Peanut sprout tryptophan purification parameter control system and method

Abstract

The invention belongs to the technical field of parameter control, and discloses a purification parameter control system and method for tryptophan in peanut buds. The method comprises the following steps: acquiring frequency shift of a chromatographic column through ultrasonic waves, analyzing to obtain flow velocity, and analyzing to obtain flow field uniformity and vortex intensity according to the flow velocity; an optogenetics probe is constructed, and initial fluorescence intensity is acquired through blue light activation and acquisition; when the uniformity of the flow field is smaller than a uniformity threshold value, the initial fluorescence intensity is compensated through the vortex intensity to obtain the fluorescence intensity; synchronously collecting temperature and ORP potential, and constructing a separation kinetic model; obtaining the enantiomer impurity concentration, and judging the process stage; setting a dual-stage optimization target according to the process stage, performing optimization in combination with the separation kinetic model to obtain an optimization purification parameter, and generating a corresponding control instruction in combination with a control barrier function; through interdisciplinary fused multi-physics field modeling and an intelligent optimization framework, accurate control and green and efficient production of the tryptophan purification process are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of parameter control, and more particularly to a parameter control system and method for purifying tryptophan in peanut sprouts. Background Art

[0002] Tryptophan, a key amino acid, is widely used in food, medicine, and feed. Purifying tryptophan from peanut sprouts holds significant economic value and market potential. However, conventional purification processes focus solely on a single chemical separation process, completely ignoring the synergistic effects of flow field dynamics and the oxidative environment on tryptophan separation. This results in an inability to quantify the oxidative losses and decreased mass transfer efficiency caused by the coupling of these two factors. Furthermore, conventional detection technologies, such as offline HPLC, have long detection cycles and lack dynamic alignment with process stages. This makes it difficult to provide real-time feedback on tryptophan concentration and impurity distribution, leading to lags in the transition between impurity elution and collection periods, further exacerbating low separation efficiency and insufficient purity. Furthermore, existing optimization algorithms target only a single objective (such as purity or time) and fail to consider the cross-effects of parameters such as flow rate and temperature on multiple physical fields. Furthermore, they lack dynamic safety constraints, making it impossible to adaptively control parameters when the flow field is abnormal or oxidation risk increases. This results in high tryptophan oxidation losses, severely restricting purification efficiency and product quality.

[0003] In view of this, the present invention proposes a control system and method for purification parameters of tryptophan in peanut sprouts to solve the above problems. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solution: a method for controlling parameters of tryptophan purification in peanut sprouts, comprising the following steps:

[0005] The frequency shift of the chromatography column is collected by ultrasound, and the flow velocity is obtained by analysis. The flow field uniformity and eddy current intensity are obtained based on the flow velocity analysis.

[0006] An optogenetic probe was constructed and fixed to the biofilm at the injection port of a simulated moving bed. The probe was activated by blue light and the initial fluorescence intensity was acquired. When the flow field uniformity was less than the uniformity threshold, the initial fluorescence intensity was compensated by the eddy current intensity to obtain the fluorescence intensity.

[0007] Temperature and ORP potential are collected simultaneously, and the separation kinetic model is constructed by combining fluorescence intensity, activation energy and diffusion penalty terms;

[0008] Obtain the enantiomeric impurity concentration and determine the process stage by combining fluorescence intensity and ORP potential;

[0009] The two-stage optimization objectives are set according to the process stages, and the constraints are set. The rolling time domain optimization is performed in combination with the separation kinetics model to obtain the optimized purification parameters, and the corresponding control instructions are generated in combination with the control barrier function.

[0010] Furthermore, the diffusion penalty term is calculated based on the second-order partial derivative of the fluorescence intensity in the axial direction of the chromatography column, and is calculated based on the molecular diffusion coefficient, eddy current intensity and cross-sectional area of ​​the chromatography column in combination with empirical constants.

[0011] Furthermore, the method for determining the process stage includes:

[0012] When the sum of the ratio of the fluorescence intensity to the enantiomeric impurity concentration and the absolute value of the first-order partial derivative of the fluorescence intensity in the axial direction of the chromatographic column is less than or equal to the dynamic threshold, it is determined to be in the impurity elution period;

[0013] When the ratio of the fluorescence intensity to the enantiomeric impurity concentration is greater than a preset threshold, and the ORP potential is greater than a dynamic threshold, it is determined to be in the tryptophan collection period;

[0014] The dynamic threshold is calculated based on the real-time ORP potential and the preset potential threshold.

[0015] Furthermore, the method for obtaining optimized purification parameters includes:

[0016] During the impurity elution period, the flow rate is integrated over the elution period to obtain the time cost. Combined with the flow field penalty term, the first goal is to minimize the sum of the time cost and the flow field penalty term. Separation kinetic model constraints are set, and the value of the separation kinetic model is less than or equal to the elution rate upper limit. Purification parameters are optimized based on the rapid gradient method.

[0017] For the tryptophan collection period, the ratio of fluorescence intensity to enantiomeric impurity concentration was calculated, the product of the ratio and the oxidation penalty factor was calculated, and the difference between the product and the diffusion penalty term was calculated, with maximizing the difference as the second objective. Flow field uniformity constraints were set, and purification parameters were optimized based on adaptive simulated annealing.

[0018] Furthermore, the method for obtaining the flow field penalty term includes:

[0019] Determine whether the uniformity index is less than a preset index threshold, if so, calculate a first penalty function according to the uniformity index, if not, the first penalty function value is 0;

[0020] Determine whether the eddy current intensity is greater than a preset intensity threshold. If so, calculate a second penalty function based on the eddy current intensity and the preset intensity threshold. If not, the second penalty function value is 0.

[0021] A flow field penalty term is obtained by performing a weighted summation on the first penalty function and the second penalty function.

