Drying process optimization method of selenium-rich tea

Abstract

The invention relates to the technical field of selenium-rich tea leaves, and discloses a drying process optimization method of selenium-rich tea leaves, which comprises the following steps: step 1, sensor configuration; 2, a data acquisition system; step 3, controlling the system; step 4, dynamic adjustment; 5, establishing a database; and step 6, performance evaluation. Data of the temperature T, the humidity H and the airflow velocity V in the tea leaf drying process are collected in real time through the sensors, the current drying state can be known in time, the control system can rapidly respond based on the actual situation through real-time monitoring, the stability of the drying environment is kept, and the drying efficiency is improved. The control system can carry out feedback adjustment according to the set target temperature and humidity, that is, the control output uT (t) and uH (t) can be adjusted according to the difference delta T and delta H between the current state and the target state, fluctuation possibly occurring in the drying process is effectively reduced, the consistency of the drying effect is ensured, and the drying efficiency is improved. And the beneficial effect of optimizing the drying process of the selenium-enriched tea industry and enabling the selenium-enriched tea to be relatively dry is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of selenium-enriched tea leaves, and in particular to a drying process optimization method for selenium-enriched tea leaves. Background Art

[0002] Drying of selenium-enriched tea is one of the key steps in the production process, which directly affects the quality, taste and retention of selenium in tea. The purpose of drying is to remove moisture from tea leaves to reduce the moisture content of tea leaves to a safe range (generally below 5%) while maintaining the color, aroma, taste and selenium-enriched characteristics of tea leaves.

[0003] At present, the drying of selenium-rich tea is achieved through electric heating rotary drying equipment, such as a selenium-rich tea efficient drying device with publication number CN218955359 U, including a base and a collection box, the surface of the base is fixedly connected to the bottom of the collection box, the surface of the base is fixedly connected to the end of the vertical plate, the outer wall of the vertical plate at the right end is fixedly connected to the outer wall of the connecting plate, and the outer wall of the vertical plate at the right end is provided with a drying device. The motor in the drying device drives the connecting rod to rotate, the connecting rod drives the first toothed belt pulley to rotate, the first toothed belt pulley drives the toothed belt to rotate, the toothed belt drives the second toothed belt pulley to rotate, and the second toothed belt pulley drives the connecting column at the right end to rotate, and the selenium-rich tea is stirred and dried, and the selenium-rich tea will not pile up, the heat is even, the drying time is shortened, and the efficiency of drying the selenium-rich tea will not be affected.

[0004] Then, drying tea leaves by electric rotary drying cannot accurately control the drying temperature of the tea leaves. If the temperature is too high during the drying process, the nutritional components of the tea leaves will be destroyed, especially the loss of selenium and other beneficial ingredients, thus affecting the health effects of the tea leaves. If the temperature is too low, the drying may not be thorough, and the residual moisture may cause the tea leaves to mold or deteriorate easily. At the same time, this type of rotary drying equipment is generally a closed structure, relying on an exhaust structure to remove moisture generated by heating. However, after the exhaust structure extracts, it is impossible to know the humidity inside the drying equipment. Excessive environmental humidity leads to low drying efficiency and increases the risk of mold growth. If the humidity is too low, the tea leaves will become brittle and hard, affecting the taste. Summary of the invention

[0005] Technical issues solved:

[0006] In view of the deficiencies in the prior art, the present invention provides a method for optimizing the drying process of selenium-rich tea, which has the advantages of optimizing the drying process of selenium-rich tea to make it drier and increasing the selenium content when drying selenium-rich tea, thereby solving the problems of the above-mentioned technology.

[0007] Technical solution:

[0008] To achieve the above object, the present invention provides the following technical solution: a method for optimizing the drying process of selenium-enriched tea leaves, comprising the following steps:

[0009] Step 1: Sensor configuration: Install temperature, humidity sensors and air flow velocity monitors in the dryer to monitor the drying process in real time;

[0010] Step 2: Data acquisition system: Use the data acquisition system to transmit the sensor data to the computer in real time for data recording and analysis;

[0011] Step 3. Control system: Combined with embedded control system, automatically adjust temperature and humidity;

[0012] Step 4: Dynamic adjustment: According to the real-time monitoring data, dynamically adjust the drying temperature and time: if the humidity is too high, increase the temperature; if the tea leaves dry too fast, reduce the temperature;

[0013] Step 5: Establish a database: store the data collected during the test: temperature, humidity, time and final product quality into the database;

[0014] Step 6. Performance evaluation: Analyze the impact of different parameter combinations on tea quality and find the best drying process parameter combination.

