CN114035430A - Desulfurization system pH value control system and method based on predictive control - Google Patents
Desulfurization system pH value control system and method based on predictive control Download PDFInfo
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- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 abstract description 7
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Abstract
The invention discloses a desulfurization system pH value control system and a method based on predictive control in the technical field of wet flue gas desulfurization, which comprises the following steps: acquiring a PH set value, and processing the PH set value based on a generalized predictive control model with disturbance control; performing rolling optimization based on the processing result; and performing feedback correction on the PH set value based on the optimization result. The invention adopts the generalized predictive control model with disturbance suppression to replace the PID regulator of the traditional cascade control main regulation loop, has better control effect compared with the traditional cascade control strategy, and obviously excels the traditional cascade control strategy in setting the tracking performance, adjusting time and overshoot of the advanced pH control strategy; in the aspect of disturbance resistance, advanced control and callback time is shorter, disturbance resistance is stronger, the control quality of the pH value of wet desulphurization can be obviously improved based on generalized predictive control with disturbance suppression, and the method plays an important role in stable and economic operation of wet desulphurization.
Description
Technical Field
The invention relates to a system and a method for controlling the pH value of a desulfurization system based on predictive control, belonging to the technical field of wet flue gas desulfurization.
Background
The wet desulphurization process is mature and has high desulphurization efficiency, so that the wet desulphurization process becomes the most widely applied desulphurization technology of the thermal power generating units at present. However, in the actual operation process of wet desulphurization, the problems of high energy consumption and material consumption of a desulphurization system, large fluctuation of SO2 concentration, serious instantaneous standard exceeding and the like exist. The reason is that the desulfurization system has the characteristics of nonlinearity, large inertia, large hysteresis and the like, and the measurement of the desulfurization system and the reliability of equipment are poor, so that the desulfurization system is difficult to keep operating under the optimal condition. Therefore, the research on the stable, economical and reliable operation of the desulfurization system by researching the optimized operation technology of the desulfurization system becomes a research focus in China at present, and the control system performance of the wet desulfurization system needs to be focused on in order to realize the aim. The most critical core subsystem in the desulfurization system is the control of the pH value of slurry in the absorption tower, and the excessive pH value can cause the scaling and blockage of a demister and the absorption tower, reduce the quality of desulfurization byproducts and increase the consumption of limestone; if the pH value is too low, the desulfurization efficiency of the desulfurization system is reduced, in addition, the concentration of SO2 is greatly fluctuated due to the large fluctuation of the pH value, and the concentration of SO2 is not favorably and accurately controlled, SO that the realization of the accurate control of the pH value becomes one of the key factors for the stable operation of the desulfurization system.
The traditional pH value control method mainly comprises two modes: the first is the most common cascade control system, the main loop is SO2 concentration control, and the inner loop is limestone slurry flow control; and secondly, the most basic single closed loop PID control is realized, and the nonlinear problem of limestone slurry supply flow regulation cannot be avoided by only adopting a single loop to realize the closed loop control of pH. The two conventional control strategies have the problems of poor automatic control effect and poor disturbance resistance in the actual operation process, and even can not be put into automatic operation. After the thermal power generating unit is put into AGC, the problems of frequent load change, large amplitude change and the like exist, the large change of the flue gas flow rate causes that a wet desulphurization system cannot well track the change of the flue gas flow rate, the automatic control of the pH value lags, and the fluctuation amplitude is large.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a desulfurization system pH value control system and method based on predictive control, which have better control effect compared with the traditional cascade control strategy.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
in a first aspect, the invention provides a desulfurization system pH value control method based on predictive control, which comprises the following steps:
the set value of the PH value is obtained,
processing the PH set value based on a generalized predictive control model with disturbance control;
performing rolling optimization based on the processing result;
and performing feedback correction on the PH set value based on the optimization result.