[0022] Furthermore, the method for optimizing the purification parameters based on the rapid gradient method includes:

[0023] Calculating the gradient of the first objective with respect to the purification parameter by the finite difference method;

[0024] Update the purification parameters in the direction of gradient descent. The purification parameters include flow rate and temperature. During the updating process, check whether the updated purification parameters meet the constraints. If not, adjust the purification parameters until the constraints are met.

[0025] Calculate the difference between the current first target value and the first target value of the previous iteration to obtain the first difference. If the first difference is less than the preset difference threshold, the algorithm is judged to have converged and the optimized purification parameters are obtained. Otherwise, return to continue calculating the gradient and updating the purification parameters.

[0026] Furthermore, the method for obtaining the oxidation penalty factor includes:

[0027] Pre-train the oxidation penalty coefficient, collect the ORP potential, combine it with the preset potential threshold, and calculate the oxidation penalty factor.

[0028] Furthermore, the method for optimizing and obtaining purification parameters based on adaptive simulated annealing includes:

[0029] Based on the current solution of the purified parameters, a neighborhood solution is generated through random perturbations;

[0030] Calculate the second objective values ​​of the current solution and the neighboring solution respectively, and calculate the difference between the second objective value of the neighboring solution and the second objective value of the current solution to obtain the second difference. If the second difference is greater than 0, accept the neighboring solution and update the current solution to the neighboring solution. If the second difference is less than or equal to 0, calculate the acceptance probability P based on the Metropolis criterion, combining the second difference and the current temperature, and generate a random number p between [0,1]. If the random number p is less than P, accept the neighboring solution and update the current solution. Otherwise, keep the current solution unchanged.

[0031] After L iterations at each temperature, the purification parameters are updated according to the parameter change rate r. When the updated purification parameters are less than or equal to the preset parameter change threshold, the algorithm is judged to have converged and the optimized purification parameters are obtained. Otherwise, the algorithm returns to continue generating neighborhood solutions and making judgments.

[0032] Furthermore, the method of generating corresponding control instructions in combination with the control barrier function includes:

[0033] When the eddy current intensity is greater than the intensity barrier threshold or the ORP potential value is greater than the potential barrier threshold, the change rate of the purification parameter is controlled to be less than or equal to the corresponding change rate threshold.

[0034] Furthermore, the method for obtaining the enantiomeric impurity concentration includes:

[0035] Prepare N sets of enantiomeric impurity standard solutions with known concentrations, covering all concentration intervals where enantiomeric impurities appear in the sample. Inject and analyze the solutions sequentially according to the preset chromatographic conditions. Record the chromatographic peak area of ​​each standard solution. Draw a standard curve with the enantiomeric impurity concentration as the abscissa and the corresponding peak area as the ordinate. Obtain the standard curve equation through linear regression.

[0036] The pretreated peanut sprout tryptophan extract sample was injected into a preset HPLC system for detection, the chromatogram was recorded, and the chromatographic peak of the enantiomeric impurity was determined according to the retention time of the chromatographic peak in the chromatogram;

[0037] The chromatographic peaks of the enantiomeric impurities were integrated to obtain the peak areas, which were then substituted into the standard curve equation to calculate the concentration of the enantiomeric impurities in the sample.

[0038] Furthermore, the method for obtaining the flow rate includes:

[0039] The flow velocity is obtained based on the frequency shift, the angle between the ultrasonic beam and the flow direction, and the sound velocity;

[0040] Methods for obtaining flow field uniformity and eddy current intensity include:

[0041] The average flow velocity and the standard deviation of the flow velocity are obtained by statistical analysis, and the flow field uniformity is calculated based on the average flow velocity and the standard deviation of the flow velocity;

[0042] According to the flow velocity, the partial derivative of the flow velocity component is calculated to obtain the velocity curl, and then the modulus of the velocity curl is calculated according to the vector modulus. The modulus of the velocity curl is integrated over the cross-sectional area of ​​the chromatography column to obtain the eddy current intensity.

[0043] A control system for purification parameters of tryptophan in peanut sprouts, implementing a method for controlling parameters of purification parameters of tryptophan in peanut sprouts, comprising:

[0044] The first analysis module: uses ultrasound to collect the frequency shift of the chromatography column, analyzes the flow velocity, and obtains the flow field uniformity and eddy current intensity based on the flow velocity analysis;

[0045] The second analysis module constructs an optogenetic probe, fixes it to the biofilm at the injection port of the simulated moving bed, activates it with blue light, and collects the initial fluorescence intensity. When the flow field uniformity is less than the uniformity threshold, the initial fluorescence intensity is compensated by the eddy current intensity to obtain the fluorescence intensity.

[0046] Model building module: synchronously collects temperature and ORP potential, and combines fluorescence intensity, activation energy and diffusion penalty terms to build a separation kinetic model;

[0047] Stage judgment module: obtains the enantiomeric impurity concentration and determines the process stage based on the fluorescence intensity and ORP potential;

[0048] Parameter optimization module: Set two-stage optimization objectives according to the process stage, set constraints, combine the separation kinetics model to perform rolling time domain optimization, obtain optimized purification parameters, and generate corresponding control instructions in combination with the control barrier function.

