Inexpensive monitoring process powered by machine learning could aid in water treatment
Small, rural drinking water treatment plants face challenges in accurately determining chlorine concentration for disinfection. However, researchers at Georgia Tech and other institutions have developed a prediction tool using machine learning to support chlorine-based disinfection processes. This innovative approach can accurately predict the concentration of free chlorine residual, even when using cost-effective, low-tech monitoring data.
The research team implemented a machine learning model that utilizes gradient boosting algorithms to generate predictions based on decision trees. By collecting extensive data from a water treatment plant in Georgia, including various monitoring records and operational process parameters, the team was able to identify correlations among multiple variables that impact the quality, efficiency, and cost of production.
Described in a publication in Frontiers of Environmental Science & Engineering, the study showcases the four iterations of the modeling approach. The researchers employed open-source software to interpret machine learning models with numerous input parameters, allowing users to visualize the impact of each parameter on prediction.
In the final iteration, the team focused on intuitive, physical relationships and water quality downstream from filtration. Their findings revealed three key insights: firstly, machine learning models can produce accurate results with a sufficient number of related input parameters; secondly, these models can be driven by correlations that may or may not have a physical basis; and lastly, machine learning models can be analogous to the experience of human operators.
This groundbreaking research offers a cost-effective solution for small, rural drinking water treatment plants, empowering them to enhance their disinfection processes. With the ability to predict free chlorine residual, plant operators can optimize chlorine dosing and achieve satisfactory concentration levels more efficiently, minimizing estimation errors.
The application of machine learning in water treatment demonstrates a significant advancement in technology. By using this prediction tool, small treatment plants can ensure better water quality and meet the required standards for safe drinking water. This breakthrough not only improves the efficiency and effectiveness of water treatment processes but also contributes to the overall well-being of communities.
The integration of machine learning into water treatment plants marks a vital milestone in the field of environmental science and engineering. With further research and development, this technology has the potential to revolutionize the way drinking water is treated, leading to safer and more sustainable water supplies for populations around the globe.
