Automatic continuous production system for cubilose peptide

Abstract

The invention relates to the technical field of cubilose peptide production, and discloses an automatic continuous cubilose peptide production system. A raw material pretreatment unit of the system performs graded cleaning and multi-stage filtering on cubilose raw materials and outputs clean raw materials; the enzymolysis reaction unit adopts a dynamic temperature control technology, and is matched with compound protease for staged enzymolysis according to the characteristics of raw materials; the membrane separation and purification unit screens a target peptide fragment through cross-flow filtration; the concentrating and drying unit performs low-temperature vacuum concentration on the product and then performs spray drying to obtain a powder finished product; the quality monitoring unit collects data in real time and generates a parameter curve, and alarms when abnormity occurs; the central control unit receives data and regulates and controls parameters of all the units, and closed-loop control over the production process is achieved. The system realizes the automatic continuous production of the cubilose peptide, and improves the production efficiency and the product quality. According to the invention, the interference of impurities on the reaction process is avoided, so that the high quality of the final product is ensured.

Description

Technical Field

[0001] This invention relates to the field of bird's nest peptide production technology, specifically to an automated continuous production system for bird's nest peptides. Background Technology

[0002] Bird's nest, a highly regarded traditional tonic, is rich in a variety of nutrients. It is high in protein, composed of various amino acids, which are essential for maintaining normal physiological functions and participating in many important physiological processes such as metabolism, cell repair, and growth. For example, arginine plays a vital role in promoting human growth and development and maintaining the normal function of the reproductive system; leucine helps regulate blood sugar levels and promotes muscle protein synthesis. Bird's nest is also rich in sialic acid, a special substance that plays a key role in enhancing immunity, promoting brain development, and improving memory, especially positively impacting the intellectual development of infants and young children by promoting nerve cell connections and signal transmission. In addition, bird's nest contains various trace elements such as calcium, iron, and phosphorus, which play irreplaceable roles in maintaining bone strength, promoting blood circulation, and participating in the synthesis of various enzymes.

[0003] Traditionally, bird's nest is mostly consumed by stewing. However, this method has limitations, making it difficult for the body to fully absorb the protein in bird's nest. The high-temperature environment during stewing can easily alter the protein structure, causing some proteins to denature and thus reducing their bioavailability. Furthermore, since most of the protein in bird's nest exists in large molecular form, the human digestive system needs to expend more time and energy to break it down and absorb it, further affecting the efficiency of protein absorption. For example, in actual consumption, the absorption rate of protein in stewed bird's nest is often low, and many nutrients are discarded along with the stewing residue, resulting in a waste of resources.

[0004] Given the limitations of traditional methods of consuming bird's nest, bird's nest peptides have emerged. Bird's nest peptides are small-molecule peptides obtained through special processing of proteins in bird's nest. They overcome the problem of poor absorption of traditional bird's nest proteins, allowing for more efficient absorption and utilization by the body. The structural characteristics of small-molecule peptides allow them to directly cross the intestinal mucosa and enter the bloodstream, bypassing complex digestion and significantly improving absorption speed and utilization. In the field of beauty and skincare, bird's nest peptides can promote the proliferation and differentiation of skin cells, increase skin elasticity and radiance, and reduce wrinkles, thus gaining popularity among consumers. Regarding boosting immunity, bird's nest peptides can activate immune cells, enhance the body's resistance, and help the body fight off various diseases.

[0005] The main production technologies for bird's nest peptides include extraction, microbial fermentation, acid hydrolysis, alkaline hydrolysis, and enzymatic hydrolysis. Extraction directly extracts natural polypeptides from biological samples; however, the quantity of naturally occurring peptides is extremely limited, making direct extraction difficult to meet the demands of large-scale production, resulting in low yields and relatively high costs. Microbial fermentation utilizes various enzyme catalysts produced during microbial growth to hydrolyze proteins in raw materials to produce hydrolyzed peptides. While this method offers advantages such as simplicity, high efficiency, low cost, and mild process conditions, making it easier to achieve industrial-scale production, it also suffers from low yields, making it difficult to meet the growing market demand for bird's nest peptides. The strong acids and alkalis used in acid and alkaline hydrolysis not only pollute the environment but may also damage the structure and activity of bird's nest peptides, affecting their quality, making industrial production difficult and remaining largely a laboratory practice. Enzymatic hydrolysis uses biological enzymes to catalyze proteins to obtain peptides, which is milder and more environmentally friendly than acid, alkaline, and microbial fermentation methods. It requires less investment, yields quick results, and is suitable for industrial production. Furthermore, peptides produced by enzymatic methods are tasteless, possess inherently green properties, have small molecular weights (mostly below 1000 Da), and can be directly absorbed without digestion, exhibiting functions as a power source, carrier, transporter, neurotransmitter, and nutrient. Traditional enzymatic hydrolysis methods are often limited by fragmented production processes and high reliance on manual labor, resulting in low efficiency and inconsistent product batches. To address this, this invention develops an automated continuous production system for bird's nest peptides. It integrates automatic control, online monitoring, and intelligent decision-making, achieving a dual leap in efficiency and quality through a closed-loop strategy. Summary of the Invention

[0006] The purpose of this invention is to provide an automated continuous production system for bird's nest peptides to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides an automated continuous production system for bird's nest peptides, the system comprising:

[0008] The raw material pretreatment unit is used to receive bird's nest raw materials and perform graded cleaning. After separating impurities through multi-stage filtration, clean raw materials are output.

[0009] The enzymatic hydrolysis reaction unit is connected to the output of the raw material pretreatment unit. It uses dynamic temperature control technology to maintain a constant temperature environment and performs staged enzymatic hydrolysis by matching compound proteases according to the characteristics of the raw materials.

[0010] The membrane separation and purification unit receives the products from the enzymatic hydrolysis unit and performs molecular weight screening, separating the target peptides from non-target components through cross-flow filtration.

[0011] The concentration and drying unit concentrates the liquid product output from the membrane separation and purification unit at low temperature under vacuum, and then uses a spray drying process to form a powdered product.

[0012] The quality monitoring unit collects production data from each unit in real time and generates process parameter curves, triggering abnormal alarm signals by comparing them with preset thresholds.

[0013] The central control unit receives data streams from the quality monitoring unit and synchronously adjusts the operating parameters of each unit to complete closed-loop control of the production process.

[0014] Preferably, when the raw material pretreatment unit performs graded cleaning:

[0015] Visual recognition technology is used to detect surface defects in raw materials, and the cleaning level is determined based on the detection results.

[0016] It is equipped with a three-stage series ultrasonic cleaning tank, with each stage of the cleaning tank operating alternately according to a preset duration and power.

[0017] A centrifugal dehydration device is installed at the outlet of the final cleaning tank. After dehydration, the raw materials are detected by a metal detector before entering the next stage.

[0018] Preferably, when the enzymatic hydrolysis reaction unit achieves staged enzymatic hydrolysis:

[0019] Establish a mapping relationship library between raw material protein content and enzymatic hydrolysis time, and call the corresponding mapping relationship according to the raw material characteristics detected in real time;

[0020] A double-helix stirrer is used to create laminar flow in the reactor, and the stirring speed is dynamically adjusted according to the enzymatic hydrolysis stage.

[0021] By interlocking the pH and temperature sensors, the reaction environment parameters are maintained within the target range.

[0022] Preferably, when the membrane separation and purification unit implements cross-flow filtration:

[0023] Ultrafiltration membrane modules with different molecular weight cutoffs are configured and arranged in descending order of molecular weight to form a series filtration channel;

[0024] Pressure sensors are installed at the inlet of each membrane module to automatically adjust the delivery frequency of the feed pump based on the transmembrane pressure difference;

[0025] Collect the retentate from each channel and perform conductivity testing. When the measured value exceeds the critical threshold, initiate the membrane module backwashing procedure.

[0026] Preferably, when the concentration and drying unit processes liquid products:

[0027] In the vacuum concentration stage, a falling film evaporator is used, and the evaporation temperature is adjusted in steps according to the viscosity of the liquid.

