CN116076765A - Cut-tobacco dryer outlet moisture prediction method based on transfer function - Google Patents

Cut-tobacco dryer outlet moisture prediction method based on transfer function Download PDF

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CN116076765A
CN116076765A CN202310058857.8A CN202310058857A CN116076765A CN 116076765 A CN116076765 A CN 116076765A CN 202310058857 A CN202310058857 A CN 202310058857A CN 116076765 A CN116076765 A CN 116076765A
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moisture
variation
outlet
outlet moisture
transfer function
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张翅远
高宇雷
周晓龙
张立斌
孔彬
杨耀晶
秦鹏
陆俊澎
张选顺
苏怡帆
韩金江
白京
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Hongta Tobacco Group Co Ltd
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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
    • 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
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F26—DRYING
    • F26B—DRYING SOLID MATERIALS OR OBJECTS BY REMOVING LIQUID THEREFROM
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Abstract

The invention discloses a cut-tobacco dryer outlet moisture prediction method based on a transfer function, which comprises the following steps of: acquiring related data in the production process of the cut-tobacco drier and determining at least one controllable technological parameter as a controllable variable; respectively establishing at least one differential equation of controllable technological parameter variation and inlet moisture variation and outlet moisture variation, and a differential equation of inlet moisture variation and outlet moisture variation; respectively carrying out Laplace transformation and processing into corresponding transfer function models; a filter is designed in a transfer function model of the inlet moisture variation and the outlet moisture variation to be used as a multiplier term; establishing a cut-tobacco drier outlet moisture prediction model; and determining specific values of parameters in the cut-tobacco dryer outlet moisture prediction model by adopting a system identification tool. According to the method, linear modeling is carried out on the controllable variables, accurate prediction of the moisture of the outlet of the baked wire is achieved, and the problem that a white box prediction model is difficult to build is solved.

Description

Cut-tobacco dryer outlet moisture prediction method based on transfer function
Technical Field
The application relates to the technical field of cigarette cut-tobacco making, in particular to a cut-tobacco drier outlet moisture prediction method based on a transfer function.
Background
In the tobacco processing process, the tobacco shred baking process is an important processing procedure for controlling the moisture of materials, and the production quality of the tobacco shred baking process can influence the quality and the taste of finished cigarettes. The outlet moisture is an important technological index in the process of drying the cut tobacco, and the accuracy of the real-time prediction of the outlet moisture has direct influence on controlling the outlet moisture of the cut tobacco dryer and the technological index of each subsequent process. Along with the research of the cut-tobacco dryer mechanism and the development of mathematical modeling, a method for establishing a mechanism prediction model is adopted in advance to predict the outlet moisture of the cut-tobacco dryer, but two difficulties exist: firstly, in the production process of the cut tobacco drying, variables related to the mechanism of the cut tobacco drying are difficult to directly collect, such as deliquescence wind scattered from a material outlet, humidity of hot air in a front chamber and the like; for some variables that can be directly collected, there is also a problem that it is difficult to evaluate the measurement error thereof, resulting in that the establishment of a white-box model of a pure mechanism is difficult to achieve. Secondly, the controllable variables of the cut-tobacco drier mainly comprise four parameters of the opening degree of a moisture removal air valve, the temperature of a cylinder wall, the hot air quantity and the hot air temperature, other parameters hardly change in the production process, and if the parameters of a whole tray are considered, the situation that the modeling is not carried out by enough data support can occur.
