CN112434867A - Intelligent prediction model for water content of blade section and application - Google Patents
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- A24—TOBACCO; CIGARS; CIGARETTES; SIMULATED SMOKING DEVICES; SMOKERS' REQUISITES
- A24B—MANUFACTURE OR PREPARATION OF TOBACCO FOR SMOKING OR CHEWING; TOBACCO; SNUFF
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Abstract
The invention relates to an intelligent prediction model for the moisture of a blade segment and application thereof, wherein historical data of each procedure set time period of a plurality of blade segments are selected as samples to be selected; selecting historical data of a set time period as sample data, and calculating by using a simple moving average method according to the characteristics of the streaming data to obtain a sample database; removing the discharged water content of the loosening and conditioning process, the discharged water content of the leaf moistening and feeding process and the discharged water content of the secondary feeding process, and screening out key parameters influencing the water content of cut leaves by applying an SCAD (sequence-characterized amplified dimension) method on the remaining sample data; constructing an intelligent prediction model with cut tobacco water content as a dependent variable by using a neural network method; and optimizing each key parameter of the intelligent prediction model by taking the NMSE as a target value, so that the precision of each key parameter reaches the set prediction precision. And (3) fully considering corresponding parameter variables, establishing a cut leaf shred water content model, and ensuring that the cut leaf shred water content is regulated and controlled dynamically in batches and is stable and consistent in batches.
Description
Technical Field
The invention belongs to the technical field of automatic control of a cigarette-oriented shredding process unit, and particularly relates to an intelligent prediction model for water content of a leaf segment and application thereof.
Background
China is the first major country of tobacco in the world, the tobacco yield and the cigarette yield are the first world, and the tobacco industry plays an important role in national economy in China. However, the current tobacco industry in China is low in informatization and intelligence level, and lacks international competitiveness, and how to improve the informatization and intelligence level of the tobacco industry in China is important.
The main component part of cigarette processing is the main importance of process quality and product quality control, and the key of improving informatization and intellectualization levels. The key of the control of the tobacco shred making process is the control of the tobacco shred making moisture, and the significant fluctuation of the tobacco shred making moisture in different batches is caused by the difference of units, personnel, external environment and the like, so that the stability and the consistency of the moisture of the finished tobacco shreds, namely the moisture content of the finished cigarettes are directly influenced.
When seasonal differences exist in the temperature and humidity of the external environment and the difficulty of water control is increased, how to adopt a scientific and effective method and further change the control method and mode is important to effectively ensure the stability of the cigarette quality.
Disclosure of Invention
The invention aims to provide an intelligent prediction model for the water content of a leaf segment and application thereof, so as to realize scientific and effective control, ensure the stability of the water content of cut tobacco and further ensure the stability of the quality of cigarettes.
The invention is realized by the following technical scheme:
an intelligent prediction model for water of a blade section is established through the following steps:
s1, determining a dependent variable of the model, wherein the dependent variable of the model is cut tobacco moisture content;
s2, selecting historical data of each procedure set time period of a plurality of leaf segments as a sample to be selected;
s3, selecting historical data of a set time period from the samples to be selected in the step S2 as sample data, and determining the stacking delay time among the working procedures;
s4, according to the characteristics of the streaming data, and on the basis of determining the stack delay among the procedures, calculating by using a simple moving average method to obtain a sample database;
s5, removing the discharged water content of the loosening and dampening procedure, the discharged water content of the leaf moistening and feeding procedure and the discharged water content of the secondary feeding procedure in the sample database of the step S4, and screening out key parameters influencing the water content of cut leaf shreds by applying an SCAD method to the remaining sample data;
s6, constructing an intelligent prediction model with cut tobacco water content as a dependent variable by using a neural network method;
and S7, optimizing each key parameter of the intelligent prediction model by using the NMSE as a target value and using the rest sample data to be selected in the step S2, so that the precision of each key parameter reaches the set prediction precision.
Preferably, the simple moving average method has the following calculation formula: ft is (At-1+ At-2+ At-3+ … … + At-n)/n, wherein Ft is a predicted value of the water content of cut tobacco leaves of the next batch, n is the number of periods of moving average and is a natural number, At-1 is an actual value of the first batch in sample data, and At-2, At-3 and At-n are actual values of the second batch, the third batch and the nth batch respectively.
Preferably, the first and second liquid crystal materials are,
wherein j and k are both natural numbers,
the application of the intelligent prediction model for the water content of the blade section utilizes any one of the intelligent prediction models on the premise that the water content of the loose moisture regaining feed material and the temperature and humidity of the environment are known.
Preferably, on the basis of an intelligent prediction model, a genetic algorithm is combined to obtain the fixed water adding amount and the secondary water adding amount of the loosening and conditioning process.
The invention has the beneficial effects that:
according to the technical scheme, corresponding parameter variables are fully considered for an optimized blade section process path, and a cut tobacco shred water content model is established, so that inter-batch dynamic regulation and control of cut tobacco shred water content are ensured, and the cut tobacco shred water content is stable and consistent in batches.
Detailed Description
The technical solutions of the present invention are described in detail below by examples, and the following examples are only exemplary and can be used only for explaining and explaining the technical solutions of the present invention, but not construed as limiting the technical solutions of the present invention.
