Efficient fermentation shaking table system

Abstract

The invention discloses an efficient fermentation shaking table system, and belongs to the technical field of bioengineering equipment. Comprising a shaking table main body unit, a pH regulation and control unit for detecting and regulating the pH value of a culture solution in real time, an OD real-time detection unit for monitoring the growth of microorganisms in a non-invasive manner, a continuous trace feeding unit for accurately conveying a nutrient solution, and a central control unit for coordinated control. The central control unit adopts a hybrid control algorithm combining fuzzy PID (Proportion Integration Differentiation) and incremental PID to realize rapid, accurate and overshoot-free adjustment of the pH value; and through a mathematical model based on growth dynamics, intelligent calculation and automatic feeding of the material supplementing rate are realized. The system supports multi-station independent parallel control. The system and the method have the beneficial effects that automation and intellectualization of a microorganism culture process are realized, the experiment efficiency, the data precision and the result repeatability are remarkably improved, laboratory research and industrial production are effectively bridged, and the process amplification risk is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of bioengineering technology, and particularly relates to a high-efficiency fermentation shaking table system. BACKGROUND

[0002] As the core equipment for microbial cultivation, strain screening and preliminary development of fermentation process in the laboratory, the technical development of microbial fermentation shaking table directly affects the efficiency of basic research and the success rate of process amplification. With the increasing demand for high-yield strains and efficient processes in the bio-manufacturing industry, the technical limitations of traditional shaking table systems have become increasingly prominent, becoming a key bottleneck connecting laboratory research and industrial production.

[0003] Firstly, in the process of microbial fermentation, pH is a key environmental parameter affecting enzyme activity, metabolic flow distribution and product synthesis. However, traditional fermentation shaking tables generally lack online pH real-time monitoring and automatic control functions. Existing technologies mainly rely on two ways: one is to add high-concentration buffer pairs (such as phosphate systems) to the culture medium to maintain a relatively stable pH; the other is to manually add acid or alkali solution for rough adjustment after measuring by offline pH meter through regular sampling. The former severely limits the design freedom of the culture medium formula, and high ionic strength may interfere with normal microbial metabolism; the latter has serious hysteresis, usually more than 10 minutes from sampling, measuring to intervention, which cannot respond to the rapid pH changes that may occur during the fermentation process. This passive and lagging pH management method causes the culture environment to frequently deviate from the optimal range, causing uncontrollable inhibition of the growth and product synthesis of pH-sensitive strains, and also makes experimental data unable to truly reflect the physiological state of the strain under steady-state conditions.

[0004] Secondly, for fed-batch fermentation or flow fermentation processes that require feeding, existing shaking table systems completely rely on manual operation by experimenters. The operation process usually includes: pausing the shaking table, opening the bottle cap or sealing film, injecting the feeding liquid using a pipette, resealing and starting the shaking table. This process has three major drawbacks: first, frequent bottle opening greatly increases the risk of contamination by foreign bacteria; second, manual operation cannot achieve continuous and trace feeding of nutrients, but only intermittent "pulse feeding", resulting in sharp fluctuations in the concentration of nutrients in the culture solution, and the microorganisms repeatedly experience "starvation-satiation" stress, which seriously affects the growth stability and product yield, and some research reports that the resulting yield loss can be more than 20%; finally, the feeding amount completely depends on the experience and accuracy of the operator, with poor repeatability, and cannot achieve standardization of feeding conditions between different batches of experiments, greatly reducing the comparability and reliability of experimental data.

[0005] In addition, in the traditional shaker experiment, the OD value must be obtained by destructive offline sampling: periodically aspirating a certain volume of culture solution from the shake flask and measuring on a spectrophotometer. This method has the following defects that cannot be ignored: first, for small-scale shake flask culture with a liquid volume of usually only 50-100 mL, frequent sampling will significantly change the volume and chemical environment of the culture system, which itself interferes with microbial growth; second, the offline measurement result lags far behind the fermentation process and cannot be used for real-time feedback control. When the depletion of nutrients is detected, the best feeding opportunity has often been missed; third, the sampling and measurement process introduces human error and instrument error, resulting in inaccurate calculation of key growth data (such as specific growth rate μ) and making it difficult to establish a reliable growth kinetics model. SUMMARY

[0006] In view of the deficiencies of the prior art, the present application provides a high-efficiency fermentation shaker system, which overcomes the deficiencies of the prior art, has a reasonable design, and realizes rapid, accurate, and non-overshoot adjustment of the pH value by using a hybrid control algorithm combining fuzzy PID and incremental PID. The intelligent calculation and automatic flow of the feeding rate are realized based on a mathematical model of growth kinetics. The automation and intelligentization of the microbial cultivation process are realized, and the experimental efficiency, data accuracy, and result repeatability are significantly improved.

