Hybrid model for discharge flow rate prediction of reciprocating multiphase pumps

Abstract

Accurate description of the quick and nonlinear change of the discharge flow rate in reciprocating multiphase pumps is important but difficult. To ensure the reliability of reciprocating multiphase pumps, it is necessary to model the relationship between the discharge flow rates of a stroke and multiphase transportation conditions. A hybrid modeling method is proposed for practical use in this work. First, a Gaussian process regression (GPR) model is adopted to online predict the discharge flow rates. Then, the probabilistic information of GPR is used to divide the flow rate curve of a stroke into four stages for individual modeling. Additionally, the process knowledge of multiphase pumps is integrated into the modeling process. Furthermore, to capture the nonlinear characteristics of the mutation stage with limited samples, the local relationship between the input variables change related to opening points and the flow rates is constructed. Consequently, the process knowledge and probabilistic information are integrated to formulate a practical hybrid model. Experimental results show the superiority of the hybrid modeling method.