Presentation of Antonio Bonomi for the Workshop on Hydrolysis Route for Cellulosic Ethanol from Sugarcane. Apresentação de Antonio Bonomi realizada no "Workshop on Hydrolysis Route for Cellulosic Ethanol from Sugarcane" Date / Data : February 10 - 11th 2009/ 10 e 11 de fevereiro de 2009 Place / Local: Unicamp, Campinas, Brazil Event Website / Website do evento: http://www.bioetanol.org.br/workshop1

1. Measuring Success of a New
Ethanol Technology:
Sustainability Assessment of PPDP Results
Centro de Ciência e Tecnologia do Bioetanol – CTBE
Antonio Bonomi
abonomi@bioetanol.org.br
Workshop on Hydrolysis Route for
Cellulosic Ethanol from Sugarcane
February, 2009

2. Virtual Biorefinery
Content:
(1) General Overview - Concepts
(2) Virtual Biorefinery
(3) Simulation
(4) Mathematical Modeling
(5) Sustainability Indicators
(6) Final Remarks

3. General Overview - Concepts
Challenge:
By the very nature of research, one cannot
know where it will lead.
One cannot predict when or which research
will lead to a discovery or outcomes that have
meaning or application.
Source: McGill University, 2005

4. General Overview - Concepts
Why Measuring and Evaluating the Success
 Aids in decisions about whether the project should be
expanded, continued, improved or curtailed;
 Increase the effectiveness of project management;
 To satisfy calls for accountability;
 To measure the impact on the core problem.
Source: RTI International, 2004

5. General Overview - Concepts
Key Concepts
 Evaluation is a process, not an event;
 Evaluation is for practical use, not to sit on the shelf;
 The questions to be answered are derived from the
project itself;
 Compares “What is” with “What would have been” and
“What should be”;
 Takes place in a setting where work/projects are
ongoing.
Source: RTI International, 2004

6. General Overview - Concepts
Project Management Success
Project Success
Project Product Success
Normal
Approach
• Within Time (qualitative)
• Intention to
• Within use / Use
• System Quality
Specifications
• Quality of project • Information Quality • Net benefits
management
• Project stakeholder • Service Quality
satisfaction • User
satisfaction
• Within Budget
Project management success Project product success

7. General Overview - Concepts
Mathematical
LDP, PPDP Modeling Net New
Others • Processes
Approach
• Unit Operations
(quantitative)
Virtual Biorefinery
• Simulation
Process • Process Optimization Assessment of
Alternative • Impacts computation Success Level
• Comparison with
Standard Biorefinery
Impacts: Economical Life Cycle Input -
• economical And Risk Assessment Output
• environmental Assessment (LCA) Assessment
• social

8. General Overview - Concepts
Biorefinery
Oil and Biomass treatment and processing
generate a large variety of products
Refinery Biorefinery
Fuels and
Fuels and Energy
Energy - Bioethanol
- Biodiesel
- Biogas
- Hydrogen
Oil Biomass
Materials and
Materials and Chemical Products
Chemical Products - Conventional chemistry
- Fine chemistry
- Biopolymers

9. General Overview - Concepts
 Parallel with the development of the research agenda, in
order to make the biorefinery technical-economic
concept viable, different alternatives should be
constructed, introducing all the routes/processes that are
being studied and the results that are being achieved.
 The whole process should be modeled, allowing its
simulation, in order to evaluate the economic, social and
environmental impacts that will be attained with the new
technologies under development.

10. General Overview - Concepts
Biorefinaria
Etanol – rota química e
bioquímica

11. Virtual Biorefinery
Steps for the Development of the Virtual Biorefinery
 Definition of a standard biorefinery, including all the production and
all the involved processes.
 Construction of the mathematical models of the different production
units.
 Construction of the tools to be used for the economic and risk
assessment, the life cycle assessment and the input-output
assessment, which will allow to calculate the economic,
environmental and social impacts, of each evaluated biorefinery
alternative.
 Comparison of the calculated impacts with the ones obtained for the
biorefinery defined as standard.

