Automatic prediction system for tobacco leaf loosening and moisture regain outlet temperature

Abstract

This invention discloses an automatic prediction system for the outlet temperature of loose and rehydrated tobacco leaves, including a data acquisition module, a prediction model module, a temperature prediction module, and a temperature control and early warning module. Based on the material mass conservation equation and energy conservation equation for the loose and rehydrated leaf stage, this system establishes a material balance analysis model for predicting the moisture content of the outlet leaves and an energy balance analysis model for predicting the outlet leaf temperature, respectively. By collecting data in real time, the system predicts the outlet temperature and displays it through a dynamic curve, achieving automatic prediction and real-time verification of the outlet temperature. This improves the accuracy of the process outlet temperature and the response speed of automatic temperature control, fundamentally overcoming the problems of high labor intensity, low efficiency, and delayed feedback associated with traditional manual monitoring and verification of outlet temperatures.

Description

Technical Field

[0001] This invention relates to the field of tobacco processing, and more specifically to an automatic prediction system for the outlet temperature of loosened and rehydrated tobacco leaves. Background Technology

[0002] Different processing temperatures have a significant impact on the sensory evaluation of tobacco leaves, meaning that processing temperature can significantly affect the gloss, aroma, harmony, off-flavors, irritation, and aftertaste of finished cigarettes. During cigarette production, by optimizing the best processing conditions, it is possible to retain the tobacco aroma to the greatest extent while balancing the moisture content of the tobacco leaves, and to volatilize the green, off-flavors and irritation in the tobacco leaves to varying degrees.

[0003] Different processing temperatures affect the chemical composition of tobacco leaves, including the content of alkaloids, polybasic acids and higher fatty acids, volatile acid compounds, sugar compounds, and aroma components. Alkaloids have a certain physiological stimulating effect on the human body. Nicotine is the most abundant alkaloid, accounting for over 95% of the total alkaloid content. Excessive nicotine content results in overly irritating tobacco leaves, while insufficient nicotine content leads to a weak flavor. Generally, tobacco leaves with a nicotine content between 1.5% and 3.5% are considered high-quality. The required nicotine content can be achieved by adjusting the temperature and moisture conditions during the production process. Sugar compounds are a key factor determining the quality of tobacco leaves, and different processing temperatures significantly influence their content during the tobacco processing. The content of aroma components in tobacco leaves is closely related to their aroma quality. Many factors influence the content of aroma components throughout the entire process from tobacco growth to processing into tobacco fibers. Temperature is a crucial factor affecting the Maillard reaction products during tobacco fiber production; the reaction temperature affects the types of reaction products, thus influencing the aroma. In addition, the content of other chemical components in tobacco leaves is also affected by the temperature conditions during the production process.

[0004] Temperature plays a crucial role in tobacco processing. However, in actual production, variations in tobacco leaf color, brand, and other factors can cause changes in the zero point of the moisture meter used for temperature measurement, leading to measurement errors. This necessitates manual calibration. The specific calibration method involves taking a sample 150mm after the outlet temperature detector once the production process has stabilized. The sample is then quickly placed in a plastic container, with the mercury bulb of a mercury thermometer positioned in the center. The container is then sealed, and the temperature reading on the detector is recorded at the time of sampling. After three minutes of temperature monitoring, the temperature reading is read and recorded. The thermometer should not be removed from the sample during the reading process.

[0005] The above steps were performed three times to obtain three sets of temperature measurement data. The average value displayed by the outlet temperature detector was compared with the actual average value measured by the mercury thermometer to calibrate the temperature detector. This calibration method requires manual verification, which is labor-intensive, inefficient, and results in delayed feedback. Therefore, it is necessary to research an automatic outlet temperature prediction and calibration system. Summary of the Invention

[0006] To address the aforementioned issues, this application provides an automatic prediction system for the outlet temperature of loose, rehydrated tobacco leaves. This invention is applicable to the tobacco processing process and is used for the automatic prediction and real-time verification of the outlet temperature of loose, rehydrated tobacco leaves. It can effectively improve the accuracy of the process outlet temperature, the verification efficiency, and the automatic control response speed.

[0007] The technical solution adopted by this invention to solve its technical problem is as follows:

[0008] Automatic temperature prediction system for loose, rehydrated tobacco leaves at the outlet, including

[0009] The data acquisition module is used to automatically collect model parameter data. The data acquisition scope includes parameters related to material mass conservation and energy conservation during the loosening and rehydration stage of the blades, such as moisture, circulating air temperature, temperature, equipment monitoring items, and environmental monitoring items. The data acquisition scope is shown in the table below:

[0010]

[0011] The prediction model module establishes a material balance analysis model for predicting the moisture content of the outlet leaf and an energy balance analysis model for predicting the temperature of the outlet leaf, based on the material mass conservation equation and energy conservation equation for the loose and rehydrated leaf stage.

