Authors:
Zoe Jing Yu Zhu
;
Yang Xiang
and
Ed McBean
Affiliation:
University of Guelph, Canada
Keyword(s):
Bayesian networks, Risk assessment, Pathogens, Reliability, Water treatment plants, Membranes, Ultra filtration.
Related
Ontology
Subjects/Areas/Topics:
Applications of Expert Systems
;
Artificial Intelligence and Decision Support Systems
;
Engineering Information System
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
Abstract:
This research investigates a method for hazard identification of modern drinking water treatment technologies. Bayesian networks are applied to quantify risk assessment. Bayesian networks represent an important formalism for representation of, and inference with, uncertain knowledge in artificial intelligence. A physicochemical ultra filtration (UF) membrane train is expressed as a Bayesian network. They can be used in quantifying understanding of the hazards at the operational level of treatment plant that impact the risk of infection from pathogens. Once such a Bayesian network is established, the risk assessment can be performed automatically using algorithms developed in artificial intelligence which facilitates risk assessment of complex water treatment domains.