Abstract
Determining the ideal size of maintenance staff is a daunting task, especially in the operation of large and complex mechanical systems such as thermal power plants. On the one hand, a significant investment in maintenance is necessary to maintain the availability of the system. On the other hand, it can significantly affect the profit of the plant. Several mathematical modeling techniques have been used in many different ways to predict and improve the availability and reliability of such systems. This work uses a modeling tool called generalized stochastic Petri net (GSPN) in a new way, aiming to determine the effect that the number of maintenance teams has on the availability and performance of a coal-fired power plant cooling tower. The results obtained through the model are confronted with a thermodynamic analysis of the cooling tower that shows the influence of this system’s performance on the efficiency of the power plant. Thus, it is possible to determine the optimal size of the repair team in order to maximize the plant’s performance with the least possible investment in maintenance personnel.
Highlights
Electricity generation from coal is one of the most important activities in fossil fuel-based economies across the globe
This work, which is an evolution of previous publications [21,22], uses a more complex type of PN, namely the generalized stochastic Petri net (GSPN), in a new way, aiming at determining the effect that a predictive maintenance policy can have on the availability, reliability and performance of a coal-fired power plant cooling tower
T3: it is an immediate transition that, if fired, makes the token go from P2 to P4, i. e., represents the machine going from the degradation zone to repair before it fails. Since it is an immediate transition, this means that as soon as the token arrives at P2, it would immediately go to P4, but in the model proposed here, this will only happen if the maintenance team is available to repair the machine; T4: it is an immediate transition that, if fired, makes the token go from P3 to P5, i. e., represents the machine going from the failed state to repair
Summary
Electricity generation from coal is one of the most important activities in fossil fuel-based economies across the globe. This work, which is an evolution of previous publications [21,22], uses a more complex type of PN, namely the generalized stochastic Petri net (GSPN), in a new way, aiming at determining the effect that a predictive maintenance policy can have on the availability, reliability and performance of a coal-fired power plant cooling tower. Availability analysis, has shown to be one of the most prominent areas of application of Petri nets, given its capability of modeling and analyzing of systems that include failure and repair processes, as well as a variability of the operating times. The application of the proposed method will help plant managers and reliability engineers to understand the availability of complex systems as a function of maintenance policy and available maintenance resources
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