The US Army is interested in looking at ways of reducing the water footprint and water and wastewater hauling and disposal costs at US Army bases. Onsite wastewater treatment and recycling can offer logistics support at forward operating base operations, but wastewater must be effectively monitored to ensure protection of human and environmental health.
THE CHALLENGE
To monitor gray water recycling and black water treatment systems in real-time, to improve the operation and performance of these systems, and to help ensure regulatory reuse and discharge standards are met. Access to conventional methods of water quality laboratory testing services, or complex sensing technologies, is not always available in the areas where the US Army operates, so a novel monitoring approach that overcomes these limitations for remote regions and harsh conditions is required.
THE SOLUTION
Ensaras developed a data-driven augmented intelligence system for real-time wastewater characterisation using noisy data from low-cost, low-maintenance, and field-ready sensors. The Bayes optimal classification approach was selected because it is the theoretically optimal approach in statistical decision theory. The AI algorithms make real-time determinations such as whether wastewater requires further treatment, is safe for discharge, is safe for non-potable reuse, or could foul reverse osmosis (RO) membranes.
RESULTS
Ensaras was able to automatically classify the US Army’s wastewater into one of several types in real-time and with high accuracy, to help ensure safe operations of wastewater treatment systems in remote regions.
At a Glance
Challenge:
- Effectively monitoring wastewater treatment systems in areas where standard laboratory testing is not available
Benefits:
- Accurate wastewater
classification using field deployable and low cost sensors, when standard laboratory methods are not available
