CN115599055A - Intelligent control method and system for water adding amount in silk making based on mechanism prediction model - Google Patents
Intelligent control method and system for water adding amount in silk making based on mechanism prediction model Download PDFInfo
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Abstract
The invention discloses a method and a system for intelligently controlling the water adding amount in the tobacco shred making process based on a mechanism prediction model, belongs to the field of intelligent control, and particularly relates to water adding amount prediction feedforward control in a loose moisture regaining process of the tobacco shred making process and a leaf moistening and feeding process of cigarettes, which is used for predicting the outlet water in advance and optimally controlling the water adding amount. According to the invention, a mechanism model is established according to material balance, and the mechanism model is extended to the stub bar stage, so that the mechanism model can be adapted to the stub bar moisture control, effective assistance is provided for solving the problem that a calculated value of water addition amount in the initial stage of material feeding, the whole process prediction control of the silk making and water addition amount is realized, the outlet moisture stability is finally ensured, the standard deviation of the outlet moisture is effectively reduced, and the problems that the control effect is not ideal and the stub bar is lack of a proper control method to ensure the outlet moisture 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 amount in the process of making silk based on a mechanism prediction model.
Background
The ultra-returning loosening and the leaf moistening feeding are used as main processing procedures of the tobacco shred quality influencing by the tobacco shred making line, and the process stability of the ultra-returning loosening and leaf moistening feeding has direct influence on the process indexes of each subsequent procedure. The control scheme of the current super-returning loosening and leaf moistening feeding process on the water content of an outlet is traditional feedback control, the control effect is not ideal, and the phenomenon that the water adding amount is not matched with the change of water at the inlet or even completely misplaced easily occurs. The current industry research mainly focuses on outlet moisture control in a steady-state production stage, few researches on the stub bar, namely, the moisture control in the production stage before the steady-state production stage are carried out, and the control of the water adding amount of the stub bar is very critical to the overall stability of the outlet moisture in the material according to the experience obtained by visiting processes, operators and observing historical data.
Disclosure of Invention
Aiming at the problems in the background art, the invention provides an intelligent control method and system for the water adding amount in the silk making process based on a mechanism prediction model, and solves the problems that the control effect is not ideal, and a stub bar is lack of a proper control method to ensure the water at an outlet. Establishing a mechanism model from inlet moisture and water adding amount to outlet moisture in a steady-state production stage, so that accurate prediction of the outlet moisture in the stage is realized; aiming at the current situation of the vacancy of the stub bar stage, a semi-mechanism model established based on the steady-state stage is extended, so that the semi-mechanism model can be adapted to the prediction of the water content of the stub bar, and effective assistance is provided for solving the problem that the calculated value of the water adding amount in the initial stage of the material is inaccurate. And finally, designing a water feeding amount feedforward control system based on the model, and assisting the traditional feedback control to overcome the problems.
In order to achieve the purpose, the invention adopts the following technical scheme:
an intelligent control method for water adding amount in silk making based on a mechanism prediction model comprises the following steps:
step 1: acquiring relevant data of each variable in the loosening and dampening or leaf moistening feeding process;
and 2, step: segmenting the working condition of the production process, tracing the measured values of the corresponding input variables at the corresponding moments according to the current outlet moisture, and then filtering the inlet moisture by adopting a Kalman filter;
and 3, step 3: establishing a mechanism model according to the fact that the moisture entering the roller is equal to the moisture out of the roller, the amount of the dry matter entering the roller is equal to the amount of the dry matter out of the roller, namely the moisture and the mass of the dry matter (tobacco leaves) are conserved;
and 4, step 4: performing transformation based on a mechanism model, updating model parameters on line by adopting a sliding average mode, further constructing a prediction model of outlet water in the material, expanding the model to a stub bar stage, respectively estimating a default model parameter off line by using respective historical data aiming at each grade, and predicting by using the default value at the stub bar;
and 5: and establishing a water adding amount calculation algorithm according to the outlet water prediction model to realize water adding amount prediction control.
In view of the fact that ultra-loosening or moistening the leaves and feeding the materials is a complex system with large time delay and hysteresis, the invention establishes a mechanism model from inlet moisture and water feeding amount to outlet moisture in a steady production stage and extends the application range of the mechanism model to a stub bar.
