UBC Theses and Dissertations
Methods for estimating reliability of water treatment processes : an application to conventional and membrane technologies Beauchamp, Nicolas
Water supply systems aim, among other objectives, to protect public health by reducing the concentration of, and potentially eliminating, microorganisms pathogenic to human beings. Yet, because water supply systems are engineered systems facing variable conditions, such as raw water quality or treatment process performance, the quality of the drinking water produced also exhibits variability. The reliability of a treatment system is defined in this context as the probability of producing drinking water that complies with existing microbial quality standards. This thesis examines the concept of reliability for two physicochemical treatment technologies, conventional rapid granular filtration and ultrafiltration, used to remove the protozoan pathogen Cryptosporidium parvum from drinking water. First, fault tree analysis is used as a method of identifying technical hazards related to the operation of these two technologies and to propose ways of minimizing the probability of failure of the systems. This method is used to compile operators’ knowledge into a single logical diagram and allows the identification of important processes which require efficient monitoring and maintenance practices. Second, an existing quantitative microbial risk assessment model is extended to be used in a reliability analysis. The extended model is used to quantify the reliability of the ultrafiltration system, for which performance is based on full-scale operational data, and to compare it with the reliability of rapid granular filtration systems, for which performance is based on previously published data. This method allows for a sound comparison of the reliability of the two technologies. Several issues remain to be addressed regarding the approaches used to quantify the different input variables of the model. The approaches proposed herein can be applied to other water treatment technologies, to aid in prioritizing interventions to improve system reliability at the operational level, and to determine the data needs for further refinements of the estimates of important variables.
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