Distribution-free hybrid schemes for process surveillance with application in monitoring chlorine content of water

Chlorination is one of the well-known and efficient methods for treating water. The amount of chlorine in drinking water must meet a specific quality standard to avoid health hazards. Hence, it is necessary to monitor the amount of chlorine in drinking water. In this article, we introduce two new statistical process monitoring schemes for monitoring a process to ensure it meets a specific quality standard. We show the application of the proposed schemes in monitoring the chlorine content of compliance water samples efficiently. The proposed methods are nonparametric and require little or no knowledge of the statistical distribution of a process. The new schemes rely on the Max and Distance combining functions to capitalise the individual advantages of the distribution-free Lepage and Cucconi statistics for simultaneous monitoring of location and scale parameters. We employ the p-value approach to construct the plotting statistic of the proposed schemes. We analyse the performance of the proposed schemes using a large scale Monte Carlo simulation. Results show that our schemes have a robust competitive performance with their existing counterparts.