5 Things To Consider When Implementing Advanced Analytics For Industrial Processes

In the previous blog post, we talked about how to measure the success of an asset performance monitoring solution. With the buzz of AI and machine learning out there, we at Plutoshift hear questions about what exactly machine learning analytics can actually do. The quick answer is a lot, but the longer and more important answers will be considered here in blog post #2 of this series. What factors should you consider when you’re implementing advanced analytics for industrial processes?

When thinking about introducing these new technologies to your company, here are the 5 considerations that will help:

1. What are the specific business goals that AI can solve?

This may sound obvious, but not identifying a key business pain point to solve is frequently the reason pilots do not progress. Even when they appear successful, they will stall at some point. Exploring new technologies and how to improve your business is the sign of a vibrant company.

However, when a pilot flies under the radar of executive’s awareness, the hurdle to take a pilot to the next level is difficult. A business objective that’s stated from the outset will improve your odds greatly. This quote from a savvy Utilities Manager is spot on:

Well, I guess it’s good to know if I needed to know it.

Some examples of business pain points that can get the right attention from the outset are:

  • Reduce unplanned downtime: You can forecast performance metrics and schedule maintenance to reduce downtime
  • Reduce energy costs: You can take advantage of off-peak energy prices
  • Reduce production material cost: You can lower chemical dosing amounts

2. What improvement in process will be attained?

When pilots succeed but don’t progress, it’s because the results were not very exciting. This doesn’t mean that the results must be a slam dunk. In fact, some of the most exciting results are when performance improvements weren’t obtained but a clear reason is determined as to why it didn’t happen. Identifying where to invest with reasonable certainty of improved results is an outstanding thing to learn.

Typically, new technology investigations have a champion at the company. Since you’re reading this article, perhaps that’s you! Your vision is vital to a successful enterprise.

The challenge is to find a project with which everyone is comfortable. The idea of getting some kind of pilot just to get an evaluation started seems reasonable. Yet, in these situations, buy-in is hard to come by. Pilots take up people’s time and goodwill runs short. You as the champion get tired of carrying the project alone. When a pilot is complete most of us are happy to be done with it. We are not all that excited to dive back in unless there is something to really entice us.

This is where concrete meaningful goals become important. Without the expectation of a real payoff, it’s hard to progress. This is certainly true with AI solutions but generally true with any project. Your vendor should be leading this improvement charge. If they can’t, consider this before making a commitment. As one old pool player, who also happens to be a Director of Plant Operations, said to me:

Call your shots! If you don’t, it really doesn’t matter whether you make it or not.

3. What access to data do you have to support the considered project?

This is specifically an AI project concern. As far as data is concerned, there are three key aspects that form the backbone of an AI project — quantity, quality, and access. AI projects use historical data to train algorithms that can predict future outcomes.

More data is always better. It may not all be used, but data scientists will want to tease out any correlations and look for causal effects. Lack of data certainly makes it challenging, but it does not mean that the project goals cannot be met.

Gaps in data can be overcome. Lacking one or more sensor inputs may be overcome. This is the type of initial investigation a data scientist team can do for you. More on this in blog #3 of this series.

4. Do you have a combination of data scientists and subject matter experts for the project proposed?

I spoke to the role of data scientists in this process. Equally important is the strong collaboration between data scientists and the SME who understands the process to be optimized. Without this, the project will likely not be successful.

This is also important because it is rare. Several solutions are available that have good AI expertise and others that have subject matter expertise. These types of projects, at least for the next couple of years, will require both of these. Both should be equally held responsible for the successful outcome.

5. How to assess a potential solution provider?

After you’ve checked all the points above, there’s still the need to evaluate the plan and execute the project. Is it to find a pure analytics company when you have your own subject matter expertise? Relying on a consulting engineering firm to organize the project? Getting a one-stop vendor to do the whole thing? All of these are viable options.
The key is to know that the analysis can be done. This is not guaranteed because historical data is crucial. Also, access to data is required in near real-time.

