CN115599055B - Intelligent control method and system for wire making and water adding quantity based on mechanism prediction model - Google Patents
Intelligent control method and system for wire making and water adding quantity based on mechanism prediction model Download PDFInfo
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Abstract
The invention discloses a method and a system for intelligently controlling water adding quantity in cut tobacco making based on a mechanism prediction model, belongs to the field of intelligent control, and particularly relates to water adding quantity prediction feedforward control of a cut tobacco loosening and conditioning process and a leaf wetting and feeding process of cigarette making, which are used for the advanced prediction of outlet water and the optimal control of water adding quantity. According to the invention, a mechanism model is established according to material balance, and is further extended to a material head stage, so that the mechanism model can be suitable for controlling water content of the material head, effective assistance is provided for solving the problem that the calculated value of the water addition amount is inaccurate in the initial stage of material feeding, the whole process of predicting and controlling the water addition amount in the process of wire making is realized, the stability of outlet water content is finally ensured, the standard deviation of the outlet water content is effectively reduced, and the problems that the control effect is not ideal and the material head lacks a suitable control method for ensuring the outlet water content are solved.
Description
Technical Field
The invention belongs to the field of intelligent control, and particularly relates to a method and a system for intelligently controlling the water adding quantity of filament making based on a mechanism prediction model.
Background
The ultra-return loosening and leaf wetting feeding materials are used as main processing procedures for influencing the quality of cut tobacco by a cut tobacco making line, and the process stability of the ultra-return loosening and leaf wetting feeding materials has direct influence on the technological indexes of the subsequent procedures. The current control scheme of the ultra-return loosening and leaf wetting feeding procedure on the water content of the outlet is traditional feedback control, the control effect is not ideal, and the phenomenon that the water content is not matched with the change of the inlet water content and even is completely misplaced easily occurs. The current industry research mainly focuses on outlet moisture control in a steady-state production stage, and few researches on the moisture control of a stub bar, namely, the production stage before entering into the production steady-state are carried out, but according to a visiting process, operators and experience obtained by observing historical data, the water addition amount control of the stub bar is very critical to the overall stability of outlet moisture in a material.
Disclosure of Invention
Aiming at the problems in the background art, the invention provides an intelligent control method and system for the water adding quantity of silk making based on a mechanism prediction model, which solve the problems of unsatisfactory control effect and lack of a proper control method for a stub bar to ensure outlet moisture. For a steady-state production stage, a mechanism model from inlet moisture and water adding amount to outlet moisture is established, so that accurate prediction of the outlet moisture of the stage is realized; aiming at the current situation of gap of the material head stage, a semi-mechanism model established based on a steady-state stage is extended, so that the model can be suitable for predicting the water content of the material head, and effective assistance is provided for solving the problem of inaccurate calculated value of the water addition amount in the initial stage of the material. Finally, based on the model, a water adding quantity feedforward control system is designed, and the traditional feedback control is assisted to overcome the problems.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
A method for intelligently controlling the water adding quantity of silk making based on a mechanism prediction model comprises the following steps:
step 1: acquiring relevant data of variables in the loosening, conditioning and leaf wetting feeding process;
Step 2: cutting the working condition of the production process, tracing the current outlet moisture to the measured value of the corresponding input variable at each corresponding moment, and then filtering the inlet moisture by adopting a Kalman filter;
step 3: according to the fact that the moisture entering the roller is equal to the moisture exiting the roller and the dry matter entering the roller is equal to the dry matter exiting the roller, namely the moisture and the dry material (tobacco leaves) are conserved in mass, a mechanism model is built;
Step 4: transforming based on a mechanism model, online updating model parameters in a moving average mode, further constructing a model for predicting the outlet moisture in the material, expanding the model to a material head stage, respectively and offline estimating a default model parameter by using respective historical data according to each brand, and predicting by using the default value at the material head;
Step 5: and (3) establishing a water adding amount calculation algorithm according to the outlet water content prediction model to realize water adding amount prediction control.
