Production temperature detection and control method for cold-soluble gelatin powder

Abstract

The invention relates to the technical field of production of cold-soluble gelatin powder, and provides a production temperature detection and control method of cold-soluble gelatin powder, which abandons single-point detection limitation in temperature detection, constructs a multi-point distributed sensor network, fully covers a reaction kettle, a drying box and a material conveying pipeline, accurately captures the temperature of each position, and improves the production efficiency of the cold-soluble gelatin powder. A detailed basis is provided for temperature control; temperature control strategies are diversified and intelligent, models are customized for different production stages through multi-model self-adaptive control, and automatic switching meets production requirements; fuzzy logic control is used for processing a complex nonlinear temperature problem, and the control precision is improved; the temperature detection and control level is greatly improved on the whole, the stability of the production process of the cold-soluble gelatin powder is guaranteed, and the product quality and the production efficiency are improved; a closed-loop system is constructed through a real-time feedback adjustment mechanism, and temperature deviation accumulation is avoided according to real-time regulation and control of detection data; and the control parameters are dynamically optimized, and the control effect is continuously optimized based on historical data and real-time feedback.

Description

Technical Field

[0001] The invention relates to the technical field of cold-soluble gelatin powder production, in particular to a method for detecting and controlling the production temperature of cold-soluble gelatin powder. Background Art

[0002] In the production of cold-soluble gelatin powder, temperature plays a decisive role in product quality. In the traditional production process, temperature detection and control methods have many disadvantages and it is difficult to meet the high-quality production needs.

[0003] Existing temperature detection often adopts a single-point detection method, which can only obtain the temperature of a specific position of the production equipment and cannot reflect the overall temperature distribution. For example, in a large reactor, the heat of material reaction causes obvious temperature differences at different heights. Single-point detection is prone to miss key temperature changes, resulting in uneven product quality. In terms of temperature control, the strategy is single and lacks real-time feedback adjustment. Most production relies on fixed set value control and cannot be flexibly adjusted according to changes in raw material characteristics, ambient temperature, etc. Once production conditions fluctuate, temperature deviations will occur immediately. For example, during the drying stage, changes in external humidity affect the drying efficiency. Traditional control methods are difficult to adjust the temperature in time, which can easily cause the product moisture content to not meet the standard. These problems seriously restrict the quality improvement and production efficiency improvement of cold-soluble gelatin powder.

[0004] Therefore, a method for production temperature detection and control of cold-soluble gelatin powder was proposed. Through multi-point detection, multi-control strategy and real-time feedback optimization mechanism, accurate temperature detection and efficient control can be achieved, providing reliable technical support for the production of cold-soluble gelatin powder and meeting the industry's urgent demand for high-quality products. Summary of the invention

[0005] Technical problem to be solved: To address the deficiencies in the prior art, the present invention provides a method for detecting and controlling the production temperature of cold-soluble gelatin powder.

[0006] Technical solution: To achieve the above-mentioned solution, the present invention provides the following technical solution: a method for detecting and controlling the production temperature of cold-soluble gelatin powder, comprising the following steps: Build a multi-point distributed sensor network, arrange temperature sensors at the top, middle and bottom of the reactor, the air inlet and outlet of the drying box, and at specific intervals on the material transmission pipeline to collect temperature data at different locations in real time; Sensor data fusion technology is used to perform weighted average fusion of sensor data to obtain a value that reflects the overall temperature state.

[0007] Multi-model adaptive control is adopted to establish corresponding temperature control models for the raw material dissolution, reaction and drying stages of cold-soluble gelatin powder production, and the models are automatically switched according to the production status.

[0008] Combined with fuzzy logic control, the temperature deviation and the temperature deviation change rate are taken as input, and the control output is determined through fuzzification, fuzzy rule reasoning and defuzzification operations in sequence.

[0009] Build a closed-loop control system to feed back temperature detection data to the temperature control unit in real time, adjust the control amount based on the deviation between the feedback data and the set temperature, and output it to the actuator.

[0010] Implement dynamic optimization of control parameters, establish a control parameter and product quality relationship model based on historical data, and use adaptive algorithms to adjust control parameters online according to real-time feedback data.

