Rapid freezing anti-adhesion treatment method and system for tapioca pearls

Abstract

The invention discloses an anti-adhesion treatment method and system for quick freezing of tapioca pearls, and aims to solve the problem that the tapioca pearls are easy to adhere during freezing. The method comprises the following steps: acquiring the surface humidity, the surface viscosity and the freezing environment temperature of the tapioca pearls; determining an adhesion risk level through comparison with a preset threshold value; and when the freezing triggering condition is met, a self-adaptive freezing anti-adhesion strategy is generated in combination with the technological parameters and the real-time data, and a freezing treatment scheme is obtained. According to the method, a humidity sensor, a viscosity detection device and a temperature sensor are adopted for collecting parameters, the risk level is calculated through difference value weighting, and the freezing rate, time, temperature stratification and airflow frequency are dynamically adjusted according to the high-risk, particle-size-dominant or composite risk state. The system comprises a parameter acquisition module, a risk assessment module and a strategy generation module which correspond to the method. Through self-adaptive control, the adhesion risk is reduced, and the freezing efficiency and the product quality are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of food processing, in particular to a quick freezing anti-sticking treatment method and system for powder balls. BACKGROUND

[0002] As a widely used food raw material in beverages and desserts, the production and freezing process of powder balls are crucial to product quality. In the prior art, powder ball freezing usually adopts a freezing process with fixed temperature and rate. However, due to differences in powder ball surface humidity, viscosity and freezing environmental conditions, powder balls are prone to sticking during the freezing process, affecting appearance, taste and subsequent processing efficiency. Existing anti-sticking methods rely on manual adjustment of freezing parameters or the addition of anti-sticking agents. The former lacks precision and automation and is difficult to adapt to dynamic production environments. The latter may affect the safety and flavor of the powder balls.

[0003] In addition, the existing technology mainly evaluates the risk of powder ball sticking based on a single parameter, lacks comprehensive analysis of multi-dimensional parameters, and the freezing strategy lacks pertinence and adaptability.

[0004] Therefore, there is an urgent need for a powder ball freezing treatment method and system that can adaptively adjust freezing conditions based on dynamic process parameters to achieve efficient anti-sticking, thereby improving freezing efficiency and product quality. SUMMARY

[0005] The present application aims to provide a quick freezing anti-sticking treatment method and system for powder balls, which addresses the problems of powder ball sticking during freezing, lack of precision in manual adjustment of freezing parameters, and the impact of adding anti-sticking agents on safety and the lack of single parameter evaluation. By obtaining multi-dimensional process parameters such as powder ball surface humidity, surface viscosity and freezing environmental temperature, determining the sticking risk level based on difference weighted calculation, and adaptively adjusting the freezing rate, time, temperature stratification and air circulation frequency based on real-time environmental data, a targeted freezing treatment scheme is generated to achieve efficient anti-sticking, ensure safety and flavor, improve freezing efficiency and product quality, and adapt to the needs of dynamic production environments.

[0006] To achieve the above-mentioned purpose, the present application provides a quick freezing anti-sticking treatment method for powder balls, which is specifically applied to powder ball production and freezing process. The method comprises:

[0007] Obtaining process parameters during powder ball production, wherein the process parameters at least include powder ball surface humidity, surface viscosity and freezing environmental temperature; determining the sticking risk level of the powder balls according to the comparison between the process parameters and the preset anti-sticking threshold; when the sticking risk level meets the freezing trigger condition, generating an adaptive freezing anti-sticking strategy based on the process parameters and real-time freezing environmental data to obtain a freezing treatment scheme.

[0008] Further, the process of obtaining the process parameters in the production of the powder ball comprises the following steps: deploying a humidity sensor on the powder ball production equipment, measuring the surface humidity of the powder ball in real time and recording it as the first parameter; measuring the surface viscosity of the powder ball using a viscosity detection device and recording it as the second parameter; monitoring the freezing environment temperature through a temperature sensor and recording it as the third parameter.

[0009] Further, the process of determining the adhesion risk level of the powder ball according to the comparison of the process parameters with the preset adhesion prevention threshold value comprises: comparing the first parameter with a preset humidity threshold value to obtain a first difference; comparing the second parameter with a preset viscosity threshold value to obtain a second difference; comparing the third parameter with a preset temperature threshold value to obtain a third difference; and determining the adhesion risk level according to the weighted calculation of the first difference, the second difference and the third difference, wherein the adhesion risk level includes low risk, medium risk and high risk.

[0010] Further, the freezing trigger condition is that the adhesion risk level is high risk or the fluctuation rate of the first difference exceeds a preset range, and the generation of the adaptive freezing anti-adhesion strategy to obtain the freezing treatment scheme comprises: judging whether the adhesion risk level is high risk or the fluctuation rate of the first difference exceeds a preset range; if any condition is met, determining that the adhesion state of the powder ball is a high risk state; adjusting the freezing rate and freezing time according to the high risk state to generate a first freezing treatment scheme; and taking the first freezing treatment scheme as the freezing treatment scheme of the powder ball.

[0011] Further, the process parameters further include powder ball particle size uniformity data, the freezing trigger condition is that the ratio of the second difference to the fourth difference exceeds a preset range, wherein the fourth difference is obtained by comparing the powder ball particle size uniformity data with a preset particle size uniformity threshold value, and the generation of the adaptive freezing anti-adhesion strategy to obtain the freezing treatment scheme comprises: calculating the ratio of the second difference to the fourth difference; if the ratio exceeds a preset range, determining that the adhesion state of the powder ball is a particle size dominant state; optimizing the airflow partition and injection rate of the freezing equipment according to the particle size dominant state to generate a second freezing treatment scheme; and taking the second freezing treatment scheme as the freezing treatment scheme of the powder ball.

[0012] Further, the process parameters include two sets of powder surface humidity data and surface viscosity data, the freezing trigger condition is that the ratio of the first difference value and the second difference value of one of the sets exceeds a preset range, and the first difference value is different from that of the other set, and the adaptive freezing anti-adhesion strategy is generated to obtain a freezing treatment scheme, including: calculating the ratio of the first difference value and the second difference value of the one set; if the ratio exceeds the preset range, comparing the first difference value of the one set with the first difference value of the other set; if different, determining that the adhesion state of the powder is a composite risk state; according to the composite risk state, adjusting the freezing temperature stratification and air flow circulation frequency to generate a third freezing treatment scheme; and taking the third freezing treatment scheme as the freezing treatment scheme of the powder.

