Systems Analysis of Natural Gas Liquid Resources for Chemical Manufacturing: Strategic Utilization of Ethane

Abstract

The increasing production of shale gas and, consequently, natural gas liquids (NGLs) provides opportunities to expand the U.S. chemical industry, leading to questions about how to best use these resources. We consider targets for the strategic use of ethane, the most abundant of the NGLs, by evaluating the impact of a potential, new catalytic dehydrogenation technology for converting ethane to ethylene and then evaluating potential, new catalytic oligomerization processes for converting ethylene to 1-butylene and to 1-octene. To conduct these evaluations, we introduce a new, nonlinear, industry-wide, optimization-based network model of the U.S. petrochemical and refining industries. Unlike previous linear models of this type, the nonlinear model accounts for changes in intermediate prices and, thus, process costs as new technology is added to the industry network. A method for propagating cost and price changes, permitting the solution of the nonlinear optimization problem as a sequence of linear problems, is developed and utilized. Using network models for this study, we account for and identify the direct and secondary consequences of introducing new technology on the rest of the industry. For each new technology evaluated, we determine the production level of the technology in the optimal industry network. By doing this over a wide range of net process cost points, a maximum adoption cost (the net process cost beyond which the technology would not be adopted) can be identified, and its sensitivity to the assumed product yield determined for each new technology can be studied. The maximum adoption costs can be viewed as targets for future catalyst research, reaction engineering, and process development work. Scenarios in which the ethane supply is constrained to current values and in which it is unconstrained are considered.

Probing the Impact of an Energy and Transportation Paradigm Shift on the Petrochemicals Industry

“…In this type of situation, the net process costs are dependent on the process utilization levels, and so the problem becomes nonlinear. To deal with this issue, Skouteris et al 22 have described a successive linear programming (SLP) approach that alternates between solving a constant-cost LP and a special cost-propagation algorithm that updates process costs and material prices based on the LP results. This approach is used in the case studies below in which new synthetic gasoline processes are added to the industry network and a variable-cost electrolysis process for green hydrogen is studied.…”

“…All commercially proven technologies appear in the network model superstructure as potential processes that can be selected as part of the minimum-cost network configuration. The process superstructure is that used by Skouteris et al, 22 but with some additional processes and components used in the superstructure to provide greater resolution of refining processes and products. Because petroleum refineries have flexibility in adjusting their product mix, the demands for gasoline, diesel, and kerosene (jet fuel) are lumped into a single liquid transportation fuel demand constraint.…”

“…The model used for this study is the optimization-based, superstructure model described by Skouteris et al, 22 enhanced with a greater resolution of refinery-based processes. As a base case, we first identify the optimal (minimum total cost) network configuration of the current industry.…”

“…The petrochemicals industry can be viewed as a complex, integrated network of individual refining and chemical manufacturing processes that are interconnected via material and energy flows . This network system acts to convert a relatively small number of primary raw materials (e.g., natural gas, NGLs, petroleum) into a large and diverse array of intermediates and end products, including polymer products. , Often, intermediates and final products can each be produced through multiple different processes, utilizing different types and amounts of feedstocks and energy sources, while also producing different byproducts. Thus, this industry network involves multiple competing pathways for transforming the primary raw materials into the desired final products.…”

“…Optimization-based network models of the petrochemicals industry originated with the work of Stadtherr and Rudd, who initially focused on minimization of resource usage and subsequently on minimization of industry cost . Many variations and extensions of this initial work, with different applications, have appeared since, as reviewed by Skouteris et al and DeRosa and Allen . Such network models represent the industry as a directed graph, in which nodes correspond to chemical processes that are connected by edges that correspond to flows between nodes.…”