基于呼吸商在线检测的谷氨酸发酵过程控制研究
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摘要
本论文以谷氨酸发酵为研究对象,在实验的基础上运用数学建模和计算机仿真对其发酵过程的优化和控制进行了研究。在利用谷氨酸棒杆菌S9114进行谷氨酸发酵过程中,通过代谢网络模型计算发现,控制不同的RQ(呼吸商)水平与代谢通量在谷氨酸、乳酸和TCA循环三者之间的分配有密切的关系。因此,确立了基于RQ在线测量的平衡代谢控制策略,以达到调节TCA循环代谢通量,从而实现提高谷氨酸浓度、抑制代谢产物的目标。通过分析RQ对于搅拌速度大辐阶跃式变化的时间响应,我们发现对RQ的控制可以通过调节转速来实现。为此我们通过转速的PI反馈控制器来实现对RQ的控制,从而使TCA循环的代谢通量保持在一个合适的水平以实现谷氨酸发酵过程的优化,但是,由于RQ和搅拌转速之间存在着响应滞后,RQ的控制性能还有待进一步提高。为此,在取得基本数据后,在RQ与搅拌转速有响应关系的前提下,建立了搅拌转速与RQ之间的动力学模型,并用Matlab软件Simplex(单纯型法)法确定了方程参数。由于在发酵实验中传统的PI反馈控制对RQ的控制性能较差,因此我们建立了一个基于自回归移动平均模型的在线自适应PI反馈控制器,试图对发酵过程控制中的反馈控制器的控制参数进行实时调节,以适应发酵过程时变性特征。通过计算机仿真发现,在不考虑滞后时,无论模型结构是否发生变化,自适应PI控制比传统的PI控制的控制性能要好,并有不需要进行人工整定PI控制参数的优势。
Glutamate fermentation was regarded as research object in the paper, the control and optimization of its fermentation process were studied via mathematical modeling and computer simulation on the basis of experimental research. During glutamate fermentation using Corynebacterium glutamicum S9114, it was found that respiratory quotient (RQ) control was closely related to the distribution of metabolic flux among glutamic acid, lactate and tricarboxylic acid cycle through metabolic reaction network model. To improve the concentration of glutamate and inhibit the metabolic byproduts, the“balanced metabolic control”(BMC) strategy based on the RQ on-line measurement for regulating metabolic flux of tricarboxylic acid cycle was established. According to the study of the time respondence of RQ to large step changes in agitation rate, we found that the RQ control could be achieved by regulating the agitation rate of fermenter. Thus, a PI feedback controller was used to control RQ, so that the metabolic flux of TCA cycle maintained at a suitable level to achieve glutamic acid fermentation process optimization. Because the delay between the RQ with the agitation rate of fermentor, how to improve the control performance must be study. Dynamic model between the agitation rate of fermentor and RQ was constructed applying differential equations on the premise of obtaining basic data and the equation parameter was determinated based on the MATLAB software,using the simplex search method. It was found that the control performance was unsatisfactory by ordinary PI feedback control. So an on-line self-adaptive PI controller based on the auto-regression and moving average model was established, which could real-time adjust PI parameters of the fermentation process. Self-adaptive PI control could improve the performance of RQ control compare to ordinary PI control through computer simulation.
Glutamate fermentation was regarded as research object in the paper, the control and optimization of its fermentation process were studied via mathematical modeling and computer simulation on the basis of experimental research. During glutamate fermentation using Corynebacterium glutamicum S9114, it was found that respiratory quotient (RQ) control was closely related to the distribution of metabolic flux among glutamic acid, lactate and tricarboxylic acid cycle through metabolic reaction network model. To improve the concentration of glutamate and inhibit the metabolic byproduts, the“balanced metabolic control”(BMC) strategy based on the RQ on-line measurement for regulating metabolic flux of tricarboxylic acid cycle was established. According to the study of the time respondence of RQ to large step changes in agitation rate, we found that the RQ control could be achieved by regulating the agitation rate of fermenter. Thus, a PI feedback controller was used to control RQ, so that the metabolic flux of TCA cycle maintained at a suitable level to achieve glutamic acid fermentation process optimization. Because the delay between the RQ with the agitation rate of fermentor, how to improve the control performance must be study. Dynamic model between the agitation rate of fermentor and RQ was constructed applying differential equations on the premise of obtaining basic data and the equation parameter was determinated based on the MATLAB software,using the simplex search method. It was found that the control performance was unsatisfactory by ordinary PI feedback control. So an on-line self-adaptive PI controller based on the auto-regression and moving average model was established, which could real-time adjust PI parameters of the fermentation process. Self-adaptive PI control could improve the performance of RQ control compare to ordinary PI control through computer simulation.
