Joint condition-based maintenance and load-sharing optimization for two-unit systems with economic dependency

Many production facilities consist of multiple and functionally exchangeable units of equipment, such as pumps or turbines, that are jointly used to satisfy a given production target. Such systems often have to ensure high levels of reliability and availability. The deterioration rates of the units typically depend on their production rates, implying that the operator can control deterioration by dynamically reallocating load among units. In this study, we examine the value of condition-based load-sharing decisions for two-unit systems with economic dependency. We formulate the system as a Markov decision process and provide optimal joint condition-based maintenance and production policies. Our numerical results show that, dependent on the system characteristics, substantial cost savings of up to 40% can be realized compared to the optimal condition-based maintenance policy under equal load-sharing. The structure of the optimal policy particularly depends on the maintenance setup cost and the penalty that is incurred if the production target is not satisfied. For systems with high setup costs, the clustering of maintenance interventions is improved by synchronizing the deterioration of the units. On the contrary, for low setup costs, the deterioration levels are desynchronized and the maintenance interventions are alternated

Planning the restoration of membranes in RO desalination using a digital twin

This paper describes the development of a decision support system (DSS) for evaluating membrane restoration strategy. The engine of the DSS is a digital twin (DT), a virtual representation of wear (degradation) and restoration of membrane elements in a reverse osmosis (RO) pressure vessel. The basis of the DT is a mathematical model that describes an RO vessel as a novel multi-component system in which the wear-states of individual elements (components) are quantified and elements can be swapped or replaced. This contrasts with the contemporary presentation in the literature of a membrane system as a single system. We estimate the parameters of the model using statistical methods. We describe our approach in the context of a case study on the Carlsbad Desalination Plant in California, which suffers from biofouling due to seasonal algae blooms. Our results show a good fit between the observed and the modelled wear-states. Competing policies are compared based on risk, cost, downtime, and number of stoppages. Projections indicate that a significant cost-saving can be achieved while not compromising the integrity of plant

Managing the restoration of membranes in reverse osmosis desalination using a digital twin

This thesis studies degradation and restoration policies for a pressure vessel in a reverse osmosis (RO) desalination plant. In the study context, biofouling is the primary cause of the degradation of the RO membrane elements, amplified by seasonal algal blooms. This research developed a decision support system (DSS) for evaluating membrane restoration strategy. The engine of the DSS is a digital twin (DT), a virtual representation of wear (degradation) and restoration of membrane elements in a RO pressure vessel. The basis of the DT is a mathematical model that describes an RO pressure vessel as a novel multi-component system in which the hidden wear-states of individual elements (components) are quantified, and elements can be swapped or replaced. This contrasts with the contemporary presentation of a membrane system as a single system in the literature. The parameters of the model are estimated using statistical methods. The research approach is described in the context of a case study on the Carlsbad Desalination Plant in California. Results show a good fit between the observed and the modelled wear-states. Competing policies are compared based on risk, cost, downtime, and the number of stoppages. Projections indicate that a significant cost-saving can be achieved while not compromising the integrity of the plant. Alternative policies 11 and 12 showed better wear management than the current policy 10 of the maintenance company while reducing costs between $0.7 to$1.7 million for the next five years.The research in the thesis contributes toward maintenance modelling. New models of multivariate degradation and imperfect repair are presented. The research makes an important contribution to desalination and water treatment engineering, providing a unique membrane maintenance management approach currently absent from the literature. The thesis also contributes to the maintenance theory. It proposes a general approach for applying a decision support system (DSS) for maintenance requirements analysis, involving a digital twin (DT) for wear and repair projections when wear is stochastic, and repair effects are not immediately apparent. The essential elements of a DSS are discussed. This research encourages a dialogue between researchers of maintenance theory and modelling and practitioners of maintenance planning about decision support systems and digital twins that not only project the when but also evaluate the what in maintenance strategy. The presented concept of a DSS driven by a DT for maintenance requirement analysis has valuable practical implications, and the thesis, in discussing this concept, makes an essential contribution to the discussion about Industry 4.0, digital twins, and maintenance