In the step 1, the related data comprises grade information, batch information, process standard, inlet material flow, inlet moisture, water adding amount, direct injection steam flow, outlet moisture and material adding amount;
further, in the step 2, the working condition of the production process is segmented into: in the material head, material middle, material tail and non-production stages, according to the current outlet moisture, the measured value of the corresponding input variable at the corresponding moment is traced back, the outlet material moisture is used as a time stamp t, according to the current outlet moisture, the measured value of the corresponding input variable at the corresponding moment is traced back, and then the corresponding time stamps of other variables are as follows:
inlet material flow rate Q mi Is t-t d，mi ；
Inlet material moisture lambda i Is t-t d，λi ；
The water addition u is t-t d，u ；
Water content lambda of the outlet material o Is t.
Then filtering the inlet moisture by adopting a Kalman filter;
further, in the step 3, the moisture and the mass of the dry material (tobacco leaves) are conserved, and the conservation equation expression is as follows:
q mi +q dstm +u+q mstm +q air，i ＝q mo +q air，o
q tbc，i ＝q tbc，o ＝q tbc
wherein q is mi For water flow into the drum, q dstm Is the flow rate of direct injection steam, u is the water addition amount, q mstm For atomizing the steam flow, q air，i The flow rate of hot air and water q mo Water flow rate of the outlet drum q air，o For removing moisture, q tbc，i For the flow of dry tobacco leaves into the drum, q tbc，o Is the flow of the dry tobacco leaves discharged from the roller.
Furthermore, the hot air/water flow rate q is higher than the water flow rate due to direct water addition air，i With direct injection steam flow q dstm All can be omitted; to have producedIn the process, the moisture exhaust air door and the atomized steam valve are kept at fixed opening degrees, and q can be considered as air，o -q air，i -q mstm -q dstm Near the fixed constant, denoted as a, the water conservation equation can be modified as:
A＝u+q mi -q mo ＝u+λ i ·Q mi +λ o ·Q mo
wherein λ is o ，λ i Respectively, the water content of the inlet and outlet materials, and Q mo ，Q mi The flow rate of the materials at the inlet and the outlet is A, and A is a fixed constant term.
Further, according to the conservation of flow of dry tobacco leaves, i.e. q tbc，i ＝q tbc，o ＝q tbc The following can be obtained:
q tbc ＝(1-λ i )Q mi ＝(1-λ o )Q mo ，
from this, the drum water flow rate can be calculated as:
furthermore, according to the water adding amount u at the current moment and q before corresponding delay mi And calculating to obtain the outlet water flow predicted value at the future moment
And outlet water content
The mathematical expression is as follows:
further, in the step 4, partial modification is made on the basis of the mechanism, the estimation of the A is updated on line in a mode of performing moving average processing on measurable variables at a material middle stage and then calculating intermediate variables, and the estimation value of the A is updated at the current time k
The mathematical expression of (a) is:
wherein k = int (T/T) s )，T s To control the algorithm program sampling period, k d，u ，k d，dstm And k d，λi Respectively calculating the time delay discretized values of the corresponding variables in the same way as k;
wherein
Is k d，u The average value of the water adding amount in a sliding window with a certain length after the time delay,
is the outlet moisture flux within a sliding window of a certain length,
is k d，λi The mathematical expression of the inlet water flow rate in the sliding window with a certain length after the delay is as follows:
further, an outlet moisture prediction value is estimated
The control variable, i.e. the timestamp of the water addition u, must be used as the reference timestamp. This requires a corresponding adjustment of the time delay of the variables: the relative size is unchanged, only the timestamp needs to be shifted:
the corresponding time stamp is:
inlet material flow rate Q mi Is composed of
Inlet material moisture lambda i Is composed of
The water adding amount u is t;
water content lambda of the outlet material o Is t + t d，u 。
Further, k is available d，u After step (d), predicting the outlet moisture of the material in the stage
Comprises the following steps:
wherein
At the head of the line there is not yet sufficient data accumulation, in which case single-point data estimation is used
Possibly unreasonable, and for each mark, respectively estimating a default by using respective historical data offlineIdentification value A brd And predicting at the head by using the default value.