[0012] The material balance analysis model includes

[0013] ① Main steam moisture balance

[0014] W C =W Ca +W Cb +W Cc ;

[0015] W Cb =W C -W Ca -W Cc This model is used to obtain the ejector steam mass flow rate W in Table 1. Cb ;

[0016] ② Moisture balance of hot air system

[0017] f(OP Ca ) = W Ca ; for W Ca The fitting function;

[0018] V G ×AH(T indoor )×RH indoor =(ΔV) E ×ΔAH(T E )×ΔRH E )+V Ea ×AH(T Ea )×RH Ea -W Ca ;

[0019] Where ΔV E ×ΔAH(T E )×ΔRH E To monitor changes in the detection interval of the circulating hot air system;

[0020] Further, we can conclude that:

[0021]

[0022] This model is used to obtain the fresh air intake volume V in Table 1. G ;

[0023] ③ Moisture balance of the dehumidification system

[0024] Fitting function: g(OP) D ) = V D ×AH(T D )×RH D y(OP) G ) = V G ;

[0025]

[0026] Further, the conversion coefficient of the ejected steam entering the blades was obtained. :

[0027]

[0028] ④ Moisture balance of the drum system

[0029]

[0030]

[0031] Further, we can conclude that:

[0032]

[0033] Transformation coefficients calculated for models ③ and ④ Compare the data to determine if there are other sources of leakage or emission.

[0034] Finally, the result is obtained by fitting the function.

[0035] ⑤ Export moisture content prediction model

[0036] Based on the material balance process for moisture content, a prediction model for the moisture content of the outlet leaves is obtained.

[0037]

[0038] The energy balance analysis model includes

[0039] ① Total Energy Balance of Hot Air System

[0040] Q4 + Q2 + Q3 = Q1;

[0041] in--

[0042] Compensation for heat released by steam Q1:

[0043]

[0044] Fresh air enters through the air damper, and the air is heated and absorbs heat (Q2).

[0045]

[0046] The heat Q3 discharged from the dehumidification branch of the hot air system:

[0047]

[0048] The heat exchanged between the heat exchange system and the drum tobacco is Q4:

[0049] Q4 = Q1 - Q2 - Q3;

[0050] Assume the heat exchange efficiency (COP) between the hot air system and the drum is:

[0051] This is the fitted function for temperature;

[0052] The amount of heat input into the drum blades by the hot air system is: COP × Q4;

[0053] ② Total Energy Balance of Drum System

[0054] Q5 + Q6 = Q7;

[0055] in--

[0056] The hot air system transfers heat Q5 into the blades:

[0057]

[0058] The heat entering the system from the water supply system is Q6:

[0059]

[0060] The heat absorbed by the blades in the drum as they heat up (Q7):

[0061]

[0062] C p,tl The specific heat capacity of tobacco leaves;

[0063]

[0064] Substituting the two sets of data into a system of equations, we can solve for C. p,tl and COP;

[0065] ③Exit blade temperature prediction model

[0066]

[0067] The temperature prediction module, based on the prediction model module and real-time data, predicts the outlet temperature in real time and displays it through a dynamic curve.

[0068] The temperature control and early warning module automatically adjusts the compensating steam opening when the predicted outlet temperature shows a temperature deviation; it automatically alarms and adjusts the compensating steam opening when the difference between the predicted and measured outlet temperature is ≥ ±5℃; and it shuts down the equipment for maintenance when the difference between the predicted and measured outlet temperature still does not meet the threshold requirement after automatic adjustment.

[0069] As an improvement to the above technical solution, after the data acquisition module completes the real-time acquisition of all parameters, it performs necessary data processing on the acquired data: anomalies are removed from the acquired data, and normal data is input into the prediction model module.

[0070] As an improvement to the above technical solution, the automatic outlet temperature prediction system also includes a model verification module, which compares and analyzes the predicted outlet temperature value with the measured value in real time / periodically to verify the accuracy of the prediction model.

[0071] The beneficial effects of this invention are as follows:

[0072] This invention can automatically predict and verify the outlet temperature of loose re-moistening, effectively improving the accuracy of the process outlet temperature and the response speed of automatic temperature control, fundamentally overcoming the problems of high labor intensity, low efficiency and delayed feedback in traditional manual monitoring and verification of outlet temperature.

