Kristjanpoller Rodriguez, Fredy Ariel

The most commonly used probabilistic model in reliability studies is the Perfect Renewal Process (PRP), which is characterized by the condition or type of maintenance represented: once the maintenance activities are executed, the equipment is restored to its original condition, leaving it “as good as new.” It is widely used since it represents an optimistic state when an item is replaced, assuming a perfect operational condition of the item after the maintenance. Some models have been developed for determining optimum preventive maintenance (PM) based on different criteria, and almost all aimed at PRP reliability modeling. The contribution of this paper is to analyze a model for determining the optimal preventive maintenance policy for a long time run under PRP and developing a general and chart-based tool for the problem, making it easier to solve the day-to-day practice and operation of equipment. As a result, a generalized chart was developed to support maintenance decisions through the elaboration of an original isometric table and complemented with a step-by-step methodology to determine the optimum time in which the preventive maintenance activities must be implemented. In most cases, these types of maintenance activities will consider a replacement activity.

In this paper, a stochastic simulation model for a standalone PV system sizing is replicated and extended to supply a dairy’s power demand. A detailed hourly-based simulation is conducted considering an hourly load profile and global solar radiation prediction model. The stochastic simulation model is based on a thorough statistical analysis of the solar radiation data and simulates the energy yield, the excess energy curtailed, and the state of charge of the batteries for the sizing month and the whole year, providing the designer autonomy factor valuesdto properly size the PV system, finding the optimum combination of installed peak powerPmand battery storage capacityCLthat meets the application load requirements, considering a preset reliability level at minimum cost. The model makes use of the NASA’S Surface Meteorology and Solar Energy database to obtain solar radiation data. Results show a substantial reduction of 44% in installed peak power and battery storage capacity when compared to conventional methodologies, considering three days of autonomy, and an 85% reduction considering four days. Considering the goodness of fit test results, the Wakeby distribution best represents the behavior of historical solar radiation data for the site in almost half of the months. This article seeks to contribute to the literature gap in the application of methodologies for the multicomponent power supply in the dairy industry through the use of renewable energy.

This article proposes an original probabilistic modelling methodology named Virtual Standby (VSB), which enables a practical simulation, analysis, and evaluation of the impact on availability and reliability achieved by potential buffering policies on the performance of complex production systems. Virtual Standby (VSB) corresponds to a design and operational characteristic where some machines under a failure scenario are capable to provide for a limited time, continuity to the subsystems downstream before suffering delay which is currently not considered when assessing availability. This feature plays a relevant role on the propagation of the effect of a failure; indeed, it could prevent the propagation by guaranteeing the isolation time needed to recover from its failure, controlling and reducing the production losses downstream. A case study of the preliminary treatment process of a wastewater treatment facility (WWTF) is developed bearing in mind the systemic behaviour in the event of a failure and the specific features of each equipment. VSB is a big advantage for the representation of this complex processes because, among other things, it considers the impact of buffering policies on the perceived availability of the system. This model allows determining different production levels, with a better and easier fitting of the reliability, availability, and production forecast of the process. Finally, the comparison between the VSB simulation results with traditional procedures that do not consider the operational continuity under a failure scenario confirms the strength and precision of the proposal for complex systems.