Long-Term Statistical Process Monitoring of an Ultrafiltration Water Treatment Process.

As water treatment technology has improved, the amount of available process data has substantially increased, making real-time, data-driven fault detection a reality. One shortcoming of the fault detection literature is that methods are usually evaluated by comparing their performance on hand-picked, short-term case studies, which yields no insight into long-term performance. In this work, we first evaluate multiple statistical and machine learning approaches for detrending process data. Then, we evaluate the performance of a PCA-based fault detection approach, applied to the detrended data, to monitor influent water quality, filtrate quality, and membrane fouling of an ultrafiltration membrane system for indirect potable reuse. Based on two short case studies, the adaptive lasso detrending method is selected, and the performance of the multivariate approach is evaluated over more than a year. The method is tested for different sets of three critical tuning parameters, and we find that for long-term, autonomous monitoring to be successful, these parameters should be carefully evaluated. However, in comparison with industry standards of simpler, univariate monitoring or daily pressure decay tests, multivariate monitoring produces substantial benefits in long-term testing.

Evaluating the enantiospecific differences of non-steroidal anti-inflammatory drugs (NSAIDs) using an ecotoxicity bioassay test battery.

Wastewater treatment plants are a major pathway for pharmaceuticals to the aquatic environment. Many pharmaceuticals, including non-steroidal anti-inflammatory drugs (NSAIDs), are chiral chemicals and the biological activity of their enantiomers can differ. Few studies have assessed the effects of different NSAID enantiomers on non-target organisms. However, this information is important for environmental risk assessment to ensure that the effects of more potent enantiomers are not overlooked. In the current study, enantiomers of naproxen, ibuprofen, ketoprofen and flurbiprofen were evaluated in bioassays with bacteria, algae and fish cells. All enantiomers induced bacterial toxicity, with (R)-naproxen more toxic than (S)-naproxen (EC50 0.75 vs 0.93 mg/L) and (S)-flurbiprofen more toxic than (R)-flurbiprofen (EC50 1.22 vs 2.13 mg/L). Both (R)-flurbiprofen and (S)-flurbiprofen induced photosystem II inhibition in green algae, with (R)-flurbiprofen having a greater effect in the assay after 24 h (EC10 5.47 vs 9.07 mg/L). Only the (R)-enantiomers of flurbiprofen and ketoprofen induced ethoxyresorufin-O-deethylase (EROD) activity in fish cells, while (S)-naproxen was 2.5 times more active than (R)-naproxen in the EROD assay. While enantiospecific differences were observed for all assays, the difference was less than an order of magnitude. This indicates that the risk of overlooking the effect of more potent NSAID enantiomers is minor for the studied test systems and supports the use of racemic (or single enantiomer) effect data for environmental risk assessment. However, further investigation of the (R)-enantiomer of commonly used NSAID ketoprofen is recommended as it was at least six times more potent in the EROD assay than the inactive (S)-ketoprofen.

Robust evaluation of performance monitoring options for ozone disinfection in water recycling using Bayesian analysis.

Ozonation of wastewater has gained popularity because of its effectiveness in removing colour, UV absorbance, trace organic chemicals, and pathogens. Due to the rapid reaction of ozone with organic compounds, dissolved ozone is often not measurable and therefore, the common disinfection controlling parameter, concentration integrated over contact time (CT) cannot be obtained. In such cases, alternative parameters have been shown to be useful as surrogate measures for microbial removal including change in UV254 absorbance (ΔUVA), change in total fluorescence (ΔTF), or O3:TOC (or O3:DOC). Although these measures have shown promise, a number of caveats remain. These include uncertainties in the associations between these measurements and microbial inactivation. Furthermore, previous use of seeded microorganisms with higher disinfection sensitivity compared to autochthonous microorganisms could lead to overestimation of appropriate log credits. In our study, secondary treated wastewater from a full-scale plant was ozonated in a bench-scale reactor using five increasing ozone doses. During the experiments, removal of four indigenous microbial indicators representing viruses, bacteria and protozoa were monitored concurrent with ΔUVA, ΔTF, O3:DOC and PARAFAC derived components. Bayesian methods were used to fit linear regression models, and the uncertainty in the posterior predictive distributions and slopes provided a comparison between previously reported results and those reported here. Combined results indicated that all surrogate parameters were useful in predicting the removal of microorganisms, with a better fit to the models using ΔUVA, ΔTF in most cases. Average adjusted determination coefficients for fitted models were high (R2adjusted>0.47). With ΔUVA, one unit decrease in LRV corresponded with a UVA mean reduction of 15-20% for coliforms, 59% for C. perfringens spores, and 11% for somatic coliphages. With ΔTF, a one unit decrease in LRV corresponded with a TF mean reduction of 18-23% for coliforms, 71% for C. perfringens spores, and 14% for somatic coliphages. Compared to previous studies also analysed, our results suggest that microbial reductions were more conservative for autochthonous than for seeded microorganisms. The findings of our study suggested that site-specific analyses should be conducted to generate models with lower uncertainty and that indigenous microorganisms are useful for the measurement of system performance even when censored observations are obtained.

Virus removal by ultrafiltration: Understanding long-term performance change by application of Bayesian analysis.

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.