[0028] The inlet air temperature and atomization pressure of the spray drying tower form a negative feedback regulation, and the temperature distribution inside the tower is monitored in real time by an infrared thermal imager.

[0029] The dried powder is collected by a cyclone separator and then enters a fluidized bed for secondary drying.

[0030] Preferably, when the quality monitoring unit generates process parameter curves:

[0031] Deploy multiple types of sensor arrays at key process nodes to collect 21 parameters, including temperature, pressure, and flow rate;

[0032] Time series analysis is used to eliminate random fluctuations in sensor data and generate smoothed parameter change trajectories.

[0033] When any parameter trajectory deviates from the reference band by more than the tolerance range, the audible and visual alarm device is activated and the abnormal timestamp is recorded.

[0034] Preferably, when the central control unit implements closed-loop control:

[0035] Construct a production topology map that includes the status of each unit device, and display the real-time operating status through the color change of topology nodes;

[0036] After receiving an abnormal alarm signal from the quality monitoring unit, it automatically generates an emergency instruction set containing the equipment number and fault code;

[0037] The neural network model is trained based on historical production data to predict the optimal combination of process parameters and output it to the actuator.

[0038] Preferably, when using visual recognition technology to detect surface defects:

[0039] Hyperspectral imagers were used to acquire reflectance spectral data of raw materials, and a spectral feature library for different defect types was established.

[0040] Image texture features are extracted using a convolutional neural network, and the detection results are classified into three levels: intact, cracked, and moldy.

[0041] After each batch of testing is completed, a heat map of raw material quality distribution is generated as a basis for adjusting cleaning parameters.

[0042] Preferably, when the ultrafiltration membrane module performs a backwashing procedure:

[0043] The backwashing intensity is calculated based on the extent to which the conductivity test value exceeds the standard, and the backwashing duration is positively correlated with the transmembrane pressure difference.

[0044] The pulse-type reverse flushing mode is adopted, and the temperature of the flushing medium is five to eight degrees Celsius higher than that of the normal operating condition.

[0045] Record the membrane flux recovery rate after each backwash. Trigger a membrane replacement prompt when the recovery rate is lower than the set standard for three consecutive times.

[0046] Preferably, the present invention also includes an optimized dynamic detection system for the molecular weight distribution of bird's nest peptides, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described optimized dynamic detection method for the molecular weight distribution of bird's nest peptides.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] The enzymatic hydrolysis unit is connected to the output of the raw material pretreatment unit and employs dynamic temperature control technology to maintain a constant temperature environment. This technology precisely controls the temperature of the enzymatic hydrolysis reaction. Because enzyme activity is highly sensitive to temperature, a suitable temperature allows enzyme activity to reach its optimal state, thereby improving the efficiency of the enzymatic hydrolysis reaction. A major innovation of this unit is the use of a compound protease for staged enzymatic hydrolysis based on the characteristics of the raw materials. Different bird's nest raw materials may have differences in protein composition and structure. The compound protease can address these differences by enzymatically hydrolyzing bird's nest proteins from multiple perspectives. Staged enzymatic hydrolysis involves using different hydrolysis conditions at different stages based on the progress of the reaction and changes in the products, ensuring a more complete hydrolysis reaction. This precise enzymatic hydrolysis method can fully release the nutrients in bird's nest, converting large protein molecules into small peptide molecules. These small peptide molecules have higher activity and can be better absorbed and utilized by the human body. Compared with traditional enzymatic hydrolysis methods, this method can more effectively retain the nutrients in bird's nest, avoiding nutrient loss and destruction, and greatly enhancing the nutritional value and efficacy of bird's nest peptides.

[0049] The membrane separation and purification unit receives the product from the enzymatic hydrolysis unit and performs molecular weight screening, separating target peptides from non-target components through cross-flow filtration. Cross-flow filtration offers unique advantages, creating turbulence on the membrane surface during filtration, reducing concentration polarization and improving filtration efficiency and separation effect. When screening for target peptides, it precisely removes non-target components such as unreacted enzymes and impurities, increasing product purity. The purified product has extremely low impurity content, significantly enhancing the quality of the bird's nest peptides. The concentration and drying unit performs low-temperature vacuum concentration on the liquid product output from the membrane separation and purification unit. The low-temperature environment prevents high temperatures from damaging the nutritional components of the bird's nest peptides, while the vacuum condition accelerates water evaporation, improving concentration efficiency. Subsequently, a spray drying process is used to form a powdered product. Spray drying rapidly converts the liquid into a dry powder with uniform particle size and good flowability. This powdered product is not only easy to store and transport but also convenient for consumers. Whether during storage or transportation, powdered bird's nest peptides maintain good stability, reducing the impact of environmental factors on product quality.

[0050] The quality monitoring unit collects production data from each unit in real time and generates process parameter curves, triggering alarm signals for abnormalities by comparing them with preset thresholds. This function enables comprehensive monitoring of the production process, allowing for timely detection of anomalies. Each production data point reflects a stage in the production process; analysis and processing of this data allows for understanding whether the production process is operating normally. When a parameter exceeds a preset threshold, an alarm signal is immediately issued, alerting staff to take timely corrective measures. The central control unit receives the data stream from the quality monitoring unit and synchronously adjusts the operating parameters of each unit, completing closed-loop control of the production process. This closed-loop control method enables precise regulation of the production process, ensuring coordinated operation of each production stage. During production, if the operating parameters of one unit change, the central control unit can adjust the operating parameters of other units in a timely manner based on the data provided by the quality monitoring unit, thereby ensuring the stability of the entire production process and the consistency of product quality. This combination of quality monitoring and central control not only improves production efficiency but also reduces production costs and minimizes losses caused by production failures and product quality issues, providing strong support for the sustainable development of the enterprise. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the working principle of the automated continuous production system for bird's nest peptides described in this invention.

[0052] Figure 2 A flowchart illustrating the grading and cleaning process for the raw material pretreatment unit;

[0053] Figure 3 A flowchart illustrating the phased enzymatic hydrolysis process of the enzymatic hydrolysis reaction unit. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Please see Figure 1This invention provides an automated continuous production system for bird's nest peptides. The system comprises a raw material pretreatment unit, an enzymatic hydrolysis unit, a membrane separation and purification unit, a concentration and drying unit, a quality monitoring unit, and a central control unit. The raw material pretreatment unit receives bird's nest raw materials and performs graded cleaning, outputting clean raw materials after separating impurities through multi-stage filtration. The output of the raw material pretreatment unit is connected to the enzymatic hydrolysis unit, which uses dynamic temperature control technology to maintain a constant temperature environment and performs staged enzymatic hydrolysis using a complex protease matched according to the characteristics of the raw materials. The membrane separation and purification unit receives the products from the enzymatic hydrolysis unit and performs molecular weight screening, separating target peptides from non-target components through cross-flow filtration. The concentration and drying unit performs low-temperature vacuum concentration on the liquid product output from the membrane separation and purification unit, followed by spray drying to form a powdered finished product. The quality monitoring unit collects production data from each unit in real time and generates process parameter curves, triggering abnormal alarm signals by comparing with preset thresholds. The central control unit receives the data stream from the quality monitoring unit and synchronously regulates the operating parameters of each unit, completing closed-loop control of the production process. The system's various units interact and transmit commands through an industrial network. The output of the raw material pretreatment unit is connected to the inlet of the enzymatic hydrolysis reaction unit via a conveying pipeline. The outlet of the reaction vessel of the enzymatic hydrolysis reaction unit is connected to the filtration device of the membrane separation and purification unit. The permeate collection tank of the membrane separation and purification unit is connected to the evaporator inlet of the concentration and drying unit. A finished product collection bin is set below the drying tower of the concentration and drying unit. The sensor nodes of the quality monitoring unit are distributed at key monitoring points in each unit. The main controller of the central control unit establishes a real-time connection with the control cabinets of each subunit through a communication module.

