Bi-bdo fermentation pH-dynamic optimization method based on online raman spectrum

Abstract

The application provides a Bi-BDO fermentation pH dissolved oxygen dynamic optimization method based on online Raman spectrum, belongs to the technical field of biological fermentation process control, and aims to solve the problems of fermentation control lag, inability to perceive the real metabolic state of cells, process instability and low efficiency in the prior art. The method realizes real-time acquisition of the concentrations of target products BDO, acetic acid, ethanol and other key by-products in the fermentation broth through online Raman spectrum. A metabolic stress index is creatively proposed and constructed. The index is obtained by weighting operation on the instantaneous generation rates of the by-products and target products, and is used for real-time and quantitative characterization of the intrinsic metabolic stress level of cells. The method does not take conventional parameters as the target, but takes maintaining the metabolic stress index in the preset optimal stable interval as the core control strategy. The application realizes the transformation from passive response to active foresight in the fermentation process, and significantly improves the yield, stability and batch-to-batch repeatability of Bi-BDO production.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biological chemical fermentation process control, in particular to a Bi-BDO fermentation pH dissolved oxygen dynamic optimization method based on online Raman spectrum. BACKGROUND

[0002] BDO is an important platform chemical, widely used in the production of engineering plastics, polyurethane, solvents and drugs. The traditional production of BDO highly depends on petrochemical route, which faces the problems of resource consumption and environmental pollution. With the development of synthetic biology and metabolic engineering technology, the biological route of BDO production through microbial fermentation has become a research hotspot and an important direction for future industrialization, because it has the advantages of renewable raw materials and green and environmentally friendly process.

[0003] The core of BDO production by biological method is the microbial fermentation process. The efficiency of this process, including product yield, production intensity and final concentration, directly determines its economic feasibility. In the fermentation process, pH and dissolved oxygen (DO) are the two most critical environmental parameters that affect microbial growth, substrate consumption and product synthesis metabolic pathways. Therefore, accurate and effective control of pH and dissolved oxygen is the prerequisite for efficient and stable production of BDO.

[0004] Currently, the control of BDO fermentation process mainly has the following technical problems:

[0005] 1. The control method based on offline sampling analysis has serious problems of hysteresis and data sparsity:

[0006] Traditional fermentation process monitoring relies on aseptic sampling from the fermentation tank at regular intervals, and then using high-performance liquid chromatography and other analytical instruments to detect the concentrations of substrates, products and byproducts in the sample. This method has fundamental defects: first, the entire analysis process takes several hours, and by the time the detection results show that the process is abnormal, the metabolic state of the fermentation system has already deviated irreversibly, and the intervention of control measures is too late to recover the loss. Secondly, to reduce the risk of contamination, the sampling frequency is low, resulting in sparse and discontinuous process data, and the control personnel cannot grasp the dynamic changes of the fermentation process during the sampling interval.

[0007] 2. The constant value control strategy based on online probe is out of touch with the dynamic physiological needs of cells:

[0008] To overcome the lag of offline analysis, online pH electrode and dissolved oxygen electrode are generally used in industrial fermentation to maintain these parameters at a constant value set in advance. However, the fundamental problem of this control strategy is that the control target is the apparent physical and chemical parameters, not the real internal physiological state of the cells. Microorganisms have different optimal requirements for the environment in different stages of fermentation. In the rapid growth period, cells need higher dissolved oxygen to support rapid proliferation; and in the product synthesis period, micro-aerobic or anaerobic conditions are needed to optimize the metabolic flow to the target product. The rigid maintenance of pH and dissolved oxygen at a fixed value cannot adapt to the dynamic metabolic demand of cells in the entire fermentation cycle, thereby limiting the maximum production potential of the strain in most of the time.

[0009] 3. The existing online monitoring technology cannot solve the problem of the superficiality of the control logic:

[0010] In recent years, process analysis technologies represented by online Raman spectroscopy have been applied to fermentation processes, which can obtain the concentration information of multiple components in the fermentation broth in real time, which to some extent solves the problem of data lag. However, the current control logic based on these online data still stays at a relatively superficial level, which adjusts the stirring speed or feed rate by monitoring the real-time generation rate of BDO. This control method is essentially a post-response, which adjusts after observing the result of the decrease in production rate, and does not touch the root cause of the rate decrease. This method cannot understand the metabolic network state inside the cells, especially cannot perceive the internal driving force that causes the metabolic flow to shift from the BDO synthesis path to the byproduct path, and the metabolic stress state such as redox imbalance inside the cells. Therefore, its control effect is still indirect and lagging, and it cannot realize the prospective guidance and active maintenance of the metabolic path.

