CN111887460A - Tobacco cut-tobacco drying moisture and temperature control prediction system and method - Google Patents
Tobacco cut-tobacco drying moisture and temperature control prediction system and method Download PDFInfo
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- CN111887460A CN111887460A CN201910368970.XA CN201910368970A CN111887460A CN 111887460 A CN111887460 A CN 111887460A CN 201910368970 A CN201910368970 A CN 201910368970A CN 111887460 A CN111887460 A CN 111887460A
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- 235000002637 Nicotiana tabacum Nutrition 0.000 title claims abstract description 78
- 238000001035 drying Methods 0.000 title claims abstract description 37
- 238000000034 method Methods 0.000 title claims abstract description 21
- 244000061176 Nicotiana tabacum Species 0.000 title 1
- 241000208125 Nicotiana Species 0.000 claims abstract description 77
- 238000003062 neural network model Methods 0.000 claims abstract description 38
- 238000012549 training Methods 0.000 claims abstract description 6
- 238000013528 artificial neural network Methods 0.000 claims description 16
- 210000002569 neuron Anatomy 0.000 claims description 8
- 230000004913 activation Effects 0.000 claims description 4
- 238000004088 simulation Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000012795 verification Methods 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 235000019504 cigarettes Nutrition 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000001808 coupling effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
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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
- A24B3/00—Preparing tobacco in the factory
- A24B3/10—Roasting or cooling tobacco
-
- 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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- Drying Of Solid Materials (AREA)
Abstract
The invention provides a method for controlling and predicting moisture and temperature of cut tobacco drying, which comprises the steps of building a neural network model, taking moisture at an inlet of a cut tobacco drying machine, tobacco flake flow, roller rotating speed, roller barrel temperature, hot air speed, hot air temperature and air door opening as input values, taking moisture and temperature at an outlet of the cut tobacco drying machine as output values, obtaining optimal weights of all layers of the neural network model through training, and verifying.
Description
Technical Field
The invention relates to the field of cut tobacco making production of tobacco enterprises, in particular to a system and a method for controlling moisture of cut tobacco drying.
Background
The tobacco shred drying process is an important process in the cigarette tobacco shred manufacturing process, and the tobacco shreds are heated and dried to reach certain moisture so as to meet the process requirements, so that the elasticity and filling capacity of the tobacco shreds are improved, and the quality of the tobacco shreds is improved. The cut tobacco drying process has important influence on the sensory quality such as filling value, cut tobacco breaking rate, aroma characteristic and the like of finished cut tobacco, and the moisture and temperature of a cut tobacco drying outlet are used as key parameters in the cut tobacco drying process, so that the accuracy and stability of the cut tobacco drying process are important evaluation indexes of cut tobacco drying effect.
The interference factors influencing the moisture at the tobacco shred outlet are more in the tobacco shred drying process, and the control process has stronger nonlinearity, uncertainty, coupling property and hysteresis. At present, the moisture control of the cut tobacco is mainly realized by adopting a PID algorithm and adding some feedforward compensation and sequential logic control means, and the control loops are relatively independent and have poor harmony, so that the real closed-loop automatic control is difficult to realize. The moisture control fluctuation of the head and tail sections in the cut tobacco drying process is relatively large, the control quality of the head and tail sections added with the feedforward compensation module is not stable enough, and the control quality of the head and tail sections in the manual control mode is greatly influenced by the experience of operators. Most of the moisture control systems in the tobacco drying process which are put into practical application are difficult to ensure higher control precision and control stability requirements, and meanwhile, the improvement and maintenance of the systems are also difficult.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, it is an object of the present invention to provide a method that can effectively improve the accuracy of the prediction and control of the moisture and temperature at the outlet of the cut-tobacco dryer.
