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On October 29th the workshop corresponding to the mid-term conference of BIM4EEB (Acronym of BIM Based Toolkit for Efficient rEnovation in Buildings, Horizon Project under grant agreement N. 820660 ) project was held during the Sustainable Places 2020 event. The main focus of the workshop was a general introduction of the project combined with vertical insights on the single tools that have been developed or are still in development. BIM4EEB grows from simple consideration: according to a BPIE analysis on EPC data, about 97% of building stock, currently not in A level, must be upgraded to achieve 2030 decarbonization objectives. As the biggest energy consumers, buildings consume about 40% of energy and they are responsible for 36% of CO2—they are strategic in order to reach the set of environmental goals and subjected to renovation and refurbishment during their lifecycle according to a specific schedule. Refurbishment becomes the crucial point of sustainability not only in construction but also in the environment in general for the coming years. The BIM4EEB project focuses on implementing a complete BIM (Building Information Modelling)-based toolkit to be adopted in the renovation of existing residential buildings to make the flow of information efficient, to enhance communication and data transfer decreasing intervention working time while improving building performances, quality, and comfort for inhabitants. The platform that controls all the tools developed for the best performance of renovation is BIMMS (Acronym of Building Information Modelling Management System platform), namely a management system linked to an operational and multifunctional toolkit for different AEC stakeholders, offering tools for increasing the adoption of BIM in renovation businesses based on an interoperable flow of information. During the workshop, six development tools have been explored in addition to the BIM Management System: BIM4EEB Fast Mapping of Buildings Toolkit, BIM4EEB BIMeaser tool, BIM4EEB BIM4Occupants tool, BIM4EEB Auteras tool, BIM4EEB BIMcpd tool, and BIM4EEB BIMPlanner tool.

Dental implants are an increasingly popular solution to missing teeth. Implants are prone to colonisation by pathogenic oral bacteria which can lead to inflammation, destruction of bone and ultimately implant failure. The aim of this study was to investigate the use of chlorhexidine (CHX) hexametaphosphate (HMP) nanoparticles (NPs) with a total CHX concentration equivalent to 5 mM as a coating for dental implants. The CHX HMP NPs had mean diameter 49 nm and composition was confirmed showing presence of both chlorine and phosphorus. The NPs formed micrometer-sized aggregated surface deposits on commercially pure grade II titanium substrates following immersion-coating for 30 s. When CHX HMP NP-coated titanium specimens were immersed in deionised water, sustained release of soluble CHX was observed, both in the absence and presence of a salivary pellicle, for the duration of the study (99 days) without reaching a plateau. Control specimens exposed to a solution of aqueous 25 µM CHX (equivalent to the residual aqueous CHX present with the NPs) did not exhibit CHX release. CHX HMP NP-coated surfaces exhibited antimicrobial efficacy against oral primary colonising bacterium Streptococcus gordonii within 8 h. The antimicrobial efficacy was greater in the presence of an acquired pellicle which is postulated to be due to retention of soluble CHX by the pellicle.

The exponential increase in data produced in recent times has had a profound impact in all areas of society. In the field of industrial engineering, the knowledge produced by this newly obtained data is driving business forward. Automating the process of capturing data from industrial machines, analyzing it and using the knowledge gained to make better decisions for the machines is the crux of the digital twin. Digital twins uncover a wealth of knowledge about the physical asset they duplicate. Sensor technology, Internet of Things platforms, information and communication technology and smart analytics allow the digital twin to transform a physical asset into a connected smart item that is now part of a cyber physical system and that is far more valuable than when it existed in isolation. The digital twin can be adopted by the maintenance engineering industry to aid in the prediction of issues before they occur thus creating value for the business. This thesis discusses the introduction of a maintenance digital twin to a large-scale manufacturing facility. Issues that hamper such work are discovered and categorized to highlight the difficulty of the practical installation of this concept. The work here highlights the difficulties when working on digital systems in manufacturing facilities and how this isn’t discussed in journal articles and the disconnect between academia and industry on this topic. To aid in the installation, a digital twin framework is created that simplifies the digital twin development process into steps that can be completed independently. Work on implementing this framework is commenced and early successes highlight the benefit of sensoring critical assets. The payback of the initial practical work is immediate, and it presents a promising outlook for the iterative development of a maintenance digital twin using the framework. The thesis’ work highlights the benefit in reducing project scale and complexity and hence risk for digital systems in manufacturing facilities by following the framework developed. The later part of the thesis discusses machine learning and how this AI topic can be integrated into the digital twin to allow the digital asset to fulfill its potential.

