467963 Assessment of Bioseparation Technology Options for Bio-Based Chemicals Generated from Microbial Cultures

Monday, November 14, 2016
Grand Ballroom B (Hilton San Francisco Union Square)
Kirti Maheshkumar Yenkie, Bioengineering, University of Illinois, Chicago, Chicago, IL, Wenzhao (Tony) Wu, Department of Chemical and Biological Engineering, University of Wisconsin – Madison, Madison, WI and Christos T. Maravelias, Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI

Assessment of bioseparation technology options for bio-based chemicals generated from microbial cultures

Kirti M. Yenkie, WenZhao Wu, Christos T. Maravelias*

Department of Chemical and Biological Engineering,

University of Wisconsin-Madison.

  Abstract Background The alarming climatic changes like global warming and constantly declining fossil reserves have established the need for alternative sources which are environmentally benign and sustainable. Production of chemicals from microbial sources is one such alternative that has potential to generate carbon neutral cycle (Fresewinkel et al., 2014). However, along with satisfaction of these crucial requirements their production must also be commercially viable. The separation of bio-based chemicals contained in process streams from microbial cultivations prove to be most challenging and can contribute to more than 70% of the total production costs. Thus, the economic feasibility is dependent upon the efficient synthesis of separation systems. Separation synthesis requires evaluation of several alternative technologies performing similar tasks, since their suitability is affected by variations in parameters like separation unit efficiency, input stream characteristics and desired product specifications. A sensitivity study involving potential variations in these parameters can provide a general guideline for technology selection while synthesizing separation systems. Previous work in this area has focused on the performance of individual technologies and has provided some general guidelines regarding their applicability to microalgae harvesting (Molina Grima et al., 2003) and biofuels (Brennan and Owende, 2010). However, a simultaneous and detailed quantitative analysis for synthesis and evaluation of bioseparation systems is not available. Methodology We develop a bioseparation superstructure framework involving four stages: (i) cell treatment, (ii) product phase isolation, (iii) concentration and purification and (iv) refinement. Each stage comprises of technology alternatives for tasks like biomass harvesting, cell disruption and product recovery based on their separation principles. Then, we generate reduced superstructures for specific product categories based on their physicochemical properties (solubility, density and volatility), physical state (solid or liquid), localization (extracellular or intracellular) and intended use (commodity or specialty). We generate a base case by assuming nominal values for parameters like plant capacity, product titer, desired purity, technology parameters, cost of raw materials, chemicals and separating agents from relevant literature (Choi and Lee, 1999; Mooibroek et al., 2007) and process simulation packages (SuperPro Designer) for economic assessment. We have a combinatorial optimization problem with an objective to minimize the overall bioseparation costs. The solution provides the cost distribution in each bioseparation stage and identifies the technologies selected. However, technologies performing intended tasks and their suitability for a specific product can vary depending on stream characteristics (biomass titer, product concentration), technology performance indices (efficiencies) or matching parameters (like mass separating agents contributing to raw material costs or energy separating agents contributing towards utility requirements) and final specifications (product purity and recovery). Hence, we perform an optimization based sensitivity analysis by varying these critical parameters and study the effects on technology selection as well as process economics. Results

To illustrate, we present some results for the product category of intracellular(IN)-insoluble (NSL)-heavy(HV)-solid(SLD)-commodity(CMD) chemical. The stage-wise cost contribution analysis predicts (Fig 1A) that stage I-cell treatment is the key cost driver, followed by stage II-product phase isolation and stage IV-refinement while stage III-concentration and purification is absent for the base case. We select the task of biomass harvesting in stage I, which can be performed by sedimentation, centrifugation and filtration, for sensitivity analysis. The results for variation in performance indices of centrifugation (efficiency) and filtration (retention factor) on total separation cost ($/kg product) can be seen in Figure 1B. The contour lines are horizontal in the region where filtration is selected and vertical where centrifuge is selected. Threshold values when there is a shift in technology selection is denoted by the white lines. We observe that total separation cost can vary from 3.12 to 8.44 $/kg. Thus, maximum improvement that can be achieved by selecting a suitable biomass harvesting operation is ~32% when compared to the base case (shown by the black point in Figure 1B).

Figure 1 Results for the product category of intracellular (IN)-insoluble (NSL)-heavy (HV)-solid (SLD)-commodity (CMD) chemical. (A) Stage-wise cost contribution analysis for the base case. (B) Sensitivity analysis for biomass harvesting task in stage I by varying performance index of centrifuge (efficiency) and filtration (retention factor). Conclusions We studied separation systems for the recovery of chemicals produced via a range of bio-conversions. We believe that such an analysis will prove valuable in (1) selecting the chemicals that can be produced economically using bioconversions, and (2) designing separation processes for bio-based chemicals. The threshold values determined by our analysis will help in deciding which technology shall be more promising for a particular task when the information about relevant parameters are known apriori. Sometimes, a particular technology selected in a previous stage also influences the technology selection in the following stages. Thus, we can make an informed decision regarding which technologies should be placed in conjunction. Most importantly, the proposed framework help us identify some promising research directions in the area of separations, a topic that has received limited attention despite its high impact on the economics of biomass-to-fuels/chemicals strategies.     References

Brennan, L., Owende, P., 2010. Biofuels from microalgae—A review of technologies for production, processing, and extractions of biofuels and co-products. Renew. Sustain. Energy Rev. 14, 557–577. doi:10.1016/j.rser.2009.10.009

Choi, J., Lee, S.Y., 1999. Factors affecting the economics of polyhydroxyalkanoate production by bacterial fermentation. Appl. Microbiol. Biotechnol. 51, 13–21. doi:10.1007/s002530051357

Fresewinkel, M., Rosello, R., Wilhelm, C., Kruse, O., Hankamer, B., Posten, C., 2014. Integration in microalgal bioprocess development: Design of efficient, sustainable, and economic processes. Eng. Life Sci. 14, 560–573. doi:10.1002/elsc.201300153

Molina Grima, E., Belarbi, E.-H., Acién Fernández, F.G., Robles Medina, A., Chisti, Y., 2003. Recovery of microalgal biomass and metabolites: process options and economics. Biotechnol. Adv. 20, 491–515. doi:10.1016/S0734-9750(02)00050-2

Mooibroek, H., Oosterhuis, N., Giuseppin, M., Toonen, M., Franssen, H., Scott, E., Sanders, J., Steinbüchel, A., 2007. Assessment of technological options and economical feasibility for cyanophycin biopolymer and high-value amino acid production. Appl. Microbiol. Biotechnol. 77, 257–267. doi:10.1007/s00253-007-1178-3

 


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