[0013] Further, the process of adjusting the freezing rate and freezing time includes: determining an upper limit of the freezing rate according to the fluctuation rate of the first difference value; calculating an adjustment coefficient of the freezing time according to the second difference value and the third difference value; and applying the freezing rate upper limit and the adjustment coefficient to the freezing equipment control parameters to generate the first freezing treatment scheme.

[0014] Further, the process of adjusting the freezing temperature stratification and air flow circulation frequency includes:

[0015] According to the composite risk state, a freezing time window is determined, the temperature stratification area of the freezing equipment is divided according to the ratio of the first difference value and the second difference value, and the freezing time window and the temperature stratification area are integrated to generate the third freezing treatment scheme.

[0016] Further, after generating the freezing treatment scheme, the method further includes: transmitting the freezing treatment scheme to a control system of a powder production line; and controlling the control system to adjust the freezing equipment parameters according to the freezing treatment scheme.

[0017] Further, a system for implementing the above-mentioned powder rapid freezing anti-adhesion treatment method includes: a parameter acquisition module configured to acquire process parameters in a powder production process, including powder surface humidity, surface viscosity and freezing environment temperature; a risk assessment module configured to determine the adhesion risk level of the powder according to comparison of the process parameters with a preset anti-adhesion threshold; and a strategy generation module configured to generate an adaptive freezing anti-adhesion strategy to form a freezing treatment scheme according to the process parameters and real-time freezing environment data when the adhesion risk level meets a freezing trigger condition.

[0018] The application provides a quick freezing anti-adhesion treatment method and system for powder balls, aiming at the problems of easy adhesion of powder balls in the prior art, lack of precision in manual adjustment, influence on food safety by adding anti-adhesion agents, and insufficient evaluation of single parameters, by acquiring multi-dimensional process parameters such as surface humidity, surface viscosity and freezing environment temperature of the powder balls, determining the adhesion risk level based on difference weighted calculation, and combining real-time environmental data to adaptively adjust the freezing rate, time, temperature stratification and airflow circulation frequency, a freezing treatment scheme is generated for high-risk, particle size dominant or composite risk states, effectively reducing the adhesion risk of the powder balls; compared with the traditional method, the application does not need to add anti-adhesion agents, ensuring the safety and flavor of food, and through the automatic and precise parameter analysis and control strategy, the freezing efficiency and product quality are significantly improved, and the needs of the dynamic production environment are met. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The flowchart of the quick freezing anti-adhesion treatment method for powder balls provided by the application is provided.

[0020] Figure 2 The flowchart of the powder ball adhesion risk level determination method provided by the application is provided.

[0021] Figure 3 The structural schematic diagram of the quick freezing anti-adhesion treatment system for powder balls provided by the application is provided.

[0022] REFERENCE NUMERALS:

[0023] The quick freezing anti-adhesion treatment system for powder balls 100, the parameter acquisition module 101, the risk assessment module 102 and the strategy generation module 103. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme of the present application will be described clearly and completely below in combination with the drawings in the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0025] The terms "first", "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features; in the description of the present application, unless otherwise specified, the meaning of "multiple" is two or more.

[0026] In order to more clearly illustrate the technical solutions of the present application, the present application will be described in detail below in conjunction with specific embodiments, but should not be understood as limiting the scope of protection of the present application.

[0027] In this embodiment, a method for realizing the quick freezing anti-adhesion treatment of powder balls is provided, as shown in Figure 1 The process based on process parameter analysis and adaptive freezing strategy generation is described to achieve efficient anti-adhesion and product quality improvement.

[0028] Step S01: Obtain the process parameters in the production process of powder balls, wherein the process parameters at least include the surface humidity, surface viscosity and freezing environment temperature of the powder balls.

[0029] Specifically, on the powder ball production line, the moisture content on the surface of the powder balls is monitored in real time using a high-precision humidity sensor to obtain the surface humidity data of the powder balls. The humidity sensor is installed above the conveying belt after the powder balls are formed, and samples are taken regularly to capture humidity changes, ensuring the real-time and accuracy of the data. Further, the surface viscosity of the powder balls is measured by a viscosity detection device, and a contact type viscometer is used to touch the surface of the powder balls and record the resistance data to obtain the surface viscosity value. Understandably, the freezing environment temperature is obtained by a temperature sensor installed in the freezing equipment, and the sensor is distributed in the key areas of the freezing chamber to record the environmental temperature fluctuations in real time. The collection of these parameters provides a comprehensive data basis for subsequent adhesion risk assessment, adapting to the production conditions of different batches of powder balls.

[0030] Step S02: Determine the adhesion risk level of the powder balls according to the comparison of the process parameters with the preset anti-adhesion threshold.

[0031] Specifically, the system compares the collected surface humidity, surface viscosity and freezing environment temperature of the powder balls with the preset anti-adhesion threshold. The preset threshold is determined based on historical production data and experimental analysis, for example, the surface humidity threshold is 60%-70%, the surface viscosity threshold is 0.5-1.0 Pa·s, and the freezing environment temperature threshold is -30℃ to -20℃. By comparing the deviation of each parameter from the corresponding threshold, a weighted average value is calculated, in which the weights of humidity and viscosity are higher to reflect their influence on adhesion. The weighted calculation result is divided into three levels of low risk, medium risk and high risk, for example, the weighted deviation less than 10% is low risk, 10%-20% is medium risk, and more than 20% is high risk. Understandably, this process is completed through an automatic control system to ensure quick and accurate determination of the adhesion risk level, providing a basis for subsequent freezing strategies.

[0032] Step S03: When the sticking risk level meets the freezing trigger condition, an adaptive freezing anti-sticking strategy is generated according to the process parameters and real-time freezing environment data to obtain a freezing treatment scheme.