[0011] In summary, the existing BDO fermentation control technology generally lacks the perception of the internal physiological state of the cells, which leads to the limitation of passive response and blind maintenance of the control strategy based on offline data or online data, and cannot fundamentally guarantee the continuous and efficient guidance of the metabolic flow to the target product. This has become the core technical bottleneck restricting the industrialization efficiency and stability of Bi-BDO technology. SUMMARY

[0012] Technical problems solved

[0013] In view of the deficiencies of the prior art, the present application provides a Bi-BDO fermentation pH and dissolved oxygen dynamic optimization method based on online Raman spectroscopy, which solves the following problems:

[0014] 1. The problem of serious lag and data blind spot in traditional fermentation control is solved: traditional control relies on offline sampling and HPLC analysis, and there is a time lag of several hours. When abnormal accumulation of by-products is detected, the cell metabolism has already deviated irreversibly, and the control measures can only be passive remediation, missing the best intervention window. At the same time, low-frequency sampling results in sparse process data, forming a large number of monitoring blind spots, which cannot capture key dynamic changes, leading to a high risk of process out of control.

[0015] 2. The problem of serious disconnection between control target and real metabolic state of cells is solved: existing control strategies generally maintain pH, dissolved oxygen and other parameters at constant values. However, the physiological needs of microorganisms are dynamically changing at different growth and production stages. This method of using apparent physical and chemical parameters as control targets is completely disconnected from the intrinsic and dynamic metabolic network state of cells, and cannot always provide the optimal growth and production environment, thereby severely limiting the full play of the production potential of the strain.

[0016] 3. The problem of shallow control logic and lack of forward-looking ability is solved: even with online concentration monitoring, existing control logic still stays at the passive response to the result indicators such as product generation rate. It cannot understand the root cause of the decline in production efficiency, i.e. the metabolic stress state inside the cell. Due to the lack of sensing ability for the precursors of metabolic imbalance, the control system cannot intervene in a truly forward-looking way, but can only correct the problem after it occurs. The control level is shallow and cannot guarantee the stability of the process from the source.

[0017] Technical scheme

[0018] To achieve the above object, the present application is implemented by the following technical scheme: a Bi-BDO fermentation pH and dissolved oxygen dynamic optimization method based on online Raman spectrum, comprising the following steps:

[0019] Sp1. Online spectrum and concentration data acquisition: in the BDO fermentation process, the online Raman spectrum data of the fermentation broth are continuously collected, and the concentration time series data of the target product BDO and the by-products related to cell metabolic stress are obtained in real time by chemometrics model analysis;

[0020] Sp2. Metabolic stress state quantification: based on the concentration time series data obtained in Sp1, the first derivative is taken to obtain the instantaneous generation rate of the target product and the by-products, and according to the preset function relationship, a comprehensive index for real-time quantification of the overall metabolic stress level of the fermentation system is calculated, which is defined as metabolic stress index;

[0021] Sp3. Metabolic imbalance tendency prediction: analyze the time series of metabolic stress index calculated in Sp2 to obtain its current value and its time variation gradient, when the value of metabolic stress index exceeds the preset baseline threshold, or its time variation gradient presents a sustained positive growth trend, it is determined that there is a precursor of metabolic imbalance in the fermentation process;

[0022] Sp4. Adjustment decision and execution: when Sp3 determines that there is a precursor of metabolic imbalance, the preset adjustment logic is started to apply a small amplitude adjustment amount to the pH and dissolved oxygen control variables, and returns to Sp1 to continue to monitor the inhibitory effect of the adjustment amount on the metabolic stress index;

[0023] Sp5. Closed-loop optimization: according to the actual inhibitory effect of the adjustment amount applied in Sp4 on the metabolic stress index, the direction and amplitude of subsequent adjustment are adaptively adjusted, and the metabolic stress index is optimized and maintained within the preset optimal interval by dynamically adjusting the pH and dissolved oxygen, so as to realize the dynamic optimization of the fermentation process.

[0024] Preferably, the by-product related to cell metabolic stress in Sp1 is one or a combination of acetic acid, ethanol, lactic acid or pyruvic acid.

[0025] Preferably, in Sp2, the weighted processing of the instantaneous generation rates of by-products and target product BDO to calculate the metabolic stress index is obtained by statistical analysis and optimization calculation of historical fermentation batch data with different pH and dissolved oxygen conditions, and the optimization target is to maximize the negative correlation between the metabolic stress index and the final BDO yield.

[0026] Preferably, in Sp3, the baseline threshold and the optimal interval of the metabolic stress index are determined by statistical analysis of the metabolic stress index data of high-yield stable fermentation batches.

[0027] Preferably, in Sp4, the adjustment logic is a hierarchical response logic: when it is determined that there is a slight imbalance precursor, only the dissolved oxygen control variable is fine-tuned; when it is determined that there is a significant imbalance precursor, the dissolved oxygen and pH control variables are adjusted cooperatively.

[0028] Preferably, in Sp4, the adjustment amount is a small amplitude adjustment amount of 0.1% to 5% of the current set value.

[0029] Preferably, in Sp5, the closed-loop optimization is an adaptive feedback optimization, which uses hill climbing algorithm or particle swarm optimization algorithm to minimize the metabolic stress index as the target to search for the optimal pH and dissolved oxygen adjustment amount online.