[0007] To achieve the above object, the present application is implemented by the following technical solutions:

[0008] A high-efficiency fermentation shaker system, comprising a shaker main unit, a pH regulation unit, an OD real-time detection unit, a continuous micro-feeding unit, and a central control unit;

[0009] The shaker main unit is used to carry the culture container and provide a constant oscillation environment;

[0010] The pH regulation unit comprises an online pH sensor immersed in the culture solution and at least one controlled liquid supplement pipeline, which is used to obtain the pH detection value of the culture solution in real time and add acid-base adjusting liquid to the culture solution according to the control instruction;

[0011] The OD real-time detection unit comprises an optical detection assembly arranged outside the culture container, which is used to non-invasively obtain the optical density detection value reflecting the growth state of the microorganism in real time;

[0012] The continuous micro-feeding unit comprises at least one controlled precision fluid delivery device, which is used to quantitatively deliver one or more nutrient feeding liquids into the culture container according to the control instruction;

[0013] The central control unit is signal-connected with the pH regulation unit, the OD real-time detection unit, the continuous micro-feeding unit, and the shaker main unit, respectively, and the central control unit is configured to:

[0014] receiving pH detection value from the pH regulating unit and optical density detection value from the OD real-time detection unit,

[0015] processing the pH detection value and the optical density detection value based on preset control logic and algorithm and generating corresponding control instruction,

[0016] sending the control instruction to the pH regulating unit, the continuous micro-feeding unit and the shaker main unit to cooperatively adjust the pH value, nutrient supply and oscillation and temperature conditions of the culture environment.

[0017] Preferably, the strategy adopted by the central control unit is specifically:

[0018] taking the output of the fuzzy PID controller as dynamic setting instruction for PID parameters, the fuzzy PID controller taking real-time deviation e(t) between the pH detection value and preset target pH value and its change rate Δe(t) as input and outputting adjustment amount of proportional coefficient Kp, integral coefficient Ki and differential coefficient Kd through built-in fuzzy inference rule;

[0019] applying the dynamically set Kp, Ki, Kd parameters to the incremental PID controller, the incremental PID controller calculating increment Δu(t) of control amount according to the following formula:

[0020] ;

[0021] wherein e(t-1) and e(t-2) represent pH deviations of previous two time points respectively;

[0022] the finally output control amount u(t) = u(t-1) + Δu(t) is used to drive the actuator in the pH regulating unit, wherein u(t-1) is the control amount already executed at the previous time point.

[0023] Preferably, the fuzzy inference rule built in the fuzzy PID controller is:

[0024] when the real-time pH deviation e(t) is large, increasing the proportional coefficient Kp to speed up the response;

[0025] when the deviation change rate Δe(t) is large, increasing the differential coefficient Kd to suppress overshoot;

[0026] when the real-time pH deviation e(t) is small, increasing the integral coefficient Ki to eliminate steady-state error.

[0027] Preferably, the shaker main unit is provided with N physically isolated culture stations, N being an integer greater than 1; each culture station is independently configured with a pH regulating unit, an OD real-time detection unit and a continuous micro-feeding unit.

[0028] Preferably, the optical detection assembly in the OD real-time detection unit comprises:

[0029] a light source module for emitting a detection light beam with a central wavelength of 600nm±4nm;

[0030] a photodetector module for receiving the light signal penetrating through the culture container and the culture solution therein or the light signal scattered by the particles in the culture solution;

[0031] The optical detection assembly is fixed to the outer wall of the culture container in a non-contact manner, and the light path of the optical detection assembly penetrates through the uniform area in the middle of the culture solution, and the optical density OD value is obtained in real time by measuring and converting the received light intensity.

[0032] Preferably, the precision fluid delivery device is a peristaltic pump or a syringe pump;

[0033] The continuous micro-feeding unit supports at least one of the following feeding modes:

[0034] Timing control mode: feeding at a predetermined time interval or a variable time interval according to a preset time program;

[0035] Signal triggering mode: automatically start feeding when the OD detection value or the pH detection value reaches a preset trigger threshold;

[0036] Custom script mode: execute the feeding rate program written by the user according to the mathematical model.