[0033] Specifically, the freezing trigger condition is set as the sticking risk level being high risk or the fluctuation rate of surface humidity deviation exceeding a preset range (such as ±5%). When the condition is met, the system dynamically adjusts the freezing parameters according to the real-time collected process parameters and freezing environment data. Further, if a high risk level is detected, the system reduces the freezing rate (such as from 5°C / min to 3°C / min) and extends the freezing time (such as from 10 minutes to 15 minutes) to reduce the sticking caused by rapid freezing of the surface moisture of the powder ball. Real-time freezing environment data is used to optimize the air flow distribution of the freezing equipment, such as by adjusting the fan speed to ensure uniform air flow in the freezing chamber and avoid local temperature being too low. Understandably, the adjusted freezing parameters are integrated into the freezing treatment scheme, which is automatically applied to the freezing equipment by the control system to ensure that the powder ball can achieve efficient anti-sticking under different production conditions, significantly improving product quality.

[0034] The embodiment obtains process parameters such as surface humidity, surface viscosity and freezing environment temperature in the production process of the powder ball, compares them with the preset anti-sticking threshold to determine the sticking risk level, and generates an adaptive freezing anti-sticking strategy according to real-time data when the freezing trigger condition is met to form a freezing treatment scheme, effectively solving the problem of easy sticking in the freezing process of the powder ball. Compared with the traditional fixed parameter freezing method, the edible safety and flavor can be ensured without adding anti-sticking agent. Through precise parameter analysis and dynamic adjustment of freezing parameters, the sticking risk is significantly reduced, the freezing efficiency and the quality of the powder ball product are improved, and the dynamic production environment demand is met.

[0035] In some embodiments, an implementation of a powder ball rapid freezing anti-sticking processing method is provided. For the step of obtaining process parameters in the production process of the powder ball, how to collect the surface humidity, surface viscosity and freezing environment temperature of the powder ball through specific equipment and technical means is described in detail to support the subsequent sticking risk assessment and freezing strategy generation. Specifically, a humidity sensor is deployed on the powder ball production equipment to measure the surface humidity of the powder ball in real time and record it as the first parameter.

[0036] It is understandable that in the forming and conveying section of the powder ball production line, a high-precision non-contact humidity sensor is installed, which is fixed above the conveying belt about 5 cm away from the surface of the powder ball to avoid direct contact affecting the shape of the powder ball. The humidity sensor uses infrared moisture measurement technology, collects data once per second, and monitors the moisture content on the surface of the powder ball in real time, such as recording the humidity value in the range of 50%-80%. Further, the sensor transmits humidity data to the control system through the data acquisition module to generate time series data as the first parameter. It is understandable that in order to ensure measurement accuracy, the system regularly calibrates the sensor and automatically adjusts the measurement range when the production environment changes (such as environmental humidity fluctuations) to adapt to the production conditions of different batches of powder balls.

[0037] Further, the viscosity detection device is used to measure the surface viscosity of the powder ball and record it as the second parameter.

[0038] Specifically, in the detection area after the powder ball is formed, a contact viscosity detection device is deployed, which is equipped with a micro probe to measure the viscous resistance by lightly touching the surface of the powder ball. The probe contacts the powder ball with constant pressure, each measurement lasts 0.5 seconds, and the recorded viscosity value is in units of Pascal seconds (Pa·s), such as a typical value of 0.4-1.2 Pa·s. Further, the viscosity detection device calculates the average value through multiple sampling (10 times for each batch of powder balls) to reduce measurement error, and the obtained average value is stored as the second parameter to the control system. It is understandable that the probe is automatically cleaned after each measurement to avoid residues affecting subsequent measurements, ensuring the reliability and consistency of the data.

[0039] Further, the temperature of the refrigerated environment is monitored by a temperature sensor and recorded as the third parameter.

[0040] Specifically, a multi-point temperature sensor network is deployed inside the refrigeration equipment, and the sensors are distributed in the upper, middle and lower areas of the refrigeration chamber to comprehensively monitor the temperature distribution of the refrigeration environment. The temperature sensor uses thermocouple technology to record the temperature data inside the refrigeration chamber in real time, such as in the range of -35°C to -25°C, updated every 10 seconds. Further, the system performs smoothing processing on the collected temperature data, calculates the average temperature of the refrigeration chamber as the third parameter, and stores it to the control system. It is understandable that the sensor network is linked with the refrigeration equipment control unit to provide real-time feedback on temperature fluctuations, ensuring the accuracy of the refrigeration environment data and providing a reliable basis for the generation of subsequent adaptive refrigeration strategies.

[0041] In some embodiments, an implementation of a powder ball rapid freezing anti-adhesion processing method is provided, as shown in Figure 2 The step of determining the adhesion risk level of the powder ball in the production process focuses on how to calculate the adhesion risk level of the powder ball by comparing the process parameters with the preset anti-adhesion threshold to support the generation of subsequent refrigeration strategies.

[0042] Step S31: comparing the first parameter with a preset humidity threshold value to obtain a first difference value.

[0043] Specifically, the system compares the surface humidity data (first parameter) collected by the humidity sensor with a preset humidity threshold value. The preset humidity threshold value is set to 60%-70% according to the experimental data of the pellet production, to reflect the humidity range of the risk of sticking. Further, the system calculates the absolute deviation of the first parameter (e.g. actual humidity value is 75%) from the median value (e.g. 65%) of the preset humidity threshold value, to obtain the first difference value (e.g. 10%). It can be understood that the comparison process is performed in real time in the automatic control system, and the deviation result is stored as an input for subsequent weighted calculation, to ensure that the humidity analysis accurately reflects the sticking tendency of the pellets.

[0044] Step S32: comparing the second parameter with a preset viscosity threshold value to obtain a second difference value.

[0045] Specifically, based on the surface viscosity data (second parameter) measured by the viscosity detection device, the system compares it with a preset viscosity threshold value. The preset viscosity threshold value is set to 0.5-1.0 Pa·s according to the material characteristics of the pellets, to represent the viscosity range of the risk of sticking. Further, the system calculates the absolute deviation of the second parameter (e.g. actual viscosity value is 1.2 Pa·s) from the median value (e.g. 0.75 Pa·s) of the preset viscosity threshold value, to obtain the second difference value (e.g. 0.45 Pa·s). It can be understood that, to improve the reliability of the comparison, the system takes the average of multiple measured viscosity data for comparison, and stores the second difference value to the control system for subsequent risk level evaluation.