[0030] Preferably, a system for online Raman spectrum-based dynamic optimization of pH and dissolved oxygen in Bi-BDO fermentation, comprising:

[0031] A Bi-BDO fermentation tank equipped with pH and dissolved oxygen sensing and execution mechanisms;

[0032] An online Raman spectrum analyzer;

[0033] A central processing unit configured to:

[0034] Receive and analyze Raman spectrum data to obtain a concentration time series;

[0035] Calculate a metabolic stress index in real time according to Sp2;

[0036] Predict a metabolic imbalance trend in real time according to Sp3;

[0037] Generate and send adjustment instructions to the execution mechanisms of the fermentation tank according to Sp4 and Sp5 to form a closed-loop control.

[0038] Preferably, the central processing unit further comprises a historical database and a model optimization module for storing historical fermentation data and for offline optimization and updating of the weighting coefficients used in calculating the metabolic stress index.

[0039] Advantages

[0040] The present application provides a method for online Raman spectrum-based dynamic optimization of pH and dissolved oxygen in Bi-BDO fermentation. The method has the following advantages:

[0041] 1. The present application uses online Raman spectrum to continuously acquire data and calculates a metabolic stress index that can represent the internal state of cells in real time, providing a continuous and high-resolution process dashboard for the control system. This fundamentally solves the serious lag problem of offline sampling analysis lasting for several hours, as well as the data sparsity and process blind spot problem caused by low-frequency sampling. The control system can instantaneously perceive the stress state fluctuations caused by subtle changes in metabolic flow, making the basis for decision-making and operation no longer outdated information from several hours ago, but the real and dynamic process state at the moment, achieving a leap from lag response to real-time insight and completely overcoming the inertia of traditional control methods.

[0042] 2、The prior art is to maintain constant pH / DO values or track product concentration changes, and the control target is an external and indirect apparent parameter, and the application directly sets the control target to maintain the metabolic stress index in the optimal stable interval, the index is a direct quantitative representation of the intracellular redox balance and energy state, and is the core of determining the metabolic flow direction, by directly controlling this core physiological indicator, the application ensures that each adjustment of pH and dissolved oxygen accurately serves the fundamental purpose of stabilizing the intracellular metabolic network, so that the environmental control and the dynamic physiological needs of the cells are always synchronized and optimally matched, thereby maximizing the production potential of the strain during the entire fermentation cycle, and realizing a profound change in the control target from an apparent parameter to a core physiological state, ensuring the accuracy and efficiency of the control.

[0043] 3、The most core advantage of the application is its foresight, by monitoring the metabolic stress index and its change gradient, the method can sensitively capture the precursor of imbalance before the cell metabolic network deviates from the large-scale and irreversible byproduct path, it occupies the valuable window period between the deviation of metabolic trend and the deterioration of production performance, and actively pulls the cells back to the healthy state of high-efficiency BDO production through the implementation of minimal and timely perturbation adjustment, which completely changes the situation of the prior art that can only passively remedy after detecting the malignant results such as yield reduction or byproduct accumulation, realizes the prevention of the fermentation process, and avoids the toxic inhibition caused by the invalid consumption of carbon source and byproduct accumulation from the source, and realizes the fundamental change of the control logic from passive remedy to active foresight and prospective stabilization.

[0044] 4、Since the application can continuously maintain the cells in the optimal metabolic interval of low stress and high activity, effectively inhibiting various metabolic unexpected turns that lead to process fluctuations and efficiency reduction, therefore, the application of the application can significantly improve the final yield and production intensity of BDO, and significantly reduce the generation of impurity byproducts such as acetic acid and ethanol, more importantly, by converting the uncertain and easily drifting cell physiological state into a quantifiable and controllable index, the application greatly enhances the stability and predictability of the fermentation process, thereby significantly improving the batch-to-batch repeatability of production, providing a key technical support for the large-scale and stable industrial production of Bi-BDO technology, and significantly improving the stability, yield and batch-to-batch repeatability of the BDO fermentation process. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 The system composition cloud map of the application;

[0046] Figure 2 The system architecture diagram of the application;

[0047] Figure 3Workflow diagram for the present application. DETAILED DESCRIPTION

[0048] 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 part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application. Embodiment one:

[0050] As shown in Figures 1-2 A Bi-BDO fermentation pH and dissolved oxygen dynamic optimization method based on online Raman spectrum includes the following steps:

[0051] Sp1. Online spectrum and concentration data acquisition: In the BDO fermentation process, the online Raman spectrum data of the fermentation broth is continuously collected, and the concentration time series data of the target product BDO and the by-product related to cell metabolic stress are obtained by real-time analysis of the chemometrics model;

[0052] Sp2. Metabolic stress state quantification: Based on the concentration time series data obtained in Sp1, the first derivative is obtained, and the instantaneous generation rate of the target product and the by-product is obtained. According to the preset function relationship, a comprehensive index for real-time quantification of the overall metabolic stress level of the fermentation system is calculated, which is defined as the metabolic stress index;