12. Virtual Biorefinery
Alternatives for Evaluation
(1) Conventional plant (producing sugar and ethanol and exporting the
surplus of co-generated electric energy).
(2) Distillery dedicated to the production of ethanol, using the surplus
bagasse and the straw for the production of ethanol through hydrolysis
(acid and enzymatic).
(3) Distillery dedicated to the production of ethanol, using the surplus
bagasse and the straw for the production of liquid fuels (BTL process).
(4) Biorefinery producing different products, besides ethanol and sugar.
(5) Other alternatives / technologies developed in CTBE’s LDP and PPDP or
in any other partner Institution.

13. Virtual Biorefinery
Objectives
 Optimization of the biorefinery processes and
concepts.
 Evaluation of different alternatives of biorefinery,
referring to their sustainability indicators (economic,
environmental and social).
 Evaluation of the level of success of the
new developed (or being developed)
technologies.

14. Simulation
 The simulation platform to be used, quantifies the process
characteristics, the energy requirements and the project
parameters for the major equipments for a particular operational
alternative. Identifies, for each equipment, the volumes, the
compositions and many other physical characteristics of the input
and output fluxes. These information are the basis for the
computation, for each equipment, of the utilities consumption and
price.
The reason to use these simulation packages
- SuperPro Designer is to facilitate the modeling, the optimization
and the technical-economic evaluation of the
- ASPEN Plus integrated processes, in a very wide spectrum
of industries.
- Hysys

15. Simulation
Simulation of Enzyme Production – Submerged Fermentation
Source: Zhuang, 2006

16. Simulation
Simulation of Enzyme Production – Solid State Fermentation
Source: Zhuang, 2006

17. Simulation
Simulation of Anhydrous Ethanol Production Source: Souza Dias, 2008

18. Simulation
Simulation of Sugar-cane Bagasse Hydrolysis Source: Souza Dias, 2008

19. Simulation
Simulation Results:
• Integrated process of ethanol production using sugar-cane juice and
bagasse
Conventional Plant ..... 84.2 l ethanol/ton of cane
Integrated Plant
hydrolyzed bagasse (60%) ..... 96.96 l ethanol/ton cane (15.1%)
hydrolyzed bagasse (70%) ..... 99.36 l ethanol/ton cane (18.0%)
hydrolyzed bagasse (80%) ..... 102.23 l ethanol/ton cane (21.4%)
Source: Souza Dias, 2008

20. Mathematical Modeling
Mathematical Models Construction
(1) Formulation of the Models
(2) Fitting the Models to experimental data
(3) Evaluation of the fitted Models
(4) Simulation of the process / operation using the Models.

21. Mathematical Modeling
Industrial Alcoholic Fermentation
Major identified phenomena:
 cell growth and ethanol and glycerol production are limited by the
glucose and fructose concentrations;
 glycerol production is also limited by the sucrose concentration;
 simultaneous, but preferential consumption of glucose in relation to
fructose for growth and ethanol and glycerol production;
 all the other nutrients are present in a non-limiting concentration;
 cell growth and ethanol and glycerol production are inhibited by
the ethanol concentration.

22. Mathematical Modeling
Industrial Alcoholic Fermentation
• Mathematical Model of the industrial alcoholic fermentation
in a fed-batch operation (IPT, 1995).
State variables:
V ......... fermentation tank volume;
X ......... viable yeast cells concentration;
P ......... ethanol concentration;
G ......... glycerol concentration;
S ......... sucrose concentration;
S1 ........ glucose concentration;
S2 ........ fructose concentration.

23. Mathematical Modeling
Industrial Alcoholic Fermentation
Operational Variables:
F ......... feed rate of the juice to be fermented;
Se ....... sucrose concentration in the feed juice;
S1e ...... glucose concentration in the feed juice;
S2e ...... fructose concentration in the feed juice.
Parameters: Fitted …... 24
Fixed …… 05

24. Mathematical Modeling
Industrial Alcoholic Fermentation
• Balance Equations: • Kinetic Equations:
dV
F Growth rate of viable yeasts:
dt
 K px  m1x S1 m 2 x S2 
dX F
 rX  * X rX     X
 K  P  K  S K  S  S 2 K 
dt V
 px  s1x 1 s2x 2 1 ix 
dP F
 rP  * P Production rate of ethanol:
dt V
dG F
 rG  * G
 K pp  m1 p S1 m 2 p S2 
rP    P
dt V

