CN109343349B - SCR flue gas denitration optimal control system and method based on ammonia injection amount compensator - Google Patents
SCR flue gas denitration optimal control system and method based on ammonia injection amount compensator Download PDFInfo
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Abstract
The invention discloses an SCR flue gas denitration optimal control method based on an ammonia injection amount compensator, which comprises the following steps: step 1, determining NO in flue gas in a coal-fired unit and an SCR system x Production and SCR reactor outlet NO x A concentration-related variable; step 2, acquiring the variable data related to the step 1 from a DCS system; step 3, estimating the input variable time delay of the model by using a fuzzy curve method to obtain a sample after phase space reconstruction; step 4, establishing an outlet NO according to the sample after the phase space reconstruction in the step 3 x Obtaining the NO at the outlet of the SCR reactor by a concentration dynamic prediction model x Predicted concentration and based on in situ outlet NO x Concentration actual value correction outlet NO x A concentration dynamic prediction model; step 5, according to the outlet NO of the SCR reactor obtained in the step 4 x The concentration predicted value is converted into ammonia spraying compensation quantity by deviation between the concentration predicted value and a set value, the ammonia spraying quantity is compensated in time, the opening degree of an ammonia spraying valve is controlled, and the NO at the outlet is ensured x The concentration was stabilized at the set point.
Description
Technical Field
The invention relates to the technical field of flue gas denitration of coal-fired units, in particular to an SCR (Selective Catalytic Reduction ) flue gas denitration optimal control system and method based on an ammonia injection amount compensator.
Background
With the increasing protrusion of the atmospheric pollution problem, the atmospheric pollutant emission of thermal power generating units in China is brought into strict supervision. At present, nitrogen Oxides (NO) of national thermal power generating units x ) The discharge amount is 200mg/m currently 3 (important area and newly built unit 100 mg/m) 3 ) Down to 50mg/m 3 In the following, the ultra-low emission index is reached. Conventional low NO x The combustion control is difficult to meet the emission standard, and a tail flue gas denitration device is required to be matched, so that an SCR (selective catalytic reduction selective catalytic reduction) flue gas denitration technology is widely adopted at home and abroad, wherein the control of an ammonia injection system is the most important. When the working condition is stable, the ammonia injection control can generally achieve better effect; however, when the running condition of the unit is changed, such as coal replacement, frequent fluctuation of AGC load instruction of the unit, and change of combustion condition (such as air distribution mode, excessive air coefficient, etc.), the NO in the flue gas is caused x The large disturbance occurs, so that the influence factors of the SCR system are more, the SCR reaction is easily influenced by the activity of the catalyst, and the system has the characteristics of nonlinearity and strong disturbance. And the SCR reaction itself requires a certain reaction time, and NO x The measurement device usually has a sampling distance of more than 20 meters, which causes measurement hysteresis, and the SCR system is characterized by large inertia and large delay. The optimal NH is difficult to be ensured by the ammonia injection amount 3 /NO x Ratio. When the ammonia injection is too small, NO is easily caused x Emission increases or even exceeds standard; when ammonia is sprayed excessively, not only the denitration efficiency is affected, but also excessive ammonia and SO in the flue gas are caused 3 Reaction to produce sulfurThe activity of the catalyst is reduced by the acid ammonia hydrogen and the ammonia sulfate, so that the ash deposition of the air preheater is blocked and corroded, the safe operation of the boiler is influenced, and meanwhile, the operation cost waste and secondary environmental pollution are caused by the increase of the ammonia escape amount. Because the reaction process of the SCR system is complex, the existing PID control is difficult to obtain a good control effect.
From the above, the existing SCR flue gas denitration system has complex reaction process, and has nonlinearity, large inertia, large delay, strong disturbance and time variability especially when working conditions change. At present, a conventional ammonia injection control system adopts a single-loop PID controller or a cascade PID controller with a feedforward signal, but the PID control is not suitable for a delay and large inertia system; and the inaccurate measurement of the flue gas flow causes NO x The total amount is inaccurate, so that the control effect of the ammonia injection amount of the system is poor, and the safe operation and the denitration efficiency of the unit are affected.