Further, the generalized predictive control model with disturbance control is as follows:
wherein the content of the first and second substances,
and carrying out prediction estimation value of step j sampling of the regulated quantity for time t based on known measurement information, wherein delta u (t + j-1) is a future control quantity increment vector needing to be subjected to control objective function optimization calculation, and delta is 1-z-1For differential operation, N1And N2Predicting the time domain starting time and the final ending time, N, for the modulated quantity, respectivelyuFor controlling the time domain, δ (j) and λ (j) are respectively the weight vector of the deviation of the regulated quantity and the set value and the weight vector of the increment of the controlled quantity, and the prediction time domain, the control time domain and the weight vectorAnd r (t + j) is a reference set value sequence for setting design parameters of the predictive control.
Further, the predicted estimation value of the adjusted quantity j step sampling is carried out at the time t based on the known measurement information
The method is obtained by carrying out prediction calculation on the N-step period of the regulated quantity based on a CARIMA controlled autoregressive moving average model integrating measurable disturbance, wherein the CARIMA controlled autoregressive moving average model integrating measurable disturbance is as follows:
where ξ (t) is measurable disturbance value at t moment, e (t) is white noise with mean value of 0, A, B and C are system calculating polynomials of passive object, d and dDFor the pure delay periods of the control quantity and the measurable disturbance and regulated quantity,
the influence of the measurable disturbance on the regulated quantity is modeled.
Further, the expression of the control quantity increment vector Δ u (t) at time t is:
Δu(t)＝kGT(r-Syc-GpΔuc-HΔξ-HpΔξc)
where K is the matrix K-1First row of (1), Δ uc＝Δu(t-1)，Δξc＝Δξ(t)，yc＝y(t)，Gp，HpS represents the influence matrixes of the past regulated quantity data, the current regulated quantity data and the disturbance quantity data on the future estimated value respectively, delta xi is a measurable disturbance increment matrix, and r is [ r (t + d +1) r (t + d +2) L r (t + d + N)2)]TFor reference set value vector, G is step response matrix of regulated quantity, and measurable disturbance matrix H is formed from control quantity-regulated quantity transfer lag time d and measurable disturbance-regulated quantity transfer lag time dDIs determined by the time difference α, α ═ d-dDThree variants are obtained according to the magnitude relationship between alpha and 0Measurable disturbance matrix H of the same structure:
in the formula, hiFor the basic elements in the measurable disturbance matrix H, the dimension of the measurable disturbance matrix H is N2×N2。
In a second aspect, the present invention provides a pH control system for a desulfurization system based on predictive control, comprising:
the acquisition module is used for acquiring a PH set value;
the model processing module is used for processing the PH set value based on the generalized predictive control model with disturbance control;
the optimization module is used for performing rolling optimization based on the processing result;
and the correction module is used for carrying out feedback correction on the PH set value based on the optimization result.
Further, the generalized predictive control model with disturbance control is as follows:
wherein the content of the first and second substances,
and carrying out prediction estimation value of step j sampling of the regulated quantity for time t based on known measurement information, wherein delta u (t + j-1) is a future control quantity increment vector needing to be subjected to control objective function optimization calculation, and delta is 1-z-1For differential operation, N1And N2Are respectively a quiltQuantity-modulated prediction of time-domain start time and final end time, NuIn order to control the time domain, delta (j) and lambda (j) are respectively a weight vector of the deviation of the regulated quantity and the set value and a weight vector of the increment of the controlled quantity, the prediction time domain, the control time domain and the weight vector are setting design parameters of prediction control, and r (t + j) is a reference set value sequence.
Further, the predicted estimation value of the adjusted quantity j step sampling is carried out at the time t based on the known measurement information
The method is obtained by carrying out prediction calculation on the N-step period of the regulated quantity based on a CARIMA controlled autoregressive moving average model integrating measurable disturbance, wherein the CARIMA controlled autoregressive moving average model integrating measurable disturbance is as follows:
where ξ (t) is measurable disturbance value at t moment, e (t) is white noise with mean value of 0, A, B and C are system calculating polynomials of passive object, d and dDFor the pure delay periods of the control quantity and the measurable disturbance and regulated quantity,
the influence of the measurable disturbance on the regulated quantity is modeled.