To achieve the above and other related objects, the present invention provides a method for predicting moisture and temperature control of a cut-tobacco, comprising the steps of: 1) collecting moisture at an inlet of a cut tobacco dryer, tobacco flake flow, roller rotating speed, roller temperature, hot air speed, hot air temperature, air door opening degree, moisture at an outlet of the cut tobacco dryer and temperature parameters as samples; 2) building a neural network model, taking the moisture content at the inlet of the cut-tobacco drier, the tobacco flake flow, the rotating speed of a roller, the temperature of the roller, the hot air speed, the hot air temperature and the opening degree of an air door as input values, and taking the moisture content and the temperature at the outlet of the cut-tobacco drier as output values; 3) carrying out simulation training on factors influencing the temperature and the moisture after the cut tobacco is dried by using a neural network to obtain the weight of each layer of the neural network; 4) and calculating or predicting the working condition of the tobacco cut-tobacco drier by using the trained neural network model.
Preferably, in the step 4), the target moisture and temperature at the outlet of the cut-tobacco drier are substituted into the neural network model to obtain the moisture at the inlet of the cut-tobacco drier, the flow rate of tobacco flakes, the rotating speed of the roller, the temperature of the roller, the speed of hot air, the temperature of the hot air and the opening degree of an air door.
Preferably, the step 4) is to bring the moisture at the inlet of the cut-tobacco drier, the flow rate of tobacco flakes, the rotating speed of the roller, the temperature of the roller, the wind speed of hot wind, the temperature of hot wind and the opening degree of the air door into a neural network model to predict the moisture and the temperature at the outlet of the cut-tobacco drier.
Preferably, the neural network model adopts a BP neural network model.
Preferably, the parameters of the BP neural network model are as follows: neuron level 2, neuron number 12, activation function tansig, error function mse, iteration number maximum value 1000 and stop error 0.01.
The patent also discloses a system for realizing the tobacco cut-tobacco drying moisture and temperature control prediction method, which comprises a neural network module, wherein the neural network module takes the moisture at the inlet of the cut-tobacco drying machine, the tobacco flake flow, the roller rotating speed, the roller barrel temperature, the hot air speed, the hot air temperature and the air door opening degree as input values, and the moisture and the temperature at the outlet of the cut-tobacco drying machine as output values.
Preferably, the neural network module adopts a BP neural network model.
As described above, the present invention has the following advantageous effects: according to the tobacco cut-tobacco drying moisture and temperature control prediction method, a neural network model is built, moisture at an inlet of a cut-tobacco drying machine, tobacco flake flow, roller rotating speed, roller barrel temperature, hot air speed, hot air temperature and air door opening degree are used as input values, moisture and temperature at an outlet of the cut-tobacco drying machine are used as output values, the optimal weight of each layer of the neural network model is obtained through training, and through verification, the neural network model can effectively predict the temperature and the moisture content of the dried cut tobacco through the method, and parameters such as the moisture at the inlet of the cut-tobacco drying machine, the tobacco flake flow, the roller rotating speed, the roller barrel temperature, the hot air speed, the hot air temperature and the air door opening degree can be reversely pushed according to the required moisture and temperature at the outlet of the cut-tobacco drying machine, so that a basis is provided for the.
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FIG. 1 is a flow chart of an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided for illustrative purposes, and other advantages and effects of the present invention will become apparent to those skilled in the art from the present disclosure.
Please refer to fig. 1. It should be understood that the structures, ratios, sizes, and the like shown in the drawings and described in the specification are only used for matching with the disclosure of the specification, so as to be understood and read by those skilled in the art, and are not used to limit the conditions under which the present invention can be implemented, so that the present invention has no technical significance, and any structural modification, ratio relationship change, or size adjustment should still fall within the scope of the present invention without affecting the efficacy and the achievable purpose of the present invention.
As shown in FIG. 1, the invention provides a method for predicting moisture and temperature control of cut tobacco drying, which comprises the following steps:
1) firstly, collecting moisture at an inlet of a cut-tobacco drier, tobacco flake flow, roller rotating speed, roller temperature, hot air speed, hot air temperature, air door opening degree, moisture at an outlet of the cut-tobacco drier and temperature parameters as samples.