Disclosure of Invention
In order to solve the problems, the invention aims to provide an SCR flue gas denitration optimal control system and method based on an ammonia injection amount compensator, which solve the problems that an SCR system presents nonlinear, large inertia, large delay, strong disturbance and time-varying influence on the existing control system, establish the ammonia injection amount compensator by using field data, and realize the NO outlet by combining the existing PID control x And (3) effectively controlling the concentration. Through the continuous adjustment of the ammonia spraying compensation quantity, the ammonia spraying quantity is reduced as much as possible while the denitration efficiency is ensured, the ammonia escape rate is reduced, the secondary pollution is avoided, and the denitration operation cost is reduced.
The invention provides an SCR flue gas denitration optimal control method based on an ammonia injection amount compensator, which comprises the following steps:
step 1, determining NO in flue gas in a coal-fired unit and an SCR system x Production and SCR reactor outlet NO x A concentration-related variable;
step 2, acquiring the variable data related to the step 1 from a DCS system;
step 3, estimating the input variable time delay of the model by using a fuzzy curve method to obtain a sample after phase space reconstruction;
step 4, reconstructing the sample according to the phase space in the step 3The present creates outlet NO x Obtaining the NO at the outlet of the SCR reactor by a concentration dynamic prediction model x Predicted concentration and based on in situ outlet NO x Concentration actual value correction outlet NO x A concentration dynamic prediction model;
step 5, according to the outlet NO of the SCR reactor obtained in the step 4 x The concentration predicted value is converted into ammonia spraying compensation quantity by deviation between the concentration predicted value and a set value, the ammonia spraying quantity is compensated in time, the opening degree of an ammonia spraying valve is controlled, and the NO at the outlet is ensured x The concentration was stabilized at the set point.
As a further development of the invention, an outlet NO is established in step 4 x The method adopted by the concentration dynamic prediction model is a wavelet kernel partial least square method, and the model updating strategy adopts a sliding window method.
As a further improvement of the invention, the step 1 is carried out with the flue gas NO x Production and SCR reactor outlet NO x The concentration-related variables include boiler load, total coal amount, ammonia injection valve opening, inlet NO x Concentration, inlet flue gas temperature, inlet flue gas flow and inlet O 2 One or more of the contents.
As a further improvement of the present invention, the fuzzy curve method in the step 3 is specifically:
the samples of the input variable x and the output variable y at the time t are recorded as Gaussian blur membership functions of the input variable x are defined as:
wherein b is 20% of the range of the value range of the variable x, { phi } t (x) The fuzzy rule of y (t) is described as { if x is phi }, the fuzzy rule of y (t) is described as { if x t (x),then y is y(t)}，
Introducing time delay to x, wherein each new variable after expansion is expressed as x (t-lambda), lambda=0, 1, … tau, tau is an estimated time delay value, and the centroid of each new variable after expansion is defuzzified as shown in the following formula:
obtaining a fuzzy curve C under the condition that the variable x time delay is lambda λ Where n is the number of samples of variable x. If the range of C (lambda) is closer to the range of y, the importance of x (t-lambda) is higher, and the fuzzy curve C is calculated λ Coverage maximum λ=argmax (C (λ) max -C(λ) min ) Time delay of variable x, C (lambda) max And C (lambda) min Is the maximum value and the minimum value of the point value range on the fuzzy curve.
As a further improvement of the invention, the kernel function in the wavelet kernel partial least square method is a Mexican Hat wavelet kernel function:
K(x)＝(1-x 2 )exp(-x 2 /2)。
as a further development of the invention, in step 4, the method is performed according to the on-site outlet NO x Concentration actual value correction outlet NO x The specific method of the concentration dynamic prediction model is as follows: measuring outlet NO by CEMS field instrument x Concentration feedback to outlet NO x Concentration dynamic prediction model for outlet NO x And correcting the concentration dynamic prediction model.
