(601b) Life Cycle Optimization of Shale Gas Supply Chains with Comprehensive Environmental Impacts and Modeling of Modular Plants

In recent decade, the rapid expansion of shale gas industry has spurred great interest in the research of sustainable shale gas energy systems [1, 2]. The mathematical programming approach has been proven as a powerful tool in the design and operations of shale gas energy systems [3-5], and some pioneering studies have successfully addressed both the economic and environmental concerns of shale gas in their models [6-10]. However, most existing studies on shale gas energy systems solely focus on certain midpoint environmental impact indicators, namely the GHG emissions and water consumption [11-14]. Despite the importance of these two environmental impact categories, other environmental impact categories being ignored, such as land occupation, ecotoxicity, resource depletion, etc., are worth considering as well [2]. Additionally, the emergence of new technologies/operations, known as the modular manufacturing, have created great opportunities in the sustainable design and optimization of shale gas energy systems [15]. Thus, there is an urgent need of research to develop corresponding models for the assessment of their potential economic and environmental benefits.

Motivated by these research gaps, we propose the first life cycle optimization framework that considers the full environmental impacts for the shale gas energy systems. In this modeling framework, an endpoint-oriented life cycle impact assessment (LCIA) method is adopted and further integrated into a functional-unit-based multiobjective life cycle optimization model. Specifically, a full life cycle inventory (LCI) is first established throughout the life cycle of shale gas. The LCI is then combined with the state-of-the-art LCIA approach ReCiPe [16], which comprises of eighteen midpoint impact categories (e.g., climate change, ozone depletion, terrestrial acidification, etc.) and three endpoint impact categories, namely damage to human health, damage to ecosystem diversity, and damage to resource availability. Next, we can integrate the decision variables in the shale gas supply chain with their full environmental impacts based on the LCI data and characterization factors provided by ReCiPe. In this model, both economic and environmental objectives are considered. The economic objective is to optimize the net present cost associated with producing one functional unit product from shale gas, and the environmental objective is formulated as a total environmental score characterized by the ReCiPe associated with one functional unit. Additionally, design decisions regarding both the conventional processing plants and modular LNG plants are considered. The presented model also captures the mobile feature of modular plants. With this integrated model, we expect to investigate the advantage of modular plants in terms of both economic and environmental performance compared with its conventional counterpart. The resulting model is formulated as a mixed-integer linear fractional program that cannot be solved efficiently by any off-the-shelf global optimizers. Therefore, a parametric algorithm is implemented to improve the computational efficiency [17].

To illustrate the application of proposed modeling framework and solution algorithm, a case study based on a real Marcellus shale gas supply chain with twelve shale sites is presented. A “well-to-wire” life cycle is considered with the functional unit defined as 1 MWh electricity generation. Major design decisions regarding the drilling schedule, water treatment technologies, locations and capacities of modular and conventional processing plants, and infrastructure constructions are all considered. Based on the optimization results, the total environmental score of the economic-oriented solution is 21% higher than that of environmental-oriented solution. However, the levelized cost of electricity (LCOE) generated in the economic-oriented solution is $59.8/MWh, 72% lower than that in the environmental-oriented solution. The adoption of modular plants is preferred when economics is the major driving force in decision-making. Nevertheless, the shale gas supply chain design with modular plants does not show obvious advantage in terms of mitigating the life cycle environmental impacts of shale gas.

References

[1] C. L. Weber and C. Clavin, "Life cycle carbon footprint of shale gas: Review of evidence and implications," Environmental Science & Technology, vol. 46, pp. 5688-5695, 2012.

[2] J. Gao and F. You, "Design and optimization of shale gas energy systems: Overview, research challenges, and future directions," Computers & Chemical Engineering. DOI: http://doi.org/10.1016/j.compchemeng.2017.01.032.

[3] O. J. Guerra, A. J. Calderón, L. G. Papageorgiou, J. J. Siirola, and G. V. Reklaitis, "An optimization framework for the integration of water management and shale gas supply chain design," Computers & Chemical Engineering, vol. 92, pp. 230-255, 2016.

[4] D. C. Cafaro and I. E. Grossmann, "Strategic planning, design, and development of the shale gas supply chain network," AIChE Journal, vol. 60, pp. 2122-2142, 2014.

[5] J. Gao and F. You, "Deciphering and handling uncertainty in shale gas supply chain design and optimization: Novel modeling framework and computationally efficient solution algorithm," AIChE Journal, vol. 61, pp. 3739-3755, 2015.

[6] J. Gao and F. You, "Shale gas supply chain design and operations toward better economic and life cycle environmental performance: MINLP model and global optimization algorithm," ACS Sustainable Chemistry & Engineering, vol. 3, pp. 1282-1291, 2015.

[7] T. V. Bartholomew and M. S. Mauter, "Multiobjective optimization model for minimizing cost and environmental impact in shale gas water and wastewater management," ACS Sustainable Chemistry & Engineering, vol. 4, pp. 3728-3735, 2016.

[8] L. F. Lira-Barragan, J. M. Ponce-Ortega, M. Serna-Gonzalez, and M. M. El-Halwagi, "Optimal reuse of flowback wastewater in hydraulic fracturing including seasonal and environmental constraints," AIChE Journal, vol. 62, pp. 1634-1645, 2016.

[9] J. Gao and F. You, "Game theory approach to optimal design of shale gas supply chains with consideration of economics and life cycle greenhouse gas emissions," AIChE Journal. DOI: 10.1002/aic.15605, 2017.

[10] 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.

[11] I. J. Laurenzi and G. R. Jersey, "Life cycle greenhouse gas emissions and freshwater consumption of Marcellus shale gas," Environmental Science & Technology, vol. 47, pp. 4896-4903, 2013.

[12] M. Jiang, C. T. Hendrickson, and J. M. VanBriesen, "Life Cycle Water Consumption and Wastewater Generation Impacts of a Marcellus Shale Gas Well," Environmental Science & Technology, vol. 48, pp. 1911-1920, 2014.

[13] M. Yang and F. You, "Comparative Techno-Economic and Environmental Analysis of Ethylene and Propylene Manufacturing from Wet Shale Gas and Naphtha," Industrial & Engineering Chemistry Research, vol. 56, pp. 4038-4051, 2017.

[14] C. He and F. You, "Deciphering the true life cycle environmental impacts and costs of the mega-scale shale gas-to-olefins projects in the United States," Energy & Environmental Science, vol. 9, pp. 820-840, 2016.

[15] S. H. Tan and P. I. Barton, "Optimal dynamic allocation of mobile plants to monetize associated or stranded natural gas, part I: Bakken shale play case study," Energy, vol. 93, Part 2, pp. 1581-1594, 2015.

[16] M. Goedkoop, R. Heijungs, M. Huijbregts, A. De Schryver, J. Struijs, and R. van Zelm, "ReCiPe 2008-A life cycle impact assessment method which comprises harmonised category indicators at the midpoint and the endpoint level," 2009.

[17] Z. Zhong and F. You, "Globally convergent exact and inexact parametric algorithms for solving large-scale mixed-integer fractional programs and applications in process systems engineering," Computers & Chemical Engineering, vol. 61, pp. 90-101, 2014.