A systematic simulation-based process intensification method for shale gas processing and NGLs recovery process systems under uncertain feedstock compositions

Gong, Jian; Yang, Minbo; You, Fengqi

doi:10.1016/j.compchemeng.2016.11.010

Cited by 39 publications

(26 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the PSE community, chance constraints are usually employed for customer demand satisfaction, product quality specification, and service level of process systems {Maranas, 1997 #745;Yue, 2013 #2254;Gupta, 2000 #2377;You, 2011 #2278; Chu, 2015 #3322;Zipkin, 2000 #2521}. Due to its practical relevance, chance constrained optimization has been applied in numerous applications, including model predictive control [76,77], process design and operations [78][79][80], refinery blend planning [81], biopharmaceutical manufacturing [82], and supply chain optimization [83][84][85][86].…”

Section: Chance Constrained Optimizationmentioning

confidence: 99%

Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming

Ning

You

2019

Computers & Chemical Engineering

Self Cite

262

View full text Add to dashboard Cite

This paper reviews recent advances in the field of optimization under uncertainty via a modern data lens, highlights key research challenges and promise of data-driven optimization that organically integrates machine learning and mathematical programming for decision-making under uncertainty, and identifies potential research opportunities. A brief review of classical mathematical programming techniques for hedging against uncertainty is first presented, along with their wide spectrum of applications in Process Systems Engineering. A comprehensive review and classification of the relevant publications on data-driven distributionally robust optimization, data-driven chance constrained program, data-driven robust optimization, and data-driven scenario-based optimization is then presented. This paper also identifies fertile avenues for future research that focuses on a closed-loop data-driven optimization framework, which allows the feedback from mathematical programming to machine learning, as well as scenario-based optimization leveraging the power of deep learning techniques. Perspectives on online learningbased data-driven multistage optimization with a learning-while-optimizing scheme is presented.

show abstract

Section: Chance Constrained Optimizationmentioning

confidence: 99%

Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming

Ning

You

2019

Computers & Chemical Engineering

Self Cite

262

View full text Add to dashboard Cite

show abstract

“…The raw shale gas produced from shale sites, after pretreatment near wellhead, is gathered and transported to midstream processing plants through pipelines. There is a set of potential processing plants to be constructed, where sales gas and natural gas liquids (NGLs) are separated and distributed to the market . Both the pipelines and processing plants need to be allocated and designed with appropriate working capacity.…”

Section: Problem Statementmentioning

confidence: 99%

Data‐driven distributionally robust optimization of shale gas supply chains under uncertainty

2018

Self Cite

View full text Add to dashboard Cite

This article aims to leverage the big data in shale gas industry for better decision making in optimal design and operations of shale gas supply chains under uncertainty. We propose a two‐stage distributionally robust optimization model, where uncertainties associated with both the upstream shale well estimated ultimate recovery and downstream market demand are simultaneously considered. In this model, decisions are classified into first‐stage design decisions, which are related to drilling schedule, pipeline installment, and processing plant construction, as well as second‐stage operational decisions associated with shale gas production, processing, transportation, and distribution. A data‐driven approach is applied to construct the ambiguity set based on principal component analysis and first‐order deviation functions. By taking advantage of affine decision rules, a tractable mixed‐integer linear programming formulation can be obtained. The applicability of the proposed modeling framework is demonstrated through a small‐scale illustrative example and a case study of Marcellus shale gas supply chain. Comparisons with alternative optimization models, including the deterministic and stochastic programming counterparts, are investigated as well. © 2018 American Institute of Chemical Engineers AIChE J, 65: 947–963, 2019

show abstract

“…Existing studies typically assumed that the composition and supply can be perfectly known and fixed in advance without variability, which may lead to over‐optimistic results and underestimation of the negative influences. For example, a mega‐scale cracking plant can consume NGLs collected from hundreds of well‐sites (rigs), while the raw gas composition and production rate of a single well‐site keep fluctuating. If this plant is only designed to convert shale gas with a high NGLs content, the plant capacity is inevitably overestimated if the true composition deviates from the designed scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Increasing scientific evidence is showing that the shale gas monetizing process is inevitably subject to mixed and multidimensional uncertainties from gas acquisition to end‐use production, which significantly affects the profitability and environmental impacts of the whole project. First, raw shale gas acquisition (the initial step of shale gas projects) is most influenced by the endogenous uncertainties of gas compositions . In fact, most of the shale gas regions (even different wells in the same region) produce raw shale gases with high compositional variabilities due to the differences in geological, geochemical, and petrophysical characteristics in these regions and wells.…”

Section: Introductionmentioning

confidence: 99%

Monetizing shale gas to polymers under mixed uncertainty: Stochastic modeling and likelihood analysis

Pan

Zhang

et al. 2018

AIChE Journal

Self Cite

View full text Add to dashboard Cite

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.

show abstract

A systematic simulation-based process intensification method for shale gas processing and NGLs recovery process systems under uncertain feedstock compositions

Cited by 39 publications

References 32 publications

Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming

Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming

Data‐driven distributionally robust optimization of shale gas supply chains under uncertainty

Monetizing shale gas to polymers under mixed uncertainty: Stochastic modeling and likelihood analysis

Contact Info

Product

Resources

About