Method and system for scaled design of internal flow field environment in industrial-scale bioreactors

Abstract

The present application provides a method for scaled design of the internal flow field environment in an industrial-scale bioreactor, including input, static simulation, dynamic simulation, and output; in the input step, built-in parameters and conditional parameters are input; in the static simulation step, calculations are performed on the built-in parameters and conditional parameters to obtain static simulation results; in the dynamic simulation step, the static simulation results, built-in parameters, and conditional parameters are cyclically iterated until the cyclic iteration terminates to obtain dynamic simulation results; in the output step, the static simulation results and dynamic simulation results are output; the present application also provides a system adopting the above method. The scaled design method and system provided by the present application can achieve the observation of the dynamic changes of key metabolic parameters during the fermentation process.

Description

Technical Field

[0001] The present application relates to the technical field of scaled - down design of the flow field environment in bioreactors, and specifically to a method and system for scaled - down design of the flow field environment in industrial - scale bioreactors. Background Art

[0002] In industrial - scale fermentation practice, due to non - ideal mixing and mass transfer limitations, as well as rapid metabolic conversion caused by high cell density, substrate concentration gradients often form in the reactor, which in turn affects the physiological metabolism of the cells. Currently, laboratory - scale multi - chamber scaled - down simulation systems reproduce the typical substrate concentration gradients experienced by cells in large - scale bioreactors by maintaining different substrate levels in different compartments (reactors), efficiently and economically exploring the metabolic regulation mechanism caused by substrate concentration gradients and realizing phenotype evaluation during the industrial scale - up process. So far, most of the studies on multi - chamber scaled - down systems for substrate concentration gradients have remained at the two levels of substrate excess and substrate limitation experienced by the cells, and the real flow field environment experienced by the cells in large - scale bioreactors has not been considered.

[0003] There are three forms of substrate utilization states in typical large - scale bioreactors, namely substrate excess, substrate limitation, and substrate starvation. There is currently no report on scaled - down design according to the above three substrate utilization states, and the large - scale flow field environment has not been considered in the reports on two - chamber and multi - chamber systems.

[0004] Generally, the chemostat culture mode is adopted in scaled - down experiments because this mode has many advantages, such as controlling specific specific growth rates, specific substrate uptake rates, oxygen uptake rates, and carbon dioxide release rates during the process, so the physiological state of the cells is repeatable. However, in scaled - down experiments, how to successfully maintain the substrate concentration gradients and cell concentration stability in three compartments during a long fermentation cycle has become a difficult point in experimental design. Some parameter deviations can easily lead to the collapse of the entire system: for example, too high a circulation rate will cause the substrate concentration gradient not to be generated, and too low a cell concentration will cause the generated gradient to disappear quickly; for another example, if the feeding sugar rate cannot be consistent with the consumption rate of the system, it will also cause the collapse of the system sugar concentration gradient. Summary of the Invention

[0005] The purpose of the present application is to provide a method and system for scaled - down design of the flow field environment in industrial - scale bioreactors, so as to solve the problems of dividing bioreaction compartments according to the above three substrate utilization states, determining parameters such as initial cell concentration, feeding rate, and feeding concentration during the early stage of fermentation in a large - scale flow field environment, and quickly predicting the dynamic changes of parameters in the subsequent chemostat process.

[0006] To achieve the above - mentioned purpose, the present application provides the following technical solutions:

[0007] A method for scaling design of the internal flow field environment in an industrial-scale bioreactor, comprising the following steps:

[0008] Input, static simulation, dynamic simulation, output;

[0009] In the input step, built-in parameters and conditional parameters are input;

[0010] In the static simulation step, the built-in parameters and conditional parameters are calculated to obtain a static simulation result;

[0011] In the dynamic simulation step, the static simulation result, built-in parameters and conditional parameters are iteratively looped until the loop iteration terminates to obtain a dynamic simulation result;

[0012] In the output step, the static simulation result and the dynamic simulation result are output.

[0013] In some embodiments of the present application, the static simulation includes residence time simulation, initial substrate concentration simulation, substrate utilization criterion, and system stability criterion.

