CN115251445A - Method for controlling moisture content of tobacco leaves at outlet of loosening and conditioning machine - Google Patents
Method for controlling moisture content of tobacco leaves at outlet of loosening and conditioning machine Download PDFInfo
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- A—HUMAN NECESSITIES
- A24—TOBACCO; CIGARS; CIGARETTES; SIMULATED SMOKING DEVICES; SMOKERS' REQUISITES
- A24B—MANUFACTURE OR PREPARATION OF TOBACCO FOR SMOKING OR CHEWING; TOBACCO; SNUFF
- A24B3/00—Preparing tobacco in the factory
- A24B3/04—Humidifying or drying tobacco bunches or cut tobacco
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- A—HUMAN NECESSITIES
- A24—TOBACCO; CIGARS; CIGARETTES; SIMULATED SMOKING DEVICES; SMOKERS' REQUISITES
- A24B—MANUFACTURE OR PREPARATION OF TOBACCO FOR SMOKING OR CHEWING; TOBACCO; SNUFF
- A24B9/00—Control of the moisture content of tobacco products, e.g. cigars, cigarettes, pipe tobacco
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
A method for controlling the moisture content of tobacco leaves at the outlet of a loosening and conditioning machine. The invention collects production process data and environmental data within one month as historical data; obtaining the latest N =15 batches of data; establishing a linear regression model I to solve and obtain the water content of the leaf storage cabinet after the leaf storage cabinet is taken out of the cabinet; establishing a probability distribution model to solve and obtain a set value of the moisture content of the tobacco leaves at the outlet of the damping machine; establishing a linear regression model II, and solving to obtain an initial value of the accumulative amount of the water for loosening and conditioning; establishing a machine learning prediction model, and solving to obtain a predicted value of the moisture content of the tobacco leaves at the outlet of the damping machine; optimizing the loosening and conditioning water adding flow of each tobacco shred yield stage by adopting an optimization algorithm; and adjusting the loosening and conditioning water adding flow according to the deviation of the moisture content of the tobacco leaves at the outlet of the conditioner and the actual value of the moisture content of the tobacco leaves at the outlet of the conditioner, which is output by the prediction model. The method has the advantages of improving the prediction accuracy of the prediction model, improving the control effect, reducing the input of manpower, and achieving the purposes of improving the quality of the remoistened tobacco leaves and reducing the cost.
Description
Technical Field
The invention relates to a method for controlling the moisture content of tobacco leaves, in particular to a method for controlling the moisture content of the tobacco leaves at the outlet of a loosening and conditioning machine, and belongs to the technical field of tobacco production and processing equipment.
Background
In each link of tobacco production, the moisture control of the tobacco leaf moisture regaining link, including the flow of water for loosening and moisture regaining, directly plays an important role in controlling the quality of tobacco shreds in the subsequent moisture regaining link. The loosening and conditioning are the first core procedure of tobacco processing, and the stability of the moisture content of the tobacco leaves at the loosening and conditioning outlet has direct influence on the process indexes of the subsequent procedures. Due to the influences of factors such as the change of physical characteristics of the tobacco flakes, the fluctuation of the flow of incoming materials, the measurement hysteresis, the temperature and the humidity of the environment and the like, the water content of the tobacco leaves at the outlet of the loosening and conditioning process has high fluctuation in the actual production, the water content of the tobacco leaves at the conditioning outlet has high deviation from a standard value, the control effect is poor, the water content of the tobacco leaves is unstable, manual intervention is needed, and the production cost is increased.
Disclosure of Invention
In order to overcome the defects in the loosening and conditioning process of the existing tobacco processing, the invention provides a method for controlling the moisture content of the tobacco leaves at the outlet of a loosening and conditioning machine.
The technical scheme adopted by the invention for solving the technical problems is as follows: a method for controlling the moisture content of tobacco leaves at the outlet of a loosening and conditioning machine comprises the following steps:
s1, collecting production process data and environmental data within one month, preprocessing the data and storing the data into a database as historical data.
And (3) carrying out data cleaning and preprocessing on the historical data, wherein the data cleaning and preprocessing method comprises the steps of eliminating null values and abnormal values.