[0037] Preferably, in the custom script mode, the mathematical model used is used to calculate the real-time feeding rate ωv, and the expression is:

[0038] ;

[0039] Wherein, μ is the preset specific growth rate, OD is the real-time detected cell concentration, V is the real-time culture volume, C is the concentration of key nutrients in the feeding solution, V0 is the unit time flow when the feeding pump runs at full speed, and t is the culture time.

[0040] Preferably, it further comprises a data communication module, which is in communication connection with the central control unit, and is used for data interaction, remote parameter setting and fault alarm notification between the central control unit and the upper computer monitoring software, the laboratory information management system or the cloud server.

[0041] Preferably, the inner cavity of the culture container is provided with at least one inclined conical baffle at the bottom, which is used to destroy the liquid flow pattern during oscillation and enhance the degree of turbulence.

[0042] Preferably, a human-computer interaction interface is further included for displaying real-time parameter curves of each culture station, including pH trend chart, OD growth curve, cumulative amount of feeding, temperature and rotating speed.

[0043] A graphical operation interface is provided for a user to set or modify control parameters and experimental schemes.

[0044] The present application provides a high-efficiency fermentation shaker system, which has the following beneficial effects: by integrating an online pH sensor and a precise peristaltic pump, the system can sense the change of the acidity and alkalinity of the culture solution in real time, and utilize a fuzzy PID and incremental PID hybrid algorithm to realize rapid, precise and non-overshoot adjustment of the pH value; and by using a mathematical model based on growth kinetics, intelligent calculation and automatic flow feeding of the feeding rate are realized. By using multi-station parallel control design, multiple groups of orthogonal experiments under different conditions can be simultaneously carried out on the same device, and the experimental process is significantly simplified, and the labor cost and operation error are greatly reduced. The present application can continuously and synchronously record multiple time series data such as pH, OD, temperature, cumulative amount of feeding, rotating speed, etc. Through correlation analysis of these data, the metabolic law of microorganisms can be more accurately analyzed, and a more realistic growth kinetics and product synthesis kinetics model can be constructed, thereby providing unprecedented detailed experimental data for metabolic engineering and systems biology research. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the description of the present application or prior art.

[0046] Fig. 1 The structural schematic diagram of the present application;

[0047] Fig. 2 The overall structure and data interaction block diagram of the present application;

[0048] Fig. 3 The intelligent control principle flow chart of pH value in the present application;

[0049] Fig. 4 The feeding control logic flow chart in the present application;

[0050] Explanation of reference numerals in the drawings:

[0051] 1, shaker main unit; 2, pH regulation unit; 3, OD real-time detection unit; 4, continuous micro-feeding unit; 5, central control unit; 6, human-computer interaction interface; 21, online pH sensor; 22, liquid supplementing pipeline; 31, light source module; 32, photodetector module; 41, precise fluid conveying device. DETAILED DESCRIPTION

[0052] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0053] Example 1, as Figs. 1 to 4 As shown, the present invention discloses a high-efficiency fermentation shaking table system, including a shaking table main unit 1, a pH control unit 2, an OD real-time detection unit 3, a continuous micro-feeding unit 4, and a central control unit 5;

[0054] The main unit 1 of the shaking table adopts a reinforced sheet metal shell and features an internal suspended eccentric wheel drive mechanism to achieve three-dimensional rotary oscillation. The core drive uses a low-noise brushless DC servo motor, connected to the eccentric wheel via a precision reducer and flexible coupling. This design ensures speed control accuracy up to ±1 rpm, within a range of 30-300 rpm, with operating noise below 50 dB. Furthermore, a photoelectric encoder is installed on the drive shaft of the brushless DC servo motor for real-time speed monitoring. The cavity employs a forced convection hot air circulation design. The heating element is a PID-controlled ceramic heater, and the cooling system utilizes an imported compressor circulation system, coupled with a high-precision platinum resistance temperature sensor (PT100), to achieve precise temperature control within the range of 4℃ to 60℃, with uniformity of ±0.5℃ and fluctuation of ±0.2℃.

