Consultancy on Large-Scale Submerged Aerobic Cultivation Process Design

27 Pages·2017·0.6 MB·English

Consultancy on Large-Scale
Submerged Aerobic Cultivation
Process Design – Final
Technical Report
February 1, 2016 — June 30, 2016
Jason Crater, Connor Galleher,
and Jeff Lievense
Genomatica, Inc.
San Diego, California
NREL Technical Monitor: James McMillan
NREL is a national laboratory of the U.S. Department of Energy
Office of Energy Efficiency & Renewable Energy
Operated by the Alliance for Sustainable Energy, LLC
This report is available at no cost from the National Renewable Energy
Laboratory (NREL) at www.nrel.gov/publications.
Subcontract Report
NREL/SR-5100-67963
May 2017
Contract No. DE-AC36-08GO28308
Consultancy on Large-Scale
Submerged Aerobic
Cultivation Process Design –
Final Technical Report
February 1, 2016 — June 30, 2016
Jason Crater, Connor Galleher,
and Jeff Lievense
Genomatica, Inc.
San Diego, California
NREL Technical Monitor: James McMillan
Prepared under Subcontract No. AFC-6-62032-01
NREL is a national laboratory of the U.S. Department of Energy
Office of Energy Efficiency & Renewable Energy
Operated by the Alliance for Sustainable Energy, LLC
This report is available at no cost from the National Renewable Energy
Laboratory (NREL) at www.nrel.gov/publications.
National Renewable Energy Laboratory Subcontract Report
15013 Denver West Parkway NREL/SR-5100-67963
Golden, CO 80401 May 2017
303-275-3000 • www.nrel.gov
Contract No. DE-AC36-08GO28308
This publication was reproduced from the best available copy
submitted by the subcontractor and received no editorial review at NREL.
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Contents
Executive Summary .................................................................................................................................... 4
Contact Information .................................................................................................................................... 5
Introduction ................................................................................................................................................. 7
Feedback ...................................................................................................................................................... 8
Modeling Methodology .......................................................................................................................... 8
Model Assumptions .............................................................................................................................. 11
Strain Selection ..................................................................................................................................... 12
Bioreactor Type .................................................................................................................................... 12
Bioreactor Scale ................................................................................................................................... 15
Bioreactor Cooling Design ................................................................................................................... 18
Bioreactor Operating Mode .................................................................................................................. 20
Seed Train Design ................................................................................................................................ 22
Recommendations .................................................................................................................................... 24
References ................................................................................................................................................. 25
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Executive Summary
NREL is developing an advanced aerobic bubble column model using Aspen Custom Modeler
(ACM). The objective of this work is to integrate the new fermentor model with existing techno-
economic models in Aspen Plus and Excel to establish a new methodology for guiding process
design. To assist this effort, NREL has contracted Genomatica to critique and make
recommendations for improving NREL’s bioreactor model and large scale aerobic bioreactor
design for biologically producing lipids at commercial scale.
While acknowledging the great work NREL has done to this point in developing a bioreactor
model, Genomatica has highlighted a few areas for improving the functionality and effectiveness
of the model. Genomatica recommends using a compartment model approach with an integrated
black-box kinetic model of the production microbe. We also suggest including calculations for
stirred tank reactors to extend the model’s functionality and adaptability for future process
designs.
Genomatica also suggests making several modifications to NREL’s large scale lipid production
process design. The recommended process modifications are based on Genomatica’s internal
techno-economic assessment experience and are focused primarily on minimizing capital and
operating costs (critical for fuel product commercial viability, see Table 1 on page 6). These
recommendations include selecting/engineering a thermotolerant yeast strain with lipid
excretion; using bubble column fermentors; increasing the volume of production fermentors;
reducing the number of vessels; employing semi-continuous operation; and recycling cell mass
and glycerol.
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Contact Information
Genomatica, Inc.
4757 Nexus Center Drive
San Diego, CA 92121
Jason Crater (Primary Contact)
Manager, Scale-up & Technology Transfer
Email: [email protected]
Phone: [phone]
Connor Galleher
Bioprocess Development Engineer
Email: [email protected]
Phone: [phone]
Jeff Lievense, Ph.D.
Senior Advisor to the CEO
Email: [email protected]
Phone: [phone]
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Table 1: List of Process Design Parameters with Associated Cost and Design Implications
Parameter Decreases Cost Increases Cost Considerations
Anaerobic fermentation eliminates
Oxygen Anaerobic Aerobic oxygen transfer costs, and may require
stirred tank reactors to mix.
