Intelligent bioreactor system based on multi-parameter real-time monitoring and feedback control

Abstract

The invention discloses an intelligent bioreactor system based on multi-parameter real-time monitoring and feedback control, and belongs to the technical field of bioengineering. The system comprises a bioreactor main body, an integrated biochemical analyzer and a feedback control system, and real-time sampling of a culture solution is realized through a bypass circulating system. The integrated biochemical analyzer comprises an optical detection module, an electrochemical sensor module and an overflow pool biochemical detection module, and can detect the OD value, pH, NH4 < + >, K < + > plasma concentration and the concentration of at least three metabolites such as glucose, lactic acid and glutamine in parallel. And the feedback control system dynamically adjusts the feeding strategy, the pH and the gas flow based on a metabolite concentration change rate prediction model and a multi-level control algorithm. The system can accurately maintain the culture environment, inhibit the accumulation of metabolic wastes, improve the nutrient utilization efficiency, obviously enhance the process stability and the production efficiency, and provide an intelligent accurate regulation and control solution for cell culture and microbial fermentation.

Description

Technical Field

[0001] The present invention relates to the field of bioengineering technology, and in particular to an intelligent bioreactor system based on multi-parameter real-time monitoring and feedback control. Background Art

[0002] As the core equipment for cell culture and microbial fermentation, the process control accuracy of bioreactors directly affects the yield, quality and production cost of biological products. In mammalian cell culture, such as monoclonal antibody preparation, and microbial fermentation, such as yeast ethanol production, the composition of the culture medium changes dynamically, such as the concentration of metabolites such as glucose, lactate, glutamine, and NH4 + , K + Plasma concentration is a key indicator of cell growth status and metabolic efficiency. However, traditional bioreactor monitoring systems generally suffer from insufficient parameter coverage, enabling only real-time detection of basic parameters such as temperature, pH, and dissolved oxygen. Key metabolites and ion concentrations that determine cellular metabolic flux and product synthesis often rely on offline sampling and analysis, resulting in long detection cycles and data lags, making it difficult to meet real-time control requirements. This lag can lead to nutrient depletion or accumulation of metabolic waste, which in turn inhibits cell growth or induces product degradation, limiting the stability and efficiency of the biomanufacturing process.

[0003] Although some advanced bioreactors have introduced online detection modules, existing technologies mostly use decentralized detection solutions, that is, different parameters need to be detected one by one through independent equipment or modules, resulting in low system integration, high maintenance costs and poor data synchronization. For example, traditional OD value detection, ion concentration analysis and metabolite monitoring are often completed by separate instruments, which not only takes up space, but also may cause the multi-parameter correlation analysis to fail due to sampling time differences. In terms of control strategy, traditional feedback systems mainly rely on single-parameter PID control of temperature and stirring speed. For multi-parameter coupling effects, such as the metabolic correlation between glucose consumption and lactate production, NH4 + The lack of effective modeling for the interplay between concentration and glutamine utilization makes it difficult to dynamically optimize feeding strategies. When the culture enters the exponential growth or induction phase, the nonlinear changes in metabolic parameters intensify. Traditional control algorithms, unable to capture the rate of change in metabolite concentrations and the synergistic relationships between parameters in real time, can easily lead to untimely or excessive feeding, resulting in nutrient waste and reduced product synthesis efficiency.

[0004] As biopharmaceuticals and industrial fermentation develop towards high-density, high-expression culture processes, higher requirements are placed on the intelligent control of bioreactors: it is necessary to integrate multi-dimensional data (cell density, metabolites, ion concentrations, etc.) in real time and dynamically adjust the feeding strategy through predictive models to maintain the optimal culture environment. However, existing technologies have significant deficiencies in the following aspects: first, the lack of real-time multi-parameter parallel detection capabilities for key metabolites and ions makes it impossible to construct a complete metabolic map; second, the feedback control system fails to effectively utilize the metabolite concentration change rate and trend prediction, resulting in feeding regulation lagging behind metabolic needs; third, the lack of multi-parameter collaborative control strategies makes it difficult for the system to cope with parameter coupling problems in complex metabolic networks. These technical bottlenecks directly restrict the scale-up efficiency and product quality consistency of the biomanufacturing process, and urgently need to be innovated through the collaboration of integrated detection modules and intelligent control algorithms. Summary of the Invention

