Simulation and optimization of biological reactor producing butanol

Number of pages: 103 File Format: word File Code: 31783
Year: 2012 University Degree: Master's degree Category: Chemical - Petrochemical Engineering
  • Part of the Content
  • Contents & Resources
  • Summary of Simulation and optimization of biological reactor producing butanol

    Master's Thesis in Chemical Engineering

    Abstract

    Simulation and optimization of biological reactor producing butanol

    Semi-fermentation Continuously, it is an efficient and beneficial method to produce valuable metabolic products such as biofuels. Mathematical modeling of semi-continuous bioreactors is a very difficult and complex issue due to the transitory and unstable nature of fermentation as well as the complexity of cellular metabolism. In this context, some researchers have presented structured models that are more accurate and efficient than unstructured models. In this research, a detailed and efficient dynamic flux balance model has been proposed to describe the behavior of Clostridium acetobutylicum bacteria. This model is the result of combining the stability model of intracellular metabolism and dynamic mass balance equations on the main extracellular components. and the growth of the microorganism, the CoAT genes are deleted in the 824_cellb model and the AAD gene is expressed more than the normal state, and the initial conditions and optimal operating parameters are used for the maximum production. These parameters are: the final operation time, the initial volume of the reactor and the input flow rate. The general process of the semi-continuous operation is divided into two phases: acid (to increase the concentration of bacteria to a significant concentration) and to increase the production. butanol) is divided with a fixed feed rate. The optimization has been done in semi-continuous mode. It is worth mentioning that the results show the importance of gene targeting and gene overexpression in determining the operating conditions of semi-continuous processes, in fact, it can be said that gene targeting and gene overexpression in anaerobic conditions will increase the amount of desired product (butanol) and reduce the amount of unwanted product (ethanol and acetone). The use of structured models based on flux balance analysis, without the need for enzymatic kinetic information, are capable of accurately modeling the behavior of microorganisms. New biology is the most diverse part of natural sciences with a surprising arrangement of various sub-disciplines such as microbiology, animal and plant anatomy, biochemistry, immunology, cell biology, plant and animal physiology, morphogenesis, systematics [1], ecology, plant paleontology, genetics and many other fields. The increasing diversity of new biology comes from the fact that after the Second World War, other scientific disciplines such as physics, chemistry, and mathematics were used in biology and made it possible to describe biological processes at the level of cells and cell nuclei.

    New biology has made an important contribution to human well-being and health. However, what has been achieved so far is very small compared to the promises that will be realized in the shadow of biotechnology.

    Biotechnology[2] has been defined as "the use of organisms or biological processes in production and service industries". Biotechnology is the science that studies the integrated application of biochemistry, microbiology and production technologies in biological systems, for their use in the interdisciplinary nature of science.

    Biotechnology will create completely new industries that require little fossil energy and will change the global economy. In most cases, biotechnological processes are carried out with low energy consumption at low temperature, and in biosynthesis [3] they mainly rely on cheap materials. The industrial activities affected by it include the production of food for humans and animals, provision of chemical raw materials instead of petrochemical sources, alternative sources of energy, circulation of waste in nature, pollution control, agriculture and the production of new materials to help and transform many aspects of medicine, veterinary and pharmaceutical sciences.Internationally, biotechnology holds as much (perhaps more) promise for commercial applications than the microelectronics revolution [4]. In particular, biotechnological industries will be mainly based on renewable and circulating materials and therefore can adapt to the needs of a society where energy is becoming more expensive and scarce. Biotechnology is in many ways still a nascent technology and its developments require skillful control, but its capabilities are wide and varied and will undoubtedly play an increasingly important role in many future industrial processes.

    Biotechnology is inherently an interdisciplinary profession. Biotechnologist [5] uses techniques from chemistry, microbiology, chemical engineering and computer science. Its main goals are innovation, development and optimal implementation of processes in which the biochemical catalyst [6] has a main and irreplaceable role. Biotechnologists should work closely with experts in other related fields such as medicine, nutrition, chemical and pharmaceutical industries, environmental protection and technology for processing waste materials. The origin of many current biotechnological processes goes back to traditional and ancient fermentations such as the production of bread, cheese, yogurt and vinegar. But the discovery of antibiotics[7] in 1929 and then their mass production in the 1940s provided the greatest advances in fermentation technology. Since then, we have witnessed the amazing development of fermentation technology, not only in the production of antibiotics, but also in the production of many useful simple or complex chemical products, for example, organic acids, polysaccharides, enzymes, vaccines, hormones, etc. The main reason for the development of fermentation processes is the close and growing relationship between biochemists, microbiologists and chemical engineers.