[0053] Sp3. Metabolic imbalance trend prediction: The time series of the metabolic stress index calculated in Sp2 is analyzed to obtain its current value and time change gradient. When the value of the metabolic stress index exceeds the preset baseline threshold, or its time change gradient presents a continuous positive growth trend, it is determined that there is a precursor of metabolic imbalance in the fermentation process;

[0054] Sp4. Adjustment decision and execution: When Sp3 determines that there is a precursor of metabolic imbalance, the preset adjustment logic is started, a small amplitude adjustment amount is applied to the pH and dissolved oxygen control variables, and Sp1 is returned to continuously monitor the inhibition effect of the adjustment amount on the metabolic stress index;

[0055] Sp5. Closed-loop optimization: According to the actual inhibition effect of the adjustment amount on the metabolic stress index in Sp4, the direction and amplitude of subsequent adjustment are adaptively adjusted, and the metabolic stress index is optimized and maintained in the preset optimal interval by dynamically adjusting the pH and dissolved oxygen, so as to realize the dynamic optimization of the fermentation process.

[0056] The core method content of a Bi-BDO fermentation pH and dissolved oxygen dynamic optimization method based on online Raman spectrum:

[0057] The core idea of the method is to construct a soft measurement index that can reflect the internal metabolic health status of microorganisms in real time and quantitatively through online pull MAH spectrum technology. This index is named metabolic stress index. Then, the pH and dissolved oxygen are dynamically optimized and adjusted in advance to maintain the index in the best stable interval, which realizes the fundamental guidance and stability of the cell metabolic flow, and ultimately achieves the high efficiency and stability of the Bi-BDO production process.

[0058] 1. Construction and connotation of metabolic stress index:

[0059] The design principle of metabolic stress index is based on the deep understanding of the imbalance of microbial metabolic network. When the microbial cells face environmental pressure, the lack of dissolved oxygen in the fermentation broth leads to redox imbalance, or the glycolysis rate exceeds the processing capacity of the tricarboxylic acid cycle under high substrate concentration, leading to energy metabolism disorder. The cell will start emergency metabolic pathways, and the generation of acetic acid is a typical sign of energy metabolism overflow, which is related to the ATP generation demand under high glycolysis flux. The generation of ethanol is a classic response to excessive reduction pressure, which acts as a temporary electron acceptor to regenerate oxidized coenzyme I, i.e. NAD+.

[0060] Therefore, the calculation of metabolic stress index is the difference between the total generation rate of stress byproducts after weighted processing and the generation rate of target products after weighted processing.

[0061] The stress byproducts here are specifically designated as acetic acid and ethanol, which have significant indicative significance for the metabolic network of Bi-BDO production strain. Their respective instantaneous generation rates are obtained by first-order derivative operation on the concentration data obtained by real-time analysis of Raman spectrum in time dimension. This rate value directly reflects the intensity of metabolic flux at this moment.

[0062] The weighting coefficients used in the calculation are not empirically set, but have a solid data basis. They are obtained by strict multivariate nonlinear regression analysis or machine learning algorithms such as gradient boosting machine training on multiple batches of historical fermentation data. The data set analyzed includes process parameters, continuous concentration curves of each component, and final BDO yield and recovery rate of each batch. The goal of the optimization algorithm is to find a combination of weighting coefficients that can make the calculated metabolic stress index and the final BDO production performance indicators present the strongest negative correlation, thereby ensuring the accuracy and reliability of the index as an indicator of cell metabolic health status.

[0063] 2. Prospective dynamic optimization control:

[0064] The control target of the method is not to track a fixed and unchanging pH or dissolved oxygen set value, but to actively maintain the metabolic stress index in a pre-marked low stress optimal interval close to zero.

[0065] The core driving force of the control logic comes from the comprehensive analysis of the current value of the metabolic stress index and its time-varying trend, where the time-varying trend is quantified by calculating the second-order derivative of the time series of the index or the linear regression slope within a short time window. This mechanism enables the control system to predict the risk of metabolic imbalance in advance through a slight positive drift trend of the metabolic stress index before the actual yield of BDO is substantially affected, and immediately start the adjustment program to achieve truly forward-looking control. Specific embodiment two:

[0067] As shown in the Bi-BDO fermentation pH dissolved oxygen dynamic optimization method based on online Raman spectrum, the operation process is as follows: Figures 1-2

[0068] The complete operation process of the method is divided into two closely linked stages: offline modeling and system initialization and online dynamic optimization.

[0069] Stage one: offline modeling and system initialization

[0070] This stage is the basis for the successful application of the entire method, and is performed once before formal production.

[0071] Diversified fermentation data collection and accurate calibration: In order to ensure the robustness and wide applicability of the model, a series of BDO fermentation batches with diversity need to be designed and run, including high-yield stable-yield "golden batches", "abnormal batches" with large amounts of by-products due to reasons, and "exploratory batches" intentionally changing pH and dissolved oxygen set points using experimental design method response surface method. During the running of all batches, two key operations are carried out simultaneously: first, the online Raman spectrum probe continuously collects spectral data at a high frequency; second, sterile sampling is strictly carried out according to the preset time point, and the concentrations of BDO, glucose, acetic acid, ethanol and other key components are immediately measured accurately using a calibrated high-performance liquid chromatograph. These offline measurement values will be used as true values or label data for subsequent model training.

