Assessing Fuel and Feedstock Energy Use in the U.S. Chemical Sector: A Supply Chain Analysis

Assessing Fuel and Feedstock Energy Use in the U.S. Chemical Sector: A Supply Chain Analysis   Scott Nicholson Alberta Carpenter Rebecca Hanes

In chemical manufacturing, fossil fuels are consumed both as an energy source and as feedstocks that are converted to higher-value chemicals. This work expands on the U.S. Department of Energy’s chemical manufacturing bandwidth study, which quantified the potential for reducing direct energy consumption in the chemicals sector through the adoption of the most energy efficient manufacturing technologies available [1]. The scope of the bandwidth study was limited to fuels consumed within the plant boundary and fuels consumed for electricity generation. Fuel consumed upstream in the supply chain and fuel consumed as feedstock (defined as fuels that are converted to chemical intermediates rather than being used as an energy source) were not quantified; by one estimate, 60% of the fossil fuels consumed in the U.S. chemical manufacturing sector are consumed as feedstocks [2]. The present study quantifies, in addition to process energy, energy consumed upstream in the supply chain and energy embedded in fossil feedstocks and identifies chemical supply chains in which an understanding of the supply chain can help to identify opportunities for reducing total fossil fuel consumption. Alternative biomass-based processes, which require no fossil feedstocks but do not necessarily consume less energy, are then analyzed to determine if the alternative processes can reduce the total fossil fuel consumption in the identified supply chains. Reducing the chemical industry’s reliance on fossil fuels could lead to greater long-term profitability in the industry, as crude oil prices are projected to rise steadily over the coming decades [3].

The Materials Flows through Industry (MFI) supply chain modeling tool, developed at the National Renewable Energy Laboratory, is employed to quantify supply chain energy and feedstock requirements for the chemicals analyzed previously in the bandwidth study. The MFI tool is a flexible framework for modeling material and energy usage in the supply chains of commodities such as chemicals, primary metals, and paper products [4]. MFI contains U.S.-specific data on energy and material inputs to 1,469 manufacturing processes for 707 commodities. U.S. commodities produced via multiple technologies are modeled using weighted averages of the current production technology mix based on market shares reported in the literature [5]. For this study, energy values are calculated at both the immediate process level and as a cumulative total for 10 steps up a chemical’s supply chain. In MFI, a “step” consists of the material and energy inputs to the intermediate products manufactured in the previous step, which then become the products of the next step in the supply chain. Material and energy inputs beyond the 10^th step have been shown to be negligible compared to the first 10 steps, so the supply chain calculations are truncated at that step.

MFI is similar to standard life cycle assessment (LCA) software packages, but the tool goes beyond these platforms in several ways. First, MFI is adaptable; processes and process weightings can be adjusted individually to model existing or future manufacturing scenarios. Modeling alternative production technologies in a particular supply chain is therefore straightforward. MFI also has the capability to disaggregate supply chain energy requirements into three types: process fuel, energy in fuels used as feedstocks, and fuel for electricity generation. For each chemical in the current analysis, the MFI model is used to calculate process and feedstock energy on a per-kilogram basis. These per-kilogram values are multiplied by annual U.S. production quantities from 2014 or later [5] to determine the energy requirements needed to produce the given chemical at the national scale. These calculations are repeated for alternative and biomass-based pathways for chemicals with feedstock-intensive supply chains, in order to compare the total fossil fuel consumption in the conventional and alternative supply chains. When comparing alternative and conventional petroleum production routes, the former may use significantly less feedstock energy, particularly for biomass-based production routes. However, alternative production routes may or may not use less process energy; in the case of advanced, non-commercial technologies, process energy is likely to increase and this increase may outweigh any savings in feedstock consumption. Both feedstock consumption and energy use must be quantified to determine which, if any, alternative technologies offer a supply-chain-level reduction in total fossil fuel consumption over the conventional technologies.