In view of the fact that the ultra-return loose or leaf-wetting feed itself is a complex system with large delay and hysteresis, the invention establishes a mechanism model from inlet moisture and water addition to outlet moisture for the steady-state production stage, and extends the application range of the mechanism model to the stub bar.
In the step 1, the related data comprise brand information, batch information, process standards, inlet material flow, inlet moisture, water adding amount, direct injection steam flow, outlet moisture and feeding amount;
further, in the step 2, the working condition of the production process is split into: the material head, the material middle, the material tail and the non-production stage, the measured value of the corresponding input variable corresponding to the current outlet moisture is traced back, the outlet material moisture is divided into a time stamp t, the measured value of the corresponding input variable corresponding to the current outlet moisture is traced back, and the corresponding time stamps of other variables are as follows:
inlet material flow Q mi is t-t d,mi;
inlet feed moisture lambda i is t-t d,λi;
the water adding amount u is t-t d,u;
The outlet material moisture lambda o is t.
Filtering the inlet water by adopting a Kalman filter;
Further, in the step 3, the conservation equation expression is as follows, according to the conservation of the moisture and the mass of the dry material (tobacco leaf):
qmi+qdstm+u+qmstm+qair,i＝qmo+qair,o
qtbc,i＝qtbc,o＝qtbc
Wherein q mi is drum inlet moisture flow, q dstm is direct injection steam flow, u is water adding amount, q mstm is atomization steam flow, q air,i is hot air moisture flow, q mo is drum outlet moisture flow, q air,o is moisture exhaust air moisture flow, q tbc,i is drum inlet dry tobacco flow, and q tbc,o is drum outlet dry tobacco flow.
Further, compared with the water flow brought by directly adding water, the hot air water flow q air,i and the direct injection steam flow q dstm are both negligible; in the production process, the moisture removal air door and the atomization steam valve are kept at fixed opening degrees, q air,o-qair,i-qmstm-qdstm can be considered to be close to a fixed constant, and the constant is recorded as A, so that the moisture conservation equation can be modified as follows:
A＝u+qmi-qmo＝u+λi·Qmi+λo·Qmo
Wherein lambda o,λi is the inlet and outlet material moisture, Q mo,Qmi is the inlet and outlet material flow, and A is a fixed constant term.
Further, according to the conservation of the flow rate of the dry tobacco, namely q tbc,i＝qtbc,o＝qtbc, the following steps are obtained:
qtbc＝(1-λi)Qmi＝(1-λo)Qmo，
the drum moisture flow can be calculated from this as:
Further, according to the water adding amount u at the current moment and q mi before the corresponding delay, calculating to obtain an outlet water flow predicted value at the future moment And outlet water contentThe mathematical expression is:
Further, in the step 4, a partial modification is made based on the above mechanism, and the estimation of a is updated online in a manner of performing a moving average treatment on the variable and then calculating the intermediate variable in the in-material stage, and the estimated value of a is estimated at the current time k The mathematical expression of (2) is:
Wherein k=int (T/T s),Ts is the sampling period of the control algorithm program, k d,u,kd,dstm and k d,λi are the values of the corresponding variables after time delay discretization, and the calculation mode is the same as k;
Wherein the method comprises the steps of Is the average value of the water adding amount in a sliding window with a certain length after k d,u time delays,For the outlet moisture flow in a length of sliding window,For the inlet moisture flow in a sliding window with a certain length after k d,λi time delays, the mathematical expression is:
Further, an outlet moisture prediction value is estimated When the control variable, i.e. the time stamp of the added water quantity u, has to be taken as a reference time stamp. This requires corresponding adjustments to the time delays of the variables: its relative size is unchanged, only the timestamp needs to be shifted:
the corresponding time stamps are:
Inlet material flow rate Q mi is
Inlet material moisture lambda i is
The water adding amount u is t;
The outlet material moisture lambda o is t+t d,u.
Further, after the k d,u step, the predicted value of the outlet moisture in the in-feed stage is obtainedThe method comprises the following steps:
Wherein the method comprises the steps of
There is not yet enough data accumulated at the stub bar, in this case using single point data estimationIt is likely that this would not be reasonable to use the respective historical data to estimate a default value a brd off-line for each brand, and use that default value for predictions at the stub bar.
