A method for predicting moisture content at the outlet of tofu drying machine based on transfer function

Abstract

The present invention discloses a method for predicting moisture at the outlet of a torrefied wire drying machine based on a transfer function, comprising the following steps: obtaining relevant data in the production process of the torrefied wire drying machine and determining at least one controllable process parameter as a controllable variable; establishing differential equations for at least one controllable process parameter change and inlet moisture change and outlet moisture change, and differential equations for inlet moisture change and outlet moisture change, respectively; performing Laplace transforms respectively and processing into corresponding transfer function models; designing filters as multiplier terms in the transfer function models of inlet moisture change and outlet moisture change; establishing a torrefied wire drying machine outlet moisture prediction model; and using a system identification tool to determine the specific values ​​of the parameters in the torrefied wire drying machine outlet moisture prediction model. The present application performs linear modeling on controllable variables, realizes accurate prediction of the outlet moisture of torrefied wire, and solves the problem of difficulty in establishing a white box prediction model.

Description

Technical Field

[0001] The present application relates to the technical field of tobacco shreds, and in particular to a method for predicting moisture at a shredded tobacco drying machine outlet based on a transfer function. Background Art

[0002] In the tobacco processing process, the drying process is an important processing step for material moisture control, and its production quality will affect the quality and taste of finished cigarettes. The outlet moisture is an important process indicator of the drying process, and the accuracy of the real-time prediction of the outlet moisture has a direct impact on the control of the outlet moisture of the drying machine and the process indicators of subsequent processes. With the development of the research on the drying mechanism and mathematical modeling, the method of establishing a mechanism prediction model is used to predict the outlet moisture of the drying machine, but there are two difficulties: First, in the production process of drying, there are variables related to the drying mechanism that are difficult to collect directly, such as the exhaust air lost from the material outlet, the humidity of the hot air in the front room, etc.; for some variables that can be collected directly, there is also a problem of difficulty in evaluating their measurement errors, which makes it difficult to establish a pure mechanism white box model. Second, the controllable variables of the drying machine are mainly the four parameters of the exhaust air door opening, the cylinder wall temperature, the hot air volume, and the hot air temperature. The other parameters hardly change during the production process. If all parameters are considered, there will be a situation where there is not enough data to support the modeling. Summary of the invention

[0003] In order to solve at least one aspect of the above problems, the present invention provides a method for predicting moisture at the outlet of a tofu drying machine based on a transfer function, comprising the following steps:

[0004] Step 1: Obtain relevant data in the production process of the tofu drying machine and determine at least one controllable process parameter as a controllable variable, wherein the relevant data at least includes brand information, batch information, inlet material flow, inlet moisture, dehumidification door opening, cylinder wall temperature, hot air volume, hot air temperature and outlet moisture;

[0005] Step 2: Establish at least one differential equation between the change in the controllable process parameter and the change in the outlet moisture content, and a differential equation between the change in the inlet moisture content and the change in the outlet moisture content;

[0006] Step 3: performing Laplace transformation on the differential equation between the change amount of at least one controllable process parameter and the change amount of outlet moisture, and the differential equation between the change amount of inlet moisture and the change amount of outlet moisture established in step 2, and processing them into corresponding transfer function models;

[0007] Step 4: Design a filter as a multiplier term in the transfer function model of the inlet moisture change and the outlet moisture change in step 3 to obtain a transfer function model of the inlet moisture change and the outlet moisture change with a filter;

[0008] Step 5: Based on the transfer function model of at least one controllable process parameter change and outlet moisture change obtained in step 3 and the transfer function model of inlet moisture change and outlet moisture change with filter obtained in step 4, a tofu drying machine outlet moisture prediction model is established;

[0009] Step 6: Use the system identification tool to determine the specific values ​​of the parameters in the dryer outlet moisture prediction model in step 5 and substitute them to obtain the final dryer outlet moisture prediction model.

[0010] Preferably, the differential equation of the inlet moisture change and the outlet moisture change in step 2 is:

[0011] Δy=K Pr Δu r (tt dr )

[0012] Among them, Δy represents the change in outlet moisture; K Pr Represents the gain coefficient; Δu r represents the change in inlet moisture; t represents time; t dr Represents the time delay from the moisture inlet to the moisture outlet of the dried silk;

[0013] After the Laplace transform and processing in step 3, the transfer function model of the inlet moisture change and the outlet moisture change is obtained as follows:

[0014]

[0015] Among them, ΔY(s) represents the change in outlet moisture before and after the inlet moisture changes; ΔU r (s) represents the change in inlet moisture; G r (s) represents the ratio of the change in outlet moisture to the change in inlet moisture before and after the inlet moisture changes; s represents the complex frequency.

[0016] Preferably, the filter in step 4 is a first-order low-pass filter.

