Ultrasonic Tremella polysaccharide extraction process temperature and flow rate coordination control system

Abstract

The ultrasonic temperature and flow rate coordination control system for the extraction of polysaccharides from Tremella fuciformis of this invention belongs to the field of automatic control technology. It includes: an oxidation potential sensing module for online detection of the hydrated suspension of Tremella fuciformis raw materials to generate an oxidation potential index that quantifies the level of oxidative stress; a dynamic parameter prediction module for receiving the oxidation potential index of the Tremella fuciformis raw materials and calculating the optimal temperature and flow rate setpoints based on a preset polysaccharide degradation risk model and extraction efficiency model; and a coordination control execution module for receiving the optimal temperature and flow rate setpoints and driving the heating system and fluid delivery system to track the setpoints. This invention solves the problem of production instability caused by the inability to perceive individual differences in raw materials in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of automatic control, specifically to a temperature and flow rate coordination control system for ultrasonic tremella polysaccharide extraction process. Background Technology

[0002] In large-scale industrial extraction processes of Tremella fuciformis polysaccharides, ultrasonic extraction equipment is typically used. This equipment usually consists of a heating system on the outside and an ultrasonic generator inside. In actual operation, the Tremella fuciformis raw material enters from the top of the equipment, and the extract flows out from the upper right side. Simultaneously, purified water enters from the lower right side, and material residue is discharged from the lower left side. Currently, this process mainly controls the entire extraction process by adjusting parameters such as ultrasonic power, action time, material-to-liquid ratio, and temperature.

[0003] However, in the industrial production of edible and medicinal fungi, the pretreatment stage of raw materials is closely linked to the subsequent extraction stage of effective components. Specifically, in order to achieve high-density, continuous mycelial culture, it is usually necessary to treat the culture medium. This process requires a broad-spectrum, strong oxidizing environment to ensure the suitability of the substrate. However, the optimal growth of mycelium and the efficient enrichment of the target product depend on a stable, weakly reducing environment, which presents a contradiction.

[0004] This contradiction manifests itself in the sharp negative correlation between the concentration of oxidizing substances in the culture medium and the growth activity of mycelium. While pursuing thorough sterilization, the substrate used for cultivation inevitably experiences a certain level of oxidative stress. This state is transmitted and ultimately manifests in the raw materials of Tremella fuciformis, profoundly altering their biochemical characteristics and leading to a technological bias that the substrate is in an ideal zero state after sterilization. When production scales up and becomes continuous, due to the compression of process time and the gradual accumulation of oxidizing substances in the dead corners of the equipment, this contradiction evolves from a controllable process parameter selection problem into a fundamental bottleneck leading to systemic instability in production.

[0005] The information disclosed in the background section above is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a temperature and flow rate coordination control system for ultrasonic tremella polysaccharide extraction process, so as to solve the problems mentioned in the background art.

[0007] The technical solution of the present invention includes:

[0008] An oxidation potential sensing module is used to detect the hydrated suspension of Tremella fuciformis raw materials online, so as to generate an oxidation potential index of Tremella fuciformis raw materials that quantifies its oxidative stress level.

[0009] The dynamic parameter prediction module is used to receive the oxidation potential index of the tremella raw material and calculate the optimal temperature setpoint and optimal flow rate setpoint based on the preset polysaccharide degradation risk model and extraction efficiency model.

[0010] The coordinated control execution module is used to receive the optimal temperature setpoint and the optimal flow rate setpoint, and drive the heating system and the fluid delivery system to track the setpoints;

[0011] The feedback adjustment module is used to monitor the quality indicators at the extract outlet and adaptively adjust the parameters in the polysaccharide degradation risk model and extraction efficiency model based on the deviation between the quality indicators and the model predictions.

[0012] Preferably, the polysaccharide degradation risk model is used to establish a mathematical relationship between the instantaneous degradation rate of polysaccharides and the oxidation potential index of the Tremella fuciformis raw material and the extraction temperature; the extraction efficiency model is used to establish a mathematical relationship between the extraction efficiency and the extraction temperature and flow rate.

[0013] Preferably, the dynamic parameter prediction module is used for:

[0014] Based on the oxidation potential index of Tremella fuciformis raw material and extraction temperature, the instantaneous degradation rate was determined by a polysaccharide degradation risk model.

[0015] Extraction efficiency is determined based on extraction temperature and flow rate using an extraction efficiency model.

[0016] Preferably, the dynamic parameter prediction module is further used for:

[0017] The cumulative degradation index is determined by combining the instantaneous degradation rate with the material residence time determined by the flow rate.

[0018] Preferably, the dynamic parameter prediction module is further used for:

[0019] A comprehensive performance index is generated by combining extraction efficiency and cumulative degradation index.

[0020] Preferably, the dynamic parameter prediction module is further used for:

[0021] By optimizing the comprehensive performance indicators, the optimal temperature setpoint and the optimal flow rate setpoint are determined.

[0022] Preferably, the polysaccharide degradation risk model is the Arrhenius equation modified by incorporating the oxidation potential index of the Tremella fuciformis raw material as a parameter.