[0046] Step S33: comparing the third parameter with a preset temperature threshold value to obtain a third difference value.

[0047] Specifically, the system compares the freezing environment temperature data (third parameter) collected by the temperature sensor with a preset temperature threshold value. The preset temperature threshold value is set to -30℃ to -20℃ according to the performance of the freezing equipment, to ensure the anti-sticking effect of the freezing process. Further, the system calculates the absolute deviation of the third parameter (e.g. actual temperature is -32℃) from the median value (e.g. -25℃) of the preset temperature threshold value, to obtain the third difference value (e.g. 7℃). It can be understood that the comparison process considers the temperature distribution in the freezing chamber, and uses the average value calculation to reduce the influence of local fluctuations, and the deviation result is stored as an input for subsequent weighted calculation.

[0048] Step S34: determining the sticking risk level according to the weighted calculation of the first difference value, the second difference value and the third difference value, wherein the sticking risk level includes low risk, medium risk and high risk.

[0049] Specifically, the system calculates the first difference (humidity deviation), the second difference (viscosity deviation), and the third difference (temperature deviation) with weights, which are set according to the importance of each parameter on the adhesion, for example, the humidity weight is 0.4, the viscosity weight is 0.4, and the temperature weight is 0.2. Further, the weighted calculation is obtained by multiplying each difference by the corresponding weight and summing up, to obtain a comprehensive deviation value. For example, the first difference is 10%, the second difference is 0.45 Pa·s, and the third difference is 7°C, and the comprehensive deviation value is 0.4x10+0.4x0.45+0.2x7=5.58. The comprehensive deviation value is divided into low risk (less than 3), medium risk (3-6), and high risk (greater than 6) according to the preset range, and in this example, it is determined to be high risk. Understandably, this calculation process is automatically completed by the algorithm module of the control system, and the adhesion risk level is stored as the basis for subsequent frozen strategy generation, ensuring that the evaluation result accurately reflects the adhesion risk of the powder ball.

[0050] In some embodiments, an implementation of a powder ball rapid freezing anti-adhesion processing method is provided, and the steps of generating an adaptive freezing anti-adhesion strategy in the powder ball production process are described in detail. How to adjust the freezing parameters to generate a freezing treatment scheme according to the adhesion risk level and the freezing trigger condition to prevent the powder ball from adhering is described.

[0051] Specifically, it is determined whether the adhesion risk level is high risk or the fluctuation rate of the first difference exceeds the preset range.

[0052] Understandably, based on the adhesion risk level (including low risk, medium risk, and high risk) obtained by the weighted calculation, it is first checked whether the high risk level is reached, for example, the comprehensive deviation value is greater than 6. Further, for the first difference calculated by the powder ball surface humidity data (the first parameter) collected by the humidity sensor, the fluctuation rate is analyzed. The fluctuation rate is calculated by the standard deviation of the first difference (such as humidity deviation) measured multiple times within 10 seconds, and the preset range is ±5%. If the fluctuation rate exceeds ±5% (for example, the standard deviation is 6%), the condition is met. Understandably, this judgment process is completed by an automatic algorithm, and the combination of the high risk level or the humidity fluctuation rate check ensures accurate identification of scenarios that require adjustment of the freezing strategy.

[0053] Further, if any condition is met, the adhesion state of the powder ball is determined to be a high risk state.

[0054] Specifically, when the sticking risk level is high risk or the fluctuation of the first difference exceeds ±5%, it is confirmed that the powder pellet is in a high risk state. For example, if the comprehensive deviation value is 6.5 (high risk) or the humidity deviation fluctuation rate reaches 6%, it is determined to be in a high risk state. Further, the high risk state indicates that the surface humidity or viscosity of the powder pellet can cause serious sticking, and the freezing parameters need to be adjusted immediately. Understandably, this determination is based on pre-set logical rules, quickly responds to the dynamic changes in the sticking risk in the production process, and ensures the pertinence of the subsequent freezing strategy.

[0055] Further, according to the high risk state, the freezing rate and freezing time are adjusted to generate a first freezing treatment scheme.

[0056] Specifically, in the high risk state, the freezing rate is adjusted according to the size of the first difference (humidity deviation), for example, when the humidity deviation is 10%, the freezing rate is reduced from the standard 5℃ / min to 3℃ / min, to slow down the freezing speed of water and reduce the sticking probability. Further, according to the second difference (viscosity deviation) and the third difference (temperature deviation), the freezing time is extended, for example, from 10 minutes to 15 minutes, to ensure that the surface of the powder pellet is fully frozen. Understandably, the adjusted freezing rate and time are integrated by the pre-set algorithm of the control unit to generate a first freezing treatment scheme, which contains specific freezing rate and time parameters, and is directly applied to the freezing equipment to achieve the anti-sticking effect.

[0057] Further, the first freezing treatment scheme is used as the freezing treatment scheme for the powder pellet.

[0058] Specifically, the generated first freezing treatment scheme containing the adjusted freezing rate (such as 3℃ / min) and freezing time (such as 15 minutes) is used as the final scheme for the freezing of the powder pellet. Further, the scheme is transmitted to the freezing equipment by the control unit to ensure that the equipment operates according to the specified parameters, for example, by adjusting the compressor power of the freezing chamber to control the freezing rate. Understandably, the implementation of the scheme ensures that the powder pellet is effectively prevented from sticking in the high risk state by optimizing the freezing parameters, improving the product quality and production efficiency.

[0059] In some embodiments, an implementation of a powder pellet rapid freezing anti-sticking treatment method is provided, and the adaptive freezing anti-sticking strategy generation step based on the particle size uniformity data in the powder pellet production process focuses on how to determine the sticking state and optimize the freezing parameters to generate a freezing treatment scheme to prevent the powder pellet from sticking by analyzing the ratio of the surface viscosity difference value to the particle size uniformity difference value.

[0060] Specifically, the ratio of the second difference to the fourth difference is calculated.

