Automating Microscopic Evaluation

The microscopic evaluation of activated sludge is still a useful tool for WRFs, especially for troubleshooting solids separation.

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Novozymes

Sophisticated image recognition helps reveal what is hiding under your microscope

While IoT-enabled smart sensors are becoming more relied upon for minute-by-minute operation of today’s water reclamation facilities, the microscopic evaluation of activated sludge is still a useful tool, especially for troubleshooting solids separation issues. Excessive growth of filamentous bacteria (filaments) is a common cause of poor settling in both municipal and industrial wastewater treatment plants and the morphologies of most filaments are well characterized.

Most filaments are associated with either specific substrates such as organic acids, sulfides, or FOG or certain selective pressures such as low DO, low F/M, or a deficiency of either nitrogen or phosphorous. If the most abundant filaments are identified, their causes can be investigated, and corrective operational actions can be implemented. Determining filamentous abundance is relatively simple, but specific training is required to properly identify filaments. Samples can be mailed to consultants or experts can be called on-site, but this can take days to organize and arranging for samples to be mailed sometimes takes weeks.

Microscopic Evaluation Made Easy

To simplify and speed-up this process, Novozymes has developed Plant Assistant, a web-based application that can identify and score common wastewater filaments from a microscopic image.

“Previously, people would send us samples from all around the world, which is both expensive and time consuming,” said Chris Flannery, Novozymes’ technical service manager.

With Plant Assistant, users simply need to obtain a microscopic image of the wastewater microbiology and upload it to the app from the preferred device. From the image, Plant Assistant instantly determines the most likely filament, scores the abundance and prescribes a course of action based on the types of filaments found.

Data Is Key for Accuracy

Data scientists and biological experts have collaborated on the app development, while using thousands of different microscopic images to train the algorithm to identify filaments and differentiate around characteristics such as branching, cell shape, and diameter.

“Any object a human can recognize, an algorithm can do as well. It just requires enough training data; in this case, images. And plenty of them,” said Mathias Gruber, senior data scientist and AI Lead at Novozymes. The more sophisticated the object is, the more training data is needed to achieve a highly accurate match. Filaments, for instance, move in three dimensions, while images only capture two. Furthermore, there are differences in microscope type and quality, magnification degree and experience of the user which all adds complexity. Reaching a critical mass of data that takes all these variations into account has been the key goal for the development team.

Currently, Plant Assistant can identify eight filaments with more than 90 percent certainty. Plans are underway to expand Plant Assistant’s algorithm to identify approximately 20 filaments as well as characterizing floc particles and bulk water and identifying higher life forms. IWW

Novozymes is a global biotech company specializing in the development and application of industrial enzymes and microorganisms. To learn more, visit www.novozymes.com.

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