[0023] Preferably, the extraction efficiency model is a phenomenological model that integrates the temperature saturation effect function and the flow rate exponential decay function.

[0024] Preferably, the feedback adjustment module is used for:

[0025] The concentration of polysaccharides or molecular weight-related physical properties at the extract outlet are detected online as quality indicators.

[0026] Preferably, the feedback adjustment module is further used for:

[0027] The actual measured values ​​of quality indicators are compared with the expected efficiency calculated based on the optimal temperature setpoint and the optimal flow rate setpoint to generate a deviation.

[0028] Based on the deviation, the pre-set process correlation coefficients in the polysaccharide degradation risk model and extraction efficiency model are corrected.

[0029] This invention provides an improved temperature and flow rate coordination control system for the ultrasonic tremella polysaccharide extraction process, which, compared with the prior art, has the following improvements and advantages:

[0030] 1. A complete technical closed loop was constructed, from raw material state perception, intelligent decision-making, precise execution to model adaptive correction, realizing refined and intelligent control of the extraction process. By setting up an oxidation potential sensing module, the system can detect the initial state of the hydrated suspension of Tremella fuciformis raw material online and generate an oxidation potential index of Tremella fuciformis raw material to quantify the level of oxidative stress. This transforms the implicit interference of batch-to-batch inconsistency introduced by upstream processes into a clear quantitative input that can be used for feedforward control, solving the problem of production instability caused by the inability of existing technologies to perceive individual differences in raw materials.

[0031] 2. Based on the measured oxidation potential index of Tremella fuciformis raw materials, the dynamic parameter prediction module can perform multi-objective optimization calculations according to the preset polysaccharide degradation risk model and extraction efficiency model to obtain the optimal temperature setpoint and optimal flow rate setpoint. This design decomposes the complex extraction process into two mutually constraining quantitative processes: product generation and product degradation. By establishing a comprehensive performance index, the conflicting objectives of high yield and high quality are unified into an optimization function. By optimizing this comprehensive performance index, the system can dynamically calculate the optimal process scheme for each batch of raw materials with different initial states in real time. This realizes a fundamental shift in control strategy from static experience setting to dynamic adaptive optimization, ensuring that the optimal process window that balances yield and quality can always be found under multivariate constraints.

[0032] 3. The coordinated control execution module can accurately track the optimal setpoint output by the prediction module, stably applying intelligent decision-making to the physical equipment. Furthermore, the system also includes a feedback adjustment module, which uses online monitoring of the physical characteristics related to polysaccharide concentration and molecular weight at the extract outlet as quality indicators, and compares the actual measured value of these indicators with the model prediction value. When systematic deviations occur, this module can adaptively adjust the process correlation coefficients in the polysaccharide degradation risk model and the extraction efficiency model. This design constitutes the highest-level closed loop of the entire control system, controlling not only the physical process but also the accuracy of the mathematical model used for decision-making. This ensures that the system can autonomously learn and adapt to slow time-varying factors such as equipment aging and environmental changes, guaranteeing the robustness and optimality of long-term operation, and significantly improving the yield and quality stability of the final product. Attached Figure Description

[0033] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0034] Figure 1 This is a flowchart of the system of the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0036] Example 1

[0037] Please see Figure 1 This invention provides a temperature and flow rate coordination control system for ultrasonic tremella polysaccharide extraction process, comprising:

[0038] An oxidation potential sensing module is used to detect the hydrated suspension of Tremella fuciformis raw materials online, so as to generate an oxidation potential index of Tremella fuciformis raw materials to quantify the level of oxidative stress.

[0039] The dynamic parameter prediction module is used to receive the oxidation potential index of the tremella raw material and calculate the optimal temperature setpoint and optimal flow rate setpoint based on the preset polysaccharide degradation risk model and extraction efficiency model.

[0040] The coordinated control execution module is used to receive the optimal temperature setpoint and the optimal flow rate setpoint, and drive the heating system and the fluid delivery system to track the setpoints;

[0041] The feedback adjustment module is used to monitor the quality indicators at the extract outlet and adaptively adjust the parameters in the polysaccharide degradation risk model and extraction efficiency model based on the deviation between the quality indicators and the model predictions.

[0042] This invention provides a temperature and flow rate coordination control system for ultrasonic tremella polysaccharide extraction process. The system aims to actively manage and compensate for batch-to-batch inconsistent oxidative stress introduced during the raw material pretreatment stage. By dynamically and collaboratively optimizing the two key parameters of temperature and flow rate during the extraction process, the system maximizes extraction efficiency while ensuring the quality of polysaccharide products, thereby achieving stability and high efficiency in the production process.

[0043] The purpose of the temperature and flow rate coordination control system for ultrasonic tremella polysaccharide extraction process is to establish a closed-loop control system that can adapt to the initial biochemical state of the raw materials. In this embodiment, the system is deployed on a continuous flow ultrasonic extraction device and consists of an oxidation potential sensing module, a dynamic parameter prediction module, a coordination control execution module, and a feedback adjustment module. The modules work together to form a complete and self-consistent technical closed loop.