[0005] Based on the above objectives, the present invention provides an intelligent bioreactor system based on multi-parameter real-time monitoring and feedback control, including:

[0006] Bioreactor body, used for cell culture or microbial fermentation;

[0007] An integrated biochemical analyzer is connected to the reactor body through a bypass circulation system and is used to detect the OD value, metabolite concentration, ion concentration and pH value in the culture medium in real time; the metabolite concentration includes at least three of glucose, lactate, glutamic acid, glutamine, glycerol, methanol, ethanol and lysine; the ion concentration includes NH4 + , K + 、Na + and Ca 2+ At least two of the following:

[0008] The feedback control system is used to receive the detection data of the biochemical analyzer and dynamically adjust the feeding strategy according to the detection data.

[0009] Furthermore, the integrated biochemical analyzer includes:

[0010] Optical detection module, used for OD value detection;

[0011] Electrochemical sensor modules for detecting pH and ion concentration; and

[0012] Overflow tank biochemical detection module, used to detect metabolite concentrations.

[0013] Furthermore, the bypass circulation system includes:

[0014] A sampling pump, used to extract the culture solution from the reactor;

[0015] In-line filtration units, including pre-filters and fine filters, for cell and debris removal; and

[0016] The reflux system is used to return the analyzed culture fluid to the reactor or discharge it to the waste liquid collector.

[0017] Furthermore, the feedback control system adopts a metabolite concentration change rate prediction model to dynamically adjust the feeding rate and component ratio according to the first-order derivative and second-order derivative of the metabolite concentration.

[0018] Furthermore, the feedback control system includes a multi-level control algorithm structure, including:

[0019] Single parameter PID control, used to control a single parameter including temperature and stirring speed;

[0020] Feed control based on the rate of change of metabolite concentration; and multi-parameter collaborative control strategy, which is used to establish the correlation matrix between parameters and make collaborative adjustments.

[0021] Furthermore, the optical detection module includes an LED light source emitter, a quartz cuvette, a photoelectric sensor and an automatic calibration system, which are used to detect the OD value of the culture solution in real time.

[0022] Furthermore, the electrochemical sensor module adopts microfluidic chip design, integrating pH electrode, K + Electrode, Na + Electrode, NH4 + Electrode and Ca 2+ electrode.

[0023] Furthermore, the overflow tank biochemical detection module adopts enzyme electrode array or biosensor technology, including glucose and lactic acid detection units, amino acid detection units, alcohol detection units and glycerol detection units.

[0024] Furthermore, the feedback control system includes:

[0025] Data acquisition and processing layer, used to collect and process detection data;

[0026] The decision control layer is used to execute the control algorithm according to the processed data; and the execution control layer is used to drive the multi-channel feeding system, pH adjustment system and gas flow control system.

[0027] The method for cell culture or microbial fermentation in the above system comprises the following steps:

[0028] S1. Setting culture process parameters and control strategies;

[0029] S2, sampling from the bioreactor to the analyzer according to the set cycle;

[0030] S3. Each detection module completes the analysis in parallel and uploads all parameter data to the control system;

[0031] S4. The control algorithm calculates the required adjustment operations based on the parameter data, sends instructions to the execution device, and the execution device performs the corresponding operations according to the instructions.

[0032] The beneficial effects of the present invention are: BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0034] Figure 1 Schematic diagram of the overall structure of the system of the present invention;

[0035] Figure 2 Schematic diagram of the system workflow of the present invention. DETAILED DESCRIPTION

[0036] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0037] It should be noted that references in the specification to "one embodiment," "an embodiment," "an exemplary embodiment," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not every embodiment necessarily includes such specific features, structures, or characteristics. In addition, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0038] In general, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending at least in part on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0039] See Figure 1 shown

[0040] 1. Overall system structure

[0041] The intelligent bioreactor system of the present invention mainly consists of three core parts: bioreactor body, integrated biochemical analyzer and feedback control system. Figure 1 As shown, the components are connected through a bypass circulation system to achieve regular sampling and analysis of the culture fluid.

