Data-Driven Natural Gas Compressor Models for Gas Transport Network Optimization

Digital Chemical Engineering

Xiang Li¹  Zaid Marfatia² 

[1] Corresponding authors.;Department of Chemical Engineering, Queen’s University, 19 Division St, Kingston, ON, Canada;

关键词: Fuel cost minimization problem;    FCMP;    Natural gas transportation;    Neural networks;    Surrogate model;    MILP;   

DOI  :  

来源: DOAJ

【 摘 要 】

The fuel cost minimization problem (FCMP) for natural gas transport is important because of the immense energy consumed by compressors to satisfy increasing natural gas demands. Current approaches to the FCMP use inaccurate simplified models, or more complex and computationally challenging models, to describe compressor performance. This paper develops two novel data-driven surrogate models, namely, the dimensionless group based model and the deep neural network (DNN) model. The DNN involves rectified linear units as activation functions, so it can be reformulated into mixed-integer linear constraints in the FCMP. The case study results show that both the dimensionless group based and the DNN models achieve better accuracy than two typical surrogate models in the literature, and they are also computationally more efficient for optimization. The computational performance of the dimensionless based model is sensitive to gas supply and demand data, while that of the DNN model is robust.

【 授权许可】

Unknown