[0044] The oxidation potential sensing module aims to sense the initial oxidative stress level of the Tremella fuciformis raw material hydrated suspension entering the extraction system in real time and quantitatively. In this embodiment, the module is implemented by deploying an immersion-type oxidation-reduction potential probe array at the material inlet pipe of the extraction equipment. The probe array continuously measures the potential signal of the suspension. The acquired raw potential signal is denoised by a digital filter, enhanced by a signal amplification circuit, and normalized by a microprocessor according to a preset calibration curve. The calibration curve is established through previous experiments and maps the potential measurement values ​​corresponding to typical oxidation environments of different intensities to the interval [0, 1].

[0045] One calibration method is as follows: using sodium hypochlorite as a typical oxide; in a standard hydrated suspension of Tremella fuciformis raw materials, with fixed pH, solid-liquid ratio, and other conditions, samples with effective chlorine concentrations of 0, 5, 10, 20, 30, and 50 mg / L are prepared respectively; the redox potential probe array is immersed in each sample, and the potential value is recorded after the reading stabilizes. The unit is mV. The measured potential value is processed using the following S-type logic function. Mapped to the dimensionless oxidation potential index of Tremella fuciformis raw materials :

[0046]

[0047] in, The dimensionless oxidation potential index of Tremella fuciformis raw material quantifies the oxidative stress level in the hydrated suspension of raw Tremella fuciformis raw material, and the value ranges from [0, 1]. When it approaches 0, it indicates that the raw material is in an ideal weak reducing state; when it approaches 1, it indicates that there is a serious oxidative environment. yes The corresponding potential value represents the midpoint of the redox state; The slope or gain of the curve determines the sensitivity of the mapping. The base of the natural logarithm; The potential value measured by the redox potential probe array, in millivolts; The midpoint of the potential value, i.e., when the oxidation potential index of the tremella raw material... The corresponding potential value at time; by using the above experimental data, By performing a nonlinear fit with the oxidation level represented by the corresponding concentration, the parameters can be determined. and For example, in this embodiment, fitting may yield... , This function ensures that the output remains consistent regardless of changes in the potential value. It is always normalized to the [0, 1] interval and can well reflect the nonlinear trend of oxidative stress level;

[0048] The processed output is the dimensionless oxidation potential index of the Tremella fuciformis raw material. Oxidation potential index of tremella raw materials This refers to the quantitative characterization of the oxidative stress level in the hydrated suspension of raw material *Tremella fuciformis*. Its function is to serve as a core input parameter driving subsequent dynamic prediction and control. The source is generated online by this oxidation potential sensing module through detection and calculation. When, it indicates that the raw material is in an ideal weakly reducing state; when When this value is high, it indicates that the raw material is in a strongly oxidized state; this index... It is transmitted in real time to the dynamic parameter prediction module;

[0049] The dynamic parameter prediction module aims to intelligently determine the optimal temperature and flow rate to achieve the best process balance based on the oxidation potential index of the current batch of raw materials (Tremella fuciformis). In this embodiment, this module is a software program embedded in an industrial control computer. The core computing unit performs multi-objective optimization calculations based on a preset polysaccharide degradation risk model and extraction efficiency model. This module receives the oxidation potential index of the raw materials from the oxidation potential sensing module. As its key real-time inputs, the preset polysaccharide degradation risk model refers to a mathematical equation describing the relationship between polysaccharide degradation rate and temperature and oxidative stress level. Its working principle is based on chemical reaction kinetics and has been modified for the scenario of oxide-catalyzed thermal degradation. The preset extraction efficiency model refers to a mathematical equation describing the relationship between polysaccharide extraction efficiency and temperature and flow rate. Its working principle integrates the temperature saturation effect in the mass transfer process and the residence time effect in the continuous flow system. This module solves the problem by executing optimization algorithms to obtain the optimal solution for the current... Under certain conditions, the optimal combination of temperature and flow rate that maximizes overall performance will ultimately output the optimal temperature setpoint. With the optimal flow rate setpoint ;

[0050] The purpose of the coordinated control execution module is to accurately and stably apply the optimal setpoint calculated by the dynamic parameter prediction module to the physical equipment. In this embodiment, the module consists of two independent PID (Proportional-Integral-Derivative) controllers, corresponding to the temperature control loop and the flow rate control loop, respectively. The temperature controller receives the optimal temperature setpoint. The actual temperature is compared with the temperature feedback from the temperature sensor installed inside the extraction tank. By adjusting the power of the electric heater supplied to the extraction tank jacket, the actual temperature can be quickly and accurately tracked. The flow rate controller receives the optimal flow rate setpoint. The flow rate is compared with the actual flow rate fed back by the flow meter, and the frequency of the frequency converter of the metering pump responsible for delivering the solvent is controlled to ensure precise matching of the actual flow rate. By precisely controlling the flow rate, the system not only determines the processing rate of materials within the equipment, but more importantly, it directly determines the effective residence time of materials in the ultrasonic action zone. Together with the raw material feed rate, it determines the material-liquid ratio, thereby achieving indirect but precise control over these two key process parameters.

