(651b) Systems Analysis of Shale-Based Resources for Chemical Manufacturing | AIChE

(651b) Systems Analysis of Shale-Based Resources for Chemical Manufacturing


Skouteris, A. - Presenter, The University of Texas at Austin
Giannikopoulos, I. - Presenter, The University of Texas at Austin
Baldea, M., The University of Texas at Austin
Edgar, T. F., McKetta Department of Chemical Engineering, The University of Texas at Austin
Allen, D., The University of Texas at Austin
Stadtherr, M., The University of Texas at Austin
In recent years, technological advancements in hydraulic fracturing and horizontal drilling have led to a significant increase in oil and gas production in the United States, particularly from shale formations [1]. Shale gas, the natural gas from shale formations, is often rich in light alkanes other than methane (ethane, propane, butanes). These natural gas liquids (NGLs) constitute some of the most important building blocks of the chemical manufacturing industry. The increase in the availability of shale gas and associated NGLs has thus provided a unique opportunity to expand the U.S. chemical manufacturing industry [2].

The aim of this work is to apply systems optimization and design principles to provide insight on how to strategically achieve this expansion, while making optimal use the light alkane resources from shale gas, with a focus on what mix of old and new technologies should be used and what should be their product slate. Assessing the potential for adoption of such new technology (i.e. how likely it is to be successfully adopted by the industry, as well as the impact it will have to the rest of the industry) requires a holistic analysis. In this paper we introduce a modeling and computational framework adapted to this purpose and based on a representation of the entire U.S. chemical industry.

The petrochemical industry itself is a highly complex, interconnected system of chemical manufacturing and refining processes. Network models of the industry are capable of modeling these interconnections and are hence employed in this work over a conventional technoeconomic comparison of different processes, which does not easily capture industry-wide effects [3]. As a starting point, we use the model developed by DeRosa and Allen [4, 5], which is based on a superstructure of hundreds of chemical processing technologies for transforming basic raw materials into several hundreds of intermediate and final products. The model is formulated as a linear program (LP) that seeks to minimize the overall network cost, subject to constraints related to material balances, as well as material supply and demand limitations. The solution of the LP determines the optimal production levels for each process technology, as well as material flows in the network. A broad class of scenarios related to production costs, raw material shortages, final product demands, etc., can be addressed.

The present work further expands on the model of DeRosa and Allen [4, 5] to represent current technologies, remove obsolete ones, cover new parts of the industry, and to account for cost propagation. To the last point, previous models treat operating costs, including prices of raw and intermediate materials as constant, independent of the level to which each process is utilized. However, if the system is perturbed by allowing the adoption of new and perhaps more cost-effective technologies for the production of intermediates, this will no longer be true, as the material costs for processes using these intermediates will change, depending on whether or not the aforementioned new technologies are adopted. This means that the costs now depend on the levels of which certain technologies are used, and since these levels are decision variables in the optimization problem, the objective function is no longer linear. Moreover, it is assumed that the price of a material is driven by the production cost at its largest producer; as the largest producer may change as a function of network operating circumstances, this leads to a discontinuous dependence of material prices and process costs on the network configuration and process utilizations. To deal with this additional mathematical complexity, we propose a novel cost-propagation algorithm that seeks to deal with the nonlinearities and discontinuities while maintaining the overall LP structure of the model. The problem is decomposed by using a sequential LP strategy, embedded in an outer loop where the price change calculations and propagations take place. We explore different approaches for carrying out these price change calculations and propagations and compare our results with the solution obtained using state of the art NLP solvers.

To demonstrate use of the network model, including the new cost-propagation algorithm, various scenarios are studied, focusing on the introduction of new processing technologies under different supply and demand limitations. It is expected that this work will facilitate the design of systems for better utilizing shale-based, light alkane resources.


[1] EIA, “Natural gas explained: Where our natural gas comes from." https://www.eia.gov/energyexplained/natural-gas/where-our-natural-gas-comes-from.php, 2019. [Online; accessed 12-April-2020].

[2] 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, no. 14, pp. 4038-4051, 2017.

[3] M. A. Stadtherr and D. F. Rudd, “Systems study of the petrochemical industry,” Chemical Engineering Science, vol. 31, no. 11, pp. 1019-1028, 1976.

[4] S. E. DeRosa and D. T. Allen, “Impact of new manufacturing technologies on the petrochemical industry in the united states: A methane-to-aromatics case study,” Industrial & Engineering Chemistry Research, vol. 55, no. 18, pp. 5366-5372, 2016.

[5] S. E. DeRosa and D. T. Allen, “Impact of natural gas and natural gas liquids supplies on the United States chemical manufacturing industry: Production cost effects and identification of bottleneck intermediates,” ACS Sustainable Chemistry & Engineering, vol. 3, no. 3, pp. 451=459, 2015.


This work is supported in part by the National Science Foundation under Cooperative Agreement No. EEC-1647722 (CISTAR – NSF Engineering Research Center for Innovative and Strategic Transformation of Alkane Resources, http://cistar.us). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.