引文
[1]尹光琳,战立克,赵根楠.发酵工业全书[M].中国医药科技出版社, 1996.
[2]张克旭.氨基酸发酵工艺学[M].中国轻工业出版社, 1992.
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[5]欧阳平凯,曹竹安,马宏建等.发酵工程关键技术及其应用[M],化学工业出版社,2005.:455-457.
[6]张嗣良,储炬.多尺度微生物过程优化[M].化学工业出版社,2003.
[7]杨正毅.计算机控制技术在发酵工业中的应用[J],计算机系统应用,1996,7:455-457.
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[10] Simon L,Karim M N. Identification and control of dissolved oxygen in hybridoma cell culture in a shear sensitive environment, Biotechnology Progress,2001,17(4):634～642
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[12] Ben Youssef C, Guillou V. Modelling and adaptive control strategy in a lactic fermentation process. Control Engineering Practice, 2000, 8(11); 1297-1307
[13] Meleiro L A C, Maciel R. A self-turning adaptive control applied to an industrial large scale ethanol production. Computers&Chemical Engineering, 2000, 24(2): 925-930
[14] Filev D P, Kishimoto M. Application of the fuzzy theory to simulation of batch fermentation[J]. Ferment Technol, 1985, 63: 545-557
[15] Siimes T, Nakajima M, Yada H, et al. Object-oriented fuzzy expert system for on-line diagnosing and control of bioprocess[J]. App Microbiol Biotechnol, 1992, 37: 756-761.
[16] Jin S, Ye K, Shimizu K. Efficient fuzzy control strategies for the application of pH-stat to fed-batch cultivation of biotechnology[J], J Chem Technol Biotechnol, 1994, 61:273-281.
[17] Srinivas M, Chidambaram M. Fuzzy logic control of an unstable bioreactor[J]. Biopro Eng, 1995, 12: 135-139.
[18] Horiuchi J I, Kishimoto M, Application of fuzzy control to industrial bioprogresses in Japan. Fuzzy Sets and Systems, 2002, 128(1): 117-124
[19] Fu K S. Learning control systems and intelligent control system: An intersection of artificial intelligent and automatic control[J], IEEE Trans, 1971, 16:70-72.
[20] Thibault J, Breusegem V V. On-line prediction of fermentation variables using prior knowledge and neural networks[J]. Biotechnol Bioeng, 1990, 36: 1041-1048.
[21] Shi Z P, Shimizu K. Fuzzy control for fed-batch fermentation with the aid of neural network[J], IFAC modeling and control of biotechnology processes, Colorado USA, 1992, 167-172.
[22] Z. Shi and Shimizu K. Neuro-fuzzy control of Bioreactor Systems with Pattern Recognition[J]. J.Biosci.Bioeng.1992:39-45
[23] Kurtanjek Z. Modeling and control by artificial neural networks in biotechnology[J]. Comput Chem Eng, 1994, 18: 627-631.
[24] Shioya S, Shimizu H, Takaguchi N. On-line metabolic flux analysis for fermentation operation. Metabolic Engineering. Marcel Dekker Inc. New York, 1999, 227-251
[25] Vallino J J, Stephanopoulos G. Metabolic flux distributions in Corynebacterium glutamicum during growth and lysine overproduction[J]. Biotechnol Bioeng, 1993, 41: 633-646
[26] Takac S, Cahk G, Mavituna F, Dervakos G. Metabolic flux distribution for the optimized production of L-glutamate[J]. Enzyme Microb Technol, 1998, 23:286-300.
[27]吴祖芳,堵国成,陈坚.从代谢流量分布角度探讨培养条件改变下对放射型根瘤菌WSH2601合成辅酶Q10的影响[J].微生物学报,2005,45(2):231-235.
[28] Van Gulik W M, de Laat WTAM, Vinke J L, Heijnen J J. Application of metabolic flux analysis for the identification of metabolic bottlenecks in the biosynthesis of penicillin G. Biotechnol Bioeng, 2000, 68(6):602-618
[29] Zhao J, Baba T, Mori H, et al. Analysis of metabolic and physiological responses to gnd knockout in Eschericha coli by using C-13 tracer experiment and enzyme activity measurement[J]. FEMS Microbiol Lett, 2003, 220:295-301.
