Measuring Success of a NewEthanol Technology:Sustainability Assessment of PPDP ResultsCentro de Ciência e Tecnologia do Bi...
Virtual BiorefineryContent:(1) General Overview - Concepts(2) Virtual Biorefinery(3) Simulation(4) Mathematical Modeling(5...
General Overview - ConceptsChallenge:      By the very nature of research, one cannot      know where it will lead.      O...
General Overview - ConceptsWhy Measuring and Evaluating the Success   Aids in decisions about whether the project should ...
General Overview - ConceptsKey Concepts   Evaluation is a process, not an event;   Evaluation is for practical use, not ...
General Overview - Concepts                                    Project Management SuccessProject Success                  ...
General Overview - Concepts                                     Mathematical       LDP, PPDP                     Modeling ...
General Overview - ConceptsBiorefinery                            Oil and Biomass treatment and processing                ...
General Overview - Concepts   Parallel with the development of the research agenda, in    order to make the biorefinery t...
General Overview - Concepts                              Biorefinaria                              Etanol – rota química e...
Virtual BiorefinerySteps for the Development of the Virtual Biorefinery   Definition of a standard biorefinery, including...
Virtual BiorefineryAlternatives for Evaluation(1) Conventional plant (producing sugar and ethanol and exporting the    sur...
Virtual BiorefineryObjectives   Optimization of the biorefinery processes and    concepts.   Evaluation of different alt...
Simulation   The simulation platform to be used, quantifies the process    characteristics, the energy requirements and t...
SimulationSimulation of Enzyme Production – Submerged Fermentation                                                       S...
SimulationSimulation of Enzyme Production – Solid State Fermentation                                                      ...
SimulationSimulation of Anhydrous Ethanol Production   Source: Souza Dias, 2008
SimulationSimulation of Sugar-cane Bagasse Hydrolysis   Source: Souza Dias, 2008
SimulationSimulation Results:• Integrated process of ethanol production using sugar-cane juice and  bagasse  Conventional ...
Mathematical ModelingMathematical Models Construction(1) Formulation of the Models(2) Fitting the Models to experimental d...
Mathematical ModelingIndustrial Alcoholic FermentationMajor identified phenomena:   cell growth and ethanol and glycerol ...
Mathematical ModelingIndustrial Alcoholic Fermentation• Mathematical Model of the industrial alcoholic fermentation  in a ...
Mathematical ModelingIndustrial Alcoholic Fermentation  Operational Variables:       F ......... feed rate of the juice to...
Mathematical ModelingIndustrial Alcoholic Fermentation• Balance Equations:              • Kinetic Equations:  dV     F   ...
Mathematical ModelingIndustrial Alcoholic Fermentation• Kinetic Equations (cont.):  Consumption rate of sucrose:         ...
Mathematical ModelingIndustrial Alcoholic Fermentation             State variable: Ethanol (P)
Sustainability IndicatorsEconomic and Risk Analysis   Usage of Economic Engineering, in order to evaluate the    obtained...
Sustainability IndicatorsLife Cycle Assessment (LCA)   The LCA methodology evaluates and quantifies the environmental    ...
Sustainability IndicatorsLife Cycle Assessment (LCA)   The major environmental impacts calculated in the LCA are:    - de...
Sustainability IndicatorsInput-Output Analysis (IO) A basic model is generally constructed from observed economic  data f...
Sustainability Indicators Social Indicators - Examples  Improve citizens’ quality of life by:   creating and retaining h...
Final Remarks   For each evaluated technology, the Virtual Biorefinery will generate    a set of indicators.   Composite...
THANK YOU !!!
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Measuring Succes of a New Ethanol Technology: Sustainability Assessment of PPDP Results

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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

Published in: Technology
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Measuring Succes of a New Ethanol Technology: Sustainability Assessment of PPDP Results

  1. 1. Measuring Success of a NewEthanol Technology:Sustainability Assessment of PPDP ResultsCentro de Ciência e Tecnologia do Bioetanol – CTBEAntonio Bonomiabonomi@bioetanol.org.brWorkshop on Hydrolysis Route forCellulosic Ethanol from Sugarcane February, 2009
  2. 2. Virtual BiorefineryContent:(1) General Overview - Concepts(2) Virtual Biorefinery(3) Simulation(4) Mathematical Modeling(5) Sustainability Indicators(6) Final Remarks
  3. 3. General Overview - ConceptsChallenge: 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. 4. General Overview - ConceptsWhy 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. 5. General Overview - ConceptsKey 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. 6. General Overview - Concepts Project Management SuccessProject 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. 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 BiorefineryImpacts: Economical Life Cycle Input -• economical And Risk Assessment Output• environmental Assessment (LCA) Assessment• social
  8. 8. General Overview - ConceptsBiorefinery 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. 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. 10. General Overview - Concepts Biorefinaria Etanol – rota química e bioquímica
  11. 11. Virtual BiorefinerySteps 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. 12. Virtual BiorefineryAlternatives 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. 13. Virtual BiorefineryObjectives 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. 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. 15. SimulationSimulation of Enzyme Production – Submerged Fermentation Source: Zhuang, 2006
  16. 16. SimulationSimulation of Enzyme Production – Solid State Fermentation Source: Zhuang, 2006
  17. 17. SimulationSimulation of Anhydrous Ethanol Production Source: Souza Dias, 2008
  18. 18. SimulationSimulation of Sugar-cane Bagasse Hydrolysis Source: Souza Dias, 2008
  19. 19. SimulationSimulation 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. 20. Mathematical ModelingMathematical 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. 21. Mathematical ModelingIndustrial Alcoholic FermentationMajor 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. 22. Mathematical ModelingIndustrial 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. 23. Mathematical ModelingIndustrial 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. 24. Mathematical ModelingIndustrial 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. 25. Mathematical ModelingIndustrial 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. 26. Mathematical ModelingIndustrial Alcoholic Fermentation State variable: Ethanol (P)
  27. 27. Sustainability IndicatorsEconomic 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. 28. Sustainability IndicatorsLife 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. 29. Sustainability IndicatorsLife 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. 30. Sustainability IndicatorsInput-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. 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. 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. 33. THANK YOU !!!

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