In recent years manufacturing industries have moved towards Smart Manufacturing, to achieve improved efficiency and production targets. Part of this innovation of current processes includes digitisation and improving access to machine information, usually through the introduction of new technology to assist with this transition. In order to maintain smooth processes and uninterrupted production, various information sources must be available on the factory floor. This project aims to provide a proof of concept for digitisation and access to necessary information during Pulse Walks. The methodology used to develop this tool is discussed. Observations during Pulse Walks were used to highlight the areas that this could be applied to, and a survey was used to determine the most useful information sources to include. Another aspect of this project is to introduce a method of digitally storing issues discussed during the Pulse Walk, to highlight recurring issues and problematic areas. This was developed to be used as part of the tool during Pulse Walks. This research will present a proof of concept for an app that will act as a digital information hub for accessing information and logging issues from the Pulse Walks. The use cases for this tool have been deliberated and the benefits clearly identified. This tool can assist with tracking recurring issues, using previously logged issues to create a historical database. The issue logging dashboard can be used for investigating reasons for machine downtime. This tool aims to improve production efficiency for a manufacturing line in a factory through issue tracking.

The advent of advanced metering infrastructure has led to vast quantities of energy data becoming available. Despite this, the typical methods employed for the performance verification of energy efficiency improvements have not progressed, as they continue to rely on expert knowledge and simplistic statistical modelling techniques. This leads to uncertainty in the quantity of savings arrived at, with this uncertainty acting as a barrier to investment in energy efficiency. In response to this, the industry is evolving towards more advanced and automated methods known as M&V 2.0. This however presents the challenge of keeping the resources required to perform M&V at a minimum level, while also improving the accuracy, reliability and trust in the process. The research presented in this thesis can be largely classified into two prominent tasks. These are the development of a machine learning-based methodology for the construction of accurate baseline energy models and the establishment of a framework and final solution for M&V 2.0. It will be shown through theoretical and practical work that: - Machine learning techniques reduce the uncertainty introduced into the performance verification process by the baseline energy regression model. Additionally, the utilisation of a broader scope of analysis with respect to traditional methods is advantageous in further improving model accuracy. - Novel, computationally efficient data processing methods, including cleaning and feature selection, can be tailored for the industrial buildings sector to minimise the resources required to carry out performance verification. - The void in knowledge resulting from the established M&V protocols can be populated by a prescriptive methodology that utilises machine learning techniques to accurately and reliably quantify energy savings; thus, empowering performance verification practitioners in the use of advanced analytics on granular data sets and removing the need for expert knowledge. - The industrial sector requires a specific framework for the application of M&V 2.0 practices. An M&V 2.0 framework is developed to offer a solution to the challenge of persisting energy savings. A performance deviation detection system enables integration with ongoing monitoring and targeting practices. - M&V 2.0 does not have to increase the resources required to carry out performance verification. A novel, cloud computing-based solution, IntelliMaV, is capable of quantifying energy savings in near real-time with minimal uncertainty at high confidence levels. This thesis addresses some of the key challenges facing the performance verification industry including utilising the large quantities of energy data available in industrial facilities and evolving practices to a level of maturity that will enable it to support M&V 2.0. The implications of such challenges are shown to be significant beyond the individual project level, with the effectiveness of European energy policy dependent on accurate and reliable M&V. The methodology, framework and IntelliMaV application developed all address these challenges, while aiding the transition to M&V 2.0 practices. Despite these advancements, this is not the final solution for the industry. A collective effort must be made to continue to modernise performance verification practices to ensure M&V remains a valued and beneficial practice in energy management into the future.

As feed-in tariffs for wind energy are gradually being replaced by market driven auction-based systems, the need for cost savings at every stage of a wind energy project is more apparent than ever. A proven and effective way of reducing maintenance costs is through a condition-based maintenance (CBM) strategy. By using supervisory control and data acquisition (SCADA) system data instead of retrofitting a dedicated condition monitoring (CM) system, CM functionality can be gained at a fraction of the cost. This thesis investigates using SCADA system data for various levels of CM: fault detection, diagnosis and prediction. First, a case study is presented on using classification techniques for CM using SCADA data. Various methods for dealing with the massive class imbalance seen in fault data are evaluated. It was found that all three levels of CM are possible using classification techniques, though with a high number of false positives. Adding a class-weight to the minority class or undersampling the majority class were found to be the best ways of dealing with class imbalance. Sources of accurate failure data can be difficult to obtain for wind turbines. The second part of this thesis presents a novel way of building a historical failure database using alarm system and availability data. This was shown to produce an accurate database of unplanned stoppages related to assembly-level failures, scheduled maintenance, or grid, noise or shadow-related events. Next, common issues with some of the classification approaches present in the literature are addressed, as well as the lack of demonstration of how these approaches would perform in the field. A formalised framework with a prescribed list of steps following best practice guidelines is presented for performing CM using classification techniques on turbine SCADA data. A case study is performed which uses a sliding window metric to evaluate field performance, showing that such a system is effective at flagging faults in advance, but more data is needed to reduce the false positive rate. It is noted throughout the thesis that turbine alarm systems have some consistent shortcomings, and do not live up to their full potential. Hence, a novel methodology is presented which uses clustering techniques to identify similar sequences of alarms as they occurred during unplanned stoppages. A case study applying the methodology showed that just under half of the 456 stoppages could be sorted into one of fifteen distinct types of alarm sequence.