[0042] The bioreactor consists of a standard culture tank, a precision stirring system, and a temperature control module. The tank is constructed of stainless steel or glass and features an internal temperature sensor that adjusts the culture temperature in real time based on the temperature control module's instructions. The stirring system is driven by a variable-frequency motor with adjustable speed between 20 and 1200 rpm, ensuring a uniform culture environment.

[0043] The integrated biochemical analyzer is the core innovation of this system and consists of three functional modules:

[0044] Optical detection module: used for OD value detection, composed of LED light source (wavelength 600nm) and photoelectric sensor, with a detection range of 0.1-50OD and an accuracy of ±0.05OD.

[0045] Electrochemical sensor module: contains a variety of ion-selective electrodes for detecting pH (range 5.5-8.5, accuracy ±0.1), K + (range 0.5-25mM, accuracy ±0.3mM), Na + (range 20-150mM, accuracy ±1.0mM), Ca 2+ (range 0.1-5mM, accuracy ±0.05mM) and NH4 + (Range 0.5-20mM, accuracy ±0.2mM) plasma concentration.

[0046] Overflow tank biochemical detection module: enzyme electrode method or fluorescence analysis method is used to detect the concentrations of metabolites such as glucose (range 0.5-30mM, accuracy ±0.3mM), lactate (range 0.2-15mM, accuracy ±0.2mM), glutamate (range 0.2-10mM, accuracy ±0.1mM), glutamine (range 0.5-10mM, accuracy ±0.2mM), glycerol (range 0.1-5%, accuracy ±0.05%), methanol (range 0.01-1%, accuracy ±0.005%), ethanol (range 0.1-15%, accuracy ±0.1%) and lysine (range 0.5-15mM, accuracy ±0.3mM).

[0047] The feedback control system consists of an industrial-grade PLC or embedded controller with a 10.4-inch color touchscreen. Built-in control algorithms include PID control, fuzzy logic control, and machine learning-based predictive control modules. The system is connected to multiple peristaltic pumps with a flow rate range of 0.1-100 mL / min to perform operations such as nutrient addition, pH adjustment, and ion balance maintenance.

[0048] 2. Bypass circulation system design

[0049] The bypass circulation system is a key component connecting the bioreactor and the analyzer. Its design is as follows:

[0050] Circulation line: Use silicone tube or PTFE tube with an inner diameter of 3mm to ensure low fluid resistance and good biocompatibility.

[0051] Sampling pump: A precision peristaltic pump is used, the flow rate can be adjusted in the range of 0.5-10mL / min, and 5-15mL of culture medium is extracted in each sampling cycle.

[0052] In-line filtration: This unit includes a pre-filter with a pore size of 5 μm and a fine filter with a pore size of 0.2 μm, ensuring the removal of cells and debris from the culture medium to prevent interference with the analyzer. The pre-filter is replaceable, and the fine filter uses a backwash system to extend its service life.

[0053] Reflux system: The analyzed culture fluid can be selectively refluxed to the reactor or discharged to the waste liquid collector to reduce disturbance to the culture system.

[0054] Cleaning system: The cleaning program is automatically started regularly, usually set to 24 hours, and the bypass system is flushed with sterile water and disinfectant to avoid bacterial growth and pipe blockage.

[0055] 3. Detailed design of detection module

[0056] 1. Optical detection module

[0057] The optical detection module uses the transmitted light method to measure the OD value. Its structure includes:

[0058] LED light source emitter: emits 600nm wavelength light, and the light intensity can be automatically adjusted to adapt to different concentration ranges.

[0059] Quartz cuvette: optical path 10mm, volume 0.5mL, equipped with automatic degassing device.

[0060] Photoelectric sensor: sensitivity 0.001OD unit, with temperature compensation function.

[0061] Automatic calibration system: Zero point and full scale calibration are performed every 24 hours using standard solution.

[0062] 2. Electrochemical sensor module

[0063] The electrochemical sensor module adopts a microfluidic chip design and integrates multiple ion-selective electrodes:

[0064] pH electrode: composite glass electrode, automatic temperature compensation, response time <30 seconds.

[0065] K + 、Na + NH4 + Electrode: Using ion-selective field-effect transistor technology, it has the characteristics of fast response (<60 seconds) and high selectivity.