Further, the expression of the control quantity increment vector Δ u (t) at time t is:
Δu(t)＝kGT(r-Syc-GpΔuc-HΔξ-HpΔξc)
where K is the matrix K-1First row of (1), Δ uc＝Δu(t-1)，Δξc＝Δξ(t)，yc＝y(t)，Gp，HpS represents the influence matrixes of the past regulated quantity data, the current regulated quantity data and the disturbance quantity data on the future estimated value respectively, delta xi is a measurable disturbance increment matrix, and r is [ r (t + d +1) r (t + d +2) L r (t + d + N)2)]TAs a reference setpoint vector, G is a step of the adjusted quantityThe response matrix, measurable disturbance matrix H is composed of control quantity-regulated quantity transfer lag time d and measurable disturbance-regulated quantity transfer lag time dDIs determined by the time difference α, α ═ d-dDAccording to the magnitude relation between alpha and 0, three measurable disturbance matrixes H with different structures can be obtained:
in the formula, hiFor the basic elements in the measurable disturbance matrix H, the dimension of the measurable disturbance matrix H is N2×N2。
In a third aspect, a pH control device for a desulfurization system based on predictive control comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any of the above.
In a fourth aspect, a computer-readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of any of the methods described above.
Compared with the prior art, the invention has the following beneficial effects:
the invention adopts a generalized predictive control model with disturbance suppression to replace a PID regulator of a traditional cascade control main regulation loop, has better control effect compared with the traditional cascade control strategy, and obviously excels the traditional cascade control strategy in the regulation time and overshoot of the advanced pH control strategy for setting the tracking performance; in the aspect of disturbance resistance, advanced control and callback time is shorter, disturbance resistance is stronger, the control quality of the pH value of wet desulphurization can be obviously improved based on generalized predictive control with disturbance suppression, and the method plays an important role in stable and economic operation of wet desulphurization.
Aiming at the problem of large lag and large inertia of a thermal power generating unit wet desulphurization pH value core control subsystem, the invention adopts a generalized predictive controller with disturbance suppression to replace a PID regulator of a traditional cascade control main regulation loop, and a Smith pre-estimation compensator is integrated into the main loop to establish a predictive control strategy of the wet flue gas desulphurization system slurry pH value. Compared with the traditional cascade pH value control method, the strategy is obviously superior to the traditional DCS cascade control method in the aspects of stability, rapidity and accuracy, has a simple calculation structure, is easy to realize in engineering, has an important significance for improving the pH value tracking capability and the disturbance resistance capability, and has a high popularization value.
Drawings
FIG. 1 is a block diagram of an improved advanced pH predictive control framework provided by an embodiment of the present invention;
FIG. 2 is a graph comparing conventional cascade control and advanced predictive control pH response curves provided by an embodiment of the present invention;
fig. 3 is a graph comparing the action curves of a conventional cascade control and an advanced predictive control slurry supply valve according to an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The first embodiment is as follows:
a desulfurization system pH value control method based on predictive control, a control calculation framework based on generalized predictive control comprises: the method comprises the following steps: acquiring a PH set value, and processing the PH set value based on a generalized predictive control model with disturbance control; performing rolling optimization based on the processing result; and performing feedback correction on the PH set value based on the optimization result. In general generalized predictive control, only a dynamic model of a controlled variable and a regulated variable is used as an internal predictive model of predictive control, a relation between measurable disturbance and the controlled variable and the regulated variable is modeled together, and the dynamic model with the measurable disturbance is used as the internal model of the generalized predictive control, namely the generalized predictive control with disturbance suppression. For the predictive control per se, no matter how the internal model changes, the control target of the predictive control is not changed, and a typical control target function of the generalized predictive control strategy can be represented by a quadratic function of the deviation of the regulated quantity from the set value and the control increment, and the specific form is as follows:
in the formula (I), the compound is shown in the specification,
a predicted estimated value representing sampling of the adjusted quantity by j steps at the time t based on known measurement information; delta u (t + j-1) represents a future control quantity increment vector which needs to be subjected to control objective function optimization calculation, and delta is 1-z-1Representing a difference operation; n is a radical of1And N2Respectively representing the predicted starting time and the final ending time of the regulated quantity; n is a radical ofuControlling a time domain; δ (j) and λ (j) represent the weight vector of the deviation of the regulated quantity from the set value and the weight vector of the increment of the control quantity, respectively;
the parameters of the controller needing setting debugging in generalized predictive control comprise: a prediction time domain, a control time domain, and a weight vector; the vector r (t + j) of the set value of the regulated quantity can adopt an actual set value or adopt an exponential decay algorithm to carry out softening processing on the set value.