[0051] The feedback adjustment module aims to monitor the system's final output and perform online correction of the core prediction model to address model mismatch issues that may arise from factors such as equipment aging and environmental changes, ensuring the system's long-term accuracy and robustness. In this embodiment, this module is implemented by installing an online viscometer or densitometer on the extract outlet pipeline. The physical characteristics measured by these instruments, such as viscosity and turbidity, are highly correlated with the polysaccharide concentration and molecular weight distribution in the product liquid and are used as quality indicators. Quality indicators refer to online measured physical quantities that can indirectly reflect the final product yield and degradation degree. Their function is to provide real-world feedback for the adaptive adjustment of model parameters, and they are obtained in real time by online detection instruments deployed at the equipment outlet, such as viscometers. This module compares the actual measured values ​​of the quality indicators with the current optimal setpoints. The calculated model predictions are compared; if a persistent systematic deviation exceeding the preset deviation threshold occurs between the two, the upper-level recursive least squares algorithm will be activated to fine-tune the process correlation coefficients in the polysaccharide degradation risk model and the extraction efficiency model; the preset deviation threshold is determined based on statistical analysis of the noise of quality index measurement signals under historical stable operating conditions, for example, it can be three times its standard deviation to ensure that only statistically significant deviations will trigger model adjustments;

[0052] This technical solution constructs a complete adaptive closed-loop control system through the tight coupling of the above four modules. The system can sense the individual differences of raw materials and oxidative stress, predict and decide the optimal combination of process parameters based on mathematical models, and then accurately execute the decision. The model is corrected by monitoring the actual output. This method solves the problem that existing technologies using fixed process parameters cannot cope with the differences between batches of raw materials. By actively managing the uncertainties introduced by upstream processes, it achieves refined and intelligent control of the extraction process, thereby significantly improving the yield and quality stability of the final product.

[0053] A polysaccharide degradation risk model was used to establish the mathematical relationship between the instantaneous degradation rate of polysaccharides and the oxidation potential index of Tremella fuciformis raw materials and extraction temperature; an extraction efficiency model was used to establish the mathematical relationship between extraction efficiency and extraction temperature and flow rate.

[0054] This embodiment further defines the two core models built into the dynamic parameter prediction module;

[0055] The purpose of the polysaccharide degradation risk model is precisely defined as establishing a quantitative mathematical relationship between the instantaneous degradation rate of polysaccharides and two key influencing factors—the oxidative potential index of the Tremella fuciformis raw material and the extraction temperature. The core function of this model is to quantify the risk of polysaccharide molecular chain breakage under specific oxidative environment and temperature conditions.

[0056] The extraction efficiency model aims to precisely establish a quantitative mathematical relationship between extraction efficiency and two other key process parameters—extraction temperature and flow rate. The core function of this model is to quantify how much target product can be dissolved from the tremella raw material under specific operating conditions.

[0057] By clearly defining the functions of these two models, the system decomposes the complex extraction process into two mutually constraining quantitative processes: product generation and product degradation. The extraction efficiency model describes the product, which is mainly affected by temperature and flow rate; the degradation model describes the polysaccharide degradation risk, which is mainly affected by oxidation potential and temperature. This deconstruction provides a clear physical and mathematical basis for establishing a unified optimization objective function and realizing the trade-off decision between opposing factors, resulting in more accurate and robust control effects.

[0058] The dynamic parameter prediction module is used for:

[0059] Based on the oxidation potential index of Tremella fuciformis raw material and extraction temperature, the instantaneous degradation rate was determined by a polysaccharide degradation risk model.

[0060] Based on extraction temperature and flow rate, the extraction efficiency is determined using an extraction efficiency model.

[0061] This embodiment further illustrates the specific operation process of the dynamic parameter prediction module, that is, how the two models are applied;

[0062] The dynamic parameter prediction module is configured to perform two parallel computational tasks; one is based on the oxidation potential index of the Tremella fuciformis raw material, which is input in real time by the oxidation potential sensing module. and extraction temperature to be optimized The instantaneous degradation rate was calculated by calling the polysaccharide degradation risk model. Instantaneous degradation rate The physical meaning is the proportion of polysaccharides that degrade per unit time, directly reflecting the rate of product quality deterioration under current operating conditions; secondly, based on the extraction temperature to be optimized. With flow rate By calling the extraction efficiency model, an extraction efficiency is calculated. Extraction efficiency The physical meaning is the proportion of the total amount of polysaccharides that can be extracted at the end of the entire process under this combination of process parameters to the theoretical total amount in the raw material.

[0063] This implementation allows the dynamic parameter prediction module to transform abstract process parameters, such as temperature, flow rate, raw material state, and oxidation potential, into two intermediate indicators with clear physical meaning and interrelationship: instantaneous degradation rate and extraction efficiency. This transformation is a key step in achieving the final optimization decision, focusing the analysis of the two core opposing indicators of efficiency and degradation on the input problem of multiple variables, thereby improving the logical clarity and accuracy of the decision-making process.

[0064] The dynamic parameter prediction module is also used for:

[0065] The cumulative degradation index is determined by combining the instantaneous degradation rate with the material residence time determined by the flow rate;

[0066] This embodiment expands the functionality of the dynamic parameter prediction module by introducing consideration of the cumulative effect of the process.

