(246i) A Superstructure-Based Assessment Framework for Downstream Bio-Separation

10A. Design, Analysis, and Optimization of Sustainable Energy Systems
and Supply Chains

A Superstructure-Based Assessment Framework for Downstream
Bio-Separation

Wenzhao Wu, Kirti Yenkie, Christos T. Maravelias^*

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

Figure
1.
The general bio-separation superstructure (including the “dimmed” parts), and
the reduced superstructure (excluding the dimmed parts) for the EX NSL LT NVL
LQD CMD case. Attributes include EX/IN:
extracellular/intracellular; SOL/NSL: soluble/insoluble in water; LT/HV: density
lower/higher than water; VOL/NVL: more/less volatile than water; LQD/SLD:
liquid/solid state; CMD/SPC: commodity/fine or specialty. Technologies include Ads: adsorption; Blc:
bleaching; Cnt: centrifugation; Chr:
chromatography; Cdr: cell disruption; Crs: crystallization; Dst:
distillation; Dry: drying; Ddg: differential
digestion; Ext: extraction;                 
Flc: flocculation; Flt: flotation; Ftt: filtration; Mbr: membrane; Prc: precipitation; Sdm:
sedimentation; Slb: solubilization.

Recent advances in synthetic biology and
metabolic engineering have enabled the production of a range of fuels and chemicals
using engineered microorganisms^1,2. Despite
the intriguing potential, the sustainable biological production of chemicals is
challenging because the product is likely to have a low titer, and the purity
specifications for high-value chemicals, as opposed to fuels, are rather
strict, which means that separation costs are likely to represent a large
fraction of the total production cost (often more than 60% of total cost³).
Thus, the efficient synthesis of bio-separation processes becomes a critical task.
Although this synthesis problem has been studied for various chemicals in the past,
these studies were mostly performed on a case-by-case basis. There has been
limited research towards the development of systematic methods for bio-separations,
applicable to different classes of chemical targets. Therefore, we develop a general
bio-separation superstructure optimization framework involving four separation
stages^4,5: (1) cell treatment, (2) product
phase isolation, (3) concentration and purification, and (4) final refinement.

Specifically, we generate a general
superstructure (Figure 1) that accounts for different types of bio-based chemical
products by implementing a set of connectivity rules^4,6.
We further develop a superstructure reduction method to solve product-specific
instances, based on product type, unit availability and suitability,
case-specific considerations, and final product specifications. The reduced
superstructure for an example case (for an EX NSL LT NVL LQD CMD product) is
shown in Figure 1. Next, a MINLP optimization model, including short-cut unit models
(e.g., Fenske-Underwood equations for distillation unit models) for the units included
in the reduced superstructure, is formulated and solved to determine the
optimal separation network and the key cost contributors. We can also determine
alternate (next best) configurations using the integer-cut method to decide
which technologies are essential and which can be changed in the optimal design
with little compromise in the process cost. Finally, we vary a combination of parameters
for the key cost contributing technologies (i.e. solve multiple optimization
problems with varying parameter values) to determine the critical values when
there is a shift in technology selection and the corresponding cost changes
(represented in “heat maps”).

Figure
2. Best,
2^nd best and 3^rd best solutions (the un-dimmed parts) for
the IN NSL HV SLD CMD case. The dimmed and the un-dimmed parts together
constitute the reduced superstructure.

The proposed framework is applied to multiple
case studies. In studying an IN NSL HV SLD CMD case⁷, we assume
nominal values for parameters like plant capacity, product titer, desired
purity, technology parameters, cost of raw materials, chemicals and separating
agents from literature, and process simulation packages (SuperPro
Designer) for economic assessment. The objective is to minimize the overall cost.
After solving the model, we identify the optimal process and also apply
integer-cuts to identify the 2^nd best and 3^rd best solutions
(Figure 2). The stage-wise cost distribution for the best solution (Figure 3A) shows
that stage 1 (cell treatment) is the key cost driver, followed by stage 2 (product
phase isolation) and stage 4 (refinement) while stage 3 (concentration and
purification) is absent. Finally, we generate a heat map that shows how the
overall process cost changes with the variation in the key design parameters in
Stage I (see Figure 3B). We observe that the total cost can vary from 4.7 to 11.44
$/kg by changing the centrifuge efficiency and filtration retention factor.
Thus, the maximum improvement that can be achieved by selecting a suitable
biomass harvesting operation is ~50% compared to the base case (shown by the
black point in Figure 4B). We further discuss the extension of the results to
generate useful insights for the class of IN NSL bio-based chemicals.

Figure
3. (A)
Stage-wise cost distribution for the best solution; (B) Overall process cost
with variation in the performance of cell harvesting technologies. Threshold
values when there is a shift in technology selection is denoted by the white
lines.

References

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[4] Wu, W.,
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[5] Yenkie, K. M., Wu, W., Clark, R. L.,
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synthesis of separation networks for the recovery of bio-based chemicals:
Matching biological and process feasibility. Biotechnology Advances, Volume 34(8), pp. 1362-1383.

[6] Wu, W., Henao, C. & Maravelias,
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for Chemical Process Synthesis. AIChE J,
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[7] Yenkie, K. M., Wu, W., Maravelias,
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