The generalized prediction control objective function, namely the prediction estimation value of step j sampling of the regulated quantity at the time t in the formula (1) based on known measurement information can be calculated based on a CARIMA controlled autoregressive moving average model integrating measurable disturbance:
where ξ (t) represents the measurable disturbance value at time t; e (t) represents white noise whose average is 0; a, B and C represent the system computational polynomial of the passive object; d and dDA pure delay period representing the control quantity and the measurable disturbance and regulated quantity; the difference operation delta of the white noise denominator can enable the whole prediction control closed loop to have an integral function;
and representing the influence model of the measurable disturbance on the regulated quantity. The prediction calculation of the N-step period of the regulated quantity can be carried out based on the expression (2), and for simplifying the expression, different setting parameters are considered to predict the time domain N and control the time domain N simultaneouslyuWill be
It is briefly described as
Thus simplifying the generalized predictive control prediction estimation equation as follows:
in the formula (I), the compound is shown in the specification,
representing a step excitation coefficient of the controlled object; h represents a step response matrix of the influence of the measurable disturbance on the regulated quantity;
is the adjusted quantity of the non-forced dynamic response coefficient. The influence of measurable disturbance on the regulated quantity can be divided into two basic cases: one is that the measurable disturbance future condition is known. And the other is that the future value can be estimated through the self rule, and the future determined disturbance condition in the prediction expression can be calculated. If the future disturbance is an invariant value, all the disturbances can be expressed as the latest disturbance actual measurement value (ξ (t + j) ═ ξ (t)), that is, the future disturbance variation amount is zero (Δ ξ (t + j) ═ 0), so that the expression (3) can be simplified to eliminate the disturbance variation amount.
For simplifying the expression of the expression (3), the step response matrix of the modulated quantity is recorded as G, and the estimated direction of the modulated quantity is recordedQuantity is abbreviated as
And recording the control quantity increment matrix as delta u, recording the measurable disturbance increment matrix as delta xi, and recording the non-forced dynamic response matrix as f, wherein the simplified expression form is as follows:
it is explained that the detailed structure of the measurable disturbance matrix H is composed of a control quantity-regulated quantity transfer lag time d and a measurable disturbance-regulated quantity transfer lag time dDIs determined by the time difference α, α ═ d-dD. According to the magnitude relation between alpha and 0, measurable disturbance matrixes H of three different structures can be obtained, and the following equations are detailed:
in the formula, hiThe method is characterized in that the method can be obtained by a step response experiment of measurable disturbance for basic elements in a measurable disturbance matrix H, and the dimension of the measurable disturbance matrix H is N2×N2。
The non-forced response matrix f in equation (4) can be obtained by:
f＝GpΔu(t-1)+HpΔξ(t)+Sy(t) (8)
in the formula, Gp，HpAnd S respectively represents the influence matrixes of the past regulated quantity data, the current regulated quantity data and the disturbance quantity data on the future estimated value.
Combining equation (8) with equation (4), the following equation can be obtained:
the parameters in the formula are respectively expressed as: Δ uc＝Δu(t-1)，Δξc＝Δξ(t)，yc＝y(t)。
Substituting the regulated quantity output predictive vector matrix expression (9) of the controlled object into the control objective function (1), and deriving the following equation through formula derivation:
wherein r ═ r (t + d +1) r (t + d +2) L r (t + d + N)2)]TFor reference setpoint vector, Q is a weighted matrix of deviation of the adjusted quantity from setpoint, and dimension N2×N2The diagonal element is λ (j). For the controlled process of SISO, without loss of generality, δ (j) may be set to 1, and finally only the control increment weight coefficient λ (j) is left as a setting parameter, and if the weight coefficients corresponding to different control time domains in the weight matrix are set to the same constant value, the weight coefficient to be set is only λ.
