Construction of collaborative system for predicting and optimizing the performance of centrifugal pumps based on machine learning, and experimental verification
Analysis reveals improved performance in centrifugal pumps with machine learning techniques, suggesting effective optimization methods may reduce energy consumption.
Key Points
Improving performance in centrifugal pumps optimized through machine learning leads to significant energy savings.
Mean absolute percentage errors for predicted performance metrics are below 4% and 2% for head and efficiency respectively.
The method leverages a regression model based on diverse datasets to enhance predictive accuracy.
These findings highlight a promising approach to centrifugal pump optimization in energy-intensive applications.