[0066] Ca 2+ Electrode: Adopting neutral carrier membrane electrode technology, it has strong anti-interference ability and good stability.

[0067] All electrode signals are processed by analog-to-digital converters and transmitted to the control system via RS-485 or Modbus communication protocols.

[0068] 3. Overflow pool biochemical detection module

[0069] The overflow tank biochemical detection module uses enzyme electrode array or biosensor technology to detect multiple metabolites:

[0070] Glucose and lactate detection unit: Adopting glucose oxidase and lactate oxidase immobilization technology, the enzyme reaction products are detected by oxygen electrode or hydrogen peroxide electrode.

[0071] Amino acid (glutamic acid, glutamine, lysine) detection unit: using the corresponding amino acid oxidase or transaminase system, through fluorescence or electrochemical detection.

[0072] Alcohols, including methanol and ethanol detection unit: uses alcohol oxidase system combined with electrochemical or optical detection principles.

[0073] Glycerol detection unit: Based on a coupled reaction system of glycerol kinase and glycerol-3-phosphate oxidase.

[0074] Each detection unit is equipped with an independent temperature control system with a temperature control range of 37±0.1℃ to ensure stable enzyme activity.

[0075] 4. Feedback Control System Design

[0076] The feedback control system is the key to achieving intelligent cultivation. Its design includes:

[0077] Data collection and processing layer:

[0078] The sampling period can be set, the default is 5 minutes / time;

[0079] Data smoothing algorithm to remove outliers;

[0080] Calculation of the first derivative of parameter change rate and the second derivative of acceleration;

[0081] Decision-making control layer:

[0082] Basic PID control: used for stable control of basic parameters such as temperature and pH;

[0083] Fuzzy control: for multi-parameter coupling situations, such as the relationship between glucose and lactate metabolism;

[0084] Predictive control: Based on historical data and current change trends, predict parameter changes within the next 3-6 hours;

[0085] Execution control layer: multi-channel feeding system: 4-8 independently controlled feeding pumps;

[0086] pH adjustment system: dual-channel precise control of alkali solution and acid solution;

[0087] Gas flow control: proportional mixing control of air, oxygen, nitrogen and carbon dioxide;

[0088] 5. Detailed description of control algorithm

[0089] This system adopts a multi-level control algorithm structure, including:

[0090] Single parameter PID control:

[0091] For single parameters such as temperature and stirring speed, standard PID control algorithm is used:

[0092] u(t)=Kp·e(t)+Ki·∫e(t)dt+Kd·de(t) / dt;

[0093] Among them, Kp, Ki, and Kd are proportional, integral, and differential coefficients respectively, which are determined by the self-tuning algorithm.

[0094] Feed control based on the rate of change of metabolite concentration:

[0095] Taking glucose as an example, the feed rate F is calculated as follows:

[0096] F=μX·V / Yx / s·(1 / Sin)+m·X·V / Sin+k·d[Glucose] / dt;

[0097] in:

[0098] μ is the specific growth rate; X is the cell concentration; V is the culture volume; Yx / s is the yield coefficient; Sin is the glucose concentration in the feed solution; m is the maintenance coefficient; k is the proportional coefficient; d[Glucose] / dt is the rate of change of glucose concentration;

[0099] Multi-parameter collaborative control strategy:

[0100] The system establishes a correlation matrix between parameters, such as:

[0101] When NH4 is detected + When the concentration is too high (>15mM) and glucose consumption slows down, the system automatically reduces the amino acid supplement ratio and increases the supply of linear carbon sources.

[0102] When an increase in the lactic acid accumulation rate and an accelerated decrease in pH are detected, the system reduces the glucose feeding rate and appropriately increases the amount of alkali solution added.

[0103] 6. Software interface and operation process

[0104] The system software interface is divided into four main functional areas:

[0105] Real-time monitoring interface: displays the current values ​​and change curves of all parameters, and automatically alarms when key parameters exceed the set range.

[0106] Parameter setting interface: allows the operator to set the target range, sampling period, control coefficient, etc. of each parameter.

[0107] Feeding strategy configuration interface: set basic feeding formula, feeding trigger conditions, control algorithm selection, etc.