[0014] In some embodiments of the present application, the residence time simulation is

[0015]

[0016] V i = V0 * k i

[0017]

[0018] τ0 = τ1 + τ2 + τ3

[0019] where i = 1, 2, 3, i represents different compartments. When i = 1, it represents the first compartment with excess substrate. When i = 2, it represents the second compartment with substrate limitation. When i = 3, it represents the third compartment with substrate starvation.

[0020] f i (t i ) is the probability that the bacteria stay in the compartment,

[0021] t i is the residence time distribution of the bacteria in the i-th compartment,

[0022] τ i is the average residence time of the bacteria in the i-th compartment,

[0023] τ0 is the total average residence time of the bacteria in the system,

[0024] V i is the liquid filling volume of the i-th compartment,

[0025] V0 is the total liquid volume of the system assembly,

[0026] k i is the ratio of the liquid volume in the i-th compartment to the total liquid volume of the system,

[0027] is the circulation flow rate of the i-th compartment.

[0028] In some embodiments of the present application, the initial substrate concentration is simulated as

[0029] C0 = M0 / F

[0030] a = F

[0031]

[0032] where C0 is the substrate concentration of the fed liquid,

[0033] M0 is the total amount of substrate fed into the system per unit time,

[0034] F is the flow rate of the feeding liquid flowing into the system during the chemostat process,

[0035] q i,s is the initial specific substrate uptake rate of the i-th compartment,

[0036] C i,x is the initial cell concentration of the i-th compartment,

[0037] q s,max is the theoretical maximum specific substrate uptake rate,

[0038] k m is the substrate affinity constant for substrate uptake,

[0039] C i,s is the initial substrate concentration of the i-th compartment.

[0040] In some embodiments of the present application, the substrate utilization criterion is

[0041]

[0042] where,

[0043] when i = 1, when i = 2, when i = 3, The system stability criterion is

[0044]

[0045] where q i,s,expectis the expected specific substrate uptake rate of the i-th compartment,

[0046] η i is the system stability criterion for the i-th compartment,

[0047] When i = 1 or 3 and η i < 1‰, the system stability is good;

[0048] When i = 2, q 2,s,expect = q 2,s , η2 = 0.

[0049] In some embodiments of the present application, the dynamic simulation includes the calculation of the dynamic substrate concentration, the calculation of the dynamic cell concentration, the simulation of the product output process, and the calculation of the oxygen uptake rate and carbon dioxide release rate.

[0050] In some embodiments of the present application, the calculation of the dynamic substrate concentration is

[0051] q 1,t,H = 0.2*μ i,t

[0052] F 1,t,H = V1*q 1,t,H *C i,t,s / C Lye

[0053]

[0054]

[0055] where q i,t,H is the hydrogen ion release rate of the i-th compartment at time t,

[0056] μ i,t is the specific growth rate of the cells in the i-th compartment at time t,

[0057] F i,t,H is the flow rate of the alkali solution replenished into the system by the i-th compartment at time t,

[0058] C i,t,s is the substrate concentration of the i-th compartment at time t,

[0059] C Lye is the concentration of the NaOH replenished,

[0060] C i,t,x is the cell concentration of the i-th compartment at time t,

[0061] F evap is the evaporation flow rate of a single compartment flowing out of the system,

[0062] qi,t,s is the specific substrate uptake rate of the i-th compartment at time t.

[0063] In some embodiments of the present application, the calculation of the dynamic cell concentration is

[0064]

[0065] where μ i,t is the specific growth rate of the i-th compartment at time t,

[0066] μ max is the theoretical maximum specific growth rate,

[0067] C x,max is the theoretical maximum cell concentration,

[0068] k s is the substrate affinity constant for cell growth.

[0069] In some embodiments of the present application, the process simulation of the product output is

[0070]

[0071]

[0072] where q i,t,p is the penicillin production rate of the i-th compartment at time t,

[0073] b is the production coefficient of penicillin,

[0074] K p is the inhibition coefficient for substrate concentration,

[0075] k dE is the decay coefficient of the enzyme,

[0076] C i,t,p is the penicillin concentration of the i-th compartment at time t.

