xylitol and bioethanol production from...

XYLITOL AND BIOETHANOL PRODUCTION FROM LIGNOCELLULOSES: A SYSTEMATIC MODEL-BASED SIMULATION

FOR A BIOREFINERY PROCESS EVALUATION

Ricardo Morales-Rodrigueza*, Divanery Rodriguez-Gomezb, Merlín Alvarado-Moralesc, Helen Denise Lugo-Mendeza, José Antonio de los Reyes-Herediaa,

Eduardo Salvador Perez-Cisnerosa

aDepartamento de Ingeniería de Procesos e Hidráulica, bDepartamento de Biotecnología, Universidad Autónoma Metropolitana-Iztapalapa, Av. San Rafael Atlixco 186, C.P. 09340, México, D.F., MÉXICO.

*e-mail: [email protected] cDepartament of Environmental Engineering, Technical University of Denmark. DK-2800 Kgs. Lyngby,

DENMARK.

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Outline

...\Introduction

...\Objective

…\Xylitol mathematical model and production pathway

...\Towards a biorefinery structure: addition of the xylitol production section to the extended version of the dynamic lignocellulosic bioethanol 1.0 model platform

…\Benchmarking criteria for the comparison of the extended process models

…\Results and conclusions

…\What Next..?

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Introduction

According to The European commission predictions, 20% of the transportation fuel and 25% of chemicals should be produced from biomass by 2030 [1].

Recent advances on producing bioproducts from biomass (Biorefinery concept) has triggered out continued research on developing new production processes, such as xylitol

Xylitol: Value added product, five-carbon sugar alcohol, used for health (dental caries and acute otitis media) and lately for the production of alcanes (C7-C12) for obtaining a biodiesel through a chemical route.

Industrially, xylitol is produced by chemical route [2]. Recently, the biotechnological production of xylitol has reached importance due to this is produced by yeast metabolism, where Candida strains has become important over Saccharomyces cerevisiae by being a natural D-xylose consumer and maintaining the reduction–oxidation balance during xylitol accumulation.

Experimental approach

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Bioethanol Production

Pretreatment Enzymatic hydrolysis

Ethanol

Downstream Co-Fermentation

Lignin

Cellulases Yeast

[3] [4] [5]

Conventional process configuration for bioethanol production via biological route for DLB 1.0

Simultaneous Saccharification Co-Fermentation (SSCF)

[6]

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Dynamic lignocellulosic bioethanol (DLB) 1.0 model platform and extension

Operational Scenario Acronyms

SHCF

1) H: Fed-batch – CF: Fed-batch FB-FB 2) H: Fed-batch – CF: Continuous FB-C 3) H: Fed-batch – CF: Continuous-Recycle FB-C_RECY 4) H: Continuous – CF: Fed-batch C-FB 5) H: Continuous – CF: Continuous C-C 6 H: Continuous – CF: Continuous-Recycle C-C_RECY 7) H: Continuous-Recycle – CF: Fed-batch C_RECY-FB 8) H: Continuous-Recycle – CF: Continuous C_RECY-C 9) H: Continuous-Recycle – CF: Continuous-Recycle C_RECY-C_RECY

SSCF 10) Fed-batch SSCF-FB 11) Continuous SSCF-C 12) Continuous-recycle SSCF-C_RECY H: Enzymatic Hydrolysis, CF: Co-Fermentation

Ethanol/DM ratio 0.10 0.10 0.13 0.09 0.11 0.11 0.10 0.14 0.16 0.13 0.12 0.18

Benchmarking performance criteria: Ethanol/Dry-Biomass Ratio

/ _

_Et dry biomassTotal Mass EtR

Total Mass Dry Biomass− =

[7]

SSCF-C_RECY

C_RECY-C_RECY

[8, 9]

C_RECY-C

Extension of DLB 1.0:

-Heat exchangers -Distillation columns

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Objective

To present an extension of the DLB 1.0 model platform by the addition of the dynamic modeling for the

conversion of xylose into xylitol. In order to evaluate the technological and economical feasibility through a

model-based simulation.

Xylitol mathematical model and production pathway

• Once xylose is inside the cell, it is reduced to xylitol by the enzyme xylose reductase. • Part of xylose is excreted as a xylitol and the other is converted to xylulose that is consumed for producing

cell mass and maintenance energy • Glucose (easily metabolized sugar) is used for producing energy and biomass, reducing xylitol consumption. • High glucose concentration can inhibit xylose transport into the cell [11].