As a further improvement of the invention, the outlet NO x The predicted value corrected by the concentration dynamic prediction model is y p (k+1)＝y m (k+1)+y(k)-y m (k) Wherein y is m (k) And y (k) is the output of the dynamic prediction model of the outlet NOx concentration and the actual output of the system respectively, y m (k+1) is the outlet NO at the next time x And outputting a concentration dynamic prediction model.
As a further improvement of the present invention, the method for calculating the ammonia injection compensation amount in the step 5 is as follows:
wherein the method comprises the steps ofQ is inlet flue gas flow, m is ammonia nitrogen mole ratio,for denitration efficiency, < >>And->NO respectively 2 And NH 3 Molar mass of>For outlet of NO x Concentration prediction value->Is NO x Concentration set point,/->Ammonia escape concentration,/->Is inlet NO x Actual concentration values.
The invention also provides an SCR flue gas denitration optimal control device based on the ammonia injection amount compensator, which comprises:
the ammonia injection quantity compensator algorithm processor is connected with the data acquisition device at the input end, the output end of the ammonia injection quantity compensator algorithm processor is connected with the input end of the improved DCS system, and the ammonia injection quantity compensator algorithm processor comprises a phase space reconstruction device, an outlet NOx concentration dynamic prediction model device and an ammonia injection compensation quantity calculation device;
the input end of the field controller is connected with the output end of the improved DCS system, the output end of the field controller is connected with the SCR denitration device, and the field controller comprises a feedforward controller, a PID controller and an ammonia injection flow controller.
As a further improvement of the present invention, the input end of the phase space reconstruction device is connected with the data collector, the output end of the phase space reconstruction device is connected with the input end of the outlet NOx concentration dynamic prediction model device, the output end of the outlet NOx concentration dynamic prediction model device is connected with the input end of the ammonia injection compensation amount calculation device, the output end of the ammonia injection compensation amount calculation device is connected with the input end of the improved DCS system, the output end of the improved DCS system is connected with the input end of the feedforward controller and the input end of the PID controller, and the output end of the feedforward controller and the output end of the PID controller are both connected with the ammonia injection flow controller.
The beneficial effects of the invention are as follows: aiming at the problem that the traditional PID can not solve nonlinearity and delay, the invention utilizes the dynamic nonlinear prediction model to comprehensively learn the related information and dynamically predict the NO of the export in advance x Concentration; at the same time according to the predicted outlet NO x Concentration and SCR System Outlet actual NO x The deviation of concentration, the compensation quantity of ammonia injection quantity is calculated, the adverse effect of the large inertia characteristic of the SCR system on control is eliminated through feedforward compensation, the ammonia injection quantity is reduced as much as possible while the denitration efficiency is ensured, the ammonia escape rate is reduced, secondary pollution is avoided, and meanwhile, the denitration operation cost is reduced.
Drawings
FIG. 1 is a flow chart of an SCR flue gas denitration optimization control method based on an ammonia injection amount compensator according to an embodiment of the invention;
fig. 2 is a schematic structural diagram of an SCR flue gas denitration optimizing control device based on an ammonia injection amount compensator according to an embodiment of the present invention;
fig. 3 is a schematic diagram of the calculation of the algorithm processor of the ammonia injection amount compensator in the SCR flue gas denitration optimizing control device based on the ammonia injection amount compensator according to the embodiment of the invention.
In the drawing the view of the figure,
1. a data collector; 2. an ammonia injection amount compensator algorithm processor; 3. an improved DCS system; 4. a field controller; 5. an SCR denitration device; 21. a phase space reconstruction device; 22. outlet NO x A concentration dynamic prediction model device; 23. an ammonia injection compensation amount calculating device; 41. a feedforward controller;42. a PID controller; 43. and an ammonia injection flow controller.
Detailed Description
The invention will now be described in further detail with reference to specific examples thereof in connection with the accompanying drawings.