[0017] Preferably, the first-order low-pass filter is:

[0018]

[0019] Among them, T Pr is the first-order low-pass filter coefficient of the inlet moisture; G LPF (s) represents a low-pass filter; s represents a complex frequency;

[0020] Then the transfer function model of the inlet moisture change and outlet moisture change with the filter in step 4 is:

[0021]

[0022] Preferably, the differential equation of the change in the controllable process parameter and the change in the outlet moisture in step 2 is:

[0023]

[0024] Where i represents the controllable process parameter, and the number of i is greater than or equal to 1; Δy represents the change in outlet moisture; t represents time; T Pi Represents the time constant; K Pi Represents the gain coefficient; Δu i Represents the change of controllable process parameters; t di It represents the reaction time delay from the change of controllable process parameters to the moisture content at the outlet of the tofu drying machine;

[0025] After the Laplace transform and processing in step 3, the transfer function model of the change in controllable process parameters and the change in outlet moisture is obtained as follows:

[0026]

[0027] Among them, ΔY(s) represents the change in outlet moisture before and after the controllable process parameters are changed; ΔU i (s) represents the change of controllable process parameters; G i (s) represents the ratio of the change in outlet moisture before and after the controllable process parameters are changed to the change in the controllable process parameters; s represents the complex frequency.

[0028] Preferably, the moisture prediction model at the outlet of the tofu drying machine in step 5 is:

[0029]

[0030] Where n represents the number of i; Y0 represents the outlet moisture at steady state before the controllable process parameters or inlet moisture change.

[0031] Preferably, the K Pr Equal to 1.

[0032] Preferably, the system identification tool in step 6 is the System Identification toolbox of Matlab.

[0033] Preferably, the controllable process parameters in step 1 are one or more of the dehumidification damper opening, the cylinder wall temperature, the hot air volume, and the hot air temperature.

[0034] The present invention provides a method for predicting moisture at the outlet of a tofu drying machine based on a transfer function, which has the following beneficial effects:

[0035] Based on the transfer function, the present application establishes a linear prediction model for the change in controllable process parameters and the change in inlet moisture and outlet moisture for the steady-state stage of the silk drying process, which simplifies the description of the silk drying process, solves the problem of difficulty in establishing a white-box prediction model, improves the calculation speed and reduces the calculation overhead; when the controllable variables are adjusted, the changing trend of the outlet moisture can be accurately predicted, and the accurate prediction of the outlet moisture of the silk drying machine is achieved, laying the foundation for the prediction model for the predictive control of the silk drying machine; by designing a first-order low-pass filter in the transfer function model of the inlet moisture change and the outlet moisture change to filter out the noise of the sensor itself and the random disturbance in the production process, the accuracy of the prediction model of the present application is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to better understand the above and other purposes, features, advantages and functions of the present invention, reference may be made to the embodiments shown in the accompanying drawings. The same reference numerals in the accompanying drawings refer to the same components. It should be understood by those skilled in the art that the accompanying drawings are intended to schematically illustrate the preferred embodiments of the present invention and have no limiting effect on the scope of the present invention, and the components in the drawings are not drawn to scale.

[0037] Figure 1 A schematic diagram showing a method for predicting moisture at a tofu drying machine outlet based on a transfer function according to an embodiment of the present invention is shown;

[0038] Figure 2 A comparison diagram between the predicted value and the actual value of the outlet moisture change caused by the change in the opening of the moisture exhaust damper according to an embodiment of the present invention is shown;

[0039] Figure 3 A comparison diagram between the predicted value and the actual value of the outlet moisture change caused by the cylinder wall temperature change according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0040] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0041] As used herein, the term "including" and its variations mean open inclusion, i.e., "including but not limited to". Unless otherwise stated, the term "or" means "and / or". The term "based on" means "based at least in part on". The terms "an example embodiment" and "an embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0042] In order to at least partially solve the above problems and one or more of other potential problems, an embodiment of the present disclosure proposes a method for predicting moisture at the outlet of a tofu drying machine based on a transfer function, such as Figure 1 As shown, the following steps are included:

[0043] Step 1: Obtain relevant data in the production process of the tofu drying machine and determine at least one controllable process parameter as a controllable variable; the relevant data include brand information, batch information, process standards, inlet material flow, inlet moisture, dehumidification air door opening, front thin plate temperature, middle thin plate temperature, rear thin plate temperature, cylinder wall temperature, hot air volume, hot air fan frequency, hot air temperature, drum speed and outlet moisture; preferably, the controllable process parameters are one or more of dehumidification air door opening, cylinder wall temperature, hot air volume and hot air temperature. In this embodiment, the controllable process parameters are selected as cylinder wall temperature and dehumidification air door opening.