[0055] Six independent workstations are located on the upper panel of the main shaker unit 1. Each workstation is equipped with a clamping module, which uses an adaptive spring clamping mechanism to securely hold standard conical shake flasks ranging from 50mL to 1000mL, ensuring that the OD detection optical path is aligned with the center of the flask during each installation. It also features two sterile Luer quick-connect interfaces and a pH sensor 21 mounting seal.

[0056] Each workstation is equipped with a high-pressure sterilized pH control unit 2 (such as a Mettler Toledo InPro3250i). The pH control unit 2 includes an online pH sensor 21 immersed in the culture medium and at least one controlled replenishment line 22, used to acquire the pH value of the culture medium in real time and add acid-base adjustment solution to the culture medium according to control instructions; the electrode signal of the online pH sensor 21 is impedance transformed and preliminarily filtered by a high input impedance, low drift amplifier board, and then connected to the central controller through a shielded cable.

[0057] The OD real-time detection unit 3 comprises an optical detection assembly arranged outside the culture container, which comprises: a light source module 31, specifically a 600 nm LED light source arranged on one side of each clamp module, for emitting a detection light beam with a central wavelength of 600 nm ± 4 nm; and a photodetector module 32 for receiving a light signal penetrating through the culture container and the culture solution therein or scattered by particles in the culture solution, specifically a silicon photodiode arranged at a corresponding position on the other side of the clamp module, and the light path passes through the sapphire glass window in the diameter direction of the shake flask. The photodiode output signal of each station is connected to an OD signal processing board. The OD signal processing board is responsible for driving the LED light source and stabilizing the current, while amplifying, filtering and digitizing the received analog signal through a 24-bit analog-to-digital converter (ADC).

[0058] The continuous micro-feeding unit 4 comprises at least one controlled precision fluid delivery device 41 for quantitatively delivering one or more nutrient feeding solutions into the culture container according to control instructions; in this embodiment, the precision fluid delivery device 41 adopts a multi-channel independently controlled precision peristaltic pump (such as Baoding Lang BT series) as the actuator for all liquid additions. Each station corresponds to a double-channel peristaltic pump head, which respectively drives the acid and alkali pipelines. Each station can be configured with a single-channel or multi-channel feeding pump as needed for adding carbon sources, nitrogen sources, inducers, etc. All pump heads are uniformly driven by a stepper motor driver in the central control unit 5, and the flow resolution can reach 0.1 μL / min. The entire fluid path from the liquid storage bottle to the shake flask adopts sterilizable silicone tubes. Before entering the shake flask, the pipeline is connected in series with an online sterilization filter with a pore size of 0.22 μm. All interfaces are quick-connection type, facilitating sterile assembly and disassembly under a clean bench.

[0059] The central control unit 5 is signal-connected with the pH regulation unit 2, the OD real-time detection unit 3, the continuous micro-feeding unit 4 and the shaker main unit 1, respectively. The central control unit 5 is equipped with a multi-channel high-precision analog input card for receiving sensor signals, and a digital output card for controlling pumps and motors.

[0060] The central control unit 5 is configured to:

[0061] receive the pH detection value from the pH regulation unit 2 and the optical density detection value from the OD real-time detection unit 3,

[0062] process the pH detection value and the optical density detection value based on a preset control logic and algorithm, and generate corresponding control instructions,

[0063] send the control instructions to the pH regulation unit 2, the continuous micro-feeding unit 4 and the shaker main unit 1, to cooperatively adjust the pH value of the culture environment, the nutrient supply and the vibration and temperature conditions.

[0064] Working principle:

[0065] Before use, the user can set target parameters for each independent culture station, including constant temperature oscillation temperature, shaking speed, desired pH target value and its allowable fluctuation range (control dead zone), and feeding strategy (such as selecting an OD trigger-based exponential flow mode). The central control unit 5 loads these set values as the basis for the entire control cycle. At the same time, the pH sensor 21 and the OD sensor complete self-checking and calibration to ensure the accuracy of the sensing data.

[0066] After the system is started, it enters a continuous running state. At this time, the online pH sensor 21 immersed in the culture solution of each flask converts the hydrogen ion activity into an electrical signal in real time based on the principle of glass electrode potential method, and feeds back the current pH value to the central control unit 5 at a frequency of several times per second. The non-invasive optical detection module fixed on the outer wall of the flask emits a light beam of a specific wavelength through the culture solution, and the detector receives the attenuated light intensity signal. This signal is processed and converted into an optical density (OD) value in real time according to the Lambert-Beer law, reflecting the instantaneous change of the bacterial concentration. The temperature sensor and the photoelectric encoder monitor the shaking bed cavity temperature and oscillation state in real time to ensure the stability of the basic culture conditions. All sensor data are collected by high-precision analog-to-digital converters and uploaded to the central control unit 5 in real time through the communication layer (such as Modbus TCP), forming a sensory network for the system to perceive the environment.