Fabrication costs, operating mode,
Fermentor Volume Larger, fewer Smaller, more gradients, mixing time, process/facility
complexity are all impacted.
Bubble column type is lower capital,
Fermentor Type Bubble column Stirred tank less maintenance, less contamination
risk, and lower OTRmax.
External loop has increased risk of
Cooling Design External loop Jacket, coils contamination, and impacts the broth
conditions in the loop.
Continuous mode uses fermentor
Semi-continuous,
Operating Mode Batch, fed-batch volume and associated system capital
continuous
equipment more efficiently.
Specific Productivity
(qp) Higher qp Lower qp Higher qp favors higher yield and rate.
Extracellular product enables cell
retention or recycle, increased yields,
Product Location Extracellular Intracellular
and lower downstream processing
(DSP) costs.
Insoluble extracellular product enables
Insoluble, low Soluble, high
Product Properties physical separation. Low boiling point
boiling point boiling point
enables direct distillation.
Lower broth temperature requires a
Broth Temperature ≥ 35oC ˂ 35oC
chiller.
Lower broth viscosity reduces heat and
Broth Viscosity Lower Higher
oxygen (aerobic only) transfer costs.
Dispose as Glycerol recycle increases carbon yield
Glycerol Byproduct Recycle
waste by 5%.
Cell retention increases yield and rate,
Cell Mass Usage Retention Single-use
but also contamination risk.
Sterility is influenced by operating
conditions, microbe, and product.
Sterility Sanitary Aseptic Aseptic design requires more
opex/capex to maintain the integrity of
the sterile boundary.

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Introduction
The National Renewable Energy Laboratory’s (NREL’s) Biochemical Platform is developing
processing strategies for producing biofuels and bio-based products from lignocellulosic
feedstocks. One approach is based on using pretreatment followed by enzymatic hydrolysis to
deconstruct the major plant carbohydrates, cellulose and hemicellulose, into monomeric sugars.
These biomass-derived sugars are then clarified using solid-liquid separation processes prior to
being concentrated and converted to products. Submerged aerobic fermentation production of
intracellular lipids from biomass-derived sugars using oleaginous yeast is one of several sugar
upgrading conversion routes being considered. Once recovered, the lipids can then be hydro-
treated and isomerized to produce a hydrocarbon biofuel (1).
NREL is developing an advanced aerobic bubble column model using Aspen Custom Modeler
(ACM). The objective of this work is to integrate the new fermentor model with existing techno-
economic models in Aspen Plus and Excel to establish a new methodology for guiding process
design. To assist this effort, NREL has contracted Genomatica to critique and make
recommendations for improving NREL’s bioreactor model and large scale aerobic bioreactor

design for biologically producing lipids at commercial scale.
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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
Feedback
Modeling Methodology
NREL is developing an advanced aerobic bubble column model using ACM that will be
integrated with existing techno-economic models in Aspen Plus and Excel. The fermentation
model in ACM is used to dynamically simulate a single batch from inoculation to harvest. The
time-dependent results from the fermentation simulation are subsequently exported to Excel for
integration and calculation of steady-state rates, which are then imported into techno-economic
models in Aspen Plus (2). Genomatica has employed a similar methodology in which multiple
software platforms (e.g. Mathematica, Excel, Aspen) are used to assess both steady state and
dynamic processes. When modeling complete processes using multiple software platforms,
Genomatica prefers to use an Excel interface with all other programs accessed on the back end,
as Excel improves the accessibility of the model; i.e., most users are familiar with and
comfortable using Excel.
Genomatica’s dynamic fermentation model is set up in Mathematica and functions as a stand-
alone program with a user interface that lets users easily manipulate model inputs (e.g.,
fermentor volume and dimensions, mixing design, process parameters, strain, etc.). The program
also features a plotting tool that allows users to visualize all parameters calculated in the model
without having to export the data to other software platforms. Additionally, the program allows
for direct comparison of multiple simulations, which lets users assess the impact of changes in
various design or process parameters on process performance. The ability to export data to other
models or function as a stand-alone application improves the flexibility and effectiveness of the
model. This enables the model to be used for various applications through all stages of a project.
Genomatica’s fermentation model has been used as a tool for large-scale bioreactor design,
techno-economic assessment, and design of bioreactor scale-down experiments. Genomatica
recommends building process modeling tools with flexibility and user-friendliness in mind to
ensure the models are leveraged at every stage of the project. Flexibility will also facilitate
adaptation of the model for future process designs.
