462461 Natural Gas to Liquids, Olefins, and Aromatics Under Uncertainty in Feedstock and Product Prices

Wednesday, November 16, 2016: 10:43 AM
Carmel I (Hotel Nikko San Francisco)
Alexander M. Niziolek1,2,3, Onur Onel1,2,3, Logan R. Matthews1,2,3, Yannis A. Guzman1,2,3 and Christodoulos A. Floudas1,2, (1)Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, (2)Texas A&M Energy Institute, Texas A&M University, College Station, TX, (3)Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ

Although natural gas to liquid transportation fuels processes have been investigated for several decades, they were considered economically unfavorable due to high feedstock costs. [1,2] Recent studies have demonstrated the profitability of such refineries can be significantly increased by coproducing valuable chemicals, such as olefins and aromatics, in tandem with liquid fuels. [3,4] Even more recently, new studies have investigated the potential of stand-alone natural gas based chemical refineries, where the main products output are mainly olefins and aromatics, and assessed the technoeconomic and environmental impacts of such processes. [5,6]

The economics of such natural gas facilities is intricately dependent on the complexity of the refinery configuration and the type, quantity, and quality of products produced (i.e., the product slate). The large chemical space available for the manufacture of liquid fuels and chemicals has been expanded even further in recent years due to novel and emerging technologies that can produce liquid fuels, olefins, and aromatics. [3-6] The types of technologies to utilize and the types of products to produce is a tremendously difficult challenge to address, one that is further complicated with the uncertainty surrounding the costs and prices of feedstocks and products, respectively. In this talk, we address these challenges through a robust optimization framework that is incorporated in a large-scale process superstructure that contains several alternatives for the production of fuels, olefins, and aromatics from natural gas. [6-10] We introduce an important production parameter that allows us to investigate the economic effects of natural gas to liquids, olefins, and aromatics refineries. The resulting large-scale, nonconvex, mixed-integer, nonlinear optimization (MINLP) model is solved using our novel global optimization algorithm. [11]

Our comprehensive optimization-based process synthesis framework contains several direct and indirect natural gas conversion technologies and numerous hydrocarbon production alternatives, including Fischer-Tropsch refining and methanol synthesis; each of which is compared to determine the optimal processing pathway. [2-6] Multiple commercial and novel technologies for the production of the olefins and aromatics, such as methanol-to-olefins and toluene alkylation with methanol, are rigorously modeled. [5,6] The key production parameter is introduced as a function of the important products output from the refinery, namely, gasoline, diesel, kerosene, aromatics, and olefins. Robust counterparts are formulated in order to address the uncertainty associated with feedstock and product prices.

Several case studies are presented to investigate the effect of plant capacity and production ratios on the overall profit of the refinery and incorporate uncertainty in the optimal solutions obtained. A discussion on the robust refinery designs that incorporate the effect of uncertainty will follow with emphasis on the major topological decisions as a function of the production parameter. The economic and environmental tradeoffs of the refineries are presented.

[1] Floudas, C. A.; Niziolek, A. M.; Onel, O.; Matthews, L. R. Multi-Scale Systems Engineering for Energy and the Environment: Challenges and Opportunities. AIChE Journal 2016, 62, 602-623.

[2] Baliban, R. C.; Elia, J. A.; Floudas, C. A. Novel natural gas to liquids (GTL) technologies: Process synthesis and global optimization strategies. AIChE Journal 2013, 59, 505–531.

[3] Onel, O.; Niziolek, A. M.; Elia, J. A.; Baliban, R. C.; Floudas, C. A. Biomass and natural gas to liquid transportation fuels and olefins (BGTL+C2_C4): Process Synthesis and Global Optimization. Industrial & Engineering Chemistry Research 2014, 54, 359-385.

[4] Niziolek, A. M.; Onel, O.; Elia, J. A.; Baliban R. C.; Floudas, C. A. Coproduction of Liquid Transportation Fuels and C6_C8 Aromatics from Biomass and Natural Gas. AIChE Journal 2015, 61, 831-856.

[5] Onel, O.; Niziolek, A. M.; Floudas, C. A. Optimal Production of Light Olefins from Natural Gas via the Methanol Intermediate. Industrial & Engineering Chemistry Research 2016, 55, 3043-3063.

[6] Niziolek, A. M.; Onel, O.; Floudas, C. A. Production of Benzene, Toluene, and Xylenes from Natural Gas via Methanol: Process Synthesis and Global Optimization. AIChE Journal 2016, 62 (5), 1531 – 1556.

[7] Li, Z.; Ding, R.; Floudas, C. A. A comparative theoretical and computational study on robust counterpart optimization: I. Robust linear optimization and robust mixed integer linear optimization. Industrial & Engineering Chemistry Research 2011, 50, 10567–10603.

[8] Li, Z.; Tang, Q.; Floudas, C. A. A comparative theoretical and computational study on robust counterpart optimization: II. probabilistic guarantees on constraint satisfaction. Industrial & Engineering Chemistry Research 2012, 51, 6769–6788.

[9] Li, Z.; Floudas, C. A. A comparative theoretical and computational study on robust counterpart optimization: III. improving the quality of robust solutions. Industrial & Engineering Chemistry Research 2014, 33, 13112–13124.

[10] Guzman, Y. A; Matthews, L. R; Floudas, C. A New a priori and a posteriori probabilistic bounds for robust counterpart optimization: I. Unknown probability distributions. Computers & Chemical Engineering 2016, 84, 568-598.

[11] Baliban, R. C.; Elia, J. A.; Misener, R.; Floudas, C. A. Global Optimization of a MINLP Process Synthesis Model for Thermochemical Based Conversion of Hybrid Coal, Biomass, and Natural Gas to Liquid Fuels. Computers and Chemical Engineering 2012, 42, 64-86.

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