And S2, acquiring the latest N =15 batches of data, and calculating the average value of each batch of sample data.
And S3, establishing a linear regression model I of the discharged water content of the leaf storage cabinet by adopting a linear regression method, and solving to obtain the discharged water content of the leaf storage cabinet.
And S4, taking the moisture difference and the water content of the leaf storage cabinet as variables, which is obtained by solving the linear regression model I, establishing a probability distribution model taking the set value of the water content of the tobacco leaves at the outlet of the damping machine as a target value, and solving to obtain the set value of the water content of the tobacco leaves at the outlet of the damping machine.
And the water difference is the difference between the out-cabinet water content of the leaf storage cabinet in historical data and the set value of the water content of the tobacco leaves at the outlet of the damping machine.
And S5, establishing a linear regression model II with the initial value of the water accumulation amount of the loose moisture regain as a target value by adopting a linear regression method by taking the set value of the moisture content of the outlet tobacco leaves of the moisture regain machine, the environmental temperature of the moisture regain machine, the environmental humidity of the moisture regain machine and the accumulated amount of the electronic scale of the slice moisture regain as variables and solving to obtain the initial value of the water accumulation amount of the loose moisture regain machine.
S6, taking the moisture content of the tobacco leaves at the outlet of the damping machine as a target value, selecting the flow of the loose damping water as a controllable variable in an input value, selecting the quantity with the correlation coefficient r being more than or equal to 0.5 as a correlation variable by adopting a correlation analysis method,
and S7, taking the correlation variable in the step S6 as input, taking the moisture content of the tobacco leaves at the outlet of the target value damping machine as output, and training by adopting a machine learning algorithm to obtain a machine learning prediction model.
And solving to obtain a predicted value of the moisture content of the tobacco leaves at the outlet of the damping machine based on a prediction model of the moisture content of the tobacco leaves at the outlet of the damping machine established by machine learning.
And S8, taking the set value of the moisture content of the tobacco leaves at the outlet of the damping machine obtained by the probability distribution model in the step S4 as a push optimization target value of the prediction model, and pushing optimization on the loosening and damping water adding flow of each tobacco yield stage by adopting an optimization algorithm.
S9, according to the initial value of the accumulative quantity of the loose conditioning and water adding obtained by the linear regression model II in the step S5, and (4) converting the initial value of the accumulated amount of the loosening and conditioning water into a loosening and conditioning water adding flow through the formula (1) in the step S6, and taking the initial value as the initial value of the loosening and conditioning water adding flow when the production is started.
Adjusting the flow rate of the loose moisture regaining water according to the deviation of the moisture content of the tobacco leaves at the outlet of the moisture regaining machine and the actual value of the moisture content of the tobacco leaves at the outlet of the moisture regaining machine output by the prediction model,
when the absolute value of the deviation is less than 0.3%, the moisture content of the moisture regain outlet is kept unchanged;
and when the absolute value of the deviation is more than or equal to 0.3%, the optimal water adding flow is solved by adopting the optimization algorithm in the step S8.
Further, in step S1, the production process data includes the moisture content of the tobacco shreds at the inlet of the warming and humidifying device, the moisture content of the tobacco leaves discharged from the storage cabinet, the set value of the moisture content at the outlet of the moisture regaining machine, the accumulated quantity of the slice moisture regaining electronic scales, the flow of the loosening and moisture regaining inlet scales, the actual opening of the moisture regaining water adding film valve, the opening of the warming and humidifying film valve, the flow of the loosening and moisture regaining machine, the flow of the loosening and moisture regaining steam, the temperature of the loosening and moisture regaining hot air, the temperature of the loosening and moisture regaining fresh air, the pressure of the loosening and moisture regaining discharge cover, the return air temperature of the loosening and moisture regaining, the flow of the feeder inlet scales, and the accumulated quantity of the slice moisture regaining electronic scales.
The environment data comprises the ambient temperature of the cut tobacco dryer, the ambient humidity of the cut tobacco dryer, the ambient temperature of the damping machine and the ambient humidity of the damping machine.
Further, in step S2, the average value of the latest N =15 batches of production process data and environmental data is obtained as fitting data of the linear regression model i, the probability distribution model, and the linear regression model ii.