Further, single-point value rather than sliding average value is used for calculating various intermediate variables and predicting outlet moisture, and the outlet moisture predicted value in the stub bar stage
Comprises the following steps:
wherein
Because the prediction difficulty of the stub bar stage is far greater than that of the material, the prediction of the outlet moisture of the stub bar stage is not aimed at obtaining a prediction curve which can be compared with the accuracy of the material, but is aimed at selecting a proper water adding amount through a prediction value so as to realize the accurate control of the outlet moisture of the initial stage in the material and avoid the overshoot phenomenon.
Further, in the step 5, the water adding increment delta u based on the current water adding amount u (k) in the material head stage 1 The mathematical expression of (a) is:
wherein u is sp (k) For setting the amount of water added, lambda o，sp Set value for outlet moisture, A brd A constant term is estimated off-line for the stub bar,
the water adding increment delta u of the material middle stage based on the current water adding amount u (k) 1 The mathematical expression of (a) is:
an intelligent control system for water addition in wire making based on mechanism prediction model is applied to an intelligent control method for water addition in wire making based on 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 a target outlet moisture 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 water adding quantity and other control signals calculated by the algorithm are issued;
the control module is used for judging the working conditions of the collected material flow and the outlet moisture, predicting the outlet moisture, calculating an optimal water adding amount set value according to the working conditions by adopting different water adding amount calculation methods and outputting the optimal water adding amount set value to the data collecting and issuing module, and comprises a working condition judging unit, an outlet moisture predicting unit, a feedforward water adding calculating unit and a feedback correcting unit;
the front end display module is used for displaying a predicted value and an actual value of the outlet water in real time, displaying a calculated value and an actual value of the water adding amount in real time, and displaying inlet water, inlet material flow and direct injection steam flow information.
Further, the control module comprises a working condition judgment unit, an outlet moisture prediction unit, a feedforward water adding calculation unit and a feedback correction unit;
and the working condition judging unit is used for dividing the production into a stub bar stage, a material middle stage, a material tail stage and a non-production stage according to the collected inlet material flow and the collected outlet moisture.
The outlet moisture prediction unit is used for establishing an outlet moisture prediction model aiming at the stub bar, the material and the material tail according to a moisture and dry material (tobacco leaf) flow conservation mechanism, and predicting the outlet moisture content in the 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 prediction model;
the feedback water adding calculation unit is used for compensating possible errors of the feedforward control and takes the actual value of the outlet water as feedback to correct system drift caused by prediction deviation.
Furthermore, the control module only performs data interaction with the data acquisition and issuing module, the data acquisition and issuing module sends acquired data related to each variable to the control module, and the control module returns the calculated value of the water addition amount to the data acquisition and issuing module after model calculation;
further, the data acquisition and issuing module writes the water adding amount calculation value and the control signal into a PLC through a 0PC Server to realize control;
further, in the control module, the respective ratios of the feedforward control part and the feedback control part in the water addition amount calculation value increment are adjusted by multiplying a feedforward ratio coefficient, and the mathematical expression of the water addition amount calculation value is as follows:
u * (k)＝u(k)+αΔu 1 (k)+(1-α)Δu 2 (k)
wherein u is * (k) A calculated value of the water addition amount output by the controller, namely a set value of the water addition amount at the next moment, u (k) is a set value of the water addition amount at the current moment, alpha represents a feedforward proportion coefficient, delta u 1 (k) For the water addition increment, deltau, of the mechanism-based prediction model 2 (k) Incremental addition of water, Δ u, for feedback correction 2 (k) The mathematical expression is:
Δu 2 (k)＝K P (λ o (k-1)-λ o (k))+K I (λ o，sp -λ o (k))+K D (λ o (k-2)+λ o (k)-2λ o (k-1))
the invention has the beneficial effects that:
the invention provides a silk making and water adding intelligent control method and system based on a mechanism prediction model, wherein the mechanism model is established according to material balance, and the mechanism model is extended to a stub bar stage to be adapted to stub bar water control, so that effective assistance is provided for solving the problem that a calculated value of water adding amount is inaccurate in the initial stage of material making, the whole silk making and water adding process prediction control is realized, the outlet water stability is finally ensured, the standard deviation of the outlet water is effectively reduced, and the problems that the control effect is not ideal and the stub bar is lack of an appropriate control method to ensure the outlet water are solved.