Disclosure of Invention
In order to solve at least one aspect of the problems, the invention provides a method for predicting the outlet moisture of a cut-tobacco dryer based on a transfer function, which comprises the following steps:
step 1: acquiring relevant data in the production process of the cut-tobacco drier and determining at least one controllable technological parameter as a controllable variable, wherein the relevant data at least comprises brand information, batch information, inlet material flow, inlet moisture, moisture-discharging air gate opening, cylinder wall temperature, hot air volume, hot air temperature and outlet moisture;
step 2: establishing a differential equation of at least one controllable process parameter variation and an outlet moisture variation, and a differential equation of an inlet moisture variation and an outlet moisture variation;
step 3: respectively carrying out Laplace transformation on the differential equation of the at least one controllable technological parameter variable quantity and the outlet water variable quantity and the differential equation of the inlet water variable quantity and the outlet water variable quantity established in the step 2, and processing the differential equation into a corresponding transfer function model;
step 4: a filter is designed in a transfer function model of the inlet moisture variation and the outlet moisture variation in the step 3 to be used as a multiplier term, and a transfer function model of the inlet moisture variation and the outlet moisture variation with the filter is obtained;
step 5: establishing an outlet moisture prediction model of the cut-tobacco dryer based on the transfer function model of the at least one controllable process parameter variable quantity and the outlet moisture variable quantity obtained in the step 3 and the transfer function model of the inlet moisture variable quantity and the outlet moisture variable quantity with the filter obtained in the step 4;
step 6: and (5) determining the specific values of the parameters in the cut-tobacco dryer outlet moisture prediction model in the step (5) by adopting a system identification tool, and substituting the specific values to obtain the final cut-tobacco dryer outlet moisture prediction model.
Preferably, the differential equation of the inlet moisture variation and the outlet moisture variation in the step 2 is:
Δy＝K Pr Δu r (t-t dr )
wherein Δy represents the outlet moisture variation; k (K) Pr Representing a gain factor; deltau r Represents the inlet moisture variation; t represents time; t is t dr Representing the time delay from the inlet moisture to the outlet moisture of the dried shreds;
then after the Laplace transformation and the processing in the step 3, the transfer function model of the obtained inlet moisture variation and the outlet moisture variation is:
Figure BDA0004060924830000021
wherein Δy(s) represents the amount of change in the outlet moisture before and after the inlet moisture is changed; deltaU r (s) represents the variation of inlet moisture; g r (s) represents the ratio of the amount of change in the outlet moisture to the amount of change in the inlet moisture before and after the change in the inlet moisture; s represents the complex frequency.
Preferably, the filter in the step 4 adopts a first order low pass filter.
Preferably, the first-order low-pass filter is:
Figure BDA0004060924830000022
wherein T is Pr A first order low pass filter coefficient for inlet moisture; g LPF (s) represents a low pass filter; s represents complex frequency;
the transfer function model of the inlet moisture variation and the outlet moisture variation with the filter in step 4 is:
Figure BDA0004060924830000023
preferably, the differential equation of the controllable process parameter variation and the outlet moisture variation in the step 2 is:
Figure BDA0004060924830000024
wherein i represents a controllable technological parameter, and the number of i is more than or equal to 1; Δy represents the outlet moisture variation; t represents time; t (T) Pi Representing a time constant; k (K) Pi Representing a gain factor; deltau i Representing the variable quantity of controllable technological parameters; t is t di Representing the reaction time delay from the change of the controllable technological parameters to the water at the outlet of the cut-tobacco drier;
then after the Laplace transformation and the processing in the step 3, the obtained transfer function model of the controllable technological parameter variation and the outlet moisture variation is as follows:
Figure BDA0004060924830000031
wherein Δy(s) represents the amount of change in outlet moisture before and after a change in a controllable process parameter; deltaU i (s) represents the variation of a controllable process parameter; g i (s) represents the ratio of the amount of change in outlet moisture to the amount of change in the controllable process parameter before and after the change in the controllable process parameter; s represents the complex frequency.
Preferably, the outlet moisture prediction model of the cut tobacco dryer in the step 5 is as follows:
Figure BDA0004060924830000032
wherein n represents the number of i; y is Y 0 Representing the controlled process parameters or the outlet moisture at steady state before the inlet moisture changes.
Preferably, the K Pr Equal to 1.