[0015] Preferably, the sensor configuration expression in step 1 is:

[0016] T(t) = real-time temperature measured by the temperature sensor;

[0017] H(t) = real-time humidity measured by the humidity sensor;

[0018] V(t) = real-time air flow velocity measured by the air flow velocity monitor;

[0019] T(t)

[0020] H(t)→Data acquisition system.

[0021] V(t)

[0022] Preferably, the data record calculation in step 2 is:

[0023]

[0024] Where: D(t) is the sensor data, which means the data obtained by the sensor at time t, including temperature T(t), humidity H(t) and air flow velocity V(t).

[0025] Preferably, in step 2, the data collection system performs data analysis and calculation as follows:

[0026]

[0027] Where: n is the number of collected data points, D(t i ) is the time at each point in time t i The sensor data collected, Represents the mean of the data.

[0028] Preferably, the data analysis in step 2 further includes regression analysis of temperature and humidity influencing factors, and the regression analysis is calculated as follows:

[0029] Y=a0+a1T(t)+a2H(t)+∈;

[0030] Where: Y is the dry mass of tea, a0, a1, and a2 are all parameters to be estimated, and ∈ is the error term.

[0031] Preferably, the control system model in step 3 is composed of control target, control output calculation, dynamic system response and combined feedback control;

[0032] The control target is calculated as:

[0033] ΔT(t)=T target -T(t);

[0034] ΔH(t)=H target -H(t);

[0035] Where: The target temperature is H target and target humidity T target , actual temperature T(t) and real-time humidity H(t) measured by the humidity sensor, ΔT(t) represents the temperature error at time t, and ΔH(t) represents the humidity error at time t;

[0036] The control output is calculated as: Based on the control error, the proportional control parameter K is used T : Temperature control gain and K H Humidity control gain to regulate temperature and humidity:

[0037] u T (t) = K T ΔT(t);

[0038] u H (t) = K H ΔH(t);

[0039] Where: u T (t) represents the control output for adjusting the temperature, u H (t) represents the control output for adjusting humidity;

[0040] The dynamic system response is calculated as follows: The temperature and humidity adjusted by the controller are represented by the dynamic equation:

[0041] T(t+1)=T(t)+u T (t);

[0042] H(t+1)=H(t)+u H (t);

[0043] The combined feedback control calculation is: In order to achieve closed-loop control, a correction term including feedback is included:

[0044] T(t+1)=T(t)+K T ·(T target -T(t));

[0045] H(t+1)=H(t)+K H ·(H target -H(t)).

[0046] Preferably, the dynamic adjustment calculation in step 4 is as follows:

[0047] ΔH(t)=H(t)-H threhold ;

[0048] Where: H threhold is the humidity threshold;

[0049] If the humidity is above the threshold, the temperature needs to be increased:

[0050] if=ΔH(t)>0then(t+1)=T(t)+ΔT increase

[0051] Where: ΔT increase is the magnitude of the temperature increase.

[0052] Preferably, the drying speed monitoring and adjustment calculation in step 4 is:

[0053] ΔV(t)=V(t)-V threshold

[0054] if=ΔV(t)>0then(t+1)=T(t)+ΔT decrease

[0055] Where: ΔT decrease is the temperature drop.

[0056] Preferably, the quality index calculation defined in step 6 is:

[0057] Q=f(T,H,t,V)

[0058] Among them: Q is the tea quality index; T is temperature; H is humidity; t is drying time; V is drying speed; f is a nonlinear mathematical model used to analyze which parameter combinations can maximize the quality of tea.

[0059] Preferably, the optimization target calculation constructed in step 6 is:

[0060] The optimization goal is set to maximize the tea quality index Q: MaxQ = f(T,H,t,V);

[0061] The constraints are:

[0062] T min ≤T≤T max ;

[0063] H min ≤H≤H max ;

[0064] t min ≤t≤t max ;

[0065] V min ≤V≤V max ;

[0066] Where: T min is the minimum allowable drying temperature, T max is the maximum allowable drying temperature; H min The minimum humidity allowed is H max is the maximum allowable humidity; t min is the minimum drying time allowed; t max is the maximum drying time allowed; V min Minimum drying speed allowed; V max is the maximum drying speed allowed.

