CN108142976B - Cut tobacco drying process parameter optimization method - Google Patents
Cut tobacco drying process parameter optimization method Download PDFInfo
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Abstract
本发明公开了一种叶丝干燥工艺参数优化方法，本发明为叶丝干燥工艺参数的优化设置提供了方法，解决了传统方法无法建立叶丝干燥工艺函数模型、难以对工艺参数进行优化设置的问题，同时提高了预测模型的运行效率和预测精度，通过建立轻量化数据驱动预测模型构建叶丝干燥过程中各工艺参数与叶丝含水率之间的映射关系，并依据该映射关系寻求叶丝含水率的最优值和其对应的叶丝干燥最优工艺参数的组合，实现了即使当叶丝干燥工艺参数较多时也可以精确的优化叶丝干燥工艺参数和叶丝含水率。
The invention discloses a method for optimizing process parameters of leaf silk drying. The invention provides a method for optimizing the setting of leaf silk drying process parameters, and solves the problem that the traditional method cannot establish a leaf silk drying process function model and is difficult to optimize the setting of process parameters. At the same time, the operation efficiency and prediction accuracy of the prediction model were improved. By establishing a lightweight data-driven prediction model, the mapping relationship between each process parameter and the moisture content of the silk in the drying process of the silk was constructed, and the silk was found according to the mapping relationship. The combination of the optimal value of moisture content and its corresponding optimal process parameters for silk drying realizes that even when there are many process parameters for silk drying, the process parameters and moisture content of silk can be precisely optimized.
Description
Technical Field
The invention relates to a method for optimizing leaf shred drying process parameters, and belongs to the field of agricultural and sideline product drying.
Background
The leaf shred drying is an important process in the drying process of agricultural and sideline products, and the water content of the leaf shreds is kept stable by optimally setting the process parameters in the drying process so as to improve and control the quality of the leaf shreds. At present, the optimized setting of the cut tobacco drying process parameters mainly depends on the experience of technicians, and the optimized setting method is difficult to adopt, mainly because the cut tobacco drying process is a complex process comprising multi-field multidisciplinary coupling of physics, chemistry and the like, the relationship between each process parameter and the cut tobacco moisture content is very complex, and the functional relationship is difficult to determine by the traditional method.
Disclosure of Invention
The invention provides a cut tobacco drying process parameter optimization method, aiming at solving the problems that a cut tobacco drying process function model cannot be established, the process parameters are difficult to optimize and set and the like in the traditional method.
The technical scheme of the invention is as follows: a cut tobacco drying process parameter optimization method comprises the following steps:
step 1, eliminating abnormal data and error data in cut tobacco drying process parameter data to obtain process parameter data to be optimized;
step 2, performing dimensionality reduction on the process parameter data to be optimized to obtain lightweight parameter data;
step 3, establishing an initial BP neural network, substituting lightweight parameter data and cut tobacco moisture content training data into the initial BP neural network for training to obtain a lightweight data-driven prediction model;
step 4, randomly screening the process parameter data to be optimized to obtain a cut tobacco drying process parameter population;
step 5, performing dimensionality reduction operation on individuals in the cut tobacco drying process parameter population;
step 6, substituting the population individual data subjected to dimensionality reduction into a lightweight data driving prediction model to obtain a predicted value of the water content of the cut tobaccoy i ；
Step 7, convergence judgment: predicting the water content of the cut tobaccoy i With set value of water content of cut tobaccoy 0Making a difference if the difference is less than or equal to the convergence accuracyeAnd outputting the individuals in the cut tobacco drying process parameter population corresponding to the predicted value of the cut tobacco water content, otherwise, not outputting.
And the dimensionality reduction treatment adopts a principal component analysis method.
In the step 7, if the cut tobacco is not output, updating the cut tobacco drying process parameter population, and repeating the steps 5 to 7 until the cut tobacco drying process parameter population is output|y i -y 0 |≤e。
The invention has the beneficial effects that: the method is provided for optimizing the setting of the cut tobacco drying process parameters, the problems that a cut tobacco drying process function model cannot be established and the process parameters are difficult to optimize in the traditional method are solved, the operation efficiency and the prediction accuracy of the prediction model are improved, the mapping relation between each process parameter and the cut tobacco moisture content in the cut tobacco drying process is established by establishing the lightweight data-driven prediction model, the combination of the optimal value of the cut tobacco moisture content and the corresponding optimal cut tobacco drying process parameter is sought according to the mapping relation, and the cut tobacco drying process parameters and the cut tobacco moisture content can be accurately optimized even when the cut tobacco drying process parameters are more.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
Example 1: as shown in fig. 1, a method for optimizing the drying process parameters of cut tobacco comprises the following steps:
step 1, eliminating abnormal data and error data in cut tobacco drying process parameter data to obtain process parameter data to be optimized;
step 2, performing dimensionality reduction on the process parameter data to be optimized to obtain lightweight parameter data;
step 3, establishing an initial BP neural network, substituting lightweight parameter data and cut tobacco moisture content training data into the initial BP neural network for training to obtain a lightweight data-driven prediction model;
step 4, randomly screening the process parameter data to be optimized to obtain a cut tobacco drying process parameter population;
step 5, performing dimensionality reduction operation on individuals in the cut tobacco drying process parameter population;
step 6, substituting the population individual data subjected to dimensionality reduction into a lightweight data driving prediction model to obtain a predicted value of the water content of the cut tobaccoy i ；
Step 7, convergence judgment: predicting the water content of the cut tobaccoy i With set value of water content of cut tobaccoy 0Making a difference if the difference is less than or equal to the convergence accuracyeAnd outputting the individuals in the cut tobacco drying process parameter population corresponding to the predicted value of the cut tobacco water content, otherwise, not outputting.