Further, instead of using a single point value rather than a sliding average value to calculate various intermediate variables and predict the outlet moisture, the outlet moisture prediction value at the stub bar stageThe method comprises the following steps:
Wherein the method comprises the steps of
The prediction difficulty of the stub bar stage is far greater than that of the material, so that the main aim of the prediction of the outlet water content of the stub bar stage is not to obtain a prediction curve which can be compared with the accuracy of the material, but to select a proper water adding amount according to the prediction value so as to realize the accurate control of the outlet water content of the initial stage in the material and avoid the phenomenon of overshoot.
Further, in the step 5, the mathematical expression of the water addition increment Δu 1 based on the current water addition amount u (k) in the stub bar stage is:
Wherein u sp (k) is a water adding quantity set value, lambda o,sp is an outlet water content set value, A brd is a stub bar off-line estimation constant term,
The mathematical expression of the water addition increment Deltau 1 of the material middle stage based on the current water addition amount u (k) is as follows:
The intelligent control system for the wire making and water adding quantity based on the mechanism prediction model is applied to the intelligent control method for the wire making and water adding quantity based on the mechanism prediction model, and is characterized in that: the system comprises a data acquisition and issuing module, a control module and a front-end display module;
The data acquisition and issuing module is used for acquiring batch information and brand information, acquiring an outlet moisture target value, and acquiring inlet material flow, inlet moisture, water adding amount, direct injection steam flow, outlet moisture and feeding amount from the OPCServer in real time; on the other hand, the method is used for controlling and linking with a central control system, and the water adding amount calculated by an algorithm and other control signals are issued;
the control module is used for judging the working condition of the collected material flow and outlet water, predicting the outlet water, adopting different water adding amount calculation methods according to the working condition, calculating an optimal water adding amount set value and outputting the optimal water adding amount set value to the data collection and issuing module, and comprises a working condition judging unit, an outlet water predicting unit, a feedforward water adding calculation unit and a feedback correction unit;
The front end display module is used for displaying the predicted value and the actual value of the outlet moisture in real time, displaying the calculated value and the actual value of the water adding quantity in real time, and displaying the information of inlet moisture, inlet material flow and direct injection steam flow.
Further, the control module comprises a working condition judging unit, an outlet water predicting unit, a feedforward water adding calculating unit and a feedback correcting unit;
The working condition judging unit is used for dividing production into a material head, a material middle part, a material tail and a non-production stage according to the collected inlet material flow and outlet moisture.
The outlet moisture prediction unit is used for establishing an outlet moisture prediction model aiming at the stub bars, the material neutralization tails according to a moisture and dry material (tobacco leaf) flow conservation mechanism, and predicting the outlet moisture content in time delay through the prediction model;
the feedforward water adding calculation unit is used for calculating the water adding amount increment through the outlet water content prediction model;
the feedback water adding calculation unit is used for compensating errors possibly existing in feedforward control, and taking the actual value of the outlet water as feedback to correct system drift caused by prediction deviation.
