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Optimal Operation of Integrated Power and Oil Transmission Systems

机译:集成电力和油传输系统的最佳运行

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This paper focuses on the optimal operation of integrated power and oil transmission systems where pumps along the oil pipeline have huge electricity consumption. Considering the contradiction between calculation speed and accuracy, a variable step mixed-integer linear programming (MILP) model is established. Additionally, to describe the diffusion effect of drag reducers on the hydraulic loss, the description based on long short-term memory recurrent neural network (RNN-LSTM) is proposed which can perform sequence-to-sequence nonlinear regression. Case studies show that the model in this paper outperforms the classical model in saving more energy and cost, and the RNN-LSTM can calculate the hydraulic loss with high accuracy. The solution is intuitive for the pipeline dispatcher and the established model is capable of industrial use, meeting the goal of energy conservation and emission reduction to relieve the energy crisis.
机译:本文重点介绍了沿石油管道泵泵的集成电源和油传输系统的最佳运行。 考虑到计算速度与精度之间的矛盾,建立了一个可变步长混合整数线性编程(MILP)模型。 另外,为了描述阻力减速器对水力损耗的扩散效果,提出了基于长短期存储器经常性神经网络(RNN-LSTM)的描述,其可以执行序列到序列的非线性回归。 案例研究表明,本文中的模型优于古典模型,以节省更多的能量和成本,并且RNN-LSTM可以以高精度计算液压损耗。 该解决方案对管道调度员直观,而建立的模型能够实现工业用途,满足节能减排的目标,以减轻能源危机。

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