National University of Ireland, Galway1
Waternomics is an innovative project aimed at improving water management within municipalities, corporations and domestic users by providing water managers and consumers with timely and actionable information relating to water usage, water availability and the state of systems within a building's water network infrastructure. A novel aspect of Waternomics is to apply Fault Detection and Diagnostics (FDD) methods and techniques to building water networks in order to identify potential operational issues including leaks, malfunctioning equipment and inefficient operation. FDD is a measurement science which has traditionally been used to identify and rectify faults in Heating Ventilation and Air-Conditioning (HVAC) systems in buildings. The results have been reduced maintenance costs, increased efficiency and energy savings of between 10 and 30% in the HVAC industry. To date, these FDD methods and tools have not been applied systematically to water infrastructure in buildings and thus it could provide the basis for significant innovation in managing water infrastructure. This paper outlines how a water wasting fault was found in a Rainwater Harvesting System (RWHS) within an engineering building at the National University of Ireland, Galway (NUIG) and how a concept FDD methodology may be applied to the RWHS to prevent such faults and water waste occurring in the future.
The application of FDD tools to a RWHS requires the development of rules and models relating to the system's operation. This can generally be divided into rule-based or model-based FDD tools:
1. Rule based FDD should utilise simple logic applied to the water network to decide whether any given system is operating as designed or not e.g. a fault could be identified if there are two identical pumps servicing the same load in a building, yet consuming different amounts of energy. Traditional flow metering may not detect such issues.
2. Model based FDD can be segregated into Law-driven and Data-driven models. Law-driven or forward models apply physical laws to the system to forecast its operation under a given set of conditions. Data-driven or inverse models require the actual water usage trends to be quantified and characterised using sensors, placed at critical locations in the RWHS of a building. Comparison of the RWHS real-time water usage to the Law and Data-driven models, in conjunction with model tolerances, can identify faults, their severity and enable rapid diagnosis.
The motivating assumption that this methodology proceeds under is that; In a mechanical system controlled from a programmed control board such as a RWHS, a malfunction of the system as a whole must be the result of one or more elemental components failing in the system. With all of the possible basic faults that could occur in the system identified, signals and readings from the system can be utilised to identify the offending basic broken component. This will then direct the repair work that needs to be completed to restore the system to its optimal running state.
The significance of the methodology development is that it is transferring intermittent expert judgement relating to the RWHS to a continuous automated process. Instead of irregular checks on a RWHS from maintenance personnel who might not necessarily be fully informed about the system, this methodology integrates all of the known knowledge relating to the optimal operation of the RWHS and provides continuous auditing to ensure minimal downtime between faults in a way that manual inspection can't.
Results and Discussion
The reason for the interest in the RWHS in the NUI Galway Engineering building was due to a significant and persistent fault that was found in the system in November 2014. The fault resulted in 150 m^3 of treated mains water per month being used unnecessarily (~15% of total mains water usage in the building per month). The fault had been present in the system for 6 months months up to November 2014 when it was found. The fault was particularly interesting as it did not impede the operation of the system due to the CWS backup but was adding additional costs to the water bills for this building.
The fault was found as part of implementing the Waternomics methodology developed in a previous study (Chambers et al., 2015). In accordance with the Assessment of Building Water Network step outlined in the previous study, the engineering buildings BMS was inspected and an apparent fault was found. As an observation and verification step, the header tanks on the roof of the Engineering building were visited physically to confirm the observation made on the BMS.
Climate change, increased urbanization and increased world population are several of the factors driving global challenges for water management. There are considerable efforts underway in many sectors to ensure water consumption is minimised. In the building sector, which accounts for 21% of all water consumed in the EU, there is considerable scope to improve the state of water consumption across the continent by improving methods of managing water in buildings alone.
A novel aspect of Waternomics is to apply fault detection and diagnostics to building water networks to identify and rectify leaks, malfunctioning equipment, inefficient operation and other water related problems.
Rainwater Harvesting is an additional service available within building water networks. These systems are gaining increased popularity across Ireland in both the residential and commercial sector. This is because of impending water charges for public houses and because of an onus on private enterprises to have a green and environmentally friendly public relations image. Although RWHS implementations are increasing a void that remains in the field of RWHS research is to develop parallel working systems which will ensure that they are more robust, resilient and proofed against faults, in the same way that HVAC systems have done.
As was shown in the case study herein, a fault in a RWHS can lead to 15% waste of a building's global usage, may be invisible to the building managers and have the potential to go unnoticed for an extended period of time. The fault presented in this study was found as a result of implementing a fault finding methodology developed in an earlier study by the Waternomics project. Chambers, N., Mccaffrey, M., & Curry, E. (2015). Assessment and Planning for the Application of Fault Detection and Diagnosis (FDD) to Building Water Networks, A WATERNOMICS Approach (pp. 1Â–11).