This means that the data analysis should at least be completed and vetted initially. Can your team or your provider tell you within certain limits that this analysis will yield prescriptive recommendations that will meet the goals of the project?

However, you combine the resources to execute this project. This initial analysis should have little to no cost. You can call it the Phase Zero of data analysis. If a sizable payment must be made before any data analysis occurs, it would mean that you’re funding the learning curve for whomever required the purchase order.

3 Questions to Ask Yourself for Improved Membrane Performance

Optimal performance of any membrane system to operate at the points of highest efficiency and lowest cost requires a delicate balance; Cleaning membranes too frequently reduces the system lifespan, too infrequently reduces product quality and increases energy costs. Providing proper maintenance of membranes is not a one-size-fits-all approach, as each system and each train are unique based on their purpose, age and placement within a treatment system.

What if it were possible to detect early warning signs of fouling to minimize the amount of time troubleshooting the system?

Maintaining membrane systems on their unique cycles and fouling rates, rather than a manufacturer’s specified time-frame, allows for maximization of operating conditions and total profits.

The challenge of membrane longevity and integrity is that each system design is unique to it’s plant location and objective. These factors also depend on the feed water source and the target product water quality. Plant managers and service engineers are required to maintain and when possible, reduce, total O&M and energy costs in order to meet achieve product margins.

How can I predict the best cleaning schedule for my membrane systems?

Analysis of the challenges of each specific train can depend on regional water quality, past performance and energy used are complicated equations based on a wide variety of factors. Applying data science to a plant’s existing data streams can provide insights to predict the ideal time to clean and service a membrane to improve and extend performance and life cycles of membrane systems to help manage these costs.

The unexpected shutdown of a membrane system can be a catastrophe for any processing plant. This can be due to the product water quality deteriorating or having to discharge the system to identify a membrane problem.

What if you could have peace-of-mind that each system was being maintained regularly and have remote monitoring to oversee the entire operation?

Remote monitoring centers now have the opportunity to use Big Data and informed decision-making to collaborate with service engineers in the field and add to the value delivered. Pluto’s predictive analytics dashboard provides data analytics and actionable insights to these big companies in order to optimize how they maintain a global fleet of membrane systems 365 days/year.

Influent Flow Forecasting Made Easy

Like the wastewater industry, most food and beverage manufacturing facilities are equipped with massive data systems to monitor and optimize the wide range of operations. These similarly regulated industries are increasingly adopting Artificial Intelligence (A.I.) into their processes to better manage systems and procedures.

Though many water industry professionals recognize the potential of A.I., the public health implications of delivering top-quality wastewater in addition to aged production infrastructure, municipal operators and engineers have not yet enjoyed the same benefits of these technologies.

Several large corporations have invested heavily to develop broad “solutions” to address the challenges of water production industries. Yet, these systems have been hit or miss due to the wide range of data streams and particularities within plants across the water industries.

For decades, water treatment process decisions have been made by plant operators based on information spread across a wide range of systems. Calculations are often made by hand and cautious decisions are chosen to avoid the vast array of potential risks – often without regard to cost or process efficiencies. Recognition of patterns of system behavior is nearly impossible as a variety of staff are tasked with administration of multiple machines on an irregular basis.

What if there was a way to recognize the risks and achieve optimal efficiencies that could address the specific challenges faced by an individual plant, without additional infrastructure investment?

One of the many benefits of the marriage between machine learning and Artificial Intelligence, as utilized by Pluto AI, is the ability to recognize the differences in individual system behavior and processes to make more informed decisions to improve plant efficiencies while controlling for potential risks.

Utilizing the existing data from each individual plant, the EZ Influent Flow Predictor will forecast influent flow and detect anomalies to help operators predict future plant behavior and upcoming challenges. The machine learning aspect of our proprietary algorithms analyze and continuously learn from the existing data that impacts incoming flow and Artificial Intelligence maps out the data to provide actionable insights to operators to determine the best course of action based on the range of potential risk factors present.