Further, in step S3, the expression of the linear regression model I with the moisture content of the leaf storage cabinet as a target value is as follows,
Y 1 ＝2.19486-0.07020X 1 -0.05841X 2 +1.18721X 3 (2)
in the formula:
Y 1 moisture content of leaves storage cabinet
X 1 -ambient temperature of cut-tobacco drier
X 2 -ambient humidity of cut-tobacco drier
X 3 The water content of the cut tobacco at the inlet of the heating and humidifying equipment.
Further, in step S5, the expression of the linear regression model II with the initial value of the accumulated amount of water for loosening and dampening as the target is as follows,
Y 2 ＝-3395.52679+11.93938X 4 -0.65249X 5 +109.58232X 6 +0.12824X 7 (3)
in the formula:
Y 2 initial value of cumulative amount of water addition for loosening and dampening
X 4 -environmental temperature of damping machine
X 5 -environmental humidity of damping machine
X 6 -set value of moisture content of tobacco leaves at outlet of damping machine
X 7 -slice conditioning electronic scale accumulation.
Further, in step S6, the correlation coefficient r is less than 0.3, i.e. no correlation exists, r is more than 0.3 and less than 0.5, low correlation is obtained, r is more than 0.5 and less than 0.8, significant correlation is obtained, and r is more than 0.8 and less than 1.0, high correlation is obtained;
the correlation variables include: weighing flow at a loosening and moisture regaining inlet, actual opening of a moisture regaining and water adding film valve, opening of a warming and moisture increasing film valve, loosening and moisture regaining and water adding flow, loosening and moisture regaining steam flow, loosening and moisture regaining hot air temperature, loosening and moisture regaining fresh air temperature, loosening and moisture regaining discharge cover pressure, loosening and moisture regaining return air temperature, moisture regaining machine environment humidity, weighing flow at a feeder inlet, and weighing by a slice moisture regaining electronic scale.
And (4) analyzing every two variables in the data by adopting a correlation analysis method, and selecting a correlation variable with the correlation coefficient r being more than or equal to 0.5 and the target value of the moisture content of the tobacco leaves at the outlet of the damping machine as a variable of the prediction model in the step S7.
And storing the collected production process data and the collected environment data into a database. Preferably, the database updates the data of the database at preset time intervals, and the updated database is used for updating the data of the moisture content prediction model of the tobacco leaves at the outlet of the damping machine.
Further, in step S8, the optimal loosening and conditioning water adding flow rate is solved by using the optimization algorithm.
Further, in the production process of the step S9, the tobacco shred production process is divided into an initial stage, a first stage, a second stage, a third stage and the like according to the nodes of 0 percent, 25 percent, 50 percent, 75 percent and 100 percent of the total tobacco shred production,
when the total tobacco shred yield is more than or equal to 0% and less than 25%, the initial value of the accumulated amount of loose moisture regaining and water addition obtained by solving the linear regression model II is used as the water addition flow value in the initial stage;
when the total tobacco shred yield is more than or equal to 25% and less than 50%, the set value of the moisture content of the tobacco shreds at the outlet of the damping machine obtained by solving the probability distribution model is used as a push-optimization target value, the prediction model is pushed and optimized by adopting an optimization algorithm, and the loose damping water adding flow push-optimization value at the moment of obtaining the total tobacco shred production amount by solving is used as the water adding flow push-optimization value at the first stage;
when the total tobacco shred yield is more than or equal to 50% and less than 75%, the set value of the moisture content of the tobacco shreds at the outlet of the damping machine obtained by solving the probability distribution model is used as a pushing optimal target value, an optimization algorithm is adopted to push the prediction model, and the loose damping water adding flow pushing optimal value at the moment of 50% of the total tobacco shred production is obtained by solving and is used as the water adding flow pushing optimal value at the second stage;
and when the total tobacco shred output is more than or equal to 75% and less than 100%, the set value of the moisture content of the tobacco shreds at the outlet of the damping machine obtained by solving the probability distribution model is used as an optimal pushing target value, the prediction model is optimized by adopting an optimization algorithm, and the optimal pushing value of the water adding flow rate of the loose damping at the moment of obtaining 75% of the total tobacco shred production is used as the optimal pushing value of the water adding flow rate of the third stage.