[0067] Subsequently, the central control unit 5 processes and judges the uploaded real-time data stream. Specifically, it includes pH regulation and feeding trigger and rate regulation. Among them, the pH regulation adopts a hybrid intelligent algorithm combining fuzzy PID and incremental PID. First, the deviation e(t) between the pH set value and the measured value and its change rate Δe(t) are calculated in real time. Then e(t) and Δe(t) are input into the fuzzy PID controller. The expert rules (such as "if the deviation is large and the deviation changes fast, then greatly enhance the proportional action and apply damping in advance") built in the controller dynamically output the adjustment amount of the PID parameters (Kp, Ki, Kd), so that the controller parameters can adapt to the complex nonlinear characteristics of the fermentation process. Specifically, Kp is the proportional coefficient, which is dynamically adjusted by the fuzzy controller to determine the response strength of the system to the change of the deviation. Ki is the integral coefficient, which is dynamically adjusted by the fuzzy controller to determine the correction strength of the system to the small deviation that exists continuously. Kd is the differential coefficient, which is dynamically adjusted by the fuzzy controller to determine the prediction and damping strength of the system to the acceleration of the deviation change.

[0068] When e(t) is large (such as a sudden drop in pH): the fuzzy rule will increase Kp, making the proportional term act more strongly to achieve rapid deviation correction.

[0069] When Δe(t) is large (e.g. pH changes rapidly): the fuzzy rules will increase Kd, strengthening the "damping" effect of the derivative term, effectively suppressing oscillation and overshoot.

[0070] When e(t) is small (pH is close to target): the fuzzy rules will increase Ki relatively, letting the integral term work finely, ensuring the eventual elimination of static error, achieving high accuracy of ±0.05 pH.

[0071] Using the optimized Kp, Ki, Kd by fuzzy logic, substitute into the incremental PID formula:

[0072] ;

[0073] Calculate the control amount fine adjustment increment Δu(t) needed to make pH return to target.

[0074] In the fermentation shaker system of the present application, Δu(t) is directly converted into the step amount to drive the acid or alkali supplement pump. A positive Δu(t) means that the alkali supplement amount needs to be increased (or the acid supplement amount needs to be reduced), and vice versa.

[0075] The total control amount at the current time u(t) = u(t-1) + Δu(t). u(t) determines the cumulative strength of the driving instruction that should be issued to the acid or alkali supplement peristaltic pump. This control signal determines the amount of acid or alkali liquid that should be added cumulatively to the fermentation broth at time t (e.g. in pulse number, step motor step number or calibrated liquid volume). The value of u(t) is a cumulative amount that ensures continuous, smooth and accurate control of the liquid supplement process.

[0076] Feeding trigger and rate control is a system that determines the current time sequence feeding, signal triggered feeding or custom script feeding mode according to the preset.

[0077] Among them, the time sequence control mode: according to the preset time program, the feeding is carried out at a fixed interval or a variable interval;

[0078] Signal trigger mode: when the OD detection value or pH detection value reaches the preset trigger threshold, the feeding is automatically started;

[0079] Custom script mode: execute the feeding rate program written by the user according to the mathematical model.

[0080] In the custom script mode, the mathematical model used to calculate the real-time feeding rate ωv is:

[0081] ;

[0082] Wherein, μ is the preset specific growth rate, OD is the real-time detected cell concentration, V is the real-time culture volume, C is the concentration of key nutrients in the feed solution, V0 is the unit time flow rate of the feed pump when running at full speed, and t is the culture time.

[0083] As in the OD-based exponential fed-batch mode, when the real-time OD value exceeds the preset trigger threshold, the system activates the feed program. At this time, the kinetic model is used to calculate the real-time feed rate . Wherein, The theoretical growth trajectory is simulated, and the precise nutrient flow required to fill the growth gap is calculated by comparing the theoretical biomass with the actual biomass . is converted into control instructions corresponding to the stepping frequency or rotational speed of the peristaltic pump.

