Predictive Modeling and Rheological Estimation of Mixed...
Transcript of Predictive Modeling and Rheological Estimation of Mixed...
Predictive Modeling and
Rheological Estimation of
Mixed Feedstocks
Narani A, Coffman P, Stettler A, Tachea F, Li
C, Pray T, and Tanjore D*
Process Engineer
Advanced Biofuels (and Bio-products) PDU (AB-PDU)
Lawrence Berkeley National Laboratory
Berkeley, CA
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Acknowledgements
DOE EERE (Energy Efficiency and Renewable Energy) –
BETO (BioEnergy Technologies Office) Division and
ARRA (American Recovery and Reinvestment Act)
Idaho National Laboratory (INL) – Kevin Kenney, Vicki Thompson, Garold Greesham, Allison Ray and David Thompson
Sandia National Laboratory (SNL) – Blake Simmons and Murthy Konda
Joint BioEnergy Institute (JBEI) – Noppadon Sathitsuksanoh, Gabriella Papa and Seema Singh
Staff of Advanced Biofuels Process Demonstration Unit
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ABPDU Process Execution & Development
Feedstock
storage
&
Size
reduction Homogenous
and/or
Heterogeneous
Pretreatment
& Catalysis
Solvent/
Catalyst
Recovery
S/L Separation
Fermentation: 0.2L to 300L
Seed Train
Process Development / Material Production
10L 50L2L
producing enzymes, biofuels, and chemicals
Sugars and
Precursors
.2L
producing long chain carbon, Sugars and Furans, Lignin, and Biofuels
Recovery (Sugars, Aldehydes,
Acids, etc.)
Recovery(Enzymes, Terpenes,
Lipids, etc.)
Centrifugation 300L
Analytics: separation sciences, calorimetry, compositional analysis, online tools (FT-NIR,
MGA, etc.), techno-economics, design of experiments
Catalysis: 10ml to 100L
Recovery and Rheology: bench-scale liquid extraction, filtration & preparative
chromatography, oscillatory stress controlled rheometer
10L 50L2L.2L S/L Separation300L
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Providing tools to de-risk investments for sugar (bio-based) production
• What are the feedstocks?
• How much will it cost?
• What treatment and process parameters should I use?
Integrate
• Least cost modeling of the available biomass (INL)
• Predicting Deconstruction Process Parameters (LBNL)
o Identify and Optimize Traditional Pretreatment Methods
o Biomass Mixture Compositions
to Maximize Sugar Yield and Minimize Furfural Production
LBNL – INL Collaboration, Objective:
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Predictive modeling defined
• “Predictive modeling is a mathematical algorithm that predicts target variable
from a number of factor variables.” - 56th Annual Canadian Reinsurance Conference
• Day to day example of Predictive modeling
• Has been applied in other renewable sectors: wind and solar
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BETO funded Mixed feedstock
Collaborative Project: FY 2015-17
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LBNL
Development of an
Applicable
Predictive Model
SNL
TEA model comparing
IL with DA
INL
Least Cost Modeling,
Feedstock blends supply and
characterization
FY 2015
• Feedstocks – Several ratio combinations
o Corn Stover (CS)
o Switchgrass (SG)
o Energy Cane (EC)
• Pretreatments – Categorical variable
o Acid (Ac) – 1% w/w H2SO4
o Alkali (Al) – 1% w/w NaOH
o Ionic Liquid (IL) - [C2 mim] [OAc]
• Temperature – Scaled variable (1 – 100%)
o Acid – 140 to 180°C
o Alkali – 55 to 120°C
o Ionic Liquid – 120 to 160°C
• Time – Scaled variable (1 – 100%)
o Acid – 5 to 60 minutes
o Alkali – 1 to 24 hours
o Ionic Liquid – 1 to 3 hours
Predictive Model Input
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Experimental Design generated by SAS JMP®
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Whole plots
PT Temp% 0C Time %
Min CS SG EC
1 IL 1 120 39 106.8 0 1 0
1 Ac 1 140 100 60 0.3 0.4 0.3
1 Al 1 55 100 1440 0 0 1
1 Al 1 55 39 589 0 0.5 0.5
1 Al 1 55 100 1440 0 1 0
1 IL 1 120 100 180 0 0 1
2 Ac 100 180 1 5 0 0.6 0.4
2 Ac 100 180 60 38 1 0 0
2 Al 100 120 1 60 0 1 0
2 Al 100 120 1 60 1 0 0
2 Al 100 120 1 60 0 0 1
2 IL 100 160 1 60 0 0 1
3 IL 39 135 100 180 0.5 0.5 0
3 Ac 39 155 1 5 0 0 1
3 IL 39 135 1 60 1 0 0
3 Al 39 80 1 60 0.3 0.4 0.3
3 Ac 39 155 1 5 0.4 0.6 0
3 Al 39 80 100 1440 1 0 0
4 Ac 80 172 80 49 0 1 0
4 IL 80 152 80 156 1 0 0
4 IL 80 152 80 156 0 1 0
4 IL 80 152 1 60 0.5 0 0.5
4 Al 80 107 80 1159 0.2 0.4 0.4
4 Ac 80 172 80 48.8 0 0 1
• Compositional Analysis of Feedstocks
– NREL Protocols
• Solids loading during Pretreatments for Mixed Feedstock
