Implementation and Field Test of Optimal Pump Scheduling in the Multiproduct Refined Oil Transmission System

Abstract

Optimal pump scheduling ensures the safe and economic operation of the oil pipelines, which is also of vital significance for energy conservation as well as carbon emission reduction. This article proposes a framework adapting the data-driven pressure loss estimation with the long short-term memory neural network (LSTM-NN) and pump characteristics fitting with quadratic polynomials to model-based optimal pump scheduling for the multiproduct refined oil transmission system. The data-driven methods are data-adaptive with periodical field data in a rolling-training manner, and thus can reflect actual working conditions in the transmission pipelines. The presented LSTM-NN for pressure loss estimation integrating the characteristics of multiproduct refined oil pipeline transmission has good performance with an acceptable mean squared error of 0.016 MPa <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in real applications. The optimization model makes the time precision down to the minute level, which is more convenient for the system dispatchers to operate the pumps following the optimal schedule. Practical implementation and field test are carried out in a real-world pipeline system, which verifies that the proposed framework is more practical and better-performed than the manual schedule.