[0011] Preferably, the sensors arranged at the top, middle and bottom of the reactor are used to monitor the temperature of the materials at different height levels, the air inlet sensor of the drying box is used to monitor the initial temperature of the hot air, the air outlet sensor of the drying box is used to monitor the exhaust gas temperature after the materials are dried, and the sensors on the material transmission pipeline are arranged every 0.5 to 2 meters to monitor the temperature changes during the material transmission process.

[0012] Preferably, in the sensor data fusion technology, the weight is determined according to the importance of the sensor position to the production process. The weight of the sensor at the bottom of the reactor is determined to be 0.4, the weight of the top sensor is determined to be 0.2, and the sensor weight is determined to be 0.4; the weight of the sensor at the air outlet of the drying box is determined to be 0.6, and the sensor weight is determined to be 0.4; the weight of the sensor near the key link of the material transmission pipeline is higher than that of other positions.

[0013] Preferably, the raw material dissolution stage model is ,in for The temperature of the moment, , are the coefficients obtained by fitting the experimental data, Indicates the influence of heating power on temperature. Indicates the influence of stirring speed on temperature.

[0014] Preferably, the reaction stage model is , is a coefficient related to the heat of reaction, determined by reaction kinetics experiments, is the initial temperature of the reaction.

[0015] Preferably, the drying stage model is , , is the coefficient determined by experiment, Indicates the influence of heating power on temperature. Indicates the influence of ventilation volume on temperature. is the drying starting temperature.

[0016] Preferably, in the fuzzy logic control, the temperature deviation e and the temperature deviation change rate ec are used as input quantities, and the temperature deviation , temperature deviation change rate ec= , using Gaussian membership function Fuzzy processing is performed, where is the central value of the membership function, is the standard deviation.

[0017] Preferably, in the closed-loop control system, the temperature detection data is transmitted to a temperature control unit using a programmable logic controller via an RS485 bus, and the PLC calculates the control quantity adjustment value according to the control algorithm and outputs it to the heating and cooling actuators.

[0018] Preferably, in the dynamic optimization control parameters, the least square method is used based on historical data to establish a control parameter and product quality relationship model. ,in As product quality indicators, is the control parameter.

[0019] Preferably, during the online adaptive optimization, the central value and standard deviation of the membership function in the fuzzy logic control are dynamically adjusted according to the real-time range of the temperature deviation and the deviation change rate, and the corresponding model coefficients are dynamically adjusted according to the real-time parameters of the reaction stage and the drying stage.

[0020] Beneficial effects: Compared with the prior art, the present invention provides a method for detecting and controlling the production temperature of cold-soluble gelatin powder, which has the following beneficial effects: 1. The production temperature detection and control method of the cold-soluble gelatin powder abandons the limitation of single-point detection in temperature detection, builds a multi-point distributed sensor network, comprehensively covers the reactor, drying box and material transmission pipeline, accurately captures the temperature of each position, and provides a detailed basis for temperature control; the temperature control strategy is diverse and intelligent, and the multi-model adaptive control customizes the model for different production stages and automatically switches to meet production needs; fuzzy logic control handles complex nonlinear temperature problems and improves control accuracy; the overall temperature detection and control level is greatly improved to ensure the stability of the cold-soluble gelatin powder production process and improve product quality and production efficiency.