Example 1
As shown in fig. 1, the embodiment of the invention discloses an SCR flue gas denitration optimization control method based on an ammonia injection amount compensator, which comprises the following steps:
step 1, determining NO in flue gas in a coal-fired unit and an SCR system x Production and SCR reactor outlet NO x A concentration-related variable;
step 2, acquiring related variable data in the step 1 from a DCS system;
step 3, estimating the input variable time delay of the model by using a fuzzy curve method to obtain a sample after phase space reconstruction;
step 4, establishing an outlet NO according to the sample after the phase space reconstruction in the step 3 x Obtaining the NO at the outlet of the SCR reactor by a concentration dynamic prediction model x Predicted concentration and based on in situ outlet NO x Concentration actual value correction outlet NO x A concentration dynamic prediction model;
step 5, according to the outlet NO of the SCR reactor obtained in the step 4 x The concentration predicted value is converted into ammonia spraying compensation quantity by deviation between the concentration predicted value and a set value, the ammonia spraying quantity is compensated in time, the opening degree of an ammonia spraying valve is controlled, and the NO at the outlet is ensured x The concentration was stabilized at the set point.
Further, in step 4, an outlet NO is established x The method adopted by the concentration dynamic prediction model is a wavelet kernel partial least square method, and the model updating strategy adopts a sliding window method. The method has the advantages of solving the problems of numerous variables and serious related industrial modeling by using a kernel partial least square method, and simultaneously introduces a multidimensional tensor product wavelet kernel function which can be allowed by a Mexican Hat parent wavelet structure and has strong capability of describing the data change trend, so as to improve the learning performance of the model.
Further, in step 1, the smoke NO x Production and SCR reactor outlet NO x Concentration-dependent variable packageIncluding boiler load, total coal quantity, ammonia injection valve opening and inlet NO x Concentration, inlet flue gas temperature, inlet flue gas flow and inlet O 2 One or more of the contents. In practical application, a technician can adjust the variable according to the practical working condition, and other NO of the smoke can be added x Production and SCR reactor outlet NO x Concentration-related variables.
Further, the fuzzy curve method in the step 3 specifically comprises the following steps:
the samples of the input variable x and the output variable y at the time t are recorded as Gaussian blur membership functions of the input variable x are defined as:
wherein b is 20% of the range of the value range of the variable x, { phi } t (x) The fuzzy rule of y (t) is described as { if x is phi }, the fuzzy rule of y (t) is described as { if x t (x),then y is y(t)}，
Introducing time delay to x, wherein each new variable after expansion is expressed as x (t-lambda), lambda=0, 1, … tau, tau is an estimated time delay value, and the centroid of each new variable after expansion is defuzzified as shown in the following formula:
obtaining a fuzzy curve C under the condition that the variable x time delay is lambda λ Where n is the number of samples of variable x. If the range of C (lambda) is closer to the range of y, the importance of x (t-lambda) is higher, and the fuzzy curve C is calculated λ Coverage maximum λ=argmax (C (λ) max -C(λ) min ) Time delay of variable x, C (lambda) max And C (lambda) min Is the maximum value and the minimum value of the point value range on the fuzzy curve.
Further, the kernel function in the wavelet kernel partial least square method is a Mexican Hat wavelet kernel function:
K(x)＝(1-x 2 )exp(-x 2 /2)。
further, root in step 4According to the on-site outlet NO x Concentration actual value correction outlet NO x The specific method of the concentration dynamic prediction model is as follows: measuring outlet NO by CEMS field instrument x Concentration feedback to outlet NO x Concentration dynamic prediction model for outlet NO x And correcting the concentration dynamic prediction model. Enhancing outlet NO x The concentration dynamic prediction model predicts the accuracy and control accuracy.
Further, for outlet NO x The predicted value corrected by the concentration dynamic prediction model is y p (k+1)＝y m (k+1)+y(k)-y m (k) Wherein y is m (k) And y (k) is respectively the outlet NO x Concentration dynamic prediction model output and system actual output, y m (k+1) is the outlet NO at the next time x And outputting a concentration dynamic prediction model.
