Towards a biomass matrix for fuels and chemicals: How PSE can … · Towards a biomass matrix for...

Towards a biomass matrix for fuels and

chemicals:

How PSE can help bridging the gap for the low

carbon economy?

Roberto de Campos Giordano

Andrew Milli Elias

Felipe Fernando Furlan

Simone de Carvalho Miyoshi Chemical Engineering Graduate Program

Federal University of São Carlos (PPGEQ-UFSCar)

PPGEQ

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4

Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem

Services (IPBES)

Nature’s Dangerous Decline ‘Unprecedented’ Species Extinction Rates

‘Accelerating’

1,000,000 species threatened with extinction www.ipbes.net/news/Media-Release-Global-Assessment

May, 2019

https://www.ipbes.net/

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SUSTAINABLE ECONOMY

ENERGY MATERIALS

Sustainable economy will rely on a

multiplicity of energy sources, with

biomass playing an important role.

6 Technology Roadmap: Delivering Sustainable Bioenergy. IEA, 2017 https://webstore.iea.org/technology-roadmap-delivering-sustainable-bioenergy

ENERGY

RTS: reference technology scenario (emissions under current Paris pledges)

7

“what does the future hold for refining-petchems

integration?”: 1,940,000 Google hits (May, 2019)

Gasoline towards olefins->polyolefins?

MATERIALS

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MATERIALS

Biorefineries: value-added molecules are important

for the economic feasibility of the biofuels production

AND…

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MATERIALS

Biorefineries: value-added molecules are important

for the economic feasibility of the biofuels production

AND…

“HOW MUCH OIL WOULD/SHOULD STAY UNDERGROUND?”

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BIOREFINERIES, similarly to oil refineries,

are defined as multipurpose plants with

backbone processes (for the fuels) and

several derived, branched products

(molecules, building blocks)

MATERIALS

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upload.wikimedia.org/wikipedia/commons/4/40/Stone_arch_bridge%2C_Portaikos_river%2C_Pyli%

2C_Trikala%2C_Greece2.jpg

An important gap still remains

to be bridged: how to make

feasible this transition in the

real economy?

Two challenges:

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real economy?

Two challenges:

“Hardware”: Continuous, persistent R&D

efforts for “better”, “feasible” (advanced?)

(bio)processes.

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real economy?

Two challenges:



(bio)processes.

“Software”: Supplying tools for techno-

economic-environmental evaluation from the

scratch.

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real economy?

Two challenges:



(bio)processes.

“Software”: Supplying tools for techno-

economic-environmental evaluation from the

scratch.

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THE “SOFTWARE”

(that’s us, PSE community…)

Big picture: Building tools for supporting

stakeholders’ decision-making and governmental

policies during the transition to low-C economy

Local problems: improving the performance of

bioprocesses, bioreactors, up/downstream unit

operations, etc (several yet unexplored, or low-

explored process solutions)

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Our approach for “big picture” problems:

Retro-techno-economic-environmental analysis

RTEEA

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Cells’ driving forces:

biological survival, reproduction Cell Factory

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biological survival, reproduction

(Bio)process industry driving forces:

economical survival, environmental

sustainability

Cell Factory

Biorefinery

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biological survival, reproduction

Our approach

Including “survival” equations into the overall

(bio)process model (together with mass, energy

balances, kinetics, thermodynamics, etc)

(Bio)process industry driving forces:

economical survival, environmental

sustainability

Cell Factory

Biorefinery

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Our approach in practice

1. How to quantify the “economical survival” potential?

Response: Classical economic metrics

NPV (Net Present Value)

IRR (Internal Rate of Return)

MSP (Minimum Selling Price), …

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1. How to quantify the “economical survival” potential?

Response: Classical economic metrics

NPV (Net Present Value)

IRR (Internal Rate of Return)

MSP (Minimum Selling Price), …

Quantitively, the “survival” limit is defined, for

instance, by:

𝑁𝑃𝑉 = −𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 + 𝑃𝑟𝑜𝑓𝑖𝑡

1 + 𝑟 𝑖= 0

𝑁

𝑖=1

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2. How to quantify the “environmental survival”

potential?

Response: LCA metrics

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2. How to quantify the “environmental survival”

potential?

Response: LCA metrics

Upstream:

Material Flow Accounting: total amount of matter required to produce a unit of mass of product

Embodied Energy Analysis: total energy requirement per unit of mass of product

Exergy Analysis: second-law efficiency of the system.

