A number of similar multi-parameter measurement platforms, most without the addition of intelligent algorithms, have been evaluated for such applications by the EPA Environmental Technology Verification (ETV) program. See the full reports at http://www.epa.gov/etv/verifications/vcenter1-35.html. These systems appear to be a good choice for detecting water quality excursions that could be linked to water security events. There are a number of advantages to using such systems. The chief advantage is that these instruments are not new. They are common everyday parameters with which the average industry worker is quite familiar, thus adding a degree of comfort in operations not afforded by other new technology. As existing technologies, these instruments have been proven to be robust and dependable in prior field deployments. They represent measurements that would be of interest and use to water utility personnel and food industry process control engineers above and beyond their role as water security devices.
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Explore This IssueFebruary/March 2007
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Process Improvement Capabilities
Through many years of experience, the best old hands at plant operations have developed “a sense” for knowing something in the system is amiss. It can be a smell, color, clarity (or lack there of), sound or just tingling in the nape of the neck. One gains these senses only by extensive experience in a particular facility. Due to the shrinking workforce and the loss of institutional knowledge at many facilities, bulk parameter monitoring in the distribution system with interpretive algorithms has the potential to become the artificial “sense” able to quickly “learn” the quirks of the distribution system and have those quirks labeled by those with extensive experience so that less experienced employees have the benefit of that knowledge without having to wait five, 10 or more years. A good phrase to describe this knowledge base would be “institutional intuition.” (Englehardt, 2005: Kroll, 2006).
With the aging of the workforce and rapid employee turnover “institutional intuition” has the chance of quickly dying out. Algorithms could be a way to circumvent this loss of knowledge and to build a knowledge base where none has previously existed. The pattern of different water quality profiles could be correlated to process or quality problems. This may be especially crucial in industries where the quality of the water used can have a direct effect on finished product quality such as the bottled beverage and brewing industries. These correlations could in turn allow improvements in system operation that may result in cost savings and definitely will result in a higher quality product being delivered to the consumer.
One of the largest advantages to this type of monitoring system is the multi-parameter array’s ability to detect such a wide variety of potential threat agents from metals to organics to bio-agents. The ability to trigger on unique unknown events is also a major plus. A disadvantage is that there are some events, which occur during normal operation, that may trigger an unknown alarm. This, however, can be an advantage if the information is used to generate the institutional intuition discussed above. Nonetheless, this learning phase is not free and requires an input of time and effort to investigate and classify these alarms so they can be placed into the database.
Many local utilities are in the process of establishing such monitoring stations in their distribution systems. One of the drawbacks for the utilities is site location. The deployment site needs to have adequate space for the instrumentation, a water supply to be tested, drain, power and communications. Many water providers have a limited number of utility owned sites that can be used for these purposes. There exists the possibility of collaboration between the food processing plant and the local utilities to provide the site for deployment in exchange for data sharing. While not a traditional arrangement, such a scheme could be mutually beneficial.
The described system makes use of an integrated array of robust common water quality monitoring sensors coupled with interpretive algorithms to recognize and classify significant water quality deviations. Extensive in house and third party verification testing as well as extensive deployment at field sites has demonstrated the suites’ ability to fill the analytical gap that currently exists for distribution network monitoring and serve the purpose of an early warning system in the water distribution network. Hopefully, the systems’ unique ability to learn and classify will result in not only increased safety from terror related events, but will morph into an operational tool that will find everyday use in improving water quality operations and ensure a better quality product to consumers.