Online Machine Learning for stream wastewater influent flow rate prediction
Predicting the influent flow rate, which is the volume of untreated wastewater entering a treatment plant, is a critical task for operators and managers of wastewater treatment facilities. The accuracy of this prediction is essential for optimizing the treatment process and ensuring that the plant can effectively handle the incoming wastewater. The prediction of influent flow is closely tied to various characteristics of the wastewater, which provide valuable information about its composition and quantity. These characteristics include:
1. Biochemical Oxygen Demand (BOD): BOD is a measure of the amount of oxygen that microorganisms need to break down organic matter in the water. It indicates the level of organic pollution in the wastewater.
2. Total Suspended Solids (TSS): TSS refers to the concentration of solid particles, such as silt and debris, suspended in the wastewater. High TSS levels can impact the efficiency of the treatment process.
3. pH Level: The pH level measures the acidity or alkalinity of the wastewater. Maintaining the right pH is crucial for the proper functioning of biological treatment processes.
Previous research has demonstrated the effectiveness of data-driven models in predicting influent flow rates. These models leverage historical data to make predictions about future flow rates. However, many of these studies have relied on a specific approach known as batch learning.
Batch Learning : Involves collecting data over a period of time and then training a machine learning model in discrete chunks or batches. It’s akin to studying and learning in chapters or segments. While this approach has shown promise, it does have limitations, especially in dynamic and rapidly changing situations.
COVID-19 Impact: The emergence of the COVID-19 pandemic significantly altered influent patterns at wastewater treatment plants. Lockdowns, changes in industrial activity, and shifts in human behavior had a profound impact on the quantity and quality of incoming wastewater. The predictability of influent flow became more challenging due to these unforeseen disruptions.
In response to these challenges, the research team decided to explore a different approach—online learning.
Online Learning : Is distinct from batch learning. In online learning, the machine learning model continuously adapts and updates as new data becomes available. It’s akin to learning and adjusting on the fly, similar to how we adapt to new information and experiences as we encounter them.
The team’s goal is to determine whether online learning models can effectively overcome the limitations posed by rapidly changing conditions, such as those experienced during the COVID-19 era. Online learning offers greater adaptability, allowing the model to respond in real-time to shifting data patterns and input-output relationships. This adaptability is particularly valuable in situations where predictability and consistency are challenged by external factors, making it a promising approach for accurate influent flow rate predictions in dynamic wastewater treatment environments.