Preferably, the system identification tool in the step 6 is a System Identification system identification kit of Matlab.
Preferably, the controllable process parameter in the step 1 is one or more of opening degree of a moisture removal air valve, temperature of a cylinder wall, hot air quantity and hot air temperature.
The cut-tobacco dryer outlet moisture prediction method based on the transfer function has the following beneficial effects:
the linear prediction model of the controllable technological parameter variation, the inlet moisture variation and the outlet moisture variation is established for the steady-state stage of the wire drying process based on the transfer function, so that the description of the wire drying process is simplified, the problem of difficult establishment of the white box prediction model is solved, the calculation speed is increased, and the calculation cost is reduced; when the controllable variable is regulated, the change trend of the outlet moisture can be accurately predicted, the accurate prediction of the outlet moisture of the cut tobacco dryer is realized, and a prediction model foundation is laid for the prediction control of the cut tobacco dryer; the noise of the sensor and the random disturbance in the production process are filtered by designing a first-order low-pass filter in a transfer function model of the inlet moisture variation and the outlet moisture variation, so that the accuracy of the prediction model of the application is improved.
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For a better understanding of the above and other objects, features, advantages and functions of the present invention, reference should be made to the embodiments illustrated in the drawings. Like reference numerals refer to like parts throughout the drawings. It will be appreciated by persons skilled in the art that the drawings are intended to schematically illustrate preferred embodiments of the invention, and that the scope of the invention is not limited in any way by the drawings, and that the various components are not drawn to scale.
FIG. 1 shows a schematic diagram of a method for predicting outlet moisture of a cut tobacco dryer based on a transfer function according to an embodiment of the present invention;
fig. 2 shows a graph of predicted versus actual values of outlet moisture variation caused by variation in the opening of the tidal elimination damper according to embodiments of the invention;
fig. 3 shows a graph of predicted versus actual changes in outlet moisture caused by changes in cylinder wall temperature according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The term "comprising" and variations thereof as used herein means open ended, i.e., "including but not limited to. The term "or" means "and/or" unless specifically stated otherwise. The term "based on" means "based at least in part on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment. The term "another embodiment" means "at least one additional embodiment". The terms "first," "second," and the like, may refer to different or the same object. Other explicit and implicit definitions are also possible below.
To at least partially address one or more of the above problems and other potential problems, one embodiment of the present disclosure proposes a cut-tobacco dryer outlet moisture prediction method based on a transfer function, as shown in fig. 1, comprising the steps of:
step 1: acquiring related data in the production process of the cut-tobacco drier and determining at least one controllable technological parameter as a controllable variable; the related data comprise brand information, batch information, process standards, inlet material flow, inlet moisture, moisture removal air door opening, front sheet temperature, middle sheet temperature, rear sheet temperature, cylinder wall temperature, hot air volume, hot air fan frequency, hot air temperature, cylinder rotating speed and outlet moisture; preferably, the controllable process parameter is one or more of the opening degree of the tide gate, the temperature of the cylinder wall, the hot air quantity and the hot air temperature, and in the embodiment, the controllable process parameter is selected from the opening degree of the tide gate and the cylinder wall temperature.
