Critical control points in DPR: Quantifying the multi-barrier approach to treatment

Direct Potable Reuse (DPR) is the supply of highly treated reclaimed water directly to a drinking water treatment plant or distribution system, with or without an engineered storage buffer. This differs from Indirect Potable Reuse (IPR) which is already practiced in many areas of the country and involves the inclusion of an environmental buffer (such as a lake, reservoir or aquifer) prior to arriving at the intake for a drinking water treatment plant. There are a number of potential benefits of DPR relative to IPR, including reduced energy requirements, reduced construction costs and reduced operational costs. DPR may even provide an opportunity to allow potable reuse in situations where a suitable environmental buffer is not available for IPR. However, a potential obstacle to implementation of DPR is related to the perception and acceptance of potential risks rather than science or engineering that quantify those risks and provide solutions to address them. A number of technical issues relating to the perceived functions of an environmental buffer need to be addressed in a transparent process, including in particular the need to ensure consistent and assured levels of reliability. Critical control points (CCPs) are defined at steps in a treatment train where control can be applied and is essential to prevent or eliminate a public health risk associated with a water quality hazard or reduce it to an acceptable level. It can include specific treatment processes but also chemical addition steps. CCPs must have both a monitoring component, which allows the operator to verify that the process is operating as intended, and a control component, which allows the operator to adjust the functioning of the unit process to improve its performance. While there are many “critical” aspects to the proper and efficient operation of a water, wastewater, or water reuse facility, there are only a handful that have direct bearing on the public health implications of water from that facility. This is a key concept as it allows operations staff to better manage various alarms and issues that will inevitably arise in any treatment process, focusing on those that are critical to public health protection. However, even with CCPs and a well-trained operations team in place it is important that multiple barriers be a component of direct potable reuse (DPR) systems to manage the total risk from chemical and microbial threats to public health. Our team has been working on a WateReuse-funded project (13-03) to demonstrate the robustness and reliability of DPR processes and to quantify the impacts of specific critical control points within a DPR system to help overcome this obstacle. Through this project we have identified critical control points (CCPs) for both a full advanced treatment (FAT) membrane based process (MF/RO/UV H₂O₂/Cl₂) and a non-membrane treatment system (Floc/Sed/O₃/BAC/GAC/Cl₂) using a hazard analysis and critical control point (HACCP) team process and risk assessment. By incorporating a combination of both operational and maintenance data from full scale operating facilities we have been able to quantify the role of each CCP and then use Monte Carlo analysis to demonstrate the robustness (that is the concentration of contaminants that a given process train can handle) and the redundancy (that is the number of processes that can handle a given contaminant) across all of the CCPs in each DPR treatment train. Inherently present in the full-scale operational data is also the reliability aspect of each unit process. Thus, when full-scale data are combined across each unit process/CCP, a Monte Carlo analysis can provide insight into the “worst” case scenarios and then quantify those with a value for probability of occurrence. This presentation will provide an overview of the CCPs selected for the two water recycling treatment trains and then will demonstrate the use of Monte Carlo to develop the probability distributions across the processes. The presentation will focus on microbial data in addition to several key chemical parameters and potential surrogate parameters.