[0073] The prediction model module establishes material balance analysis models and energy balance analysis models based on the material mass conservation equation and energy conservation equation during the loose and rehydrated blade stage, respectively. It realizes real-time prediction of production parameters by means of data flow relationship. The model has high accuracy and keeps in sync with feedback control. Multiple prediction models are set for different production grades to meet the differences between different grades and improve the universality of this automatic temperature prediction system.

[0074] This system is equipped with a model verification module, which can compare and analyze the predicted and measured values ​​of the outlet temperature in real time or periodically to verify the accuracy of the prediction model. It can also use historical production parameters and big data systems to perform error compensation / correction on the prediction model based on season, environment, etc., to improve the system's error prevention capability. Attached Figure Description

[0075] The present invention will be further described below with reference to the accompanying drawings and specific embodiments:

[0076] Figure 1 A block diagram of an automatic prediction system for the outlet temperature of loose, rehydrated tobacco leaves;

[0077] Figure 2 A schematic diagram showing the relationship between material mass and energy conservation data parameters during the loosening and rehydration stage of the blades;

[0078] Figure 3 The control flowchart is for an automatic prediction system of outlet temperature for loose, rehydrated tobacco leaves. Detailed Implementation

[0079] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0080] Example 1

[0081] Reference Figure 1 Automatic temperature prediction system for loose, rehydrated tobacco leaves at the outlet, including

[0082] The data acquisition module is used to automatically collect model parameter data. The data acquisition scope includes parameters related to material mass conservation and energy conservation, such as moisture, circulating air temperature, temperature, equipment monitoring items, and environmental monitoring items, during the loosening and rehydration stage of the blades (the stage requiring softened water). See Table 1 for details.

[0083] Table 1. Scope and methods of automatic parameter data acquisition by the data acquisition module

[0084]

[0085]

[0086]

[0087] After the data acquisition module completes the real-time acquisition of all parameters based on the parameter items in Table 1, the corresponding parameter acquisition location, and the acquisition instrument, the data acquisition module performs necessary data processing on the acquired data: it removes abnormal data such as interrupted flow and 3σ, and inputs normal data into the prediction model module.

[0088] The prediction model module establishes material balance analysis models for predicting the moisture content of the outlet leaves and energy balance analysis models for predicting the temperature of the outlet leaves, based on the material mass conservation equation and energy conservation equation for the loose and rehydrated leaf stage. Figure 2 This diagram illustrates the relationship between material mass and energy conservation data parameters during the loosening and rehydration stage of the blades.

[0089] The material balance analysis model includes

[0090] ① Main steam moisture balance

[0091] W C =W Ca +W Cb +W Cc ;

[0092] W Cb =W C -W Ca -W Cc This model is used to obtain the ejector steam mass flow rate W in Table 1. Cb ;

[0093] ② Moisture balance of hot air system

[0094] f(OP Ca ) = W Ca ; for W Ca The fitting function;

[0095] V G ×AH(T indoor )×RH indoor =(ΔV) E ×ΔAH(T E )×ΔRH E )+V Ea ×AH(T Ea )×RH Ea -W Ca ;

[0096] Where ΔV E ×ΔAH(TE )×ΔRH E To monitor changes in the detection interval of the circulating hot air system;

[0097] Further, we can conclude that:

[0098]

[0099] This model is used to obtain the fresh air intake volume V in Table 1. G ;

[0100] ③ Moisture balance of the dehumidification system

[0101] Fitting function: g(OP) D ) = V D ×AH(T D )×RH D y(OP) G ) = V G ;

[0102]

[0103] Further, the conversion coefficient of the ejected steam entering the blades was obtained. :

[0104]

[0105] ④ Moisture balance of the drum system

[0106]

[0107]

[0108] Further, we can conclude that:

[0109]

[0110] Transformation coefficients calculated for models ③ and ④ Compare the data to determine if there are other sources of leakage or emission.

[0111] Finally, the result is obtained by fitting the function.

[0112] ⑤ Export moisture content prediction model

[0113] Based on the material balance process for moisture content, a prediction model for the moisture content of the outlet leaves is obtained.

[0114]

[0115] The energy balance analysis model includes

[0116] ① Total Energy Balance of Hot Air System

[0117] Q4 + Q2 + Q3 = Q1;

[0118] in--

[0119] Compensation for heat released by steam Q1:

[0120]

[0121] Fresh air enters through the air damper, and the air is heated and absorbs heat (Q2).

