Plutoshift APM Brings Direct Financial Impact To The Process Industry Using AI

Companies in the process industry today are expected to generate more revenue using fewer resources and without buying new assets. There’s greater pressure than ever to be efficient and nimble, worsened by the potential of a global trade war. Manufacturers have questioned if artificial intelligence (AI) could be cost-effectively harnessed to transform the industry like it promises for so many others.

AI is only as good as the outcomes it supports, so our team’s biggest priority is to make it as easy as possible for our customers to leverage AI to generate the ROI that matters most to them.

Today we announced our cloud-based, AI-driven asset performance management (APM) platform designed specifically for the process industry. Using AI, Plutoshift automatically and continuously connects asset data with financial metrics, letting you easily measure performance, achieve your business outcomes, and increase profit margins.

We worked closely with our Fortune 500 customers in verticals that would push the limits, including food, beverage and chemical, to solve their critical pain points. The vast amount of industrial sensor and IIoT data manufacturers rely on to overcome challenges is often trapped in legacy systems. These aging, on-premise systems can’t correlate the impact of asset performance over future revenue metrics. Plus, these tools have not kept pace with the mobility and ease-of-use demands that today’s savvy end users expect.

That was just the beginning.

We created a platform that lets plant managers discover process inefficiencies and new opportunities to increase throughput, speed up ticket resolution, reduce resource consumption, and eliminate waste. Plutoshift’s proprietary algorithms leverage both existing historical and real-time data, extracting actionable insights between asset behavior and revenue. We work with all of your existing data systems seamlessly, providing you with immediate ROI.

Plutoshift can be accessed safely from anywhere, empowering those front-line end-users who expect an on-demand experience. While new, it’s proven – the only solution vetted by global forums of leading industrial technology evaluation committees – and is accompanied by mature features, including:

  • Deep analysis and intelligence: Easily connects to data streams including SCADA, ERP and CMMS to produce actionable insights on the costs, risks and efficiencies of plant operations.
  • Agile integration: Integrates with every process historian on the market today.
  • On-demand insights: Interactive, easy-to-use dashboards and alerts enables operators to work effectively from anywhere.
  • Pre-built asset templates: A growing library includes membranes, cooling towers, CIP systems, clarifiers, dryers, and more.

We built Plutoshift APM to help companies bridge the relationship between the data and financial performance of their assets. Contact us today for a free demo.

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.


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.