[0072] Construction and verification of chemometrics model: Using the collected spectral data and corresponding offline concentration true values, a mathematical model that can accurately predict the concentrations of multiple components from complex spectral signals is constructed using the partial least squares regression algorithm. During model construction, special attention should be paid to the Raman characteristic peak regions related to the molecular vibrations of each component, including the C-H stretching vibration region of glucose, the C-O stretching vibration region of BDO and ethanol, and the C=O carbonyl stretching vibration region of acetic acid. After the model is built, it must undergo a strict verification procedure, including internal cross-validation and external validation using an independent test data set, to ensure the prediction accuracy and generalization ability of the model and prevent data overfitting.

[0073] ​Model parameterization of metabolic stress index: Using the validated chemometric model, all the historical batch-wise full-process spectral data are converted into continuous, high-resolution concentration profiles. Combining the final fermentation performance evaluation index of each batch, the optimal weighting coefficients used in the calculation of metabolic stress index are calculated and determined through the aforementioned multivariate regression or machine learning method.

[0074] Precise quantification of control threshold: Select the top 10% of "golden batches" with the best performance, and perform detailed statistical analysis on their full-process metabolic stress index curves. The range of the mean value plus or minus twice the standard deviation of these curves is defined as the low stress optimal interval in the control target. The 95th percentile value of these curves is set as the early warning threshold for triggering the correction control.

[0075] System initialization and deployment: Load the final determined chemometric model file, the weighting coefficients of metabolic stress index, and the quantitative control threshold parameters into the central processor or industrial computer of the fermentation control system, complete the system deployment and preparation work.

[0076] Phase two: online dynamic optimization;

[0077] This phase is the real-time closed-loop control process performed during each actual fermentation production.

[0078] Start and high-frequency monitoring: Start a new BDO fermentation process, and the system automatically starts the online Raman spectrometer. A 785 nm laser wavelength is used to reduce biological fluorescence interference. The acquisition parameters are set to ensure data quality and real-time performance.

[0079] Data real-time processing cycle:

[0080] Spectrum input: Collect a new frame of raw Raman spectrum data.

[0081] Spectrum pretreatment: Automatically perform a series of pretreatment steps. First, subtract the dark spectrum. Second, use the median filter algorithm to remove the spike noise caused by cosmic rays. Then, apply the asymmetric least squares method for baseline correction to eliminate the fluorescence background. Finally, normalize the spectrum by total area to correct for minor fluctuations in laser power.

[0082] Concentration analysis: Input the clean spectral data into the loaded chemometric model, and instantly calculate the current concentrations of BDO, glucose, acetic acid, ethanol, and other components.

[0083] Rate calculation: Add the newly analyzed concentration points to their respective time series. The system uses the Savitzky-Golay filter to process the time series window containing the latest data points. This filter can directly calculate the first derivative of the smoothed curve, i.e., the instantaneous generation rate of each component, while smoothing the noise through local polynomial fitting.

[0084] State Quantification: Call the metabolic stress index model parameters, and substitute the instantaneous generation rate of each component into the weighted operation to calculate the metabolic stress index value at the current time, and at the same time calculate its linear regression slope in the recent time window as its change gradient.

[0085] State Determination and Decision: The system compares the calculated metabolic stress index and its gradient with the preset threshold to automatically determine the state level of the current process.

[0086] Control Command Output: The control system sends specific adjustment commands to the programmable logic controller or distributed control system at the bottom layer of the fermenter through the standard industrial communication protocol OPCUA according to the determination result.

[0087] Uninterrupted Circulation: The system returns to the input step of the data real-time processing cycle and continues the closed-loop operation of "monitoring-treatment-decision-execution" until the operator issues the fermentation end instruction. Specific Embodiment Three:

[0089] As shown in Figures 1-2 A Bi-BDO fermentation pH dissolved oxygen dynamic optimization method based on online Raman spectrum, the system determination scheme:

[0090] The system has a three-level state determination scheme that automatically switches according to the real-time quantification results of the metabolic stress index, achieving automation and standardization of decision-making.

[0091] State level State name Decision condition System action Level 1 Steady-state optimization zone Metabolic stress index value is within the low-stress optimal interval, and its linear regression slope over the past 15 minutes is less than a pre-set positive micro-threshold. Maintain: No active intervention, keep current pH and DO setpoints. System continues high-frequency monitoring. Level 2 Metabolic warning zone Metabolic stress index value exceeds the "low-stress optimal interval" but is below the "warning threshold", or its linear regression slope over the past 15 minutes is consistently positive and statistically significant. Feedforward fine-tuning: Decision as a precursor to metabolic imbalance. Initiate a minimal disturbance regulation sequence, preferentially fine-tuning the DO setpoint, and closely observe the subsequent metabolic stress index response. Level 3 Imbalance control zone Metabolic stress index value has clearly broken the "warning threshold". Correction control: Decision as a clear shift in metabolism has occurred. Execute a more forceful co-regulation, simultaneously adjusting both pH and DO setpoints according to a pre-set control map.