[0084] The central control unit 5 issues the calculated control instructions (u(t) and , etc.) to the execution layer. According to the positive or negative and size of u(t), the control unit drives the high-precision peristaltic pump of the corresponding acid or alkali pipeline. The pump injects μL / min level of adjusting liquid through a sterile interface in the form of pulse or continuous microflow. The incremental algorithm ensures that the amount added is extremely small and smooth each time, avoiding sudden changes in pH. According to the calculated feed rate , the nutrient feed peristaltic pump is controlled to work at the corresponding flow rate, continuously and uniformly delivering concentrated nutrient solution to the fermentation broth. According to the temperature feedback, the heating or refrigeration module is controlled to work; according to the process requirement or indirect judgment of dissolved oxygen, the speed of the shaker is fine-tuned.

[0085] In this embodiment, when the OD value rises rapidly (into the logarithmic phase), the system can moderately reduce the shaker speed while increasing the feed rate, to reduce the shear damage at high cell concentration. When the pH starts to drop due to the accumulation of organic acids, the system can simultaneously start to add alkali and increase the proportion of nitrogen source in the feed rate to balance the carbon-nitrogen metabolism. The main control algorithm can learn the dynamic characteristics of a specific fermentation process, fine-tune the fuzzy rules or model parameters, and continuously optimize the control behavior over time.

[0086] During the entire working process, all sensor data, control instructions, and alarm events are synchronized with timestamps and recorded at high density in local storage or uploaded to the cloud through the communication module. The human-machine interaction interface 6 displays the trend curves of each parameter in real time, providing intuitive process monitoring. The complete data chain ensures the complete traceability of the experimental process.

[0087] Example Two (taking high-density culture of Escherichia coli as an example):

[0088] The prepared LB medium (100 mL / 500 mL flask) was assembled with pH sensor 21 (calibrated), feed line (connected to a feed bottle containing 50% glucose solution) into a sterile unit. The connections were wrapped with aluminum foil and placed in an autoclave for 20 minutes at 121 °C.

[0089] The sterilized and cooled flask unit was installed into the designated station clamp, and the online pH sensor 21 plug and feed line quick connector were connected. The system was powered on, and the station was selected on the touch screen for one or two-point calibration of the pH electrode (using standard buffers). The OD detection "dark current" and "blank calibration" were performed (using fresh medium as blank).

[0090] The following parameters were entered in the human-machine interface 6 corresponding to the station:

[0091] Basic parameters: temperature 37.0 °C, rotation speed 250 rpm.

[0092] pH control parameters: target pH = 7.0, dead zone ± 0.05. Associated acid (1M H3PO4) and base (2M NaOH) pumps.

[0093] Feeding strategy: "Exponential fed-batch based on OD" was selected.

[0094] Trigger OD: 0.6 (600 nm).

[0095] Set μ: 0.35 h -1 .

[0096] Feeding liquid concentration C: 500 g / L glucose.

[0097] Initial volume V0: 100 mL.

[0098] In the clean bench, the E. coli seed solution was inoculated at a 1% inoculum from the overnight culture. The station "start" button was clicked on the touch screen. The system started recording time t = 0 and entered the automatic control state.

[0099] Growth lag phase and early log phase: the system continuously monitored, the OD slowly increased, and the pH stabilized around 7.0, without the need for feeding and adjustment.

[0100] Mid-log phase (OD > 0.6): when the OD value first exceeded 0.6, the feed pump automatically started, and the initial rate was calculated according to the model to be about 0.15 mL / h. With the rapid growth of the bacteria and the consumption of glucose, acid may be produced, causing the pH to drop. When the pH is lower than 6.95, the fuzzy PID controller calculates the amount of base needed to be added, and the base pump works in pulse mode with a very small flow rate (e.g. 0.5 μL / min) to accurately pull the pH back to 7.0.

[0101] Late log and stationary phase: feed rate is dynamically increased according to model and real-time OD value, up to 5 mL / h, to meet the demand of high-density growth. The system uses pH trend (e.g. rapid pH rise often indicates carbon source depletion) as an auxiliary judgment, complementary to the OD model, to ensure sufficient but not excessive nutrient supply.

[0102] After the preset time (e.g. 24h) or OD growth enters the plateau, the experiment is stopped manually or automatically. All historical data of the station are exported to a USB in the software. Data analysis shows that the final OD600 of this culture reaches 45, the pH is controlled between 6.98-7.02 throughout the process, the feed curve is smooth, and high-density culture is successfully achieved.