[0108] Data management interface: provides historical data query, data export, report generation and other functions.

[0109] See Figure 2 shown

[0110] The operation process is as follows:

[0111] The system starts and initializes, and performs a self-test procedure.

[0112] The operator inputs the culture process parameters and selects the appropriate control strategy.

[0113] After the system is started, samples are collected from the bioreactor and transferred to the analyzer at a set period (e.g., 5 minutes).

[0114] Each detection module completes the analysis in parallel, and all parameter data are uploaded to the control system.

[0115] The control algorithm calculates the required adjustment operations based on the parameter data and sends instructions to the execution device.

[0116] The execution equipment (such as the feed pump) performs the corresponding operations according to the instructions.

[0117] The system continues to cycle the above process until the cultivation is completed.

[0118] Example 1: Multi-parameter monitoring and feedback control during mammalian cell culture

[0119] Experimental conditions

[0120] Reactor specifications: 5 L stirred tank bioreactor, working volume 3.5 L;

[0121] Cultured: CHO-K1 cell line, expressing humanized monoclonal antibodies;

[0122] Basal culture medium: modified DMEM / F12 serum-free medium;

[0123] Feed solution composition: 10-fold concentrated nutrient mixture containing glucose (200 mM), glutamine (40 mM) and essential amino acids and vitamins;

[0124] Culture conditions:

[0125] Temperature: 37±0.1℃;

[0126] Initial pH: 7.2 ± 0.05;

[0127] Dissolved oxygen level: 30% ± 2%;

[0128] Stirring speed: 70-120rpm (automatically adjusted according to dissolved oxygen);

[0129] Total culture period: 14 days;

[0130] System Settings

[0131] Parameter monitoring settings:

[0132] Sampling frequency: 10 mL of culture medium is extracted through the bypass system every 5 minutes

[0133] Optical detection: Real-time monitoring of OD values ​​to assess cell growth status

[0134] Metabolite monitoring: glucose (target 5-10mM), lactate (alert value 3mM), glutamine (target 2-4mM);

[0135] Ion monitoring: K + (target 4-6mM), Na + (target 130-145 mM), Ca 2+ (target 1-2mM), NH4 + (warning value 5mM);

[0136] pH monitoring: target range 7.0-7.2;

[0137] Control parameter settings:

[0138] Glucose feeding trigger threshold: when the concentration is lower than 5.5mM or the decrease rate exceeds 0.5mM / h;

[0139] Glutamine feeding trigger threshold: when the concentration is lower than 2.5mM or the decrease rate exceeds 0.3mM / h;

[0140] Lactate control strategy: start the reduced feeding mode when the concentration exceeds 2.5mM;

[0141] pH adjustment strategy: Use 1 M NaOH and 1 M HCl to maintain the pH within the set range;

[0142] Detailed description of the operation process

[0143] Cultivation start-up phase (0-48 hours):

[0144] Seeding density: 3×10 5 cells / mL;

[0145] The system works in basic monitoring mode, recording all parameters every 5 minutes;

[0146] Glucose consumption is slow (about 0.2 mM / h), and no additional feeding is required;

[0147] Glutamine consumption was relatively rapid (approximately 0.25 mM / h), and the system started the feed pump for the first time at the 48-hour point;

[0148] Exponential growth phase (48-144 hours):

[0149] The cell density quickly increased to 2×10 6 cells / mL;

[0150] The glucose consumption rate increased to 0.6-0.8 mM / h;

[0151] The system automatically adjusts the feeding rate according to the glucose consumption rate to maintain the concentration at 8 ± 0.5 mM;

[0152] Glutamine concentration was maintained at 3 ± 0.2 mM;

[0153] Lactic acid begins to accumulate at a rate of approximately 0.15 mM / h;

[0154] Key regulatory phase (144-192 hours):

[0155] The lactate concentration gradually increased and reached 2.8 mM at the 168th hour of culture;

[0156] The system detects that the lactate level is approaching the warning value and automatically performs the following adjustments:

[0157] The target glucose concentration was lowered from 8 mM to 6 mM;

[0158] The feed rate was reduced from 15 mL / h to 10 mL / h;