Drawings
FIG. 1 is a schematic flow chart of an intelligent control method for water addition in silk making based on a mechanism prediction model according to the present invention;
FIG. 2 is a schematic diagram of an intelligent control system for water addition and silk making based on a mechanism prediction model according to the present invention;
FIG. 3 is a comparison graph of the predicted value and the actual value of the water at the outlet of the loosening and conditioning system predicted by the mechanism prediction model of the invention;
fig. 4 is a comparison graph before and after a certain leaf moistening and feeding device adopts intelligent control of silk making and water feeding amount based on a mechanism prediction model in embodiment 2 of the invention.
Detailed Description
The present invention will be described in further detail with reference to examples.
Embodiment 1, a method for intelligently controlling a water addition amount in a wire making process based on a mechanism prediction model, as shown in fig. 1, includes the following steps:
step 1, collecting relevant data of each variable in the loosening and conditioning production process, wherein the relevant data comprises brand information, batch information, process standard, inlet material flow, inlet moisture, water adding amount, direct injection steam flow and outlet moisture.
Step 2, segmenting the working condition of the production process, tracing the measured values of the corresponding input variables at respective corresponding moments according to the current outlet moisture, and then filtering the inlet moisture by adopting a Kalman filter, wherein the steps comprise:
a) The production process is divided into: a stub bar stage, a material middle stage, a material tail stage and a non-production stage;
b) Tracing the measured values of the corresponding input variables at the corresponding moments according to the current outlet moisture to export the materialsMoisture is the timestamp t, and the timestamp aligned to other variables is: inlet material flow rate Q mi Is t-t d，mi (ii) a Inlet material moisture lambda i Is t-t d，λi (ii) a The water addition u is t-t d，u (ii) a Water content lambda of the outlet material o Is t;
c) And filtering the inlet moisture by adopting a Kalman filter, and configuring different filtering strengths according to different brands.
Step 3, establishing a mechanism model according to the fact that the moisture entering the roller is equal to the moisture out of the roller, the dry matter entering the roller is equal to the dry matter out of the roller, namely the moisture and the dry matter (tobacco leaves) are in conservation, and accordingly according to the water adding amount u at the current moment and the q before corresponding delay mi And calculating to obtain the outlet water flow predicted value at the future moment
And water content at outlet
Comprises the following steps:
and 4, transforming based on a mechanism model, updating model parameters on line by adopting a sliding average mode, further constructing a prediction model of the outlet water in the material, expanding the model to a stub bar stage, respectively estimating a default model parameter off line by using respective historical data aiming at each grade, and predicting by using the default value at the stub bar, wherein the method comprises the following steps:
a) The estimation of A is updated on line in a mode of carrying out moving average processing on measurable variables at the material middle stage and then calculating intermediate variables:
where k = int (T/T) s )，T s For controlling algorithm programsSampling period, k d，u ，k d，dstm And k d，λi Respectively calculating the time delay discretized values of the corresponding variables in the same way as k;
b) And taking the timestamp of the water adding amount u as a reference timestamp, and correspondingly adjusting the time delay of each variable:
inlet material flow rate Q mi Is composed of
Inlet material moisture lambda i Is composed of
The water adding amount u is t; water content lambda of the outlet material o Is t + t d，u ；
C) Predicting the water content at the outlet of the material to obtain a predicted value
Comprises the following steps:
wherein
D) In the stub bar stage, aiming at each mark, a default value A is respectively estimated off line by using respective historical data brd Predicting at the head of the material by using the default value
Comprises the following steps:
wherein
a) Calculating a water adding increment delta u based on the current water adding amount u (k) in the material head stage 1 ：
Wherein λ is o，sp Set value for outlet moisture, A brd A constant term is estimated for the head of the material,
b) Calculating the water adding increment delta u based on the current water adding amount u (k) in the material in the stage 1 ：
As shown in FIG. 3, the effect of predicting the moisture at the outlet of the ultra-loosening element by the method of the present invention is shown, and the predicted value is very close to the actual value, and the calculation correlation coefficient is 0.82, which shows that the method of the present invention can realize the accurate prediction of the moisture at the outlet of the ultra-loosening element.