The method has the advantages that variables which have large influence on the system are selected by adopting a correlation analysis method, and data preprocessing is carried out on historical data; the method comprises the steps of establishing a linear regression model I, a probability distribution model and a linear regression model II, establishing a prediction model based on a machine learning algorithm, optimizing the prediction model by using an optimization algorithm when the deviation between a prediction target value output by the prediction model and an actual target value is more than or equal to 0.3%, solving the optimal loosening and conditioning water adding flow rate by using the optimization algorithm and the machine learning prediction model through a parameter optimization scheme, and automatically adjusting the water adding flow rate value to provide the control precision of the moisture content of outlet tobacco leaves of a conditioning machine, so that the prediction accuracy of the prediction model is improved, the control effect is improved, the input of manpower is reduced, and the purposes of improving the quality of the conditioning tobacco leaves and reducing the cost are achieved.
Drawings
FIG. 1 is a flow chart of linear regression and probability distribution model modeling in the present invention.
FIG. 2 is a flow chart of machine learning model modeling in the present invention.
FIG. 3 is a control logic diagram for the production run stage of the present invention.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the drawings. However, it should be understood by those skilled in the art that the present invention is not limited to the specific embodiments listed, and should be included within the scope of the present invention as long as the spirit of the present invention is met.
See figures 1-3. The invention discloses a method for controlling the moisture content of tobacco leaves at the outlet of a loosening and conditioning machine, which comprises the following steps:
s1, collecting production process data and environmental data within one month, preprocessing the data and storing the data into a database as historical data.
And carrying out data cleaning and preprocessing on the historical data, wherein the data cleaning and preprocessing method comprises the steps of eliminating null values and abnormal values.
Further, the production process data comprises the moisture content of the tobacco shreds at the inlet of the temperature-increasing and humidifying equipment, the moisture content of the tobacco leaves discharged from the tobacco storage cabinet, the set value of the moisture content at the outlet of the moisture regaining machine, the accumulated quantity of the slice moisture regaining electronic scales, the flow of the loose moisture regaining inlet scales, the actual opening of the moisture regaining water adding film valves, the opening of the temperature-increasing and humidifying film valves, the flow of the loose moisture regaining machine, the flow of the loose moisture regaining steam, the temperature of the loose moisture regaining hot air, the temperature of the loose moisture regaining fresh air, the pressure of the loose moisture regaining discharge cover, the temperature of the loose moisture regaining return air, the flow of the feeder inlet scales and the accumulated quantity of the slice moisture regaining electronic scales.
Further, the environmental data comprises the ambient temperature of the cut tobacco dryer, the ambient humidity of the cut tobacco dryer, the ambient temperature of the damping machine and the ambient humidity of the damping machine.
And S2, acquiring the latest N =15 batches of data, and calculating the average value of each batch of sample data.
And S3, establishing a linear regression model I of the water content of the leaf storage cabinet by adopting a linear regression method, and solving to obtain the water content of the leaf storage cabinet.
The expression of the linear regression model I taking the moisture content of the leaf storage cabinet as a target value is as follows,
Y 1 ＝2.19486-0.07020X 1 -0.05841X 2 +1.18721X 3 (2)
in the formula:
Y 1 moisture content of leaves storage cabinet
X 1 -ambient temperature of cut-tobacco drier
X 2 -ambient humidity of cut-tobacco drier
X 3 The moisture content of the cut tobacco at the inlet of a heating and humidifying device (HT for short).
And S4, taking the moisture difference and the water content of the leaf storage cabinet obtained by solving the linear regression model I as variables, establishing a probability distribution model taking the moisture content set value of the tobacco leaves at the outlet of the damping machine as a target value, and solving to obtain the moisture content set value of the tobacco leaves at the outlet of the damping machine.
And the water difference is the difference between the out-cabinet water content of the leaf storage cabinet in historical data and the set value of the water content of the tobacco leaves at the outlet of the damping machine.
And S5, establishing a linear regression model II with the initial value of the water accumulation amount of the loose moisture regain as a target value by adopting a linear regression method by taking the set value of the moisture content of the outlet tobacco leaves of the moisture regain machine, the environmental temperature of the moisture regain machine, the environmental humidity of the moisture regain machine and the accumulated amount of the electronic scale of the slice moisture regain as variables and solving to obtain the initial value of the water accumulation amount of the loose moisture regain machine.
