Submitted by Sandra Pereira (srpereira@ufrrj.br) on 2017-08-18T13:57:44Z
No. of bitstreams: 1
2010 - Diego Dias Carneiro.pdf: 2540734 bytes, checksum: 49c9a669764fa3f9360256c27d7f24e7 (MD5) / Made available in DSpace on 2017-08-18T13:57:44Z (GMT). No. of bitstreams: 1
2010 - Diego Dias Carneiro.pdf: 2540734 bytes, checksum: 49c9a669764fa3f9360256c27d7f24e7 (MD5)
Previous issue date: 2010-03-05 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior, CAPES, Brasil. / Beer is the oldest alcoholic beverage in the world, and its processing has been evolving along the time. Nowadays, beer trading occupies an important position in the economic market since it is the most consumed beverage in Brazil and around the world. Due to this economic significance, the search for more efficient processes that are able to keep the sensorial attributes to the final product represents a great interest for breweries. Fermentation is an important stage of the beer process since in this stage the products and by-products resulted from the yeast metabolism are formed. The detailed study of the fermentative stage of the beer production allows analyzing how the main process variables influence the fermentation and the way they interact each other. To reach this goal, mathematical modeling and computational simulation, were used in this work as a tool for studying the fermentative process. The goals of this study were: i) Select and reproduce through computational simulation, phenomenological models that describe the brewing process; ii) Investigate the effect of manipulate process variables (temperature, pressure and/or flows) over the dynamic behavior of the products and by-products of interest, and; iii) Propose a control strategy that be able to implement optimal temperature profiles in the beer fermentation process. A few dynamics mathematical models that describe the fermentation process were found in the literature. Based on the experimental validation and on the process variables considered, three phenomenological models were selected for the development of this work. It was observed that the manipulate process variables usually affect the dynamic of the fermentation temperature and, as a consequence, the dynamic of the other process variables. A simple control strategy, capable to heat up and refrigerate the fermentation vessel according to the process needs, was proposed in this work to better drive the fermentative process. The proposed control strategy shows very efficient, providing to the process operator facilities to the application of optimal temperature profiles in order to obtain a satisfactory fermentation and leading to a final product with appropriate sensorial attributes for the customer. / A cerveja ? a bebida alco?lica mais antiga do mundo e seu processamento vem evoluindo ao longo do tempo. Atualmente, a comercializa??o da cerveja ocupa uma posi??o de destaque no mercado econ?mico, pois ? a bebida alco?lica mais consumida no Brasil e no mundo. Devido a esta import?ncia econ?mica, a busca por processos mais eficientes e com capacidade de manter a qualidade sensorial do produto final ? de grande interesse para as cervejarias. A fermenta??o ? uma etapa importante do processo cervejeiro, pois ? nessa fase que se formam os produtos e sub-produtos do metabolismo das leveduras. O estudo detalhado sobre a etapa fermentativa da produ??o de cerveja permite analisar como as principais vari?veis de processo influenciam a fermenta??o e o modo como elas interagem. Para atingir esta meta, a modelagem matem?tica, aliada ? simula??o computacional, foi utilizada nessa disserta??o como ferramenta de estudo do processo fermentativo. Os objetivos desta disserta??o foram: i) Selecionar e reproduzir atrav?s de simula??o computacional modelos matem?ticos fenomenol?gicos da etapa de fermenta??o do processo de produ??o cervejeira; ii) Investigar o efeito das vari?veis manipul?veis de processo (temperatura, press?o e/ou vaz?es) sobre o comportamento din?mico dos produtos e subprodutos de interesse, e; iii) Propor uma estrat?gia de controle que seja capaz de implementar de modo eficiente perfis ?timos de temperatura no processo cervejeiro. Foram encontrados poucos modelos din?micos na literatura que representam a etapa fermentativa da produ??o da cerveja. Para o desenvolvimento dessa disserta??o foram utilizados tr?s modelos fenomenol?gicos escolhidos com base em sua valida??o experimental e nas vari?veis de processo consideradas. Observou-se que as vari?veis manipul?veis de processo normalmente influenciam a din?mica da temperatura da fermenta??o e, consequentemente, a din?mica das demais vari?veis do processo. Para a melhor condu??o do processo fermentativo uma estrat?gia de controle simples, capaz de aquecer e refrigerar o tanque de fermenta??o conforme a necessidade do processo, foi proposta nessa disserta??o. A estrat?gia de controle proposta se mostrou bastante eficiente, proporcionando ao operador a possibilidade da aplica??o de perfis ?timos de temperatura que proporcionem a condu??o satisfat?ria da fermenta??o cervejeira, levando a um produto final com os atributos sensoriais adequados para o consumidor.