Further, the method for calculating the ammonia injection compensation amount in the step 5 is as follows:
wherein Q is inlet flue gas flow, m is ammonia nitrogen mole ratio,for denitration efficiency, < >>And->NO respectively 2 And NH 3 Molar mass of>For outlet of NO x Concentration prediction value->Is NO x Concentration set point,/->Ammonia escape concentration,/->Is inlet NO x Actual concentration values.
Example 2
As shown in fig. 2-3, the embodiment of the invention is an SCR flue gas denitration optimizing control device based on an ammonia injection amount compensator, which comprises:
the input end of the ammonia injection quantity compensator algorithm processor 2 is connected with the data acquisition device 1, the output end of the ammonia injection quantity compensator algorithm processor 2 is connected with the input end of the improved DCS system 3, and the ammonia injection quantity compensator algorithm processor 2 comprises a phase space reconstruction device 21, an outlet NOx concentration dynamic prediction model device 22 and an ammonia injection compensation quantity calculation device 23. The phase space reconstruction device 21, the outlet NOx concentration dynamic prediction model device 22 and the ammonia injection compensation amount calculation device 23 are arranged inside the ammonia injection amount compensator algorithm processor 2.
The input end of the field controller 4 is connected with the output end of the modified DCS system 3, the output end of the field controller 4 is connected with the SCR denitration device 5, and the field controller 4 comprises a feedforward controller 41, a PID controller 42 and an ammonia injection flow controller 43.
The data collector 1 collects the related parameter data of the coal-fired unit and the SCR system, and the data of the existing PID control system, namely the export NO x The concentration dynamic prediction model sample is prepared. The phase space reconstruction means 21 estimate the outlet NO from the fuzzy curve method x Each input variable and outlet NO of concentration dynamic prediction model x Time delay of concentration and for outlet NO x Sample phase space reconstruction of concentration dynamic prediction model, solving the problem of large inertia and large delay existing in SCR system on outlet NO x Influence of concentration dynamic prediction model precision; outlet NO x The concentration dynamic prediction model device 22 is based on wavelet kernel partial least square method and is reconstructed in phase spaceA sliding window updating strategy is adopted on the basis of setting 21 reconstructed samples, and relevant factors and export NO are established x Dynamic concentration prediction model for realizing advanced dynamic prediction of outlet NO x Concentration and according to on-site outlet NO x A concentration actual value correction model; the ammonia injection compensation amount calculating device 23 calculates NO at the outlet of the SCR denitration device 5 x The deviation of the predicted concentration value and the set value is converted into ammonia spraying amount, and the ammonia spraying amount is timely compensated to ensure that the NO is discharged x The concentration was stabilized at the set point. The improved DCS system 3 transmits the ammonia injection compensation quantity output by the ammonia injection quantity compensator algorithm processor 2 to the site controller 4, the feedforward controller 41 and the PID controller 42 control the ammonia injection device to perform ammonia injection compensation, and the ammonia injection flow controller 43 converts the compensated ammonia injection signal into a control instruction to control the SCR denitration device 5 to perform denitration treatment. The improved DCS system 3 relates to logic improvement of the traditional DCS system, an ammonia injection compensation quantity signal is added, and the ammonia injection compensation quantity signal is added with the original ammonia injection signal to obtain a compensated ammonia injection signal.