The expression of the linear regression model II taking the initial value of the accumulated amount of the water for loosening and conditioning as the target is as follows,
Y 2 ＝-3395.52679+11.93938X 4 -0.65249X 5 +109.58232X 6 +0.12824X 7 (3)
in the formula:
Y 2 initial value of cumulative amount of loose moisture regain water addition
X 4 -the environmental temperature of the damping machine
X 5 -environmental humidity of damping machine
X 6 -set value of moisture content of tobacco leaves at outlet of damping machine
X 7 -slice conditioning electronic scale accumulation.
S6, taking the moisture content of the tobacco leaves at the outlet of the damping machine as a target value, selecting the flow of the loose damping water as a controllable variable in an input value, selecting the quantity with the correlation coefficient r being more than or equal to 0.5 as a correlation variable by adopting a correlation analysis method,
when the correlation coefficient r is less than 0.3, the correlation is absent, r is more than or equal to 0.3 and less than 0.5, the low correlation is present, r is more than or equal to 0.5 and less than 0.8, the significant correlation is present, and r is more than or equal to 0.8 and less than or equal to 1.0, the high correlation is present.
The correlation variables include: weighing flow at a loosening and moisture regaining inlet, actual opening of a moisture regaining water adding film valve, opening of a warming and humidifying film valve, loosening and moisture regaining water adding flow, loosening and moisture regaining steam flow, loosening and moisture regaining hot air temperature, loosening and moisture regaining fresh air temperature, loosening and moisture regaining discharge cover pressure, loosening and moisture regaining return air temperature, environmental temperature of a moisture regaining machine, environmental humidity of the moisture regaining machine, weighing flow at a feeder inlet, accumulated metering by a slicing and moisture regaining electronic scale and the like.
S7, taking the correlation variable in the step S6 as input, taking the moisture content of the tobacco leaves at the outlet of the target value conditioner as output, and training by adopting a machine learning algorithm to obtain a machine learning prediction model;
and solving to obtain a predicted value of the moisture content of the tobacco leaves at the outlet of the damping machine based on a prediction model of the moisture content of the tobacco leaves at the outlet of the damping machine established by machine learning.
And analyzing every two variables in the data by adopting a correlation analysis method, and selecting a correlation variable with the target value of the moisture content of the tobacco leaves at the outlet of the damping machine and the correlation coefficient r being more than or equal to 0.5 as a variable of the prediction model.
And S8, taking the set value of the moisture content of the tobacco leaves at the outlet of the damping machine obtained by the probability distribution model in the step S4 as a push optimization target value of the prediction model, and pushing optimization on the loosening and damping water adding flow of each tobacco yield stage by adopting an optimization algorithm.
And solving the optimal loosening and conditioning water adding flow by using the optimization algorithm.
And S9, converting the initial value of the accumulated amount of the loose moisture regaining water into the flow rate of the loose moisture regaining water through the formula (1) in the step S6 according to the initial value of the accumulated amount of the loose moisture regaining water obtained by the linear regression model II in the step S5, and using the flow rate of the loose moisture regaining water as the initial value of the flow rate of the loose moisture regaining water when the production is started.
And adjusting the loosening and conditioning water adding flow according to the deviation of the moisture content of the tobacco leaves at the outlet of the conditioner and the actual value of the moisture content of the tobacco leaves at the outlet of the conditioner, which is output by the prediction model. When the absolute value of the deviation is less than 0.3%, the moisture content of the moisture regain outlet is kept unchanged; and when the absolute value of the deviation is more than or equal to 0.3%, the optimal water adding flow is solved by adopting the optimization algorithm in the step S8.