[0056] Example 1: See Figure 2 During the graded cleaning process in the raw material pretreatment unit, visual recognition technology is used to detect surface defects in the raw materials. The visual recognition system integrates a hyperspectral imager and an image processing module. The hyperspectral imager collects reflectance spectral data of the bird's nest raw materials in the 400-2500 nm wavelength range, and the image processing module runs a convolutional neural network algorithm to analyze the collected data in real time. The raw materials pass through the detection area on a conveyor belt at a speed of 0.5 m / s. The scanning frequency of the hyperspectral imager is synchronized with the speed of the conveyor belt. After each batch of raw materials is inspected, the system automatically generates a heat map of the raw material quality distribution. The hardware platform of the visual recognition system includes a spectral imaging lens, an LED light source array, and an embedded processor. The light source array provides uniform illumination of 1200 lux, and the spectral imaging lens acquires 30 frames of hyperspectral images per second. The convolutional neural network adopts the ResNet-50 architecture, with the input image resolution set to 512×512 pixels. The output layer uses the Sigmoid activation function to classify the detection results into three levels: intact, cracked, and moldy. The raw material quality distribution heat map generated after each batch of inspection visually displays the spatial distribution of surface defects in the raw materials in pseudo-color image form.

[0057] The three-stage series ultrasonic cleaning tank is made of 304 stainless steel, with each stage having a volume of 200 liters. Each tank is equipped with an automatic liquid level control system. The first-stage cleaning tank uses an ultrasonic frequency of 40kHz and a power adjustment range of 500-800W, primarily removing visible deposits from the raw material surface. The second-stage cleaning tank uses 80kHz high-frequency ultrasound with a power range of 300-500W, specifically for handling fine impurities. The third-stage cleaning tank uses pulsed ultrasound, with frequencies alternating between 28-40kHz and a stable power of 400W for final rinsing. Each cleaning tank is equipped with a digital temperature sensor to monitor the cleaning solution temperature in real time and maintain it at 30±2℃. The drainage system of the cleaning tank is controlled by a pneumatic butterfly valve, with the drainage cycle linked to the cleaning process. The centrifugal dehydrator has a drum diameter of 800 mm and a steplessly adjustable speed within the range of 800-1200 rpm. The dehydration time is automatically set to 3-5 minutes based on the moisture content of the raw material. The metal detector employs dual-channel balanced coil technology, achieving sensitivity capable of detecting metal impurities larger than 0.5 mm in diameter. Upon detecting a metal signal, a pneumatic diversion device guides the material into the waste bin within 0.5 seconds. The visual recognition system's spectral feature library contains over 100,000 standard spectral curves, each corresponding to a defect type feature. The convolutional neural network is trained using a dataset containing 500,000 labeled images, with network weights updated using new data after every 200 production batches.

[0058] The heatmap of raw material quality distribution is generated based on the statistical aggregation of detection results. A kernel density estimation algorithm is used to calculate the probability of defect distribution, establishing a one-to-one correspondence between the heatmap coordinates and the conveyor belt position coordinates. The time-power curve of the three-stage ultrasonic cleaning tank is dynamically adjusted by the central control unit. The first-stage cleaning time is set to 3 minutes, the second stage to 5 minutes, and the third stage to 2 minutes. The cleaning fluid circulation system is equipped with a 5-micron precision bag filter, which automatically backwashes every 8 hours of operation. The centrifugal dehydrator uses an S-shaped acceleration / deceleration mode, with the starting acceleration controlled within 0.3 m / s² to prevent damage to the raw materials due to rapid acceleration. The metal detector's signal processing algorithm employs adaptive filtering technology, effectively distinguishing between metal signals and background noise generated by damp raw materials.

[0059] The visual recognition system's calibration procedure is executed automatically daily, using a standard calibration board for spectral response correction. The hyperspectral imager's optical system is sealed within a nitrogen-filled protective enclosure to prevent moisture condensation from affecting image quality. The image processing module employs a parallel computing architecture, equipped with a 6-core processor and 8GB of video memory, ensuring real-time processing capabilities. The raw material quality distribution heatmap is updated in sync with the production cycle, updating the display after every 50 kg of raw material is processed. The ultrasonic generator utilizes IGBT inverter technology, achieving a conversion efficiency of over 92%. The transducers in the cleaning tank are made of piezoelectric ceramic material, with 15 transducer units arranged per square meter to form a uniform sound field distribution. The centrifugal dehydrator's vibration damping system uses a combination of rubber springs and air bladders to control vibration intensity below 4.5 mm / s. The metal detector's detection head features a fully sealed structure with an IP67 protection rating, suitable for long-term use in humid environments.

[0060] The visual recognition system communicates with the cleaning equipment using the industrial Ethernet protocol, achieving a data transmission rate of 100Mbps. The inference time of the convolutional neural network model is optimized to within 150 milliseconds, meeting the real-time requirements of online detection. Data storage for the raw material quality distribution heatmap employs a lossy compression algorithm with a compression ratio of 10:1, minimizing storage space usage while maintaining image quality. The ultrasonic cleaning tank's power adjustment uses phase angle control for precise power regulation. The centrifugal dehydrator's braking system uses DC braking, with a stopping time controlled within 20 seconds. The metal detector's automatic compensation function adapts to raw materials with varying humidity levels, ensuring detection stability. The visual recognition system's self-diagnostic function detects issues such as abnormal camera focusing and light source attenuation. The ultrasonic cleaning tank's sound intensity distribution is periodically calibrated using a sound intensity meter to ensure uniform cleaning performance. The centrifugal dehydrator's rotational speed is detected using encoder feedback, achieving a control accuracy of ±1 rpm. The metal detector's log system records all alarm events, including the time of occurrence, metal size, and location within the raw material. The equipment status data of the entire raw material pretreatment unit is uploaded to the central control unit via the OPCUA protocol, realizing digital monitoring of the entire process.

[0061] Example 2: See Figure 3The enzymatic hydrolysis unit establishes a mapping relationship database between raw material protein content and hydrolysis time during staged enzymatic hydrolysis. This database is stored in a relational database, with tables including raw material batch number, protein concentration value, and optimal hydrolysis time. Real-time detection of raw material protein content is achieved using a near-infrared spectroscopy analyzer. The analyzer probe is inserted into the raw material delivery pipeline, and the spectral data is converted into protein percentage concentration using a pre-calibrated model. The reactor volume of the enzymatic hydrolysis unit is determined based on the designed processing capacity. The inner wall of the reactor is coated with a food-grade polytetrafluoroethylene anti-corrosion coating. The blade angle of the double-helix stirrer is adjustable, and the stirrer is driven by a servo-driven geared motor. The speed control signal comes from a distributed control system. The stirring speed is set to low speed in the initial stage of enzymatic hydrolysis to promote thorough wetting and dispersion of the raw materials. In the middle stage of enzymatic hydrolysis, the stirring speed is increased to medium speed to enhance the contact efficiency between the complex protease and the substrate. In the final stage of enzymatic hydrolysis, the stirring speed is reduced to low speed to avoid excessive shearing force on the dissociated peptide chains.

[0062] The mapping relationship between raw material protein content and enzymatic hydrolysis time was constructed based on regression analysis of historical production data. The regression analysis algorithm used the least squares method. The model input variables included raw material origin code, storage temperature, storage humidity, and other factors. When the mapping relationship was invoked based on the real-time detected raw material characteristics, the structured query statement retrieved the most matching historical records in the database based on the real-time protein concentration value. The laminar flow motion mode of the twin-helix stirrer was optimized through computational fluid dynamics simulation software. The ratio of blade pitch to vessel diameter was determined through multiple iterative calculations. The instruction sequence for dynamic adjustment of stirring speed was generated by a programmable timing controller, and the speed change curve adopted a smooth transition algorithm to prevent mechanical shock. pH and temperature sensors were installed at different heights in the reactor. The pH sensor used a composite glass electrode structure, and the temperature sensor was a PT1000 platinum resistance thermometer. The sensor signals were converted into 4-20mA standard current signals by a signal transmitter and transmitted to the process controller. The interlocking control logic of the pH and temperature sensors set the target parameter range. The pH target range was set to a weakly acidic range based on the optimal pH range of the complex protease, and the temperature target range was determined based on the activity temperature curve of the complex protease. Dynamic temperature control technology is achieved through a heat medium circulation system within the reactor jacket. The heat medium temperature is jointly regulated by an electric heater and a plate cooling coil. The temperature control algorithm uses a proportional-integral-derivative triple-acting regulator, and the regulator parameters are tuned using the Ziegler-Nichols method. The feeding system of the enzymatic hydrolysis unit uses a precision metering pump to deliver the complex protease solution. The flow coefficient of the metering pump is correlated with the enzyme concentration working curve. The exhaust valve of the reactor is connected to a shell-and-tube condenser, and volatile substances are condensed, recovered, and then introduced into the treatment facility. The process monitoring interface for the staged enzymatic hydrolysis displays real-time parameter trend graphs, and the operator can manually intervene in the stage switching points through the human-machine interface. The calibration cycle for the raw material protein content detection module is set to be performed once per production batch. The calibration operation uses a series of standard solutions with concentrations covering the typical protein content range of bird's nest raw materials.

