(420d) 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_repor...

[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