Explaining AI: Differences Between LLMs And Machine Learning Models And Their Applications In Wastewater

As artificial intelligence moves from a buzzword to a functional utility in wastewater treatment, clarity on its distinct forms is essential for operational success. While often grouped together, machine learning (ML) and large language models (LLMs) serve vastly different roles within a facility. Machine learning excels at analyzing numerical data to predict effluent concentrations, optimize chemical dosing, and forecast process upsets based on historical patterns. These models are the "engines" of predictive maintenance and process stability.

In contrast, LLMs act as the "translators," converting complex technical data into plain-language summaries and assisting with operator training or documentation retrieval. They do not predict biological failures, but they can explain why a specific alarm triggered. Understanding these differences prevents the trap of false expectations. By integrating predictive numerical insights with natural language interfaces, facilities can bridge the gap between complex data and actionable decision-making. Explore the full breakdown of these technologies to refine your digital strategy.