CN118286838B - Combustion-pollutant treatment full-flow intelligent regulation pollution-reduction and carbon-reduction method and system - Google Patents
Combustion-pollutant treatment full-flow intelligent regulation pollution-reduction and carbon-reduction method and system Download PDFInfo
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Abstract
The invention discloses a method and a system for intelligently regulating, controlling, reducing pollution and reducing carbon in a whole flow of combustion-pollutant treatment, wherein the control method comprises the following steps: based on accurate prediction models of all controlled variables of a boiler combustion system of a coal-electricity/thermoelectric unit, predicted values of the controlled variables are obtained in advance, a control strategy of the boiler combustion system is formulated by combining the predicted values, key equipment adjustment of the combustion system is performed in advance, adjustment lag time is shortened, unit steam coal consumption and emission concentration of various pollutants are reduced, and pollution and carbon reduction of a source are realized; based on an accurate prediction model of a target controlled variable of the pollutant removal system, the change of the concentration of various pollutants at a boiler outlet is predicted in advance, the intelligent accurate regulation and control of the multi-device key parameter steps of the whole-flow multi-device in the boiler combustion-flue gas treatment process are combined, the ultra-high precision clamping control of the pollutant emission concentration under variable load/fuel is realized, the material consumption of a pollutant removal device is reduced in the whole-flow manner, and the effective reduction of CO 2 emission is realized cooperatively.
Description
Technical Field
The invention relates to the field of energy conservation and environmental protection, in particular to a full-flow intelligent regulation and control pollution reduction and carbon reduction method and system for combustion-pollutant treatment.
Background
In an ultra-low emission system, a certain amount of energy and materials are required to be input to efficiently remove pollutants in the flue gas, but the energy consumption and the operation cost of the ultra-low emission system are increased. According to statistics, the station service power consumption of the ultra-low emission system can reach more than 2 percent. Along with the large-scale access of renewable energy sources such as wind energy and light energy and the mixed combustion of zero carbon/low carbon complex fuels, the load fluctuation of the coal-fired unit is frequent (30% -100%), and new requirements are provided for the running stability, economy and adjustability of the ultra-low emission system. Therefore, how to realize high-efficiency stable ultralow emission of the coal-fired unit under complicated fuel blending and deep peak shaving, reduce the energy consumption and the material consumption of the system, and improve the stability, the economy and the reliability of the ultralow emission system is a problem to be solved.
However, the conventional pollution and carbon reduction control mode of the existing boiler combustion and pollutant treatment system is generally controlled by a DCS system, manual control is used as assistance, timely adjustment and response are difficult to achieve after the operation working condition changes, pollutant concentration fluctuation is large under the variable load/fuel working condition, and larger energy consumption and material consumption are easy to generate. Through manual adjustment by operators, the reaction speed is low, the adjustment is delayed, and the requirements on the operators are high (such as familiarity degree of the system, operation experience and the like); the control parameters have strong coupling, other parameters are not correspondingly optimized and adjusted after the working conditions are changed, the global change of the boiler operation is not considered, and the energy waste and the pollutant emission exceeding are easily caused; meanwhile, the operation parameters and the outlet concentration of the pollutant removal equipment are influenced by load fluctuation and coal quality change, the manual control level is limited, and sometimes, in order to meet the ultra-low emission requirement, materials are excessively input or the operation power is increased when the equipment is operated, so that the input cost is too high, and resource waste and secondary pollution are easily caused.
Therefore, the invention constructs an operation monitoring and database covering the boiler-ultra-low emission system, and establishes a 4-layer structure intelligent regulation and control system comprising a boiler combustion-pollutant treatment system layer, a knowledge-data coupling modeling layer, a model parameter identification optimizing layer and a step intelligent accurate regulation and control layer.