In the production process, the tobacco shred production process is divided into an initial stage, a first stage, a second stage, a third stage and the like according to the nodes of 0, 25 percent, 50 percent, 75 percent and 100 percent of the total tobacco shred production,
when the total yield of the tobacco shreds is more than or equal to 0% and less than 25% of the total yield of the tobacco shreds, taking the initial value of the accumulated loose moisture regaining water adding amount obtained by solving the linear regression model II as the water adding flow value of the initial stage;
when the total tobacco shred yield is more than or equal to 25% and less than 50%, the set value of the moisture content of the tobacco shreds at the outlet of the damping machine obtained by solving the probability distribution model is used as a push-optimization target value, an optimization algorithm is adopted to push the prediction model, the push-optimization value of the water adding flow of the loose damping at the moment when the total tobacco shred yield is 25% is obtained by solving, and the push-optimization value is used as the water adding flow push-optimization value of the first stage;
when the total tobacco shred yield is more than or equal to 50% and less than 75% of the total tobacco shred yield, taking the set value of the moisture content of the tobacco shreds at the outlet of the damping machine obtained by solving the probability distribution model as an optimal pushing target value, pushing the prediction model by adopting an optimization algorithm, solving to obtain an optimal pushing value of the water flow rate of the loose damping water addition at the moment when the total tobacco shred yield is 50%, and taking the optimal pushing value as the water flow rate optimal pushing value at the second stage;
and when the total tobacco shred yield is more than or equal to 75% and less than 100%, using the set value of the moisture content of the tobacco shreds at the outlet of the damping machine obtained by solving the probability distribution model as a push-optimization target value, pushing the prediction model by adopting an optimization algorithm, solving to obtain a loose damping water adding flow push-optimization value at the moment of 75% of the total tobacco shred production, and using the push-optimization value as a water adding flow push-optimization value at the third stage.
In the production regulation and modeling process, the loose conditioning water adding flow is more visual and accurate than the expression of the loose conditioning water adding accumulated amount.
Further, in step S2, the average value of the latest N =15 batches of production process data and environmental data is obtained as fitting data of the linear regression model i, the probability distribution model, and the linear regression model ii.
Storing the collected production process data and environmental data in a database; and the database updates the data of the database at preset time intervals, and the updated database is used for updating the data of the moisture content prediction model of the tobacco leaves at the outlet of the damping machine. Preferably, the preset time interval for updating the data in the database is 200 to 400 seconds, and more preferably, the preset time interval is 250 seconds, 300 seconds or 350 seconds.
According to the method for controlling the moisture content of the tobacco leaves at the outlet of the loosening and conditioning machine, variables which have large influence on a system are selected by building a machine learning model and adopting a correlation analysis method; by means of the parameter optimization scheme, the moisture regaining and water adding flow of each node is optimized according to the moisture regaining outlet moisture content required, the controllable variable is automatically adjusted, the input of manpower is reduced, and the purposes of improving the tobacco shred quality and reducing the cost are achieved.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design many alternative embodiments without departing from the scope of the appended claims.
Claims (10)
1. A method for controlling the moisture content of tobacco leaves at the outlet of a loosening and conditioning machine comprises the following steps:
s1, collecting production process data and environmental data within one month, preprocessing the data and storing the data into a database as historical data;
performing data cleaning and preprocessing on the historical data, wherein the data cleaning and preprocessing method comprises the steps of eliminating null values and abnormal values;
s2, obtaining the latest N =15 batches of data, and solving the average value of each batch of sample data;
s3, establishing a linear regression model I of the discharged water content of the leaf storage cabinet by adopting a linear regression method, and solving to obtain the discharged water content of the leaf storage cabinet;
s4, taking the moisture difference and the water content of the leaf storage cabinet as variables, which is obtained by solving the linear regression model I, establishing a probability distribution model taking the set value of the water content of the tobacco leaves at the outlet of the damping machine as a target value, and solving to obtain the set value of the water content of the tobacco leaves at the outlet of the damping machine;
the water difference is the difference value between the water content of the leaf storage cabinet in the historical data and the set value of the water content of the tobacco leaves at the outlet of the damping machine;