    The most important reason for the growing awareness of biotechnology was the realization that fossil fuel resources are limited. Therefore, humans should look for ways to make direct and indirect solar energy usable by using biomass[8]. Many essential chemicals for human survival will be obtained from this biological mass. Although traditional fermentation industries will always play a central role in biotechnology, the hope of biotechnologists today mainly lies in the applications of two biological discoveries, which are:

    a) the development of enzyme technology or engineering, i.e. the use of biological units separated by enzymes in industry and medicine.

    b) genetic engineering, i.e. the use of the newly acquired human ability to transfer genetic information between Completely unrelated and distant organisms, such as plants, animals, and microorganisms.

    These fields basically seek to exploit the discoveries of molecular biology and enzymology[9], and the term biomolecule engineering is used to refer to the combination of these two.

    1-2- Biotechnology- a central core with two components

    Basically, biotechnology can be considered as a central core with two components, in which one component seeks to achieve the best catalyst [10] for a special process or function, and the other, by providing the building and technical implementation, seeks to create the best possible environment for the use of the catalyst.

    In most of the cases that have been developed so far, the most effective, The most suitable and stable form for a catalyst in a biotechnological process has been the whole organism, and for this reason most biotechnological work is based on microbial processes. This issue does not prevent the use of organic organisms, especially the cultivation of plant and animal cells, which will play an important and increasing role in biotechnology. Microorganisms can be considered both as the first stabilizers of photosynthetic energy and as systems that make changes in almost all types of natural and man-made organic molecules. Collectively, they have an infinite gene pool that provides almost unlimited analytical and synthetic potential. In addition, microorganisms have a very fast growth rate compared to all higher organisms such as plants and animals. So, under suitable environmental conditions, in a short period of time, huge amounts of them can be produced.

  • Contents & References of Simulation and optimization of biological reactor producing butanol

    List:

    Title..   Page

    1- Introduction. 2

    1- 1- An introduction to biotechnology. 2

    1-2- Biotechnology- a central core with two components 4

    1-3- An introduction to fermentation processes. 5

    1-3-1- The main parts of the fermentation process. 7

    1-3-2- industrial fermentation culture medium. 8

    2- Review of past works. 11

    2-1- An overview of the applications of semi-continuous culture (non-continuously fed) 11

    2-2- An overview of butanel production through microbial culture. 13

    2-3- An overview of optimization of semi-continuous fermentation processes. 13

    3- Process. 16

    3-1- Fermentor design 17

    3-2- Semi-continuous culture (non-continuous fed) 19

    3-2-1- Advantages of semi-continuous culture (non-continuous fed) 20

    3-3-Butanol (butyl alcohol) 22

    3-3-1- Butanol production methods. 25

    3-3-2-1- Use of butanol as a substitute for fossil fuels. 25

    3-3-1-2-Research conducted in the field of biological production of butanol 27

    Chapter four. 29

    4- Modeling. 30

    4-1- Semi-continuous bioreactor model. 30

    4-2- Growth models of microorganisms 31

    4-2-1- Unstructured models. 31

    4-2-1-1- Monod, Haldane, Konak, Tissier and Moser models 31

    4-2-1-2- Neural network model. 33

    4-2-2- Structured models. 33

    4-2-2-1- Models based on flux balance analysis (FBA) 35

    4-2-2-2- Models based on dynamic flux balance analysis (DFBA) 39

    4-3- Modeling used in this research. 40

    4-3-1- Governing equations. 41 4-4-1- dynamic flux balance analysis model for discontinuous culture of natural (wild) species of Clostridium acetobutylicum bacteria 42 4-4-1-1- determination of optimal parameters of food absorption equations 43 4-4-2 dynamic flux balance analysis model for semi-continuous culture of mutated species of Clostridium acetobutylicum bacteria 50

    5- Optimization. 59

    5-1- Operational strategy. 61

    6- Results, discussion and conclusion. 64

    6-1- The results of optimization. 64

    6-2- Thematic studies. 67

    6-3- Discussion and conclusion. 68

    Resources. 70

    Appendix one. 74

    Appendix Two 80

    Source:

     

    Shuja Al-Sadati, Abbas (1381). Industrial biotechnology. First edition. Tehran: Tarbiat Modares University, Scientific Works Publishing Office.

    Farazmand, Ali (1371). Biotechnology. First edition. Tehran: Allameh Tabatabai University Press.

    Rosta, Ali Akbar (1382). Application of neural networks in obtaining chemical reactor model. Master thesis, Shiraz University.

     

    Agarwal, L. et al. (2007). "Statistical optimization for Butanol production from E. coli in the cost-effective medium." Appl Biochem Biotechnol., Vol. 142, pp. 158-167.

     

    Angira, R. and Babu. B. V. (2006). Optimization of process synthesis and design problems: A modified differential evolution approach. Chemical Engineering Science. 61,4707 – 4721.

     

    Azevedo, S.F. et al. (1997). "Hybrid modeling of biochemical processes: a comparison with conventional approach." Computers Chem Eng., Vol. 21, pp. 751-756.