[0067] After calculating the instantaneous degradation rate, the dynamic parameter prediction module is further used to determine a cumulative degradation index. The calculation logic for this indicator is as follows: based on the set flow rate. and the known effective volume of the ultrasonic extraction cavity Determine the average residence time of the material in the effective working zone. The relationship is Average stay time This refers to the average time it takes for material to travel from entering the extraction chamber to leaving. Its function is to integrate the instantaneous degradation rate over time to assess the cumulative damage throughout the process; it is based on the instantaneous degradation rate δ and the average residence time. The cumulative degradation index was calculated using a first-order reaction kinetic model. It represents the proportion of polysaccharides that are degraded throughout the entire process: ;in, The average residence time of materials in the effective working zone; : The effective volume of the ultrasonic extraction cavity; : The set flow rate; : Cumulative degradation index, used to estimate the total proportion of polysaccharides degraded during the entire extraction process; Instantaneous degradation rate;

[0068] Introducing the concept of cumulative degradation index enables the system to not only assess the degradation risk at a specific moment but also predict the total proportion of polysaccharides degraded throughout the entire extraction process. This overcomes the limitation that instantaneous rate cannot fully reflect the process impact, because even if the instantaneous degradation rate is very low, excessive residence time, i.e., excessively low flow rate, can still lead to a serious decline in product quality. By coupling the effect of flow rate on degradation, the effect of residence time on degradation in conjunction with temperature and oxidation potential, and instantaneous rate, this approach makes the prediction of the final product quality more comprehensive and accurate, thereby significantly improving the coordination and control of process parameters.

[0069] The dynamic parameter prediction module is also used for:

[0070] A comprehensive performance index is generated by combining extraction efficiency and cumulative degradation indicators;

[0071] This embodiment further clarifies how the dynamic parameter prediction module integrates mutually restrictive performance indicators;

[0072] After determining the extraction efficiency and cumulative degradation index, the dynamic parameter prediction module is also used to fuse these two indicators to generate a single comprehensive performance index. The purpose of this indicator is to provide a unified benchmark for evaluating any set of process parameters, such as temperature. Flow rate In a specific raw material state The overall advantages and disadvantages; in this embodiment, the overall performance index Constructed for extraction efficiency With a quality factor representing the product's integrity rate The product of, i.e. ;in, Comprehensive performance index: a unified standard for evaluating the overall merits and demerits of any set of process parameters; Extraction efficiency; Cumulative degradation index;

[0073] This design unifies the two core objectives of the extraction process—high yield and high quality—into a single objective function; increasing the temperature or decreasing the flow rate may increase extraction efficiency. However, this also leads to the cumulative degradation of indicators. An increase, thereby reducing the quality factor. Conversely, the same applies to comprehensive performance indicators. The mathematical form aptly reflects this inherent contradiction and trade-off relationship; by constructing such a complete and clearly physical single optimization objective, the system can transform a complex multi-objective decision problem into a single-objective optimization problem, greatly simplifying the solution process and ensuring the optimality and globality of the decision results, thereby obtaining the best process window that balances yield and quality.

[0074] The dynamic parameter prediction module is also used for:

[0075] The optimal temperature setpoint and the optimal flow rate setpoint are determined by optimizing the comprehensive performance indicators.

[0076] This embodiment clarifies the decision output mechanism of the dynamic parameter prediction module;

[0077] The dynamic parameter prediction module generates comprehensive performance indicators. After obtaining the expression, the final task is to determine the optimal process parameter settings by optimizing the comprehensive performance index; for each oxidation potential index of the Tremella fuciformis raw material input by the sensing module... Each prediction module will execute a numerical optimization algorithm, such as gradient descent or particle swarm optimization, to find a method that optimizes the objective function. Temperature at which the maximum value is reached and flow rate The combination;

[0078] When performing optimization, the algorithm's search space is constrained by actual process and equipment capabilities, including temperature variables. and flow rate variables The optimization range is limited to a preset interval, that is... as well as For example, based on equipment safety and process experience, the temperature range is set to [323K, 363K], i.e., 50°C to 90°C, and the flow rate range is [0.5L / min, 2.0L / min]. These constraints ensure that the set values ​​given by the optimization algorithm are always physically achievable and safe, avoiding the optimal solution from exceeding the equipment operating boundary. The hyperparameters of the optimization algorithm, such as the number of iterations and the population size, are also pre-tuned to ensure convergence to the global optimum within a limited computation time.

[0079] The optimal combination obtained by solving the problem is determined as the optimal temperature setpoint. and optimal flow rate setpoint ;

[0080]

[0081] in, Optimal temperature setpoint; Optimal flow rate setting; A function representing the search for the objective function. Parameters that reach the maximum value and The combination; A function representing a comprehensive performance index. Given the oxidation potential index of the raw material of Tremella fuciformis Under the conditions of temperature and flow rate Decision made;

[0082] This step constitutes a complete logical closed loop from state perception to control command generation. It transforms the modeling, analysis, and quantitative evaluation of the physical process in the preceding steps into specific control commands that can be directly used by the execution module. By performing optimization solutions, the system no longer relies on static, empirical parameter tables, but can calculate a unique optimal process scheme in real time and dynamically for each batch of raw materials with different initial states. This realizes the transformation of the control strategy from fixed parameters to dynamic adaptation, which is the core technical effect of this invention in achieving refined control and maximizing benefits.