Further, the control module only performs data interaction with the data acquisition and issuing module, the data acquisition and issuing module sends the acquired variable related data to the control module, and the control module returns the water adding amount calculated value to the data acquisition and issuing module after calculation through the model;
Further, the data acquisition and issuing module writes the water addition calculated value and the control signal into the PLC through the 0PC Server to realize control;
Further, in the control module, the respective duty ratios of the feedforward control part and the feedback control part in the water addition calculated value increment are adjusted by multiplying the feedforward duty ratio coefficient, and the water addition calculated value mathematical expression is:
u*(k)＝u(k)+αΔu1(k)+(1-α)Δu2(k)
Wherein u * (k) is a calculated water adding amount value output by the controller, namely a water adding amount set value at the next moment, u (k) is a water adding amount set value at the current moment, alpha represents a feedforward duty ratio coefficient, deltau 1 (k) is a water adding amount increment based on the mechanism prediction model, deltau 2 (k) is a feedback corrected water adding amount increment, and Deltau 2 (k) has the mathematical expression:
Δu2(k)＝KP(λo(k-1)-λo(k))+KI(λo,sp-λo(k))+KD(λo(k-2)+λo(k)-2λo(k-1))
the invention has the beneficial effects that:
The invention provides a method and a system for intelligently controlling the water adding quantity of a wire making based on a mechanism prediction model, which are characterized in that the mechanism model is established according to material balance, and the mechanism model is extended to a stub bar stage, so that the stub bar can be suitable for controlling the water adding quantity, effective power assistance is provided for solving the problem of inaccurate calculated water adding quantity value in the initial stage of material, the whole process of predicting and controlling the water adding quantity of the wire making is realized, the stability of outlet water is finally ensured, the standard deviation of the outlet water is effectively reduced, and the problems of unsatisfactory control effect and the lack of a proper control method for the stub bar to ensure the outlet water are solved.
Drawings
FIG. 1 is a schematic flow chart of a method for intelligently controlling the water addition quantity of a wire making machine based on a mechanism prediction model;
FIG. 2 is a schematic diagram of a system for intelligent control of the water addition amount of filament making based on a mechanism prediction model;
FIG. 3 is a graph of predicted versus actual values of loose conditioning outlet moisture predicted using the mechanism prediction model of the present invention;
fig. 4 is a front-rear comparison chart of a certain leaf-wetting and feeding device in the embodiment 2 of the invention, which adopts intelligent control of the wire-making and water-feeding amount based on a mechanism prediction model.
Detailed Description
The present invention will be described in further detail with reference to examples.
Embodiment 1, a method for intelligently controlling the water addition amount of filament making based on a mechanism prediction model, as shown in fig. 1, comprises the following steps:
Step 1, collecting relevant data of various variables in the loose and moisture regain production process, wherein the relevant data comprise brand information, batch information, process standards, inlet material flow, inlet moisture, water adding amount, direct injection steam flow and outlet moisture.
Step 2, cutting the working condition of the production process, tracing the current outlet moisture to the measured value of the corresponding input variable at each corresponding moment, and filtering the inlet moisture by adopting a Kalman filter, wherein the steps comprise:
A) The production process is divided into: a stub bar, a middle material, a tail material and a non-production stage;
b) According to the measured value of the current outlet moisture traced to the corresponding time of the corresponding input variable, the outlet material moisture is used as a time stamp t, and the time stamps of other variables are aligned as follows: inlet material flow Q mi is t-t d,mi; inlet feed moisture lambda i is t-t d,λi; the water adding amount u is t-t d,u; the moisture lambda o of the outlet material is t;
C) And filtering the inlet moisture by adopting a Kalman filter, and configuring different filtering intensities according to different brands.
Step 3, according to the water entering the roller is equal to the water entering the roller and the dry matter entering the roller is equal to the dry matter entering the roller, namely the water and the dry matter (tobacco leaf) are conserved, a mechanism model is built, and the predicted value of the outlet water flow at the future moment is calculated according to the water adding amount u at the current moment and q mi before the corresponding delayAnd outlet water contentThe method comprises the following steps:
and 4, transforming based on a mechanism model, updating model parameters on line in a moving average mode, further constructing an outlet moisture prediction model in the material, expanding the model to a material head stage, respectively and off-line estimating a default model parameter by using respective historical data according to each brand, and predicting by using the default value at the material head, wherein the steps comprise:
a) The estimation of A is updated on line in a mode of carrying out moving average treatment on the variable in the material stage and then calculating the intermediate variable, wherein the estimation is as follows:
Wherein k=int (T/T s),Ts is the sampling period of the control algorithm program, k d,u,kd,dstm and k d,λi are the values of the corresponding variables after time delay discretization, and the calculation mode is the same as k;
B) Taking the timestamp of the added water u as a reference timestamp, and correspondingly adjusting the time delay of each variable:
Inlet material flow rate Q mi is Inlet material moisture lambda i isThe water adding amount u is t; the moisture lambda o of the outlet material is t+t d,u;
C) Predicting the water content of the outlet in the material, and predicting the value The method comprises the following steps:
Wherein the method comprises the steps of
D) In the stub bar stage, a default value A brd is respectively estimated offline by using respective historical data for each brand, and the stub bar is used for prediction, wherein the predicted valueThe method comprises the following steps:
Wherein the method comprises the steps of
And 5, calculating the water adding amount, wherein the water adding amount is used for controlling the water adding amount, and the steps are as follows:
A) Calculating a water addition increment Deltau 1 based on the current water addition amount u (k) in the stub bar stage:
Wherein lambda o,sp is the outlet moisture set point, A brd is the stub bar estimation constant term,
B) The water addition increment Deltau 1 based on the current water addition amount u (k) in the material stage is calculated:
As shown in FIG. 3, the effect of the method for predicting the moisture of the ultra-back loose outlet is shown, the predicted value and the actual value are very close, the calculated correlation coefficient is 0.82, and the method can realize the accurate prediction of the moisture of the ultra-back loose outlet.