[0122]

[0123] The heat Q3 discharged from the dehumidification branch of the hot air system:

[0124]

[0125] The heat exchanged between the heat exchange system and the drum tobacco is Q4:

[0126] Q4 = Q1 - Q2 - Q3;

[0127] Assume the heat exchange efficiency (COP) between the hot air system and the drum is:

[0128] This is the fitted function for temperature;

[0129] The amount of heat input into the drum blades by the hot air system is: COP × Q4;

[0130] ② Total Energy Balance of Drum System

[0131] Q5 + Q6 = Q7;

[0132] in--

[0133] The hot air system transfers heat Q5 into the blades:

[0134]

[0135] The heat entering the system from the water supply system is Q6:

[0136]

[0137] The heat absorbed by the blades in the drum as they heat up (Q7):

[0138]

[0139] C p,tl The specific heat capacity of tobacco leaves;

[0140]

[0141] Substituting the two sets of data into a system of equations, we can solve for C.p,tl and COP;

[0142] ③Exit blade temperature prediction model

[0143]

[0144] The temperature prediction module, based on the prediction model module and real-time data, predicts the outlet temperature in real time and displays it through a dynamic curve.

[0145] The temperature control and early warning module automatically adjusts the compensating steam opening when the predicted outlet temperature shows a temperature deviation; it automatically alarms and adjusts the compensating steam opening when the difference between the predicted and measured outlet temperature is ≥ ±5℃; and it shuts down the equipment for maintenance when the difference between the predicted and measured outlet temperature still does not meet the threshold requirement after automatic adjustment.

[0146] The model validation module compares and analyzes the predicted and measured values ​​of the outlet temperature in real time or periodically to verify the accuracy of the prediction model.

[0147] Figure 3 The control flowchart of this automatic prediction system for the outlet temperature of loose, rehydrated tobacco leaves is shown.

[0148] After receiving the production instruction, the system starts running and retrieves the grade information and corresponding prediction model. Data is collected in real time by acquisition instruments set up at various locations on the equipment, and abnormal data is removed. Normal data is automatically entered into the model prediction module for real-time prediction of outlet moisture and outlet temperature. The prediction results are displayed through a dynamic curve. The curve is tracked in real time and the data is compared and analyzed. When the predicted outlet temperature value shows a temperature deviation, the control system automatically adjusts the compensation steam opening to make up for the deviation. When the difference between the predicted outlet temperature value and the measured value is ≥±5℃ (this threshold is adjustable), the system automatically issues an abnormal alarm and automatically adjusts the compensation steam opening. If the difference meets the threshold requirement, production continues. If the difference between the predicted outlet temperature value and the measured value still does not meet the threshold requirement after automatic adjustment, the equipment is shut down for maintenance.

[0149] Example 2

[0150] An automatic prediction method for the outlet temperature of loose, rehydrated tobacco leaves, including...

[0151] Step 1 Data Collection

[0152] Automatically collect model parameter data;

[0153] The data collection scope includes parameters related to the loosening and rehydration of blades, such as moisture, circulating air temperature, temperature, equipment monitoring items, and environmental monitoring items, which are related to the conservation of material mass and energy.

[0154] Step 2: Building the Model

[0155] Based on the material mass conservation equation and energy conservation equation for the loose and rehydrated leaf stage, material balance analysis models for predicting the moisture content of the outlet leaf and energy balance analysis models for predicting the temperature of the outlet leaf are established respectively.

[0156] The material balance analysis model includes a main steam moisture balance operator model, a hot air system moisture balance operator model, a dehumidification system moisture balance operator model, a drum system moisture balance operator model, and an outlet moisture content prediction model; the energy balance analysis model includes a hot air system total energy balance operator model, a drum system total energy balance operator model, and an outlet blade temperature prediction model.

[0157] Step 3 Temperature Prediction

[0158] Based on the prediction model established in Step 2 and the real-time data collected in Step 1, the outlet temperature is predicted in real time and displayed through a dynamic curve.

[0159] Step 4 Temperature Control and Early Warning

[0160] When the predicted outlet temperature shows a temperature deviation, the steam opening is automatically adjusted to compensate.

[0161] When the difference between the predicted and measured outlet temperature is ≥ ±5℃, an alarm will be automatically triggered, and the compensating steam opening will be automatically adjusted.

[0162] If the difference between the predicted and measured outlet temperatures after automatic adjustment still does not meet the threshold requirement, the equipment will be shut down and repaired.