Step 2: establishing a differential equation of at least one controllable process parameter variation and an outlet moisture variation, and a differential equation of an inlet moisture variation and an outlet moisture variation; preferably, a first order differential equation of the controllable process parameter variation and the outlet moisture variation and a first order differential equation of the inlet moisture variation and the outlet moisture variation are established;
preferably, the first order differential equation of the controllable process parameter variation and the outlet moisture variation is:
Figure BDA0004060924830000051
wherein i represents a controllable technological parameter, and the number of i is more than or equal to 1; Δy represents the outlet moisture variation; t represents time; t (T) pi Representing a time constant; k (K) Pi Representing a gain factor; deltau i Representing the variable quantity of controllable technological parameters; t is t di Representing the reaction time delay from the change of the controllable technological parameters to the water at the outlet of the cut-tobacco drier;
in this embodiment, if the number of i is 2,1 represents the opening of the tidal elimination damper, and 2 represents the wall temperature of the cylinder, the first-order differential equation of the opening variation of the tidal elimination damper and the outlet moisture variation is:
Figure BDA0004060924830000052
the first-order differential equation of the cylinder wall temperature variation and the outlet water variation is as follows:
Figure BDA0004060924830000053
preferably, the first order differential equation of the inlet moisture variation and the outlet moisture variation:
Δy＝K Pr Δu r (t-t dr )
wherein Δy represents the outlet moisture variation; k (K) pr Represents the gain factor, preferably K pr Equal to 1; deltau r Represents the inlet moisture variation; t represents time; t is t dr Representing the time delay from the inlet moisture to the outlet moisture of the baked wire.
Step 3: respectively carrying out Laplace transformation on the differential equation of the at least one controllable technological parameter variable quantity and the outlet water variable quantity and the differential equation of the inlet water variable quantity and the outlet water variable quantity established in the step 2, and processing the differential equation into a corresponding transfer function model;
specifically, the Laplace transformation is carried out on two sides of a differential equation of the controllable process parameter variation and the outlet moisture variation simultaneously:
Figure BDA0004060924830000054
the processing into a transfer function model is as follows:
Figure BDA0004060924830000061
wherein Δy(s) represents the amount of change in outlet moisture before and after a change in a controllable process parameter; deltaU i (s) represents the variation of a controllable process parameter; g i (s) represents the ratio of the amount of change in outlet moisture to the amount of change in the controllable process parameter before and after the change in the controllable process parameter; s represents complex frequency;
Figure BDA0004060924830000062
Figure BDA0004060924830000063
in this embodiment, the transfer function model of the opening variation of the tidal elimination damper and the outlet moisture variation is:
Figure BDA0004060924830000064
the transfer function model of the cylinder wall temperature variation and the outlet moisture variation is as follows:
Figure BDA0004060924830000065
specifically, the laplace transform is performed simultaneously on both sides of the differential equation of the inlet moisture variation amount and the outlet moisture variation amount:
Figure BDA0004060924830000066
the processing into a transfer function model is as follows:
Figure BDA0004060924830000067
wherein Δy(s) represents the amount of change in the outlet moisture before and after the inlet moisture is changed; deltaU r (s) represents the variation of inlet moisture; g r (s) represents the ratio of the amount of change in the outlet moisture to the amount of change in the inlet moisture before and after the change in the inlet moisture; s represents complex frequency;
Figure BDA0004060924830000068
step 4: designing a filter in the transfer function model of the inlet moisture variation and the outlet moisture variation in the step 3 as a multiplier term, wherein the filter preferably adopts a first-order low-pass filter to obtain the transfer function model of the inlet moisture variation and the outlet moisture variation with the filter;
the first order low pass filter is preferably:
Figure BDA0004060924830000069
wherein T is Pr A first order low pass filter coefficient for inlet moisture; g LPF (s) represents a low pass filter; s represents complex frequency;
the transfer function model of the inlet moisture variation and the outlet moisture variation with the filter is specifically:
Figure BDA00040609248300000610