Embodiment 2, a system for intelligent control of filament making and water adding based on a mechanism prediction model, as shown in fig. 2, comprises the following modules:
The data acquisition and issuing module acquires batch information and brand information through a data interface, acquires the outlet moisture target value, the inlet material flow, the inlet moisture, the water adding amount, the direct injection steam flow, the outlet moisture and the feeding amount from the OPC Server in real time; on the other hand, the water adding amount calculated by the algorithm and other control signals are issued through the OPC Server;
The control module is used for judging the working condition of the collected material flow and outlet water, predicting the outlet water, adopting different water adding amount calculation methods according to the working condition, calculating an optimal water adding amount set value and outputting the optimal water adding amount set value to the data collection and issuing module, and comprises a working condition judging unit, an outlet water predicting unit, a feedforward water adding calculation unit and a feedback correction unit;
the front end display module is used for displaying the predicted value and the actual value of the outlet moisture in real time, displaying the calculated value and the actual value of the water adding quantity in real time, and displaying the information of inlet moisture, inlet material flow and direct injection steam flow.
The control module comprises a working condition judging unit, an outlet water predicting unit, a feedforward water adding calculating unit and a feedback correcting unit;
And the working condition judging unit is used for dividing the production into a material head, a material middle part, a material tail and a non-production stage according to the collected inlet material flow and outlet moisture.
The outlet moisture prediction unit is used for establishing an outlet moisture prediction model aiming at the stub bars, the material neutralization tails according to a moisture and dry material (tobacco leaf) flow conservation mechanism, and predicting the outlet moisture content in time delay through the prediction model;
the feedforward water adding calculation unit is used for calculating the water adding amount increment through the outlet water content prediction model;
And the feedback water adding calculation unit is used for compensating errors possibly existing in feedforward control, and taking the actual value of the outlet water as feedback to correct the system drift caused by the prediction deviation.
In the control module, the respective duty ratio of the feedforward control part and the feedback control part in the increment of the water adding calculated value is adjusted by multiplying the feedforward duty ratio coefficient, and the mathematical expression of the water adding calculated value is as follows:
u*(k)＝u(k)+αΔu1(k)+(1-α)Δu2(k)
Wherein u * (k) is a calculated water adding amount value output by the controller, namely a water adding amount set value at the next moment, u (k) is a water adding amount set value at the current moment, alpha represents a feedforward duty ratio coefficient, deltau 1 (k) is a water adding amount increment based on the mechanism prediction model, deltau 2 (k) is a feedback corrected water adding amount increment, and Deltau 2 (k) has the mathematical expression:
Δu2(k)＝KP(λo(k-1)-λo(k))+KI(λo,sp-λo(k))+KD(λo(k-2)+λo(k)-2λo(k-1))
furthermore, the data acquisition and issuing module acquires the relevant data of the leaf wetting and feeding process based on the data communication software through the on-site Profinet industrial control loop network. The data acquisition and issuing module transmits the obtained data to the control module, the data are calculated by the model to obtain the optimal water adding amount at the moment, the optimal water adding amount is transmitted to the data issuing module, and the water adding amount is written into the PLC control network through the data communication software.