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[10]

Xylitol mathematical model and production pathway

• Mathematical model [10]: – Biomass growth rate, – The rate of glucose and xylose uptake related with biomass

concentration – The production rate of intracellular and extracellular xylitol.

• Original model for xylitol production considers a reactor operating in batch.

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The model was extended for a continuous operation mode, considering the residence time with higher xylitol production…!!

Towards a biorefinery structure: addition of the xylitol production section to the extended version of the dynamic lignocellulosic bioethanol 1.0 model platform

• The xylitol production by yeast can be affected by several factors such as, culture conditions, initial xylose concentration, and the presence of inhibitor (such as glucose, acetic acid, furfural, 5-hydroxymethylfurfural (HFM) and phenolic compounds) released by acid hydrolysis.

• The inhibition effect of ethanol on Candida genus has not been completely studied and none available information was found

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For the sake of the process design, the xylitol production section was added after ethanol was eliminated from process streams.

Where do we add the xylitol production section?


10

C_RECY-C

C_RECY-C-RECY


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SSCF-C

C_RECY-C-RECY

Benchmarking criteria for the comparison of the extended process models

• Production costs and potential profit for bioethanol and xylitol as the benchmarking criteria.

• The production costs considered were: – feedstock – utilities (low-pressure steam, high-pressure steam and cooling water) – additives (enzyme loading in the enzymatic hydrolysis and sulfuric acid loading in

the pretreatment)

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Note that only utilities cost (cooling water) is considered for xylitol production, since after the distillation columns the temperature at the

waste streams must be decreased to carry out the fermentation of xylose to xylitol.

= + + =−Feedstock utilities AdditivesUSDPr oduction _ Cost _ Ethanol c c c

gal ethanol

= =−utilitiesUSDPr oduction _ Cost _ Xylitol c

gal xylitol

Results

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Process configuration

Production cost for

bioethanol, USD/gal-ethanol

Production cost for xylitol,

USD/gal-xylitol

% of difference on bioethanol

profit with respect to SSCF

configuration

% of difference on potential profit with

respect to SSCF configuration

% of difference on total profit

with respect to SSCF

configuration

C_RECY-C 2.42 0.0078 -90.4% 20.9% -44.4% C_RECY-C_RECY 2.15 0.0078 -49.8% 20.3% -20.8%

SSCF-C 1.53 0.0054 - - - SSCF-C_RECY 1.71 0.0077 29.9% 18.4% 25.1%

• C_RECY-C – SSCF-C: xylitol potential profit 20.9% higher for the C_RECY-C, the ethanol production cost was 58.2% higher for SSCF-C process configuration, for total profit analysis was 44.4 % less in the C_RECY-C than in the SSCF-C.

• C_RECY-C_RECY – SSCF-C: for xylitol profit the results were quite similar, but for the bioethanol production cost this was 1.4 times higher than SSCF-C and the bioethanol profit was lower by 20.8 %

• SSCF-C – SSCF-C_RECY: xyliltol potential income for SSCF-C_RECY was 1.84 higher than the reference process configuration and the variation for bioethanol production cost was found to be 11.8% more expensive for SSCF-C_RECY, but the total potential income for ethanol and xylitol production was 25.1% higher for the SSCF-C_RECY

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Discussion

• At first relying on the production cost, the SSCF-C seemed to be the best process

configuration, but whether the incomes from bioethanol and xylitol productions were

taken into account, the best process configuration was the SSCF-C_RECY.

• A previous study [7] showed that ethanol/dry-biomass ratio was 0.18 and 0.12 for

SSCF-C_RECY and SSCF-C, respectively. The obtained profit for SSCF-C_RECY

was reasonability understandable since the production was 1.5 times higher with

respect to SSCF-C.

• Xylitol production: difference was due to the flowrate in the streams leaving from the

distillation columns. In the SSCF-C_RECY, the amount of xylose and glucose

available in the streams was higher when compared with SSCF-C.

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Concluding remarks

This study has presented the addition of a xylitol production section to the

previous extended version of the DLB 1.0 modeling platform biorefinery

modeling platform.

Detailed analysis in terms of the likely profit; where the best process

configuration when combining both products was the SSCF-C_RECY having

25.1% more incomes compared to the SSCF-C.