[0049] The technical effects and advantages of the control system and method for purifying tryptophan in peanut sprouts of the present invention are as follows:

[0050] The present invention uses ultrasound to collect flow field parameters in real time and combines the eddy current intensity compensation mechanism of the optogenetic probe to reduce fluorescence detection errors and accurately solve the detection distortion problem caused by flow field fluctuations; constructs a separation kinetics model, combines two-stage rolling time domain optimization and control barrier function, realizes quantitative regulation of multi-physical field coupling effects, reduces tryptophan oxidation loss, and improves tryptophan purity, breaking through the technical bottleneck that traditional single parameter optimization cannot cope with the synergistic effect of multiple fields; also through the process stage judgment method of HPLC standard curve and multi-parameter dynamic threshold fusion, shortens the switching delay between the impurity elution period and the collection period, cooperates with the two-stage optimization of adaptive simulated annealing algorithm and fast gradient method, improves the purification rate, and reduces energy consumption at the same time; the present invention systematically solves the problems of detection lag, model inaccuracy, high oxidation loss and difficulty in parameter coupling regulation in the existing technology through interdisciplinary integration of multi-physical field modeling and intelligent optimization framework, and realizes precise control of the tryptophan purification process and green and efficient production. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flow chart of a method for controlling parameters of tryptophan purification in peanut sprouts according to the present invention;

[0052] Figure 2 This is a data flow diagram of the present invention;

[0053] Figure 3 A flow chart is constructed for the separation kinetics model of the present invention;

[0054] Figure 4 The present invention is a schematic structural diagram of a parameter control system for trying to purify peanut sprouts. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] Example 1

[0057] See also Figure 1 As shown, this embodiment provides a method for controlling parameters of tryptophan purification in peanut sprouts, comprising the following steps:

[0058] The frequency shift of the chromatography column is collected by ultrasound, and the flow velocity is obtained by analysis. The flow field uniformity and eddy current intensity are obtained based on the flow velocity analysis.

[0059] Methods for obtaining flow rate include:

[0060] The flow velocity is calculated based on the frequency shift, the angle between the ultrasonic beam and the flow direction, and the sound velocity. ;in, is the flow rate of the fluid in the chromatography column; is the frequency shift collected by the ultrasonic wave; is the propagation speed of ultrasound in the fluid; is the emission frequency of ultrasonic waves; is the angle between the ultrasonic beam and the flow direction;

[0061] Methods for obtaining flow field uniformity and eddy current intensity include:

[0062] The average flow velocity and the standard deviation of the flow velocity are obtained by statistical analysis, and the flow field uniformity is calculated based on the average flow velocity and the standard deviation of the flow velocity; ;in, for process uniformity; is the average flow velocity during the statistical period; is the standard deviation of flow rate;

[0063] According to the flow velocity, the partial derivative of the flow velocity component is calculated to obtain the velocity curl, and then the modulus of the velocity curl is calculated according to the vector modulus. The modulus of the velocity curl is integrated over the cross-sectional area of ​​the chromatography column to obtain the eddy current intensity.

[0064] The frequency shift of the chromatography column is collected ultrasonically and the flow velocity is analyzed accordingly. The average flow velocity and standard deviation are then obtained based on flow velocity statistics to calculate flow field uniformity. The partial derivatives of the flow velocity components are combined with vector modulus integrals to obtain the eddy strength. This allows for real-time and non-invasive acquisition of flow field dynamic parameters. During the impurity elution period, flow field uniformity and eddy strength are used to construct a flow field penalty term, which is used together with time cost as an optimization target. The flow velocity and temperature are adjusted using a rapid gradient method to ensure efficient elution of impurities while avoiding a decrease in mass transfer efficiency caused by flow field anomalies. During the tryptophan collection period, these parameters can be embedded in the separation kinetics model, and a linkage penalty function is constructed in combination with the redox potential. The parameter combination is optimized using an adaptive simulated annealing algorithm to suppress the oxidation risk and diffusion loss caused by eddy currents, thereby achieving the multiple goals of improving tryptophan purity, reducing oxidation loss, and optimizing process energy consumption. This effectively addresses the problems of flow field control lag and difficulty in multi-parameter coupling optimization in traditional purification methods, providing a quantitative basis for accurate judgment and dynamic optimization of the process stages.

[0065] Construct an optogenetic probe, fix the probe to the biofilm at the injection port of the simulated moving bed, activate it with blue light, and collect the initial fluorescence intensity; when the flow field uniformity is less than the uniformity threshold, the uniformity threshold is determined by the flow field-fluorescence signal calibration experiment: select a fluid that is consistent with the actual process and contains the target detection object and the same biofilm probe, and construct a gradient flow field uniformity environment at the inlet of the simulated moving bed. This can be achieved by adjusting the eddy current intensity through a fluid spoiler, and collecting the fluorescence signal deviation rate compared with the ideal flow field fluorescence value under different uniformities. When the deviation rate exceeds the process allowable error for the first time, such as when it is set to 5% in tryptophan purification, the corresponding flow field uniformity value is the uniformity threshold. The fluorescence intensity is obtained by compensating the initial fluorescence intensity with the eddy current intensity. ; Construct an optogenetic probe and fix it on the biofilm at the injection port of the simulated moving bed, and collect the initial fluorescence intensity after blue light activation ; The flow field uniformity U is calculated in real time through the flow field monitoring module. When U is less than the threshold determined by the experiment, the eddy current intensity Ω is substituted into the compensation model Calculate compensation amount ,in, is the flow field-fluorescence response coefficient, determined by the probe calibration experiment; is the basic compensation intercept to offset the background deviation under uniform flow field; the final compensated fluorescence intensity is , to solve the signal distortion problem caused by flow field fluctuations.

[0066] An optogenetic probe is constructed and fixed to the biofilm at the injection port of a simulated moving bed. The initial fluorescence intensity is collected by blue light activation. When the flow field uniformity is less than the uniformity threshold, the initial fluorescence intensity is compensated using the eddy current intensity. This technical solution can solve the key problem of fluorescence detection signal distortion caused by flow field fluctuations in the existing technology. Traditional optogenetic detection methods do not consider the interference of flow field unevenness on probe distribution and fluorescence signals. Fluorescence intensity measurement deviations are easily caused by fluid micro-cluster mixing caused by eddy currents, which in turn causes misjudgment of tryptophan concentration and incorrect switching of process stages. The present invention establishes a dynamic mapping relationship between eddy current intensity and fluorescence compensation to correct the impact of flow field anomalies on signals in real time and improve fluorescence detection accuracy. The inventive concept breaks through the conventional thinking of those skilled in the art who only focus on the chemical properties of the probe. For the first time, fluid mechanics parameters are coupled with biological detection signals, and a substantial improvement in detection accuracy is achieved through interdisciplinary technology integration. In the case that the existing technology has not solved the problem of detection distortion caused by flow field interference, the present invention fits the eddy current compensation model through a large number of experiments and creatively introduces fluid mechanics parameters into the biological detection correction system.