[0021] 2. The production temperature detection and control method of the cold-soluble gelatin powder builds a closed-loop system through a real-time feedback adjustment mechanism, and performs real-time regulation based on the detection data to avoid the accumulation of temperature deviations; dynamically optimizes control parameters, and continuously optimizes control effects based on historical data and real-time feedback. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the steps of the present invention; Figure 2 It is a schematic diagram of the framework structure of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] See also Figure 1-2 The present invention provides a method for detecting and controlling the production temperature of cold-soluble gelatin powder, comprising the following contents: 1. Temperature detection system 1. Multi-point distributed sensor network 1. Sensor layout implementation Reactor: As a key equipment for gelatin powder production, the internal temperature distribution of the reactor is affected by factors such as material convection and reaction heat. Installing a sensor at the top (position A) can monitor the temperature of the upper area of ​​the reactor, which is crucial for understanding the gas escape and heat accumulation at the top during the reaction. For example, when the gelatin raw materials and additives are mixed and reacted, the temperature change at the top can reflect the impact of the volatile substances produced by the reaction on the temperature of the top space. The middle (position B) sensor can obtain the main body temperature of the middle layer of the reactor. The temperature at this position is relatively stable and can represent the average reaction temperature of the material in the reactor. The bottom (position C) sensor is particularly important because the reaction heat usually accumulates at the bottom, and the bottom material is less affected by factors such as stirring. Its temperature change can directly reflect the intensity of the reaction. For example, in an exothermic reaction, the temperature at the bottom rises faster, and it can be monitored in time whether the reaction is proceeding normally.

[0025] Drying box: The temperature of the air inlet (position D) of the drying box determines the initial temperature of the hot air entering the drying box, which directly affects the starting conditions for material drying. The temperature of the air outlet (position E) reflects the temperature of the exhaust gas after the material is dried, which can reflect the degree of material drying. By comparing the air inlet and outlet temperatures, the heat utilization efficiency in the drying box and whether the material is dried sufficiently can be determined. For example, if the air outlet temperature is too low, it may mean that the drying is insufficient; if the air outlet temperature is too high, there may be a risk of over-drying the material.

[0026] Material transmission pipeline: The temperature of materials in the transmission pipeline will change continuously due to heat exchange with the pipeline wall and the influence of the external environment. A sensor is arranged every L meters. The L value can be determined according to the pipeline length, material characteristics and production process requirements, and is generally between 0.5 and 2 meters. Assume that n sensors are arranged, and the positions are as follows: These sensors can monitor the temperature changes of materials in the transmission process in real time, so as to promptly detect temperature anomalies caused by poor pipe insulation or long material residence time. For example, in a long-distance transmission pipeline, the front section pipeline sensor close to the heating source can monitor the temperature rise of the material, and the rear section sensor can monitor whether the temperature drops too quickly due to heat dissipation.

[0027] 2. Sensor selection and implementation The platinum resistance temperature sensor is selected. Its temperature measurement principle is based on the property that the resistance value of metal platinum changes with temperature. In the range of 0-100℃, the resistance value of platinum resistance is approximately linear with temperature, which can be calculated by the formula Indicates that is the resistance value of the platinum resistor at t℃, is the resistance value of the platinum resistor at 0°C, is the temperature coefficient of platinum resistance, generally 0.003851 / ℃. The sensor has an accuracy of ±0.1℃ and a short response time, generally within 1 to 3 seconds. It can quickly and accurately capture temperature changes and meet the needs of cold-soluble gelatin powder production for high-precision and real-time temperature detection. It has high stability, and the resistance value drifts little during long-term use, which can ensure the reliability of temperature detection.

[0028] 2. Sensor data fusion technology Weighted average fusion algorithm implementation Assume there are m sensors in total, and the temperature data collected are The corresponding weights are The temperature value T after fusion is:

[0029] Weight The determination of the sensor position should take into account the importance of the production process. In the reactor, the bottom temperature has the greatest impact on the reaction process. , because the bottom temperature change directly reflects the heat release of the reaction, which has a significant impact on the reaction rate and product quality. Top sensor weight The top temperature is mainly affected by gas escape and convection, and has relatively little effect on the overall reaction. , the middle temperature can better represent the main reaction temperature of the material. In the drying oven, the outlet temperature is more critical to the determination of the dryness of the product. , because the outlet temperature directly reflects the state of the exhaust gas after the material is dried, and is closely related to the moisture content of the product. , although the air inlet temperature is important, its direct impact on the final product quality is less than that of the air outlet temperature. In the material transmission pipeline, the weight of the sensor close to the key production link (such as before entering the reactor or before entering the drying oven) is relatively high. For example, the weight of the transmission pipeline sensor close to the reactor can be set to 0.3, and the weights of sensors at other locations decrease in turn according to the distance from the key link. By setting the weight reasonably, the fused temperature value can more accurately reflect the overall temperature state, providing a reliable basis for subsequent temperature control.

