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Monetizing shale gas to polymers under mixed uncertainty: Stochastic modeling and likelihood analysis
Author(s) -
He Chang,
Pan Ming,
Zhang Bingjian,
Chen Qinglin,
You Fengqi,
Ren Jingzheng
Publication year - 2018
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16058
Subject(s) - kriging , monetization , stochastic modelling , raw material , computer science , sample (material) , oil shale , econometrics , mathematical optimization , engineering , mathematics , economics , statistics , machine learning , waste management , chemistry , organic chemistry , chromatography , macroeconomics
A novel framework based on stochastic modeling methods and likelihood analysis to address large‐scale monetization processes of converting shale gas to polymers under the mixed uncertainties of feedstock compositions, estimated ultimate recovery, and economic parameters is presented. A new stochastic data processing strategy is developed to quantify the feedstock variability through generating the appropriate number of scenarios. This strategy includes the Kriging‐based surrogate model, sample average approximation, and the integrated decline‐stimulate analysis curve. The feedstock variability is then propagated through performing a detailed techno‐economic modeling method on distributed‐centralized conversion network systems. Uncertain economic parameters are incorporated into the stochastic model to estimate the maximum likelihood of performance objectives. The proposed strategy and models are illustrated in four case studies with different plant locations and pathway designs. The results highlight the benefits of the hybrid pathway as it is more amenable to reducing the economic risk of the projects. © 2018 American Institute of Chemical Engineers AIChE J , 64: 2017–2036, 2018