 K  P  K  S K  S  S 2 K 
 * S e  S   rS  pp  s1 p 1 ip 
dS F s2 p 2 1
dt V
Production rate of glycerol:
 * S 1e  S 1   rS 1
dS 1 F
dt V
 Kpg  m1g S1 m2g S2 msgS 
 * S 2 e  S 2   rS 2
dS 2 F rG      X
 K  P  K  S K  S  S2 K K  S 
dt V
 pg  s1g 1 s 2g 2 1 ig sg 

25. Mathematical Modeling
Industrial Alcoholic Fermentation
• Kinetic Equations (cont.):
Consumption rate of sucrose:
 1  Vm S 
rS  
Y 
 X

 s1s 2 s  K m  S 
Consumption rate of glucose:
 V S   1 K px  m1x S1 
rS1    m  
 K  S X Y K  P K  S X 

 m   xs1 px s1 x 1 
 1 K pp  m1 p S1   1 K pg  m1g S1 
 X  X
   
 Y ps1 K pp  P K s1 p  S1   Ygs1 K pg  P K s1g  S1 
Consumption rate of fructose:
 Vm S   1 K px m2xS2 
rS 2   
 X 
  X 

 K m  S   Y xs 2 K px  P K s 2 x  S 2  S1 K ix 
2
 1 K pp m2 pS2   1 K pg m2g S2 
 X  X
Y K  P K  Y K  P K  S  S K 
s 2 p  S 2  S 1 K ip
2 2
 ps 2 pp   gs 2 pg s2g 2 1 ig 

26. Mathematical Modeling
Industrial Alcoholic Fermentation
State variable: Ethanol (P)

27. Sustainability Indicators
Economic and Risk Analysis
 Usage of Economic Engineering, in order to evaluate the
obtained rentability and the required investments of a new
technology.
 Risk analysis is a technique to identify and assess factors that
may jeopardize the success of a project. This technique also
helps to define preventive measures to reduce the probability
of these factors from occurring and identify countermeasures
to successfully deal with these constraints. Probabilistic risk
assessment is a systematic methodology to evaluate risk
associated with a complex engineered technology.

28. Sustainability Indicators
Life Cycle Assessment (LCA)
 The LCA methodology evaluates and quantifies the environmental
impacts, accounting for the total usage of natural resources and
pollutant emissions.
 LCA methodology accounts the major fluxes of mass and energy,
computing the environmental impacts of the whole production
chain, including the stages from nature extraction of raw-materials
to final disposal of products and co-products.

29. Sustainability Indicators
Life Cycle Assessment (LCA)
 The major environmental impacts calculated in the LCA are:
- depletion of biotic and abiotic resources;
- climate changes;
- stratospheric ozone depletion;
- human toxicity;
- ecotoxicity;
- photo-oxidation formation;
- acidification;
- eutrophication;
- others.

30. Sustainability Indicators
Input-Output Analysis (IO)
 A basic model is generally constructed from observed economic
data for a specific geographic region (for example, a country). One
is concerned about the activity of a group of industries that both
produce goods (outputs) and consume goods from other industries
(inputs) in the process of producing each industry’s own output.
 In CTBE “sustainability program”, an IO model will be developed
and integrated with the virtual biorefinery, in order to evaluate
social-economic and environmental impacts from new technologies
to produce ethanol as well as new products.
 The model is able to compute both direct and indirect effects
involved in the whole production chain to offer the amount required
by final demand.

31. Sustainability Indicators
 Social Indicators - Examples
Improve citizens’ quality of life by:
 creating and retaining high quality jobs;
 creating and retaining high quality companies
(generally in technology-based industries);
 improving the stability and/or competitiveness of
local and regional economy through the innovation.

32. Final Remarks
 For each evaluated technology, the Virtual Biorefinery will generate
a set of indicators.
 Composite indicators should be used with caution, mainly if
weightings are applied to the individual indicators.
 No single composite indicator will ever be able to capture entirely
the results of a new developed technology, risking to generate an
unwarranted impression of accuracy and reliability.
 The measure of success of a new technology will take into account
the set of resulting individual indicators and, through a strategic
analysis, will reach a final conclusion.

33. THANK YOU !!!