[0077] In some embodiments of the present application, the calculation of the oxygen uptake rate and carbon dioxide release rate is

[0078] q i,t,PAA =-q i,t,p

[0079]

[0080] where q i,t,p is the penicillin production rate of the i-th compartment at time t, q i,t,PAA is the phenylacetic acid production rate of the i-th compartment at time t,

[0081] is the specific oxygen uptake rate of the i-th compartment at time t,

[0082] is the specific carbon dioxide uptake rate of the i-th compartment at time t,

[0083] OUR i,t is the total oxygen uptake rate of the i-th compartment at time t,

[0084] CER i,t is the total carbon dioxide release rate of the i-th compartment at time t.

[0085] In some embodiments of the present application, the condition parameters are the total amount of substrate replenished into the system per unit time, the flow rate of the feeding liquid flowing into the system during the chemostat process, the concentration of NaOH replenished, the circulation flow rate of the i-th compartment, the evaporation flow rate of a single compartment flowing out of the system, the initial cell concentration of the i-th compartment, the desired specific substrate uptake rate of the i-th compartment, the liquid volume of the i-th compartment, and the ratio of the liquid volume of the i-th compartment to the total liquid volume of the system;

[0086] The built-in parameters are the substrate affinity constant for substrate uptake, the theoretical maximum specific substrate uptake rate, the substrate affinity constant for cell growth, the theoretical maximum cell concentration, the theoretical maximum specific growth rate, the decay coefficient of the enzyme, the inhibition coefficient for substrate concentration, and the production coefficient of penicillin;

[0087] The static output results are the average residence time of the cells in the i-th compartment, the total average residence time of the cells in the system, the initial substrate concentration of the i-th compartment, the initial specific substrate uptake rate of the i-th compartment, and the system stability criterion of the i-th compartment;

[0088] The dynamic output results are the cell concentration of the i-th compartment at time t, the substrate concentration of the i-th compartment at time t, the penicillin production rate of the i-th compartment at time t, the total oxygen uptake rate of the i-th compartment at time t, the total carbon dioxide release rate of the i-th compartment at time t, and the penicillin concentration of the i-th compartment at time t.

[0089] In some embodiments of the present application, the cells include Penicillium chrysogenum.

[0090] To achieve the above object, the present application also provides a technical solution:

[0091] A system for scaling design of the internal flow field environment in an industrial-scale bioreactor, adopting the above method, includes an input module, a static simulation module, a dynamic simulation module, and an output module;

[0092] In the input module, built-in parameters and condition parameters are input;

[0093] In the static simulation module, the built-in parameters and conditional parameters are calculated to obtain a static simulation result;

[0094] In the dynamic simulation module, the static simulation result, built-in parameters, and conditional parameters are iterated cyclically until the cyclic iteration terminates to obtain a dynamic simulation result;

[0095] In the output module, the static simulation result and the dynamic simulation result are output.

[0096] In some embodiments of the present application, the static simulation module includes residence time simulation, initial substrate concentration simulation, substrate utilization criterion, and system stability criterion;

[0097] The dynamic simulation module includes operations on the dynamic concentration of the substrate, operations on the dynamic concentration of the thallus, simulation of the production process of the product, operations on the oxygen absorption rate and carbon dioxide release rate.

[0098] In some embodiments of the present application, the conditional parameters are the total amount of substrate replenished into the system per unit time, the flow rate of the feed liquid flowing into the system during the chemostat process, the concentration of NaOH replenished, the circulation flow rate of the i-th compartment, the evaporation flow rate of a single compartment flowing out of the system, the initial thallus concentration of the i-th compartment, the desired specific substrate absorption rate of the i-th compartment, the liquid volume of the i-th compartment, and the ratio of the liquid volume of the i-th compartment to the total liquid volume of the system;

[0099] The built-in parameters are the substrate affinity constant for substrate absorption, the theoretical maximum specific substrate absorption rate, the substrate affinity constant for thallus growth, the theoretical maximum thallus concentration, the theoretical maximum specific growth rate, the decay coefficient of the enzyme, the inhibition coefficient for substrate concentration, and the production coefficient of penicillin;

[0100] The static output result is the average residence time of the thallus in the i-th compartment, the total average residence time of the thallus in the system, the initial substrate concentration of the i-th compartment, the initial specific substrate absorption rate of the i-th compartment, and the system stability criterion of the i-th compartment;

[0101] The dynamic output result is the thallus concentration of the i-th compartment at time t, the substrate concentration of the i-th compartment at time t, the penicillin production rate of the i-th compartment at time t, the total oxygen absorption rate of the i-th compartment at time t, the total carbon dioxide release rate of the i-th compartment at time t, and the penicillin concentration of the i-th compartment at time t.

