In the evolving field of water and wastewater treatment, machine learning is paving the way for greater efficiency and improved performance. Carollo Engineers’s Nick Guho recently sat down with Wastewater Digest to discuss how machine learning technology is being used to optimize primary clarifiers at wastewater treatment plants.
Why Machine Learning Matters for Primary Clarifiers
Primary clarifiers play a crucial role in the treatment process, and optimizing their performance can lead to significant improvements. Nick Guho, principal technologist at Carollo, explains that through AI, utilities can now analyze real-time data to achieve better settling performance, boost operational efficiency, and reduce energy consumption and operational costs. These benefits not only improve plant operations but also contribute to long-term sustainability efforts.
A Smarter Future with AI-Driven Insights
By leveraging machine learning, utilities can gain a more predictive view of how their clarifiers will perform under various conditions. “The benefit to utilities, and process engineers like myself,” Nick shares, “is that we will have a much wider sense of how those primary clarifiers may perform in the future…and then we will be able to improve our overall model predictions as a result.” This predictive capability empowers operators and engineers to make more informed, data-driven decisions that enhance both efficiency and reliability.
Watch the video below to learn more from Nick Guho about how AI and machine learning are transforming primary clarifier operations and creating new opportunities in wastewater treatment.