The application provides an intelligent prediction model for water content of a blade section, which is established through the following steps:
s1, determining a dependent variable of the model, wherein the dependent variable of the model is cut tobacco moisture content; the technical scheme of the application is established by using a model with cut tobacco water content as a dependent variable.
S2, selecting historical data of each procedure set time period of a plurality of leaf segments as a sample to be selected; in the technical solution of the present application, the number of the part is usually at least 3 or more, if the number is too small, the precision of each key parameter is not accurate, the set time period may be set as required, and may be a certain time period in one batch, or may be two or more batches included in a certain time period.
S3, selecting historical data of a set time period from the samples to be selected in the step S2 as sample data, and determining the stacking delay time among the working procedures; in the case of continuous production, the stack delay time between processes of each batch is not substantially different.
S4, according to the characteristics of the streaming data, in the technical scheme of the application, the streaming data is one of the existing data processing modes, and the calculation speed is between the real-time data and the offline data. On the basis of determining the stack delay among the procedures, calculating by using a simple moving average method to obtain a sample database; the simple moving average method has the calculation formula as follows: ft is (At-1+ At-2+ At-3+ … … + At-n)/n, wherein Ft is a predicted value of the water content of cut tobacco leaves of the next batch, n is the number of periods of moving average and is a natural number, At-1 is an actual value of the first batch in sample data, and At-2, At-3 and At-n are actual values of the second batch, the third batch and the nth batch respectively. The simple moving average has the function of smoothing or smoothening the original sequence, so that the up-and-down fluctuation of the original sequence is weakened, and the larger the average period number n is, the stronger the smoothing function on the number sequence is, thereby realizing the calculation of the fixed water adding amount in the loosening and dampening process.
S5, removing the discharged water content of the loosening and dampening procedure, the discharged water content of the leaf moistening and feeding procedure and the discharged water content of the secondary feeding procedure in the sample database of the step S4, and screening out key parameters influencing the water content of cut leaf shreds by applying an SCAD method to the remaining sample data; the calculation formula of the SCAD method is a conventional calculation, and can be obtained as required through the existing network, and is not described in detail herein.
S6, constructing an intelligent prediction model with the cut-tobacco water content as a dependent variable by using a neural network method; the neural network method is a conventional data processing method, a boolean network can be used, and a self-adjusting network data processing method can also be used.
S7, using NMSE ((normalized average absolute error) as a target value, and using the rest sample data to be selected in the step S2 to optimize each key parameter of the intelligent prediction model, so that the precision of each key parameter reaches the set prediction precision.
Wherein j and k are both natural numbers,
the application also provides an application of the intelligent prediction model for the moisture of the blade sections, on the premise that the moisture content of the loose moisture regaining feed material and the temperature and humidity of the environment are known, the intelligent prediction model is utilized, and on the basis of the intelligent prediction model, the feedforward prediction of the intelligent prediction model is realized by combining a genetic algorithm so as to enable the moisture content of cut tobacco threads to be closer to a central value, and the fixed water adding amount and the secondary water adding amount of the loose moisture regaining process are obtained.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. An intelligent prediction model for water of a blade section is characterized by being established through the following steps:
s1, determining a dependent variable of the model, wherein the dependent variable of the model is cut tobacco moisture content;
s2, selecting historical data of each procedure set time period of a plurality of leaf segments as a sample to be selected;
s3, selecting historical data of a set time period from the samples to be selected in the step S2 as sample data, and determining the stacking delay time among the working procedures;
s4, according to the characteristics of the streaming data, and on the basis of determining the stack delay among the procedures, calculating by using a simple moving average method to obtain a sample database;
s5, removing the discharged water content of the loosening and dampening procedure, the discharged water content of the leaf moistening and feeding procedure and the discharged water content of the secondary feeding procedure in the sample database of the step S4, and screening out key parameters influencing the water content of cut leaf shreds by applying an SCAD method to the remaining sample data;
s6, constructing an intelligent prediction model with cut tobacco water content as a dependent variable by using a neural network method;
and S7, optimizing each key parameter of the intelligent prediction model by using the NMSE as a target value and using the rest sample data to be selected in the step S2, so that the precision of each key parameter reaches the set prediction precision.
2. The intelligent blade segment moisture prediction model of claim 1, wherein the simple moving average method has the following formula: ft is (At-1+ At-2+ At-3+ … … + At-n)/n, wherein Ft is a predicted value of the water content of cut tobacco leaves of the next batch, n is the number of periods of moving average and is a natural number, At-1 is an actual value of the first batch in sample data, and At-2, At-3 and At-n are actual values of the second batch, the third batch and the nth batch respectively.
3. The intelligent blade segment moisture prediction model of claim 1,
4. the application of an intelligent prediction model for the moisture of a blade section is characterized in that the intelligent prediction model of any one of claims 1 to 3 is utilized on the premise that the moisture content of a loose moisture regaining feed material and the temperature and humidity of the environment are known.
5. The application of the intelligent prediction model for the water content of the blade segment as claimed in claim 4, wherein the fixed water adding amount and the secondary water adding amount of the loosening and conditioning process are obtained by combining a genetic algorithm on the basis of the intelligent prediction model.
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Cited By (1)
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Application publication date: 20210302 |