| Identifer | oai:union.ndltd.org:IBICT/oai:localhost:jspui/1973 |
| Date | 05 March 2010 |
| Creators | Carneiro, Diego Dias |
| Contributors | Meleiros, Luiz Augusto da Cruz, Souza Junior, Maur?cio Bezerra de, Almeida, Andr? de, Henriques, Anderson Wilson da Silva |
| Publisher | Universidade Federal Rural do Rio de Janeiro, Programa de P?s-Gradua??o em Ci?ncia e Tecnologia de Alimentos, UFRRJ, Brasil, Instituto de Tecnologia |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Format | application/pdf |
| Source | reponame:Biblioteca Digital de Teses e Dissertações da UFRRJ, instname:Universidade Federal Rural do Rio de Janeiro, instacron:UFRRJ |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | ANDR?S-TORO B.; G?RON-SIERRA J. M.; L?PEZ-OROZCO J. A.; FERNANDEZ-CONDEZ C.; PEINADO J. M.; GARCIA-OCHOA F. A kinetic model for beer production under industrial operational conditions. Mathematics and Computers in Simulation. v. 48, p. 65-74, 1998. ANDR?S-TORO B.; G?RON-SIERRA J. M.; FERNANDEZ-BLANCO P.; L?PEZ-OROZCO J. A.; BESADA-PORTAS E. Multiobjective optimization and multivariable control of the beer fermentation process with the use of evolutionary algorithms. Journal of the Zhejiang University ? SCIENCE. v. 5, n. 4), p. 378-389, 2004. ANDRIETTA S. R. Modelagem, Simula??o e Controle de Fermenta??o Alco?lica Cont?nua em Escala Industrial, Tese de Doutorado, Universidade Estadual de Campinas ? Faculdade de Engenharia de Alimentos ? FEA ? UNICAMP, Campinas ?SP ? Brasil. BAMFORTH C. BEER: Tap Into The Art of Science of Brewing. 2. ed. Oxford University, 2003. BAMFORTH C. BEER: Health and Nutrition. 2. ed. Blackwell Publishing, 2004. BAMFORTH C.; KANAUCHI M. Enzymology of Vicinal Diketone Reduction in Brewer?s Yeast. Journal of the Institute of Brewing. v. 110, n. 2, p.83-93, 2004. BEQUETTE B. W. Behavior of a CSTR with a Recirculating Jacket Heat Transfer System. Proceedings of the American Control Conference. p. 8-10, 2002. BORZANI, W., SCHMIDELL, W., LIMA, U.A. e AQUARONE, E. Biotecnologia Industrial ? Volume 1: Fundamentos. Editora Edgard Blucher. 2001. BRASIL. Minist?rio da Agricultura. Decreto n? 2.314, de 4 de setembro de 1997. Regulamenta a Lei n? 8.918, de 14 de julho de 1994, que disp?e sobre a padroniza??o, a classifica??o, o registro, a inspe??o, a produ??o e a fiscaliza??o de bebidas. Bras?lia, 1997. BRIGGS D. E.; Brewing Science and Practice. 1. ed. Woodhead Publishing, 2004. BROWN A. K.; HAMMOND J. R. M. Flavour control in small-scale beer fermentations. Trans IChemE, v. 81, p. 40-49, 2003. CERVESIA. A Historia da Cerveja. 2005. Dispon?vel em: < http://www.cervesia.com.br/historia_da_cerveja.asp> Acesso em: 5 dez. 2008. CISA ? Centro de Informa??es Sobre Sa?de e Alco?l. Alco?l e Sistema Hep?tico. 2009. Dispon?vel em: <http://www.cisa.org.br/categoria.html?FhIdCategoria=3af284f8c4b878ce6e44b277189ec95f> Acesso em: 11 nov. 2009. 