step 5: establishing an outlet moisture prediction model of the cut-tobacco dryer based on the transfer function model of the at least one controllable process parameter variable quantity and the outlet moisture variable quantity obtained in the step 3 and the transfer function model of the inlet moisture variable quantity and the outlet moisture variable quantity with the filter obtained in the step 4;
preferably, the cut-tobacco dryer outlet moisture prediction model is:
Figure BDA0004060924830000071
wherein n represents the number of i; y is Y 0 Representing a controllable process parameter or outlet moisture at steady state before the inlet moisture changes;
from the above, in this embodiment, the number of i is 2, so n is equal to 2, and the cut-tobacco dryer outlet moisture prediction model is:
Figure BDA0004060924830000072
step 6: determining specific values of parameters in the cut-tobacco dryer outlet moisture prediction model in the step 5 by adopting a system identification tool, and substituting the specific values to obtain a final cut-tobacco dryer outlet moisture prediction model; the system identification tool is preferably a System Identification system identification tool kit of Matlab, and in this embodiment, the parameter K in the cut-tobacco dryer outlet moisture prediction model in step 5 is set P 、T P 、t d The identification is carried out, and the identification result is as follows:
i K Pi T Pi t di
1 -0.05661 8.8483 20
2 -0.1221 38.146 20
r 1 20 380
the final cut-tobacco dryer outlet moisture prediction model is specifically:
Figure BDA0004060924830000073
fig. 2 is a graph showing the comparison between the predicted value and the actual value of the outlet moisture change caused by the change of the opening of the moisture removal valve in the present embodiment, wherein the abscissa is time, the ordinate is outlet material moisture, the black line represents the actual value of the outlet moisture, and the gray line represents the predicted value of the outlet moisture; as shown in fig. 3, in this embodiment, the predicted value and the actual value of the outlet moisture change caused by the cylinder wall temperature change are compared, wherein the abscissa is time, the ordinate is outlet material moisture, the black line represents the actual value of the outlet moisture, and the gray line represents the predicted value of the outlet moisture; the fact that the predicted value accords with the trend of the actual value is shown in fig. 2 and 3, and the error is small shows that the method for predicting the outlet moisture of the baked wire has the capability of capturing the outlet moisture change brought by the adjustment of the controllable variable, and accurate prediction of the outlet moisture of the baked wire is realized.
The foregoing description of the embodiments of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the technical improvement in the marketplace, or to enable others of ordinary skill in the art to understand the disclosure.

Claims (9)

1. A cut-tobacco dryer outlet moisture prediction method based on transfer function is characterized by comprising the following steps: the method comprises the following steps:
step 1: acquiring relevant data in the production process of the cut-tobacco drier and determining at least one controllable technological parameter as a controllable variable, wherein the relevant data at least comprises brand information, batch information, inlet material flow, inlet moisture, moisture-discharging air gate opening, cylinder wall temperature, hot air volume, hot air temperature and outlet moisture;
step 2: establishing a differential equation of at least one controllable process parameter variation and an outlet moisture variation, and a differential equation of an inlet moisture variation and an outlet moisture variation;
step 3: respectively carrying out Laplace transformation on the differential equation of the at least one controllable technological parameter variable quantity and the outlet water variable quantity and the differential equation of the inlet water variable quantity and the outlet water variable quantity established in the step 2, and processing the differential equation into a corresponding transfer function model;
step 4: a filter is designed in a transfer function model of the inlet moisture variation and the outlet moisture variation in the step 3 to be used as a multiplier term, and a transfer function model of the inlet moisture variation and the outlet moisture variation with the filter is obtained;
step 5: establishing an outlet moisture prediction model of the cut-tobacco dryer based on the transfer function model of the at least one controllable process parameter variable quantity and the outlet moisture variable quantity obtained in the step 3 and the transfer function model of the inlet moisture variable quantity and the outlet moisture variable quantity with the filter obtained in the step 4;
step 6: and (5) determining the specific values of the parameters in the cut-tobacco dryer outlet moisture prediction model in the step (5) by adopting a system identification tool, and substituting the specific values to obtain the final cut-tobacco dryer outlet moisture prediction model.