– 10% w/w i.e. 1 g in 10 ml total volume in 25 ml (or 21 ml) SS316 tube (or Glass) reactors
• Enzymatic Hydrolysis on unwashed solids with CTec2 and HTec2
– Variable solids loading: < 4% w/w i.e.10ml pretreated slurry was brought to 25 ml total volume
– Variable pH adjustments for each test
– 2 enzyme loadings: 40 and 10 mg protein/g glucan in untreated biomass
• Sugar and furfural analysis on Dionex HPLC
– Aminex HPX 87 H-column
Methods and Materials
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Pretreatment and Enzymatic Hydrolysis of
Mixed Feedstocks
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Data Interpretation through Excel
can be Limiting
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Understanding Predictive Model through
Ternary Plots
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Mixture profile for predetermined yields from DA treated biomass
40 mg protein/ g glucan
Predictive Modeling to define Feedstock Mixtures
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Application of the Model
Glucose Yield did not drop drastically
with Enzyme loading
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Mixture profile for predetermined yields from DA treated biomass
40 mg protein/ g glucan 10 mg protein/ g glucan
Building an Applicable model…
Building the model
• A spectrum of data
• Several data points
Applying the model: Questions that need to be addressed,
• Does rheology impact scale-up sugar yields at high solids loading?
• What about commercialization?
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BETO funded Mixed feedstock
Collaborative Project: FY 2015-17
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LBNL
Development of an
Applicable
Predictive Model
SNL
TEA model comparing
IL with DA
LBNL
Scale up (10 and 100L)
deconstruction tests
LBNL
Scale up (300 L)
hydrolysate
fermentation
INL
Least Cost Modeling,
Feedstock blends supply and
characterization
LBNL
Rheological studies to
determine behavior of
mixed feedstocks at high
solids loading
UCR/ LBNL
High throughput
deconstruction for a
robust predictive model
LBNL
Validate the predictive
model
Why Rheology?Data from a 2011 study…
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Rheology – Science of Deformation and Flow
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Rotational Rheometer Parallel Plate Geometry
Oscillatory Measurements
Complex Modulus (G*) =Shear Stress
Shear Strain
Elastic Modulus G′ =Stress
Strain× Cos (phase angle) Viscous Modulus G" =
Stress
Strain× Sin (phase angle)
Variation in Rheology with Increasing
Solid loading
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Energy Cane
10% 15% 30%
Energy Cane Blended with Switchgrass (50% each w/w)
Determination of Rheological parameters
for Alkali Pretreated Biomass at 5Hz
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Oscillatory stress sweep of alkali pretreated energy cane at various solid loadings
Determination of Rheological parameters
for Alkali Pretreated Biomass blends at 5Hz
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Oscillatory stress sweep of alkali pretreated energy cane blended with
switchgrass (50% w/w each) at various solid loadings
Comparing the Rheology of Single and Mixed
Feedstocks at 10% (w/w)
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Comparing the Rheology of Single and Mixed
Feedstocks at 15% (w/w)
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Comparing the Rheology of Single and Mixed
Feedstocks at 30% (w/w)
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Rheological Estimation of IL Pretreated
Biomass at various solids loading
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IL Pretreatment in Globe reactor
Corn Stover solid loading (w/w)
i) 10% ii) 15% iii) 30%
• Predictive Model could identify the
• Pretreatment catalyst and treatment conditions for an “optimal”
Biomass Mixture
• Optimal Biomass Mixture for a particular Pretreatment System
• In the future
• More deconstruction tests for a robust model
• Scale-up of deconstruction test to 10 and 100L
• Continue to better understand rheological implications on
pretreatment and enzymatic hydrolysis of mixed feedstocks
• Ferment-ability tests, i.e. sugar conversion to ethanol and other
molecules
Summary
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Suggestions
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