    Banga, J. R. et al. (2005). "Dynamic optimization of bioprocesses: Efficient and robust numerical strategies." Journal of Biotechnology., Vol. 117, pp.407-419.

    Burgard, A.P. and Van Dien, S.J (2007). Methods and organisms for the growth-coupled production of succinate. Patent Cooperation Treaty (PCT).

     

    Chaundhuri, B. and Modak, J. M (1998). "Optimization of fed-batch bioreactor using neural network model." Bioprocess Engineering., Vol. 19, pp. 71-79.

    Cheon Lee, P. et al. (2000). "Fermentative production of Butanol from glucose and corn steep liquor by Anaerobiospirillum succiniciproducens." Biotechnol Bioprocess Eng., Vol. 5, pp. 379-381. Dhir, S. et al. (2000). "Dynamic optimization of hybridoma growth in a fed-batch bioreactor." Biotechnology and Bioengineering., Vol. 67, No. 2, pp. 197-205.

    Duarte,C. et al. (2004). "Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model." Genome Research., Cold Spring Harbor Laboratory Press ISSN 1088-9051/04, pp.1-13.

     

    Feist, A. M. et al. (2007). "A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information." Molecular Systems Biology., Vol. 3, article number 121, pp.1-18. Feist, A. M. et al. (2006). "Modeling methanogenesis with a genome-scale metabolic reconstruction of Methanosarcina barkeri." Molecular Systems Biology., doi:10.1038/msb4100046, article number: 2006.0004, pp. 1-14.

     

    Hagan. M. T., Demuth H. B., Beale. M (1995). "Neural Network Design", MHB. Inc.

     

    He. et al. (2005). "Batch and fed-batch production of butyric acid by Clostridium butyricum ZJUCB." Journal of Zhejiang Univ. SCI., pp. 1076-1080.

     

    Henson, M.A. (2006). "Exploiting cellular biology to manufacture high-value products." IEEE Control Systems Magazine., pp.54-62.

     

    Hjersted, J.L. et al. (2007). "Genome-scale analysis of Saccharomyces cerevisiae metabolism and ethanol production in fed-batch culture." Biotechnology and Bioengineering., Vol. 97, No. 5, pp. 1190-1204.

     

    Hjersted, J. and Henson, M.A (2006). "Optimization of fed-batch Saccharomyces cerevisiae fermentation using dynamic flux balance models." Biotechnol Prog., Vol. 22, pp. 1239-1249. Isar, J. et al. (2006). "A statistical method for enhancing the production of Butanol from Escherichia coli under anaerobic conditions." Bioresource Technology., Vol. 97, pp.1443–1448.

     

    Ito, T. et al. (1991). "Efficient ethanol production by repeated fed-batch fermentation using two fermentors." Applied Microbiology and Biotechnology., Vol. 36, No. 3, pp. 295-299. Kaelo P. and Ali M.M (2006). "A numerical study of some modified differential evolution algorithms", European Journal of Operational Research. Vol. 169, pp. 1176–1184. Kauffman, K. J. et al. (2003).  "Advances in metabolic flux analysis." Curr Opin Biotechnol., Vol. 14, pp. 491-496.

    Kim, T. Y. et al. (2007). "Genome-Scale Analysis of Mannheimia succiniciproducens Metabolism." Biotechnology and Bioengineering., Vol. 97, No. 4, pp. 657-671.

    Kim, J. S. and Hong S. I (2002). "Ethanol production from xylose by Clostridium thermoaceticum." Theories and Applications of Chem Eng., Vol. 8, No. 2, pp. 3291-3294.

     

    Lee, P. C (2003). "Batch and continuous cultures of Mannheimia succiniciproducens MBEL55E for the production of Butanol from whey and corn steep liquor." Bioprocess Biosyst Eng., Vol. 26, pp. 63-67. Mahadevan, R. et al. (2002). "Dynamic flux balance analysis of diauxic growth in Escherichia coli." Biophysical Journal., Vol. 83, pp. 1331-1340.

     

    McKinlay, J.B., Vieille, C., Zeikus, J.G (2007). Prospects for a bio-based succinate industry. Applied Microbial Biotechnology, 76, 727-740.

     

     

    Oliviera, R (2004). "Combining first principles modeling and artificial neural networks: a general framework." Computers and Chemical Engineering., Vol. 28, pp. 755-766. Parekh, S. and Cheryan, M (1990). "Fed-batch fermentation of glucose to acetate by an improved strain of Clostridium thermoaceticum." Biotechnology Letters., Vol. 12, No. 11, pp. 861-864.

     

    Senger, J.L., Vo, T.D., Schilling, C.H. , Palsson, B.O (2012). An expanded genome-scale model of Clostridium acetobutylicum K-12 (iJR904 GSM/GPR). Genome Biology, 4, R54.

     

    Rocha, I. and Ferreira, E.C (2002). "Optimization methods for improving fed-batch cultivation of E.

Simulation and optimization of biological reactor producing butanol