[0067] The temperature and ORP potential are collected simultaneously, and the separation kinetic model is constructed by combining the fluorescence intensity, activation energy and diffusion penalty term; for details, please refer to Figure 3 , where the diffusion penalty term is calculated based on the second-order partial derivative of the fluorescence intensity along the axial direction of the column: ,in, is the axial coordinate of the chromatography column; is the axial sampling interval; is the axial coordinate position of the chromatography column , The fluorescence intensity collected at each moment; is the axial coordinate position of the chromatography column , The fluorescence intensity collected at each moment; is the axial coordinate position of the chromatography column , The fluorescence intensity collected at each moment; through the molecular diffusion coefficient (Can be obtained by looking up the table), eddy current intensity , cross-sectional area of ​​chromatography column and effective column length , calculate the effective diffusion impact factor, such as the effective diffusion impact factor ; Combined with empirical constants , generally takes a value of 0.5-1.5, and calculates the diffusion penalty term, such as the diffusion penalty term ; With the goal of minimizing the mean square error of fluorescence intensity, other correlation coefficients were fitted by the least squares method; the activation energy was obtained by preliminary experiments combining tryptophan with a chiral stationary phase.

[0068] The above method introduces a diffusion penalty term to quantify the impact of axial diffusion on concentration distribution, combines the ORP potential to reflect the oxidation risk in real time, and couples the temperature, flow field parameters and biological detection signals across scales, so that the model can dynamically predict the concentration changes, oxidation loss and diffusion loss during the tryptophan separation process, thereby reducing the prediction error; incorporates fluid mechanics parameters, electrochemical parameters and biosensor signals into a unified modeling framework, and forms a multi-physical field collaborative kinetic model through experimental fitting of diffusion penalty terms and activation energy parameters, which solves the problem of insufficient model accuracy and inability to adapt to flow field fluctuations in existing technologies.

[0069] The enantiomeric impurity concentration is obtained by detection and analysis, and the process stage is determined by combining the fluorescence intensity and ORP potential;

[0070] Methods for obtaining enantiomeric impurity concentrations include:

[0071] Prepare N sets of enantiomeric impurity standard solutions with known concentrations, covering all concentration intervals where enantiomeric impurities appear in the sample. Inject and analyze the solutions sequentially according to the preset chromatographic conditions. Record the chromatographic peak area of ​​each standard solution. Draw a standard curve with the enantiomeric impurity concentration as the abscissa and the corresponding peak area as the ordinate. Obtain the standard curve equation through linear regression.

[0072] A sample of the pretreated peanut sprout tryptophan extract was injected into a pre-set HPLC system for detection, and the chromatogram was recorded. The chromatographic peaks of the enantiomeric impurities were determined based on the retention times of the chromatographic peaks in the chromatogram. Specifically, tryptophan and the enantiomeric impurities had different interactions with the stationary phase of the chiral chromatographic column, resulting in different retention behaviors in the chromatographic column. Based on the retention time of tryptophan and the theoretical difference in the retention times of the two, the chromatographic peaks of the enantiomeric impurities could be determined in the chromatogram.

[0073] The chromatographic peaks of the enantiomeric impurities were integrated to obtain the peak areas, which were then substituted into the standard curve equation to calculate the concentration of the enantiomeric impurities in the sample.

[0074] By preparing multiple sets of enantiomeric impurity standard solutions with known concentrations and constructing a standard curve, the peanut sprout tryptophan extract is detected using HPLC combined with a chiral chromatographic column. The impurity chromatographic peak is accurately located based on the difference in retention time caused by the difference in interaction between tryptophan and the enantiomeric impurity on the stationary phase, and then the enantiomeric impurity concentration is calculated. This is then combined with the fluorescence intensity reflecting the tryptophan concentration and the ORP potential characterizing the oxidation state. The process stage can be dynamically and accurately judged. When the sum of the ratio of fluorescence intensity to enantiomeric impurity concentration and the absolute value of the first-order partial derivative of fluorescence intensity is less than or equal to the dynamic threshold, it is determined to be the impurity elution period, and when the ratio is greater than the preset threshold and the ORP potential is greater than the dynamic threshold, it is determined to be the tryptophan collection period. The above method can dynamically switch the optimization target according to the real-time monitored impurity concentration, tryptophan concentration and oxidation potential. In the elution period, the goal is to minimize the sum of time cost and flow field penalty term to efficiently elute impurities. In the collection period, the goal is to maximize the purity correction value to inhibit oxidation and improve purity, such as: purity correction value ,in, To integrate the chiral chromatographic peaks by HPLC and calculate the proportion of tryptophan peak area; In order to utilize the standard curve, the real-time fluorescence intensity is reversed into the theoretical purity; thereby achieving precise control of the entire process, effectively improving the yield and purity of tryptophan, and reducing oxidation loss and impurity residues.

[0075] Methods for determining the process stage include:

[0076] When the sum of the ratio of the fluorescence intensity to the enantiomeric impurity concentration and the absolute value of the first-order partial derivative of the fluorescence intensity in the axial direction of the chromatographic column is less than or equal to the dynamic threshold, it is determined to be in the impurity elution period;

[0077] When the ratio of fluorescence intensity to enantiomeric impurity concentration is greater than a preset threshold, and the ORP potential is greater than a dynamic threshold, it is determined to be in the tryptophan collection period; wherein, the preset threshold is determined through a full-process simulation experiment, collecting fluorescence intensity, enantiomeric impurity concentration, and ORP potential data at different process stages (impurity elution period, tryptophan collection period), drawing a scatter plot of fluorescence intensity / enantiomeric impurity concentration and ORP potential, and taking the data dividing point of the elution-collection stage as the preset threshold.