[0061] It can be understood that the ratio of the second difference value (e.g. 0.45 Pa·s) calculated based on the surface viscosity data of the powder pellets measured by the viscosity detection device and the fourth difference value (e.g. 0.3 mm) obtained by comparing the particle size uniformity data measured by the particle size detection equipment with the preset particle size uniformity threshold (e.g. standard deviation 0.2 mm) is calculated. Further, the ratio is calculated by dividing the second difference value by the fourth difference value, for example, 0.45 ÷ 0.3 = 1.5. It can be understood that the calculation is performed in the automatic control unit, and the particle size uniformity data obtained by the optical particle size analyzer ensures that the ratio accurately reflects the combined influence of surface viscosity and particle size uniformity on the risk of adhesion.

[0062] Further, if the ratio exceeds the preset range, it is determined that the adhesion state of the powder pellets is a particle size dominant state.

[0063] Specifically, the preset ratio range is set to 0.8-1.2 according to experimental data, and if the calculated ratio (e.g. 1.5) exceeds this range, it is determined that the adhesion state of the powder pellets is a particle size dominant state. Further, the particle size dominant state indicates that the particle size non-uniformity of the powder pellets significantly affects adhesion, for example, larger particle size powder pellets are more likely to adhere due to increased contact area. It can be understood that this determination is completed by a preset logical algorithm, which quickly identifies the adhesion risk caused by particle size non-uniformity and provides targeted basis for subsequent freezing parameter optimization.

[0064] Further, according to the particle size dominant state, the airflow partition and injection rate of the freezing device are optimized to generate a second freezing treatment scheme.

[0065] Specifically, in the particle size dominant state, the airflow partition of the freezing device is adjusted according to the size of the fourth difference value (particle size uniformity deviation), for example, when the particle size deviation is 0.3 mm, the freezing chamber is divided into upper and lower airflow zones, and the lower airflow intensity is enhanced for larger particle size powder pellets. Further, the airflow injection rate is adjusted according to the second difference value (viscosity deviation), for example, the injection rate is increased from 2 m / s to 3 m / s to enhance the separation effect of the freezing airflow on the surface of the powder pellets. It can be understood that the optimized airflow partition and injection rate parameters are integrated into the second freezing treatment scheme, which is applied to the freezing device through the control unit to ensure effective reduction of the adhesion risk caused by particle size non-uniformity.

[0066] Further, the second freezing treatment scheme is used as the freezing treatment scheme for the powder pellets.

[0067] Specifically, the second freezing process scheme including optimized airflow partition (e.g. upper and lower partitions) and jet speed (e.g. 3 m / s) is selected as the final scheme for powder pellet freezing. Further, the scheme is transmitted to the freezing device through the control unit to adjust the airflow distribution valve and fan speed to achieve the specified parameters. Understandably, the implementation of the scheme effectively prevents powder pellet adhesion by precise airflow control for particle size dominant state, improving freezing efficiency and product quality.

[0068] In some embodiments, an implementation of a powder pellet rapid freezing anti-adhesion processing method is provided, and the adaptive freezing anti-adhesion strategy generation step based on two groups of humidity and viscosity data in the powder pellet production process focuses on how to determine the compound risk state and optimize the freezing parameters to generate the freezing process scheme to prevent powder pellet adhesion through difference ratio analysis and cross-group comparison.

[0069] Specifically, the ratio of the first difference and the second difference of one of the groups is calculated.

[0070] Understandably, based on the two groups of powder pellet surface humidity data (first parameter) and surface viscosity data (second parameter) collected by the humidity sensor and viscosity detection device, the ratio of the first difference (humidity deviation, e.g. 10%) and the second difference (viscosity deviation, e.g. 0.45 Pa·s) is calculated for one of the groups (e.g. the first group). Further, the ratio is obtained by dividing the first difference by the second difference, e.g. 10 ÷ 0.45 ≈ 22.22. Understandably, the calculation is performed in the automated control unit based on the average deviation value of multiple measurements of the first group to ensure that the ratio accurately reflects the relative influence between humidity and viscosity, providing a basis for subsequent risk state determination.

[0071] Further, if the ratio exceeds the preset range, the first difference of one of the groups is compared with the first difference of the other group.

[0072] Specifically, the preset ratio range is set to 15-20 according to experimental data, and if the calculated ratio (e.g. 22.22) exceeds this range, the first difference of the first group (e.g. 10%) is further compared with the first difference of the second group (e.g. 8%). Further, the comparison is completed by calculating the absolute difference of the two groups of humidity deviation (e.g. 10% - 8% = 2%), to determine the difference in humidity characteristics between the two groups of data. Understandably, the comparison process is automatically executed through a preset algorithm, quickly identifying whether the humidity change leads to a complex adhesion risk, providing support for the determination of the compound risk state.

[0073] Further, if different, the adhesion state of the powder pellet is determined as a compound risk state.

[0074] Specifically, if the first difference between the first group and the second group is different (e.g., 2% is greater than the preset threshold 0.5%), it is determined that the agglomeration state of the powder ball is a complex risk state. Further, the complex risk state indicates that the interaction of the surface humidity and viscosity of the powder ball and the humidity difference between batches jointly cause a higher agglomeration risk, for example, different batches of powder balls intensify agglomeration due to humidity fluctuations. Understandably, this determination is based on logical rules to ensure accurate identification of complex agglomeration risk scenarios and provide targeted basis for subsequent freezing parameter adjustment.

[0075] Further, according to the complex risk state, the freezing temperature stratification and air flow circulation frequency are adjusted to generate a third freezing processing scheme.

[0076] Specifically, in the complex risk state, according to the ratio of the first difference and the second difference (e.g., 22.22), the freezing chamber is divided into a high temperature zone (-20°C) and a low temperature zone (-30°C) for temperature stratification to adapt to the freezing needs of powder balls of different humidity. Further, according to the difference (e.g., 2%) between the first differences of the two groups, the air flow circulation frequency is adjusted, for example, from 1 time / minute to 2 times / minute, to enhance the air flow uniformity in the freezing chamber and reduce humidity-induced agglomeration. Understandably, the adjusted temperature stratification and air flow circulation frequency parameters are integrated into the third freezing processing scheme, which is applied to the freezing equipment to ensure effective anti-agglomeration for the complex risk.

[0077] Further, the third freezing processing scheme is used as the freezing processing scheme for the powder ball.