The data communication software is Kepware, and the acquisition process is as follows: subscribing data points corresponding to all labels in Kepware OPC Server to collect data in the bottom layer PLC address; the writing process is as follows: and writing data into a corresponding data tag in Kepware OPC Server, and writing the data into a PLC address corresponding to the tag through the OPC tag.
As shown in FIG. 4, in order to achieve the batch effect of intelligently controlling the water addition amount of a certain leaf-wetting feeding device by adopting the system, before the system is used for controlling, the inter-batch standard deviation average value of the outlet water is 0.17, after the system is used for intelligently controlling the water addition amount, the inter-batch standard deviation average value of the outlet water is reduced to 0.11, the traditional control is changed to the edge big data calculation control, the stability and consistency of the inter-batch quality in the whole process of the silk production are ensured, and the control effect of the system is better.
It should be noted that:
the invention may take the form of an entirely hardware embodiment, an entirely software embodiment, an embodiment containing both hardware and software elements, such that the means for implementing the flow-making function in the flowchart, or flows, are generated by the computer.
Examples 1 and 2 mentioned in the specification illustrate, for preference, the export moisture forecast and the water addition control of the wet leaf addition for the super-back loosening and wet leaf addition, respectively, and the method and system of the present invention are applicable to both the export moisture forecast and the water addition control of the super-back loosening and wet leaf addition, respectively.
Finally, it is noted that the above-mentioned preferred embodiments are only intended to illustrate rather than limit the invention, and that, although the invention has been described in detail by means of the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention as defined by the appended claims.
Claims (4)
1. A method for intelligently controlling the water adding quantity of silk making based on a mechanism prediction model comprises the following steps:
step 1: acquiring relevant data of variables in the loosening, conditioning and leaf wetting feeding process;
Step 2: cutting the working condition of the production process, tracing the current outlet moisture to the measured value of the corresponding input variable at each corresponding moment, and then filtering the inlet moisture by adopting a Kalman filter;
Step 3: according to the fact that the moisture entering the roller is equal to the moisture exiting the roller and the quantity of dry matters entering the roller is equal to the quantity of dry matters exiting the roller, namely the moisture and the tobacco are conserved in mass, a mechanism model is built;
Step 4: transforming based on a mechanism model, online updating model parameters in a moving average mode, further constructing a model for predicting the outlet moisture in the material, expanding the model to a material head stage, respectively and offline estimating a default model parameter by using respective historical data according to each brand, and predicting by using the model parameter at the material head;
the method comprises the following steps: according to the outlet water content prediction model, a water adding amount calculation algorithm is established, and water adding amount prediction control is realized;
In the step 4, the estimation of A is updated online in a manner of carrying out the moving average processing on the variable and then calculating the intermediate variable, and the estimation value of A is estimated at the current moment The mathematical expression of (2) is:
Wherein, a is the model parameter, k is the current moment, k=int (T/T s),Ts is the control algorithm program sampling period, k d,u,kd,datm and k d,λi are the values after time delay discretization of the corresponding variables respectively, and the calculation mode is the same as k; Is the average value of the water adding amount in a sliding window with a certain length after k d,u time delays, For the outlet moisture flow in a length of sliding window,For the inlet moisture flow in a sliding window with a certain length after k d,λi time delays, the mathematical expression is:
wherein N u is the length of the sliding window, AndCalculation mode and of (2)The calculation mode is the same, and the calculation is performed by carrying out the moving average treatment on the variable, and then calculating, wherein the delay of the indirect measurement quantity is determined by the minimum delay in the direct measurement;
Calculating predicted value of outlet material moisture When the control variable, namely the time stamp of the added water quantity u is used as the reference time stamp, the estimation is neededAnd correspondingly adjusting the time delay of each variable: the relative size is unchanged, only the time stamp needs to be shifted, and the corresponding time stamp is as follows:
Inlet material flow rate Q mi is
Inlet material moisture lambda i is