Downstream:

Global Warming Potential: expressed in gram of CO2 equivalent

Acidification Potential: expressed in gram of SO2 equivalent

Eutrophication potential: expressed in gram of PO4-3 equivalent

Tropospheric ozone & photosmog formation potential: expressed in gram of ethene equivalent

Stratospheric ozone depletion potential: expressed in gram of CFC-11 equivalent

Ecotoxicity potential: expressed in 1,4-dichlorobenzene equivalent

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RTEEA

NPV = 0, GWP = % of fossil ...

Classical TEEA

NPV CI ...

Performance parameters: Yield Selectivity Productivity

RTEEA

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RTEEA

1 – Solving TEA + environmental equations in simulation

time

2 – Replacing specification of a key variable for the

“survival equation” (e.g. NPV = 0, …), keeping DF = 0

Obs: Equation-oriented simulators avoid external loops…

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*Soares, R. P.; Secchi, A. R. EMSO: A new environment for

modelling, simulation and optimization. Comput.-Aided Chem. Eng.,

2003, 14, 947

EMSO*

- Open models, easily including sizing and

calculation of capital costs

- Equation-oriented: all equations (including

“survival”) are solved simultaneously, so

RTEEA can run together with the simulation of

the overall process (much easier than the trial-

and-error procedure that a modular simulator

would demand)

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RTEEA results,

an illustration

Succinic Acid Production

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Efe Ç, van der Wielen LAM, Straathof AJJ. Techno-economic analysis of succinic acid production using adsorption from fermentation medium. Biomass & Bioenergy 56:479-792, 2013.

Succinic Acid Production

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Biocatalyst productivity (g/(kg h)) Conversion Suc. Acid final concentration (g/L) Selectivity (gAc. Suc./gEthanol)

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Succinic Acid RTEA: “isoeconomic curves”

Final Suc. Ac. concentration in the bioreactor as a function of selectivity for distinct biocatalyst productivities

Biocatalyst productivity (g/(kgh))

Infeasible region

Furlan FF, Costa CBB, Secchi AR, Woodley JM, Giordano RC. Retro-Techno-Economic Analysis: Using (Bio)Process Systems Engineering Tools to Attain Process Target Values. Industrial & Engineering Chemistry Research 55:9865-9872, 2016.

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Infeasible region

NPV ≥ 0

Feasible


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It is better trying to increase product final concentration than selectivity

Infeasible region


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Succinic Acid RTEEA: what about

environmental impacts?

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Succinic Acid RTEEA: what about

environmental impacts?

To improve economics: optimize bioreactor operation (bioreactor engineering)

To reduce C intensity: back to the clone for higher selectivity (systems biology)

1G-2G Bioethanol from Sugarcane Biorefinery (EMSO)

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Dimensionless cash flow: burning sugarcane

trash (50%); C6 + C5 fermentation

Infeasible

region

Furlan FF et al. Comp. Chem. Eng. (2012) 43:1-9

Furlan FF et al. Biotechnol. Biofuels, (2013) 6:142.

Dimensionless cash flow: burning sugarcane

trash (50%); only C6 fermentation

Bagasse partition in the biorefinery.

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Análise de Ciclo de Vida (ACV)

Environmental footprint, 1G and 1G-2G bioethanol

Economic allocation, CML – IA methodology

a- Global Warming Potentials 100 years’ horizon, in kg CO2eq/MJ ethanol

b- Abiotic depletion, in kg Sb eq./MJ ethanol

c- Ozone layer depletion, in kg CFC-11 eq./MJ ethanol

d- Human toxicity, in kg 1,4DB eq./MJ ethanol

e- Fresh water aquatic ecotoxicity, in kg 1,4DB eq./MJ ethanol

f- Marine aquatic ecotoxicity, in kg 1,4DB eq./MJ ethanol

g- Terrestrial ecotoxicity, in kg 1,4DB eq./MJ ethanol

h- Photochemical oxidation, in kg C2H4 eq./MJ ethanol

i- Acidification, in kg SO2 eq. /MJ ethanol

j- Eutrophication, in kg PO4-3 eq./MJ ethanol

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1G 1G-2G

Overview of some points

we’re presently working on

LCA: consistency of databases (uncertainty…)

Improving robustness

Key variables: screening & global sensitivity

analysis

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LCA: consistency of databases for

inventory

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Brazilian sugarcane GWP 100

a SimaPro(9.0): 0.1690 kg CO2eq/kgsugarcane b RenovaCalc : 0.0413 kg CO2eq/kgsugarcane


inventory

a 1 kg Sugarcane {BR}| market for, IPCC 2013 GWP 100a V1 b Seabra, J.E., Macedo, I.C., 2011. Comparative analysis for power generation and ethanol production from sugarcane residual biomass in Brazil.