[0159] Increase the frequency of adding pH adjusting solution (1 M NaOH) to maintain the pH in the range of 7.1-7.2;

[0160] After adjustment, the lactate production rate dropped from 0.15 mM / h to 0.05 mM / h, and the concentration gradually stabilized below 2.5 mM;

[0161] NH4 + The concentration was controlled below 4 mM to avoid the inhibitory effect of ammonia;

[0162] Stable production stage (192-336 hours):

[0163] The cell density was maintained at 8-9×10 6 cells / mL plateau;

[0164] The system accurately maintains glucose concentration within the range of 5.5-6.5mM;

[0165] Glutamine concentration was stabilized at 2.5-3.0 mM;

[0166] The lactate concentration stabilized at 2.3-2.5 mM and no longer increased;

[0167] Antibody production continued to increase, reaching a final concentration of 4.2 g / L;

[0168] Parameter change data recording

[0169] Dynamic changes in metabolite concentrations:

[0170]

[0171]

[0172] Cell growth and product formation data:

[0173] Culture time (h) Cell density (x106 cells / mL) Cell viability (%) Antibody concentration (g / L) 0 0.3 98.5 0.0 48 0.8 97.8 0.1 96 2.5 97.0 0.4 144 5.2 96.3 1.0 168 7.1 95.5 1.6 192 8.4 94.8 2.2 240 8.9 93.2 3.0 288 8.8 91.0 3.7 336 8.5 88.5 4.2

[0174] Lactic acid accumulation control effect in Example 1:

[0175] The system successfully controlled lactate at a safe level of <3mM, avoiding the inhibition of cell growth by acidosis; the key intervention occurred at 168 hours, effectively inhibiting lactate production by reducing glucose concentration and feeding rate; the pH adjustment strategy was implemented in conjunction to successfully maintain the stability of the culture environment.

[0176] Nutrient utilization efficiency: total glucose consumption: 82.5g; total glutamine consumption: 15.8g

[0177] Antibody yield: 4.2 g / L; glucose conversion efficiency increased by 25% compared to conventional batch feeding.

[0178] Compared with the traditional process, the maximum cell density increased by 32%, from 6.7×10 6 cells / mL increased to 8.9×10 6 cells / mL; the final antibody concentration increased by 38%, from 3.05g / L to 4.2g / L; the culture utilization rate increased by 25%; and the labor intensity of the process was reduced by 80%.

[0179] System stability evaluation: Parameter fluctuations were controlled within the set range: glucose ±0.5mM, glutamine ±0.2mM, pH ±0.05; the system ran continuously for 336 hours without any failure, and the bypass sampling system was not blocked; the sensor calibration was stable, with no obvious drift.

[0180] Example 2: Methanol Monitoring and Glycerol Feed Optimization during Yeast Ethanol Fermentation

[0181] Experimental conditions

[0182] Reactor specifications: 10 L stirred tank bioreactor, working volume 7 L;

[0183] Bacterial strain: Recombinant methylotrophic yeast, Pichia pastoris X-33;

[0184] Culture medium: Basic YNB medium supplemented with biotin and trace elements;

[0185] Main carbon source: glycerol in batch culture stage, methanol in induction stage;

[0186] Culture conditions:

[0187] Temperature: 30±0.2℃;

[0188] pH: 5.5 ± 0.1, adjusted with 4 M NaOH and 2 M H3PO4;

[0189] Dissolved oxygen: >20%, adjusted by stirring speed and ventilation volume;

[0190] Stirring speed: 400-1000rpm;

[0191] Total culture period: 120 hours;

[0192] System Settings

[0193] Parameter monitoring settings:

[0194] Sampling frequency: 8 mL of culture medium was extracted through the bypass system every 3 minutes;

[0195] Optical detection: real-time monitoring of OD value (biomass assessment);

[0196] Metabolite monitoring: methanol, target <0.1%; glycerol, 0.5-4% during batch phase; ethanol;

[0197] Ion monitoring: K + 、Na + NH4 + ;

[0198] pH monitoring: target range 5.5 ± 0.1;

[0199] Control parameter settings:

[0200] Methanol addition strategy: Maintain a low concentration range of 0.05-0.08%;

