Nyad

Wastewater microscopy becomes an operating signal

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As captured
Anonymous plant microscopy sample from April 1 showing healthy floc.
Anonymous plant microscopy sample from April 7 showing stable biology.
Anonymous plant microscopy sample from April 22 showing early filament warning signs.
Anonymous plant microscopy sample from April 29 showing worsening filament growth.
Anonymous plant microscopy sample from May 5 showing upset biology.
With Nymph AI Vision
Anonymous plant microscopy sample from April 1 with Nymph AI Vision overlaid.
Healthy
Anonymous plant microscopy sample from April 7 with Nymph AI Vision overlaid.
Stable
Anonymous plant microscopy sample from April 22 with Nymph AI Vision overlaid.
Warning
Anonymous plant microscopy sample from April 29 with Nymph AI Vision overlaid.
Worsening
Anonymous plant microscopy sample from May 5 with Nymph AI Vision overlaid.
Upset

Foresee the upset hidden behind a healthy SVI

April - May biology target index SVI MLSS Vision risk Target
Biologist microscopy and process target-index chart SVI, MLSS, and Nymph Vision risk are plotted as percent of target from April 1 through May 5, with 100 percent marking the target line.

The people building tools for clean water.

Caleb Van Geffen

Caleb Van Geffen

Founding Engineer

Building the tools.

Marathoner & aspiring Olympian.

Sophia Lopez

Sophia Lopez

Environmental Intelligence

Building the knowledge.

Antarctic explorer.

Daniel Coburn

Daniel Coburn

Sales & Growth

Finding the operators.

Clothing Designer.

Patent

Automated organism detection for wastewater treatment.

A patented system for capturing microscopy images of activated sludge, classifying organisms and floc characteristics using deep learning, and generating corrective action recommendations in real time.

Fig. 1 - System architecture

End-to-end pipeline from basin to corrective action

An operator collects a sludge sample and places it on a slide, which is then imaged through a microscope and sensor. That data routes through the cloud to a five-component processor stack: Preprocessing (30), Training (32), Attribute Recognition (34), Density Recognition (36), and Correction (38).

prediction database stores all training data and deployed models. Results are delivered to a user interface device, giving operators actionable output without manual microscopy or lab turnaround.

Patent system architecture figure
Interfloc bridging microscopy figure

Filaments connecting floc particles.

FIG. 10 - Interfloc bridging

FIG. 10 shows interfloc bridging at 100x magnification - filamentous organisms growing between and connecting individual floc particles. This is a key early indicator of filamentous bulking, where the sludge mass knits together, impairing settling performance in the clarifier before the problem becomes severe.

Elevated polysaccharide microscopy figure

Normal floc vs. Zooglea.

FIG. 11 - Elevated polysaccharide

FIG. 11 contrasts normal stain penetration with lack of stain penetration, which indicates elevated polysaccharide - a condition associated with Zooglea-dominant sludge. Elevated polysaccharide is typically caused by high organic acid loading or low dissolved oxygen, and results in a viscous, poorly settling sludge.

Stain penetration analysis microscopy figure

Detecting polysaccharide in the floc matrix.

FIG. 12 - Stain penetration analysis

FIG. 12 shows a real sample with stain penetration analysis, indicating elevated polysaccharide within the floc matrix itself. The system detects this characteristic automatically from the image - without requiring an operator to manually interpret staining results or compare against reference slides.

Filament density grading scale

A six-level grading scale.

FIG. 13 - Filament density

The system quantifies filamentous organism density across six levels: Few (13A), Some (13B), Common (13C), Very Common (13D), Abundant (13E), and Excessive (13F). Density grading determines the urgency and specificity of the corrective recommendation.

Live object detection output

Organisms identified and labeled in real time.

FIG. 14 - Live object detection

FIG. 14 shows live detection output on an actual sludge sample. The system draws bounding boxes around detected organisms, classifying each one by type and filtering out irrelevant objects in the same frame. Here, Nocardia is identified across multiple locations within a single microscopy frame.

Patent details

US 11,565,946 B2

Application No. 16/701,725
Filed: December 3, 2019
Granted: January 31, 2023
Expires: December 3, 2039
Status: Active

Inventors

Edward Bryan Arndt
Francis John DeOrio
Patrick Joseph Campbell

Classification

Systems & Methods for Treating Wastewater

Computer Vision · Deep Learning · Microscopy Image Analysis · Real-Time Process Control