Embodiment 2, a system for intelligently controlling the amount of water added in a wire making process based on a mechanism prediction model, as shown in fig. 2, includes the following modules:
the data acquisition and issuing module acquires batch information and brand information through a data interface, acquires and acquires an outlet moisture target value, an inlet material flow, inlet moisture, a water adding amount, a direct injection steam flow, outlet moisture and a feeding amount from the OPC Server in real time; on the other hand, the water adding amount and other control signals calculated by the algorithm are issued through an OPC Server;
the control module is used for judging the working conditions of the collected material flow and the outlet moisture, predicting the outlet moisture, calculating an optimal water adding amount set value according to different water adding amount calculation methods and outputting the optimal water adding amount set value to the data collecting and issuing module according to the working conditions, and comprises a working condition judging unit, an outlet moisture predicting unit, a feedforward water adding calculating unit and a feedback correcting unit;
and 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 amount in real time, and displaying the inlet moisture, the inlet material flow and the direct injection steam flow information.
The control module comprises a working condition judging unit, an outlet moisture 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 stub bar stage, a material middle stage, a material tail stage and a non-production stage according to the collected inlet material flow and the collected outlet moisture.
The outlet moisture prediction unit is used for establishing an outlet moisture prediction model aiming at the stub bar, the material and the material tail according to a moisture and dry material (tobacco leaf) flow conservation mechanism, and predicting the outlet moisture content in the 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 prediction model;
and the feedback water adding calculation unit is used for compensating possible errors of the feedforward control and takes the actual value of the outlet moisture as feedback to correct the system drift caused by the prediction deviation.
In the control module, the respective ratio of a feedforward control part and a feedback control part in the water adding amount calculation value increment is adjusted by multiplying a feedforward ratio coefficient, and the mathematical expression of the water adding amount calculation value is as follows:
u * (k)＝u(k)+αΔu 1 (k)+(1-α)Δu 2 (k)
wherein u is * (k) A calculated value of the water addition amount output by the controller, namely a set value of the water addition amount at the next moment, u (k) is a set value of the water addition amount at the current moment, alpha represents a feedforward proportion coefficient, and delta u 1 (k) For the water addition increment, deltau, of the mechanism-based prediction model 2 (k) Incremental addition of water, deltau, for feedback correction 2 (k) The mathematical expression is:
Δu 2 (k)＝K P (λ o (k-1)-λ o (k))+K I (λ o，sp -λ o (k))+K D (λ o (k-2)+λ o (k)-2λ o (k-1))
furthermore, the data acquisition and issuing module acquires the relevant data of the leaf moistening and feeding process through an on-site Profinet industrial control ring network based on data communication software. The data acquisition and issuing module transmits the obtained data to the control module, the optimal water adding amount at the moment is obtained through model calculation and then is transmitted to the data issuing module, and the water adding amount is written into a PLC control network through data communication software.
The data communication software is Kepware, and the acquisition process is as follows: data points corresponding to all labels in a Kepware OPC Server are subscribed to realize the acquisition of data in the bottom layer PLC address; the writing process is as follows: and writing the data into a corresponding data tag in a 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 moistening feeding device by using the system of the present invention, before the control by using the present invention, the batch standard deviation mean value of the outlet water is 0.17, and after the intelligent control by using the present invention is performed, the batch standard deviation mean value of the outlet water is reduced to 0.11, so that the conversion from the traditional control to the edge big data calculation control is realized, the stability and the consistency of the quality among batches in the whole silk making process are ensured, and the control effect of the system of the present invention is better.
It should be noted that:
the present invention may implement each of the procedures in the flowcharts by computer program instructions, which may be deployed to a general purpose computer or a special purpose computer to produce a machine, such that the computer creates means for implementing one or more of the procedures specified in the flowcharts.
Examples 1 and 2 mentioned in the specification exemplify, for preference, prediction of the outlet moisture of ultra-return loosening and control of the water addition of a leaf-moistening charge, respectively, and the method and system of the present invention are applicable to both prediction of the outlet moisture of ultra-return loosening and control of the water addition of a leaf-moistening charge.
Finally, it is noted that the above-mentioned preferred embodiments illustrate rather than limit the invention, and that, although the invention has been described in detail with reference to the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.