119 CHUNG, C.A. Simulation Modeling Handbook: A Practical Approach. 1. ed. CRC Press, 2004. CHYLLA, R. W.; RANDALL HAASE, D. Temperature control of semibatch polymerization reactors. Computers & Chemical Engineering. v. 17, n. 3, p. 257-264, 1993. COUTINHO C.A.T. A Historia da cerveja no Brasil. 2005. Dispon?vel em: <http://www.cervesia.com.br/historia_da_cerveja_no_brasil.htm> Acesso em: 5 dez. 2008. de la ROZA C.; LACA A.; GARCIA L. A.; DIAZ M. Ethanol and ethyl acetate production during the cider fermentation from laboratory to industrial scale. Process Biochemistry. v. 38., p. 1451-/1456, 2003. DOBRE T.G.; MARCANO J.G.S. Chemical Engineering - Modelling, Simulation and Similitude. 1. ed. : WILEY-VCH Verlag GmbH & Co. KgaA, 2007. FENNEMA, O. R. Food Chemistry. Ed. Acribia, Zaragoza, 1997. FLEET G. H. Yeasts in foods and beverages: impact on product quality and safety. Current Opinion in Biotechnology. v. 18, p. 170-175, 2007. GARCIA A.; GARCIA L. A.; DIAZ M. Prediction of ester production in industrial beer fermentation. Enzyme and Microbiology Technologv. 16, p. 66-71, 1994. GEE, D. A.; RAMIREZ, W. F. Optimal Temperature Control for Batch Beer Fermentation. Biotechnology and Bioengeneering. v. 31, p. 224-234, 1988. GEE, D. A. Modelling, Optimal Control, State Estimation, and Parameter Identification Applied to a Batch Fermentation Process. Tese de Ph.D., University of Colorado, 1990. GEE, D. A.; RAMIREZ, W. F. A Flavour Model for Beer Fermentation. Journal of the Institute of Brewing. v. 100, p. 321-329, 1994. GEE D. A.; RAMIREZ W. F. On-Line State Estimation and Parameter Identification for Batch Fermentation. Biotechnol. Prog. v. 12, p. 132-140, 1996. G?MEZ N. InBev: Budweiser ocupar? mercado premium no Brasil. Revista Exame. S?o Paulo, jul. 2008. Se??o Neg?cios. Dispon?vel em: <http://portalexame.abril.com.br/ae/negocio/m0164107.html>. Acesso em: 31 jul. 2008. GVAZDAITJS G.; BEIL S.; KREIBAUM U.; SIMUTIS R.; HAVLIK I.; DORS M.; SCHNEIDER F.; L?BBERT A. Temperature Control in Fermenters: Application of Neural Nets and Feedback Control in Breweries. Journal of the Institute of Brewing. v. 100, p. 99-104, 1994. HEPWORTH N.; BROWN A. K.; HAMMOND J. R.; BOYD J. W. R.; VARLEY J. The use of laboratory-scale fermentations as a tool for modelling beer fermentations. Trans IChemE. v. 81, p. 50-56, mar. 2003. 120 HUI Y.H. Food biochemistry and food processing. 1. ed. Blackwell Publishing, 2006. InBev compra fabricante da Budweiser por US$ 52 bi e torna-se l?der global. Folha Online. S?o Paulo, jul. 2008. Se??o Dinheiro. Dispon?vel em: <http://www1.folha.uol.com.br/folha/dinheiro/ult91u422102.shtml>. Acesso em: 31 jul. 2008. JAY J.M. Modern Food Microbiology. 