[0102] The beneficial effects of the present application are:

[0103] 1. The present application provides a scaling design method for assisting the flow field environment of a bioreactor with a simple and easy-to-understand operating system. Relying on Matlab, for the substrate concentration gradient (substrate excess, substrate limitation, and substrate starvation) in a large-scale penicillin fermenter, the determination of initial parameters including feeding rate, feeding concentration, circulation rate, cell concentration, and substrate concentration, etc. in the design process of a laboratory-scale three-compartment scaling reactor is realized, as well as the prediction of the dynamic changes of key metabolic parameters including cell concentration, specific production rate, substrate concentration, specific substrate consumption rate, oxygen uptake rate, etc. during the subsequent continuous fermentation experiment.

[0104] 2. The present application supports the functions of uploading and saving the kinetic parameters of Penicillium chrysogenum, which is convenient for data entry. The simulated data is convenient for export and saving, and can be used for further processing.

[0105] 3. The dynamic simulation process of the present application is rapid and accurate, and a 100-hour fermentation process can be completed within 20 seconds. BRIEF DESCRIPTION OF THE DRAWINGS

[0106] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0107] Figure 1 It is a schematic flow chart of a method and system for scaling design of the flow field environment in an industrial-scale bioreactor. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0108] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all of them. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0109] The present application provides a method and system for scaling design of the flow field environment in an industrial-scale bioreactor, which will be described in detail below. It should be noted that the description order of the following embodiments does not limit the preferred order of the embodiments of the present application. And in the following embodiments, each embodiment is described with emphasis. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0110] Embodiment 1

[0111] As Figure 1As shown, in the embodiments of the present application, a method for scaling design of the internal flow field environment in an industrial-scale bioreactor includes the following steps:

[0112] Input, static simulation, dynamic simulation, output;

[0113] In the input step, built-in parameters and conditional parameters are input;

[0114] In the static simulation step, the built-in parameters and conditional parameters are calculated to obtain a static simulation result;

[0115] In the dynamic simulation step, the static simulation result, built-in parameters, and conditional parameters are iteratively looped until the loop iteration terminates to obtain a dynamic simulation result;

[0116] In the output step, the static simulation result and the dynamic simulation result are output.

[0117] In some embodiments of the present application, the static simulation includes residence time simulation, initial substrate concentration simulation, substrate utilization criterion, and system stability criterion.

[0118] In some embodiments of the present application, the residence time simulation is

[0119]

[0120] V i = V0 * k i

[0121]

[0122] τ0 = τ1 + τ2 + τ3

[0123] where i = 1, 2, 3, i represents different compartments. When i = 1, it represents the first compartment with excess substrate. When i = 2, it represents the second compartment with substrate limitation. When i = 3, it represents the third compartment with substrate starvation.

[0124] f i (t i ) is the probability that the bacteria stay in the compartment,

[0125] t i is the residence time distribution of the bacteria in the i-th compartment,

[0126] τ i is the average residence time of the bacteria in the i-th compartment,

[0127] τ0 is the total average residence time of the bacteria in the system,

[0128] V iis the liquid filling volume of the i-th compartment,

[0129] V0 is the total liquid filling volume of the system,

[0130] k i is the ratio of the liquid filling volume of the i-th compartment to the total liquid filling volume of the system,

[0131] is the circulation flow rate of the i-th compartment.

[0132] In some embodiments of the present application, the initial substrate concentration is simulated as

[0133] C0 = M0 / F

[0134] a = F

[0135]

[0136]

[0137] where C0 is the substrate concentration of the supplemented feed liquid,

[0138] M0 is the total amount of substrate supplemented to the system per unit time,

[0139] F is the flow rate of the supplemented feed liquid flowing into the system during the chemostat process,

[0140] q i,s is the initial specific substrate uptake rate of the i-th compartment,

[0141] C i,x is the initial cell concentration of the i-th compartment,

[0142] q s,max is the theoretical maximum specific substrate uptake rate,

[0143] k m is the substrate affinity constant for substrate uptake,

[0144] C i,s is the initial substrate concentration of the i-th compartment.

