Municipalities across the United States are under constant pressure to do more with fewer resources and to meet rigorous safety regulations. At the same time, they are tasked with rebuilding aging infrastructure and modernizing facilities, often while facing funding gaps. The statistics are daunting: Drinking water utilities in the U.S. need $472.6 billion in infrastructure investments over 20 years, according to the U.S. Environmental Protection Agency’s (EPA) latest national assessment of public water system infrastructure. In other areas of the world, growth and development places greater demands on the water services in major population centers, and new facilities are being built to keep up with the population surge.
In such a demanding environment, the ability to manage water treatment processes and systems efficiently and expertly will play an important role in meeting the needs of communities and populations around the world. And this is the very environment where the powerful technologies of the Industrial Internet of Things (IIoT) can be leveraged to deliver meaningful and measurable benefits that address the multiple pressures the industry is facing.
We’ve already seen the promise of digital transformation, powered by automation, payoff across multiple industries, from power and chemical to life sciences and oil and gas. The payoff can come in many forms — reduced costs, improved efficiency of processes and personnel, and greater reliability and safety due to the ability to predict and detect issues before they lead to damage or unplanned downtime, among others.
But, given the unique challenges municipalities face, coupled with a cost-sensitive procurement model, is digital transformation realistic? Absolutely. First and foremost, it’s important to remember that digital transformation is a journey — not a destination. And, as such, it is inherently scalable — there is no need to feel the pressure to “go big or go home.” It is a continuum that enables organizations to start small and gain valuable experience that allows people in the organization to adopt, accept and have confidence in these new technologies. And this is a critical aspect that is often lost when discussing digital transformation. The investment in the technology should be viewed as an investment in people, enabling them to add more value and creating a better, more engaging work environment.
Of course, industries in general, and individual organizations in particular, vary greatly in their readiness to embrace the shift. Some are advanced in their operating philosophies and are pushing the limits of how to drive their digital transformation. Others have been more historically cautious but are stepping up to incorporate new sensor technology into their controls, adopt highly secure digital pathways, and embrace data mobility and analytics software to improve the reliability of equipment and overall process performance.
Unlike other industries, which can halt operations in an emergency, municipalities have an obligation to treat water 24/7, regardless of what is happening in the world; even a global pandemic. Indeed, COVID-19 has accelerated the need to respond to some difficult questions: How can an organization operate and maintain facilities when faced with reduced staffing and limited access?
The good news is that the technology exists to help solve these problems. The not-so-good-news is that until recently, it may have been difficult to make the investments due to the cost pressures the industry faces. A prime example is instrumentation. Prior to the pandemic, a plant could monitor and control assets with minimal instrumentation integrated into the plant automation platform because personnel could monitor them during rounds. But, for plants forced to operate with a skeleton crew, someone may not be available to read a gauge or open an isolation valve. Additional instrumentation for monitoring asset and process health can provide a longer response window should an unforeseen interruption of service or staffing levels occur.
While the pandemic may be a once-in-a-lifetime event, brain drain is an ongoing industry challenge, as roughly 1 in 4 plant employees is eligible to retire in the next five years. When they leave, decades of experience may walk out the door with them. Many experienced operators and technicians can look closely at process conditions and use their decades of experience to see trends and patterns that reveal health and performance issues essential to operation — a skill that takes decades for new employees to learn.
It’s true that analytics offers a major opportunity to transform operations, but for this to occur a few things need to fall into place. For instance, some municipalities have gone down the path to interrogate their historical data to find patterns, but have found that some of the data is not usable or available for a variety of reasons:
- Cost pressures may have caused municipalities to make choices in their instrumentation investments, investing in instrumentation that is required to control a process, but not additional instrumentation that can monitor the health of an asset.
- In the instances where instrumentation was installed, it might not have been given the priority on the maintenance schedule to ensure the instrumentation was validated and accurate. This is primarily a result of a challenging workload municipalities have — they need to prioritize time and budgets to keep the plant running.
- Historical data is often not configured properly. Deadbands are put in place to minimize hard drive space, but often they are set too wide. When this happens, there is no granularity in process changes.
And of course, we know that generating vast amounts of data without the ability to interpret it can not only be overwhelming, but also counterproductive. Fortunately, new tools are emerging to help plant personnel make sense of the vast amounts of critical data they collect. These smart tools and algorithms can monitor equipment and process efficiency and health, and advanced pattern recognition (APR) can be used to predict failures, provided there is reliable field data.
Using a common automation platform with embedded simulation makes it possible to build bridges between silos of data by bringing data from disparate systems together. This capability helps users establish patterns and identify critical data points and deviations in process values that are often imperceptible to a person poring over data sheets. Operators can use these tools to help bring context to their data and turn data points into actionable information to increase performance and operational reliability.
One upcoming tool will be able to develop a predictive model of equipment that plant personnel can compare to live data to thoroughly evaluate how plant assets are running. One way to understand the enhanced value this brings is by analyzing and classifying equipment performance during various operational scenarios to determine what is “normal.”
Those in the water and wastewater treatment industries certainly understand that pumps play a critical role in the energy/water nexus. Since they are a large expenditure and consume a lot of electricity, it’s natural to gravitate toward these assets to realize additional benefits.
Of course, monitoring pump performance is not new, as municipalities have been doing it in some capacity for some time. Understanding the efficiency, costs to run the equipment and tracking degraded performance is all good information to make business decisions. Unfortunately, that information rarely gets back into the hands of the operators for them to understand the impact their actions have on the bottom line. This can usually be attributed to two reasons: this information was either calculated offline by a group that had little interaction with the operations team, or the municipality has this information, but it is a part of a separate software package not integrated with the plant control system.
Given today’s environment of reduced staffing and the need to keep costs in check, it’s more important than ever to have an early warning system to identify process or equipment issues before they turn into bigger problems and empowering operators to be proactive instead of reactive.
Again, the fundamental question about equipment and processes is, what is normal? For instance, how do you know that influent pumps are running with no issues? Perhaps that is the assumption because there are no alarms. A better way to determine “normal” operation would be to compare how the pump is operating today to how it has been behaving in similar conditions yesterday, last week, last month, etc. If there is little change in the operating parameters, it’s a likely indication there are no issues. But what about tomorrow? And the day, week or month after that? APR provides the necessary context to arm operators with actionable information related to future performance. This is important for equipment as well as processes like backwashing or chemical treatment.
Without a doubt, the pandemic has accelerated the adoption of IIoT in numerous industries. The evolution in analytics technology, specifically, holds the promise of delivering smart data that empowers personnel and improves operation. Faced with the need to meet regulatory, cost and safety pressures — with the added layer of today’s unique uncertainties — there has never been a better time for the water and wastewater industries to dip their toes into the digital transformation waters. WW
About the Author: Pete Gabor is business development manager for water automation solutions with Emerson Power & Water Solutions.