[0063] The enzymatic hydrolysis unit terminates the reaction by adding a food-grade terminator. The terminator injection valve is triggered by a pH change signal, and the injection volume is automatically calculated based on the reaction liquid volume. The mapping database update mechanism includes a data mining module; new production data is automatically added to the database and triggers the model retraining process. The optical window of the near-infrared spectrometer is equipped with an automatic cleaning device, which performs a purging procedure after each detection cycle. The pressure safety valve of the reactor is set above the upper limit of the working pressure; the safety valve is periodically calibrated, and the calibration data is recorded. The mechanical seal of the double-helix stirrer adopts a double-end-face structure, and the sealing fluid system maintains lubrication and cooling of the sealing surface. The pH sensor is calibrated using a standard buffer solution, and the calibration data is stored in the sensor transmitter memory. The cleaning and sterilization process of the enzymatic hydrolysis unit uses an online cleaning system; the cleaning fluid circulation path covers all material contact surfaces, and the sterilization temperature is monitored by a temperature sensor, recording the sterilization curve. The storage conditions of the complex protease are controlled in a low-temperature, light-protected environment, and the usage time limit of the enzyme solution after preparation is automatically timed and alarmed by the system. The liquid level detection of the reactor uses a radar level gauge, and the liquid level data is used in the feed rate control algorithm. An outlier removal mechanism is established using a mapping database of raw material protein content and enzymatic hydrolysis time, with outlier judgment criteria based on the three-sigma criterion. The matching degree between real-time monitoring data and the mapping database is calculated using the Euclidean distance algorithm, and the matching degree threshold is configurable. Embedded temperature sensors are used for bearing temperature monitoring of the twin-helix stirrer, and temperature data is used in equipment health status assessment.

[0064] The energy consumption of the enzymatic hydrolysis unit is metered by a smart meter, and the meter data is integrated into the energy management system. The jacket pressure of the reactor prevents heat transfer medium leakage, and a pressure-locked pump stop protection mechanism is in place. The pH sensor's electrode aging is detected by response time, with a response time delay triggering an electrode replacement prompt. The temperature sensor's measurement accuracy is periodically verified using a standard thermometer comparison method. The process data for the enzymatic hydrolysis unit is stored in a time-series database, and a data compression algorithm balances storage space and query efficiency. Updates to the spectral model of the raw material protein content detection module require authorized operator confirmation, and the model version management system records change history. The drive motor current monitoring of the twin-helix stirrer reflects load changes, and abnormal current fluctuations trigger torque protection. The equipment layout of the enzymatic hydrolysis unit considers maintenance space, and the reserved passage width meets safety regulations. Pipe connections use quick-connect clamps, with clamp material matching the pipe material. The reactor's sight glass lighting uses explosion-proof lamps, and the lamp protection level meets the requirements for explosive environments. The grounding system resistance of the enzymatic hydrolysis unit is measured periodically, and the measurement report is archived for future reference. Lightning protection devices are installed in the distribution box, and their status indicators are visible. Emergency flushing devices are installed around the reactor, and the flushing water pressure meets emergency handling standards. Safety signs are posted in prominent locations on the equipment, and the content of the signs complies with industry standards.

[0065] The automated control system of the enzymatic hydrolysis unit adopts a redundant design, with the main controller and backup controller operating in hot standby mode. Network communication uses the industrial Ethernet protocol, and the switch ports are configured with flow control. The software system has user access management, with hierarchical permissions corresponding to operational responsibilities. The alarm management system processes alarm information by category, prioritizing alarms based on urgency. The historical data query interface supports multi-condition filtering, and query results can be exported in a standard format. Report generation is scheduled, and report templates are customizable. The system log records all operational events, and log files are automatically archived and saved. The validation scheme for the enzymatic hydrolysis unit includes three stages: installation verification, operational verification, and performance verification. The verification report is reviewed by the quality department. Change control process management parameters are modified, and change requests require approval from multiple parties. Training materials are tailored to different positions, and training effectiveness is evaluated through assessment. Preventive maintenance plans are developed based on equipment uptime, and maintenance tasks are automatically assigned to responsible personnel. Spare parts inventory management sets a minimum inventory level, and a purchase request is generated when inventory is insufficient. The performance indicator monitoring system calculates indicator values ​​in real time, and the indicator dashboard visually displays trends.

[0066] The interface protocol between the enzymatic hydrolysis reaction unit and upstream and downstream units adopts international standards, and the protocol converter handles data format differences. Material transfer pipelines are designed for hygiene, with pipe slopes ensuring thorough evacuation. Sampling valves are installed in representative locations, and sampling operations comply with aseptic requirements. A weighing module detects the weight of reactants, and the weight signal is used for batch control. A nitrogen protection system maintains an inert reaction environment, and nitrogen purity is regularly monitored and recorded. The waste gas treatment device is connected to the workshop exhaust system, and emission concentrations meet environmental standards. Vibration monitoring of the enzymatic hydrolysis reaction unit is performed using accelerometers, and vibration spectrum analysis diagnoses equipment status. Noise levels are measured using sound level meters, and results are recorded in environmental monitoring reports. Lighting intensity meets operational requirements, and illuminance meters are regularly calibrated. Emergency power automatically activates in case of power grid failure, and its load-bearing capacity has been tested. Anti-static measures include equipment grounding and personnel protection; electrostatic discharge data is recorded and archived. Cleanliness verification uses a particle counter, and the cleanliness level meets the specified standards. The computerized system verification of the enzymatic hydrolysis reaction unit follows the GAMP5 guidelines, and the verification documentation is structurally complete. Data integrity assurance measures include audit trails, and data modifications require electronic signatures. The electronic record backup strategy includes off-site storage, and the backup and recovery process has been rehearsed. System time synchronization uses the Network Time Protocol, and timestamp accuracy meets regulatory requirements. Access control policies are role-based, and the permission approval process is electronic. The system decommissioning plan includes a data migration scheme, and decommissioning records are permanently stored.

[0067] Example 3: When implementing cross-flow filtration in the membrane separation and purification unit, ultrafiltration membrane modules with different molecular weight cutoffs are configured. These modules are arranged in descending order of molecular weight to form a series filtration channel. The first-stage ultrafiltration membrane module has a molecular weight cutoff of 50 kDaltons to remove undigested large protein fragments. The second-stage ultrafiltration membrane module has a molecular weight cutoff of 10 kDaltons to match the molecular weight range of the target active peptides. The third-stage ultrafiltration membrane module has a molecular weight cutoff of 3 kDaltons to separate small peptides and impurities such as salts. A piezoresistive pressure sensor is installed in the inlet pipe of each ultrafiltration membrane module. The pressure sensor signal is input to a programmable logic controller (PLC). The PLC calculates the real-time transmembrane pressure differential setpoint. The frequency converter of the feed pump adjusts the motor speed based on the pressure differential deviation signal. The formula for adjusting the feed pump's delivery frequency is:

[0068]

[0069] in: The pump frequency representing the current sampling period. This indicates the pump frequency of the previous sampling period. It is a proportionality coefficient. It is the target transmembrane pressure difference. This is the current measured pressure difference value. The cross-flow filtration system is designed with tangential flow, where the feed liquid flows along the membrane surface at a linear velocity of two meters per second. This flow state slows down concentration polarization. Each channel's retentate collection tank is equipped with a four-electrode conductivity sensor, whose electrodes directly contact the liquid to measure changes in ion concentration in real time.