[0145] In some embodiments of the present application, the substrate utilization criterion is

[0146]

[0147] where,

[0148] when i = 1, when i = 2, when i = 3,

[0149] The system stability criterion is

[0150]

[0151] where q i,s,expect is the expected specific substrate uptake rate of the i-th compartment,

[0152] η i is the stability criterion of the i-th compartment system,

[0153] When i = 1 or 3 and η i < 1‰, the system stability is good;

[0154] When i = 2, q 2,s,expect = q 2,s , η2 = 0.

[0155] In some embodiments of the present application, the dynamic simulation includes the calculation of the dynamic substrate concentration, the calculation of the dynamic cell concentration, the simulation of the product output process, and the calculation of the oxygen uptake rate and the carbon dioxide release rate.

[0156] In some embodiments of the present application, the calculation of the dynamic substrate concentration is

[0157] q 1,t,H = 0.2 * μ i,t

[0158] F 1,t,H = V1 * q 1,t,H * C i,t,s / C Lye

[0159]

[0160] where q i,t,H is the hydrogen ion release rate of the i-th compartment at time t,

[0161] μ i,t is the specific growth rate of the cells in the i-th compartment at time t,

[0162] F i,t,H is the flow rate of the lye supplemented into the system by the i-th compartment at time t,

[0163] C i,t,s is the substrate concentration of the i-th compartment at time t,

[0164] C Lye is the concentration of the added NaOH,

[0165] C i,t,x is the cell concentration of the i-th compartment at time t,

[0166] F evap is the evaporation flow rate of a single compartment flowing out of the system,

[0167] q i,t,s is the specific substrate uptake rate of the i-th compartment at time t.

[0168] In some embodiments of the present application, the calculation of the dynamic cell concentration is

[0169]

[0170]

[0171] where μ i,t is the specific growth rate of the i-th compartment at time t,

[0172] μ max is the theoretical maximum specific growth rate,

[0173] C x,max is the theoretical maximum cell concentration,

[0174] k s is the substrate affinity constant for cell growth.

[0175] In some embodiments of the present application, the process simulation of the product output is

[0176]

[0177]

[0178] where q i,t,p is the penicillin production rate of the i-th compartment at time t,

[0179] b is the production coefficient of penicillin,

[0180] K p is the inhibition coefficient for substrate concentration,

[0181] k dE is the decay coefficient of the enzyme,

[0182] C i,t,p is the penicillin concentration of the i-th compartment at time t.

[0183] In some embodiments of the present application, the calculation of the oxygen uptake rate and carbon dioxide release rate is

[0184] q i,t,PAA =-q i,t,p

[0185]

[0186] where q i,t,p is the penicillin production rate of the i-th compartment at time t,

[0187] q i,t,PAA The production rate of phenylacetic acid in the i-th compartment at time t,

[0188] The specific oxygen uptake rate in the i-th compartment at time t,

[0189] The specific carbon dioxide uptake rate in the i-th compartment at time t,

[0190] OUR i,t The total oxygen uptake rate in the i-th compartment at time t,

[0191] CER i,t The total carbon dioxide release rate in the i-th compartment at time t.

[0192] In some embodiments of the present application, the condition parameters are the total amount of substrate replenished into the system per unit time, the flow rate of the feed solution flowing into the system during the chemostat process, the concentration of NaOH replenished, the circulation flow rate of the i-th compartment, the evaporation flow rate of a single compartment flowing out of the system, the initial cell concentration of the i-th compartment, the desired specific substrate uptake rate of the i-th compartment, the liquid volume of the i-th compartment, and the ratio of the liquid volume of the i-th compartment to the total liquid volume of the system;

[0193] The built-in parameters are the substrate affinity constant for substrate uptake, the theoretical maximum specific substrate uptake rate, the substrate affinity constant for cell growth, the theoretical maximum cell concentration, the theoretical maximum specific growth rate, the decay coefficient of the enzyme, the inhibition coefficient for substrate concentration, and the production coefficient of penicillin;

[0194] The static output results are the average residence time of the cells in the i-th compartment, the total average residence time of the cells in the system, the initial substrate concentration of the i-th compartment, the initial specific substrate uptake rate of the i-th compartment, and the system stability criterion of the i-th compartment;

[0195] The dynamic output results are the cell concentration in the i-th compartment at time t, the substrate concentration in the i-th compartment at time t, the penicillin production rate in the i-th compartment at time t, the total oxygen uptake rate in the i-th compartment at time t, the total carbon dioxide release rate in the i-th compartment at time t, and the penicillin concentration in the i-th compartment at time t.