2) And (3) building a neural network model, wherein the moisture content at the inlet of the cut-tobacco drier, the tobacco flake flow rate, the rotating speed of the roller, the temperature of the roller, the hot air speed, the hot air temperature and the opening degree of the air door are used as input values, and the moisture content and the temperature at the outlet of the cut-tobacco drier are used as output values. The neural network model adopts a BP neural network model, and each parameter of the BP neural network model is as follows: neuron level 2, neuron number 12, activation function tansig, error function mse, iteration number maximum value 1000 and stop error 0.01.
3) And (3) performing simulation training on factors influencing the temperature and the moisture after the cut tobacco is dried by using the neural network model to obtain the weight of each layer of the neural network model.
4) Calculating or predicting the working condition of the tobacco cut-tobacco drier by using the trained neural network model: specifically, the target moisture and temperature at the outlet of the cut-tobacco drier can be substituted into the neural network model, and the moisture at the inlet of the cut-tobacco drier, the flow rate of tobacco flakes, the rotating speed of the roller, the temperature of the roller, the hot air speed, the hot air temperature and the opening degree of an air door can be obtained through calculation of the neural network model. Or parameters such as moisture at the inlet of the cut-tobacco drier, tobacco flake flow, roller rotating speed, roller barrel temperature, hot air speed, hot air temperature, air door opening degree and the like are wholly or partially brought into the neural network model, and the moisture and the temperature at the outlet of the cut-tobacco drier are predicted through the neural network model.
As a specific embodiment:
1. 7 input values and 2 output values required by research are stored in a historical database of a tobacco processing workshop of a Guiyang cigarette factory, and the parameter time of a tobacco dryer is 2018-10-20-2018-12-13. In this study, 527 samples were collected, of which 490 were used in the modeling to build the neural network model, and the remaining 37 to test the model.
2. The parameters of the BP neural network are as follows: neuron level: 2, the number of neurons: 12, activation function: tansig, error function: mse, maximum number of iterations: 1000, stop error 0.01.
3. BP neural network weight analysis, as shown in table 1.
TABLE 1 weights of respective layers of neural networks
4. Further validation was performed using the 37 data obtained, as shown in table 2. The results show that: the average relative error of the temperature is 0.86 percent, and the maximum relative error is 3.16 percent; the average relative error of the water content is 0.11%, and the maximum relative error is 4.80%. The established BP neural network model can be used for predicting the temperature and the water content of the dried tobacco shreds.
TABLE 2 model verification results
5. The target moisture content of the cut tobacco dryer was set to 12.5%, the exit temperature was set to 60 ℃, and the results were as follows:
under the condition of minimum error, parameters: inlet moisture 20%; rotating speed: 10; the flow rate is 2950 kg/h; the temperature of hot air is 103 ℃; the temperature of the cylinder wall is 152 ℃; the wind speed of the hot wind is 0.53 m/s; the damper opening 52.
And obtaining a parameter 238 group meeting the conditions under the conditions that the absolute error of the water content is less than 0.2 percent and the absolute error of the outlet temperature is less than 0.2 percent.
The patent also discloses a system for realizing the tobacco cut-tobacco drying moisture and temperature control prediction method, which comprises a neural network module, wherein the neural network module takes the moisture at the inlet of the cut-tobacco drying machine, the tobacco flake flow, the roller rotating speed, the roller barrel temperature, the hot air speed, the hot air temperature and the air door opening degree as input values, and the moisture and the temperature at the outlet of the cut-tobacco drying machine as output values. The neural network module adopts a BP neural network model.