[0078] The dynamic threshold is calculated based on the real-time ORP potential and the preset potential threshold, such as: ,in, It is a preset reference potential, which is calibrated by experiments, such as the tryptophan stable zone potential of 300mV; is the response coefficient, which ranges from 0.8 to 1.2 and is used to adjust the sensitivity of the threshold to potential fluctuations; It is the real-time ORP potential. When the impurities are different, the dynamic threshold can also be adaptively adjusted according to the actual situation.

[0079] Set dual-stage optimization objectives according to the process stages and set constraints to limit the adjustment range of purification parameters (including flow rate, temperature and OPR) to avoid flow field / oxidation out of control; ,in, is the reference flow rate; is the real-time flow rate; ,in, is the optimal temperature of the stage; is the real-time temperature; when adjusting the parameters, , to avoid violent oxidation caused by potential mutation; combined with the separation kinetics model, rolling time domain optimization is performed to obtain the optimized purification parameters, and combined with the control barrier function to generate the corresponding control instructions.

[0080] The process stage is judged by comparing the ratio of fluorescence intensity to enantiomeric impurity concentration, the sum of the absolute values ​​of the first-order partial derivatives of fluorescence intensity in the axial direction of the chromatographic column with the dynamic threshold, and combining the ratio with whether the ORP potential meets the preset conditions. The dynamic threshold can be flexibly adjusted according to the real-time ORP potential, the preset potential threshold and the impurity characteristics, and can accurately and dynamically identify the impurity elution period and the tryptophan collection period, providing an effective means to solve the problems of delayed stage judgment and lack of adaptability of fixed thresholds in traditional processes; a two-stage optimization target is set based on the judgment result, and the time cost and flow rate are used to optimize the process stage in the impurity elution period. The goal is to minimize the sum of field penalty terms and combine it with the constraints of the separation kinetics model. During the collection period, the goal is to maximize the purity correction value and set constraints such as flow field uniformity. The purification parameters are dynamically adjusted through rolling time domain optimization, and then combined with the control barrier function to ensure that the parameter changes are within a safe range to generate control instructions. The above method realizes the closed-loop control of the entire process from accurate stage judgment to dynamic optimization of parameters, effectively solving technical problems such as incomplete impurity elution during the purification process, low purity of tryptophan collection, parameter control lag and system operation risks, and significantly improves the efficiency, purity and process stability of tryptophan purification.

[0081] Methods for obtaining optimized purification parameters include:

[0082] During the impurity elution period, the flow rate is integrated over the elution period to obtain the time cost. Combined with the flow field penalty term, the first objective is to minimize the sum of the time cost and the flow field penalty term. Separation kinetic model constraints are set, specifically, the value of the separation kinetic model is less than or equal to the elution rate upper limit. Purification parameters are optimized based on the rapid gradient method.

[0083] Methods for obtaining flow field penalty terms include:

[0084] Determine whether the uniformity index is less than a preset index threshold. If so, calculate the first penalty function based on the uniformity index. If not, set the first penalty function value to 0. The preset index threshold is determined through a flow field calibration experiment. A tracer with properties consistent with those of the tryptophan solution is injected into a simulated moving bed, and the axial flow velocity distribution under different flow field disturbances is collected to calculate the uniformity index. When the flow field uniformity decreases, resulting in a 5% decrease in the tryptophan separation purity, the uniformity index at this time is recorded as the preset index threshold.

[0085] Determine whether the eddy current intensity is greater than a preset intensity threshold. If so, calculate a second penalty function based on the eddy current intensity and the preset intensity threshold. If not, the second penalty function value is 0. In the same simulated moving bed, a gradient eddy current intensity Ω is generated by a fluid spoiler, and chromatographic peak broadening data under different eddy current intensities are collected. When the half-peak width increases by 20%, the corresponding eddy current intensity is used as the preset intensity threshold.

[0086] The first penalty function and the second penalty function are weightedly summed to obtain a flow field penalty term, wherein the weight coefficient is determined through experiments or experience according to specific process requirements and flow field characteristics.

[0087] Methods for optimizing purification parameters based on the rapid gradient method include:

[0088] Calculating the gradient of the first objective with respect to the purification parameter by the finite difference method;

[0089] Update the purification parameters, including flow rate and temperature, in the direction of gradient descent. During the update process, check whether the updated purification parameters meet the constraints. If not, adjust the purification parameters until the constraints are met.

[0090] Calculate the difference between the current first target value and the first target value of the previous iteration to obtain the first difference. If the first difference is less than the preset difference threshold, the algorithm is judged to have converged and the optimized purification parameters are obtained. Otherwise, return to continue calculating the gradient and updating the purification parameters. In the simulation experiment of tryptophan purification, the convergence process of the first target value during the iteration of the fast gradient method is recorded. The first target value is such as the purity correction value, yield, etc. When the statistical iteration is stable, if the process parameter fluctuation is less than 1%, the upper limit of the 95% confidence interval of the difference between adjacent iterative target values ​​is taken as the preset difference threshold.

[0091] For the tryptophan collection period, the ratio of fluorescence intensity to enantiomeric impurity concentration was calculated, the product of the ratio and the oxidation penalty factor was calculated, and the difference between the product and the diffusion penalty term was calculated, with maximizing the difference as the second objective. Flow field uniformity constraints were set, and purification parameters were optimized based on adaptive simulated annealing.