[0196] In some embodiments of the present application, the cells include Penicillium chrysogenum.

[0197] The above method for scaling design of the internal flow field environment in an industrial-scale bioreactor can use Matlab as a platform to perform the actions of the above input, static simulation, dynamic simulation, and output, and obtain the static simulation result and the dynamic simulation result. It can also use other programming platforms or programming languages according to the actual situation to achieve the same effect.

[0198] Embodiment 2

[0199] As Figure 1 shown, in the embodiment of the present application, a system for scaling design of the internal flow field environment in an industrial-scale bioreactor adopts the method described in Embodiment 1, and includes an input module, a static simulation module, a dynamic simulation module, and an output module;

[0200] In the input module, built-in parameters and conditional parameters are input;

[0201] In the static simulation module, the built-in parameters and conditional parameters are calculated to obtain a static simulation result;

[0202] In the dynamic simulation module, the static simulation result, built-in parameters, and conditional parameters are iterated cyclically until the cyclic iteration terminates, and a dynamic simulation result is obtained;

[0203] In the output module, the static simulation result and the dynamic simulation result are output.

[0204] In some embodiments of the present application, the static simulation module includes residence time simulation, initial substrate concentration simulation, substrate utilization criterion, and system stability criterion;

[0205] The dynamic simulation module includes operations of dynamic substrate concentration, dynamic cell concentration, simulation of product production process, oxygen absorption rate, and carbon dioxide release rate.

[0206] In some embodiments of the present application, the conditional parameters are the total amount of substrate replenished into the system per unit time, the flow rate of the feed solution flowing into the system during the chemostat process, the concentration of NaOH replenished, the circulation flow rate of the i-th compartment, the evaporation flow rate of a single compartment flowing out of the system, the initial cell concentration of the i-th compartment, the desired specific substrate absorption rate of the i-th compartment, the liquid volume of the i-th compartment, and the ratio of the liquid volume of the i-th compartment to the total liquid volume of the system;

[0207] The built-in parameters are the substrate affinity constant for substrate absorption, the theoretical maximum specific substrate absorption rate, the substrate affinity constant for cell growth, the theoretical maximum cell concentration, the theoretical maximum specific growth rate, the enzyme decay coefficient, the inhibition coefficient for substrate concentration, and the penicillin production coefficient;

[0208] The static output results are the average residence time of the bacteria in the i-th compartment, the total average residence time of the bacteria in the system, the initial substrate concentration in the i-th compartment, the initial specific substrate uptake rate in the i-th compartment, and the system stability criterion for the i-th compartment;

[0209] The dynamic output results are the concentration of bacteria in the i-th compartment at time t, the substrate concentration in the i-th compartment at time t, the penicillin production rate in the i-th compartment at time t, the total oxygen uptake rate in the i-th compartment at time t, the total carbon dioxide release rate in the i-th compartment at time t, and the penicillin concentration in the i-th compartment at time t.

[0210] In some embodiments of the present application, the bacteria include Penicillium chrysogenum.

[0211] The above system for scaling design of the internal flow field environment in an industrial-scale bioreactor can perform the actions of input, static simulation, dynamic simulation, and output with Matlab as the platform to obtain the static simulation results and dynamic simulation results. Alternatively, other programming platforms or programming languages can be used according to actual situations to achieve the same effect.

