(735e) Optimal Design and Synthesis of Shale Gas Processing and Ngls Recovery Process

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.

References

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[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.

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