A system designed to address the problem of distribution system monitoring is described here. The developed system employs an array of common analytical instrumentation, such as pH and chlorine monitors, coupled with advanced interpretive algorithms housed in an event monitor to provide detection/identification-response networks that are capable of enhancing system security and quality. A variety of real world venues and testing protocols were used to verify the efficacy of the system. Deployed systems are shown to demonstrate the capability of learning base line in a rapid timeframe while being capable of detecting and characterizing system anomalies related to security and basic water quality operations. Included are data generated from several real world events including caustic overfeeds, rain events, street work and major line breaks among others.
Distribution system monitoring has typically included a minimal set of water quality parameters, acquired at low frequency.
The parameter set, and frequency of data acquisition are insufficient for the surveillance of typical distribution systems' water quality in the event of agent introduction.
An improved methodology is discussed. The method includes a more complete set of water quality parameters acquired at higher frequency, mathematical processing to alarm on deviations from operational baseline, pattern recognition of deviations, statistical analysis of recurring events, and a learning function which allows recurring events to be recognized and categorized as normal operation or unknown. Examples of events from distribution systems are presented and discussed.