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Updated: Sep 9, 2025

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

NDMA soft-sensors for potable reuse: A model development study.

K B Newhart¹, K A Thompson², A Branch²

¹Oregon State University, 105 SW 26th St., Corvallis, OR 97331, USA.

|August 29, 2025

Summary

This summary is machine-generated.

Real-time monitoring of N-nitrosodimethylamine (NDMA) is challenging. This study developed a data-driven soft sensor using machine learning to optimize UV treatment, achieving significant energy savings in water reuse facilities.

Keywords:

Data-driven decision making Indirect potable reuse Machine learning Model development Reverse osmosis Trace organic contaminants

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Area of Science:

Environmental Engineering
Water Treatment Technologies
Analytical Chemistry

Background:

N-nitrosodimethylamine (NDMA) is a carcinogenic contaminant in potable water.
Current NDMA monitoring and treatment are often energy-intensive due to a lack of real-time analytical methods.
Indirect potable reuse facilities face challenges in optimizing treatment for NDMA removal.

Purpose of the Study:

To develop a data-driven soft sensor for real-time N-nitrosodimethylamine (NDMA) prediction.
To optimize UV disinfection dose control for NDMA removal in indirect potable reuse.
To simulate and quantify potential energy savings in water treatment processes.

Main Methods:

Utilized process data from a full-scale reverse osmosis (RO) indirect potable reuse facility.
Applied statistical and machine learning (ML) methods, including Principal Component Analysis (PCA) and Support Vector Machines (SVM).
Developed a soft sensor model for predicting NDMA levels to control UV dose.

Main Results:

Principal Component Analysis (PCA) significantly improved the accuracy of statistical and ML models by reducing sensor noise.
Support Vector Machines combined with PCA achieved 13-31% energy reduction compared to conventional methods.
Simulations demonstrated energy savings based on varying regulatory limits and safety factors for NDMA.

Conclusions:

Data-driven modeling and soft sensors offer a viable solution for real-time NDMA control in water treatment.
Implementing advanced data analysis techniques can lead to substantial energy savings in potable reuse facilities.
This approach enables precise control of critical treatment units, addressing challenges in sensorized environments.