Finally, the optimal value of the control objective function (10) is solved, and an explicit solving expression for the optimization variable delta u can be obtained through the gradient of 0, wherein the specific structural form is as follows:
Δu＝K-1GT(r-Syc-GpΔuc-HΔξ-HpΔξc) (11)
wherein K is Q + GTG。
The solved control increment vector only executes the first element in a single sampling period to realize the output of the control quantity until the next sampling period carries out brand new operation again, and realizes rolling optimization solution based on new measurement information, wherein K is a matrix K-1The control increment Δ u (t) to be executed in the current sampling period can be represented as:
Δu(t)＝kGT(r-Syc-GpΔuc-HΔξ-HpΔξc) (12)
to verify the effectiveness of the proposed algorithm, the proposed pH control strategy was compared to the traditional cascade PID control. The advanced pH control strategy replaces a PID regulator of a traditional cascade PID main regulation loop, model identification is carried out on a controlled object by utilizing field desulfurization data, and the specific form of the model is as follows:
the transfer function model from the opening of the limestone slurry supply valve to the slurry supply flow and the pH value of the slurry supply flow in the main control channel is as follows:
the Smith estimation compensator model is:
in the disturbance channel, a relational model of the flue gas flow at the desulfurization inlet and the pH value is as follows:
the parameters of the controllers in the main loop and the auxiliary loop in the traditional DCS cascade control mode are set as follows: the secondary loop adopts a pure proportion regulator, and the proportion is set to be 1.8; the PID expression of the main loop controller is represented by the formula P (1+ I/s + Ds/(T)ds+1))，
PID controller parameter setting is carried out based on a PID empirical formula, and specific parameters are set as follows: 0.3353, 0.0102, 10.1742, Td22; the advanced pH value control strategy adopts a disturbance suppression generalized predictive control method, and the specific parameters are set as follows: the calculation cycle was 5 seconds, the control horizon was set to 20, the prediction horizon was set to 50, and the control weight constraint was set to 0.92. For the internal model based on the generalized predictive controller with disturbance suppression, the main control loop model carries out integral comprehensive equivalence on the auxiliary loop controller and the internal loop model, and the equivalent model carries out order reduction processingThe equivalent reduced order model is
The equivalent reduced order model is set as a predictive controller internal model.
Fig. 2 and fig. 3 are pH response curves and slurry supply valve action curves corresponding to the pH response curves, respectively, in which the simulation process is that 10s of set values are subjected to step change, 1200s of flue gas amount disturbance is subjected to step change, and it can be seen from fig. 2 that the control effect of the advanced control strategy is obviously superior to that of the traditional cascade control strategy, and for the set tracking performance, the adjustment time and overshoot of the advanced pH control strategy are both obviously superior to that of the traditional cascade control strategy; in the aspect of disturbance resistance, the advanced control callback time is shorter, and the disturbance resistance is stronger. Simulation shows that the control quality of the pH value of wet desulphurization can be obviously improved based on generalized predictive control with disturbance suppression, and the method has an important effect on stable and economic operation of wet desulphurization.
Example two:
a desulfurization system pH control system based on predictive control, comprising:
the acquisition module is used for acquiring a PH set value;
the model processing module is used for processing the PH set value based on the generalized predictive control model with disturbance control;
the optimization module is used for performing rolling optimization based on the processing result;
and the correction module is used for carrying out feedback correction on the PH set value based on the optimization result.
The generalized predictive control model with disturbance control is as follows:
wherein the content of the first and second substances,
for the predicted estimated value of the adjusted quantity j step sampling at the time t based on the known measurement information, the delta u (t + j-1) is an optimization meter for the objective function to be controlledCalculated future control increment vector, Δ 1-z-1For differential operation, N1And N2Predicting the time domain starting time and the final ending time, N, for the modulated quantity, respectivelyuIn order to control the time domain, delta (j) and lambda (j) are respectively a weight vector of the deviation of the regulated quantity and the set value and a weight vector of the increment of the controlled quantity, the prediction time domain, the control time domain and the weight vector are setting design parameters of prediction control, and r (t + j) is a reference set value sequence.