6. ed. Aspen Publishers, 2000. KOBAYASHI K.; KUSAKA K.; TAKAHASHI T.; SATO K. Method for the Simultaneous Assay of Diacetyl and Acetoin in the Presence of a-Acetolactate: Application in Determining the Kinetic Parameters for the Decomposition of a-Acetolactate. Journal of Bioscience and Bioengineering. v. 99, p. 502?507, 2005. KOBAYASHI M.; SHIMIZU H.; SHIOYA S. Beer Volatile Compounds and Their Application to Low-Malt Beer Fermentation. Journal of Bioscience and Bioengineering. v. 106, n. 4, p. 317-323, 2008. KUNZE W. Technology of Brewing and Malting. International Edition.VLB, Berlim, 1999. LANDAUD S.; LIEBEN P.; PICQUE D. Quantitative analysis of diacetyl, pentanedione and their precursors during beer fermentation by an accurate GC /MS method. Journal of the Institute of Brewing. v. 104, p. 93?99, 1997. LINKO M.; HAIKARA A.; RITALA A.; PENTILLA M. Recent advances in the malting and brewing industry. Journal of Biotechnology. v. 75, p. 85-98, 1998. LUYBEN, W.L. Process Modeling, Simulation, and Control for Chemical Engineers. 2. ed. McGraw-Hill, 1996. MADAR J.; ABONYI J. BALASKO B.; SZEIFERT F. Interactive Evolutionary Computation for Model based Optimization of Batch Fermentation. Computers & Chemical Engineering. v. 26, 2002. MADAR J.; SZEIFERT F.; NAGY L.; CHOV?N T.; ABONYI J. Tendency model-based improvement of the slave loop in cascade temperature control of batch process units. Computers & Chemical Engineering. v. 28, n. 5, p. 737-744, 2004. MITTAL G. S. Food Biotechnology. Technoni Publishing Co., Lancaster, 1992. MOREIRA A. R.; WALLACE K.K. Computer-based studies on bioprocess engineering ii - tools for process operation. Computer and Information Science Applications in Bioprocess Engineering, v.1, p. 27-49, 1996. NELSON M. The Barbarian's Beverage: A History of Beer in Ancient Europe. 1. ed. Routledge, 2005. OLIVEIRA, J. A.; GAMBETTA, R.; PINTO, J. C. Pega Ponto 1.0, Rio de Janeiro, 2006, CD-ROM. 121 PALMER J. Sensory Analysis of Beer. Zymurgy Magazine. 2007. Dispon?vel em: <http://www.beertown.org/homebrewing/zymurgy_magazine/pdf/MJzym07_SensoryAnalysis.pdf>. Acesso em: 31 nov. 2009. PAPAZIAN C. The New Complete Joy of Home Brewing. 2. ed. Avon Books, 1991. PHISALAPHONG M.; SRIRATTANA N.; TANTHAPANICHAKOON W. Mathematical modeling to investigate temperature effect on kinetic parameters of ethanol fermentation. Biochemical Engineering Journal. v. 28, p. 36?43, 2006. RAMIREZ W. F.; MACIEJOWSKI J. Optimal Beer Fermentation. Journal of the Institute of Brewing. v. 113, p. 325?333, 2007. RENGER R. S.; VAN HATENER S. H.; LUYBEN K. C. A. M. The formation of esters and higher alcohols during beer fermentation; the effect of carbon dioxide pressure. Journal of the Institute of Brewing. v. 98, p. 509-513, 1992. RIVEROL C.; COONEY J. Estimation of the ester formation during beer fermentation using neural networks. Journal of Food Engineering. v. 82, p. 585?588, 2007. ROSENSTOCK T. R.; COIMBRA V. S. Cerveja. 