(735c) Integrated Biomass and Natural Gas Refineries for the Co-Production of Liquid Fuels, Olefins, and Aromatics: Optimization Under Uncertainty

Biomass and natural gas are attractive feedstocks for the synthesis of alternative refineries towards the production of fuels and chemicals [1-3]. Biomass can provide environmental benefits through reduced greenhouse gas (GHG) emissions, whereas inexpensive natural gas can improve process economics. Recent work [1-3] suggested that these refineries could be economically competitive with chemicals production significantly increasing the plant profitability. However, volatility of the crude oil prices can shift the process economics significantly. Crude oil prices are around $50/bbl in 2015 compared to $100/bbl in 2014 [4]. In tandem, refinery gasoline prices fell to as low as $1.36/gal in 2015 from as high as $2.89/gal in 2014 [4]. Similarly, petrochemicals price index fell to as low as $850/MT in 2015 compared to a value of $1450/MT in 2014 [5].

A refinery design that is highly profitable at 2014 prices may become infeasible to operate at 2015 levels. Therefore, uncertainties in feedstock and product prices need to be systematically addressed. To this motive, we have incorporated a robust optimization framework [6-8] into the process synthesis superstructure developed for the production of liquid fuels and chemicals [1-3]. The process superstructure incorporates a hybrid feedstock for the coproduction of liquid fuels, olefins, and aromatics with multiple conversion pathways, and upgrading technologies. The large-scale non-convex non-linear optimization model (MINLP) for the process superstructure is improved with the robust counterpart constraint for each uncertain price parameter.

A set of case studies is investigated to evaluate different uncertainty sets, robust counterpart formulations, and probability of constraint violation. Each case study is solved to global optimality within a branch-and-bound framework to maximize the profit. Important topological trade-offs (which process to utilize, which products to produce) triggered by the robust optimization framework will be presented.  The strength of robust optimization framework will be demonstrated via a comparison with deterministic case study results.

References:

[1]: Baliban, R. C.; Elia, J. A.; Floudas, C. A.; Biomass and Natural Gas to Liquid Transportation Fuels: Process Synthesis, Global Optimization, and Topology Analysis, 2013, Industrial and Engineering Chemistry Research, 52(9), 3381-3406.

[2]: 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, 2015, Industrial and Engineering Chemistry Research, 54, 359-385.

[3]: 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, 2015, AIChE Journal, 61(3), 831-856.

[4]: Energy Information Administration, This Week in Petroleum (May 6, 2015), 2015.

[5]: Platts Global Petrochemicals Index, 2015

[6]: 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, 2011, Industrial and Engineering Chemistry Research, 50, 10567-10603.

[7]: Li, Z.; Tang, Q.; Floudas, C. A.; A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: II. Probabilistic Guarantees on Constraint Satisfaction, 2012, Industrial and Engineering Chemistry Research, 51(19), 6769-6788.

[8]: Li, Z.; Floudas, C. A.; A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: III. Improving the Quality of Robust Solutions, 2014, Industrial and Engineering Chemistry Research, 53(33), 13112-13124.