[0103] As can be seen from the above embodiment two, first in terms of accuracy, pH control accuracy ± 0.02, OD detection online real-time, and feeding can be accurately modeled. Second, from inoculation to harvest, no manual intervention is required except for initial settings, especially avoiding frequent opening of the cover for sampling. And 6 stations run independently, different conditions can be compared simultaneously, and the data are good in parallel. The growth curve, feed curve, pH dynamics and other data obtained can directly provide key references for fermentation tank scale-up process, greatly reducing the risk of scale-up.

[0104] The present application integrates online pH sensor 21 with precise peristaltic pump, the system can real-time sense the change of acidity and alkalinity of the culture medium, and use fuzzy PID and incremental PID hybrid algorithm to complete decision and micro-compensation within seconds, control the pH fluctuation range within ± 0.05. This completely changes the passive mode of relying on buffer and manual sampling, so that the microorganism is always in the most suitable acid-base environment, laying a foundation for obtaining real physiological data and high product synthesis efficiency. And by using multi-station parallel control design, multiple groups of orthogonal experiments under different conditions can be simultaneously carried out on the same device, combined with automatic feeding and monitoring function, the experimental process is significantly simplified, and the labor cost and operation error are greatly reduced. At the same time, the automatic control system reduces the opening times of the flask from 3-7 times of the traditional method to 1 time through non-invasive OD detection and closed feeding design, significantly reduces the pollution risk, and further ensures the continuity of the experiment.

[0105] And the present application can continuously and synchronously record multiple time series data such as pH, OD, temperature, cumulative amount of feed, and speed. Through correlation analysis of these data (such as the correspondence of pH inflection point and substrate depletion time, and the dynamic relationship between OD growth rate and feed linkage), the metabolic rules of microorganisms can be more accurately analyzed, and more realistic growth kinetics and product synthesis kinetics models can be constructed, providing unprecedented detailed experimental data for metabolic engineering and systems biology research.

[0106] High reliability of experimental results is achieved by a three-level precision control system: pH control precision reaches ±0.05 (±0.3 for traditional shake flask), overshoot of regulation is reduced by 50% and response time is shortened to 1 / 3 of that of traditional PID by using fuzzy PID algorithm; OD detection CV value is ≤2.3%, which is reduced by 73.6% compared with traditional offline detection (±8.7%), and data difference between different plates and wells is significantly reduced; product concentration variation coefficient between batches is reduced from 15% to 5%, and uniformity is doubled. This high-precision control makes the repeatability of experimental results reach the level of fermentation tank, providing reliable data support for process scaling.

[0107] And by maintaining the optimal pH environment and implementing precise nutrient flow, the system can effectively eliminate substrate inhibition, product feedback inhibition and other problems in the metabolic process, so that the microbial cells are in a more efficient and stable metabolic flow state. In the verification experiment, for various products such as glutamic acid and monoclonal antibodies, the system has achieved a 20%-30% yield increase in shake flask scale, with higher product purity and lower residual sugar content, fully proving its direct value in improving fermentation efficiency.

[0108] The above examples are only used to illustrate the technical solutions of the present application, but not to limit it; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A high-efficiency fermentation shaking table system, characterized in that: It includes a shaking table main unit (1), a pH control unit (2), an OD real-time detection unit (3), a continuous micro-feeding unit (4), and a central control unit (5). The main unit (1) of the shaker is used to support the culture container and provide a constant oscillation environment; The pH control unit (2) includes an online pH sensor (21) immersed in the culture medium and at least one controlled replenishment pipeline (22) for real-time acquisition of the pH detection value of the culture medium and adding acid-base adjustment solution to the culture medium according to control instructions. The real-time OD detection unit (3) includes an optical detection component disposed outside the culture container, which is used to non-invasively acquire optical density detection values ​​that reflect the growth status of microorganisms in real time. The continuous micro-feeding unit (4) includes at least one controlled precision fluid delivery device (41) for quantitatively delivering one or more nutrient supplement solutions into the culture container according to control instructions; The central control unit (5) is connected to the pH control unit (2), the real-time OD detection unit (3), the continuous micro-feeding unit (4), and the shaking table main unit (1) via signals. The central control unit (5) is configured as follows: It receives the pH detection value from the pH control unit (2) and the optical density detection value from the real-time OD detection unit (3). Based on preset control logic and algorithms, the system processes pH and optical density detection values ​​and generates corresponding control commands. The control commands are sent to the pH control unit (2), the continuous micro-feeding unit (4), and the shaker main unit (1) to coordinate the pH value, nutrient supply, and shaking and temperature conditions of the culture environment.