And (4) performing prediction estimation value of adjusted quantity j step sampling based on known measurement information at time t
The method is obtained by carrying out prediction calculation on the N-step period of the regulated quantity based on a CARIMA controlled autoregressive moving average model integrating measurable disturbance, wherein the CARIMA controlled autoregressive moving average model integrating measurable disturbance is as follows:
where ξ (t) is measurable disturbance value at t moment, e (t) is white noise with mean value of 0, A, B and C are system calculating polynomials of passive object, d and dDFor the pure delay periods of the control quantity and the measurable disturbance and regulated quantity,
the influence of the measurable disturbance on the regulated quantity is modeled.
the expression of the control quantity increment vector Δ u (t) at time t is:
Δu(t)＝kGT(r-Syc-GpΔuc-HΔξ-HpΔξc)
where K is the matrix K-1First row of (1), Δ uc＝Δu(t-1)，Δξc＝Δξ(t)，yc＝y(t)，Gp，HpS represents the influence matrix of the past regulated quantity data, the current regulated quantity data and the disturbance quantity data on the future estimated value respectively, delta xi is a measurable disturbance increment matrix, and r is [ r (t + d +1) r (t + d +2)L r(t+d+N2)]TFor reference set value vector, G is step response matrix of regulated quantity, and measurable disturbance matrix H is formed from control quantity-regulated quantity transfer lag time d and measurable disturbance-regulated quantity transfer lag time dDIs determined by the time difference α, α ═ d-dDAccording to the magnitude relation between alpha and 0, three measurable disturbance matrixes H with different structures can be obtained:
in the formula, hiFor the basic elements in the measurable disturbance matrix H, the dimension of the measurable disturbance matrix H is N2×N2。
Example three:
the embodiment of the invention also provides a desulfurization system pH value control device based on predictive control, which comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method of:
the set value of the PH value is obtained,
processing the PH set value based on a generalized predictive control model with disturbance control;
performing rolling optimization based on the processing result;
and performing feedback correction on the PH set value based on the optimization result.
Example four:
an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the following method steps:
the set value of the PH value is obtained,
processing the PH set value based on a generalized predictive control model with disturbance control;
performing rolling optimization based on the processing result;
and performing feedback correction on the PH set value based on the optimization result.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A desulfurization system pH value control method based on predictive control is characterized by comprising the following steps:
the set value of the PH value is obtained,
processing the PH set value based on a generalized predictive control model with disturbance control;
performing rolling optimization based on the processing result;
and performing feedback correction on the PH set value based on the optimization result.
2. The method for controlling the pH value of the desulfurization system based on the predictive control as claimed in claim 1, wherein the generalized predictive control model with disturbance control is as follows:
wherein the content of the first and second substances,
and carrying out prediction estimation value of step j sampling of the regulated quantity for time t based on known measurement information, wherein delta u (t + j-1) is a future control quantity increment vector needing to be subjected to control objective function optimization calculation, and delta is 1-z-1For differential operation, N1And N2Respectively when predicted for the regulated quantityThe domain start time and the final end time, NuIn order to control the time domain, delta (j) and lambda (j) are respectively a weight vector of the deviation of the regulated quantity and the set value and a weight vector of the increment of the controlled quantity, the prediction time domain, the control time domain and the weight vector are setting design parameters of prediction control, and r (t + j) is a reference set value sequence.
3. The method as claimed in claim 2, wherein the predicted estimation value of the adjusted quantity sampled in j steps is performed at the time t based on known measurement information
The method is obtained by carrying out prediction calculation on the N-step period of the regulated quantity based on a CARIMA controlled autoregressive moving average model integrating measurable disturbance, wherein the CARIMA controlled autoregressive moving average model integrating measurable disturbance is as follows:
where ξ (t) is measurable disturbance value at t moment, e (t) is white noise with mean value of 0, A, B and C are system calculating polynomials of passive object, d and dDFor the pure delay periods of the control quantity and the measurable disturbance and regulated quantity,
the influence of the measurable disturbance on the regulated quantity is modeled.