Our unique system of dashboard insights and alerts have helped customers achieve compliance and save thousands in operational costs.  A pilot version of the EZ Influent Flow Predictor is available for free to a limited number of treatment plants, learn more about how to enroll.

Predict Tomorrow’s Influent Flow With Today’s Data

Wastewater plant operations make important operational decisions based on the influent flow rate to the plant, and despite the ample availability of sensors, there is no accurate industry standard for predicting influent flow rate to the plant.

Knowing the performance of a collection system is difficult because there are few industry-recognized benchmarks on what “performance” is and how it should be determined. Performance of sewer collection systems are often simply educated guesses. Quantifying the areas of highest inflow and infiltration can be difficult due to large networks of pipes, the expense of water monitoring, and varying weather conditions impacting soil saturation.

Municipal sanitary sewer collection and conveyance systems are an extensive, valuable, and complex part of the nation’s infrastructure. Collection systems consist of pipelines, conduits, pumping stations, force mains, and any other facility collecting wastewater and conveying it to facilities that provide treatment prior to discharge to the environment

Plant operators are responsible for ensuring there is enough treated water available for pumping into the distribution or discharge system as well as enough water to maintain ongoing operations. Many operators overlook production water in addition to effluent pumping rates when determining influent rate, this factor ensures treatment is consistent.

Influent flow rates are usually estimated by the operators based on experience and local weather forecasts. These back-of-the-napkin calculations are necessary to engage in master planning for the future of the facility. Determination of the future capacity should be based on needs and sizing, as well as the plant’s ability to meet regulations in the future, and expected timing to update or build new facilities, are all impacted by the irregular and unpredictable amount of influent entering a system.

EPA estimates that the more than 19,000 collection systems across the country would have a replacement cost of $1-2 trillion dollars. The collection system of a single large municipality can represent an investment worth billions of dollars. Usually, the asset value of the collection system is not fully recognized and the collection system operation and maintenance programs are given low priority compared with wastewater treatment needs and other municipal responsibilities.

Typically, small amounts of infiltration and inflow are anticipated and tolerated. Yet, unpredictable weather can increase this load and cause overflows. Management of these events are costly in terms of unplanned labor expenditures, repair of damaged equipment and health and environmental impacts sometimes incurring monetary fines and coverage on the evening news.

As one of the most serious and environmentally threatening problems, sanitary sewer overflows are a frequent cause of water quality violations and are a threat to public health and the environment. Beach closings, flooded basements and overloaded treatment plants are some symptoms of collection systems with inadequate capacity and improper management, operation, and maintenance. The poor performance of many sanitary sewer systems and resulting potential health and environmental risks highlight the need to optimize operation and maintenance of these systems.

Wastewater collection systems suffer from inadequate investment in maintenance and repair often due in large part to the “out-of-sight, out-of-mind” nature of the wastewater collection system. The lack of proper maintenance has resulted in deteriorated sewers with subsequent basement backups, overflows, cave-ins, hydraulic overloads at treatment plants, and other safety, health, and environmental problems.

Managing these complex water systems relies on heavy physical infrastructure and reactive governing attitudes. This is changing with the development of cyber-physical systems, real-time monitoring, big data analysis and machine learning with advanced control systems through the Internet of Things (IoT). These “smarter” systems; in which technology, components, and devices talk to each other and feed information to each other in a more sophisticated way bring about a more optimized, efficient process.

Data provided by weather radar are important in weather forecasting. Rainfall data are typically introduced to provide stormwater information at different locations in the vicinity of the wastewater treatment plant. Several consecutive days of rainfall appears to correlate with increased WWTP flows, indicating a trend that is historically related to interflow.