[0070] When the ultrafiltration membrane module performs a backwashing procedure, the backwashing intensity is calculated based on the extent to which the conductivity reading exceeds the standard. The calculation formula uses a linear relationship between the excess percentage and the backwashing pressure. The backwashing duration is positively correlated with the transmembrane pressure difference, and the positive correlation coefficient is determined through experimental data fitting. The pulse frequency of the pulse-type backwashing mode is controlled by a digital timer. The temperature of the backwashing medium is increased by five to eight degrees Celsius compared to normal operating conditions, and this temperature increase is achieved through a plate heat exchanger. After each backwash, the membrane flux recovery rate is recorded. The recovery rate is calculated as the percentage of flux after backwashing to the initial flux. When the recovery rate is lower than the set standard of 85% for three consecutive backwashes, the monitoring system triggers a membrane replacement prompt signal. The ultrafiltration membrane module is made of polyethersulfone polymer material. The uniformity of the membrane pore size distribution is verified by scanning electron microscopy. The connecting pipes of the series filtration channels are made of 316L stainless steel, and the inner wall of the pipes is electropolished to achieve a roughness of 0.4 micrometers. The pressure sensor's range covers the operating pressure range of 0 to 1 MPa, and the sensor calibration certificate is updated quarterly. The feed pump's delivery frequency adjustment algorithm employs a fuzzy control strategy, with a fuzzy rule base containing the mapping relationship between differential pressure change rate and frequency adjustment amount. Electromagnetic flow meters are installed in the circulation pipeline of the cross-flow filtration system, and the flow meter data participates in the system's mass balance calculation. The critical threshold for conductivity detection is set at 500 microSiemens per centimeter based on product purity requirements; modification of this threshold requires authorization from operators with three levels of access.

[0071] In addition to exceeding conductivity limits, the backwashing procedure is initiated based on a timed triggering mechanism, with preventative flushing intervals calculated cumulatively based on equipment operating time. The pulse width of the pulse-type backwashing mode is adjustable from 200 milliseconds to 2 seconds; narrow pulses are suitable for minor contamination, while wide pulses are used for severe clogging. Temperature control of the flushing medium is achieved through a steam injection system, with a temperature sensor feedback signal used to regulate the steam valve opening in a closed loop. The membrane flux recovery rate record table includes fields such as timestamp and operator employee number, and the data is archived for five years. Membrane replacement alerts are sent to the maintenance management platform, which automatically generates a maintenance work order and assigns it to the responsible engineer. The ultrafiltration membrane module's sealing structure uses a double O-ring design, and the sealing material is EPDM rubber resistant to cleaning agent corrosion. The pressure sensor's installation location considers fluid stability, with the pressure tap positioned in the middle of the straight pipe section. The feed pump impeller is dynamically balanced, with vibration controlled below 2.5 mm / s. The cross-flow filtration system's pressure protection is equipped with a safety valve, with a trip pressure of 1.5 times the operating pressure. The electrodes of the conductivity sensor have built-in cleaning brushes that automatically rotate and scrape away dirt before and after each measurement.

[0072] The backwashing process uses purified water with a conductivity requirement of less than 10 microsiemens per centimeter. The pneumatic valve response time for the pulse-type backwashing mode is less than 100 milliseconds, and the valve life reaches one million cycles. The heat calculation for the temperature increase of the backwashing medium considers specific heat capacity and heat supply and loss balance. The statistical analysis method for membrane flux recovery rate uses the moving average method, with a moving window size of ten data points. The membrane replacement prompt trigger logic includes a progressive alarm, with the alarm level increasing as the recovery rate decreases. The ultrafiltration membrane module frame structure is made of 304 stainless steel, and the frame surface is passivated. The pressure sensor signal transmission uses shielded twisted-pair cable, with the shielding layer grounded at a single point to avoid loop interference. The feed pump motor insulation class reaches F, and the motor protection class is IP55. The cleaning procedure for the cross-flow filtration system is divided into two stages: alkaline washing and acid washing, with online monitoring of the cleaning agent concentration. The conductivity sensor calibration solution uses potassium chloride standard solution, with calibration points covering the range of 0 to 1000 microsiemens.

[0073] The backwashing program's pressure curve is programmable, including pressure ramping, holding, and depressurization sections. The pulse shape for the pulse-type backwashing mode can be selected as either a rectangular or trapezoidal wave, with the waveform selection determined by the membrane fouling type. The backwash medium temperature control accuracy is ±0.5 degrees Celsius, and the temperature sensor accuracy is zero-point level. Historical data trend analysis of membrane flux recovery rate uses the least squares method, with the trend line slope reflecting the rate of membrane performance degradation. The membrane replacement confirmation process requires dual verification, and verification records are electronically signed and archived. The ultrafiltration membrane module end caps adopt a quick-opening structure, reducing maintenance time. The pressure sensor's process connector is a compression fitting, ensuring reliable sealing. The feed pump's bearing lubrication system automatically injects oil, with the injection interval calculated based on operating time. The cross-flow filtration system's vent valve is located at a high point and automatically opens upon system startup. The conductivity sensor's cable length compensation function eliminates signal attenuation, and the cable length compensation coefficient is configurable.

[0074] The water-saving mode of the backwashing program can be enabled, adjusting the flushing water volume to match the degree of fouling. The pressure peak limiting function of the pulse-type backwashing mode protects the membrane elements; the pressure peak limit value is configurable. The heating rate of the flushing medium temperature is controllable, and the heating rate affects the cleaning effect. Outliers in the membrane flux recovery rate are eliminated using the Grubbs test to avoid false alarms. The spare parts inventory query function for membrane replacement prompts is automatically triggered; a purchase request is generated when spare parts inventory falls below the safety stock level. The lifting points of the ultrafiltration membrane module have undergone stress calculations, with a safety factor greater than five times. The lightning protection device for the pressure sensor absorbs surge energy, with a response time in the nanosecond range. The shock absorber of the feed pump base is made of rubber, and its natural frequency avoids the excitation frequency. The pre-embedded depth of the anchor bolts in the cross-flow filtration system meets specifications, and the pull-out strength of the anchor bolts has been calculated. The grounding resistance of the conductivity sensor is required to be less than one ohm, and the grounding resistance is checked and recorded monthly.

[0075] The remote start function of the backwashing program is access-controlled, requiring on-site personnel to confirm safety before remote start. The pulse-type backwashing mode's fault self-diagnosis function detects valve status, covering over 95% of fault modes. A safety interlock for the flushing medium temperature prevents overheating; the safety interlock trigger temperature is set at 90 degrees Celsius. Membrane flux recovery rate data backup employs a cross-machine backup strategy, with data synchronized daily. Membrane replacement work order status is updated in real-time, including statuses such as pending, in progress, and completed. The ultrafiltration membrane module's nameplate information is complete and traceable, including model number and production date. Pressure sensor calibration labels are clearly visible, indicating their expiration date. The feed pump's operating indicator light displays equipment status, with colors conforming to specifications. Pipe markings in the cross-flow filtration system indicate the flow direction, with different colors distinguishing different functions. The conductivity sensor's protection level meets IP67 standards, ensuring normal operation even in harsh environments. The backwashing program's optimization algorithm is based on machine learning, with training data derived from historical operation records. The pulsed backwash mode features adaptive parameter adjustment, dynamically optimizing parameters based on the washing effect. The uniformity of the washing medium temperature distribution is verified through computational fluid dynamics simulation, with a uniformity coefficient required to be greater than 0.9. The membrane flux recovery rate prediction model uses time series analysis, providing early warnings of performance degradation. Economic analysis of membrane replacement prompts calculates replacement costs, and the economic analysis report assists management decision-making.