[0030] 2. Temperature Control Strategy 1. Multi-model adaptive control 1. Model establishment and implementation Raw material dissolution stage model: The main goal of this stage is to make the gelatin raw material dissolve quickly and evenly. Assume that the heating power is , stirring speed is The temperature change rate is , build the model:

[0031] in, for The temperature of the moment, , are model coefficients obtained by fitting the experimental data. It represents the influence coefficient of heating power on temperature. For example, in many experiments, when the heating power increases by 100W, the temperature rises by 0.5℃, then k1=0.005℃ / W. Indicates the influence coefficient of stirring speed on temperature. If the stirring speed increases by 100r / min, the temperature decreases by 0.2℃, then . is the model error term, reflecting the interference of other unconsidered factors on temperature, such as ambient temperature fluctuations, initial temperature differences of raw materials, etc. The model takes into account the comprehensive effects of heating power and stirring speed on temperature. By adjusting these two parameters, the temperature of the raw material dissolution stage can be effectively controlled to improve the dissolution efficiency and uniformity.

[0032] Reaction stage model: The temperature needs to be precisely controlled during the reaction stage to ensure the reaction proceeds. Let the reaction time be t, the reaction rate be r, and the temperature be , build the model:

[0033] It is the initial reaction temperature, which is set by the production process and is generally determined according to the type of gelatin powder and the reaction requirements. For example, the initial reaction temperature of a specific gelatin powder is set at 50°C. is a coefficient related to the heat of reaction, determined by reaction kinetics experiments. For example, it is experimentally determined that when the reaction rate increases When the temperature rises by 0.3℃, . It is the model error term at this stage, reflecting the influence of other factors on temperature during the reaction process, such as changes in catalyst activity, heat dissipation in the reaction vessel, etc. This model uses the reaction rate as the key variable, and controls the temperature of the reaction stage by controlling the reaction rate, ensuring that the reaction proceeds as expected and improving product quality and yield.

[0034] Drying stage model: In the drying stage, the temperature needs to be controlled within an appropriate range to ensure that the moisture content of the product meets the standard. Assume that the heating power is The ventilation volume is , drying time is , the temperature is , build the model:

[0035] It is the starting temperature of drying, which is determined according to product requirements and drying process. For example, the starting temperature of drying of a certain cold-soluble gelatin powder is 60℃. , are model coefficients, determined through experiments. Indicates the influence coefficient of heating power on temperature. For example, when the heating power increases by 100W, the temperature rises by 0.4℃. . Indicates the influence coefficient of ventilation volume on temperature. If the ventilation volume increases When the temperature drops by 0.1℃, . It is the error term of the drying stage model, reflecting the influence of other factors on the temperature during the drying process, such as the tightness of the drying box, material loading, etc. The model comprehensively considers the influence of heating power and ventilation volume on temperature. By adjusting these two parameters, the temperature of the drying stage can be effectively controlled to ensure that the moisture content of the product meets the standard.

[0036] 2. Model switching implementation During the production process, the production status parameters are monitored to determine the production stage and automatically switch the control model.

[0037] In the raw material dissolution stage, the dissolution degree of the material is detected to determine whether the dissolution is complete. Online concentration detection instruments, such as near-infrared spectrometers, can be used to monitor the material dissolution rate in real time based on the relationship between the characteristic spectrum of the gelatin solution and the concentration. When the material dissolution rate reaches more than 95%, the raw material dissolution stage is judged to be over, and the system automatically switches to the reaction stage model for temperature control.

[0038] In the reaction stage, the reaction progress is judged by monitoring the concentration of the reaction progress indicator. The concentration of a specific reaction intermediate in the reaction system is detected by high performance liquid chromatography. When the concentration of the intermediate reaches 90% of the expected reaction progress, the reaction is judged to be nearly completed and ready to switch to the drying stage model.

