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.

Four-Pronged Strategy For Asset Management In Water

When you look at the water treatment facilities, assets are very critical to their operations. These assets can be pumps, pipes, evaporators, chlorinators, and so on. Most of the inefficiencies like water leakage, monetary losses, or compliance related fines can be directly attributed to assets’ performance. So why don’t water facilities just replace the assets when they go down in efficiency? One of the biggest problems here is that assets are very expensive. Replacing them is not an option until it completely dies down. Given this situation, what can the water facilities do to solve their problems?

What are the main problems?

Water and wastewater treatment facilities face enormous challenges when it comes to managing their operations. These challenges represent significant expenses to operators. Some of the highest ranking problems include asset health prediction, anomaly detection, performance forecasting, combined sewer overflow avoidance, and many more. Understanding the asset health and learning how to predict it can open up a lot of doors, especially when we can’t replace them frequently.

Understanding the definition

Before we dig into asset health prediction, we need to understand asset management. What exactly is asset management anyway? Sounds like it’s just managing the assets, right? Well, there’s a lot more to it than that. When it comes to wastewater asset management, we need to be aware of all the variables that impact a particular asset’s health. It includes the operation, maintenance, and replacement of assets on the critical path. For example, the critical path for a water utility will be retrieving, purifying, and dispersing clean water. This path will include water pumps, water transportation pipes, stormwater sites, and many other components.

What exactly is the problem?

One of the first and foremost questions that comes to mind is — What’s the big deal here? Why can’t we just use simple thresholds to get alerted about assets? The problem is that the data is very unstructured. This data is usually a combination of numbers, free form text, SCADA, ERP, event logs, and more. It’s usually referred to as a “data lake”. Extracting meaningful insights from this data lake takes several areas of expertise like:

  • Automatic data processing engine to parse the data
  • Natural Language Processing to understand text
  • Time-series modeling to analyze sensor data
  • Predictive analytics for event prediction
  • In reference to the title of the post, these are the four prongs we need to build anything meaningful. Modern water facilities are gathering data using many different sources, so we need to make sure we use all that data to drive efficiency upwards.
Okay I understand the problem, but what’s the solution here?

We need a solution that can extract wisdom from this data lake consisting of large amounts of unstructured data. More importantly, we need wisdom that’s specific to water. We don’t need some generic “Artificial Intelligence platform” that uses the same model for many verticals like healthcare, energy, mining, and so on. Artificial Intelligence is an amazing tool that can solve really difficult problems, but only if we use it in the right way. Water is a very unique vertical that has a lot of nuances associated with it. An Artificial Intelligence solution that takes this into account when extracting wisdom will totally outperform a generic Artificial Intelligence platform. Artificial Intelligence deserves to be used in the right (and slightly constrained) way so that it can have a meaningful impact.