2. The cut-tobacco dryer outlet moisture prediction method based on transfer function as claimed in claim 1, wherein: the differential equation of the inlet moisture variation and the outlet moisture variation in the step 2 is as follows:
Δy＝K Pr Δu r (t-t dr )
wherein Δy represents the outlet moisture variation; k (K) Pr Representing a gain factor; deltau r Represents the inlet moisture variation; t represents time; t is t dr Representing the time delay from the inlet moisture to the outlet moisture of the dried shreds;
then after the Laplace transformation and the processing in the step 3, the transfer function model of the obtained inlet moisture variation and the outlet moisture variation is:
Figure FDA0004060924810000011
wherein Δy(s) represents the amount of change in the outlet moisture before and after the inlet moisture is changed; deltaU r (s) represents the variation of inlet moisture; g r (s) represents the ratio of the amount of change in the outlet moisture to the amount of change in the inlet moisture before and after the change in the inlet moisture; s represents the complex frequency.
3. The cut-tobacco dryer outlet moisture prediction method based on transfer function as claimed in claim 2, wherein: the filter in the step 4 adopts a first-order low-pass filter.
4. The method for predicting the outlet moisture of the cut-tobacco dryer based on a transfer function as claimed in claim 3, wherein the method comprises the following steps of: the first-order low-pass filter is as follows:
Figure FDA0004060924810000021
wherein T is Pr A first order low pass filter coefficient for inlet moisture; g LPF (s) represents a low pass filter; s represents complex frequency;
the transfer function model of the inlet moisture variation and the outlet moisture variation with the filter in step 4 is:
Figure FDA0004060924810000022
5. the method for predicting outlet moisture of cut-tobacco dryer based on transfer function as claimed in claim 4, wherein: the differential equation of the controllable technological parameter variation and the outlet moisture variation in the step 2 is as follows:
Figure FDA0004060924810000023
wherein i represents a controllable technological parameter, and the number of i is more than or equal to 1; Δy represents the outlet moisture variation; t represents time; t (T) Pi Representing a time constant; k (K) Pi Representing a gain factor; deltau i Representing the variable quantity of controllable technological parameters; t is t di Representing the reaction time delay from the change of the controllable technological parameters to the water at the outlet of the cut-tobacco drier;
then after the Laplace transformation and the processing in the step 3, the obtained transfer function model of the controllable technological parameter variation and the outlet moisture variation is as follows:
Figure FDA0004060924810000024
wherein Δy(s) represents the amount of change in outlet moisture before and after a change in a controllable process parameter; deltaU i (s) represents the variation of a controllable process parameter; g i (s) represents the variation of outlet moisture before and after the variation of the controllable process parametersThe ratio of the amount to the amount of variation of the controllable process parameter; s represents the complex frequency.
6. The method for predicting the outlet moisture of a cut-tobacco dryer based on a transfer function as claimed in claim 5, wherein: the outlet moisture prediction model of the cut tobacco dryer in the step 5 is as follows:
Figure FDA0004060924810000025
wherein n represents the number of i; y is Y 0 Representing the controlled process parameters or the outlet moisture at steady state before the inlet moisture changes.
7. The cut-tobacco dryer outlet moisture prediction method based on transfer function as claimed in claim 2, wherein: the K is Pr Equal to 1.
8. The cut-tobacco dryer outlet moisture prediction method based on transfer function as claimed in claim 1, wherein: the system identification tool in the step 6 is a System Identification system identification tool kit of Matlab.
9. The cut-tobacco dryer outlet moisture prediction method based on transfer function as claimed in claim 1, wherein: the controllable technological parameters in the step 1 are one or more of the opening degree of a moisture removal air valve, the temperature of the cylinder wall, the hot air quantity and the hot air temperature.
CN202310058857.8A 2023-01-18 2023-01-18 Cut-tobacco dryer outlet moisture prediction method based on transfer function Pending CN116076765A (en)

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Publication number Priority date Publication date Assignee Title
CN117329830A (en) * 2023-11-28 2024-01-02 北京万通益生物科技有限公司 Automatic monitoring control system of lactobacillus powder heating and drying equipment
CN117329830B (en) * 2023-11-28 2024-04-05 北京万通益生物科技有限公司 Automatic monitoring control system of lactobacillus powder heating and drying equipment

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