1999. In: Universidade Federal de S?o Paulo ? Escola Paulista de Medicina. Dispon?vel em: < http://www.virtual.epm.br/material/tis/curr-bio/trab99/alcool/index.htm> Acesso em: 5 dez. 2008. RUSSEL I.; STEWART G.G. Brewing. In: NAGODAWITHANA T.W.; REED G. Vol. 9. Enzymes, biomass, food and feed. 2. ed. Weinheim: WILEY-VCH Verlag GmbH & Co. KgaA, 1995. SEBORG D. E.; EDGAR T. F.; MELLICHAMP D. A. Process Dynamics and Control. 1.ed. John WIley & Sons, 1989 SENAD ? Secretaria Nacional Antidrogas. Cerveja ? a bebida mais consumida no Brasil. Dispon?vel em: <www.senad.gov.br/releases/Cerveja_Brasil.pdf> Acesso em: 21 fev. 2009. SHENGLI Z.; DETANG W.; XINHONG Z.; ZHIZONG Z. Modelling and control of beer fermenting technology process. Proceedings of the IEEE International Conference. v. 5, n. 9, p. 775-778, 1994. SHETTY K.; PALIYATH G.; POMETTO A.; LEVIN R. E. Food Biotechnology. 2. ed. Taylor & Francis Group, 2006. SMOGROVICOVA D.; DOMENY Z. Beer volatile by-product formation at different fermentation temperature using immobilised yeasts. Process Biochemistry . v. 34, p. 785?794, 1998. SOARES M. R. Alternativas tecnol?gicas para otimizar o processo da produ??o de cerveja. 1. ed. 2005 122 TITICA M.; LANDAUD S.; TRELEA I. C.; LATRILLE E.; CORRIEU G.; CHERUY A. Modeling of the Kinetics of Higher Alcohol and Ester Production Based on CO2 Emission with a View to Control of Beer Flavor by Temperature and Top Pressure. Journal of the American Society of Brewers and Chemistries. v. 58, n. 4, p. 167-174, 2000. TRELEA I. C.; LATRILLE E.; LANDAUD S.; CORRIEU G. Reliable estimation of the key variables and of their rates of change in the alcoholic fermentation. Bioprocess and Biosystems Enginnering. v. 24, p. 227-237, 2001a. TRELEA I. C.; TITICA M.; LANDAUD S.; LATRILLE E.; CORRIEU G.; CHERUY A. Predictive modelling of brewing fermentation: from knowledge-based to black-box models. Mathematics and Computers in Simulation. v. 56, p. 405?424, 2001b. TRELEA I. C.; LANDAUD S.; LATRILLE E.; CORRIEU G. Prediction of Confidence Limits for Diacetyl Concentration During Beer Fermentation Journal of the American Society of Brewers and Chemistries. v. 59, p. 77-87, 2002. TRELEA I. C.; TITICA M.; CORRIEU G. Dynamic optimisation of the aroma production in brewing fermentation. Journal of Process Control. v. 14 , p. 1-16, 2004. WILLAERT R.; NEDOVIC V. A. Review Primary beer fermentation by immobilized yeast ? a review on flavour formation and control strategies. Journal of Chemistry Technology and Biotechnology. v. 81, p.1353?1367, 2006. WOLF-HALL C. E. Mold and mycotoxin problems encountered during malting and brewing. International Journal of Food Microbiology. v. 119, p. 89?94, 2007. XIAO J.; ZHOU Z.; ZHANG G. Ant colony system algorithm for the optimization of beer fermentation control. Univ SCI. v. 5, n.. 12, p.1597-1603, 2004. |
Page generated in 0.0041 seconds