[0044] Step 2: Establish at least one differential equation between the change in the controllable process parameter and the change in the outlet moisture content, and a differential equation between the change in the inlet moisture content and the change in the outlet moisture content; preferably, establish a first-order differential equation between the change in the controllable process parameter and the change in the outlet moisture content, and a first-order differential equation between the change in the inlet moisture content and the change in the outlet moisture content;

[0045] Preferably, the first-order differential equation of the change in controllable process parameters and the change in outlet moisture is:

[0046]

[0047] Where i represents the controllable process parameter, and the number of i is greater than or equal to 1; Δy represents the change in outlet moisture; t represents time; T pi Represents the time constant; K Pi Represents the gain coefficient; Δu i Represents the change of controllable process parameters; t di It represents the reaction time delay from the change of controllable process parameters to the moisture content at the outlet of the tofu drying machine;

[0048] In this embodiment, the number i is 2, 1 represents the opening of the dehumidification damper, and 2 represents the cylinder wall temperature. Then the first-order differential equation of the change in the dehumidification damper opening and the change in the outlet moisture is:

[0049]

[0050] The first-order differential equation of the change in cylinder wall temperature and outlet moisture is:

[0051]

[0052] Preferably, the first-order differential equation of the inlet moisture change and the outlet moisture change is:

[0053] Δy=K Pr Δu r (tt dr )

[0054] Among them, Δy represents the change in outlet moisture; K pr represents the gain factor, preferably, K pr =1;Δu r represents the change in inlet moisture; t represents time; t dr Represents the time delay from the moisture at the inlet to the outlet of the drying wire.

[0055] Step 3: performing Laplace transformation on the differential equation between the change amount of at least one controllable process parameter and the change amount of outlet moisture, and the differential equation between the change amount of inlet moisture and the change amount of outlet moisture established in step 2, and processing them into corresponding transfer function models;

[0056] Specifically, Laplace transform is performed on both sides of the differential equation of the change in controllable process parameters and the change in outlet moisture content:

[0057]

[0058] The transfer function model is:

[0059] Among them, ΔY(s) represents the change in outlet moisture before and after the controllable process parameters are changed; ΔU i (s) represents the change of controllable process parameters; G i (s) represents the ratio of the change in outlet moisture before and after the controllable process parameters are changed to the change in the controllable process parameters; s represents the complex frequency;

[0060] In this embodiment, the transfer function model of the change in the opening of the dehumidification damper and the change in the outlet moisture is:

[0061]

[0062] The transfer function model of the change in cylinder wall temperature and outlet moisture is:

[0063]

[0064] Specifically, Laplace transform is performed on both sides of the differential equation of the inlet moisture change and the outlet moisture change:

[0065]

[0066] The transfer function model is:

[0067] Among them, ΔY(s) represents the change in outlet moisture before and after the inlet moisture changes; ΔU r (s) represents the change in inlet moisture; G r (s) represents the ratio of the change in outlet moisture to the change in inlet moisture before and after the inlet moisture changes; s represents the complex frequency;

[0068] Step 4: Design a filter as a multiplier term in the transfer function model of the inlet moisture change amount and the outlet moisture change amount in step 3. Preferably, the filter adopts a first-order low-pass filter to obtain a transfer function model of the inlet moisture change amount and the outlet moisture change amount with the filter;

[0069] The first-order low-pass filter is preferably:

[0070] Among them, T Pr is the first-order low-pass filter coefficient of the inlet moisture; G LPF (s) represents a low-pass filter; s represents a complex frequency;

[0071] Then the transfer function model of the inlet moisture change and outlet moisture change with the filter is as follows:

[0072]

[0073] Step 5: Based on the transfer function model of at least one controllable process parameter change and outlet moisture change obtained in step 3 and the transfer function model of inlet moisture change and outlet moisture change with filter obtained in step 4, a tofu drying machine outlet moisture prediction model is established;

[0074] Preferably, the moisture prediction model at the outlet of the tofu drying machine is:

[0075]

[0076] Where n represents the number of i; Y0 represents the outlet moisture at steady state before the controllable process parameters or inlet moisture change;

[0077] As can be seen from the above, in this embodiment, the number of i is 2, so n is equal to 2, and the moisture prediction model of the tofu drying machine outlet is:

[0078]

[0079] Step 6: Use a system identification tool to determine the specific values ​​of the parameters in the dryer outlet moisture prediction model in step 5 and substitute them into the final dryer outlet moisture prediction model; the system identification tool is preferably Matlab's SystemIdentification system identification toolbox. In this embodiment, the parameter K in the dryer outlet moisture prediction model in step 5 is P , T P ,t d The identification results are as follows:

[0080] i <![CDATA[K Pi ]]> <![CDATA[T Pi ]]> <![CDATA[t di ]]> 1 -0.05661 8.8483 20 2 -0.1221 38.146 20 r 1 20 380