s5, using the set value of the moisture content of the tobacco leaves at the outlet of the damping machine, the environment temperature of the damping machine, the environment humidity of the damping machine and the accumulated quantity of the electronic scale for slice damping, which are obtained by solving through the probability distribution model, as variables, establishing a linear regression model II with the initial value of the water accumulation for loosening and damping as a target value by adopting a linear regression method, and solving to obtain the initial value of the water accumulation for loosening and damping;
s6, taking the moisture content of the tobacco leaves at the outlet of the damping machine as a target value, selecting the flow rate of loose damping water addition as a controllable variable in an input value, selecting the quantity with a correlation coefficient r being more than or equal to 0.5 as a correlation variable by adopting a correlation analysis method,
s7, taking the correlation variable in the step S6 as input, taking the moisture content of the tobacco leaves at the outlet of the target value damping machine as output, and training by adopting a machine learning algorithm to obtain a machine learning prediction model;
solving and obtaining a predicted value of the moisture content of the tobacco leaves at the outlet of the damping machine based on a prediction model of the moisture content of the tobacco leaves at the outlet of the damping machine established by machine learning;
s8, taking the set value of the moisture content of the tobacco leaves at the outlet of the damping machine obtained by the probability distribution model in the step S4 as a push optimization target value of a prediction model, and pushing optimization on the loosening and damping water adding flow of each tobacco yield stage by adopting an optimization algorithm;
s9, converting the initial value of the accumulated amount of the loose moisture regaining water into the flow of the loose moisture regaining water through the formula (1) in the step S6 according to the initial value of the accumulated amount of the loose moisture regaining water obtained by the linear regression model II in the step S5, and taking the flow as the initial value of the flow of the loose moisture regaining water when the production is started;
adjusting the flow rate of the loosening and conditioning water according to the deviation of the moisture content of the tobacco leaves at the outlet of the conditioner and the actual value of the moisture content of the tobacco leaves at the outlet of the conditioner output by the prediction model,
when the absolute value of the deviation is less than 0.3%, the moisture content of the moisture regain outlet is kept unchanged;
and when the absolute value of the deviation is more than or equal to 0.3%, the optimal water adding flow is solved by adopting the optimization algorithm in the step S8.
2. The method for controlling the moisture content of the tobacco leaves at the outlet of the loosening and conditioning machine according to claim 1, which is characterized by comprising the following steps: in the step S1, the first step is performed,
the production process data comprises the moisture content of cut tobacco at an inlet of a heating and humidifying device, the moisture content of a leaf storage cabinet, the set value of the moisture content at an outlet of a moisture regaining machine, the accumulated quantity of a slice moisture regaining electronic scale, the flow of a loosening and moisture regaining inlet scale, the actual opening of a moisture regaining water adding film valve, the opening of a heating and humidifying film valve, the flow of the loosening and moisture regaining machine, the flow of loosening and moisture regaining steam, the temperature of loosening and moisture regaining hot air, the temperature of loosening and moisture regaining fresh air, the pressure of a loosening and moisture regaining discharging cover, the temperature of loosening and moisture regaining return air, the flow of a feeder inlet scale and the accumulated quantity of the slice moisture regaining electronic scale;
the environment data comprises the ambient temperature of the cut tobacco dryer, the ambient humidity of the cut tobacco dryer, the ambient temperature of the damping machine and the ambient humidity of the damping machine.
3. The method for controlling the moisture content of the tobacco leaves at the outlet of the loosening and conditioning machine according to claim 1, which is characterized in that: in step S2, the average of the latest N =15 batches of production process data and environmental data is obtained as fitting data of the linear regression model i, the probability distribution model, and the linear regression model ii.
4. The method for controlling the moisture content of the tobacco leaves at the outlet of the loosening and conditioning machine according to claim 1, which is characterized in that: in step S3, the expression of the linear regression model I with the water content of the leaf storage cabinet as a target value is as follows,
Y 1 ＝2.19486-0.07020X 1 -0.05841X 2 +1.18721X 3 (2)
in the formula:
Y 1 moisture content of leaves storage cabinet
X 1 -ambient temperature of cut-tobacco drier
X 2 -ambient humidity of cut-tobacco drier
X 3 The water content of the cut tobacco at the inlet of the heating and humidifying equipment.