[0092] System Control Logic:

[0093] Steady-state logic: In level 1 state, the control system is in "observer" mode, and its core task is to ensure the continuity and accuracy of monitoring data, providing high-quality basis for state change.

[0094] Feedforward logic: In level 2 state, the system enters "precautioner" mode, and the core of this logic is an active "detection-evaluation-decision" sequence. The system first increases the DO set value by a small step, 0.5% saturation, and then continuously calculates the response gradient of the metabolic stress index in the next 20-minute observation window. When the gradient turns negative, it proves that the adjustment is effective, and the system will lock the new DO set value and try to return to level 1. When the gradient is still positive or has no significant change, the system determines that simply adjusting the dissolved oxygen is ineffective, and will restore the original DO value and try to slightly decrease the pH set value by 0.05 units, and enter the observation and evaluation cycle again. This logic aims to quickly locate and solve potential problems with minimal environmental disturbance.

[0095] Correction logic: in level 3 state, the system enters "intervener" mode, when no more tentative fine-tuning is performed, but a multi-dimensional control map built in offline modeling stage is directly invoked, which is a response surface model with input variables including current metabolic stress index value, main by-product relative contribution degree causing index exceeding, and current substrate concentration, and output variables are calculated optimal ΔpH and ΔDO adjustment values of pH and DO set points. The system adds current set points to these two adjustment values to obtain new control targets, in order to pull metabolic state back to pre-warning zone as fast as possible.

[0096] Data content processing, input and output:

[0097] System input:

[0098] Real-time dynamic data: raw spectral data collected by Raman spectrometer, which is represented as high-dimensional floating-point number vector containing photon counting value and Raman shift value, and real-time measurement values of pH and DO by online sensors of fermenter, as control feedback and current state record.

[0099] Static configuration data: chemometrics model file containing regression coefficient vector for prediction, metabolic stress index model parameters including by-product Raman characteristic peak list for monitoring and corresponding weighting coefficient values, control scheme parameters including upper and lower limits of "low stress optimal interval" and specific values of "pre-warning threshold", and multi-dimensional control map model file used by correction logic.

[0100] Core data processing:

[0101] Spectrum pretreatment: automatically execute standardization process including dark spectrum subtraction, cosmic ray removal, asymmetric least squares baseline correction and spectrum total area normalization.

[0102] Concentration prediction: perform matrix operation between pretreated spectrum vector and regression coefficient vector of chemometrics model, and output predicted concentration values of each component.

[0103] Time series processing and rate calculation: use a Savitzky-Golay filter containing 11 data point window to perform smoothing and first-order differential processing on concentration time series, and directly output instantaneous generation rate.

[0104] Metabolic stress index synthesis: perform linear combination operation on instantaneous generation rates of each component according to preset weighting coefficients, and synthesize single metabolic stress index scalar value.

[0105] State determination and decision: input synthesized index and its gradient into preset determination rule and control logic module for comparison and judgment.

[0106] System output:

[0107] Control instruction: send new pH setpoint to the controller of pH control unit, send new DO setpoint to the controller of DO control unit. All control instructions are transmitted safely and reliably through industrial standard protocols such as OPCUA.

[0108] Process data and log: record and store the concentration curve of each component, the change curve of metabolic stress index in real time in the form of CSV file or writing into time series database, record each switch of system state in detail in the dedicated event log, and the specific value, execution time and response result of all pH and DO adjustment actions automatically executed by the system. These structured data provide a solid foundation for subsequent process analysis, fault diagnosis and continuous iterative optimization of the model. Specific embodiment four:

[0110] As shown in Figures 1-2 According to the content in the above specific embodiments, the following content is further disclosed, and the following provides a specific use case:

[0111] Background and scenario:

[0112] In a 500-liter Bi-BDO fermentation tank of pilot scale, the fermentation process enters the late logarithmic growth phase, which is the key stage of the fastest BDO synthesis rate. The fermentation tank uses a standard distributed control system, and the pH and DO are set at the best constant values of historical experience, which are 6.8 and 15% saturation, respectively. During this period, due to the slight fluctuation of the total air pipe pressure in the workshop, the air flow supplied to the fermentation tank decreases slightly, but the decrease is very small, and the reading of the online DO probe still fluctuates between 14.8% and 15% in a narrow range, and does not trigger the traditional low DO alarm.

[0113] System monitoring and data performance:

[0114] At the 28th hour of fermentation, the operator saw from the monitoring interface that the conventional parameters such as pH, DO, temperature and feed rate were all normal, and the slope of the BDO concentration curve also remained good. However, the background data of the dynamic optimization system of the present application showed abnormal signals.

[0115] From 28h10min, the metabolic stress index calculated by the system started to slowly but steadily climb up from the steady-state baseline of -0.5. By 28h40min, the index had reached +1.2, and by analyzing its constituents, the system found that the increase in the index was mainly due to a significant increase in the instantaneous rate of ethanol production. At the same time, the system calculated the linear regression slope of the index over the past 30 minutes to be positive with a high degree of confidence, indicating a sustained worsening trend rather than a random fluctuation in the data.