[0078] Specifically, the third freezing processing scheme containing temperature stratification (e.g., high temperature zone -20°C, low temperature zone -30°C) and air flow circulation frequency (e.g., 2 times / minute) is used as the final scheme for powder ball freezing. Further, the scheme is transmitted to the freezing equipment through the control unit to adjust the refrigeration unit and air flow circulation device to achieve the specified parameters. Understandably, this scheme effectively deals with the complex agglomeration risk through precise temperature stratification and air flow control, improving the efficiency of powder ball freezing and product quality.

[0079] In some embodiments, an implementation of a powder ball rapid freezing anti-agglomeration processing method is provided, which focuses on how to optimize freezing parameters to generate a first freezing processing scheme to prevent powder ball agglomeration according to humidity, viscosity and temperature deviation in a high risk state for the step of adjusting freezing rate and freezing time in the powder ball production process.

[0080] Specifically, according to the fluctuation rate of the first difference, the upper limit of the freezing rate is determined.

[0081] It is understood that the first difference (humidity deviation, for example, 10%) calculated based on the surface humidity data of the powder ball collected by the humidity sensor is used to calculate the volatility rate. The volatility rate is calculated by the standard deviation of the first difference measured multiple times within 10 seconds, for example, the volatility rate is 6%. Further, according to the size of the volatility rate, the upper limit of the freezing rate is determined, if the volatility rate exceeds the preset range ±5% (for example, 6%), the upper limit of the freezing rate is set to 3℃ / min instead of the standard 5℃ / min, to slow down the rapid freezing caused by the fluctuation of humidity to reduce the risk of adhesion. It is understood that this process is completed by an automated algorithm, and the volatility rate analysis ensures that the upper limit of the freezing rate adapts to the dynamic changes of the surface humidity of the powder ball, reducing the risk of adhesion.

[0082] Further, according to the second difference and the third difference, an adjustment coefficient of the freezing time is calculated.

[0083] Specifically, based on the second difference (viscosity deviation, for example, 0.45 Pa·s) measured by the viscosity detection device and the third difference (temperature deviation, for example, 7℃) measured by the temperature sensor, the adjustment coefficient of the freezing time is calculated. Further, the adjustment coefficient is obtained by weighted average of the second difference and the third difference (for example, the weights are 0.6 and 0.4 respectively), such as 0.6x0.45+0.4x7=3.07. The adjustment coefficient is used to prolong the freezing time, for example, multiply the standard time 10 minutes by the coefficient (3.07 / 2) and take the integer, about 15 minutes. It is understood that this calculation process is performed by a preset algorithm, which ensures that the freezing time is reasonably prolonged according to the viscosity and temperature deviation, enhancing the anti-adhesion effect.

[0084] Further, the upper limit of the freezing rate and the adjustment coefficient are applied to the control parameters of the freezing equipment to generate the first freezing treatment scheme.

[0085] Specifically, the determined upper limit of the freezing rate (for example, 3℃ / min) and the adjusted freezing time (for example, 15 minutes) are integrated into the control parameters of the freezing equipment. Further, these parameters are converted into specific instructions of the freezing equipment by the control unit, for example, adjusting the compressor power to achieve a freezing rate of 3℃ / min, and setting a timer to ensure that the freezing lasts for 15 minutes. It is understood that the generated control parameters are integrated into the first freezing treatment scheme, which is directly applied to the freezing equipment, ensuring that the powder ball is effectively prevented from adhesion by slow freezing and prolonged freezing time in high-risk conditions.

[0086] Further, the first freezing treatment scheme is used as the freezing treatment scheme of the powder ball.

[0087] Specifically, the first freezing treatment scheme including the upper limit of freezing rate (3℃ / min) and freezing time (15 minutes) is taken as the final scheme for the freezing of the powder pellets. Further, the scheme is transmitted to the freezing equipment through the control unit to automatically adjust the equipment operating parameters to perform the freezing process. Understandably, the implementation of the scheme effectively reduces the risk of powder pellet adhesion and improves the freezing efficiency and product quality by precisely controlling the freezing rate and time for the high-risk state.

[0088] In some embodiments, an implementation of a powder pellet rapid freezing anti-adhesion treatment method is provided, which focuses on how to optimize the freezing parameters according to the composite risk state to generate a third freezing treatment scheme to prevent powder pellet adhesion for the step of adjusting the freezing temperature stratification and air flow circulation frequency in the powder pellet production process.

[0089] Specifically, the freezing time window is determined according to the composite risk state.

[0090] Further, based on the composite risk state confirmed by the ratio analysis and cross-group comparison (for example, the first group humidity and viscosity difference ratio is 22.22, and the two groups humidity difference difference is 2%), the appropriate freezing time window is determined. Further, according to the high-risk characteristics of the composite risk state, the freezing time window is set to 12-18 minutes to ensure that the powder pellets are fully frozen under the interaction of humidity fluctuation and viscosity. Understandably, this time window is determined by analyzing historical production data and the severity of the composite risk, for example, when the ratio exceeds 20 and the humidity difference is greater than 1%, a longer 18 minutes is preferred to ensure the anti-adhesion effect.

[0091] Further, the temperature stratification area of the freezing equipment is divided according to the ratio of the first difference and the second difference.

[0092] Specifically, based on the ratio (for example, 22.22) of the first difference (humidity deviation, for example, 10%) and the second difference (viscosity deviation, for example, 0.45 Pa·s) of the first group, the temperature stratification area of the freezing equipment is divided. Further, if the ratio exceeds the preset range of 15-20, the freezing chamber is divided into a high-temperature zone (-20℃) and a low-temperature zone (-30℃), the high-temperature zone is used to process high-humidity powder pellets to avoid rapid freezing leading to adhesion, and the low-temperature zone is used for high-viscosity powder pellets to ensure complete freezing. Understandably, the division is achieved by adjusting the power of the refrigeration unit in the freezing equipment, for example, the upper half of the freezing chamber is maintained at -20℃, and the lower half is maintained at -30℃, to ensure that the temperature distribution adapts to the adhesion characteristics of the powder pellets.

[0093] Further, the freezing time window and the temperature stratification area are integrated to generate the third freezing treatment scheme.

