Intelligent Dynamic Prediction Model of Pump Pressure Lift for a Petroleum Pipeline System with Nonisothermal Batch Transportation

Accurate prediction of pump characteristics is pivotal for optimizing transportation schemes and device operations in long-distance petroleum pipelines. In response to the variable nature of pump characteristics due to production circumstances, a novel dynamic prediction model of pump pressure lift is proposed by coupling the neural network, the intelligent evolutionary strategy, and an adaptive method of a neural network coefficient matrix. This model enables parameter adjustments to align with optimal directions through intelligent optimization strategies in which the differential evolution performs better than the genetic algorithm and particle swarm optimization. The proposed model exhibits superior accuracy and computational efficiency compared with classical static and dynamic prediction models. Notably, the model achieves a prediction error reduction of 1.34%, surpassing existing dynamic models while offering an 87.93% computation time reduction when contrasted with the static model