[0083] The polysaccharide degradation risk model is an Arrhenius equation modified by incorporating the oxidation potential index of tremella raw material as a parameter.

[0084] This embodiment provides a specific and implementable mathematical form for the polysaccharide degradation risk model.

[0085] The polysaccharide degradation risk model was concretized into an Arrhenius equation modified by incorporating the oxidation potential exponent of the Tremella fuciformis raw material as a parameter. The pre-defined Arrhenius equation is a classic model in chemical reaction kinetics describing the relationship between the rate constant of a chemical reaction and temperature. Its working principle is that the reaction rate increases exponentially with increasing temperature. In this embodiment, the standard Arrhenius equation was modified to describe the thermal degradation process co-catalyzed by oxides. The modified model was used to calculate the instantaneous degradation rate. The format is as follows:

[0086]

[0087] in:

[0088] Instantaneous degradation rate: Represents the proportion of polysaccharides degraded per unit time, with dimensions of _____. ;

[0089] The oxidation potential index of tremella raw material is a dimensionless parameter, and its value is obtained by online measurement and calculation by the oxidation potential sensing module.

[0090] : Absolute temperature, in Kelvin, K, is the control variable to be optimized in the system;

[0091] The baseline degradation ratio represents the thermal degradation rate under no oxidative stress, and its unit is 1000 kJ / m². ; : Oxidative catalytic degradation coefficient, characterizing the accelerating effect of oxidative stress on degradation, in units of ;

[0092] The activation energy-related constant is physically proportional to the activation energy of the degradation reaction, and in the Arrhenius equation, it is equivalent to... Item, of which For activation energy, This is the ideal gas constant, measured in Kelvin (K); this parameter was also obtained through calibration using previous experimental data.

[0093] This modified Arrhenius equation can accurately capture the synergistic accelerating effect of temperature and oxidative stress on polysaccharide degradation in a theoretically sound and concise manner; the exponential term form... Ensured instantaneous degradation rate With temperature The physical law that the oxidation potential of the tremella raw material increases exponentially with the increase of its value is completely consistent with the theory of chemical reaction kinetics, ensuring the correctness of the model's physical meaning; especially the linear introduction of the oxidation potential exponent of the tremella raw material. This allows the model to sensitively reflect the direct impact of differences in the initial state of raw materials on degradation risk. This specific model implementation not only makes the technical solution fully operable, but also has better generalization ability and physical interpretability compared to purely empirical models, thereby improving the prediction accuracy and reliability of the control system.

[0094] Example 2

[0095] The extraction efficiency model is a phenomenological model that integrates the temperature saturation effect function and the flow rate exponential decay function;

[0096] This embodiment provides a specific and implementable mathematical form for the extraction efficiency model;

[0097] The extraction efficiency model is concretized as a phenomenological model that integrates the temperature saturation effect function and the flow rate exponential decay function. The presupposed phenomenological model refers to a mathematical model established based on the observation and summarization of macroscopic phenomena rather than delving into the underlying microscopic mechanisms; its working principle is to grasp the main influencing trends. In this embodiment, the model is built upon two basic understandings: first, the extraction rate increases with increasing temperature, but due to solvent saturation or mass transfer limits, this increasing effect gradually weakens, exhibiting a saturation trend; second, in a continuous flow system, the extraction efficiency is positively correlated with the residence time of the material in the effective working zone, while the residence time is inversely proportional to the flow rate, exhibiting an exponential decay relationship. Based on this, the extraction efficiency... Modeled as temperature and flow rate Functions:

[0098]

[0099] in:

[0100] Extraction efficiency is a dimensionless parameter.

[0101] : Temperature and flow rate, respectively, are the control variables to be optimized in the system;

[0102] Maximum theoretical efficiency, dimensionless, represents the highest extraction efficiency achievable under ideal conditions; this parameter is determined by experimental data under extreme conditions and is derived from experimental calibration.

[0103] : Extraction reference temperature, in Kelvin (K), represents the reference point or baseline temperature at which the extraction process begins to change significantly;

[0104] Reference flow rate, in L / min;

[0105] Process correlation coefficient The dimensions are , These are dimensionless parameters, representing the sensitivity of efficiency to temperature changes and the sensitivity to flow rate changes, respectively. These parameters were calibrated by conducting multiple orthogonal experiments with different temperature-flow rate combinations under a baseline low-oxidative stress feedstock, and then fitting the obtained experimental data to the model using multivariate nonlinear regression. The base of the natural logarithm;

[0106] One calibration example is as follows:

[0107] Step 1: Calibrate the extraction efficiency model parameters , and

[0108] Oxidation potential index of a batch of pretreated white fungus raw materials was selected. Using near-zero ideal tremella fuciformis raw material as a benchmark, an orthogonal experiment of temperature and flow rate covering the entire processing range was designed. For example, three temperature levels were selected: T1=333K, T2=343K, T3=353K, and three flow rate levels: V1=0.5L / min, V2=1.0L / min, V3=1.5L / min, for a total of 9 sets of experiments. Under each set of (T, V) conditions, the continuous flow ultrasonic extraction system was run until stable. The final extract was collected and its total polysaccharide content was measured to calculate the actual extraction efficiency. ; these 9 sets of data points , , Substitute into the extraction efficiency model formula:

[0109]

[0110] The parameters are obtained by fitting using a nonlinear least squares method such as the Levenberg-Marquardt algorithm. , and The optimal estimate; for example, in this embodiment, it may be obtained by fitting:

[0111] , ,

[0112] Reference temperature Compared with reference flow rate It can be preset to commonly used device values, such as , ;

[0113] Step 2: Calibrate the parameters of the polysaccharide degradation risk model and

[0114] Using the same batch of reference raw materials, they were divided into several groups. One group was used as a blank control. In addition, different concentrations of typical oxidants, such as sodium hypochlorite, were added to the remaining groups, and the oxidation potential index of the raw material with a clear value was measured using a calibrated oxidation potential sensing module. A series of samples, such as ;

[0115] Extraction of these samples was conducted at a constant high flow rate to shorten residence time, primarily investigating transient degradation and different temperature levels, such as T1=343K, T2=353K, and T3=363K. The changes in the average molecular weight of the polysaccharides before and after extraction were measured using techniques such as gel permeation chromatography, and the molecular weights of the polysaccharides were calculated accordingly. Instantaneous degradation rate under T conditions ; these experimental data points, , , Substitute into the polysaccharide degradation risk model formula:

[0116]

[0117] These experimental data points, Substituting the parameters into the polysaccharide degradation risk model formula, the parameters are solved using a nonlinear fitting algorithm, such as the Levenberg-Marquardt algorithm. , and The optimal estimate; for example, in this embodiment, it is possible to obtain , , ;

[0118] This phenomenological model can describe the nonlinear effects of temperature and flow rate on extraction efficiency in a structured manner. This model form can capture key physical trends, such as saturation and decay, while avoiding the huge cost and uncertainty of building complex microscopic mass transfer models. This specific model implementation provides the system with a practical and effective tool to predict product yield under different process parameters, which is a necessary prerequisite for achieving precise optimization control, thereby improving the practicality and engineering feasibility of the entire system.

[0119] Example 3

[0120] The feedback adjustment module is used for:

[0121] The concentration of polysaccharides or molecular weight-related physical properties at the extract outlet are detected online as quality indicators.

[0122] This embodiment provides a more detailed explanation of the functions of the feedback adjustment module;

[0123] The feedback adjustment module is configured to obtain quality indicators by online detection of specific physical characteristics at the extract outlet. In this embodiment, the module installs an online viscometer on the finished product output pipe of the extraction equipment. Since the viscosity of the Tremella polysaccharide solution is closely related to its concentration and average molecular weight—the higher the concentration and the larger the molecular weight, i.e., the lower the degree of degradation, the higher the viscosity—the real-time measured viscosity value is used as a comprehensive quality indicator. Alternatively, an online turbidimeter or polarimeter can be used to measure the turbidity or optical rotation as a quality indicator.

[0124] By detecting these physical properties directly related to polysaccharide concentration or molecular weight online, the system obtains a real-time, continuous feedback signal about the actual state of the final product. Compared with offline, lagging laboratory chemical analysis, this method greatly improves the timeliness of feedback. Using these easily measurable physical quantities as quality indicators provides a fast and reliable data source for achieving closed-loop online calibration of the model, thereby significantly enhancing the system's adaptability to unknown disturbances and slowly time-varying characteristics, and ensuring the stability and optimality of long-term operation.

[0125] The feedback adjustment module is also used for:

[0126] The actual measured values ​​of quality indicators are compared with the expected efficiency calculated based on the optimal temperature setpoint and the optimal flow rate setpoint to generate a deviation.

[0127] Based on the deviation, the preset process correlation coefficients in the polysaccharide degradation risk model and the extraction efficiency model are corrected;

[0128] This embodiment further describes in detail the closed-loop correction logic inside the feedback adjustment module;

[0129] After obtaining the actual measured values ​​of the quality indicators, the feedback adjustment module also performs comparison and correction actions; the module compares the actual measured values ​​of the quality indicators with the expected values ​​to generate deviations. This expected value is based on the optimal temperature setpoint currently used by the system. and optimal flow rate setpoint The theoretical quality index prediction values ​​are obtained by calling the extraction efficiency model and the degradation risk model in a forward calculation.