[0039] In the drying stage, the moisture content of the product is detected by an online moisture detector. When the moisture content of the product reaches the target range (such as the target moisture content is 5% to 8%), the drying is considered complete. Through this model switching mechanism based on real-time production status parameters, it can ensure that temperature control always matches the production stage, improving production efficiency and product quality.

[0040] 2. Fuzzy logic control 1. Input quantity determination and implementation The temperature deviation e and the temperature deviation change rate ec are used as input. , is the current actual temperature, For example, in the reaction stage, the set temperature is 60℃, the current actual temperature T is 60.5℃, then the temperature deviation e=0.5℃. Temperature deviation change rate ec= , is the temperature at the previous moment, is the time interval. Assume that the temperature at the last moment is is 60.3℃, time interval For 1 minute, the temperature deviation change rate ec = By accurately calculating these two input quantities, the deviation between the current temperature and the set temperature and the temperature change trend can be reflected, providing basic data for subsequent fuzzy logic control.

[0041] 2. Fuzzy processing implementation Divide e and ec into multiple fuzzy subsets, such as negative large (NB), negative medium (NM), negative small (NS), zero (Z), positive small (PS), positive medium (PM), and positive large (PB). Take the membership function of temperature deviation e as an example, and assume a Gaussian membership function:

[0042] in, is the membership degree of e to the fuzzy subset A, is the central value of the membership function, is the standard deviation, and different fuzzy subsets correspond to different and For example, for the negative large (NB) fuzzy subset, let , , when the temperature deviation Substituting into the membership function, we can get , indicating that the degree of belonging to the negative large fuzzy subset when the temperature deviation is -6℃ is 0.88. By fuzzifying e and ec, the precise temperature deviation and deviation change rate are converted into fuzzy linguistic variables, which is convenient for subsequent fuzzy rule reasoning.

[0043] 3. Fuzzy rule reasoning implementation Determine fuzzy rules based on experience and experiments. For example, if e is NB and ec is NB, then the control output (such as the heating power adjustment amount) ) is PB; if e is Z and ec is PS, then NS, etc. These rules constitute the fuzzy rule base. When constructing the fuzzy rule base, various situations in the production process must be fully considered. For example, in the reaction stage, when the temperature deviation is negative and the deviation change rate is also negative, it means that the current temperature is far below the set temperature and is still dropping rapidly. At this time, the heating power needs to be greatly increased, so the control output is positive. In practical applications, the fuzzy rule base can be verified and optimized through a large number of experiments and production data to ensure its rationality and effectiveness.

[0044] 4. Defuzzification operation implementation The centroid method is used to perform defuzzification and calculate the final control output. Suppose the fuzzy output is , and its calculation formula is:

[0045] in, is an element in the fuzzy output set, For example, after fuzzy rule reasoning, the fuzzy output set contains three elements: , and the corresponding membership degrees are , , , then the final control output Through the defuzzification operation, the fuzzy control output is converted into a precise control quantity, which is used to adjust the heating, cooling and other actuators to achieve precise control of the temperature.

[0046] 3. Real-time feedback adjustment mechanism 1. Construction of closed-loop control system 1. System structure implementation The temperature data collected by the temperature detection system is processed by data fusion and transmitted to the temperature control unit through a data transmission line (such as RS485 bus). The temperature control unit generally uses a programmable logic controller (PLC) with a built-in temperature control algorithm. The PLC calculates the control adjustment value based on the deviation between the feedback data and the set temperature. For example, in the reaction stage, if the set temperature is 60°C, the actual temperature after data fusion is 60.5°C. The PLC calculates the need to reduce the heating power based on the control algorithm. Then, the PLC outputs the control signal to the heating and cooling actuators. The heating actuator can use an electric heating element to control the heating power by adjusting the current; the cooling actuator can use a cold water circulation device to control the cooling water volume by adjusting the valve opening. The actuator adjusts the temperature of the production equipment according to the control signal output by the PLC to form a closed-loop control. In the entire closed-loop control system, the real-time and accuracy of data transmission are crucial. The RS485 bus has the advantages of long transmission distance and strong anti-interference ability, which can ensure that the temperature data is transmitted to the temperature control unit quickly and accurately.