[0116] System decision and control execution:

[0117] At 28h41min, the system automatically switched the current state from the steady-state optimization zone at level 1 to the metabolic warning zone at level 2 according to the decision scheme. The system determined that although the apparent parameters were normal, there was a slight redox imbalance inside the cell, i.e., a reduction pressure, which led to a partial carbon flow diversion to ethanol synthesis to regenerate NAD+.

[0118] The system immediately started the feedforward fine-tuning control logic. First, it instructed the DO control unit to slightly increase the dissolved oxygen setpoint from 15.0% to 15.5%. Over the next 20 minutes, the system continuously monitored the response of the metabolic stress index at a high frequency. The data showed that after the DO was increased, the instantaneous rate of ethanol production began to decrease, and the metabolic stress index began to fall from the high point of +1.2. By 29h15min, the index had successfully returned to -0.2, re-entering the low stress optimal interval.

[0119] Results and values:

[0120] Thanks to the proactive intervention of the system, a "silent" metabolic drift triggered by a slight oxygen deficiency, which could not be detected by traditional methods, was successfully nipped in the bud. Without the system, this state would have continued to worsen, and the operator would not have discovered the accumulation of ethanol and the decrease in BDO yield from offline HPLC data for several hours, but by then irreversible carbon source waste would have been caused. This case fully demonstrates the ability of the method to prevent problems from occurring, by making minimal interventions at the initial stage of the problem, ensuring the stability of metabolic flow in the key production stage, and estimating that the final BDO yield of this batch has been improved by at least 5%. Specific embodiment five:

[0122] As Figures 1-2 shown, according to the content in the above specific embodiments, the following content is further disclosed, and the following provides a specific use case:

[0123] Background and scenario:

[0124] In an industrial production scale 50 cubic meter BDO fermentor, the fermentation process was in stable phase, due to the pump failure of downstream processing unit, the back pressure of fermentor off gas line increased instantaneously, which seriously affected the mass transfer efficiency of fermentor, causing the dissolved oxygen in the tank dropped rapidly from 20% to 5% in a short time, although the engineers repaired the fault in 15 minutes, and manually restored the dissolved oxygen to 20% of the set value, but this strong anoxic disturbance had caused a huge metabolic impact on the strain.

[0125] System monitoring and data performance:

[0126] During the sudden drop of dissolved oxygen, the system of the present application monitored the metabolic stress index skyrocketing, from-0.3 to +8.5 in just 15 minutes, far exceeding the preset warning threshold of +3.0, the contribution analysis of the index showed that the instantaneous generation rate of acetic acid and ethanol both showed explosive growth, indicating that under anoxic stress, the energy metabolism and redox balance of the cells collapsed at the same time.

[0127] When the engineer manually restored the dissolved oxygen to 20%, the conventional control system task was completed, but the system of the present application showed that although the dissolved oxygen was restored, the metabolic stress index only decreased slightly to +7.9 in a short time, still at a very high stress level, indicating that the cell metabolic network had fallen into a "vicious cycle" and could not recover by itself by restoring the dissolved oxygen.

[0128] System determination and control execution:

[0129] At the time of the sudden drop of dissolved oxygen, the system immediately determined the state as level 3 imbalance control area. After the dissolved oxygen was manually restored, the system determination process was still at level 3, and the correction control logic was immediately started.

[0130] Instead of trying to fine-tune, the system directly calls the built-in multi-dimensional control atlas, according to the current input "MSI value = +7.9, acetic acid and ethanol are jointly dominant, substrate concentration is sufficient", the optimal correction strategy calculated by the atlas model is: first, lower the pH set value from 6.5 to 6.2 to suppress the dissociative toxicity of acetic acid and change the metabolic balance; second, further increase the DO set value from 20% to 25% for a short time of "oxygen overexposure" to help the cells quickly consume the accumulated reducing power.

[0131] The system automatically executed this coordinated regulation instruction.

[0132] Results and value:

[0133] Within about 2 hours after the system performed the coordinated regulation, the metabolic stress index was quickly and effectively suppressed, successfully dropped from +7.9 to +2.5, into the warning zone, and then the system automatically switched back to the feedforward fine-tuning logic, gradually guided the pH and DO back to the regular setpoints within the following 4 hours, and finally returned the metabolic stress index to the steady-state optimization zone. Although this strong disturbance led to some loss of the production phase, through the system's precise, fast, and multi-variable coordinated corrective control, the strain was successfully "rescued" from the severe metabolic collapse, avoiding the disastrous consequence of discarding the whole batch of fermentation broth, and recovering huge economic loss.

[0134] It should be noted that the relational terms herein, such as first and second, and the like, are used solely to distinguish one from another entity or action without necessarily requiring or implying any actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. The terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0135] While the embodiments of the application have been illustrated and described, it will be understood by those skilled in the art that various changes, modifications, substitutions, and alterations can be made therein without departing from the spirit and scope of the application, which is defined by the following claims and their equivalents.