One of the chemicals in the current analysis is 1,3-butadiene, a chemical intermediate used predominantly in the production of synthetic rubber [6]. Essentially all commercial-scale production of butadiene in the U.S. derives from the byproduct streams of ethylene cracking [7]. Since ethylene is derived from fossil feedstocks, there are significant feedstock-embedded energy inputs in the conventional butadiene supply chain. Preliminary results show that supply chain feedstock consumption for 1,3-butadiene is 220 GJ/tonne, which exceeds both supply chain scale process energy consumption (21 GJ/tonne) and the onsite energy consumption reported by the bandwidth study (18 GJ/tonne) by a full order of magnitude. The alternative manufacturing processes for 1,3-butadiene are all biomass-based and begin with the fermentation of sugars [8]. These processes, because they do not involve ethylene cracking, are expected to reduce feedstock consumption in the 1,3-butadiene supply chain significantly. However, these processes have yet to be optimized for commercial scale production, and may show an increase in both onsite and supply chain energy consumption. The other chemicals analyzed in this study are caprolactam, para-xylene, and propylene glycol, all of which have biomass-based alternative processes under development [8].

This analysis quantifies the technological potential of alternative chemical manufacturing processes to reduce fossil fuel consumption. The economic implications of transitioning chemical manufacturing to next-generation energy-efficient and/or biomass-based production technologies are outside the scope of this study, as are the logistical complexities involved in shifting from a fossil-based to a biomass-based chemical sector. Still, the results of this study could be used to inform decisions around the commercialization of advanced manufacturing processes. They can be used to determine whether it is more worthwhile, from the perspective of reducing fossil fuel consumption, to upgrade an existing manufacturing process or to move to another technology entirely. Future extensions of this work may explicitly analyze the process economics of these alternative production technologies by incorporating costs of material and energy inputs. If these results show that the bio-based technologies can both reduce feedstock consumption and be economically competitive, then a stronger argument can be made for commercialization in the near future. In contrast, an alternative process that is significantly more expensive would still require further development. Lastly, the MFI tool accounts for energy use from resource extraction through production (cradle-to-gate); it does not currently look at use-phase or end-of-life energy requirements (i.e. the full cradle-to-grave). These latter stages are beyond the scope of this chemical sector analysis, since the downstream products manufactured from these commodity chemicals are used across many sectors —not just the chemical sector. With additional data, MFI has the ability to capture use-phase and end-of-life, and this is another potential area of future research.

References

[1] Brueske, S., Kramer, C., & Fisher, A. (2015). Bandwidth Study on Energy Use and Potential Energy Saving Opportunities in US Chemical Manufacturing (No. DOE/EE-1229). Energetics. Retrieved from https://energy.gov/sites/prod/files/2015/08/f26/chemical_bandwidth_report.pdf

[2] U.S. Department of Energy, Energy Information Administration. Office of Energy Consumption and Efficiency Statistics. 2010 Manufacturing Energy Consumption Survey. https://www.eia.gov/ consumption/manufacturing/data/2010/

[3] U.S. Department of Energy, Energy Information Administration. Annual Energy Outlook 2017 with projections to 2050 (No. DOE/EIA- 0383(2017)). Retrieved from http://www.eia.gov/forecasts/ aeo/pdf/0383(2017).pdf.

[4] Hanes, R.J. & Carpenter, A. (2017) Evaluating opportunities to improve material and energy impacts in commodity supply chains. Environ Syst Decis 37: 6. doi: 10.1007/s10669-016-9622-5

[5] IHS (2017). Chemical Economics Handbook (CEH). https://www.ihs.com/products/chemical-economics-handbooks.html.

[6] White, W. C. (2007). Butadiene production process overview. Chemico-biological interactions, 166(1), 10-14. doi: 10.1016/j.cbi.2007.01.009

[7] Sriram, P., Hyde, B., & Smith, K. (2016). Butadiene. Chemical Economics Handbook. IHS.

[8] Biddy, M. J., Scarlata, C., & Kinchin, C. (2016) Chemicals from Biomass: A Market Assessment of Bioproducts with Near-Term Potential. National Renewable Energy Laboratory (NREL). Retrieved from http://www.nrel.gov/docs/fy16osti/65509.pdf