[0076] Pressure test records for the ultrafiltration membrane module are archived. The pressure test employed a 1.5 times design pressure hold for 30 minutes. The zero-point drift of the pressure sensor is less than 0.1%, and this drift is checked annually. The feed pump's energy efficiency label meets the Level 1 energy efficiency standard and is prominently displayed. The cross-flow filtration system's operation manual includes an emergency shutdown procedure, with clear and concise steps. The conductivity sensor's measurement delay is less than three seconds, meeting real-time control requirements. Energy-saving modes for the backwashing procedure are studied to reduce energy consumption; these modes are achieved through optimized backwashing parameters. A simulation model of the pulsed backwashing mode verifies parameter settings; the simulation model was built using AMESim software. Gradient control of the backwashing medium temperature attempts segmented heating, improving cleaning efficiency. Correlation analysis of membrane flux recovery rate identifies influencing factors; the correlation analysis uses Pearson correlation coefficients. A decision support system for membrane replacement prompts integrates multi-dimensional data, providing replacement recommendations.

[0077] Example 4: In the concentration and drying unit, a falling film evaporator is used in the vacuum concentration stage when processing liquid products. The heat exchange tube bundle of the falling film evaporator is arranged vertically. The liquid product distributor evenly spreads the liquid onto the tube wall to form a liquid film. The evaporation temperature is adjusted in steps according to the liquid viscosity. The viscosity is detected online using a rotational viscometer, and the viscosity signal controls the opening of the steam regulating valve. The inlet air temperature and atomization pressure of the spray drying tower form a negative feedback regulation. The inlet air temperature sensor is located at the hot air distributor at the top of the tower, and the atomization pressure sensor monitors the inlet pressure of the high-pressure nozzle. The output signal of the negative feedback regulator simultaneously adjusts the power of the gas heater and the speed of the compressed air compressor. The temperature distribution inside the tower is monitored in real time by an infrared thermal imager, which scans the side wall of the tower to generate a pseudo-color image of the temperature field. The dried powder is collected by a cyclone separator. The discharge valve of the cyclone separator is in an intermittent opening mode. The powder enters the fluidized bed for secondary drying. The airflow velocity of the fluidized bed is controlled by a variable frequency fan, and the airflow temperature is 20 degrees Celsius lower than the outlet temperature of the spray drying tower.

[0078] When generating process parameter curves, the quality monitoring unit deploys multi-type sensor arrays at key process nodes. These arrays include twenty-one sensor types, such as temperature, pressure, and flow rate sensors. Sensor data is transmitted to a data acquisition card via a PROFIBUS-DP bus network, and the sampling frequency of the data acquisition card is set according to the parameter characteristics. When processing sensor data using time series analysis, the raw sampled data undergoes sliding window averaging. The window width dynamically expands and contracts with the parameter change rate; when the change rate is fast, the window width automatically narrows to five sampling points, and when the change rate is slow, the window width expands to twenty sampling points. A wavelet transform algorithm separates high-frequency noise from low-frequency trend components in the signal. The wavelet basis function is selected as the fourth type of Daubechies wavelet. A Markov chain state transition model is used to predict the parameter fluctuation range within the next three sampling periods. The model's state space is discretized into ten levels. The operating parameter settings for the concentration and drying unit follow the specifications outlined in Table 1.

[0079] Table 1: Control Range of Process Parameters for Concentration and Drying Unit

[0080] Parameter name Control range Sampling frequency alarm threshold Falling film evaporator temperature 45-65℃ 1 time / second <40℃ or >70℃ spray drying tower inlet air temperature 160-180℃ 2 times / second <150℃ or >190℃ atomization pressure 0.8-1.2MPa 5 times / second <0.7MPa or >1.3MPa vacuum degree -0.085 to -0.095 MPa 1 time / second >-0.08MPa Product moisture content 2.5-3.5% 1 time / minute >4.0%

[0081] The vacuum system of the falling film evaporator is maintained by a water ring vacuum pump, with the sealing water temperature controlled below 25 degrees Celsius. A step-wise adjustment rule for liquid viscosity is preset with three threshold ranges; the evaporation temperature is lowered by five degrees Celsius for each threshold range crossed. The atomizer structure of the spray drying tower is a centrifugal atomizing disc, with the disc rotation speed and atomization pressure coupled for control. The parameters of the negative feedback regulator are tuned based on the critical proportionality method to prevent continuous system oscillation. The infrared thermal imager has a resolution of 320 x 240 pixels, and the image processing algorithm identifies localized overheating areas and marks their coordinates. The design efficiency of the cyclone separator is verified using computational fluid dynamics software, and the separation efficiency curve is stored in the distributed control system database. The fluidized bed thickness sensor uses radar ranging; the bed thickness controls the airflow to prevent powder agglomeration within the bed.

[0082] The sensor array layout of the quality monitoring unit considers spatial coverage, with densely packed sensors at key nodes such as the outlet of the falling film evaporator and the conical section of the spray drying tower. The sliding window averaging algorithm of the time series analysis method dynamically adjusts the window size, with the window width inversely proportional to the absolute value of the first derivative of the signal. The wavelet transform algorithm adaptively selects the number of decomposition layers, increasing to six layers when the signal-to-noise ratio is low. The state transition probability matrix of the Markov chain is trained from historical data, with the training cycle set to update every seven days. The predicted results of parameter fluctuation ranges are displayed as 95% confidence intervals, allowing operators to adjust the process setpoints by referring to the confidence interval boundaries. Random fluctuations in sensor data are eliminated using a Kalman filter algorithm, with the gain of the Kalman filter algorithm calculated online in real time. The heat exchange tube bundle of the falling film evaporator is made of 316L stainless steel, and the tube bundle surface is polished to reduce the probability of scaling. The liquid distributor is designed with a perforated plate structure, with the orifice diameter gradually varying along the length of the tube bundle. The condenser in the vacuum concentration stage uses a plate heat exchanger, with the temperature difference between the cooling water inlet and outlet controlled within ten degrees Celsius. The spray drying tower has a 150mm thick insulation layer, made of rock wool. The orifice diameter of the high-pressure nozzles is selected based on the throughput, and nozzle wear is monitored through periodic checks of the spray pattern. The cyclone separator's inlet velocity is designed to be 18 meters per second, and the separator pressure drop is displayed in real-time on the operating interface.

[0083] The fluidized bed's inlet filter is H13 grade, with a differential pressure alarm indicating replacement time. The quality monitoring unit uses a time-series database for data storage, and data compression algorithms save storage space. Sensor calibration records are electronically managed, with automatic reminders for calibration expiration. Anomaly detection in the time series analysis method uses the isolated forest algorithm; anomaly scores exceeding a threshold trigger a review. Wavelet transform thresholding uses a soft threshold function, with the threshold size adaptively determined based on noise levels. The Markov chain assumes a uniform initial state distribution, and state transition matrix regularization avoids zero-probability events. The falling film evaporator's cleaning procedure performs online cleaning, with cleaning agents recycled until the concentration falls below the standard. The spray drying tower's shutdown procedure includes a cooling curve, and cooling rate control prevents thermal stress damage. The cyclone separator's wear-resistant liner is periodically measured for thickness; liners are replaced when the thickness falls below a safe value. The fluidized bed's vibrating motor frequency is adjustable, matching the vibration frequency to material characteristics. The quality monitoring unit's communication protocol converter handles data from sensors of different brands. The time series analysis method meets the control cycle's real-time requirements, with algorithm computation time optimized to the millisecond level. The boundary effect of wavelet transform is handled by the symmetric extension method, and the extension length is selected to be twice the wavelet support length.

