422011 Optimal Design and Synthesis of Shale Gas Processing and Ngls Recovery Process

Thursday, November 12, 2015: 4:39 PM
Salon E (Salt Lake Marriott Downtown at City Creek)
Jian Gong and Fengqi You, Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL

The past decades have witnessed the fast development of the global shale gas industry. It is predicted that shale gas production will be substantially expanded in the future, resulting in a need for additional facilities to absorb the burgeoning supplies of shale gas [1]. As midstream infrastructure in the shale gas value chain, shale gas processing plants separate valuable constituents, such as natural gas liquids (NGL), from raw shale gas feedstocks, and remove the undesired constituents to meet product specifications in the downstream distribution network. Multiple technology alternatives have been developed for various separation operations in a shale gas processing plant, but the optimal extent of constituent separation and removal, as well as the best technology selection and operation strategy has not been determined for the entire shale gas processing and NGLs recovery. Moreover, sustainability has emerged as a critical consideration in the design and synthesis of energy systems [2]. Therefore, it is worth research effort to develop sustainable shale gas processing plants that demonstrate the best process economics while minimizing the overall environmental impacts.

In this work, we propose a comprehensive superstructure of shale gas processing and NGLs recovery for the production of sale gas and NGLs from raw shale gas. The superstructure is divided into four major sections, namely acid gas removal, dehydration, NGLs recovery, and nitrogen rejection [3, 4]. Received raw shale gas is compressed before sent to the acid gas removal processes to separate carbon dioxide and hydrogen sulfide. The purification alternatives in this section are based on the same chemical absorption technology, but equipped with various absorbents. Later in the second section, the purified gas from acid gas removal undergoes dehydration, which can be achieved by three alternative processes using absorption or condensation technologies. Next, the dry sweet gas go through the third section to recover NGLs via a distillation column and further fractionate major constituents of NGLs through a distillation train. The distillation column for NGLs recovery, or the demethanizer, is operated under relatively low temperature, and requires a complex utility system. Accordingly, we consider several NGLs recovery process alternatives, which differ with each other in terms of the configuration of the heat exchanger network. In the last section, extra nitrogen is split from the gas product before the final sale gas can be sold to the downstream customers. In addition to the mentioned technologies and processes, sulfur recovery is also integrated into the superstructure to enhance the overall economic performance [5].

Based on the proposed superstructure, we develop a multi-objective mixed integer nonlinear programming (MINLP) model to determine the most cost-effective and environmentally sustainable design of a shale gas processing plant. This model includes four types of constraints. Mass balance constraints accounts for the mass relationships in each equipment units using continuous design variables and integer variables for technology selection [6, 7]. Energy balance constraints describe the energy conservation of all equipment units [8]. Following the life cycle optimization framework [9], we conduct a techno-economic analysis in the economic evaluation constraints and minimize the annual total cost per unit amount of sale gas produced as one of the objective functions to determine the economic viability of the optimal process design. The remaining constraints evaluate the environmental impact of the sale gas product based on a cradle-to-gate life cycle analysis [10]. Accordingly, the system boundary covers the emissions embedded in raw shale gas acquisition, transportation, and sale gas production [11]. Both direct greenhouse gas emissions in the shale gas processing plant and indirect greenhouse gas emissions in the background processes are considered and aggregated into a carbon footprint indicator [12]. The other objective function, therefore, is to minimize the carbon footprint indicator per unit amount of sale gas produced. The resulting multi-objective MINLP problem is solved using a global optimization framework [13, 14]. The optimal results are plotted in a Pareto-optimal curve which reveals the tradeoffs between the competing objectives.


[1]        J. F. Goellner. (2012) Expanding the shale gas infrastructure. Chemical Engineering Progress. 49-59.

[2]        B. R. Bakshi and J. Fiksel, "The quest for sustainability: Challenges for process systems engineering," AIChE Journal, vol. 49, pp. 1350-1358, Jun 2003.

[3]        C. He and F. Q. You, "Shale Gas Processing Integrated with Ethylene Production: Novel Process Designs, Exergy Analysis, and Techno-Economic Analysis," Industrial & Engineering Chemistry Research, vol. 53, pp. 11442-11459, Jul 16 2014.

[4]        C. He and F. You, "Toward more cost-effective and greener chemicals production from shale gas by integrating with bioethanol dehydration: Novel process design and simulation-based optimization," AIChE Journal, vol. 61, pp. 1209-1232, 2015.

[5]        C. He, F. Q. You, and X. Feng, "A Novel Hybrid Feedstock to Liquids and Electricity Process: Process Modeling and Exergoeconomic Life Cycle Optimization," AIChE Journal, vol. 60, pp. 3739-3753, Nov 2014.

[6]        B. Wang, B. H. Gebreslassie, and F. Q. You, "Sustainable design and synthesis of hydrocarbon biorefinery via gasification pathway: Integrated life cycle assessment and technoeconomic analysis with multiobjective superstructure optimization," Computers & Chemical Engineering, vol. 52, pp. 55-76, May 10 2013.

[7]        D. J. Garcia and F. You, "Multiobjective optimization of product and process networks: General modeling framework, efficient global optimization algorithm, and case studies on bioconversion," AIChE Journal, vol. 61, pp. 530-554, 2015.

[8]        B. H. Gebreslassie, M. Slivinsky, B. L. Wang, and F. Q. You, "Life cycle optimization for sustainable design and operations of hydrocarbon biorefinery via fast pyrolysis, hydrotreating and hydrocracking," Computers & Chemical Engineering, vol. 50, pp. 71-91, Mar 5 2013.

[9]        F. Q. You and B. Wang, "Life Cycle Optimization of Biomass-to-Liquid Supply Chains with Distributed-Centralized Processing Networks," Industrial & Engineering Chemistry Research, vol. 50, pp. 10102-10127, Sep 7 2011.

[10]      B. H. Gebreslassie, R. Waymire, and F. You, "Sustainable design and synthesis of algae-based biorefinery for simultaneous hydrocarbon biofuel production and carbon sequestration," AIChE Journal, vol. 59, pp. 1599-1621, 2013.

[11]      Q. Zhang, J. Gong, M. Skwarczek, D. J. Yue, and F. Q. You, "Sustainable Process Design and Synthesis of Hydrocarbon Biorefinery through Fast Pyrolysis and Hydroprocessing," AIChE Journal, vol. 60, pp. 980-994, Mar 2014.

[12]      J. Gong and F. Q. You, "Optimal Design and Synthesis of Algal Biorefinery Processes for Biological Carbon Sequestration and Utilization with Zero Direct Greenhouse Gas Emissions: MINLP Model and Global Optimization Algorithm," Industrial & Engineering Chemistry Research, vol. 53, pp. 1563-1579, Jan 29 2014.

[13]      J. Gong and F. Q. You, "Global Optimization for Sustainable Design and Synthesis of Algae Processing Network for CO2 Mitigation and Biofuel Production Using Life Cycle Optimization," AIChE Journal, vol. 60, pp. 3195-3210, Sep 2014.

[14]      J. Gong and F. You, "Value-Added Chemicals from Microalgae: Greener, More Economical, or Both?," ACS Sustainable Chemistry & Engineering, vol. 3, pp. 82-96, 2015.

Extended Abstract: File Not Uploaded
See more of this Session: Energy Systems Design & Operations
See more of this Group/Topical: Computing and Systems Technology Division