Virus removal by ultrafiltration: Understanding long-term performance change by application of Bayesian analysis. (1st October 2017)

• Record Type: Journal Article
• Authors: Carvajal, Guido; Branch, Amos; Sisson, Scott A.; Roser, David J.; van den Akker, Ben; Monis, Paul; Reeve, Petra; Keegan, Alexandra; Regel, Rudi; Khan, Stuart J.
• Is Part Of: Water research. Volume 122(2017)
• Page Start: 269
• Page End: 279
• Publication Date: 2017-10-01
• Subjects: Bayesian hierarchical model -- Validation -- Ultrafiltration -- MS2 bacteriophage -- Log reduction value; Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2017.05.057
• Languages: English
• ISSNs: 0043-1354

Abstract

Abstract: Ultrafiltration is an effective barrier to waterborne pathogens including viruses. Challenge testing is commonly used to test the inherent reliability of such systems. Performance validation seeks to demonstrate the adequate reliability of the treatment system. Appropriate and rigorous data analysis is an essential aspect of validation testing. In this study we used Bayesian analysis to assess the performance of a full-scale ultrafiltration system which was validated and revalidated after five years of operation. A hierarchical Bayesian model was used to analyse a number of similar ultrafiltration membrane skids working in parallel during the two validation periods. This approach enhanced our ability to obtain accurate estimations of performance variability, especially when the sample size of some system skids was limited. This methodology enabled the quantitative estimation of uncertainty in the performance parameters and generation of predictive distributions incorporating those uncertainties. The results indicated that there was a decrease in the mean skid performance after five years of operation of approximately 1 log reduction value (LRV). Interestingly, variability in the LRV also reduced, with standard deviations from the revalidation data being decreased by a mean 0.37 LRV compared with the original validation data. The model was also useful in comparing the operating performance of the various parallel skids within the same year. Evidence of differences was obtained in 2015 for one of the membrane skids. A hierarchical Bayesian analysis of validation data provides robust estimations of performance and the incorporation of probabilistic analysis which is increasingly important for comprehensive quantitative risk assessment purposes. Graphical abstract: Highlights: Validation of a full-scale UF plant was analysed through Bayesian modelling. Hierarchical models fitted the data better than non-hierarchical models. Skid-specific and skid-independent predictive LRV distributions were obtained. The system showed a decrease on the mean performance of 1 LRV after 5 years. Censored values were incorporated into the analysis.