Wastewater treatment is a critical and necessary task every human settlement is obligated to address. If not, the consequences might be catastrophic, not just for humans but for the ecosystems as well, pushing research into finding new ways to improve wastewater treatment processes to make them safer and more efficient. Hence, there is a need to address matters, such as reliability and maintainability of Wastewater Treatment Plants (WWTP), when analyzing the availability and operational conditions. These should be addressed by analyzing the plant operational effectiveness impact (P-OEI), and in this article specifically, a WWTP study case to identify design flaws or improvement opportunities. A vital aspect of a complex system is to determine the contribution to resilience, reliability, and availability of every element embedded in the system. This is performed by adapting and applying the P-OEI methodology and real data of a WWTP located in Chile. This methodology breaks down the system into several levels of disaggregation similar to RBD methodology, analyzing the upstream for availability and the downstream for the P-OEI analysis from the system itself to the individual elements within subsystems. The potential impact on the overall system’s lack of efficiency is also quantified by an Expected Operational Impact (EOI) index, which is also calculated by the methodology. The P-OEI and EOI analyses performed in this study are powerful tools to assess the design and performance of complex systems and WWTP in particular.
In this article, we report on the design and implementation of a reportability tool using Microsoft Power BI embedded with Python script to assess opportunistic grouping schemes under a preventive maintenance policy. The reportability tool is based on specially developed indicators based on current maintenance standards for better implementation and considers a formerly developed grouping strategy with poor embedded performance indicators as an implementation case for the tool. Performance indicators were carefully developed considering a stochastic perspective when possible; this enables decisions to be influenced by risk assessment under a costs view. Reporting is focused on six maintenance sub-functions, enabling the decision maker to easily assess performance of any maintenance process, thereby improving the quality of decisions. The developed tool is a step forward for grouping (or any scheduling scheme) strategies due to its flexibility to be implemented in almost any case, enabling comparison between different grouping algorithms.
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