[30] Hoque M A, Siddiquee K A, Shimizu K. Metabolic control analysis of gene-knockout Eschericha coli based on the inverse flux analysis with experimental verification[J]. Biochem Eng J, 2004, 19:53-59.
[31] Jin S, Ye K, Shimizu K. Metabolic pathway analysis of recombinant saccharomyces cerevisiae with a galactose-inducible promotor based on a signal flow model approach[J]. J Biosci Bioeng, 1995, 80:541-551.
[32] Shi H, Shimizu K. On-line metabolic pathway analysis based on metabolic signal flow diagram[J]. Biotechnol Bioeng, 1998 ,58:139-148.
[33] Savinell J M, Palsson B O, Network analysis of intermediary metabolism using linear optimization– Development of mathematical formalism[J]. J Theor Biol, 1992, 154:421-454.
[34] Varma A, Boesch B W, Palsson B O, Biochemical production capabilities of Escherichia coli[J]. Biotechnol Bioeng, 1993, 42:59-73.
[35]高学金,王普,孙崇正.微生物发酵过程建模与优化控制.控制工程[J],2006,13(2):152-157
[36]潘丰,李海波,吕光帅.发酵过程的建模与优化方法研究.计算机与应用化学[J],2006,23(11): 1112-1116
[37]刘藻珍,魏华梁.系统仿真[M].北京理工大学出版社,1998
[38]张成燕.利用代谢网络模型和线性规划法在线推定谷氨酸发酵中产物浓度[D].江南大学,2005.
[39] Zhang C Y, Shi Z P, Gao P, et al. On-line prediction of products concentrations on glutamate fermentation using metabolic network model and linear programming [J]. Biochem Eng J, 2005, 25:99-108.
[40]郜培,史仲平等.溶氧浓度对谷氨酸发酵关键酶的影响.食品与发酵工业[J]. 2005,31(10):72-75.
[2]张克旭.氨基酸发酵工艺学[M].中国轻工业出版社, 1992.
[3]王树青,元英进.生化过程自动化技术[M].化学工业出版社, 1999
[4]史仲平,潘丰.发酵过程解析、控制与检测技术[M],化工出版社,2005.
[5]欧阳平凯,曹竹安,马宏建等.发酵工程关键技术及其应用[M],化学工业出版社,2005.:455-457.
[6]张嗣良,储炬.多尺度微生物过程优化[M].化学工业出版社,2003.
[7]杨正毅.计算机控制技术在发酵工业中的应用[J],计算机系统应用,1996,7:455-457.
[8] Luedeking R, and Piret L. A kinetic study of the lactic acid fermentation: Batch process at controlled pH[J]. J Biochem Microbial Technol Eng, 1959, 1: 393-412
[9] Fredrickson A G, Megee R D, Tsuchiya H M. Mathematical models for fermentation process[J]. Adv Appl Microbiol, 1970, 13: 419-465
[10] Simon L,Karim M N. Identification and control of dissolved oxygen in hybridoma cell culture in a shear sensitive environment, Biotechnology Progress,2001,17(4):634～642
[11] Gustasson T K. Dynamic modeling and reaction invariant control of pH[J]. Chem Eng Sci, 1983, 38: 389-398
[12] Ben Youssef C, Guillou V. Modelling and adaptive control strategy in a lactic fermentation process. Control Engineering Practice, 2000, 8(11); 1297-1307
[13] Meleiro L A C, Maciel R. A self-turning adaptive control applied to an industrial large scale ethanol production. Computers&Chemical Engineering, 2000, 24(2): 925-930
[14] Filev D P, Kishimoto M. Application of the fuzzy theory to simulation of batch fermentation[J]. Ferment Technol, 1985, 63: 545-557
[15] Siimes T, Nakajima M, Yada H, et al. Object-oriented fuzzy expert system for on-line diagnosing and control of bioprocess[J]. App Microbiol Biotechnol, 1992, 37: 756-761.
[16] Jin S, Ye K, Shimizu K. Efficient fuzzy control strategies for the application of pH-stat to fed-batch cultivation of biotechnology[J], J Chem Technol Biotechnol, 1994, 61:273-281.