Further, the dimensionality reduction processing can be set to adopt a principal component analysis method.
Example 2: as shown in fig. 1, a method for optimizing the drying process parameters of cut tobacco comprises the following steps:
step 1, eliminating abnormal data and error data in cut tobacco drying process parameter data to obtain process parameter data to be optimized;
step 2, performing dimensionality reduction on the process parameter data to be optimized to obtain lightweight parameter data;
step 3, establishing an initial BP neural network, substituting lightweight parameter data and cut tobacco moisture content training data into the initial BP neural network for training to obtain a lightweight data-driven prediction model;
step 4, randomly screening the process parameter data to be optimized to obtain a cut tobacco drying process parameter population;
step 5, performing dimensionality reduction operation on individuals in the cut tobacco drying process parameter population;
step 6, substituting the population individual data subjected to dimensionality reduction into a lightweight data driving prediction model to obtain a predicted value of the water content of the cut tobaccoy i ；
Step 7, convergence judgment: predicting the water content of the cut tobaccoy i With set value of water content of cut tobaccoy 0Making a difference if the difference is less than or equal to the convergence accuracyeOutputting individuals in the cut tobacco drying process parameter population corresponding to the predicted value of the cut tobacco water content, otherwise updating the cut tobacco drying process parameter population, and repeating the steps 5 to 7 until the step is finished|y i -y 0 |≤e。
Further, the dimensionality reduction processing can be set to adopt a principal component analysis method.
Example 3: as shown in fig. 1, a method for optimizing the drying process parameters of cut tobacco comprises the following steps:
step 1, removing abnormal data and error data in the cut tobacco drying process parameter data to obtain process parameter data to be optimized, as shown in table 1:
table 1:
variable x in Table 14The data of (2) is no longer changed from the previous data starting from line 8, variable x8All the data of (1) are 0 from the 9 th row, so the data after the 8 th row are eliminated; variable x9All the data of (1) are 0, so the variable x is eliminated9Obtaining the data of the process parameters to be optimized shown in the table 2:
table 2:
step 2, performing dimensionality reduction on the process parameter data to be optimized to obtain lightweight parameter data, such as: establishing the following dimensionality reduction formula by using a principal component analysis method:
principal component 1= 0.050027 × x1+0.020110*x2-0.257952*x3-0.035438*x4+0.386489*x5
+0.443235*x6-0.000825*x7+0.144985*x8+0.014484*x10-0.004486*x11-0.027720*x12
-0.051757*x13+0.080297*x14+0.013425*x15-0.018750*x16-0.016826*x17
Principal component 2= -0.055614 × x1-0.030874*x2+0.506334*x3-0.012715*x4-0.103676*x5
-0.210429*x6-0.052316*x7+0.222817*x8+0.015285*x10-0.015888*x11+0.029754*x12
+0.049319*x13+0.310010*x14+0.283559*x15-0.037438*x16-0.005331*x17
Principal component 3= -0.186842 × x1+0.033591*x2-0.069666*x3-0.150656*x4-0.002547*x5
+0.013832*x6+0.018056*x7-0.102104*x8+0.391237*x10-0.316293*x11-0.102645*x12
+0.029974*x13+0.073417*x14+0.137466*x15+0.094621*x16+0.404656*x17
Dividing the data (x is divided) of the process parameters x 1-x 17 to be optimized, which are obtained in the step 19) Respectively substituting the dimensionality reduction formulas of the main components 1-3 to obtain the lightweight parameter data and the cut tobacco water content shown in the table 3Training data:
table 3:
step 3, establishing an initial BP neural network, substituting lightweight parameter data and cut tobacco moisture content training data into the initial BP neural network for training to obtain a lightweight data-driven prediction model, wherein the lightweight data-driven prediction model comprises the following steps: taking the principal components 1-3 as input variables, taking y as an output variable, substituting the y into a neural network for training, wherein the constructed mapping relation between the principal components 1-3 and y is a lightweight data-driven prediction model;
and 4, randomly screening the process parameter data to be optimized to obtain a cut tobacco drying process parameter population, wherein the process parameter population comprises the following steps: obtaining data (x is divided) of the process parameters x 1-x 17 to be optimized from the step 19) Respectively randomly screening one data to obtain one individual of the population, repeating the screening process for 5 times to obtain 5 individuals, wherein the 5 individuals form a cut tobacco drying process parameter population, and the process parameter population is shown in table 4:
table 4:
and 5, performing dimensionality reduction treatment on individuals in the cut tobacco drying process parameter population, such as: substituting 5 individuals of the cut leaf drying process parameter population in the step 4 into the dimensionality reduction formula in the step 2 respectively, wherein the result is shown in a table 5:
table 5:
step 6, substituting the population individual data subjected to dimensionality reduction into a lightweight data driving prediction model to obtain a predicted value of the water content of the cut tobaccoy i(ii) a Such as: each time a group of data of main components 1-3 is input, a predicted value of the water content of the cut tobacco can be obtainedy iAs shown in table 6:
table 6:
and 7, judging convergence. Specifically, the predicted value of the water content of the cut tobacco leavesy iWith set value of water content of cut tobaccoy 0Making a difference if the difference is less than or equal to the convergence accuracyeObtaining the optimal value of the water content of the cut tobacco, outputting the combination of the optimal technological parameters of cut tobacco drying corresponding to the optimal value, and ending the step; if the difference is greater than the convergence accuracyeUpdating the cut tobacco drying process parameter population, and repeating the steps 5 to 7 until the cut tobacco drying process parameter population is updated|y i-y 0 |≤e. Such as: convergence accuracy measuree=0.001, the set value of the water content of the cut tobacco leaves is takeny 0= 12.7, wheny iWhen the power is not less than = 12.699915,|y i -y 0 |if =0.000585 ≦ 0.001, theny i= 12.699915 is the optimal value of water content of cut tobacco, and the corresponding data of cut tobacco drying process parameters x 1-x 17 before individual 3 dimensionality reduction (except x)9) The combination of the optimal process parameters is obtained, and the step is finished; when in use|y i -y 0 |And (3) when the water content of the cut tobacco is more than or equal to 0.001, if the combination of the optimal value of the water content of the cut tobacco and the optimal process parameters is not obtained, selecting, crossing and mutating the individuals 1-5 by utilizing a genetic algorithm, generating new individuals and populations, and repeatedly performing the steps 5-7 by using the new individuals and the new populations until the convergence condition is met.
While the present invention has been described in detail with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.
Claims (2)
1. A cut tobacco drying process parameter optimization method is characterized by comprising the following steps: the method comprises the following steps:
step 1, eliminating abnormal data and error data in cut tobacco drying process parameter data to obtain process parameter data to be optimized;
step 2, performing dimensionality reduction on the process parameter data to be optimized to obtain lightweight parameter data;
step 3, establishing an initial BP neural network, substituting lightweight parameter data and cut tobacco moisture content training data into the initial BP neural network for training to obtain a lightweight data-driven prediction model;
step 4, randomly screening the process parameter data to be optimized to obtain a cut tobacco drying process parameter population;
step 5, performing dimensionality reduction operation on individuals in the cut tobacco drying process parameter population;
step 6, substituting the population individual data subjected to dimensionality reduction into a lightweight data driving prediction model to obtain a predicted value y of the water content of the cut tobaccoi；
Step 7, convergence judgment: predicting the water content of the cut tobaccoiAnd the set value y of the water content of the cut tobacco0Making a difference, if the difference value is less than or equal to the convergence precision e, outputting individuals in the cut tobacco drying process parameter population corresponding to the predicted value of the cut tobacco moisture content, otherwise, not outputting the individuals;
in the step 7, if the cut tobacco is not output, updating the cut tobacco drying process parameter population, and repeating the steps 5 to 7 until the value of y is up toi-y0|≤e。
2. The method for optimizing the parameters of the leaf shred drying process according to claim 1, wherein the method comprises the following steps: and the dimensionality reduction treatment adopts a principal component analysis method.
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| CN109506450B (en) * | 2018-10-24 | 2020-06-02 | 浙江工业大学 | A neural network predictive control method for humidity in the automatic drying process of Chinese herbal decoction pieces |
| CN110973686B (en) * | 2019-12-13 | 2022-02-08 | 红云红河烟草(集团)有限责任公司 | Method for establishing accurate moisture control model in silk making process |
| CN112380760B (en) * | 2020-10-13 | 2023-01-31 | 重庆大学 | Multi-objective process parameter intelligent optimization method based on multi-algorithm fusion |
| CN112257948A (en) * | 2020-10-30 | 2021-01-22 | 红云红河烟草(集团)有限责任公司 | Method, device and equipment for predicting moisture content at the outlet of moistening leaf |
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