According to the tobacco cut-tobacco drying moisture and temperature control prediction method, a neural network model is built, moisture at an inlet of a cut-tobacco drying machine, tobacco flake flow, roller rotating speed, roller barrel temperature, hot air speed, hot air temperature and air door opening degree are used as input values, moisture and temperature at an outlet of the cut-tobacco drying machine are used as output values, the optimal weight of each layer of the neural network model is obtained through training, and through verification, the neural network model can effectively predict the temperature and the moisture content of the dried cut tobacco through the method, and parameters such as the moisture at the inlet of the cut-tobacco drying machine, the tobacco flake flow, the roller rotating speed, the roller barrel temperature, the hot air speed, the hot air temperature and the air door opening degree can be reversely pushed according to the required moisture and temperature at the outlet of the cut-tobacco drying machine, so that a basis is provided for the. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (7)
1. A method for controlling and predicting moisture and temperature of tobacco cut-tobacco drying is characterized by comprising the following steps:
1) collecting moisture at an inlet of a cut tobacco dryer, tobacco flake flow, roller rotating speed, roller temperature, hot air speed, hot air temperature, air door opening degree, moisture at an outlet of the cut tobacco dryer and temperature parameters as samples;
2) building a neural network model, taking the moisture content at the inlet of the cut-tobacco drier, the tobacco flake flow, the rotating speed of a roller, the temperature of the roller, the hot air speed, the hot air temperature and the opening degree of an air door as input values, and taking the moisture content and the temperature at the outlet of the cut-tobacco drier as output values;
3) carrying out simulation training on factors influencing the temperature and the moisture after the cut tobacco is dried by using a neural network to obtain the weight of each layer of the neural network;
4) and calculating or predicting the working condition of the tobacco cut-tobacco drier by using the trained neural network model.
2. The tobacco cut-tobacco moisture and temperature control prediction method of claim 1, characterized by: and step 4) substituting the target moisture and temperature at the outlet of the cut-tobacco drier into the neural network model to obtain the moisture at the inlet of the cut-tobacco drier, the flow rate of tobacco flakes, the rotating speed of the roller, the temperature of the roller, the speed of hot air, the temperature of hot air and the opening degree of an air door.
3. The tobacco cut-tobacco moisture and temperature control prediction method of claim 1, characterized by: and step 4) introducing parameters of the inlet moisture of the cut tobacco dryer, the tobacco flake flow, the roller rotating speed, the roller temperature, the hot air speed, the hot air temperature and the air door opening degree into a neural network model to predict the outlet moisture and temperature of the cut tobacco dryer.
4. The tobacco cut-tobacco moisture and temperature control prediction method of claim 1, characterized by: the neural network model adopts a BP neural network model.
5. The tobacco cut-tobacco moisture and temperature control prediction method of claim 1, characterized by: the parameters of the BP neural network model are as follows: neuron level 2, neuron number 12, activation function tansig, error function mse, iteration maximum 1000 and stop error 0.01.
6. A tobacco cut-tobacco drying moisture and temperature control prediction system is characterized in that: the tobacco shred drying machine comprises a neural network module, wherein the neural network module takes moisture at an inlet of the tobacco shred drying machine, tobacco flake flow, roller rotating speed, roller temperature, hot air speed, hot air temperature and air door opening degree as input values, and moisture and temperature at an outlet of the tobacco shred drying machine as output values.
7. The tobacco cut-tobacco moisture and temperature control prediction system of claim 1, wherein: the neural network module adopts a BP neural network model.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112434868A (en) * | 2020-11-30 | 2021-03-02 | 红云红河烟草(集团)有限责任公司 | Sheet drying process accurate control intelligent prediction model and application |
