Recent advancements in horizontal drilling and hydraulic fracturing have led to an increase in domestic natural gas and natural gas liquids (NGL) production. This increase in availability, at low cost, of natural gas and associated liquids has spurred changes in both feedstock and energy use throughout the chemical manufacturing industry. Because of their low cost and high domestic availability, there is an incentive for manufacturers to use natural gas and NGL as a feedstock where possible, replacing heavy liquids such as naphtha. The increased use of NGL as a feedstock can impact the production costs of downstream materials and therefore the competitiveness of some process technologies.
Because of the complex material connections in the chemical manufacturing industries, as more NGL are used as feedstocks, many different supply chains will be impacted. Structural changes will not be restricted to the direct supply chains of NGL use (ethylene, propylene, etc.) but will also propagate throughout the entire network of chemical manufacturing operations because of the importance of byproducts in many reactions. For example, butadiene, a byproduct of ethylene cracking, is used in synthetic elastomer production, so changes in ethylene cracking technology could impact supply and cost of raw materials for rubber production.
To quantify the effects that changing NGL price and supply have on downstream chemicals, a network model of the U.S. petrochemical industry was constructed. The linear program uses stoichiometric relationships between manufacturing processes to form the structure of the network. Due to the interdependent nature of the industry, changes in feedstock availability and price can have impacts that propagate throughout the entire network, influencing production costs and the feasibility of specific processing pathways. Previously developed network models of the chemical processing industries have sought to discern the optimal industry structure (technologies chosen that meet all end product demand constraints) [1, 2]. This work extends that modeling framework to determine not only optimal technologies, but also the impact on the production costs of all downstream materials as natural gas and NGL prices change. Understanding which downstream materials are impacted by natural gas/NGL prices and the magnitude of that cost effect is important because the relationship between upstream raw material price and production costs for farther downstream materials is not always apparent. For example, a reduction in ethane feedstock price for an ethylene cracker does not mean that every product from the cracking operation will become cheaper (as evidenced by the increase in U.S. butadiene spot price from 2008 to 2012 even as ethane prices decreased) . Through a series of pricing and supply scenarios, the relationship between upstream primary raw materials and 276 downstream intermediate/end product production costs is explored.
With an exogenous change in natural gas prices, 32 downstream chemicals show a change in production cost. As the selection of optimal technologies adapts to changing natural gas prices, acetaldehyde is identified as a potential bottleneck intermediate. As NGL prices are changed, 65 downstream chemicals show a change in production cost. Changes in the ratio of NGL prices did not substantially impact the industry’s structure. It was also found that while availability of natural gas and NGLs and quality of crude oil do impact industry structure, raw material price more than supply availability will influence technology choices and utilization levels .
The abundance of these lighter feedstocks also allows for the possibility of designing new processing pathways to take advantage of their availability. This work involves using the shadow prices of materials in the dual linear program to calculate the characteristics that a new process must have to be accepted as part of the optimal solution. These results can be used to direct development of new processes. For example, considerable catalyst design work has been conducted to convert methane into a variety of aromatics [5, 6, 7]. Each different catalyst design has a different aromatic selectivity. Analyzing chemical shadow prices in the dual linear program enables calculation of the optimal aromatic selectivity that will allow for inclusion of the new process as part of the industry structure. Understanding the behavior of the target process operating in the complete industry network also allows for a robust calculation of the process cost that would make the new process competitive. This process cost can act as a starting point during further catalyst and process development.
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 DeRosa, S; Allen, D “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 Chem. Eng., 3 (3), 451–459 (2015).
 Ma, S; Guo, X; Zhao, L; Scott, S; Bao, X “Recent Progress in Methane Dehydroaromatization: From Laboratory Curiosities to Promising Technology,” J Energy Chem, 22, 1-20 (2013).
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