[0081] The final prediction model of moisture content at the outlet of the tofu drying machine is as follows:

[0082]

[0083] like Figure 2 : is a comparison chart of the predicted value and actual value of the outlet moisture change caused by the change in the opening of the moisture exhaust damper in this embodiment, wherein the horizontal axis is time, the vertical axis is the outlet material moisture, the black line represents the actual value of the outlet moisture, and the gray line represents the outlet moisture predicted value; Figure 3 The figure shows the comparison between the predicted value and the actual value of the outlet moisture change caused by the change of the drum wall temperature in this embodiment, wherein the horizontal axis is time, the vertical axis is the outlet material moisture, the black line represents the actual value of the outlet moisture, and the gray line represents the outlet moisture predicted value; Figure 2 and Figure 3 It can be seen that the trend of the predicted value is consistent with the actual value, and the error is small, which shows that the dried silk outlet moisture prediction method of the present application is capable of capturing the changes in outlet moisture caused by the adjustment of controllable variables and realizing accurate prediction of the dried silk outlet moisture.

[0084] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the present invention.

Claims

1. A method for predicting moisture at the outlet of a tofu drying machine based on a transfer function, characterized in that: The steps include: Step 1: Obtain relevant data in the production process of the tofu drying machine and determine at least one controllable process parameter and inlet moisture as input variables, and outlet moisture as output variables, wherein the relevant data at least includes brand information, batch information, inlet material flow, inlet moisture, dehumidification door opening, cylinder wall temperature, hot air volume, hot air temperature and outlet moisture; the controllable process parameter is one or more of dehumidification door opening, cylinder wall temperature, hot air volume and hot air temperature; Step 2: Establish at least one differential equation between the change in the controllable process parameter and the change in the outlet moisture content, and a differential equation between the change in the inlet moisture content and the change in the outlet moisture content; The differential equation of the change in controllable process parameters and the change in outlet moisture is: Where i represents the controllable process parameter, and the number of i is greater than or equal to 1; Δy represents the change in outlet moisture; t represents time; T Pi Represents the time constant; K Pi Represents the gain coefficient; Δu i Represents the change of controllable process parameters; t di It represents the reaction time delay from the change of controllable process parameters to the moisture content at the outlet of the tofu drying machine; The differential equation of the inlet moisture change and the outlet moisture change is: Δy=K Pr Δu r (t-t dr ) Among them, Δy represents the change in outlet moisture; K Pr Represents the gain coefficient; Δu r represents the change in inlet moisture; t represents time; t dr Represents the time delay from the moisture inlet to the moisture outlet of the dried silk; Step 3: performing Laplace transformation on the differential equation between the change amount of at least one controllable process parameter and the change amount of outlet moisture, and the differential equation between the change amount of inlet moisture and the change amount of outlet moisture established in step 2, and processing them into corresponding transfer function models; The transfer function model of the change in controllable process parameters and the change in outlet moisture is: Among them, ΔY(s) represents the change in outlet moisture before and after the controllable process parameters are changed; ΔU i (s) represents the change of controllable process parameters; G i (s) represents the ratio of the change in outlet moisture before and after the controllable process parameters are changed to the change in the controllable process parameters; s represents the complex frequency; The transfer function model of the inlet moisture change and the outlet moisture change is: Among them, ΔY(s) represents the change in outlet moisture before and after the inlet moisture changes; ΔU r (s) represents the change in inlet moisture; G r (s) represents the ratio of the change in outlet moisture to the change in inlet moisture before and after the inlet moisture changes; s represents the complex frequency; Step 4: Design a first-order low-pass filter in the transfer function model of the inlet moisture change and the outlet moisture change in step 3. As a multiplier term, the transfer function model of the inlet moisture change and the outlet moisture change with the filter is obtained as follows: Among them, T Pr is the first-order low-pass filter coefficient of the inlet moisture; G LPF (s) represents a low-pass filter; s represents a complex frequency; Step 5: Based on the transfer function model of at least one controllable process parameter change and outlet moisture change obtained in step 3, and the transfer function model of inlet moisture change and outlet moisture change with filter obtained in step 4, a prediction model of outlet moisture of the tofu drying machine is established as follows: Where n represents the number of i; Y0 represents the outlet moisture at steady state before the controllable process parameters or inlet moisture change; Step 6: Use the system identification tool to determine the specific values ​​of the parameters in the dryer outlet moisture prediction model in step 5 and substitute them to obtain the final dryer outlet moisture prediction model.

2. The method for predicting moisture at the outlet of a tofu drying machine based on transfer function according to claim 1, characterized in that: The K Pr is equal to 1.

3. The method for predicting moisture at the outlet of a tofu drying machine based on transfer function according to claim 1, characterized in that: The system identification tool in step 6 is the System Identification toolbox of Matlab.

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