[0212] As described above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims. In addition, specific examples are used in the specification to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application, and the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for scaled - down design of the internal flow field environment in an industrial - scale bioreactor, characterized in that, It includes the following steps: Input, static simulation, dynamic simulation, and output; In the said input step, built-in parameters and conditional parameters are input; In the said static simulation step, calculations are performed on the built-in parameters and conditional parameters to obtain a static simulation result; In the said dynamic simulation step, loop iterations are performed on the static simulation result, built-in parameters, and conditional parameters until the loop iteration terminates to obtain a dynamic simulation result; In the said output step, the static simulation result and the dynamic simulation result are output; The said static simulation includes residence time simulation, initial substrate concentration simulation, substrate utilization criterion, and system stability criterion; The said residence time simulation is V i = V0 * k i τ0 = τ1 + τ2 + τ3 where i = 1, 2, 3, i represents different compartments. When i = 1, it represents the first compartment with excess substrate. When i = 2, it represents the second compartment with substrate limitation. When i = 3, it represents the third compartment with substrate starvation, f i (t i ) is the probability that the bacterial cells stay in the said compartment. t i is the residence time distribution of the microbial cells in the i-th compartment, τ i is the average residence time of the bacteria in the i-th compartment, τ0 is the total average residence time of the bacteria in the system, V i is the liquid filling volume of the i-th compartment, V0 is the total liquid volume in the system, k i is the ratio of the liquid filling volume of the i-th compartment to the total liquid filling volume of the system. is the circulation flow rate of the i-th compartment; The said initial substrate concentration simulation is C0 = M0 / F a = F where C0 is the substrate concentration of the supplementary feed liquid, M0 is the total amount of substrate supplemented into the system per unit time, F is the flow rate of the supplementary feed liquid flowing into the system during the chemostat process, q i,s is the initial specific substrate uptake rate of the i-th compartment, C i,x is the initial cell concentration of the i-th compartment, q s,max is the theoretical maximum specific substrate uptake rate, k m is the substrate affinity constant for substrate uptake, C i,s is the initial substrate concentration in the i-th compartment; The said substrate utilization criterion is where, When i = 1, When i = 2, When i = 3, The system stability criterion is where q i,s,expect is the desired specific substrate uptake rate of the i-th compartment, η i is the stability criterion for the i-th compartment system When i = 1 or 3 and η i <1‰, the stability of the system is good; When i = 2, q 2,s,expect = q 2,s , η2 = 0.

2. The method for scaled design of the internal flow field environment in an industrial-scale bioreactor according to claim 1, characterized in that: The said dynamic simulation includes operations on the dynamic substrate concentration, operations on the dynamic bacteria concentration, simulation of the product output process, operations on the oxygen absorption rate, and operations on the carbon dioxide release rate.

3. The method for scaled design of the internal flow field environment in an industrial-scale bioreactor according to claim 2, characterized in that: The operations on the dynamic substrate concentration are q 1,t,H = 0.2 * μ i,t F 1,t,H = V1 * q 1,t,H * C i,t,s / C Lye where q i,t,H is the hydrogen ion release rate of the i-th compartment at time t, μ i,t is the specific growth rate of the bacteria in the i-th compartment at time t, F i,t,H is the lye flow rate replenished into the system for the i-th compartment at time t, C i,t,s is the substrate concentration in the i-th compartment at time t, C Lye is the concentration of NaOH added, C i,t,x is the cell concentration in the i-th compartment at time t, F evap is the evaporation flow rate for a single-compartment effluent system, q i,t,s is the specific substrate uptake rate of the i-th compartment at time t.

4. The method for scaled design of the internal flow field environment in an industrial-scale bioreactor according to claim 3, characterized in that: The operations on the dynamic bacteria concentration are where μ i,t is the specific growth rate of the i-th compartment at time t, μ max is the theoretical maximum specific growth rate, C x,max is the theoretical maximum cell concentration k s is the substrate affinity constant for cell growth.

5. The method for scaled design of the internal flow field environment in an industrial-scale bioreactor according to claim 4, characterized in that: The simulation of the product output process is where q i,t,p is the penicillin production rate of the i-th compartment at time t, b is the production coefficient of penicillin, K p is the inhibition coefficient for the substrate concentration k dE is the decay coefficient of the enzyme, C i,t,p is the penicillin concentration in the i-th compartment at time t.

6. The method for scaled design of the internal flow field environment in an industrial-scale bioreactor according to claim 5, characterized in that: The operations on the oxygen absorption rate and the carbon dioxide release rate are q i,t,PAA = -q i,t,p Among them, q i,t,p is the production rate of penicillin in the i-th compartment at time t, q i,t,PAA is the production rate of phenylacetic acid in the i-th compartment at time t, is the specific oxygen uptake rate of the i-th compartment at time t, is the carbon dioxide specific absorption rate of the i-th compartment at time t, OUR i,t is the total oxygen uptake rate of the i-th compartment at time t, CER i,t is the total carbon dioxide release rate of the i-th compartment at time t.