Energy Policy, 39(1), 421-428. https://doi.org/10.1016/j.enpol.2010.10.019

Cavalett, O., Junqueira, T.L., Dias, M.O., Jesus, C.D., Mantelatto, P.E., Cunha, M.P., Franco, H.C.J.; Cardoso, T.F., Maciel Filho, R., Rossel,

C.E.V., Bonomi, A., 2012. Environmental and economic assessment of sugarcane first generation biorefineries in Brazil. Clean Technol.

Environ. Policy, 14, 399-410. https://doi.org/10.1007/s10098-011-0424-7

Matsuura, M.I., Scachetti, M.T., Chagas, M.F., Seabra, J.E., Moreira, M.M., Bonomi, A.M., Bayma, G., Picoli, J.F., Morandi, M.A.B., Ramos, N.P.,

Cavallet, O., Novaes, R.M.L, 2018. Nota Técnica RenovaCalc: Método e ferramenta para a contabilidade da Intensidade de Carbono de

Biocombustíveis no Programa RenovaBio. RenovaBio.


inventory

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1G 1G-2G

g CO2eq/MJethanol g CO2eq/MJethanol

SimaPro 9.0 database 69.90 64.76

Replacing only sugarcane by

RenovaCalc data 23.72 22.48

Bioethanol GWP 100*

* Energetic allocation, electricity and anhydrous ethanol as co-products.

The 1G and 1G-2G biorefinery: 833 tons of sugarcane per hour.

- 1G base case produces 89.81 L/ton of sugarcane.

- 1G-2G base case produces 120.92 L/ton of sugarcane.


inventory

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SimaPro:

Database extrapolated from 2006 to 2016; Ecoinvent v.2.0 a .

RenovaCalc:

Agriculture data from Ecoinvent v.3.1 b.

a Jungbluth N., Chudacoff M., Dauriat A., Dinkel F., Doka G., Faist Emmenegger M., Gnansounou E., Kljun N., Spielmann M., Stettler C. and

Sutter J. (2007) Life Cycle Inventories of Bioenergy. Final report ecoinvent data v2.0 No. 17. Swiss Centre for Life Cycle Inventories, Dübendorf,

CH.

b WERNET, G., et al. (2016). "The ecoinvent database version 3 (part I): overview and methodology." The International Journal of Life Cycle

Assessment, 21(9): 1218-1230


inventory

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SimaPro

• 20 % mechanical harvesting, 80 % manual

• Average annual sugarcane yield: 68.7 t/ha (Macedo et al. 2004).

• Pesticides: Amount of active ingredient, averages for Brazil from CETESB (1988)

• Fertilizing : Based on diesel consumption of sugarcane machinery Brazil (Macedo 1996)

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Improving robustness: virtual heat exchange networks (including

pinch analysis within process simulations)

An example:

• Fraction of solids (FS) in the hydrolysis reactor a the key variable in the

techno-economic-environmental analysis of the 2G ethanol process;

• Depending on FS, heat exchanger (E602) would be a cooler, a heater or

would not even exist.

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LONGATI, ANDREZA A. ; Lino, Anderson R.A. ; Giordano, Roberto C. ;

Furlan, Felipe F. ; Cruz, Antonio J.G. . Defining research & development

process targets through retro-techno-economic analysis: The sugarcane

biorefinery case. BIORESOURCE TECHNOLOGY, v. 263, p. 1-9, 2018.

b a

Improving robustness: virtual heat exchange networks (including

pinch analysis within process simulations)

Before pinch After pinch

Metamodels: Kriging (enzymatic saccharification of sugar cane bagasse)

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Metamodels: Kriging (enzymatic saccharification of sugar cane bagasse)

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Metamodels: Multilinear lookup tables (ethanol distillation train)

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Metamodels: Multilinear lookup tables (ethanol distillation train)

Selection of key variables: screening & global sensitivity analysis

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Screening methods a

• 1G-2G biorefinery: app. 103 specified variables: experience/heuristics before

systematic search

Variance-based global sensitivity analysis (GSA) a

• Model independency;

• Capacity to capture the influence of the full range of variation of each input factor;

• Appreciation of interaction effects among input factors;

a Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S., 2007. Global Sensitivity Analysis. The Primer,

Global Sensitivity Analysis. The Primer. John Wiley & Sons, Ltd, Chichester, UK. https://doi.org/10.1002/9780470725184

b Soares, R.P., Secchi, A.R., 2004. Modifications, simplifications, and efficiency tests for the CAPE-OPEN numerical open interfaces. Comput. Chem.