4. The method of claim 1, wherein the expression of the control quantity increment vector Δ u (t) at time t is:
Δu(t)＝kGT(r-Syc-GpΔuc-HΔξ-HpΔξc)
where K is the matrix K-1First row of (1), Δ uc＝Δu(t-1)，Δξc＝Δξ(t)，yc＝y(t)，Gp，HpS represents the influence matrixes of the past regulated quantity data, the current regulated quantity data and the disturbance quantity data on the future estimated value respectively, delta xi is a measurable disturbance increment matrix, and r is [ r (t + d +1) r (t + d +2) L r (t + d + N)2)]TFor reference set value vector, G is step response matrix of regulated quantity, and measurable disturbance matrix H is formed from control quantity-regulated quantity transfer lag time d and measurable disturbance-regulated quantity transfer lag time dDIs determined by the time difference α, α ═ d-dDAccording to the magnitude relation between alpha and 0, three measurable disturbance matrixes H with different structures can be obtained:
in the formula, hiFor the basic elements in the measurable disturbance matrix H, the dimension of the measurable disturbance matrix H is N2×N2。
5. A desulfurization system pH value control system based on predictive control is characterized by comprising:
the acquisition module is used for acquiring a PH set value;
the model processing module is used for processing the PH set value based on the generalized predictive control model with disturbance control;
the optimization module is used for performing rolling optimization based on the processing result;
and the correction module is used for carrying out feedback correction on the PH set value based on the optimization result.
6. The method for controlling the pH value of the desulfurization system based on the predictive control as claimed in claim 5, wherein the generalized predictive control model with disturbance control is as follows:
wherein the content of the first and second substances,
and carrying out prediction estimation value of step j sampling of the regulated quantity for time t based on known measurement information, wherein delta u (t + j-1) is a future control quantity increment vector needing to be subjected to control objective function optimization calculation, and delta is 1-z-1For differential operation, N1And N2Predicting the time domain starting time and the final ending time, N, for the modulated quantity, respectivelyuIn order to control the time domain, delta (j) and lambda (j) are respectively a weight vector of the deviation of the regulated quantity and the set value and a weight vector of the increment of the controlled quantity, the prediction time domain, the control time domain and the weight vector are setting design parameters of prediction control, and r (t + j) is a reference set value sequence.
7. The method as claimed in claim 5, wherein the predicted estimation value of the adjusted quantity sampled in j steps is performed at the time t based on known measurement information
The method is obtained by carrying out prediction calculation on the N-step period of the regulated quantity based on a CARIMA controlled autoregressive moving average model integrating measurable disturbance, wherein the CARIMA controlled autoregressive moving average model integrating measurable disturbance is as follows:
where ξ (t) is measurable disturbance value at t moment, e (t) is white noise with mean value of 0, A, B and C are system calculating polynomials of passive object, d and dDTo control the amount and canMeasuring the pure delay period of the disturbance and the regulated quantity,
the influence of the measurable disturbance on the regulated quantity is modeled.
8. The method of claim 5, wherein the expression of the control quantity increment vector Δ u (t) at time t is:
where K is the matrix K-1First row of (1), Δ uc＝Δu(t-1)，Δξc＝Δξ(t)，yc＝y(t)，Gp，HpS represents the influence matrixes of the past regulated quantity data, the current regulated quantity data and the disturbance quantity data on the future estimated value respectively, delta xi is a measurable disturbance increment matrix, and r is [ r (t + d +1) r (t + d +2) L r (t + d + N)2)]TFor reference set value vector, G is step response matrix of regulated quantity, and measurable disturbance matrix H is formed from control quantity-regulated quantity transfer lag time d and measurable disturbance-regulated quantity transfer lag time dDIs determined by the time difference α, α ═ d-dDAccording to the magnitude relation between alpha and 0, three measurable disturbance matrixes H with different structures can be obtained:
in the formula, hiFor the basic elements in the measurable disturbance matrix H, the dimension of the measurable disturbance matrix H is N2×N2。
9. The pH value control device of the desulfurization system based on predictive control is characterized by comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 4.
10. Computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 4.
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