[0094] Specifically, the determined freezing time window (e.g. 18 minutes) and temperature stratification zone (e.g. high temperature zone -20℃, low temperature zone -30℃) are integrated into the control parameters of the freezing equipment. Further, these parameters are converted into specific instructions by the control unit, such as setting the operating temperature of the upper and lower refrigeration units of the freezing chamber, and configuring the timer to ensure freezing for 18 minutes. Understandably, the integrated parameters form a third freezing treatment scheme, which is directly applied to the freezing equipment and effectively addresses the adhesion problem under the complex risk state through stratified temperature control.

[0095] Further, the third freezing treatment scheme is used as the freezing treatment scheme for the powder ball.

[0096] Specifically, the third freezing treatment scheme containing the freezing time window (18 minutes) and temperature stratification zone (high temperature zone -20℃, low temperature zone -30℃) is used as the final scheme for powder ball freezing. Further, this scheme is transmitted to the freezing equipment by the control unit, which automatically adjusts the refrigeration unit and timer to perform the freezing process. Understandably, this scheme effectively prevents powder ball adhesion by precise temperature stratification and time control, and improves freezing efficiency and product quality.

[0097] In some embodiments, an implementation of a powder ball rapid freezing anti-adhesion processing method is provided, which focuses on how to transmit the generated freezing treatment scheme to the production line control system and adjust the freezing equipment parameters to achieve the anti-adhesion effect for the application steps of the freezing treatment scheme in the powder ball production process.

[0098] Specifically, the freezing treatment scheme is transmitted to the control system of the powder ball production line.

[0099] Understandably, the generated freezing treatment scheme (e.g. containing freezing rate 3℃ / min, freezing time 15 minutes, or temperature stratification zone -20℃ and -30℃) is transmitted to the central control system of the powder ball production line through a data interface. Further, the transmission process uses an industrial Ethernet protocol to ensure that the scheme data is quickly and stably transmitted from the generation module to the control system, and the data packet includes specific freezing parameters and execution instructions. Understandably, the scheme data is checked before transmission to confirm the integrity of the parameters, avoid transmission errors affecting the operation of the freezing equipment, and ensure that the scheme is accurately applied to the production line.

[0100] Further, the control system is controlled to adjust the freezing equipment parameters according to the freezing treatment scheme.

[0101] Specifically, after receiving the freezing treatment scheme, the central control system parses the parameters in the scheme (such as freezing rate, freezing time or temperature stratification setting), and converts them into control instructions for the freezing equipment. Further, for example, for a freezing rate of 3°C / min, the control system adjusts the compressor power of the freezing chamber to achieve gradual cooling; for a freezing time of 15 minutes, a timer is set to ensure that the freezing lasts for a specified duration; for temperature stratification (such as a high-temperature zone of -20°C and a low-temperature zone of -30°C), the operating temperature of the upper and lower refrigeration units of the freezing chamber is adjusted respectively. Understandably, the adjustment process is completed through the automation program of the control system, which monitors the equipment operating state in real time to ensure the accurate application of parameters, thereby effectively preventing the adhesion of the powder balls and improving the freezing efficiency and product quality.

[0102] The present application also provides a powder ball rapid freezing anti-adhesion treatment system 100, as shown in Figure 3 For the system function of realizing rapid freezing and anti-adhesion in the powder ball production process, the specific implementation of the parameter acquisition module 101, the risk assessment module 102 and the strategy generation module 103 is described in order to prevent the adhesion of the powder balls. The system 100 comprises:

[0103] The parameter acquisition module 101 is configured to acquire process parameters in the powder ball production process, including powder ball surface humidity, surface viscosity and freezing environment temperature.

[0104] Specifically, the parameter acquisition module 101 realizes data acquisition through the humidity sensor, viscosity detection device and temperature sensor deployed in the powder ball production line. The humidity sensor is installed above the conveying belt, uses non-contact infrared humidity measurement technology, samples once per second, and acquires powder ball surface humidity data, for example, the humidity value is in the range of 50%-80%. Further, the viscosity detection device uses a micro probe to contact the powder ball surface, measures the viscous resistance, and records the average viscosity value (such as 0.4-1.2 Pa·s). The temperature sensor is distributed in the upper, middle and lower regions of the freezing chamber, uses thermocouple technology, and records the freezing environment temperature (such as -35°C to -25°C) once every 10 seconds. Understandably, the collected data is transmitted to the processing unit in real time through the data acquisition module to ensure the accuracy and real-time nature of the parameters, providing a basis for subsequent risk assessment.

[0105] The risk assessment module 102 is configured to determine the adhesion risk level of the powder balls according to the comparison of the process parameters with the preset anti-adhesion threshold.

[0106] Specifically, the risk assessment module 102 receives the humidity, viscosity and temperature data from the parameter collection module 101, and compares them with the preset anti-adhesion threshold values (humidity 60%-70%, viscosity 0.5-1.0 Pa·s, temperature -30℃ to -20℃). Further, the module calculates the absolute deviation of each parameter from the median value of the threshold (e.g. humidity deviation 10%, viscosity deviation 0.45 Pa·s, temperature deviation 7℃), and obtains a comprehensive deviation value by weighted calculation (weights: humidity 0.4, viscosity 0.4, temperature 0.2), for example, 0.4x10+0.4x0.45+0.2x7=5.58. The comprehensive deviation value determines the adhesion risk level according to the range (less than 3 is low risk, 3-6 is medium risk, and greater than 6 is high risk), and in this example, it is high risk. Understandably, this process is automatically completed by the embedded algorithm module, and the evaluation result is stored as the basis for subsequent strategy generation.

[0107] The strategy generation module 103 is configured to generate an adaptive freezing anti-adhesion strategy to form a freezing treatment scheme according to the process parameters and real-time freezing environment data when the adhesion risk level meets the freezing trigger condition.

[0108] Specifically, the strategy generation module 103 is triggered when the adhesion risk level is high risk or the humidity deviation fluctuation rate exceeds ±5%, and adjusts the freezing parameters according to the real-time process parameters and freezing environment data. Further, for example, in the high risk state, the module reduces the freezing rate to 3℃ / min according to the humidity deviation of 10%, prolongs the freezing time to 15 minutes in combination with the viscosity and temperature deviation, and optimizes the air flow distribution to ensure the uniformity of freezing. The generated freezing treatment scheme includes specific parameters (such as freezing rate 3℃ / min, time 15 minutes). Understandably, this scheme is integrated and transmitted to the freezing equipment control unit through the algorithm, ensuring precise anti-adhesion for high risk state, improving the efficiency of powder ball freezing and product quality.