[0047] 2. Control algorithm implementation The proportional-integral-derivative (PID) control algorithm is used. , control output for:

[0048] in, is the proportionality coefficient, is the integration coefficient, is the differential coefficient. In practical applications, by adjusting these three coefficients, the system can quickly and stably adjust the temperature to the set value. For example, in the drying stage, when the temperature deviation is large, the proportional term Plays a major role in quickly reducing deviations. , temperature deviation , the proportional term output is 4. The integral term Used to eliminate steady-state errors. As time accumulates, the integral term continues to accumulate deviations, prompting the system to gradually eliminate steady-state errors. Adjust the control amount in advance according to the deviation change rate to prevent temperature overshoot. For example, when the temperature deviation change rate is 0.5℃ / min and Kd=1, the differential output is 0.5. By reasonably adjusting the PID parameters, the closed-loop control system can achieve efficient and stable temperature control in different production stages.

[0049] 2. Dynamic optimization control parameters 1. Optimize implementation based on historical data The system records the historical temperature data, control parameters and product quality data during the production process. Through data analysis algorithms, such as the least squares method, a relationship model between control parameters and product quality is established. Let the product quality index be Q and the control parameter be , build the model:

[0050] in, To control the functional relationship between parameters and product quality, is the error term. For example, in the drying stage, the control parameter is the heating power, is the ventilation volume, and the product quality index Q is the moisture content of the product. By collecting a large amount of historical production data, the functional relationship is obtained by fitting using the least squares method. By continuously updating historical data, the model is optimized to obtain the optimal control parameter combination. For example, when the target moisture content of the product is 6%, the model calculates that the optimal heating power is 300W and the ventilation volume is 15m³ / min.

[0051] 2. Online Adaptive Optimization Implementation In the production process, adaptive algorithms are used to adjust control parameters based on real-time feedback data. For example, in fuzzy logic control, the central value and standard deviation of the membership function are dynamically adjusted according to the real-time range of temperature deviation and deviation change rate, so that the fuzzy control rules are more in line with the actual production situation. Specifically, assuming the real-time range of temperature deviation is , the real-time range of the temperature deviation change rate is .when When it is relatively small, it means that the temperature is relatively stable. At this time, the standard deviation of the membership function can be appropriately reduced. , making the division of fuzzy subsets more refined and improving the sensitivity of control. For example, the central value of the membership function of the original negative small (NS) fuzzy subset , standard deviation , if the real-time temperature deviation range is reduced to , then you can Adjust to , so that when the temperature deviation is small, the control quantity can be adjusted more accurately according to the fuzzy rules.

[0052] For the reaction stage, if the real-time monitoring shows that the reaction rate fluctuates greatly, the reaction stage model can be used to , dynamically adjust the coefficients related to the reaction heat By online monitoring of key parameters in the reaction system, such as reactant concentration changes, reaction heat release rate, etc., an adaptive algorithm is used to calculate Assume that through a series of calculations and analyses, it is found that when the reaction rate is accelerated, the actual temperature rise is greater than expected, indicating that the original The value is too large, it can be reduced appropriately , to ensure the accuracy of temperature control. , after adaptive adjustment, it can be reduced to .

[0053] In the drying stage, if the real-time feedback data of the product moisture content shows a large deviation from the target moisture content, the drying stage model can be used to , and adjust the heating power influence coefficient and ventilation rate influence coefficient For example, when the moisture content of the product is higher than the target value and the temperature in the drying oven is low, the temperature can be appropriately increased. , improve the effect of heating power on temperature, and adjust reasonably according to the influence of ventilation volume on temperature , to optimize the drying process. Through this online adaptive optimization mechanism, the system can automatically and quickly adjust the control parameters according to the real-time changes in the production process, ensuring that the temperature control is always in the best state, effectively improving the stability of product quality and production efficiency.