2. The high-efficiency fermentation shaking table system according to claim 1, characterized in that: The strategy adopted by the central control unit (5) is as follows: The output of the fuzzy PID controller is used as a dynamic tuning instruction for the PID parameters. The fuzzy PID controller takes the real-time deviation e(t) between the pH detection value and the preset target pH value and its rate of change Δe(t) as input, and outputs the adjustment amount of the proportional coefficient Kp, integral coefficient Ki and derivative coefficient Kd through the built-in fuzzy inference rules. The dynamically tuned parameters Kp, Ki, and Kd are applied to an incremental PID controller, which calculates the increment Δu(t) of the control quantity according to the following formula: ； Where e(t-1) and e(t-2) represent the pH deviations at the first two time points, respectively; The final output control quantity u(t) = u(t-1) + Δu(t) is used to drive the actuator in the pH control unit (2), where u(t-1) is the control quantity executed at the previous moment.

3. The high-efficiency fermentation shaking table system according to claim 2, characterized in that: The fuzzy inference rules built into the fuzzy PID controller are as follows: When the real-time pH deviation e(t) is large, the proportionality coefficient Kp is increased to speed up the response. When the rate of change of deviation Δe(t) is large, the differential coefficient Kd is increased to suppress overshoot; When the real-time pH deviation e(t) is small, the integral coefficient Ki is increased to eliminate the steady-state error.

4. The high-efficiency fermentation shaking table system according to claim 1, characterized in that: The main unit (1) of the shaker is provided with N physically isolated culture stations, where N is an integer greater than 1; each culture station is independently equipped with a pH control unit (2), an OD real-time detection unit (3) and a continuous micro-feeding unit (4).

5. The high-efficiency fermentation shaking table system according to claim 1, characterized in that: The optical detection components in the real-time OD detection unit (3) include: The light source module (31) is used to emit a detection beam with a center wavelength of 600nm±4nm; The photodetector module (32) is used to receive light signals that penetrate the culture container and the culture medium therein or light signals scattered by particles in the culture medium; The optical detection component is fixed to the outer wall of the culture container in a non-contact manner. The optical path of the optical detection component penetrates the uniform area in the middle of the culture medium. By measuring and converting the received light intensity, the optical density OD value is obtained in real time.

6. The high-efficiency fermentation shaking table system according to claim 1, characterized in that: The precision fluid delivery device (41) is a peristaltic pump or an injection pump; The continuous micro-feeding unit (4) supports at least one of the following feeding modes: Timing control mode: Replenishing materials at preset time intervals or variable time intervals according to a preset time program; Signal triggering mode: When the OD detection value or pH detection value reaches the preset trigger threshold, feeding is automatically started; Custom script mode: Executes the feeding rate program written by the user based on the mathematical model.

7. The high-efficiency fermentation shaking table system according to claim 6, characterized in that: In the custom script mode, the mathematical model used to calculate the real-time feeding rate ωv is expressed as follows: ； Where μ is the preset specific growth rate, OD is the real-time detected cell concentration, V is the real-time culture volume, C is the concentration of key nutrients in the feed solution, V0 is the flow rate per unit time when the feed pump is running at full speed, and t is the culture time.

8. The high-efficiency fermentation shaking table system according to claim 1, characterized in that: It also includes a data communication module, which is connected to the central control unit (5) for data interaction, remote parameter setting and fault alarm notification between the central control unit (5) and the host computer monitoring software, laboratory information management system or cloud server.

9. The high-efficiency fermentation shaking table system according to claim 1, characterized in that: The bottom of the inner cavity of the culture container is provided with at least one inclined conical baffle, which is used to disrupt the liquid flow pattern and enhance the degree of turbulence during oscillation.

10. The high-efficiency fermentation shaking table system according to claim 1, characterized in that: It also includes a human-machine interface (6), which is used to display the real-time parameter curves of each culture station. The real-time parameter curves include pH trend graph, OD growth curve, cumulative feed amount, temperature and rotation speed. It also provides a graphical user interface for users to set or modify control parameters and experimental schemes.

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