A data-driven segmented model based on variance information for centrifugal pump efficiency prediction

Efficiency prediction is an essential prerequisite for dynamic decision to ensure safe, stable and energy-saving operation of centrifugal pumps in various industrial applications. This paper presents a novel segmented efficiency model that integrates both process data and centrifugal pump knowledge. The consistent trend in efficiency curves, as revealed by the affinity law, is leveraged: rapid increase to the best efficiency point followed by a gradual decrease at different rotational speeds. To accentuate the methodology employed, the prediction variance of a global Gaussian Process Regression (GPR) model is utilized to partition the curve into two intervals. Local GPR models are subsequently established for each interval to predict efficiency. A posterior probability index is introduced to assess data reliability by combining GPR predictive variance with the Bayesian theorem. Data subsets with higher approximations are selected as the training set, thereby enhancing the predictive performance of the model. Validation results demonstrate the superiority of the segmented model in terms of prediction accuracy and robustness, with an average root mean square error of 0.5948 for four testing sample sets, significantly lower than three alternative models. This study provides an accurate prediction model for centrifugal pump efficiency, offering valuable insights into the integration of process data and knowledge for making dynamic operation decisions in complex applications.