Monitoring techniques to enhance everyday operations, quality and security in the distribution system

By Dan Kroll

Spurred by the desire for technologies that can detect intentional water contamination, online monitoring in the drinking water distribution system has made large strides in recent years. Numerous communities have deployed such systems to protect against intentional attack. For the past several years, scientists at Hach Homeland Security Technologies (HST) have been actively engaged in the development and deployment of several early warning systems for detecting water quality problems. Below is a brief summary of how the system operates and presents real life case studies.

The Hach HST approach was to utilize a sensor suite of commonly available water quality sensors linked together in an intelligent network. The logic behind this approach is that these are tried and true technologies that have been extensively deployed in the water supply industry for a number of years and have proven to be stable in such situations. Then, a proprietary, patented, non-classical method was derived to make analyzing all of this data easier and effective.

In the system, signals from 5 separate parameters of water quality (pH, Conductivity, Turbidity, Chlorine Residual, TOC) are processed into a single trigger signal in an Event Monitor using algorithms. A deviation of the signal from the established baseline is calculated. The magnitude of the trigger / deviation signal is then compared to a user-set threshold. If the signal exceeds the threshold, the trigger is activated. 

Fig. 1. The use of intelligent algorithms with standard bulk parameter monitoring equipment allows for a robust system that is capable of triggering on and classifying a wide diversity of threat agents including unknown events.

Field Testing of the Developed System

The following are a few examples of incidents that have been recorded during these real world deployments. These incidents help to demonstrate the systems ability to learn and to become a useful tool not just for security but also for every day operational improvements.

Grab Sample Versus On-line Case Study

In this situation, the local water utility had in place an extensive system of water monitoring through grab samples. All indications were that the water quality was good. After installation of several water monitoring panels in the distribution system they found that the turbidity spiked to levels as high as 20 NTU during the night and early morning hours when typically no grab samples would have been collected. They also found extremely high variability in chlorine levels during these time periods. 

A series of changes to their treatment plant operations and distribution system procedures allowed the system to regain control of the water quality in the distribution system. They were able to lower the turbidity spikes to around 1.5 NTU at night and maintain more consistency in chlorine residual levels resulting in better water quality and consistency for the end consumers. 

Main Break Event

In this situation the system had only just been installed a few days previously. The instruments were behaving abnormally and were giving strange readings. An investigation of the sensors found no problems. Almost three day later a 36 inch major water delivery main ruptured in a catastrophic mode. The system was able to detect the perturbation in water quality parameters that were precursors to the main break and trigger upon them. Unfortunately, the system was newly installed and the event was not recognized until it was too late.

Figure 13. Photo of main break event along with graph of trigger signal.

Caustic Overfeed Event

The plant uses caustic feed to control water pH. The system experienced a trigger that when investigated was identified as an operational problem that resulted in the feed of excess caustic. 

The reason behind this was that the vendor from which the casuistic was being purchased had delivered the wrong concentration of the solution. No one had checked to see if the concentration was correct before feeding in the material. New procedures were put in place to verify incoming raw materials. The Event Monitor learned this Plant Event and can identify a recurrence of the event in the future if there is another failure in the system and it is repeated.

Figure 2. Caustic Event.

Fluoride Overfeed

In this scenario the water utility was forced to revert to the utilization of an older water treatment plant while maintenance was being done to the new plant. A pump responsible for dosing fluoride into the treated water malfunctioned causing the dose to increase over time. In this case the monitor not only alarmed but also classified the likely cause of the problem to be a fluoride overfeed as that fingerprint was in the agent library. This allowed a rapid response before consumers were exposed to potentially dangerous levels of fluoride.   

Conclusion

These types of monitoring systems appear to be a good choice for detecting water quality excursions that could be linked to water security events and are definitely linked to water quality. The deployment incidents detailed in this paper further confirm this and also demonstrate the applicability of utilizing these everyday parameters by linking them with advanced algorithms. The field deployment studies not only demonstrate robustness in the field and the ability to recognize a wide variety of events, but these studies also demonstrate such system's ability to learn. These devices are much more than a system that is capable of detecting security related events. They are a critical tool for improving everyday operations.

For example, through many years of experience, the best old hands at treatment plant operations have developed "a sense" for knowing something in the treatment 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 this sense only by extensive experience in a particular facility. These senses do not exist in distribution systems because there has typically been little measurement done upon which to gain these "senses" and, therefore; "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 5, 10 or more years. A good phrase to describe this knowledge base would be "institutional intuition."

With the aging of the work force and rapid employee turnover "institutional intuition" has the chance of quickly dying out. Above and beyond their obvious security benefits, algorithms could be a way to circumvent this loss of knowledge and to build a knowledge base where none has previously existed. This in turn could allow improvements is system operation that may result in cost savings and definitely will result in a higher quality product, the water they drink, being delivered to the consumer.