Claims (8)
1. An intelligent control method for water adding amount in silk making based on a mechanism prediction model is characterized in that: the method comprises the following steps:
step 1: acquiring relevant data of each variable in the loosening and dampening or leaf moistening feeding process;
step 2: segmenting the working condition of the production process, tracing the measured values of the corresponding input variables at the corresponding moments according to the current outlet moisture, and then filtering the inlet moisture by adopting a Kalman filter;
and step 3: establishing a mechanism model according to the fact that the moisture entering the roller is equal to the moisture leaving the roller, the dry matter entering the roller is equal to the dry matter leaving the roller, namely the moisture and the dry matter (tobacco leaves) are conserved;
and 4, step 4: performing transformation based on a mechanism model, updating model parameters on line by adopting a sliding average mode, further constructing a prediction model of outlet water in the material, expanding the model to a stub bar stage, respectively estimating a default model parameter off line by using respective historical data aiming at each grade, and predicting by using the default value at the stub bar;
step 5: and establishing a water adding amount calculation algorithm according to the outlet water prediction model to realize water adding amount prediction control.
2. The intelligent control method for the water adding amount in the wire making process based on the mechanism prediction model as claimed in claim 1, characterized in that: in the step 1, the related data comprises grade information, batch information, process standard, inlet material flow, inlet moisture, water adding amount, direct injection steam flow, outlet moisture and material adding amount.
3. The intelligent control method for the water adding amount in the wire making process based on the mechanism prediction model as claimed in claim 1, characterized in that: in the step 2, working condition segmentation is carried out on the production process, and the segmentation is as follows: in the material head stage, the material middle stage, the material tail stage and the non-production stage, according to the current outlet moisture, the measured value of the corresponding input variable at the corresponding moment is traced back, the outlet material moisture is taken as a time stamp t, and the time stamps corresponding to other variables are as follows:
inlet material flow rate Q mi Is t-t d，mi ； (1)
Inlet material moisture lambda i Is t-t d，λi ； (2)
The water addition u is t-t d，u ； (3)
Water content lambda of the outlet material o Is t (4).
4. The intelligent control method for the water adding amount in the silk making process based on the mechanism prediction model as claimed in claim 1, characterized in that: in the step 3, the flow of the moisture and the dry material (tobacco leaves) is conserved in the processes of super-return and leaf-moistening production, and the mathematical expression is as follows:
q mi +q dstm +u+q mstm +q air，i ＝q mo +q air，o (5)
q tbc，i ＝q tbc，o ＝q tbc (6)
wherein q is mi For water flow into the drum，q dstm Is the flow rate of direct injection steam, u is the water addition amount, q mstm For atomizing the steam flow, q air，i Is the flow rate of hot air and water q mo Water flow rate of the outlet drum q air，o For removing moisture, q tbc，i For the flow of dry tobacco leaves into the drum, q tbc，o Is the flow of the dry tobacco leaves discharged from the roller.
The moisture exhaust air door and the atomized steam valve are kept at fixed opening degrees, and the moisture conservation equation (1) can be modified into:
A＝u+q mi -q mo ＝u+λ i ·Q mi +λ o ·Q mo (7)
wherein λ is o ，λ i Respectively, the water content of the materials at the inlet and outlet, and Q mo ，Q mi The flow rate of the materials at the inlet and the outlet is shown as A, and A is a fixed constant term.
Based on the conservation of flow of the dry tobacco leaf, equation 6 can be modified as follows:
q tbc ＝(1-λ i )Q mi ＝(1-λ o )Q mo (8)
the water flow of the roller is calculated as follows:
according to the water adding amount u at the current moment and q before corresponding delay mi And calculating to obtain the outlet water flow predicted value at the future moment
And water content at outlet
The mathematical expression is:
5. the intelligent control method for the water adding amount in the wire making process based on the mechanism prediction model as claimed in claim 1, characterized in that: in the step 4, the estimation value of A is updated on line in a mode of carrying out moving average processing on the measurable variable and then calculating the intermediate variable, and the estimation value of A is estimated at the current time k
The mathematical expression of (a) is:
wherein k = int (T/T) s )，T s To control the algorithm program sampling period, k d，u ，k d，dstm And k d，λi Respectively calculating the time delay discretized values of the corresponding variables in the same way as k;
wherein
Is k d，u The average value of the water addition amount in a sliding window with a certain length after the time delay,
is the outlet moisture flux within a sliding window of a certain length,
is k d，λi The mathematical expression of the inlet water flow rate in the sliding window with a certain length after the delay is as follows:
N u i.e. the length of the sliding window,
and
in a manner of calculation of
The calculation method is the same, and the calculation is performed after the measurable variable is subjected to the moving average processing, and the delay of the indirect measurement is determined by the minimum delay of the direct measurement.