A multidisciplinary team in the process design step, due to the intrinsic

complexity of the biological processes.

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Concluding remarks

Future work:

• Extension in the DLB 1.0 modeling platform for the remaining process

configurations presented by Morales-Rodriguez et al. [7],

• Addition of the unit operations for the production of xylitol and other high value-

added products through a chemical and biological route under the biorefinery

concept, thereby, providing a global vision of the scope of biomass processing.

• Control strategies for the different unit operations. (Poster: 936, simulación y

control)

• Risk analysis in the process configurations. (Poster: 737, energía)

References

1. Biofuels in the European Union. A vision for 2030 and beyond. http://ec.europa.eu/research/energy/pdf/biofuels_vision_2030_en.pdf. Accessed January 9, 2013.

2. Granström, T.B., Izumori, K, Leisola, M., “A rare sugar xylitol. Part II: biotechnological production and future applications of xylitol”, Appl Microbiol Biotechnol, Vol. 74, p. 273–276, 2007.

3. Kadam, K.L. Rydholm, E.C. McMillan, J.D., “Development and Validation of a Kinetic Model for Enzymatic Saccharification of Lignocellulosic Biomass”. Biotechnology Progress, 20, 698-705 (2004).

4. Krishnan, M.S., Ho, N.W.Y., Tsao, G.T., “Fermentation Kinetics of Ethanol production from Glucose and Xylose by recombinant Saccharomyces 1400(pLNH33)”. Applied biochemistry and biotechnology, 77-79, 373-388 (1999).

5. Lavarack B.P., Griffin G.J. Rodman D., “The acid hydrolysis of sugarcane baggase hemicellulose to produce, xylose, arabinose, glucose and other products”. Biomass Bioenergy. 23, 367-380 (2002).

6. Morales-Rodriguez, R., Gernaey, K.V., Meyer, A.S., Sin, G., “A Mathematical Model for Simultaneous Saccharification and Co-Fermentation”. Chinese Journal of Chemical Engineering. 19, 185-191 (2011a).

7. Morales-Rodriguez, R., Meyer, A.S., Gernaey, K.V., Sin, G., “Dynamic Model-Based Evaluation of Process configuration for Integrated operation and Hydrolysis and Co-Fermentation for Bioethanol Production from Lignocellulose”. Bioresource Technology. 102, 1174-1184 (2011b).

8. Morales-Rodriguez, R., Meyer, A.S., Gernaey, K.V. & Sin, G. (2012a). “A Framework for Model-Based Optimization of Bioprocesses under Uncertainty: Lignocellulosic Ethanol Production Case”. Computers and Chemical Engineering (ISSN: 0098-1354), 42, 115-129.

9. Morales-Rodríguez, R., Rodriguez-Gomez, D., Alvarado-Morales, M., Lugo-Mendez, H.D. (2012b). “A Model-Based Process Configurations Comparison for Bioethanol Production from Lignocellulose Feedstocks”. 1° Iberoamerican Conference on Biorefineries. San José del Cabo, BJS, México. Pages: 544-550. ISBN: 978-607-441-200-0. Poster Presentation.

10.Tochampa, W., Sirisansaneeyakul, S., Vanichsriratana, W., Srinophakun, P., Bakker, H.H.C., Chisti, Y., “A model of xylitol production by the yeast Candida mogii”, Bioprocess Biosyst Eng, Vol. 28, p. 175-183, 2005.

Thank you for your attention

XYLITOL AND BIOETHANOL PRODUCTION FROM LIGNOCELLULOSES: A SYSTEMATIC MODEL-BASED SIMULATION

FOR A BIOREFINERY PROCESS EVALUATION

Ricardo Morales-Rodrigueza*, Divanery Rodriguez-Gomezb, Merlín Alvarado-Moralesc, Helen Denise Lugo-Mendeza, José Antonio de los Reyes-Herediaa,

Eduardo Salvador Perez-Cisnerosa

aDepartamento de Ingeniería de Procesos e Hidráulica, bDepartamento de Biotecnología, Universidad Autónoma Metropolitana-Iztapalapa, Av. San Rafael Atlixco 186, C.P. 09340, México, D.F., MÉXICO.

*e-mail: [email protected] cDepartament of Environmental Engineering, Technical University of Denmark. DK-2800 Kgs. Lyngby,

DENMARK.

mailto:[email protected]�

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