The water adding amount u is t;
The moisture lambda o of the outlet material is t+t d,u;
Predicted value of stage outlet moisture in the batch The mathematical expression is:
Wherein,
The stub bar stage no longer uses real-time estimationFor each brand, using respective historical data and based onRespectively off-line estimating a default value A brd, predicting the material head by using the default value, namely, calculating various intermediate variables and predicting the outlet moisture by using a single-point value instead of a sliding average value, and outputting the moisture predicted value in the material head stageThe mathematical expression is:
Wherein,
In the step 5, the mathematical expression of the water adding increment based on the current water adding amount in the stub bar stage is as follows:
the mathematical expression of the water adding increment of the material middle stage based on the current water adding amount is as follows:
Wherein u sp (k) is a water adding quantity set value, u (k) is the current water adding quantity, deltau 1 is a water adding increment, lambda o,sp is an outlet water set value, A brd is a stub bar off-line estimation constant term,
2. The intelligent control method for the water adding quantity in the filament making process based on the mechanism prediction model as claimed in claim 1, wherein the intelligent control method is characterized in that: in the step 1, the related data comprise brand information, batch information, process standards, inlet material flow, inlet moisture, water adding amount, direct injection steam flow, outlet moisture and feeding amount.
3. The intelligent control system for producing silk and adding water based on the mechanism prediction model is applied to the intelligent control method for producing silk and adding water based on the mechanism prediction model as claimed in claim 1, and is characterized in that: the system comprises a data acquisition and issuing module, a control module and a front-end display module;
The data acquisition and issuing module is used for acquiring batch information, brand information and outlet moisture target values, acquiring inlet material flow, inlet moisture, water adding amount, direct injection steam flow, outlet moisture and feeding amount from the OPC Server in real time, controlling and linking with the central control system, and issuing the water adding amount calculated by an algorithm;
The control module comprises a working condition judging unit, an outlet water content predicting unit, a feedforward water adding calculating unit and a feedback correcting unit, wherein the working condition judging unit judges the collected material flow and outlet water content, predicts the outlet water content, adopts different water adding amount calculating algorithms according to the working condition, calculates an optimal water adding amount set value and outputs the optimal water adding amount set value to the data collecting and issuing module;
The front end display module displays the predicted value and the actual value of the outlet moisture in real time, the calculated value and the actual value of the water adding amount in real time, and the inlet moisture, the inlet material flow and the direct injection steam flow information.
The working condition judging unit is used for dividing production into a stub bar, a middle material, a tail material and a non-production stage according to the collected inlet material flow and outlet moisture;
The outlet moisture prediction unit is used for establishing an outlet moisture prediction model aiming at the stub bars, the material neutralization tails according to a moisture and tobacco flow conservation mechanism, and predicting the outlet moisture content in time delay through the prediction model;
The feedforward water adding calculation unit is used for calculating the water adding amount increment through the outlet water predicting model and multiplying the water adding amount increment by a feedforward duty ratio coefficient to adjust the respective duty ratio of the feedforward control part and the feedback control part in the water adding amount calculation value increment;
the feedback correction unit is used for compensating errors possibly existing in feedforward control, and taking the actual value of the outlet moisture as feedback to correct system drift caused by prediction deviation.
4. The intelligent control system for making and adding water based on mechanism prediction model as claimed in claim 3, wherein: in the control module, the mathematical expression of the water addition calculated value is as follows:
u*(k)＝u(k)+αΔu1(k)+(1-α)Δu2(k)
Wherein u * (k) is a calculated water adding amount value output by the controller, namely a water adding amount set value at the next moment, u (k) is a water adding amount set value at the current moment, alpha represents a feedforward duty ratio coefficient, deltau 1 (k) is a water adding amount increment based on the mechanism prediction model, deltau 2 (k) is a feedback corrected water adding amount increment, and Deltau 2 (k) has the mathematical expression:
Δu2(k)＝KP(λo(k-1)-λo(k))+KI(λo,sp-λo(k))+KD(λo(k-2)+λo(k)-2λo(k-1)).
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