[0092] Methods for obtaining the oxidation penalty factor include:

[0093] Pre-training the oxidation penalty coefficient, such as fitting the purification experiment under different polyphenol concentrations, collecting the ORP potential, combining the preset potential threshold, and calculating the oxidation penalty factor; such as the oxidation penalty factor ,in, is the low oxidation potential of tryptophan, determined experimentally, such as 250 mV; The potential at which oxidation loss increases significantly, such as 400 mV;

[0094] Methods for optimizing purification parameters based on adaptive simulated annealing include:

[0095] Based on the current solution of the purified parameters, a neighborhood solution is generated through random perturbations;

[0096] Calculate the second objective values ​​of the current solution and the neighboring solution respectively, and calculate the difference between the second objective value of the neighboring solution and the second objective value of the current solution to obtain the second difference. If the second difference is greater than 0, accept the neighboring solution and update the current solution to the neighboring solution. If the second difference is less than or equal to 0, calculate the acceptance probability P based on the Metropolis criterion, combining the second difference and the current temperature, and generate a random number p between [0,1]. If the random number p is less than P, accept the neighboring solution and update the current solution. Otherwise, keep the current solution unchanged.

[0097] After L iterations at each temperature, the purification parameters are updated according to the parameter change rate r. When the updated purification parameters are less than or equal to the preset parameter change threshold, the algorithm is judged to have converged and the optimized purification parameters are obtained. Otherwise, the algorithm returns to continue generating neighborhood solutions and making judgments. Among them, the preset parameter change threshold is tested through a disturbance experiment to test the influence of the parameter change rate on the process stability, and the critical value at which the flow field fluctuation / oxidation rate begins to exceed the standard is taken as the preset parameter change threshold.

[0098] By minimizing the sum of time cost and flow field penalty term during the impurity elution period, combining the separation kinetics model constraint and the fast gradient method to optimize the flow rate and temperature, and calculating the flow field penalty term based on the uniformity index and eddy current intensity to suppress flow field anomalies, the problem of incomplete impurity elution or flow field turbulence caused by unreasonable flow rate in the traditional elution process can be solved, and a balance between efficient elution and flow field stability can be achieved; in the tryptophan collection period, the goal is to maximize the difference between the ratio of fluorescence intensity to enantiomeric impurity concentration combined with the oxidation penalty factor and the diffusion penalty term, using the adaptive simulated annealing algorithm and setting the flow field uniformity constraint, and at the same time through pre-training The oxidation penalty coefficient is dynamically adjusted in combination with the ORP potential, which can accurately deal with the oxidation risk and diffusion loss of tryptophan during the collection period, and solve the contradiction between purity improvement and oxidation loss in traditional methods; the entire optimization method realizes precise parameter control of the entire process from impurity elution to tryptophan collection through dynamic adaptation of the two-stage objective function and constraint conditions, combined with the fast gradient method and adaptive simulated annealing algorithm for efficient search, effectively solving technical problems in traditional purification processes such as single optimization target, difficult parameter coupling control, high oxidation loss and unstable flow field, and significantly improving the tryptophan yield, purity and process stability.

[0099] The method of generating corresponding control instructions in combination with the control barrier function includes:

[0100] When the eddy current intensity is greater than the intensity barrier threshold or the ORP potential value is greater than the potential barrier threshold, the change rate of the purification parameters is controlled to be less than or equal to the corresponding change rate threshold; among them, on the tryptophan purification pilot plant, the eddy current intensity is gradually increased, such as through spoiler adjustment, and the eddy current intensity value when the flow field fluctuation causes the chromatographic peak to broaden ≥15%, that is, exceeds the separation accuracy requirement, is collected as the intensity barrier threshold; through the oxidation loss experiment, the oxidation rate of tryptophan under different ORP potentials is recorded, and when the oxidation rate is ≥3% / h, that is, exceeds the allowable value of process loss, the corresponding ORP potential is used as the potential barrier threshold; under stable process conditions, the maximum safe change rate of the test parameters (flow rate, temperature, etc.) - when the parameter change rate causes a sudden change in the ratio of flow field fluctuation to oxidation rate, 80% of the critical change rate is taken, that is, a safety margin is reserved as the change rate threshold.

[0101] By using the control barrier function to control the change rate of the purification parameters within the corresponding change rate threshold when the eddy current intensity is greater than the intensity barrier threshold or the ORP potential value is greater than the potential barrier threshold, the process stability problem caused by violent fluctuations in the flow field or increased oxidation risk during the tryptophan purification process can be effectively solved. When the eddy current intensity exceeds the standard, limiting the change rate of parameters such as flow velocity and temperature can avoid further disorder of the flow field, prevent the decrease in mass transfer efficiency and the broadening of chromatographic peaks, and ensure the separation effect of impurity elution and tryptophan collection; when the ORP potential exceeds the standard, the adjustment range of the constrained parameters can inhibit the intensification of the oxidation reaction, reduce the oxidation loss of tryptophan, and avoid the system operation risk caused by parameter mutation, thereby setting a safety boundary for the process parameters in the dynamic optimization process, realizing robust control of the purification process, ensuring the stability of tryptophan yield and purity, and solving the technical problem of the lack of dynamic protection mechanism for abnormal working conditions in traditional control methods.

[0102] Example 2

[0103] This embodiment proposes a second objective optimization method applied to embodiment 1, including the following steps:

[0104] Add an eddy current-oxidation linkage penalty term to the second objective, calculate the ratio of fluorescence intensity to enantiomeric impurity concentration, calculate the product of the ratio and the oxidation penalty factor, calculate the difference between the product and the diffusion penalty term, and then calculate the difference between the difference and the eddy current-oxidation linkage penalty term to obtain the optimized difference, and maximize the optimized difference as the second objective;

[0105] Methods for obtaining the eddy-oxidation linkage penalty term include:

[0106] The eddy current-oxidation linkage penalty term is calculated through the eddy current intensity, the preset intensity threshold, the ORP potential deviation value (obtained by collecting the ORP value in real time and subtracting it from the preset potential threshold), and the maximum allowable deviation value of the ORP potential (which can be obtained through preliminary experiments). The weight coefficients involved are determined by experimental fitting or NSGA-III algorithm optimization.