[0201] Glycerol feeding trigger standard: When the methanol consumption rate exceeds 0.15% / h, auxiliary glycerol is added;

[0202] Dissolved oxygen control strategy: When dissolved oxygen is lower than 25%, increase the stirring speed first, then increase the ventilation volume;

[0203] Metabolite monitoring: ethanol production rate and concentration;

[0204] Detailed description of the operation process

[0205] Batch growth phase, time 0-24 hours:

[0206] Initial glycerol concentration 4%;

[0207] The cells quickly adapted to and consumed glycerol, with OD600 increasing from 0.5 to approximately 40;

[0208] The system continuously monitors the glycerol consumption rate and enters the methanol pre-induction preparation stage when the concentration drops below 0.2%;

[0209] During this stage, system monitoring data showed that the glycerol consumption rate reached a maximum of 0.35% / h;

[0210] Induction start-up phase, duration: 24-36 hours:

[0211] After the system confirms that glycerol is depleted (<0.05%), the methanol induction procedure will automatically begin;

[0212] Methanol was added to 0.3% for the first time, while the stirring speed was increased from 500 rpm to 700 rpm;

[0213] The methanol sensor monitors methanol consumption in real time. Initial consumption is slow, about 0.05% / h.

[0214] The system gradually increases the frequency of methanol addition according to the consumption rate to achieve yeast adaptation to methanol metabolism;

[0215] Mixed carbon source coordinated control stage, time 36-72 hours:

[0216] The methanol consumption rate is stable at 0.12-0.15% / h, and the system maintains the methanol concentration at 0.05-0.08%;

[0217] When the system detects that methanol consumption is accelerating, >0.15% / h, it automatically starts the glycerol auxiliary feeding program;

[0218] The glycerol addition rate was set to 20% of the methanol consumption equivalent, calculated based on the carbon source;

[0219] Real-time monitoring data showed that the mixed carbon source strategy increased the ethanol production rate from 0.18 g / L / h to 0.22 g / L / h;

[0220] The dissolved oxygen level is maintained within the range of 20-30% to ensure adequate oxygen supply;

[0221] Optimize the product formation stage, time 72-120 hours:

[0222] The system establishes a dynamic feeding model based on the methanol consumption pattern in the previous 36 hours

[0223] The basic addition rate of methanol is set at 0.12% / h, and the auxiliary addition rate of glycerol is dynamically adjusted.

[0224] When the residual methanol concentration approaches the upper limit (the upper limit is 0.08%), the system automatically reduces the addition frequency

[0225] When the methanol consumption rate is higher than 0.18% / h, the system increases the glycerol ratio to 30% of the methanol carbon equivalent.

[0226] The ethanol production rate is stable at 0.25-0.28g / L / h, which is significantly higher than the conventional single carbon source process.

[0227] Parameter change data recording

[0228] Metabolite concentration and consumption rate:

[0229]

[0230] Biomass and dissolved oxygen data:

[0231] Culture time (h) OD600 value Dry weight (g / L) Dissolved oxygen (%) Stirring speed (rpm) 0 0.5 0.2 90 400 24 42.3 15.8 35 500 36 50.1 18.7 28 700 48 64.8 24.2 25 750 60 78.5 29.3 22 850 72 89.2 33.4 20 900 84 98.7 36.8 21 950 96 103.5 38.6 22 950 108 106.2 39.7 23 950 120 108.3 40.5 24 950

[0232] The methanol control effect in the above embodiment 2:

[0233] The system successfully controls the residual methanol concentration within the range of 0.05-0.08%, avoiding substrate inhibition and accumulation; responds to fluctuations in methanol consumption in real time, dynamically adjusting the addition rate to ensure a stable supply; and avoids large fluctuations in methanol concentration compared to traditional intermittent addition, which fluctuates from 0.01-0.5% with traditional methods.

[0234] Glycerol feeding optimization effect: A dynamic glycerol feeding strategy based on methanol consumption rate successfully improved yeast growth viability; the mixed carbon source strategy alleviated the toxic pressure of pure methanol metabolism and increased cell density by 108.3OD vs. 91.5OD of the traditional method; precise control of the glycerol carbon source ratio avoided product metabolism deviation.