[0084] Energy consumption metering in the concentration and drying unit is performed by sub-equipment, and energy consumption data is used in energy efficiency calculations. A vortex flow meter with temperature and pressure compensation is installed to meter the steam consumption of the falling film evaporator. The calorific value of the fuel gas in the spray drying tower is periodically tested, and the air-fuel ratio is adjusted accordingly. The emission concentration of the cyclone separator is continuously monitored, and the monitoring data is uploaded to the environmental protection platform. The humidity sensor at the fluidized bed outlet uses a capacitive principle, and the humidity signal is used in secondary drying control. Data integrity checks in the quality monitoring unit use checksum algorithms, and a data transmission error retransmission mechanism is implemented. Trend extraction in time series analysis uses an exponentially weighted moving average, and the weighting factor is adjusted based on data stability. The control loop settings of the falling film evaporator are recorded and saved; changes to settings parameters require authorization. The explosion-proof door design of the spray drying tower meets pressure release standards, and the explosion-proof door operating pressure is calculated. The material leg sealing device of the cyclone separator maintains stable negative pressure, and automatic wear compensation is implemented for the sealing device. The opening ratio of the gas distribution plate in the fluidized bed is optimized through testing, and the pressure drop of the distribution plate is displayed on the operating interface. The network security protection of the quality monitoring unit is implemented with a firewall, and the firewall rules are updated periodically. The time series analysis model validation uses cross-validation, with validation scores used to evaluate model performance. The emergency plan for the concentration and drying unit includes equipment failure handling procedures, and emergency drills are conducted quarterly. Leak detection for the falling film evaporator uses acoustic detection technology, achieving leak location accuracy within one meter.

[0085] The fire suppression system of the spray drying tower covers the entire tower, and the extinguishing agent concentration meets design standards. The cyclone separator's insulation and heating system is freeze-proof in winter, with automatic temperature adjustment. The electrostatic grounding resistance of the fluidized bed is monitored daily, and the values ​​are recorded. Data backup in the quality monitoring unit employs an incremental backup strategy, with backup data stored off-site. Parameter optimization for time series analysis uses a grid search method, balancing calculation accuracy and resource consumption through grid density. The falling film evaporator's operation log includes start-up and shutdown records, and log analysis reveals operational patterns. Product quality traceability codes for the spray drying tower are printed on the packaging, and these codes are linked to production batch data. Operating noise of the cyclone separator is monitored regularly, and causes for noise exceeding standards are analyzed. Fluidized bed cleanliness validation uses the ATP biofluorescence method, with validation standards strictly adhered to. Software version control for the quality monitoring unit uses a Git system, with detailed version change records. The real-time display interface for time series analysis supports simultaneous display of multiple parameters, with automatic scaling of the display ratio.

[0086] Example 5: When the central control unit implements closed-loop control, it constructs a production topology map containing the status of each unit's equipment. The production topology map is visualized on the monitoring screen in a node-edge format. Nodes represent specific equipment instances, such as the ultrasonic cleaning tank of the raw material pretreatment unit, the reaction vessel of the enzymatic reaction unit, and the ultrafiltration membrane module of the membrane separation and purification unit. Edges represent material flow pipelines or data flow connection lines. The node color coding rule defines the mapping relationship of operating status: green indicates that the equipment is in normal operation, yellow indicates that the equipment is in a parameter warning state, and red indicates that the equipment is in a fault shutdown state. After the central control unit receives the abnormal alarm signal sent by the quality monitoring unit, the signal parsing module extracts the equipment number field and fault code field from the alarm signal. The emergency command set generation module maps the fault code to the corresponding command sequence in the preset operation sequence library. The operation sequence includes specific operation steps such as equipment soft shutdown command, parameter reset command, and standby equipment switching procedure. The neural network model is trained using historical production data from the past three months as the training set. The data preprocessing stage includes data normalization and feature selection. The neural network structure adopts a multilayer perceptron model with three hidden layers, and the ReLU function is used as the activation function for the hidden layers. The trained neural network model predicts the optimal combination of process parameters. After the predicted output is verified by the verification module, it is sent to the field actuators, which include the frequency converter of the raw material pretreatment unit, the regulating valve of the enzymatic reaction unit, and the heater of the concentration and drying unit.

[0087] The production topology map is constructed based on equipment ledger information in the system configuration database. The database records each device's unique identifier, equipment type, installation location, and other attribute fields. The topology map rendering engine uses WebGL technology to achieve 3D visualization on the browser side, allowing users to zoom, rotate, and click to view details via mouse. Node color change logic is implemented using a finite state machine model. State transition conditions depend on real-time data streams uploaded by the quality monitoring unit; state transitions are triggered when sensor data exceeds threshold ranges. Abnormal alarm signal transmission follows the MQTT protocol standard, with signal payloads encapsulated in JSON format. The JSON object contains fields such as timestamp, device ID, parameter type, current value, upper threshold, and lower threshold. Emergency command set generation relies on an expert system based on a rule engine. The rule base stores hundreds of verified fault handling rules, with rule priorities configurable at three levels based on equipment criticality. The neural network model training platform integrates the TensorFlow machine learning framework, with training data covering the entire process parameters of more than six production batches. The feature selection algorithm calculates the Pearson correlation coefficient between each process parameter and product quality indicators, retaining feature parameters with an absolute correlation coefficient greater than 0.8. The objective function for predicting the optimal combination of process parameters is defined as a weighted sum of product yield and purity, with constraints considering the upper and lower limits of each piece of equipment's operation. The prediction output verification module compares the predicted values ​​with the safe operating range of the equipment; predicted values ​​exceeding the safe range are marked as invalid and trigger recalculation. Control signals from the actuators are sent to the equipment controller via the PROFIBUS fieldbus, with a signal transmission response time required to be less than one hundred milliseconds. Stability analysis of the closed-loop control uses the Nyquist frequency domain criterion, and control loop gain adjustment prevents the system from approaching a critical stable state.

[0088] The production topology map is updated every second in real time, and historical status data is archived every ten minutes for post-audit analysis. The online learning function of the neural network model supports incremental update mode; once new production data reaches a certain scale, the model retraining process is automatically triggered. The equipment node details dialog box displays real-time operating parameter curves, which support multi-parameter overlay and time axis scaling. The alarm signal processing queue uses a priority scheduling algorithm; high-priority alarms can interrupt the processing of low-priority alarms. The rule engine's rule execution log records the triggering conditions and execution results of each rule; the log file is retained for three years. The feature importance analysis of the neural network model uses a ranking importance method; the feature importance ranking results assist in process optimization. The numerical range dictionary of the predicted value verification module is reviewed and updated monthly, and dictionary updates require approval from the quality department. The fieldbus network topology is a redundant star network; in the event of a network failure, path switching can be completed within fifty milliseconds. Control loop gain adjustment parameters are stored in non-volatile memory, and parameter settings are not lost after power failure.

[0089] The production topology map's equipment filtering function supports filtering by multiple conditions such as unit type and equipment status, with the filtered results highlighted on the topology map. An alarm signal confirmation mechanism requires operators to manually confirm each alarm; unconfirmed alarms flash continuously as a reminder. The rule base's version management uses semantic version number rules, and version change records include detailed descriptions of rule additions, deletions, and modifications. The neural network model's prediction deviation monitoring calculates the average absolute percentage error between predicted and actual values; continuous errors exceeding the standard trigger model retraining. Actuator status feedback signals are incorporated into equipment health assessments; health scores below a threshold generate maintenance reminders. Fieldbus communication quality monitoring tracks message loss rates; a loss rate exceeding one percent initiates network diagnostics. Control loop parameter tuning records include fields such as tuning time, tuning personnel, parameters before tuning, and parameters after tuning. The production topology map's printing function supports selecting time points for screenshots; printed output includes a topology map legend and a system time watermark. The alarm signal history query interface supports filtering by multiple dimensions such as time range, equipment type, and alarm level. The rule base's test environment simulates various fault scenarios, requiring rule test coverage of over 95%. The inference time performance of the neural network model is optimized using model quantization technology, compressing the model size to 40% of its original size. Actuator action count statistics are used for predictive maintenance; replacement work orders are generated in advance when the action count approaches the lifespan threshold. Network configuration information of fieldbus nodes is backed up to the configuration management system, and the configuration is automatically restored after node replacement. The manual / bumper-free switching function of the control loop ensures continuous output signal when switching from automatic to manual mode.