[0067] Compared with the prior art, the present invention provides a method for optimizing the drying process of selenium-enriched tea leaves, which has the following beneficial effects:

[0068] 1. The present invention collects data of temperature T, humidity H and air flow velocity V in real time during the tea drying process through sensors, so as to timely understand the current drying state. Real-time monitoring enables the control system to respond quickly based on the actual situation and maintain the stability of the drying environment. The control system can make feedback adjustments according to the set target temperature and humidity, that is, control the output u T (t) and u H (t) will be adjusted according to the gap ΔT and ΔH between the current state and the target state, effectively reducing the fluctuations that may occur during the drying process and ensuring the consistency of the drying effect. thresholdIf the humidity is detected to be too high, the system will automatically increase the temperature to accelerate the evaporation of water. Conversely, if the drying speed is too fast ΔV>0, the temperature can be lowered in time to avoid excessive drying of the tea or deterioration in quality, thereby achieving the beneficial effect of optimizing the drying process of selenium-rich tea and making it drier.

[0069] 2. The present invention adopts a real-time monitoring system to dynamically adjust the drying conditions, temperature, humidity, time and air flow speed, and can accurately control the drying process according to the real-time status of the tea. By establishing a data recording and analysis system, the selenium content in each batch of dried tea can be monitored, providing a basis for subsequent process analysis and improvement. The continuous feedback and optimization process can gradually increase the selenium content of each batch of tea, and form a virtuous circle in production, achieving the beneficial effect of increasing the selenium content when drying selenium-enriched tea. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a schematic diagram of the process steps of the present invention; DETAILED DESCRIPTION

[0071] 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.

[0072] See also Figure 1 The drying process optimization method of selenium-enriched tea leaves comprises the following steps:

[0073] Step 1: Sensor configuration: Install temperature, humidity sensors and air flow velocity monitors in the dryer to monitor the drying process in real time;

[0074] Step 2: Data acquisition system: Use the data acquisition system to transmit the sensor data to the computer in real time for data recording and analysis;

[0075] Step 3. Control system: Combined with embedded control system, automatically adjust temperature and humidity;

[0076] Step 4: Dynamic adjustment: According to the real-time monitoring data, dynamically adjust the drying temperature and time: if the humidity is too high, increase the temperature; if the tea leaves dry too fast, reduce the temperature;

[0077] Step 5: Establish a database: store the data collected during the test: temperature, humidity, time and final product quality into the database;

[0078] Step 6. Performance evaluation: Analyze the impact of different parameter combinations on tea quality and find the best drying process parameter combination.

[0079] By adopting a real-time monitoring system and dynamically adjusting the drying conditions, temperature, humidity, time and air flow speed, the drying process can be accurately controlled according to the real-time status of the tea. By establishing a data recording and analysis system, the selenium content in each batch of dried tea can be monitored, providing a basis for subsequent process analysis and improvement. The continuous feedback and optimization process can gradually increase the selenium content of each batch of tea and form a virtuous circle in production.

[0080] Specifically, the sensor configuration expression in step 1 is:

[0081] T(t) = real-time temperature measured by the temperature sensor;

[0082] H(t) = real-time humidity measured by the humidity sensor;

[0083] V(t) = real-time air flow velocity measured by the air flow velocity monitor;

[0084] T(t)

[0085] H(t)→Data acquisition system.

[0086] V(t)

[0087] Specifically, the data record calculation in step 2 is:

[0088]

[0089] Where: D(t) is the sensor data, which means the data obtained by the sensor at time t, including temperature T(t), humidity H(t) and air flow velocity V(t).

[0090] Dynamic data collection:

[0091] Through real-time measurement of temperature sensors, humidity sensors and air flow velocity monitors, T(t), H(t, V(t), the environmental status is obtained in real time during the drying process, which can promptly reflect the changes in the drying process and ensure that the drying conditions are always in the best state.

[0092] Specifically, in step 2, the data collection system performs data analysis and calculation as follows:

[0093]

[0094] Where: n is the number of collected data points, D(t i ) is the time at each point in time t i The sensor data collected, Represents the mean of the data.

[0095] Specifically, the data analysis in step 2 also includes regression analysis of temperature and humidity influencing factors, and the regression analysis calculation is:

[0096] Y=a0+a1T(t)+a2H(t)+∈;

[0097] Where: Y is the dry mass of tea, a0, a1, and a2 are all parameters to be estimated, and ∈ is the error term.