One important distinction between Genomatica’s and NREL’s fermentation modeling
methodologies is Genomatica uses a compartment model approach. Rather than modeling the
fermentation broth as a single component, Genomatica’s model compartmentalizes the broth
based on assumed mixing patterns for bubble columns (based on L/D) and stirred tank reactors
(based on impeller type and location). Gas and liquid phase component balances for O and CO
2 2
are solved for each compartment simultaneously and iteratively using an ordinary differential
equation (ODE) solver built in to Mathematica. Genomatica uses the same component balances
and literature correlations for mass transfer coefficients, mass transfer rates, and gas hold-up
reported for NREL’s model (2). However, it is important to note that many of these literature
correlations have correction factors for temperature, pressure, and/or viscosity, which can have a
significant impact on the calculations. The impact of these operating parameters is highlighted in
the Bioreactor Type section below (see page 12). It is also important to note that literature
correlations should be cautiously applied, as >95% of these correlations are developed in lab
scale or small pilot fermentors.
The advantage of using a compartment model is it provides insight into how fermentor volume
and geometry impact axial gradients that the microbe will encounter at scale. Not only does this
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aid in the initial bioreactor design, but it also facilitates the design of process scale-down studies
to mimic the anticipated large-scale conditions in the laboratory. See the Bioreactor Scale section
(page 15) for more details. Klaas van’t Riet’s and Johannes Tramper’s Basic Bioreactor Design
provides a detailed example (chapter 18, example 18.1) on how to set up a compartment model
for bubble column reactors (3). Klaas van’t Riet’s and Rob van der Laans’ “Mixing in bioreactor
vessels” provides useful information on broth mixing and compartmentalization in bubble
column and stirred tank reactors (4).
In order to maximize the effectiveness of the compartment model, a black-box kinetic model of
the production microbe should also be incorporated. Linking the kinetics of the host microbe’s
metabolism to the bioreactor model provides important insight into how the design (volume,
geometry) impacts the environment (pH, temperature, pressure, pO , pCO , substrate
2 2
concentration, etc.) the microbe experiences in different zones of the fermentor. The black box
model can be used to calculate characteristic times for important process parameters, such as
substrate, product, pH, temperature, O , CO , ammonium, byproducts, etc. These characteristic
2 2
times can then be compared with mixing time estimates to assess the degree of broth
heterogeneity for each parameter.
For example, substrate consumption rate and mixing time can be used to calculate the gradient in
residual substrate concentration from the top (near the substrate addition point) to the bottom of
the fermentor. If the microbe is sensitive to gradients in residual substrate concentration then the
bioreactor volume, geometry, or number of substrate feed points may need to be adjusted to
maximize process performance. Similarly, rates of O consumption and CO production can be
2 2
used to calculate axial gradients in transfer rates and dissolved concentrations of O and CO ,
2 2
respectively. Sensitivity to these parameters may also be addressed by modifying the bioreactor
design (e.g., volume, aspect ratio), or by adjusting process parameters (e.g., aeration rate).
NREL’s current model assumes a fully aerobic fermentation process with nitrogen (ammonium)
limitation used as a lever to limit cell mass propagation and turn on TAG production. A simple
black box model can be developed using the process reactions outlined in NREL’s statement of
work (1). Because ammonium is the limiting nutrient, a relation between specific growth rate (µ)
and residual ammonium concentration is required. For this the Monod growth equation for
microbes can be used (5):
Equation 1:
𝑆𝑆
Where µ is the microbe specific growth rate (h𝜇𝜇r-1=), µ𝜇𝜇𝑚𝑚𝑚𝑚 𝑚𝑚is∗ t h𝐾𝐾e𝑆𝑆 +m𝑆𝑆aximum microbe specific growth
max
rate (hr-1), S is the concentration of the limiting nutrient (ammonium), and K is the half-velocity
S
constant (value of S where µ = 0.5*µ ).
max
Additionally, a relation for specific product formation rate (q ) as a function of µ is required. The
p
q (µ) relation can be easily determined from experimental data. If experimental data are not
p
available, then assumptions must be made based on the anticipated relationship between µ and q
p
and the kinetics of product formation (q ). For an aerobic process producing a product that
p,max
costs energy (i.e., the biosynthetic pathway has a net consumption of biochemical energy and
thus a positive change in free energy), it is difficult to predict the algebraic form of the q (µ)
p
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