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了解变异性,以减少美国乙烯生产的能源和温室气体足迹。

Understanding Variability To Reduce the Energy and GHG Footprints of U.S. Ethylene Production.

机构信息

Department of Chemical & Biological Engineering, Northwestern University , 2145 Sheridan Road, Evanston, Illinois 60201, United States.

Global Security Sciences Division, Argonne National Laboratory , 9700 South Cass Avenue, Argonne, Illinois 60439, United States.

出版信息

Environ Sci Technol. 2015 Dec 15;49(24):14704-16. doi: 10.1021/acs.est.5b03851. Epub 2015 Nov 18.

DOI:10.1021/acs.est.5b03851
PMID:26523461
Abstract

Recent growth in U.S. ethylene production due to the shale gas boom is affecting the U.S. chemical industry's energy and greenhouse gas (GHG) emissions footprints. To evaluate these effects, a systematic, first-principles model of the cradle-to-gate ethylene production system was developed and applied. The variances associated with estimating the energy consumption and GHG emission intensities of U.S. ethylene production, both from conventional natural gas and from shale gas, are explicitly analyzed. A sensitivity analysis illustrates that the large variances in energy intensity are due to process parameters (e.g., compressor efficiency), and that large variances in GHG emissions intensity are due to fugitive emissions from upstream natural gas production. On the basis of these results, the opportunities with the greatest leverage for reducing the energy and GHG footprints are presented. The model and analysis provide energy analysts and policy makers with a better understanding of the drivers of energy use and GHG emissions associated with U.S. ethylene production. They also constitute a rich data resource that can be used to evaluate options for managing the industry's footprints moving forward.

摘要

由于页岩气热潮,美国乙烯产量的近期增长正在影响美国化学工业的能源和温室气体(GHG)排放足迹。为了评估这些影响,开发并应用了一种从摇篮到大门的乙烯生产系统的系统的、第一原理模型。明确分析了估算美国传统天然气和页岩气生产乙烯的能源消耗和 GHG 排放强度的方差。敏感性分析表明,能源强度的巨大差异是由于工艺参数(例如压缩机效率),而 GHG 排放强度的巨大差异是由于上游天然气生产的逸散排放。基于这些结果,提出了减少能源和 GHG 足迹的最具影响力的机会。该模型和分析为能源分析师和政策制定者提供了更好的理解,了解与美国乙烯生产相关的能源使用和 GHG 排放的驱动因素。它们还构成了一个丰富的数据资源,可以用于评估管理该行业未来足迹的选择。

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