[0098] Specifically, the control system model in step 3 is composed of control target, control output calculation, dynamic system response and combined feedback control;

[0099] The control target is calculated as:

[0100] ΔT(t)=T target -T(t);

[0101] ΔH(t)=H target -H(t);

[0102] Where: The target temperature is H target and target humidity T target , actual temperature T(t) and real-time humidity H(t) measured by the humidity sensor, ΔT(t) represents the temperature error at time t, and ΔH(t) represents the humidity error at time t;

[0103] The control output is calculated as: Based on the control error, the proportional control parameter K is used T : Temperature control gain and K H humidity control gain to adjust temperature and humidity:

[0104] u T (t) = K T ΔT(t);

[0105] u H (t)= K H·ΔH(t);

[0106] Where: u T (t) represents the control output for adjusting the temperature, u H (t) represents the control output for adjusting humidity;

[0107] The dynamic system response is calculated as follows: The temperature and humidity adjusted by the controller are represented by the dynamic equation:

[0108] T(t+1)=T(t)+u T (t);

[0109] H(t+1)=H(t)+uH (t);

[0110] The combined feedback control calculation is: In order to achieve closed-loop control, a correction term including feedback is included:

[0111] T(t+1)=T(t)+K T ·(T target -T(t));

[0112] H(t+1)=H(t)+K H ·(H target -H(t)).

[0113] Specifically, the dynamic adjustment calculation in step 4 is as follows:

[0114] ΔH(t)=H(t)-H threshold ;

[0115] Where: H threshold is the humidity threshold;

[0116] If the humidity is above the threshold, the temperature needs to be increased:

[0117] if=ΔH(t)>0then(t+1)=T(t)+ΔT increase

[0118] Where: ΔT increase is the magnitude of the temperature increase.

[0119] Specifically, the drying speed monitoring and adjustment calculation in step 4 is:

[0120] ΔV(t)=V(t)-V threshold

[0121] if=ΔV(t)>0then(t+1)=T(t)+ΔT decrease

[0122] Where: ΔT decrease is the temperature drop.

[0123] Specifically, the quality index calculation defined in step 6 is:

[0124] Q=f(T,H,t,V)

[0125] Among them: Q is the tea quality index; T is temperature; H is humidity; t is drying time; V is drying speed; f is a nonlinear mathematical model used to analyze which parameter combinations can maximize the quality of tea.

[0126] Specifically, the optimization target calculation constructed in step 6 is:

[0127] The optimization goal is set to maximize the tea quality index Q: MaxQ = f(T,H,t,V);

[0128] The constraints are:

[0129] T min ≤T≤T max ;

[0130] H min ≤H≤H max ;

[0131] t min ≤t≤t max ;

[0132] V min ≤V≤V max ;

[0133] Where: T min is the minimum allowable drying temperature, T max is the maximum allowable drying temperature; H min The minimum humidity allowed is H max is the maximum allowable humidity; t min is the minimum drying time allowed; t max is the maximum drying time allowed; V min Minimum drying speed allowed; V max is the maximum drying speed allowed.

[0134] The sensor collects the data of temperature T, humidity H and air flow velocity V in real time during the tea drying process, and can timely understand the current drying status. Real-time monitoring enables the control system to respond quickly based on the actual situation and maintain the stability of the drying environment. The control system can make feedback adjustments according to the set target temperature and humidity, that is, control the output u T (t) and u H (t) will be adjusted according to the gap ΔT and ΔH between the current state and the target state, effectively reducing the fluctuations that may occur during the drying process and ensuring the consistency of the drying effect. threshold If the humidity is detected to be too high, the system will automatically increase the temperature to accelerate the evaporation of water. Conversely, if the drying speed is too fast (ΔV>0), the temperature can be lowered in time to avoid over-drying of the tea or deterioration of its quality.

[0135] 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 method for optimizing the drying process of selenium-enriched tea, characterized in that: The following steps are involved: Step 1: Sensor configuration: Install temperature, humidity sensors and air flow velocity monitors in the dryer to monitor the drying process in real time; Step 2: Data acquisition system: Use the data acquisition system to transmit the sensor data to the computer in real time for data recording and analysis; Step 3. Control system: Combined with embedded control system, automatically adjust temperature and humidity; Step 4: Dynamic adjustment: According to the real-time monitoring data, dynamically adjust the drying temperature and time: if the humidity is too high, increase the temperature; if the tea leaves dry too fast, reduce the temperature; Step 5: Establish a database: store the data collected during the test: temperature, humidity, time and final product quality into the database; Step 6. Performance evaluation: Analyze the impact of different parameter combinations on tea quality and find the best drying process parameter combination.