5. The method for controlling the moisture content of the tobacco leaves at the outlet of the loosening and conditioning machine according to claim 1, which is characterized in that: in step S5, the expression of the linear regression model II taking the initial value of the accumulated amount of the loose conditioning and the water addition as the target is as follows,
Y 2 ＝-3395.52679+11.93938X 4 -0.65249X 5 +109.58232X 6 +0.12824X 7 (3)
in the formula:
Y 2 initial value of cumulative amount of water addition for loosening and dampening
X 4 -the environmental temperature of the damping machine
X 5 -environmental humidity of damping machine
X 6 -set value of moisture content of tobacco leaves at outlet of damping machine
X 7 -slice conditioning electronic scale accumulation.
6. The method for controlling the moisture content of the tobacco leaves at the outlet of the loosening and conditioning machine according to claim 1, which is characterized by comprising the following steps:
in step S6, the correlation coefficient r is less than 0.3, i.e. no correlation exists, r is more than 0.3 and less than 0.5, low correlation exists, r is more than 0.5 and less than 0.8, significant correlation exists, and r is more than 0.8 and less than 1.0, high correlation exists;
the correlation variables include: weighing flow at a loosening and conditioning inlet, actual opening of a conditioning and water adding film valve, opening of a warming and humidifying film valve, loosening and conditioning water adding flow, loosening and conditioning steam flow, loosening and conditioning hot air temperature, loosening and conditioning fresh air temperature, loosening and conditioning discharge cover pressure, loosening and conditioning return air temperature, conditioning machine environment humidity, weighing flow at a feeder inlet, and weighing by a slicing and conditioning electronic scale;
and (4) carrying out pairwise analysis on the variables in the data by adopting a correlation analysis method, and selecting a correlation variable with the target value of the moisture content of the tobacco leaves at the outlet of the damping machine and the correlation coefficient r being more than or equal to 0.5 as the variable of the prediction model in the step S7.
7. The method for controlling the moisture content of the tobacco leaves at the outlet of the loosening and conditioning machine according to claim 1, which is characterized in that: and storing the collected production process data and the collected environment data into a database.
8. The method for controlling the moisture content of the tobacco leaves at the outlet of the loosening and conditioning machine according to claim 7, which is characterized in that: and the database updates the data of the database at preset time intervals, and the updated database is used for updating the data of the moisture content prediction model of the tobacco leaves at the outlet of the damping machine.
9. The method for controlling the moisture content of the tobacco leaves at the outlet of the loosening and conditioning machine according to claim 1, which is characterized by comprising the following steps: in step S8, the optimal loosening and conditioning water adding flow is solved by the optimization algorithm.
10. The method for controlling the moisture content of the tobacco leaves at the outlet of the loosening and conditioning machine according to claim 8, which is characterized in that: in the production process of the step S9, the tobacco shred production process is divided into an initial stage, a first stage, a second stage, a third stage and the like according to the nodes of 0 percent, 25 percent, 50 percent, 75 percent and 100 percent of the total tobacco shred production,
when the total tobacco shred yield is more than or equal to 0% and less than 25%, the initial value of the accumulated amount of loose moisture regaining and water addition obtained by solving the linear regression model II is used as the water addition flow value in the initial stage;
when the total tobacco shred yield is more than or equal to 25% and less than 50%, the set value of the moisture content of the tobacco shreds at the outlet of the damping machine obtained by solving the probability distribution model is used as a push-optimization target value, the prediction model is pushed and optimized by adopting an optimization algorithm, and the loose damping water adding flow push-optimization value at the moment of obtaining the total tobacco shred production amount by solving is used as the water adding flow push-optimization value at the first stage;
when the total tobacco shred yield is more than or equal to 50% and less than 75%, the set value of the moisture content of the tobacco shreds at the outlet of the damping machine obtained by solving the probability distribution model is used as a pushing optimal target value, an optimization algorithm is adopted to push the prediction model, and the loose damping water adding flow pushing optimal value at the moment of 50% of the total tobacco shred production is obtained by solving and is used as the water adding flow pushing optimal value at the second stage;
and when the total tobacco shred output is more than or equal to 75% and less than 100%, the set value of the moisture content of the tobacco shreds at the outlet of the damping machine obtained by solving the probability distribution model is used as an optimal pushing target value, the prediction model is optimized by adopting an optimization algorithm, and the optimal pushing value of the water adding flow rate of the loose damping at the moment of obtaining 75% of the total tobacco shred production is used as the optimal pushing value of the water adding flow rate of the third stage.
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