Hedging Against Uncertain Feedstock Compositions in Shale Gas Processing System Designs with Intensified Equipment Capacities

Abstract Handling uncertainty in feedstock compositions is an important challenge for shale gas processing and natural gas liquids (NGL) recovery process systems. If the process system is designed without considering uncertain feedstock compositions, the product specifications could be easily violated. To address this challenge, we develop a systematic simulation-based process intensification method. This method consists of three steps, namely process simulation, capacity-oriented process intensification, and design validation. An iterative feature of the proposed method guarantees the intensified design hedged against uncertain feedstock compositions. The proposed method is illustrated on a conventional process system and a novel condensation-based system. In the novel system, a condensation process is considered in the gas dehydration section and it is integrated with a turboexpander process to improve the overall energy utilization efficiency. The intensified design of the novel system shows a lower total annualized cost than that of the conventional system.

[1]  Fengqi You,et al.  Unraveling Optimal Biomass Processing Routes from Bioconversion Product and Process Networks under Uncertainty: An Adaptive Robust Optimization Approach , 2016 .

[2]  Fengqi You,et al.  Optimal supply chain design and operations under multi-scale uncertainties: Nested stochastic robust optimization modeling framework and solution algorithm , 2016 .

[3]  Fengqi You,et al.  Deciphering and handling uncertainty in shale gas supply chain design and optimization: Novel modeling framework and computationally efficient solution algorithm , 2015 .

[4]  Johan Grievink,et al.  Process intensification and process systems engineering: A friendly symbiosis , 2008, Comput. Chem. Eng..

[5]  F. You,et al.  Shale Gas Supply Chain Design and Operations toward Better Economic and Life Cycle Environmental Performance: MINLP Model and Global Optimization Algorithm , 2015 .

[6]  Fengqi You,et al.  Deciphering the true life cycle environmental impacts and costs of the mega-scale shale gas-to-olefins projects in the United States , 2016 .

[7]  James Thomas,et al.  Measurements of methane emissions at natural gas production sites in the United States , 2013, Proceedings of the National Academy of Sciences.

[8]  Fengqi You,et al.  Toward more cost‐effective and greener chemicals production from shale gas by integrating with bioethanol dehydration: Novel process design and simulation‐based optimization , 2015 .

[9]  Ignacio E. Grossmann,et al.  An index for operational flexibility in chemical process design. Part I: Formulation and theory , 1985 .

[10]  Fengqi You,et al.  A computational framework and solution algorithms for two‐stage adaptive robust scheduling of batch manufacturing processes under uncertainty , 2016 .

[11]  Nguyen Van Duc Long,et al.  Techno-economic analysis of potential natural gas liquid (NGL) recovery processes under variations of feed compositions , 2013 .

[12]  Fengqi You,et al.  Optimal design and operations of supply chain networks for water management in shale gas production: MILFP model and algorithms for the water‐energy nexus , 2015 .

[13]  Fengqi You,et al.  Optimal processing network design under uncertainty for producing fuels and value‐added bioproducts from microalgae: Two‐stage adaptive robust mixed integer fractional programming model and computationally efficient solution algorithm , 2017 .

[14]  Keith A. Bullin,et al.  Compositional nal variety complicates processing plans for US shale gas , 2009 .

[15]  Rafiqul Gani,et al.  Process intensification: A perspective on process synthesis , 2010 .