[17] Srinivas M, Chidambaram M. Fuzzy logic control of an unstable bioreactor[J]. Biopro Eng, 1995, 12: 135-139.
[18] Horiuchi J I, Kishimoto M, Application of fuzzy control to industrial bioprogresses in Japan. Fuzzy Sets and Systems, 2002, 128(1): 117-124
[19] Fu K S. Learning control systems and intelligent control system: An intersection of artificial intelligent and automatic control[J], IEEE Trans, 1971, 16:70-72.
[20] Thibault J, Breusegem V V. On-line prediction of fermentation variables using prior knowledge and neural networks[J]. Biotechnol Bioeng, 1990, 36: 1041-1048.
[21] Shi Z P, Shimizu K. Fuzzy control for fed-batch fermentation with the aid of neural network[J], IFAC modeling and control of biotechnology processes, Colorado USA, 1992, 167-172.
[22] Z. Shi and Shimizu K. Neuro-fuzzy control of Bioreactor Systems with Pattern Recognition[J]. J.Biosci.Bioeng.1992:39-45
[23] Kurtanjek Z. Modeling and control by artificial neural networks in biotechnology[J]. Comput Chem Eng, 1994, 18: 627-631.
[24] Shioya S, Shimizu H, Takaguchi N. On-line metabolic flux analysis for fermentation operation. Metabolic Engineering. Marcel Dekker Inc. New York, 1999, 227-251
[25] Vallino J J, Stephanopoulos G. Metabolic flux distributions in Corynebacterium glutamicum during growth and lysine overproduction[J]. Biotechnol Bioeng, 1993, 41: 633-646
[26] Takac S, Cahk G, Mavituna F, Dervakos G. Metabolic flux distribution for the optimized production of L-glutamate[J]. Enzyme Microb Technol, 1998, 23:286-300.
[27]吴祖芳,堵国成,陈坚.从代谢流量分布角度探讨培养条件改变下对放射型根瘤菌WSH2601合成辅酶Q10的影响[J].微生物学报,2005,45(2):231-235.
[28] Van Gulik W M, de Laat WTAM, Vinke J L, Heijnen J J. Application of metabolic flux analysis for the identification of metabolic bottlenecks in the biosynthesis of penicillin G. Biotechnol Bioeng, 2000, 68(6):602-618
[29] Zhao J, Baba T, Mori H, et al. Analysis of metabolic and physiological responses to gnd knockout in Eschericha coli by using C-13 tracer experiment and enzyme activity measurement[J]. FEMS Microbiol Lett, 2003, 220:295-301.
[30] Hoque M A, Siddiquee K A, Shimizu K. Metabolic control analysis of gene-knockout Eschericha coli based on the inverse flux analysis with experimental verification[J]. Biochem Eng J, 2004, 19:53-59.
[31] Jin S, Ye K, Shimizu K. Metabolic pathway analysis of recombinant saccharomyces cerevisiae with a galactose-inducible promotor based on a signal flow model approach[J]. J Biosci Bioeng, 1995, 80:541-551.
[32] Shi H, Shimizu K. On-line metabolic pathway analysis based on metabolic signal flow diagram[J]. Biotechnol Bioeng, 1998 ,58:139-148.
[33] Savinell J M, Palsson B O, Network analysis of intermediary metabolism using linear optimization– Development of mathematical formalism[J]. J Theor Biol, 1992, 154:421-454.
[34] Varma A, Boesch B W, Palsson B O, Biochemical production capabilities of Escherichia coli[J]. Biotechnol Bioeng, 1993, 42:59-73.
[35]高学金,王普,孙崇正.微生物发酵过程建模与优化控制.控制工程[J],2006,13(2):152-157
[36]潘丰,李海波,吕光帅.发酵过程的建模与优化方法研究.计算机与应用化学[J],2006,23(11): 1112-1116
[37]刘藻珍,魏华梁.系统仿真[M].北京理工大学出版社,1998
[38]张成燕.利用代谢网络模型和线性规划法在线推定谷氨酸发酵中产物浓度[D].江南大学,2005.
[39] Zhang C Y, Shi Z P, Gao P, et al. On-line prediction of products concentrations on glutamate fermentation using metabolic network model and linear programming [J]. Biochem Eng J, 2005, 25:99-108.
[40]郜培,史仲平等.溶氧浓度对谷氨酸发酵关键酶的影响.食品与发酵工业[J]. 2005,31(10):72-75.