Eng. 28, 1611–1621. https://doi.org/10.1016/j.compchemeng.2003.12.008

EMSO CAPE-OPEN interface b

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Análise de Sensibilidade Global (ASG)

Variables Metrics

ODP AD HT FWET MAET TET EU AC PO GWP100 NPV

HSMF 0.34 0.08 0.08 0.09 0.08 0.08 0.06 0.07 0.00 0.01 0.01

HEL 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.02 0.53 0.37 0.72

HC 0.20 0.33 0.32 0.33 0.33 0.33 0.37 0.36 0.28 0.33 0.17

XC 0.03 0.08 0.08 0.08 0.08 0.08 0.09 0.09 0.05 0.06 0.02

XRT 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

PSMF 0.40 0.45 0.46 0.45 0.46 0.45 0.40 0.41 0.07 0.16 0.00

PT 0.00 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.00 0.00

PCGC 0.02 0.02 0.01 0.01 0.01 0.01 0.02 0.02 0.04 0.04 0.06

PHXC 0.01 0.03 0.03 0.04 0.03 0.04 0.03 0.03 0.02 0.02 0.00

Sobol first order index, normalized

GSA

HSMF: hydrolysis reactor solid mass fraction

HEL: hydrolysis reactor enzymatic load

HC: hydrolysis conversion (C6)

XC: xylose conversion (C5)

XRT: xylose reaction time

PSMF: pretreatment reactor solid mass fraction

PT: pretreatment reactor temperature

PCGC: pretreatment cellulose to glucose conversion

PHXC: pretreatment hemicellulose to xylose conversion

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feasible

RTEEA - 1G/2G bioethanol

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80 ton cane/ha CI = 19 gC02eq/MJ eth

N residual

Industry (6%) Fertilizers

Machinery

diesel

Distribution

Correctives

Burning straw

1G Ethanol in Brazil: contributions for C footprint

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N residual


Machinery

diesel

Distribution

Correctives

Burning straw


Industry impact is comparatively small, BUT since the

biorefinery is multiproduct, allocation will occur in it,

during the production process

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N residual


Machinery

diesel

Distribution

Correctives

Burning straw





Nowadays: global allocation, for instance, “at the gate”

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N residual


Machinery

diesel

Distribution

Correctives

Burning straw





Nowadays: global allocation, for instance, “at the gate”

QUESTION: aren’t there more sound criteria? We’re

working on them…

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A word of advice: the importance of

re-thinking land use, with

sustainability as a keystone…

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apublica.org/2019/04/coquetel-com-27-agrotoxicos-foi-achado-na-agua-

de-1-em-cada-4-municipios-consulte-o-seu/

“Local” problems:

Complex enzymatic reactions:

Fuzzy consortium of simplified models for saccharification of

biomass

Modeling enzymatic esterification/transesterification…

Microbial cultivations:

Metabolic flux-oriented control of bioreactors

Advanced softsensoring: information from NIR, UV,

capacitance probes (less and less expensive)

Data-driven induction in high cell density cultivations for

recombinant m.o.: constructive neural networks (machine

learning)

…

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Rational, structural changes in chains of

production, from field to industry to

distribution...

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Rational, structural changes in chains of

production, from field to industry to

distribution...

A most urgent task, where

surely PSE has a role

62 May 17th, 2019

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“I don’t want you to be hopeful. I want you to panic. I

want you to feel the fear I feel every day. And then I

want you to act,” Greta Thunberg at Davos, January

2019

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Roberto C. Giordano

Antonio C. L. Horta

Antonio J. G. Cruz

Felipe F. Furlan

Marcelo P. A. Ribeiro

Ruy Sousa Jr

Andrew M. Elias

Andreza A. Longati

Christian O. Martins

Ediane S. Alves

Erich Potrich

Gustavo Batista

Harikishan R. Ellamla

Simone C. Miyoshi

Vitor B. Furlong

Wellington M. Santos

…

Argimiro R. Secchi

Roymel Rodríguez-Carpio…

Rafael P. Soares

Collaborations (more frequent)

Teresa C. Zangirolami

Thiago Mesquita…

Raquel L. C. Giordano

Felipe S. Corradini…

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OBRIGADO [email protected]

ACKNOWLEDGEMENTS

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