(205c) Cost-Effective Technology Identification & Uncertainty Quantification of Stranded Sour Gas Desulfurization Processes

The maximum utilization of natural gas resources motivates us to have efficient and cost-effective desulfurization strategies. There have been significant advancements in the development of new technologies, but the screening of these technologies is expensive and time-consuming. Computational models can help accelerate the screening of these novel desulfurization technologies. Optimization of detailed equipment models can help find the best process conditions and risks. Moreover, a better understanding of the models and their uncertainties can help transfer technologies from lab to commercial scale.

In this work, different natural gas desulfurization processes, e.g., SourCat® [1], iron-chelate redox systems LO-CAT® [2], Claus [3], and Triazine based scavenger [4] are simulated at different scales (1 to 1,000 MMSCFD) and inlet H₂S concentrations (500 to 2,500 ppm). For each combination of operating conditions, the desulfurization cost, which is defined as the net present value during the life period of the desulphurization system, is calculated for each process. The process with the lowest desulfurization cost is identified for each operating condition. This information is used to generate a map that envelopes the operating conditions of the cost-competitive processes. From our initial analysis, we found that the newly developed oxidative sulfur removal (OSR) SourCat® process is a viable and cost-competitive option for stranded gas sweetening in the wide range of 3,000 MSCFD to 75 MMSCFD at 500 to 2,000 ppm H₂S concentration, among the processes that were compared. Triazine based scavenger process becomes cost-effective at a lower flow rate (3,000 MSCFD) and low H₂S concentrations. In contrast, the Claus process is efficient at a much higher flowrate (45,000 MMSCFD) or higher H₂S concentrations.

The desulfurization cost depends on solvents, catalysts, equipment costs, electricity costs, and fuel costs. Of the parameters that impact desulfurization cost, solvent cost, catalyst cost, capital investment cost and fuel cost are most uncertain. A systematic sensitivity analysis was performed to identify the significant parameters to understand the impact of these uncertainties on the desulfurization cost and the resulting map of the operational envelope, specifically focusing on the boundaries. Then, the uncertainties of the significant parameters were propagated to estimate the uncertainty of desulfurization cost for each process. These estimates allowed us to update the map to include transition boundaries where there may be more than one process that is cost-competitive.

References:

1. “Sour Gas Has a Sweeter Future,” Chemical Engineering Progress, January 2017 https://www.aiche.org/sites/default/files/cep/20170112.pdf
2. “Removing H2S from Gas Streams”, https://www.merichem.com/technology/sulfur-recovery-with-lo-cat/?doing_wp_cron=1588006246.7422919273376464843750
3. “Steady State Simulation and Optimization of an Integrated Gasification Combined Cycle (IGCC) Plant with CO Capture,” Bhattacharyya, D., R. Turton, and S. E. Zitney, Ind. Eng. Chem. Res. 50 (2011): 1674–1690.
4. “Hydrolysis of 1,3,5-Tris(2-hydroxyethyl)hexahydro- s -triazine and Its Reaction with H 2 S”. Bakke, J.M.; Buhaug, J.; Riha, J. Ind. Eng. Chem. Res. 2002, 40, 6051–6054.