[0235] Ethanol production improvement effect: The final ethanol concentration reached 22.3g / L, an increase of 12.1% compared to the 19.9g / L of the traditional process; the ethanol yield increased by 9.8%; and the production efficiency increased from 0.17g / L / h to 0.19g / L / h.

[0236] System stability assessment: The methanol sensor remained stable during 120 hours of continuous operation, with a deviation of <3%. The bypass system was not blocked and maintained a normal sampling frequency throughout the entire process. The feedback control logic responded promptly, with an average adjustment delay of <1 minute.

[0237] Process consistency: Batch-to-batch reproducibility was significantly improved, with the coefficient of variation in yield reduced from 12% to 4.5%. The cell growth curves had a high degree of overlap between batches, indicating the accuracy of process control.

[0238] This example demonstrates that the system can achieve precise control of methanol concentration and dynamic optimization of glycerol feed, effectively increasing ethanol yield during yeast fermentation. Compared with traditional processes, this not only increases yield but also significantly enhances process stability and repeatability, providing reliable support for industrial production.

[0239] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0240] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. An intelligent bioreactor system based on multi-parameter real-time monitoring and feedback control, characterized in that: include: Bioreactor body, used for cell culture or microbial fermentation; An integrated biochemical analyzer is connected to the reactor body through a bypass circulation system and is used to detect the OD value, metabolite concentration, ion concentration and pH value in the culture medium in real time; the metabolite concentration includes at least three of glucose, lactic acid, glutamic acid, glutamine, glycerol, methanol, ethanol and lysine; the ion concentration includes NH4 + , K + 、Na + and Ca 2+ At least two of the following; as well as The feedback control system is used to receive the detection data of the biochemical analyzer and dynamically adjust the feeding strategy according to the detection data.

2. The system according to claim 1, wherein: The integrated biochemical analyzer comprises: Optical detection module, used for OD value detection; Electrochemical sensor modules for detecting pH and ion concentration; and Overflow tank biochemical detection module, used to detect metabolite concentrations.

3. The system according to claim 1, wherein: The bypass circulation system comprises: A sampling pump, used to extract the culture solution from the reactor; In-line filtration units, including pre-filters and fine filters, for cell and debris removal; and The reflux system is used to return the analyzed culture fluid to the reactor or discharge it to the waste liquid collector.

4. The system according to claim 1, wherein: The feedback control system adopts a metabolite concentration change rate prediction model to dynamically adjust the feeding rate and component ratio according to the first-order derivative and second-order derivative of the metabolite concentration.

5. The system according to claim 4, characterized in that The feedback control system includes a multi-level control algorithm structure, including: Single parameter PID control, used to control a single parameter including temperature and stirring speed; Feed control based on the rate of change of metabolite concentration; and multi-parameter collaborative control strategy, which is used to establish the correlation matrix between parameters and make collaborative adjustments.

6. The system according to claim 1, wherein: The optical detection module includes an LED light source emitter, a quartz cuvette, a photoelectric sensor and an automatic calibration system, and is used to detect the OD value of the culture solution in real time.

7. The system according to claim 1, wherein: The electrochemical sensor module adopts microfluidic chip design, integrating pH electrode, K + Electrode, Na + Electrode, NH4 + Electrode and Ca 2+ electrode.

8. The system according to claim 1, wherein: The overflow tank biochemical detection module adopts enzyme electrode array or biosensor technology, including glucose and lactic acid detection units, amino acid detection units, alcohol detection units and glycerol detection units.

9. The system according to claim 1, wherein: The feedback control system comprises: Data acquisition and processing layer, used to collect and process detection data; The decision control layer is used to execute the control algorithm according to the processed data; and the execution control layer is used to drive the multi-channel feeding system, pH adjustment system and gas flow control system.

10. A method for cell culture or microbial fermentation using the system according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1. Setting culture process parameters and control strategies; S2, sampling from the bioreactor to the analyzer according to the set cycle; S3. Each detection module completes the analysis in parallel and uploads all parameter data to the control system; S4. The control algorithm calculates the required adjustment operations based on the parameter data, sends instructions to the execution device, and the execution device performs the corresponding operations according to the instructions.

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