[0163] Step 5 Model Validation

[0164] The predicted and measured values ​​of the outlet temperature are compared and analyzed in real time or periodically to verify the accuracy of the prediction model.

[0165] When an abnormal alarm is triggered and the control system automatically adjusts the compensating steam opening, the data is manually verified and entered into the system for comparison with the predicted data. This operation is equivalent to adding an error prevention function to avoid prediction or adjustment errors.

[0166] This method utilizes information technology to input real-time collected data into a prediction model, thereby predicting the theoretical outlet moisture and temperature of the loose, rehydrated outlet blades in real time. By comparing this prediction with the actual online moisture and temperature, it can provide a basis for regulating steam volume, shifting from qualitative to quantitative control. It can also verify the accuracy of the online moisture meter.

[0167] This method can also be applied to the automatic prediction of outlet moisture and outlet temperature of loose, rehydrated sheets.

[0168] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An automatic prediction system for the outlet temperature of loose, rehydrated tobacco leaves, characterized in that: include The data acquisition module is used to automatically collect model parameter data. The scope of data acquisition includes parameters related to the material mass conservation and energy conservation during the loosening and rehydration stage of the blades, such as moisture, circulating air temperature, temperature, equipment detection items, and environmental detection items. The prediction model module establishes a material balance analysis model for predicting the moisture content of the outlet blades and an energy balance analysis model for predicting the temperature of the outlet blades, based on the material mass conservation equation and energy conservation equation during the loosening and rehydration stage of the blades. The material balance analysis model includes a main steam moisture balance operator model, a hot air system moisture balance operator model, a dehumidification system moisture balance operator model, a drum system moisture balance operator model, and an outlet moisture content prediction model. The energy balance analysis model includes a hot air system total energy balance operator model, a drum system total energy balance operator model, and an outlet blade temperature prediction model. The temperature prediction module, based on the prediction model module and real-time data, predicts the outlet temperature in real time and displays it through a dynamic curve. The temperature control and early warning module automatically adjusts the compensating steam opening when the predicted outlet temperature shows a temperature deviation; it automatically alarms and adjusts the compensating steam opening when the difference between the predicted and measured outlet temperature is ≥ ±5℃; and it shuts down the equipment for maintenance when the difference between the predicted and measured outlet temperature still does not meet the threshold requirement after automatic adjustment. The data acquisition range of the data acquisition module includes: After the data acquisition module completes real-time parameter acquisition, it performs necessary data processing on the acquired data: it removes abnormal data and inputs normal data into the prediction model module. The material balance analysis model includes: ① Main steam moisture balance operator model ； This model is used to obtain the calculated ejector steam mass flow rate. ; ②Moisture balance operator model of hot air system ;for The fitting function; ； in To monitor changes in the detection interval of the circulating hot air system; Further, we can conclude that: ； This model is used to obtain the calculated fresh air intake volume. ; ③ Moisture balance operator model of the tidal discharge system Fitting function: , ; ； Further, the conversion coefficient of the ejected steam entering the blades was obtained. : ； ④ Moisture balance operator model of the drum system ； ； Further, we can conclude that: ； Transformation coefficients calculated for models ③ and ④ Compare the data to determine if there are other sources of leakage or emission. Finally, the result is obtained by fitting the function. ; ⑤ Export moisture content prediction model Based on the material balance process for moisture content, a prediction model for the moisture content of the outlet leaves is obtained. ； The energy balance analysis model includes: ① Total Energy Balance Sub-model of Hot Air System ; in-- Compensation for heat released by steam : ； Fresh air enters through the fresh air inlet and is heated to absorb heat. : ； Heat discharged from the dehumidification branch of the hot air system : ; The heat exchanged between the heat exchange system and the drum tobacco : ； Assuming the heat exchange efficiency between the hot air system and the drum... for: , is the fitting function for temperature; The amount of heat input into the drum blades by the hot air system is: ; ② Total Energy Balance Operator Model of the Drum System ； in-- The hot air system transfers heat into the blades. : ； Heat entering the system from the water supply system : ； The heat absorbed by the blades in the drum as they heat up : ； The specific heat capacity of tobacco leaves; ； Substituting the two sets of data into a system of equations, we can solve for the following: and ; ③Exit blade temperature prediction model 。 2. The automatic prediction system for the outlet temperature of loose, rehydrated tobacco leaves according to claim 1, characterized in that: The automatic outlet temperature prediction system also includes a model verification module, which compares and analyzes the predicted outlet temperature with the measured value in real time or periodically to verify the accuracy of the prediction model.

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