Calculating the predicted value of the water content of the outlet material
The control variable, i.e. the time stamp of the water addition u, must be used as a reference time stamp, which requires an estimation
And correspondingly adjusting the time delay of each variable: the relative size is unchanged, only the timestamp needs to be displaced, and the corresponding timestamp is as follows:
inlet material flow rate Q mi Is composed of
Inlet material moisture lambda i Is composed of
The water adding amount u is t;
water content lambda of the outlet material o Is t + t d，u 。
In-feed stage outlet moisture prediction
The mathematical expression is:
wherein
Without using real-time estimation in the head stage
For each brand, using respective historical data and based on
Respectively estimating a default value A off-line brd The default value is used for prediction at the stub bar, namely, a single-point value rather than a sliding average value is used for calculating various intermediate variables and predicting the outlet moisture, and the outlet moisture predicted value is used at the stub bar stage
The mathematical expression is as follows:
wherein
6. The intelligent control method for the water adding amount in the silk making process based on the mechanism prediction model as claimed in claim 1, characterized in that: in the step 5, the water adding increment delta u based on the current water adding amount u (k) in the material head stage 1 The mathematical expression of (a) is:
wherein u is sp (k) For a set value of the amount of water added, lambda o，sp As outlet moisture set point, A brd A constant term is estimated off-line for the stub bar,
the water adding increment delta u of the material middle stage based on the current water adding amount u (k) 1 The mathematical expression of (a) is:
7. an intelligent control system for water and wire making based on mechanism prediction model is applied to an intelligent control method for water and wire making based on 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, brand information and an outlet moisture target value, acquiring inlet material flow, inlet moisture, water adding quantity, direct injection steam flow, outlet moisture and feeding quantity from an OPC Server in real time, performing control connection with a central control system, and issuing the water adding quantity calculated by an algorithm;
the control module comprises a working condition judgment unit, an outlet water prediction unit, a feedforward water adding calculation unit and a feedback correction unit, the working condition judgment is carried out on the collected material flow and the outlet water, the outlet water is predicted, different water adding calculation algorithms are adopted according to the working conditions, and the optimal water adding set value is calculated and output to the data acquisition and sending module;
the front end display module displays a predicted value and an actual value of the outlet moisture in real time, displays a calculated value and an actual value of the water adding amount in real time, and displays inlet moisture, inlet material flow and direct injection steam flow information.
The working condition judging unit is used for dividing production into a stub bar stage, a material middle stage, a material tail stage and a non-production stage according to the collected inlet material flow and outlet water;
the outlet moisture prediction unit is used for establishing an outlet moisture prediction model aiming at the stub bar, the material and the material tail according to a moisture and dry material (tobacco leaf) flow conservation mechanism, and predicting the outlet moisture content in the time delay through the prediction model;
the feedforward water adding calculation unit is used for calculating a water adding amount increment through the outlet water prediction model and multiplying the water adding amount increment by a feedforward proportion coefficient to adjust the proportion of the feedforward control part and the feedback control part in the water adding amount calculation value increment;
and the feedback water adding calculation unit is used for compensating possible errors of the feedforward control and taking the actual value of the outlet water as feedback to correct the system drift caused by the prediction deviation.
8. The intelligent control system for the water adding amount during the wire making based on the mechanism prediction model as claimed in claim 7, wherein: in the control module, the mathematical expression of the calculated value of the water adding amount is as follows:
u * (k)＝u(k)+αΔu 1 (k)+(1-α)Δu 2 (k) (24)
wherein u is * (k) A calculated value of the water addition amount output by the controller, namely a set value of the water addition amount at the next moment, u (k) is a set value of the water addition amount at the current moment, alpha represents a feedforward proportion coefficient, and delta u 1 (k) For the water addition increment, deltau, of the mechanism-based prediction model 2 (k) Incremental addition of water, Δ u, for feedback correction 2 (k) The mathematical expression is:
Δu 2 (k)＝K P (λ o (k-1)-λ o (k))+K I (λ o，sp -λ o (k))+K D (λ o (k-2)+λ o (k)-2λ o (k-1)) (25)。
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