[0054] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A production temperature detection and control method for cold-soluble gelatin powder, characterized in that: The following steps are involved: Build a multi-point distributed sensor network, arrange temperature sensors at the top, middle and bottom of the reactor, the air inlet and outlet of the drying box, and at specific intervals on the material transmission pipeline to collect temperature data at different locations in real time; Using sensor data fusion technology, the data of each sensor is weighted averaged and fused to obtain a value reflecting the overall temperature state; Adopting multi-model adaptive control, corresponding temperature control models are established for the raw material dissolution, reaction and drying stages of cold-soluble gelatin powder production, and the models are automatically switched according to the production status; Combined with fuzzy logic control, the temperature deviation and the temperature deviation change rate are used as inputs, and the control output is determined by fuzzification, fuzzy rule reasoning and defuzzification operations in sequence; Build a closed-loop control system to feed back temperature detection data to the temperature control unit in real time, adjust the control amount according to the deviation between the feedback data and the set temperature, and output it to the actuator; Implement dynamic optimization of control parameters, establish a control parameter and product quality relationship model based on historical data, and use adaptive algorithms to adjust control parameters online according to real-time feedback data.

2. The method for detecting and controlling the production temperature of cold-soluble gelatin powder according to claim 1, characterized in that: The sensors arranged at the top, middle and bottom of the reactor are used to monitor the temperature of the materials at different height levels. The air inlet sensor of the drying box is used to monitor the initial temperature of the hot air. The air outlet sensor of the drying box is used to monitor the temperature of the exhaust gas after the materials are dried. The sensors on the material transmission pipeline are arranged every 0.5 to 2 meters to monitor the temperature changes during the material transmission process.

3. The method for detecting and controlling the production temperature of cold-soluble gelatin powder according to claim 1, characterized in that: In the sensor data fusion technology, the weight is determined according to the importance of the sensor position to the production process. The weight of the sensor at the bottom of the reactor is determined to be 0.4, the weight of the top sensor is determined to be 0.2, and the sensor weight is determined to be 0.4; the weight of the sensor at the air outlet of the drying box is determined to be 0.6, and the sensor weight is determined to be 0.4; the weight of the sensor near the key link of the material transmission pipeline is higher than that of other positions.

4. The method for detecting and controlling the production temperature of cold-soluble gelatin powder according to claim 1, characterized in that: The raw material dissolution stage model is: ,in for The temperature of the moment, , are the coefficients obtained by fitting the experimental data, Indicates the influence of heating power on temperature. Indicates the influence of stirring speed on temperature.

5. The method for detecting and controlling the production temperature of cold-soluble gelatin powder according to claim 4, characterized in that: The reaction stage model is , is a coefficient related to the heat of reaction, determined by reaction kinetics experiments, is the initial temperature of the reaction.

6. The method for detecting and controlling the production temperature of cold-soluble gelatin powder according to claim 5, characterized in that: The drying stage model is , , is the coefficient determined by experiment, Indicates the influence of heating power on temperature. Indicates the influence of ventilation volume on temperature. is the drying starting temperature.

7. The method for detecting and controlling the production temperature of cold-soluble gelatin powder according to claim 1, characterized in that: In the fuzzy logic control, the temperature deviation e and the temperature deviation change rate ec are used as input quantities. , temperature deviation change rate ec = , using Gaussian membership function Fuzzy processing is performed, where is the central value of the membership function, is the standard deviation.

8. The method for detecting and controlling the production temperature of cold-soluble gelatin powder according to claim 1, characterized in that: In the closed-loop control system, the temperature detection data is transmitted to the temperature control unit using a programmable logic controller via the RS485 bus, and the PLC calculates the control amount adjustment value according to the control algorithm and outputs it to the heating and cooling actuators.

9. The method for detecting and controlling the production temperature of cold-soluble gelatin powder according to claim 1, characterized in that: In the dynamic optimization control parameters, the least square method is used based on historical data to establish the relationship model between the control parameters and product quality. ,in As product quality indicators, is the control parameter.

10. The method for detecting and controlling the production temperature of cold-soluble gelatin powder according to claim 1, characterized in that: During the online adaptive optimization, the central value and standard deviation of the membership function in the fuzzy logic control are dynamically adjusted according to the real-time range of the temperature deviation and the deviation change rate, and the corresponding model coefficients are dynamically adjusted according to the real-time parameters of the reaction stage and the drying stage.

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