[0109] The above only describes exemplary embodiments of the present application, and does not limit the patent scope of the present application. Any equivalent structural transformation made by using the content of the present application specification and drawings, or direct / indirect application in other related technical fields is included in the patent protection scope of the present application.

Claims

1. A method for preventing the adhesion of a frozen powder ball, characterized by, The application is applied to the production and freezing process of powder balls, comprising: obtaining process parameters in the production process of powder balls, wherein the process parameters at least include the surface humidity, surface viscosity and freezing environment temperature of the powder balls; determining the adhesion risk level of the powder balls according to the comparison between the process parameters and the preset adhesion prevention threshold value; when the adhesion risk level meets the freezing trigger condition, generating an adaptive freezing anti-adhesion strategy according to the process parameters and real-time freezing environment data to obtain a freezing treatment scheme.

2. The quick freezing anti-blocking treatment method of the powder ball according to claim 1, characterized by, The process of obtaining the process parameters in the production process of powder balls comprises the following steps: deploying a humidity sensor on the powder ball production equipment to measure the surface humidity of the powder balls in real time and record it as the first parameter; measuring the surface viscosity of the powder balls by using a viscosity detection device and recording it as the second parameter; monitoring the freezing environment temperature by using a temperature sensor and recording it as the third parameter.

3. The quick freezing anti-blocking treatment method of the powder ball according to claim 2, characterized by, The determination of the adhesion risk level of the powder balls according to the comparison between the process parameters and the preset adhesion prevention threshold value comprises: comparing the first parameter with the preset humidity threshold value to obtain a first difference value; comparing the second parameter with the preset viscosity threshold value to obtain a second difference value; comparing the third parameter with the preset temperature threshold value to obtain a third difference value; determining the adhesion risk level according to the weighted calculation of the first difference value, the second difference value and the third difference value, wherein the adhesion risk level includes low risk, medium risk and high risk.

4. The quick freezing anti-blocking treatment method of the powder ball according to claim 3, characterized by, The freezing trigger condition is that the adhesion risk level is high risk or the fluctuation rate of the first difference value exceeds the preset range, and the generation of the adaptive freezing anti-adhesion strategy to obtain the freezing treatment scheme comprises: determining whether the adhesion risk level is high risk or the fluctuation rate of the first difference value exceeds the preset range; if any condition is met, determining that the adhesion state of the powder balls is a high risk state; adjusting the freezing rate and freezing time according to the high risk state to generate a first freezing treatment scheme; taking the first freezing treatment scheme as the freezing treatment scheme of the powder balls.

5. The method of quick freezing and anti-blocking treatment of pellets according to claim 3, characterized in that, The process parameters further include the particle size uniformity data of the powder balls, the freezing trigger condition is that the ratio of the second difference value to the fourth difference value exceeds the preset range, wherein the fourth difference value is obtained by comparing the particle size uniformity data of the powder balls with the preset particle size uniformity threshold value, and the generation of the adaptive freezing anti-adhesion strategy to obtain the freezing treatment scheme comprises: calculating the ratio of the second difference value to the fourth difference value; if the ratio exceeds the preset range, determining that the adhesion state of the powder balls is a particle size dominant state; optimizing the airflow partition and injection rate of the freezing equipment according to the particle size dominant state to generate a second freezing treatment scheme; taking the second freezing treatment scheme as the freezing treatment scheme of the powder balls.

6. The quick freezing anti-blocking treatment method of the powder ball according to claim 3, characterized by, The process parameters include two groups of surface humidity data and surface viscosity data of the powder balls, the freezing trigger condition is that the ratio of the first difference value to the second difference value of one group exceeds the preset range and is different from the first difference value of the other group, and the generation of the adaptive freezing anti-adhesion strategy to obtain the freezing treatment scheme comprises: calculating the ratio of the first difference value to the second difference value of the one group; If the ratio is out of the preset range, compare the first difference value of one group with the first difference value of another group; If different, determine the agglomeration state of the powder as a complex risk state; According to the complex risk state, adjust the freezing temperature stratification and air flow circulation frequency to generate a third freezing treatment scheme; The third freezing treatment scheme is used as the freezing treatment scheme of the powder.

7. The quick freezing anti-blocking treatment method of the powder ball according to claim 4, characterized by, The process of adjusting the freezing rate and freezing time includes: According to the fluctuation rate of the first difference value, determine the upper limit of the freezing rate; According to the second difference value and the third difference value, calculate the adjustment coefficient of the freezing time; Apply the freezing rate upper limit and adjustment coefficient to the freezing equipment control parameters to generate the first freezing treatment scheme.

8. The quick freezing anti-blocking treatment method of the powder ball according to claim 6, characterized by, The process of adjusting the freezing temperature stratification and air flow circulation frequency includes: According to the complex risk state, determine the freezing time window; According to the ratio of the first difference value and the second difference value, divide the temperature stratification area of the freezing equipment; Integrate the freezing time window and temperature stratification area to generate the third freezing treatment scheme.

9. The quick freezing anti-blocking treatment method of pellets according to any one of claims 1 to 8, characterized in that, After generating the freezing treatment scheme, it also includes: Transmit the freezing treatment scheme to the control system of the powder production line; Control the control system to adjust the freezing equipment parameters according to the freezing treatment scheme.

10. A system for the quick freezing anti-blocking treatment of pellets, characterized in that it comprises: The system for realizing the powder quick freezing anti-adhesion treatment method of claims 1-9 includes: A parameter acquisition module configured to obtain process parameters in the powder production process, including powder surface humidity, surface viscosity and freezing environment temperature; A risk assessment module configured to determine the agglomeration risk level of the powder according to the comparison of the process parameters with the preset anti-adhesion threshold value; A strategy generation module configured to generate an adaptive freezing anti-adhesion strategy to form a freezing treatment scheme according to the process parameters and real-time freezing environment data when the agglomeration risk level meets the freezing trigger condition.

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