[0107] A vortex-oxidation linkage penalty term is added to the second objective. This term is calculated by multiplying the ratio of fluorescence intensity to enantiomeric impurity concentration by an oxidation penalty factor, subtracting a diffusion penalty term, and then subtracting a linkage penalty term calculated based on vortex intensity, a preset intensity threshold, the ORP potential difference, and the maximum allowable deviation. The goal is to maximize the optimization difference. This accurately quantifies the synergistic effect of vortex intensity and redox potential during the tryptophan collection period, addressing the problem that traditional optimization objectives fail to consider multi-physics field coupling effects. The vortex-oxidation linkage penalty term dynamically adjusts the penalty intensity for vortex and oxidation deviation using a weight coefficient. When vortex anomalies or oxidation risk increase, the penalty term's value increases significantly, driving the optimization algorithm to prioritize parameter combinations that reduce flow rate to weaken vortex and adjust temperature to improve ORP potential, thereby suppressing tryptophan degradation caused by vortex-induced oxidation. A control barrier function is also incorporated to limit the rate of parameter change, avoiding sudden flow field changes caused by drastic parameter adjustments. This achieves the multiple goals of improving tryptophan purity during the collection period, reducing oxidation losses, and enhancing process stability. This effectively addresses the disconnect between purity optimization and oxidation control, and the difficulty of multi-parameter coupling control in traditional methods.

[0108] Example 3

[0109] See also Figure 4 As shown, this embodiment provides a control system for purification parameters of tryptophan in peanut sprouts, comprising:

[0110] The first analysis module: uses ultrasound to collect the frequency shift of the chromatography column, analyzes the flow velocity, and obtains the flow field uniformity and eddy current intensity based on the flow velocity analysis;

[0111] The second analysis module constructs an optogenetic probe, fixes it to the biofilm at the injection port of the simulated moving bed, activates it with blue light, and collects the initial fluorescence intensity. When the flow field uniformity is less than the uniformity threshold, the initial fluorescence intensity is compensated by the eddy current intensity to obtain the fluorescence intensity.

[0112] Model building module: synchronously collects temperature and ORP potential, and combines fluorescence intensity, activation energy and diffusion penalty terms to build a separation kinetic model;

[0113] Stage judgment module: obtains the enantiomeric impurity concentration and determines the process stage based on the fluorescence intensity and ORP potential;

[0114] Parameter optimization module: Set two-stage optimization objectives according to the process stage, set constraints, combine the separation kinetics model to perform rolling time domain optimization, obtain optimized purification parameters, and generate corresponding control instructions in combination with the control barrier function.

[0115] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

[0116] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for controlling parameters of tryptophan purification in peanut sprouts, characterized in that: The steps include: The frequency shift of the chromatography column is collected by ultrasound, and the flow velocity is obtained by analysis. The flow field uniformity and eddy current intensity are obtained based on the flow velocity analysis. An optogenetic probe was constructed and fixed to the biofilm at the injection port of a simulated moving bed. The probe was activated by blue light and the initial fluorescence intensity was collected. When the flow field uniformity is less than the uniformity threshold, the fluorescence intensity is obtained by compensating the initial fluorescence intensity with the eddy current intensity; Temperature and ORP potential are collected simultaneously, and the separation kinetic model is constructed by combining fluorescence intensity, activation energy and diffusion penalty terms; Obtain the enantiomeric impurity concentration and determine the process stage by combining fluorescence intensity and ORP potential; The two-stage optimization objectives are set according to the process stages, and the constraints are set. The rolling time domain optimization is performed in combination with the separation kinetics model to obtain the optimized purification parameters, and the corresponding control instructions are generated in combination with the control barrier function.

2. The method for controlling parameters of tryptophan purification in peanut sprouts according to claim 1, wherein: The diffusion penalty term is calculated based on the second-order partial derivative of the fluorescence intensity in the axial direction of the chromatography column, and is calculated based on the molecular diffusion coefficient, eddy current intensity and cross-sectional area of ​​the chromatography column in combination with empirical constants.

3. The method for controlling parameters of tryptophan purification in peanut sprouts according to claim 1, wherein: Methods for determining the process stage include: When the sum of the ratio of the fluorescence intensity to the enantiomeric impurity concentration and the absolute value of the first-order partial derivative of the fluorescence intensity in the axial direction of the chromatographic column is less than or equal to the dynamic threshold, it is determined to be in the impurity elution period; When the ratio of the fluorescence intensity to the enantiomeric impurity concentration is greater than a preset threshold, and the ORP potential is greater than a dynamic threshold, it is determined to be in the tryptophan collection period; The dynamic threshold is calculated based on the real-time ORP potential and the preset potential threshold.

4. The method for controlling parameters of tryptophan purification in peanut sprouts according to claim 1, wherein: Methods for obtaining optimized purification parameters include: During the impurity elution period, the flow rate is integrated over the elution period to obtain the time cost. Combined with the flow field penalty term, the first goal is to minimize the sum of the time cost and the flow field penalty term. Separation kinetic model constraints are set, and the value of the separation kinetic model is less than or equal to the elution rate upper limit. Purification parameters are optimized based on the rapid gradient method. For the tryptophan collection period, the ratio of fluorescence intensity to enantiomeric impurity concentration was calculated, the product of the ratio and the oxidation penalty factor was calculated, and the difference between the product and the diffusion penalty term was calculated, with maximizing the difference as the second objective. Flow field uniformity constraints were set, and purification parameters were optimized based on adaptive simulated annealing.