| CN113048777A (en) * | 2021-04-12 | 2021-06-29 | 江苏丰尚智能科技有限公司 | Dryer control method and device based on discharged water content and computer equipment |
| CN113053469A (en) * | 2021-04-12 | 2021-06-29 | 江苏丰尚智能科技有限公司 | Method and device for predicting moisture of drying material, computer equipment and storage medium |
| CN113096747A (en) * | 2021-04-12 | 2021-07-09 | 江苏丰尚智能科技有限公司 | Method and device for predicting moisture content of discharged material of dryer and computer equipment |
| CN113128764A (en) * | 2021-04-19 | 2021-07-16 | 广西中烟工业有限责任公司 | Generation of tobacco dryer outlet water content prediction model and regulation and control method based on same |
| CN113576017A (en) * | 2021-07-15 | 2021-11-02 | 河南中烟工业有限责任公司 | Cut tobacco drying water control method |
| CN113673679A (en) * | 2021-08-19 | 2021-11-19 | 浙江中烟工业有限责任公司 | Cut tobacco drying process parameter selection method based on particle swarm optimization neural network |
| CN114115393A (en) * | 2021-11-04 | 2022-03-01 | 湖北中烟工业有限责任公司 | Method for controlling moisture and temperature at outlet of cut tobacco dryer for sheet cut tobacco making line |
| CN114403486A (en) * | 2022-02-17 | 2022-04-29 | 四川大学 | Intelligent control method of airflow type cut-tobacco drier based on local peak value coding circulation network |
| US11354561B2 (en) * | 2020-07-10 | 2022-06-07 | Zhangjiakou Cigarette Factory Co., Ltd | System and method for on-line analysis of structure of dried shredded tobacco |
| CN115997960A (en) * | 2022-11-30 | 2023-04-25 | 上海烟草集团有限责任公司 | Method, device and system for controlling outlet moisture of cut stem drying machine |
| CN116035249A (en) * | 2023-03-02 | 2023-05-02 | 广东中烟工业有限责任公司 | Cut tobacco water content control method, device, equipment and storage medium |
| CN116172231A (en) * | 2023-03-21 | 2023-05-30 | 上海益实智能科技有限公司 | Method and device for controlling outlet water content of sheet cut-tobacco dryer |
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| US11354561B2 (en) * | 2020-07-10 | 2022-06-07 | Zhangjiakou Cigarette Factory Co., Ltd | System and method for on-line analysis of structure of dried shredded tobacco |
| CN112434868A (en) * | 2020-11-30 | 2021-03-02 | 红云红河烟草(集团)有限责任公司 | Sheet drying process accurate control intelligent prediction model and application |
| CN113096747A (en) * | 2021-04-12 | 2021-07-09 | 江苏丰尚智能科技有限公司 | Method and device for predicting moisture content of discharged material of dryer and computer equipment |
| CN113053469A (en) * | 2021-04-12 | 2021-06-29 | 江苏丰尚智能科技有限公司 | Method and device for predicting moisture of drying material, computer equipment and storage medium |
| CN113048777A (en) * | 2021-04-12 | 2021-06-29 | 江苏丰尚智能科技有限公司 | Dryer control method and device based on discharged water content and computer equipment |
| CN113053469B (en) * | 2021-04-12 | 2022-06-21 | 江苏丰尚智能科技有限公司 | Method and device for predicting moisture of drying material, computer equipment and storage medium |
| CN113128764A (en) * | 2021-04-19 | 2021-07-16 | 广西中烟工业有限责任公司 | Generation of tobacco dryer outlet water content prediction model and regulation and control method based on same |
| CN113576017A (en) * | 2021-07-15 | 2021-11-02 | 河南中烟工业有限责任公司 | Cut tobacco drying water control method |
| CN113576017B (en) * | 2021-07-15 | 2022-09-23 | 河南中烟工业有限责任公司 | Cut tobacco drying water control method |
| CN113673679A (en) * | 2021-08-19 | 2021-11-19 | 浙江中烟工业有限责任公司 | Cut tobacco drying process parameter selection method based on particle swarm optimization neural network |
| CN114115393A (en) * | 2021-11-04 | 2022-03-01 | 湖北中烟工业有限责任公司 | Method for controlling moisture and temperature at outlet of cut tobacco dryer for sheet cut tobacco making line |
| CN114403486A (en) * | 2022-02-17 | 2022-04-29 | 四川大学 | Intelligent control method of airflow type cut-tobacco drier based on local peak value coding circulation network |
| CN115997960A (en) * | 2022-11-30 | 2023-04-25 | 上海烟草集团有限责任公司 | Method, device and system for controlling outlet moisture of cut stem drying machine |
| CN116035249A (en) * | 2023-03-02 | 2023-05-02 | 广东中烟工业有限责任公司 | Cut tobacco water content control method, device, equipment and storage medium |
| CN116172231A (en) * | 2023-03-21 | 2023-05-30 | 上海益实智能科技有限公司 | Method and device for controlling outlet water content of sheet cut-tobacco dryer |
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