Claims

1. A method for dynamic optimization of pH and dissolved oxygen in a Bi-BDO fermentation based on online Raman spectroscopy, characterized in that: Comprising the following steps: Sp1. Online spectrum and concentration data acquisition: during the BDO fermentation process, the online Raman spectrum data of the fermentation broth is continuously collected, and the concentration time series data of the target product BDO and the by-products related to cell metabolic stress are obtained by real-time analysis of the chemometrics model; Sp2. Metabolic stress state quantification: based on the concentration time series data obtained in Sp1, the first derivative is taken to obtain the instantaneous generation rate of the target product and the by-products, and a comprehensive index for real-time quantification of the overall metabolic stress level of the fermentation system is calculated according to the preset function relationship, defined as the metabolic stress index; Sp3. Metabolic imbalance trend prediction: analyze the time series of the metabolic stress index calculated in Sp2 to obtain its current value and its time change gradient, when the value of the metabolic stress index exceeds the preset baseline threshold, or its time change gradient presents a continuous positive growth trend, it is determined that there is a precursor of metabolic imbalance in the fermentation process; Sp4. Adjustment decision and execution: when Sp3 determines that there is a precursor of metabolic imbalance, the preset adjustment logic is started to apply a small amplitude adjustment to the pH and dissolved oxygen control variables, and returns to Sp1 to continuously monitor the inhibition effect of the adjustment on the metabolic stress index; Sp5. Closed-loop optimization: according to the actual inhibition effect of the adjustment amount applied in Sp4 on the metabolic stress index, the direction and amplitude of subsequent adjustment are adaptively adjusted, and the metabolic stress index is optimized and maintained within the preset optimal interval by dynamically adjusting the pH and dissolved oxygen, so as to realize the dynamic optimization of the fermentation process.

2. The method for dynamic optimization of pH and dissolved oxygen in a Bi-BDO fermentation based on online Raman spectroscopy according to claim 1, characterized in that: The by-products related to cell metabolic stress in Sp1 are one or a combination of acetic acid, ethanol, lactic acid or pyruvic acid.

3. The method of claim 1, wherein the method is a method for dynamic optimization of pH and dissolved oxygen in a Bi-BDO fermentation based on online Raman spectroscopy. In Sp2, the weighting coefficients for weighting the instantaneous generation rates of the by-products and the target product BDO to calculate the metabolic stress index are obtained by statistical analysis and optimization calculation of historical fermentation batch data with different pH and dissolved oxygen conditions, and the optimization target is to maximize the negative correlation between the metabolic stress index and the final BDO yield.

4. The method for dynamic optimization of pH and dissolved oxygen in a Bi-BDO fermentation based on online Raman spectroscopy according to claim 1, characterized in that: The baseline threshold of the metabolic stress index in Sp3 and the optimal interval are determined by statistical analysis of the metabolic stress index data of high-yield stable fermentation batches.

5. The method of claim 1, wherein the method is a method for dynamic optimization of pH and dissolved oxygen in a Bi-BDO fermentation based on online Raman spectroscopy. The adjustment logic in Sp4 is a hierarchical response logic: when it is determined that there is a slight imbalance precursor, only the dissolved oxygen control variable is fine-tuned; when it is determined that there is a significant imbalance precursor, the dissolved oxygen and pH control variables are adjusted coordinately.

6. The method of claim 1, wherein the method is a method of online Raman spectroscopy-based dynamic optimization of pH and dissolved oxygen in a Bi-BDO fermentation. The adjustment amount in Sp4 is a small amplitude adjustment amount of 0.1% to 5% of the current set value.

7. The method of claim 1, wherein the method is a method of online Raman spectroscopy-based dynamic optimization of pH and dissolved oxygen in a Bi-BDO fermentation. The closed-loop optimization in Sp5 is an adaptive feedback optimization, which uses hill climbing algorithm or particle swarm optimization algorithm to minimize the metabolic stress index as the target to search for the optimal pH and dissolved oxygen adjustment amount online.

8. The system for online Raman spectroscopy-based dynamic optimization of pH and dissolved oxygen in a Bi-BDO fermentation according to any one of claims 1-7, characterized in that, Comprising: Bi-BDO bioreactor equipped with pH and dissolved oxygen sensing and execution mechanism; Online Raman spectrum analyzer; Central processing unit configured to: receive and analyze Raman spectrum data to obtain concentration time series; According to Sp2, the metabolic stress index is calculated in real time; According to Sp3, the metabolic imbalance trend is predicted in real time; According to Sp4 and Sp5, adjustment instructions are generated and sent to the actuators of the fermenter to form a closed-loop control.

9. The system of online Raman spectrum-based dynamic optimization of pH and dissolved oxygen for a Bi-BDO fermentation according to claim 8, characterized in that: The central processor further comprises a historical database and a model optimization module, which are used to store historical fermentation data and to perform offline optimization and update of the weighting coefficients used in the calculation of the metabolic stress index.

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