[0130] To perform this calculation, a connection model output needs to be established. , With online measurement of physical quantities, such as viscosity The bridging relationship between them; based on polymer solution theory, solution viscosity is related to polysaccharide concentration C and average molecular weight Mw; while extraction efficiency Proportional to concentration C, product integrity rate Proportional to the final average molecular weight Mw; therefore, the following phenomenological prediction model can be established to calculate the theoretical viscosity value. :

[0131]

[0132] in, Calculate the theoretical viscosity value, for example, in Pascal-seconds (Pa·s) or millipascal-seconds (mPa·s). This is the theoretical maximum soluble concentration of polysaccharides in the raw material, expressed in units such as grams per liter (g / L) or kilograms per cubic meter (kg / m³). It is the initial average molecular weight of the raw polysaccharide, which is an inherent property of the material and can be determined in advance through offline analysis. For example, the unit is Dalton, Da or g / mol. , , This is an empirical constant characterizing the properties of a specific polysaccharide solution. It needs to be calibrated during the initial deployment of the system by measuring the viscosity of a series of standard polysaccharide solutions with known concentrations and molecular weights. Once calibrated, this formula can be calculated by the dynamic parameter prediction module. and Determined by To and Vo, the theoretical viscosity prediction is calculated in real time. This is used to compare the actual measured value with that of the online viscometer;

[0133] Adaptive algorithms, such as recursive least squares, will adjust to this deviation. The size and sign of the coefficients correlated with the preset processes in the polysaccharide degradation risk model and the extraction efficiency model, i.e., the aforementioned... The parameters calibrated through previous experiments are then made minor, iterative corrections. The preset process correlation coefficient refers to the empirical constant obtained by fitting offline experimental data in the early stage of model building. Its function is to enable the general model to reflect the process characteristics of specific equipment and materials.

[0134] This implementation constructs the highest-level closed loop of the entire control system; it not only controls the temperature and flow rate of the physical process, but also the accuracy of the mathematical model used for decision-making. By continuously correcting the model parameters with actual output results, the system can autonomously learn and adapt to changes in process conditions, such as seasonal changes in raw material sources or slight wear of equipment components. This adaptive adjustment mechanism ensures the long-term effectiveness of the predictive model, thereby giving the entire control system strong robustness and adaptability to environmental changes, resulting in continuously stable optimization performance.

[0135] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A temperature and flow rate coordination control system for ultrasonic extraction of polysaccharides from Tremella fuciformis, characterized in that, include: An oxidation potential sensing module is used to detect the hydrated suspension of Tremella fuciformis raw materials online, so as to generate an oxidation potential index of Tremella fuciformis raw materials to quantify the level of oxidative stress. The dynamic parameter prediction module is used to receive the oxidation potential index of the tremella raw material and calculate the optimal temperature setpoint and optimal flow rate setpoint based on the preset polysaccharide degradation risk model and extraction efficiency model. The coordinated control execution module is used to receive the optimal temperature setpoint and the optimal flow rate setpoint, and drive the heating system and the fluid delivery system to track the setpoints; The feedback adjustment module is used to monitor the quality indicators at the extract outlet and adaptively adjust the parameters in the polysaccharide degradation risk model and extraction efficiency model based on the deviation between the quality indicators and the model predictions. The polysaccharide degradation risk model is used to establish the mathematical relationship between the instantaneous degradation rate of polysaccharides and the oxidation potential index and extraction temperature of Tremella fuciformis raw materials. An extraction efficiency model is used to establish the mathematical relationship between extraction efficiency and extraction temperature and flow rate; The polysaccharide degradation risk model is the Arrhenius equation modified by introducing the oxidation potential index of the Tremella fuciformis raw material as a parameter. The extraction efficiency model is a phenomenological model that integrates the temperature saturation effect function and the flow rate exponential decay function. The feedback adjustment module is used for: The concentration of polysaccharides or molecular weight-related physical properties at the extract outlet are detected online as quality indicators.

2. The temperature and flow rate coordination control system for the ultrasonic Tremella fuciformis polysaccharide extraction process according to claim 1, characterized in that, The dynamic parameter prediction module is used for: Based on the oxidation potential index of Tremella fuciformis raw material and extraction temperature, the instantaneous degradation rate was determined by a polysaccharide degradation risk model. Extraction efficiency is determined based on extraction temperature and flow rate using an extraction efficiency model.

3. The temperature and flow rate coordination control system for the ultrasonic Tremella fuciformis polysaccharide extraction process according to claim 2, characterized in that, The dynamic parameter prediction module is also used for: The cumulative degradation index is determined by combining the instantaneous degradation rate with the material residence time determined by the flow rate.

4. The temperature and flow rate coordination control system for the ultrasonic tremella polysaccharide extraction process according to claim 3, characterized in that, The dynamic parameter prediction module is also used for: A comprehensive performance index is generated by combining extraction efficiency and cumulative degradation index.

5. The temperature and flow rate coordination control system for the ultrasonic Tremella fuciformis polysaccharide extraction process according to claim 4, characterized in that, The dynamic parameter prediction module is also used for: By optimizing the comprehensive performance indicators, the optimal temperature setpoint and the optimal flow rate setpoint are determined.

6. The temperature and flow rate coordination control system for the ultrasonic Tremella fuciformis polysaccharide extraction process according to claim 1, characterized in that, The feedback adjustment module is also used for: The actual measured values ​​of quality indicators are compared with the expected efficiency calculated based on the optimal temperature setpoint and the optimal flow rate setpoint to generate a deviation. Based on the deviation, the pre-set process correlation coefficients in the polysaccharide degradation risk model and extraction efficiency model are corrected.

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