5. The method for controlling parameters of tryptophan purification in peanut sprouts according to claim 4, wherein: The methods for obtaining flow field penalty items include: Determine whether the uniformity index is less than a preset index threshold, if so, calculate a first penalty function according to the uniformity index, if not, the first penalty function value is 0; Determine whether the eddy current intensity is greater than a preset intensity threshold. If so, calculate a second penalty function based on the eddy current intensity and the preset intensity threshold. If not, the second penalty function value is 0. A flow field penalty term is obtained by performing a weighted summation on the first penalty function and the second penalty function.

6. The method for controlling parameters of tryptophan purification in peanut sprouts according to claim 4, wherein: Methods for optimizing purification parameters based on the rapid gradient method include: Calculating the gradient of the first objective with respect to the purification parameter by the finite difference method; Update the purification parameters in the direction of gradient descent. The purification parameters include flow rate and temperature. During the updating process, check whether the updated purification parameters meet the constraints. If not, adjust the purification parameters until the constraints are met. Calculate the difference between the current first target value and the first target value of the previous iteration to obtain the first difference. If the first difference is less than the preset difference threshold, the algorithm is judged to have converged and the optimized purification parameters are obtained. Otherwise, return to continue calculating the gradient and updating the purification parameters.

7. The method for controlling parameters of tryptophan purification in peanut sprouts according to claim 4, wherein: Methods for obtaining the oxidation penalty factor include: Pre-train the oxidation penalty coefficient, collect the ORP potential, combine it with the preset potential threshold, and calculate the oxidation penalty factor.

8. The method for controlling parameters of tryptophan purification in peanut sprouts according to claim 4, wherein: Methods for optimizing purification parameters based on adaptive simulated annealing include: Based on the current solution of the purified parameters, a neighborhood solution is generated through random perturbations; Calculate the second objective values ​​of the current solution and the neighboring solution respectively, and calculate the difference between the second objective value of the neighboring solution and the second objective value of the current solution to obtain the second difference. If the second difference is greater than 0, accept the neighboring solution and update the current solution to the neighboring solution. If the second difference is less than or equal to 0, calculate the acceptance probability P based on the Metropolis criterion, combining the second difference and the current temperature, and generate a random number p between [0,1]. If the random number p is less than P, accept the neighboring solution and update the current solution. Otherwise, keep the current solution unchanged. After L iterations at each temperature, the purification parameters are updated according to the parameter change rate r. When the updated purification parameters are less than or equal to the preset parameter change threshold, the algorithm is judged to have converged and the optimized purification parameters are obtained. Otherwise, the algorithm returns to continue generating neighborhood solutions and making judgments.

9. The method for controlling parameters of tryptophan purification in peanut sprouts according to claim 1, wherein: The method of generating corresponding control instructions in combination with the control barrier function includes: When the eddy current intensity is greater than the intensity barrier threshold or the ORP potential value is greater than the potential barrier threshold, the change rate of the purification parameter is controlled to be less than or equal to the corresponding change rate threshold.

10. The method for controlling parameters of tryptophan purification in peanut sprouts according to claim 1, wherein: Methods for obtaining enantiomeric impurity concentrations include: Prepare N sets of enantiomeric impurity standard solutions with known concentrations, covering all concentration intervals where enantiomeric impurities appear in the sample. Inject and analyze the solutions sequentially according to the preset chromatographic conditions. Record the chromatographic peak area of ​​each standard solution. Draw a standard curve with the enantiomeric impurity concentration as the abscissa and the corresponding peak area as the ordinate. Obtain the standard curve equation through linear regression. The pretreated peanut sprout tryptophan extract sample was injected into a preset HPLC system for detection, the chromatogram was recorded, and the chromatographic peak of the enantiomeric impurity was determined according to the retention time of the chromatographic peak in the chromatogram; The chromatographic peaks of the enantiomeric impurities were integrated to obtain the peak areas, which were then substituted into the standard curve equation to calculate the concentration of the enantiomeric impurities in the sample.

11. The method for controlling parameters of tryptophan purification in peanut sprouts according to claim 1, wherein: Methods for obtaining flow rate include: The flow velocity is obtained based on the frequency shift, the angle between the ultrasonic beam and the flow direction, and the sound velocity; Methods for obtaining flow field uniformity and eddy current intensity include: The average flow velocity and the standard deviation of the flow velocity are obtained by statistical analysis, and the flow field uniformity is calculated based on the average flow velocity and the standard deviation of the flow velocity; According to the flow velocity, the partial derivative of the flow velocity component is calculated to obtain the velocity curl, and then the modulus of the velocity curl is calculated according to the vector modulus. The modulus of the velocity curl is integrated over the cross-sectional area of ​​the chromatography column to obtain the eddy current intensity.

12. A control system for purification parameters of tryptophan in peanut sprouts, implementing the method for purification parameters of tryptophan in peanut sprouts according to any one of claims 1 to 11, characterized in that: include: The first analysis module: uses ultrasound to collect the frequency shift of the chromatography column, analyzes the flow velocity, and obtains the flow field uniformity and eddy current intensity based on the flow velocity analysis; The second analysis module: construct an optogenetic probe, fix the probe to the biofilm at the injection port of the simulated moving bed, activate it with blue light, and collect the initial fluorescence intensity; When the flow field uniformity is less than the uniformity threshold, the fluorescence intensity is obtained by compensating the initial fluorescence intensity with the eddy current intensity; Model building module: synchronously collects temperature and ORP potential, and combines fluorescence intensity, activation energy and diffusion penalty terms to build a separation kinetic model; Stage judgment module: obtains the enantiomeric impurity concentration and determines the process stage based on the fluorescence intensity and ORP potential; Parameter optimization module: Set two-stage optimization objectives according to the process stage, set constraints, combine the separation kinetics model to perform rolling time domain optimization, obtain optimized purification parameters, and generate corresponding control instructions in combination with the control barrier function.

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