Model based prognostic maintenance as applied to small scale PVRO systems for remote communities
Author(s)Kelley, Leah C. (Leah Camille)
Massachusetts Institute of Technology. Department of Mechanical Engineering.
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Many systems degrade as functions of their operation and require maintenance to extend their productivity. When operating under steady conditions, prescheduled maintenance can be used to ensure such systems meet their required levels of productivity at lowest cost. However, using pre-scheduled maintenance on systems that degrade as functions of their operation under uncertain, varying conditions will not guarantee that they meet their productivity at lowest cost. They require maintenance schedules that accommodate changes in their operating conditions and degradation. This research develops a prognostic maintenance methodology that ensures a system degrading with its operation under variable, uncertain operating conditions meets its desired productivity at the lowest cost. An example of a degrading system under variable, uncertain operating conditions is a photovoltaic-powered reverse osmosis (PVRO) desalination system. PVRO desalination can provide drinking water to remote communities in sunny areas with saline water sources. Such systems produce clean water and degrade as functions of their operating conditions, including solar radiation, water chemistry and community demand. These conditions are not constant, but vary stochastically. Maintenance (system flushing and cleaning) will extend a PVRO system's productivity, but requires time, chemicals and use of the clean product water. Hence, it has a substantial impact on the total cost of water production and should be adjusted in response to variations in operation. The community members who generally operate and maintain PVRO systems do not have the training or experience to determine the best type and timing of maintenance to ensure their water demand is met at lowest cost, and require a method to do so. Here, prognostic maintenance methodology is developed and applied to community-scale PVRO desalination. Degradation (fouling) and remediation (cleaning) of the RO membrane have the largest impact on the system productivity and water cost, and hence are the focus of this study. Fouling and cleaning are complex functions of water chemistry and system operation. Physics-based mathematical models of fouling and cleaning rely on two critical unknown parameters: fouling rate and cleaning effectiveness. They can be determined using system identification methods in real time, using measurements of the PVRO feed water pressure and clean water production rates. The identified fouling and cleaning models are combined with statistical models of the expected future PV power and community water demand to predict the type and timing of future maintenance procedures. The maintenance protocols are adjusted in real time in response to changes in identified fouling. The prognostic algorithm developed here is suitable for implementation on a PVRO system's embedded microcontroller. Case studies presented here show that the prognostic maintenance methodology provides non-expert operators with near optimal maintenance protocols when compared with conventional periodic scheduling, especially under varying degradation, solar radiation and demand. In this example study, annual maintenance happens to be nearly optimal, so the prognostic maintenance algorithm produces a nearly annual cleaning schedule that minimizes maintenance costs. Since the statistical nature of this example prevents demand from being met 100% of the time, the prognostic maintenance method is used to minimize cost and water loss. On average, following the prognostic maintenance protocol results in less than 4% loss of water over a 5-year period at lowest cost. Although developed in the domain of PVRO, the prognostic maintenance methodology developed here is anticipated to be applicable to other systems that degrade as functions of their operation, including machine systems, vehicle fleets and transportation networks.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2015.Cataloged from PDF version of thesis.Includes bibliographical references (pages 141-147).
DepartmentMassachusetts Institute of Technology. Department of Mechanical Engineering.
Massachusetts Institute of Technology