7. The method for scaling design of the internal flow field environment in an industrial-scale bioreactor according to claim 6, wherein: The said conditional parameters are the total amount of substrate supplemented into the system per unit time, the flow rate of the supplementary feed liquid flowing into the system during the chemostat process, the concentration of NaOH supplemented, the circulation flow rate of the i-th compartment, the evaporation flow rate of a single compartment flowing out of the system, the initial bacteria concentration of the i-th compartment, the desired specific substrate absorption rate of the i-th compartment, the liquid volume of the i-th compartment, and the ratio of the liquid volume of the i-th compartment to the total liquid volume of the system; The said built-in parameters are the substrate affinity constant for substrate absorption, the theoretical maximum specific substrate absorption rate, the substrate affinity constant for bacteria growth, the theoretical maximum bacteria concentration, the theoretical maximum specific growth rate, the decay coefficient of the enzyme, the inhibition coefficient for substrate concentration, and the production coefficient of penicillin; The said static output results are the average residence time of the bacteria in the i-th compartment, the total average residence time of the bacteria in the system, the initial substrate concentration of the i-th compartment, the initial specific substrate absorption rate of the i-th compartment, and the system stability criterion of the i-th compartment; The said dynamic output results are the bacteria concentration in the i-th compartment at time t, the substrate concentration in the i-th compartment at time t, the penicillin production rate in the i-th compartment at time t, the total oxygen absorption rate in the i-th compartment at time t, the total carbon dioxide release rate in the i-th compartment at time t, and the penicillin concentration in the i-th compartment at time t.

8. A system for scaled - down design of the internal flow field environment in an industrial - scale bioreactor, characterized in that: Adopt the method as described in any one of claims 1-7, including an input module, a static simulation module, a dynamic simulation module, and an output module; In the input module, input built-in parameters and conditional parameters; In the static simulation module, calculate the built-in parameters and conditional parameters to obtain a static simulation result; In the dynamic simulation module, perform cyclic iteration on the static simulation result, built-in parameters, and conditional parameters until the cyclic iteration terminates to obtain a dynamic simulation result; In the output module, output the static simulation result and the dynamic simulation result.

9. The system for scaled design of the internal flow field environment in an industrial-scale bioreactor according to claim 8, characterized in that: The static simulation module includes residence time simulation, initial substrate concentration simulation, substrate utilization criterion, and system stability criterion; The dynamic simulation module includes operations on the dynamic concentration of the substrate, operations on the dynamic concentration of the thallus, simulation of the product output process, operations on the oxygen absorption rate and carbon dioxide release rate.

10. The system for scaling design of the internal flow field environment in an industrial-scale bioreactor according to claim 8, wherein: The conditional parameters are the total amount of substrate replenished into the system per unit time, the flow rate of the feed solution flowing into the system during the chemostat process, the concentration of NaOH replenished, the circulation flow rate of the i-th compartment, the evaporation flow rate of a single compartment flowing out of the system, the initial thallus concentration of the i-th compartment, the desired specific substrate absorption rate of the i-th compartment, the liquid volume of the i-th compartment, and the ratio of the liquid volume of the i-th compartment to the total liquid volume of the system; The built-in parameters are the substrate affinity constant for substrate absorption, the theoretical maximum specific substrate absorption rate, the substrate affinity constant for thallus growth, the theoretical maximum thallus concentration, the theoretical maximum specific growth rate, the decay coefficient of the enzyme, the inhibition coefficient for substrate concentration, and the production coefficient of penicillin; The static output result is the average residence time of the thallus in the i-th compartment, the total average residence time of the thallus in the system, the initial substrate concentration of the i-th compartment, the initial specific substrate absorption rate of the i-th compartment, and the system stability criterion of the i-th compartment; The dynamic output result is the thallus concentration of the i-th compartment at time t, the substrate concentration of the i-th compartment at time t, the penicillin production rate of the i-th compartment at time t, the total oxygen absorption rate of the i-th compartment at time t, the total carbon dioxide release rate of the i-th compartment at time t, and the penicillin concentration of the i-th compartment at time t.

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