid groot is omdat het systeem, wanneer het goed functioneert, naar verwachting wel degelijk door een groot deel van de bestuurders zal worden gebruikt. Echter, de technische state-of-the-art is op dit moment nog niet ver genoeg gevorderd om deze potentie ook te verwerkelijken. Met name de digitale infrastructuren die nodig zijn voor kwalitatief goede data in het systeem zijn niet goed genoeg ontwikkeld en/of op elkaar afgestemd. De bestuurder merkt daar de nadelige en soms zelfs gevaarlijke gevolgen van tijdens het rijden, wat de adoptie van het systeem uiteraard niet ten goede komt. Op basis van de resultaten van ACTI-I stellen wij daarom dat zowel technologieontwikkelaars als beleidsmakers snel(ler) moeten handelen om met name de digitale infrastructuur op orde te krijgen. De fysieke infrastructuur zal daarbij ook moeten worden ‘geüpdatet’ om de integratie van camerabeelden in het systeem te optimaliseren.
An efficient and sustainable logistics process is essential for logistics companies to remain competitive and to manage the dynamic demands and service requirements. Specifically, the first- and last-mile hub-to-hub (inter) logistics is one of the most difficult operations to manage due to low volumes, repetitive operation and short-distance transport, and relatively high waiting times. With the advancements in Industry 4.0 technologies (Internet of Things, Big Data, Cloud computing, Artificial Intelligence), the consortium partners expect that the intelligent and connected technology is a viable solution to improve operational efficiency, coordination, and sustainability of this inter-hub logistics. Despite the promising potential, the impact of technology on inter- and intra-hub (inside hub) logistics operations (such as transportation, communication, and planning) is not well-established. The focus of STEERS is to explore the real-life challenges associated with the logistics operation in a small-to-medium size logistics hub and investigate the potential of intelligent and connected technology to address such challenges. This project will investigate the requirements for the application of automated vehicles in inter-hub transportation and simultaneously explore the potential of intelligent inter-hub corridors. Additionally, inter-hub communications will also provide the opportunity to explore their potential impact on the planning and coordination of intra-hub activities, with an explicit focus on the changing role of human planners. It combines the knowledge of education and research institutes (Hogeschool van Arnhem en Nijmegen, The University of Twente and Hogeschool Rotterdam), logistics industry partners (Bolk Container Transport and Combi Terminal Twente) and public institutes (XL Business Park, Port of Twente and Regio Twente). The insights obtained in this exploratory study will serve as a foundation for the follow-up RAAK-PRO project, in which real-world demonstrators will be developed and tested inside XL Business Park.
The goal of UPIN is to develop and evaluate a scalable distributed system that enables users to cryptographically verify and easily control the paths through which their data travels through an inter-domain network like the Internet, both in terms of router-to-router hops as well as in terms of router attributes (e.g., their location, operator, security level, and manufacturer). UPIN will thus provide the solution to a very relevant and current problem, namely that it is becoming increasingly opaque for users on the Internet who processes their data (e.g., in terms of service providers their data passes through as well as what jurisdictions apply) and that they have no control over how it is being routed. This is a risk for people’s privacy (e.g., a malicious network compromising a user’s data) as well as for their safety (e.g., an untrusted network disrupting a remote surgery). Motivating examples in which (sensitive) user data typically travels across the Internet without user awareness or control are: - Internet of Things for consumers: sensors such as sleep trackers and light switches that collect information about a user’s physical environment and send it across the Internet to remote services for analysis. - Medical records: health care providers requiring medical information (e.g., health records of patients or remote surgery telemetry) to travel between medical institutions according to specified agreements. - Intelligent transport systems: communication plays a crucial role in future autonomous transportation systems, for instance to avoid freight drones colliding or to ensure smooth passing of trucks through busy urban areas. The UPIN project is novel in three ways: 1. UPIN gives users the ability to control and verify the path that their data takes through the network all the way to the destination endpoint, both in terms of hops and attributes of routers traversed. UPIN accomplishes this by adding and improving remote attestation techniques for on-path routers to existing path verification mechanisms, and by adopting and further developing in-packet path selection directives for control. 2. We develop and simulate data and control plane protocols and router extensions to include the UPIN system in inter-domain networking systems such as IP (e.g., using BGP and segment routing) and emerging systems such as SCION and RINA. 3. We evaluate the scalability and performance of the UPIN system using a multi-site testbed of open programmable P4 routers, which is necessary because UPIN requires novel packet processing functions in the data plane. We validate the system using the earlier motivating examples as use cases. The impact we target is: - Increased trust from users (individuals and organizations) in network services because they are able to verify how their data travels through the network to the destination endpoint and because the UPIN APIs enable novel applications that use these network functions. - More empowered users because they are able to control how their data travels through inter-domain networks, which increases self-determination, both at the level of individual users as well as at the societal level.