[0090] The production topology diagram's element library supports custom device icons, with uploads required to be in SVG vector graphics format. The alarm signal linkage video retrieval function associates with nearby surveillance cameras, automatically displaying real-time video feeds upon alarm trigger. The rule base's syntax checker verifies the logical correctness of rules; rules with syntax errors cannot be deployed to the production environment. The neural network model's input data anomaly detection uses the Isolation Forest algorithm; abnormal input data triggers a manual review process. Actuator calibration records are electronically managed, with reminders sent one week before calibration expiration. The fieldbus network's physical layer diagnostics detect cable impedance changes, pinpointing fault locations based on impedance anomalies. Control loop setpoint change trend records can be exported to CSV format for trend analysis to aid process optimization. The production topology diagram's device hierarchy expansion function allows drilling down to view the status of internal components, with component status data obtained from the device's embedded system. The intelligent alarm signal push function filters irrelevant alarms based on operator permissions. The rule base's performance monitoring tracks rule execution time; rules with excessively long execution times trigger optimization reminders. Multiple versions of the neural network model run in parallel, with version performance comparisons using A / B testing. The spare parts inventory management for the actuators is integrated with the ERP system; when inventory levels fall below safety stock, a purchase requisition is automatically generated. The fieldbus network traffic monitoring graphically displays the communication load of each node, and a load balancing algorithm dynamically adjusts the communication path. The control loop's pattern recognition function automatically identifies oscillation patterns, and the oscillation identification results recommend the direction for adjusting regulator parameters.

[0091] The equipment group management function of the production topology diagram supports batch operation of equipment groups, and the equipment group definition is based on process logic relationships. The root cause analysis tool for alarm signals constructs a fault propagation map to assist in quickly locating the root cause of the fault. The rule dependency relationships in the rule base are visualized, avoiding circular dependency conflicts. The confidence interval calculation for the prediction results of the neural network model uses the Bootstrap method, and the confidence interval width assesses the prediction reliability. The actuator life prediction model integrates multi-dimensional data such as running time and load intensity, achieving a prediction accuracy of over 90%. The network security policy of the fieldbus network prohibits unauthorized device access, and device authentication uses a digital certificate mechanism. The parameter tuning auxiliary tool for control loops provides Ziegler-Nichols tuning rule calculation functionality, with a graphical guide to the tuning process. The real-time data export function of the production topology diagram supports the OPCUA standard interface, allowing third-party systems to subscribe to data through the standard interface. The intelligent filtering algorithm for alarm signals learns operator confirmation patterns and automatically filters false alarm signals. The rule simulation execution function of the rule base predicts the consequences of rule modifications, and the simulation execution results generate an impact analysis report. The training data quality of the neural network model is checked using statistical process control methods, and data quality indicators are continuously monitored. The remote diagnostic function of the actuator allows manufacturer experts to access diagnostic data online, with access time limited to one hour. Message analysis tools for the fieldbus network parse communication protocol details, aiding in troubleshooting. Performance evaluation reports for the control loop are automatically generated monthly, with evaluation indicators including the integral square of the control deviation.

[0092] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0093] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An automated continuous production system for bird's nest peptides, characterized in that, include: The raw material pretreatment unit is used to receive bird's nest raw materials and perform graded cleaning. After separating impurities through multi-stage filtration, clean raw materials are output. The enzymatic hydrolysis reaction unit is connected to the output of the raw material pretreatment unit. It uses dynamic temperature control technology to maintain a constant temperature environment and performs staged enzymatic hydrolysis by matching compound proteases according to the characteristics of the raw materials. The membrane separation and purification unit receives the products from the enzymatic hydrolysis unit and performs molecular weight screening, separating the target peptides from non-target components through cross-flow filtration. The concentration and drying unit concentrates the liquid product output from the membrane separation and purification unit at low temperature under vacuum, and then uses a spray drying process to form a powdered product. The quality monitoring unit collects production data from each unit in real time and generates process parameter curves, triggering abnormal alarm signals by comparing them with preset thresholds. The central control unit receives data streams from the quality monitoring unit and synchronously adjusts the operating parameters of each unit to complete closed-loop control of the production process.

2. The automated continuous production system for bird's nest peptides according to claim 1, characterized in that, When the raw material pretreatment unit performs graded cleaning: Visual recognition technology is used to detect surface defects in raw materials, and the cleaning level is determined based on the detection results. It is equipped with a three-stage series ultrasonic cleaning tank, with each stage of the cleaning tank operating alternately according to a preset duration and power. A centrifugal dehydration device is installed at the outlet of the final cleaning tank. After dehydration, the raw materials are detected by a metal detector before entering the next stage.

3. The automated continuous production system for bird's nest peptides according to claim 1, characterized in that, When the enzymatic hydrolysis reaction unit achieves staged enzymatic hydrolysis: Establish a mapping relationship library between raw material protein content and enzymatic hydrolysis time, and call the corresponding mapping relationship according to the raw material characteristics detected in real time; A double-helix stirrer is used to create laminar flow in the reactor, and the stirring speed is dynamically adjusted according to the enzymatic hydrolysis stage. By interlocking the pH and temperature sensors, the reaction environment parameters are maintained within the target range.

4. The automated continuous production system for bird's nest peptides according to claim 1, characterized in that, When cross-flow filtration is implemented in the membrane separation and purification unit: Ultrafiltration membrane modules with different molecular weight cutoffs are configured and arranged in descending order of molecular weight to form a series filtration channel; Pressure sensors are installed at the inlet of each membrane module to automatically adjust the delivery frequency of the feed pump based on the transmembrane pressure difference; Collect the retentate from each channel and perform conductivity testing. When the measured value exceeds the critical threshold, initiate the membrane module backwashing procedure.

5. The automated continuous production system for bird's nest peptides according to claim 1, characterized in that, When the concentration and drying unit processes liquid products: In the vacuum concentration stage, a falling film evaporator is used, and the evaporation temperature is adjusted in steps according to the viscosity of the liquid. The inlet air temperature and atomization pressure of the spray drying tower form a negative feedback regulation, and the temperature distribution inside the tower is monitored in real time by an infrared thermal imager. The dried powder is collected by a cyclone separator and then enters a fluidized bed for secondary drying.

6. The automated continuous production system for bird's nest peptides according to claim 1, characterized in that, When the quality monitoring unit generates process parameter curves: Deploy multiple types of sensor arrays at key process nodes to collect twenty-one parameters, including temperature, pressure, and flow rate; Time series analysis is used to eliminate random fluctuations in sensor data and generate smoothed parameter change trajectories. When any parameter trajectory deviates from the reference band by more than the tolerance range, the audible and visual alarm device is activated and the abnormal timestamp is recorded.

7. The automated continuous production system for bird's nest peptides according to claim 1, characterized in that, When the central control unit implements closed-loop control: Construct a production topology map that includes the status of each unit device, and display the real-time operating status through the color change of topology nodes; After receiving an abnormal alarm signal from the quality monitoring unit, it automatically generates an emergency instruction set containing the equipment number and fault code; The neural network model is trained based on historical production data to predict the optimal combination of process parameters and output it to the actuator.

8. The automated continuous production system for bird's nest peptides according to claim 2, characterized in that, When using visual recognition technology to detect surface defects: Hyperspectral imagers were used to acquire reflectance spectral data of raw materials, and a spectral feature library for different defect types was established. Image texture features are extracted using a convolutional neural network, and the detection results are classified into three levels: intact, cracked, and moldy. After each batch of testing is completed, a heat map of raw material quality distribution is generated as a basis for adjusting cleaning parameters.

9. The automated continuous production system for bird's nest peptides according to claim 4, characterized in that, When the ultrafiltration membrane module performs a backwashing procedure: The backwashing intensity is calculated based on the extent to which the conductivity test value exceeds the standard, and the backwashing duration is positively correlated with the transmembrane pressure difference. The pulse-type reverse flushing mode is adopted, and the temperature of the flushing medium is five to eight degrees Celsius higher than that of the normal operating condition. Record the membrane flux recovery rate after each backwash. Trigger a membrane replacement prompt when the recovery rate is lower than the set standard for three consecutive times.

10. A dynamic detection system for optimizing the molecular weight distribution of bird's nest peptides, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the optimized dynamic detection method for molecular weight distribution of bird's nest peptides as described in any one of claims 1 to 9.

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