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(681a) Screening for Economically Promising Bio-Based Chemicals

Authors: 
Wu, W., University of Wisconsin – Madison
Long, M., University of Wisconsin – Madison
Reed, J., University of Wisconsin Madison
Maravelias, C. T., University of Wisconsin-Madison

10A. Process Design: Innovation for Sustainability

Screening for Economically Promising Bio-Based Chemicals

Wenzhao Wu, Matthew Long, Jennifer Reed,
Christos T. Maravelias*

Dept. of
Chemical and Biological Engineering, University of Wisconsin-Madison,
Madison, WI 53706

The last decade has seen tremendous progress in metabolic
engineering and synthetic biology1,2. These advances enable the use
of engineered microorganisms such as Escherichia coli and yeast for the
production of chemicals that are currently derived mainly from fossil fuel
feedstocks3,4. However, it is still unclear which chemicals have the
highest economic potential, which is key in developing a sustainable chemical
production system. To this end, we develop a framework for the identification
of economically promising chemicals that can be produced biologically using
microorganisms (“bio-based chemicals”).

We first examine the US high-production-volume (HPV) chemicals5,
which are manufactured in or imported into the United States in amounts equal
to or greater than 454 metric tons (MT) per year. HPV chemicals include all
commodity chemicals and a portion of fine chemicals. We establish an HPV
chemical database (3574 chemicals) by compiling several HPV lists published by the
EPA over the past two decades. Then we intersect the HPV chemical database with
the KEGG database, which includes most chemicals produced by characterized
reactions in biological systems, and thus 613 overlapping chemicals are found.
These chemicals are then imported into a genome-scale metabolic model we
develop, and 170 chemicals are identified to be producible by microorganisms,
often with the addition of heterologous reactions. These 170 chemicals are the
complete pool of candidate targets for bio-based production. In addition,
market volume and price data is collected for them.

We first use the following two screening criteria to quantify
economic potential.

Criterion 1: market volume. The market volume
(MT/year) should be greater than an expected production capacity.

Criterion 2: market value. The market value
($/year) should be large enough to attract investment and recover capital cost
within an expected time horizon.

The difference between the price and the production cost also
plays a key role in determining the economic potential of a product. The larger
the difference between a chemical’s selling price and its total production
cost, the larger its economic potential. However, directly implementing a third
criterion based on such a difference is challenging because the downstream
separation cost is highly product-dependent and difficult to estimate. To this
end, we use the “separation cost margin” as Criterion 3, which is
the difference between the price and the upstream cost (including raw material
supply cost and bio-conversion cost). We calculate the upstream cost using base
cost data from the literature as well as the theoretical yield and productivity
calculated using our metabolic model. For a specific product, if the margin is
negative, then the product fails Criterion 3; if the margin is positive, then
we compare it with the expected separation cost. In general, the separation
cost depends on (1) the product properties, e.g., separation of extracellular
insoluble products tends to be easier than that of extracellular soluble ones,
especially when the product concentration is low; and (2) case-specific
information, e.g., whether a known solvent exists for a particular product, and
special product requirement such as colorlessness and dryness.

To account for these major separation cost drivers, we introduce a general
downstream bio-separation superstructure optimization framework to provide
guidance in the preliminary design of separation networks6,7. We then apply this
framework and formulate superstructure models for different classes of
bio-based chemicals8 based on their properties such as localization
(intracellular/extracellular), solubility (soluble/insoluble) and density with
respect to water (light/heavy). Further, we identify the optimal separation
processes for base cases and the key cost drivers (such as titer and technology
performances). Finally, we generate sensitivity curves (e.g. Figure 1A) and
“heat maps” (e.g. Figure 1B) to show how the overall cost changes with the
variation in the key cost drivers, which can help us roughly estimate the cost
of separation for different classes of products.

Figure
1.
Cost estimates for
intracellular insoluble products under varying parameters. (A) Sensitivity
curve showing cost changes with the variation in titer; (B) “Heap map” showing
the change of cost and the optimal cell harvesting technology with the
variation in technology performances (centrifuge efficiency and filtration
retention factor). The white lines show the threshold values where the optimal
technology switches. The asterisk represents the base case.

As a preliminary result, we identify three chemicals as
the most promising ones: glutaric acid, acrylamide and propanal, by setting a
uniform separation cost margin threshold for all chemicals to be greater than 1.8
$/kg (an estimated margin for a case product - polyhydroxyalkanoate), a volume
greater than 31751 MT/year (an estimated production capacity), and a market
size greater than 200 million $/year (to recover capital cost in ~5 years
supposing a 50% market share). More accurate results can be obtained by using
the aforementioned bio-separation framework with a different separation cost
margin.

References

[1] Gavrilescu,
M. & Chisti, Y., 2005. Biotechnology- a sustainable alternative for
chemical industry. Biotechnology advances, Volume 23, pp. 471-499.

[2] Bornscheuer,
U. T. & Nielsen, A. T., 2015. Editorial overview: chemical biotechnology:
interdisciplinary concepts for modern biotechnological production of
biochemicals and biofuels. Current Opinion in Biotechnology, Volume 35,
pp. 133-134.

[3] Wilson,
S. A. & Roberts, S. C., 2014. Metabolic engineering approaches for
production of biochemicals in food and medicinal plants. Current Opinion in
Biotechnology,
Volume 26, pp. 174-182.

[4] Zhang, X.,
Tervo, C. J. & Reed, J. L., 2016. Metabolic Assessment of E. coli as a
Biofactory for Commercial Products. Metabolic Engineering, Volume 35,
pp. 64-74.

[5] US EPA, 2004.
High-production-volume (HPV) chemicals status and future directions of the HPV
Challenge Program. Office of Pollution Prevention and Toxics, Washington
DC.

[6]
Wu, W., Yenkie, K.M.
& Maravelias, C.T., 2017. A superstructure-based framework for bio-separation
network synthesis. Computers & Chemical Engineering, Volume 96, pp.
1-17.

[7] Yenkie,
K. M., Wu, W., Clark, R. L., Pfleger, B. F., Root, T. W., & Maravelias, C.
T., 2017. A roadmap for the synthesis of separation networks for the recovery
of bio-based chemicals: Matching biological and process feasibility. Biotechnology
Advances
, Volume 34(8), pp. 1362-1383.

[8] Yenkie,
K. M., Wu, W., Maravelias, C.T., 2017. Synthesis and analysis of separation
networks for the recovery of intracellular chemicals generated from
microbial-based conversions. Biotechnology for Biofuels