Goals of prediction to prevent overflows:
  • Reduce ratepayer costs by implementing all cost-effective I&I reduction projects
  • Minimize liability from water pollution and public health risks by eliminating storm-related SSOs
  • Proactive reduce overall I&I to avoid capital costs of capacity expansion in anticipation of future population growth
  • Eliminate enough I&I to offset the environmental and regulatory impact of sewer system expansion and increased water demand

Though sensors helped to combat the overflows in South Bend, Indiana for a while, they could only read out that they were being overwhelmed in a recent storm. Yet, if the data from those sensors flowed into a system powered by Artificial Intelligence, operators could have a forecast to predict that storm and may have be able to proactively divert in preparation.

Predictive influent flow rate information is helpful to determine the the most cost-efficient schedule of operating wastewater pumps. Pluto AI has developed a state-of-the-art prediction system which delivers a high accuracy influent flow forecast based on weather forecasts, recent influent flow trends, and the hydraulics of the plant and sewer system to predict influent flow into a wastewater plant.

To assess extraneous water entering your system at least a year of influent flow data to the treatment facility should be examined. Pluto recommends two. Contact us to learn more about integrating predictive forecasting for overflow prevention into your system.

Sources:
https://www.southbendtribune.com/news/local/south-bend-s-smart-sewers-overwhelmed-by-floodwaters/article_cb75b63c-aaa9-5b39-9c9c-df4fcd2b62b3.html
https://www.mass.gov/eea/docs/dep/water/laws/i-thru-z/omrguide.pdf
https://www.globalw.com/support/inflow.html
https://www.ce.utexas.edu/prof/maidment/giswr2012/TermPaper/Boersma.pdf
https://www.mountainview.gov/civicax/filebank/blobdload.aspx?blobid=6979

Highlights from the 2018 Membrane Technology Conference

Back in March, I attended the opening day of the AWWA & AMTA Membrane Technology Conference in West Palm Beach, Florida to meet Pluto customers. I wanted to learn more about the challenges facing them and explore the new processes and solutions being employed to meet those challenges.

The conference opened with an inspiring keynote address given by Water for People CEO, Eleanor Allen. Her speech offered a glimpse into the progress made through collaborative partnerships of social entrepreneurs around the world to provide potable water to the millions in need. Distinct from the technologically-focused presentations given throughout the day, this talk was an uplifting reminder of the life-sustaining impact of the advancements and efforts of the water industry’s products, services, and people.

After the lunch hour, Val Frenkel Ph.D., PE, D.WRE., of Greely and Hansen, presented a thought-provoking presentation entitled “What We Don’t Know About RO.” Dr. Frenkel provided a comprehensive review of the history of RO systems and the introduction to the commercial marketing dating back to the 1970s. He discussed the impact of specific system configurations to enable different types of RO systems to achieve individual targets of product quality or meet specific operating procedures for different applications.

Dr. Frenkel went on to describe pretreatment of membranes as a cost-effective way to insure integrity. Now that the performance of RO systems is no longer a question of achievability, the longevity and integrity of the RO membrane is the new focus for furthering system performance.

Another talk that stood out was a presentation by Pierre Kwan of HDR, regarding the Basin Creeks membrane operation, “All-Gravity Membrane Filtration: Design and Operational Considerations.” Kwan described an almost certainly unique circumstance of having a water reservoir with enough altitude above the plant to not only eliminate to the expensive pumping usually required but, created the complication of managing high pressure, instead.

Building a sustainable operation under these conditions had several interesting ramifications. Along with this gravity challenge was the high-water quality requirement, the two-stage membrane process implemented was impressive. The net result of this unique system design was that this facility consumed only 5% of the energy typically expected of a membrane plant. Kwan painted a vivid description of how thoughtful, custom design can overcoming the geographical and infrastructure challenges; the result was an compelling speech about how to achieve energy efficiency in the face of adversity.

Overall, the advancements in membrane integrity analysis and the appetite for increasing efficiencies is a rich area for predictive technologies. Pluto’s predictive analytics dashboard has helped several utilities and companies determine convenient cleaning schedules and discover optimal points for normalization of RO membrane trains, typically with a 3-5x ROI. Click here for more information (link to Demo)