2. The drying process optimization method for selenium-enriched tea leaves according to claim 1, characterized in that: The sensor configuration expression in step 1 is: T(t) = real-time temperature measured by the temperature sensor; H(t) = real-time humidity measured by the humidity sensor; V(t) = real-time air velocity measured by the air velocity monitor; 3. The drying process optimization method for selenium-enriched tea leaves according to claim 2, characterized in that: The data record calculation in step 2 is: Where: D(t) is the sensor data, which means the data obtained by the sensor at time t, including temperature T(t), humidity H(t) and air flow velocity V(t).

4. The drying process optimization method for selenium-enriched tea leaves according to claim 3, characterized in that: In step 2, the data acquisition system performs data analysis and calculation as follows: Where: n is the number of collected data points, D(t i ) is the time at each point in time t i The sensor data collected, Represents the mean of the data.

5. The drying process optimization method for selenium-enriched tea leaves according to claim 4, characterized in that: The data analysis in step 2 also includes regression analysis of temperature and humidity influencing factors, and the regression analysis calculation is: Y=a0+a1T(t)+a2H(t)+∈; Where: Y is the dry mass of tea, a0, a1, and a2 are all parameters to be estimated, and ∈ is the error term.

6. The drying process optimization method for selenium-enriched tea leaves according to claim 5, characterized in that: The control system model in step 3 is composed of control target, control output calculation, dynamic system response and combined feedback control; The control target is calculated as: ΔT(t)=T target -T(t); ΔH(t)=H target -H(t); Where: The target temperature is H target and target humidity T target , actual temperature T(t) and real-time humidity H(t) measured by the humidity sensor, ΔT(t) represents the temperature error at time t, and ΔH(t) represents the humidity error at time t; The control output is calculated as: Based on the control error, the proportional control parameter K is used T : Temperature control gain and K H Humidity control gain to regulate temperature and humidity: u T (t)=K T ·ΔT(t); u H (t)=K H ·ΔH(t); Where: u T (t) represents the control output for adjusting the temperature, u H (t) represents the control output for adjusting humidity; The dynamic system response is calculated as follows: The temperature and humidity adjusted by the controller are represented by the dynamic equation: T(t+1)=T(t)+u T (t); H(t+1)=H(t)+u H (t); The combined feedback control calculation is: In order to achieve closed-loop control, a correction term including feedback is included: T(t+1)=T(t)+K T ·(T target -T(t)); H(t+1)=H(t)+K H ·(H target -H(t))。 7. The drying process optimization method for selenium-enriched tea leaves according to claim 6, characterized in that: The dynamic adjustment calculation in step 4 is: ΔH(t)=H(t)-H threshold ; Where: H threshold is the humidity threshold; If the humidity is above the threshold, the temperature needs to be increased: if=ΔH(t)>0then(t+1)=T(t)+ΔT increase Where: ΔT increase is the magnitude of the temperature increase.

8. The drying process optimization method for selenium-enriched tea leaves according to claim 7, characterized in that: The drying speed monitoring and adjustment calculation in step 4 is: ΔV(t)=V(t)-V threshold if=ΔV(t)>0then(t+1)=T(t)+ΔT decrease Where: ΔT decrease is the temperature drop.

9. The drying process optimization method for selenium-enriched tea leaves according to claim 8, characterized in that: The quality index calculation defined in step 6 is: Q=f(T,H,t,V) Among them: Q is the tea quality index; T is temperature; H is humidity; t is drying time; V is drying speed; f is a nonlinear mathematical model used to analyze which parameter combinations can maximize the quality of tea.

10. The drying process optimization method for selenium-enriched tea leaves according to claim 9, characterized in that: The optimization target calculation constructed in step 6 is: The optimization goal is set to maximize the tea quality index Q: MaxQ = f(T,H,t,V); The constraints are: T min ≤T≤T max ; H min ≤H≤H max ; t min ≤t≤t max ; In min ≤V≤V max ; Where: T min is the minimum allowable drying temperature, T max is the maximum allowable drying temperature; H min The minimum humidity allowed is H max is the maximum allowable humidity; t min is the minimum drying time allowed; t max is the maximum drying time allowed; V min Minimum drying speed allowed; V max is the maximum drying speed allowed.

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