Further, the input end of the phase space reconstruction device 21 is connected with the data collector 1, the output end of the phase space reconstruction device 21 is connected with the input end of the outlet NOx concentration dynamic prediction model device 22, the output end of the outlet NOx concentration dynamic prediction model device 22 is connected with the input end of the ammonia injection compensation amount calculation device 23, the output end of the ammonia injection compensation amount calculation device 23 is connected with the input end of the improved DCS system, the output end of the improved DCS system 3 is connected with the input end of the feedforward controller 41 and the input end of the PID controller 42, and the output end of the feedforward controller 41 and the output end of the PID controller 42 are connected with the ammonia injection flow controller 43.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. An SCR flue gas denitration optimal control method based on an ammonia injection amount compensator is characterized by comprising the following steps:
step 1, determining NO in flue gas in a coal-fired unit and an SCR system x Production and SCR reactor outlet NO x Concentration-related variables including boiler load, total coal quantity, ammonia injection valve opening, inlet NO x Concentration, inlet flue gas temperature, inlet flue gas flow and inlet O 2 One or more of the contents;
step 2, acquiring the variable data related to the step 1 from a DCS system;
step 3, estimating the time delay of an input variable of a model by using a fuzzy curve method to obtain a sample after phase space reconstruction, wherein the fuzzy curve method specifically comprises the following steps:
the samples of the input variable x and the output variable y at time t are denoted as (x (t), y (t)), and the gaussian fuzzy membership function of the input variable x is defined as:
wherein b is 20% of the range of the value range of the variable x, { phi } t (x) The fuzzy rule of y (t) is described as { if x is phi }, the fuzzy rule of y (t) is described as { if x t (x),then y is y(t)}，
Introducing time delay to x, wherein each new variable after expansion is expressed as x (t-lambda), lambda=0, 1, … tau, tau is an estimated time delay value, and the centroid of each new variable after expansion is defuzzified as shown in the following formula:
obtaining a fuzzy curve C under the condition that the variable x time delay is lambda λ Where n is the number of samples of the variable x, the more important x (t- λ) is the closer the C (λ) range is to the y range, the more fuzzy curve C is calculated λ Coverage maximum λ=argmax (C (λ) max -C(λ) min ) Time delay of variable x, C (lambda) max And C (lambda) min The maximum value and the minimum value of the point value range on the fuzzy curve are obtained;
step 4, according to the steps3, establishing an outlet NO by the sample after phase space reconstruction x Obtaining the NO at the outlet of the SCR reactor by a concentration dynamic prediction model x Predicted concentration and based on in situ outlet NO x Concentration actual value correction outlet NO x A concentration dynamic prediction model;
step 5, according to the outlet NO of the SCR reactor obtained in the step 4 x The concentration predicted value is converted into ammonia spraying compensation quantity by deviation between the concentration predicted value and a set value, the ammonia spraying quantity is compensated in time, the opening degree of an ammonia spraying valve is controlled, and the NO at the outlet is ensured x The concentration is stabilized at a set value, wherein the calculation method of the ammonia injection compensation quantity comprises the following steps:
wherein Q is inlet flue gas flow, m is ammonia nitrogen mole ratio,for denitration efficiency, < >>And->NO respectively 2 And NH 3 Molar mass of>For outlet of NO x Concentration prediction value->Is NO x Concentration set point,/->Ammonia escape concentration,/->Is inlet NO x Actual concentration values.
2. The SCR flue gas denitration optimization control method according to claim 1, wherein the step 4 establishes the outlet NO x The method adopted by the concentration dynamic prediction model is a wavelet kernel partial least square method, and the model updating strategy adopts a sliding window method.
3. The SCR flue gas denitration optimization control method according to claim 2, wherein the wavelet kernel partial least square method is a Mexican Hat wavelet kernel function:
K(x)＝(1-x 2 )exp(-x 2 /2)。
4. the SCR flue gas denitration optimization control method according to claim 1, wherein in step 4, NO is outputted according to the site x Concentration actual value correction outlet NO x The specific method of the concentration dynamic prediction model is as follows: measuring outlet NO by CEMS field instrument x Concentration feedback to outlet NO x Concentration dynamic prediction model for outlet NO x And correcting the concentration dynamic prediction model.
5. The optimized control method for SCR flue gas denitration according to claim 4, wherein the outlet NO x The predicted value corrected by the concentration dynamic prediction model is y p (k+1)＝y m (k+1)+y(k)-y m (k) Wherein y is m (k) And y (k) is respectively the outlet NO x Concentration dynamic prediction model output and system actual output, y m (k+1) is the outlet NO at the next moment x And outputting a concentration dynamic prediction model.
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