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Comprehensive method of natural gas pipeline efficiency evaluation based on energy and big data analysis

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  • Fan, Mu-wei
  • Ao, Chu-chu
  • Wang, Xiao-rong

Abstract

Pipeline companies deploy a series of pipeline resource optimal configurations and efficiency enhancement strategies with the purpose of improving financial benefits. Thus far, pipeline owners and stakeholders have usually focused on efficiency evaluation processes based on the technological aspects of transmission. In this context, here we derive a pipeline efficiency evaluation method from the perspective of the pipeline energy input–output by monitoring the energy and transmission amount changes along the pipeline. The parameter of volumetric work, which is defined as the energy consumption required for transporting a certain amount of gas over a certain distance along the pipeline, measures the energy change except for the intrinsic energy and mechanical pressure energy. With no distribution stations being taken into consideration, the gas amount change due to the pipeline's self-energy consumption is regarded as the equivalent fuel gas while also considering the volumetric work change caused by gas consumption. Upon accounting for all factors via volumetric work and pressure energy, we utilise the input–output variation to develop a data envelopment analysis (DEA) model, which affords the relative efficiency via calculation and evaluation of the operating efficiency of the pipeline and related facilities. Subsequently, analytic hierarchy process (AHP) is introduced to determine the impact from different facilities. Our result, which presents the difference between the ideal objective efficiency and current efficiency based on the measured volumetric work can aid in identifying the most efficient situation. Here, we note that owing to factors such as the inlet pressure and equipment configuration, the first station usually exhibits lower efficiency than the other stations. In terms of energy for the whole pipeline, the efficiency (unit consumption) grows in inverse proportion to the transmission amount. In the final phase of the study, we consider an actual pipeline case study in our analysis and verify the practical utility of this method. Our results also indicate that the transmission efficiency and economic efficiency exhibit a trade-off relationship.

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  • Fan, Mu-wei & Ao, Chu-chu & Wang, Xiao-rong, 2019. "Comprehensive method of natural gas pipeline efficiency evaluation based on energy and big data analysis," Energy, Elsevier, vol. 188(C).
    • Handle: RePEc:eee:energy:v:188:y:2019:i:c:s0360544219317645
    DOI: 10.1016/j.energy.2019.116069
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    1. Yang, Zhaoming & Liu, Zhe & Zhou, Jing & Song, Chaofan & Xiang, Qi & He, Qian & Hu, Jingjing & Faber, Michael H. & Zio, Enrico & Li, Zhenlin & Su, Huai & Zhang, Jinjun, 2023. "A graph neural network (GNN) method for assigning gas calorific values to natural gas pipeline networks," Energy, Elsevier, vol. 278(C).
    2. Zhou, Dengji & Jia, Xingyun & Ma, Shixi & Shao, Tiemin & Huang, Dawen & Hao, Jiarui & Li, Taotao, 2022. "Dynamic simulation of natural gas pipeline network based on interpretable machine learning model," Energy, Elsevier, vol. 253(C).
    3. Zhang, Jinrui & Meerman, Hans & Benders, René & Faaij, André, 2021. "Techno-economic and life cycle greenhouse gas emissions assessment of liquefied natural gas supply chain in China," Energy, Elsevier, vol. 224(C).
    4. Zhu, Zhu & Liao, Qi & Liang, Yongtu & Qiu, Rui & Zhang, ZeZhou & Zhang, Haoran, 2022. "The era of renewables: Infrastructure disposal strategies under market decline of oil products," Energy, Elsevier, vol. 249(C).

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