Industrial cyber-physical systems are the primary enabling technology for Industry 4.0, which combine legacy industrial and control engineering, with emerging technology paradigms (e.g. big data, internet-of-things, artificial intelligence, and machine learning), to derive self-aware and self-configuring factories capable of delivering major production innovations. However, the technologies and architectures needed to connect and extend physical factory operations to the cyber world have not been fully resolved. Although cloud computing and service-oriented architectures demonstrate strong adoption, such implementations are commonly produced using information technology perspectives, which can overlook engineering, control and Industry 4.0 design concerns relating to real-time performance, reliability or resilience. Hence, this research compares the latency and reliability performance of cyber-physical interfaces implemented using traditional cloud computing (i.e. centralised), and emerging fog computing (i.e. decentralised) paradigms, to deliver real-time embedded machine learning engineering applications for Industry 4.0. The findings highlight that despite the cloud’s highly scalable processing capacity, the fog’s decentralised, localised and autonomous topology may provide greater consistency, reliability, privacy and security for Industry 4.0 engineering applications, with the difference in observed maximum latency ranging from 67.7% to 99.4%. In addition, communication failures rates highlighted differences in both consistency and reliability, with the fog interface successfully responding to 900,000 communication requests (i.e. 0% failure rate), and the cloud interface recording failure rates of 0.11%, 1.42%, and 6.6% under varying levels of stress.

The world’s current utilisation of water, allied to the forecasted increase in our dependence on it, has led to the realisation that water as a resource needs to be managed. Whilst it is generally accepted that enhancements in water management are required worldwide, the relatively low apparent financial cost of water is inhibiting the essential changes, as the true cost of the water is not known. The water-energy nexus has been researched and details of significant, predominantly industrial, water management studies are outlined. A common trend has been identified, whereby the true cost of the water is rarely determined and hence unappreciated. In order to assist with addressing this situation within the pharmaceutical/healthcare manufacturing industry, a novel framework for establishing the true cost of water by analysing the cost added has been developed. The application of this decision support tool is described. The true cost provides a valuable insight into the operation of the facility, a means for internal and external benchmarking, and also the data necessary to financially justify any modifications required. The data may also be used to assist with the calculation of a water footprint or a life-cycle cost. For the case-study, a model was created and data over a three year period was analysed. It was determined that the true cost of the deionised water was, on average, €13.20/m3. The Cost-Added Factor relevant to this system is 14.05, originating from a water supply cost of €0.94/m3. The total true expenditure on deionised water was €26,025 whereas the perceived expenditure was €1,853. The true cost and Cost-Added Factors for other water services typical of the industry were also calculated. The results were analysed for various scenarios and tested for sensitivity and uncertainty. The methodology developed and results obtained will assist with the transition to more sustainable practices.

Comfort is, in essence, satisfaction with the environment, and with respect to the indoor environment it is primarily satisfaction with the thermal conditions and air quality. Improving comfort has social, health and economic benefits, and is more financially significant than any other building cost. Despite this, comfort is not strictly managed throughout the building lifecycle. This is mainly due to the lack of an appropriate system to adequately manage comfort knowledge through the construction process into operation. Previous proposals to improve knowledge management have not been successfully adopted by the construction industry. To address this, the BabySteps approach was devised. BabySteps is an approach, proposed by this research, which states that for an innovation to be adopted into the industry it must be implementable through a number of small changes. This research proposes that improving the management of comfort knowledge will improve comfort. ComMet is a new methodology proposed by this research that manages comfort knowledge. It enables comfort knowledge to be captured, stored and accessed throughout the building life-cycle and so allowing it to be re-used in future stages of the building project and in future projects. It does this using the following: Comfort Performances – These are simplified numerical representations of the comfort of the indoor environment. Comfort Performances quantify the comfort at each stage of the building life-cycle using standard comfort metrics. Comfort Ratings - These are a means of classifying the comfort conditions of the indoor environment according to an appropriate standard. Comfort Ratings are generated by comparing different Comfort Performances. Comfort Ratings provide additional information relating to the comfort conditions of the indoor environment, which is not readily determined from the individual Comfort Performances. Comfort History – This is a continuous descriptive record of the comfort throughout the project, with a focus on documenting the items and activities, proposed and implemented, which could potentially affect comfort. Each aspect of the Comfort History is linked to the relevant comfort entity it references. These three components create a comprehensive record of the comfort throughout the building lifecycle. They are then stored and made available in a common format in a central location which allows them to be re-used ad infinitum. The LCMS System was developed to implement the ComMet methodology. It uses current and emerging technologies to capture, store and allow easy access to comfort knowledge as specified by ComMet. LCMS is an IT system that is a combination of the following six components: Building Standards; Modelling & Simulation; Physical Measurement through the specially developed Egg-Whisk (Wireless Sensor) Network; Data Manipulation; Information Recording; Knowledge Storage and Access.Results from a test case application of the LCMS system - an existing office room at a research facility - highlighted that while some aspects of comfort were being maintained, the building’s environment was not in compliance with the acceptable levels as stipulated by the relevant building standards. The implementation of ComMet, through LCMS, demonstrates how comfort, typically only considered during early design, can be measured and managed appropriately through systematic application of the methodology as means of ensuring a healthy internal environment in the building.