Disclosure of Invention
The invention provides a method and a system for intelligently regulating, controlling and reducing pollution and carbon in a whole flow of combustion-pollutant treatment, and provides an intelligent regulation and control method for a carbon pollution source emission reduction-ultralow emission system based on the thought of intelligent and accurate regulation and control of multiple steps of multiple systems and devices in the whole flow of a boiler combustion-flue gas treatment process, which is used for realizing advanced and accurate prediction of target controlled variables of a boiler combustion system and a flue gas multiple pollutant removal system, accurate control of key parameters and collaborative optimization of pollution reduction and carbon reduction in the whole flow. The method comprises the steps of acquiring predicted values of controlled variables in advance based on an accurate prediction model of each controlled variable of a boiler combustion system, formulating a control strategy of the boiler combustion system by combining the predicted values, adjusting key equipment of the combustion system in advance, shortening adjustment lag time, reducing unit steam coal consumption, reducing pollutant emission concentration and realizing pollution and carbon reduction of a source; based on an accurate prediction model of a target controlled variable of a pollutant removal system, the change of the pollutant concentration at the outlet of a boiler is predicted in advance, the intelligent accurate regulation and control of multiple device key parameter steps of the whole-flow multi-device in the boiler combustion-flue gas treatment process are combined, the ultra-high precision clamping control of the pollutant emission concentration under variable load/fuel is realized, the material energy consumption of a pollutant removal device is reduced in the whole-flow manner, and the effective reduction of CO 2 emission is realized cooperatively.
In order to achieve the above object, the present invention provides the following solutions:
the utility model provides a combustion-pollutant treatment whole flow intelligent regulation and control subtracts dirty carbon reduction system, the system includes: a boiler combustion-pollutant treatment system layer, a knowledge-data coupling modeling layer, a model parameter identification optimizing layer and a cascade intelligent accurate regulation layer;
The flow of the combustion-pollutant treatment full-flow intelligent regulation pollution-reducing and carbon-reducing system is as follows, a knowledge-data coupling modeling layer is used for establishing a boiler combustion-pollutant treatment full-flow multi-section pollutant concentration generation and step removal mechanism model by a pollutant generation and removal mechanism based on a full-flow device in a boiler combustion-pollutant treatment system layer, and a knowledge-data fusion-driven multi-section pollutant concentration prediction model is further established by combining historical operation data and system operation priori knowledge in the boiler combustion-pollutant treatment system layer;
In the model parameter identification optimization layer, taking a target controlled variable as an intelligent regulation target, constructing an optimization problem, carrying out model parameter identification and optimization solution by adopting a PSO algorithm, a WOA algorithm, a particle swarm-gradient descent algorithm and an enumeration algorithm according to the characteristics of the optimization problem, and simultaneously carrying out rolling optimization solution on the optimization solution parameters by combining offline excavation and online iteration, thereby establishing a key parameter prediction model driven by the process mechanism of the boiler combustion-flue gas treatment process and multi-working-condition segmentation machine learning in a cooperative manner;
The multi-section pollutant concentration of a boiler outlet and a pollutant removal system is accurately predicted through a cascade intelligent accurate regulation layer, the corresponding relation between the pollutant emission concentration of the outlet of different pollutant treatment devices and the key regulation parameters is obtained through the boiler low-carbon/zero-carbon fuel blending combustion quantity, the frequency of a boiler section fan (a primary fan, a secondary fan and a draught fan) under different working conditions, the ammonia injection quantity of different areas of a denitration device, the operation secondary voltages of different types of power supplies of different electric fields of an electric dust collector, different circulating pump combinations of slurry of a wet desulphurization device and key regulation parameters of the circulating pump frequencies and the slurry pH of the wet desulphurization device, the effect of the particulate matters, SO 3 and heavy metal multi-pollutants on the total-flow cascade emission reduction of the flue gas, and the cascade multi-device control strategy with energy consumption-material consumption-pollutant emission multi-target synergy is established, SO that the total-flow pollutant optimal control and the energy conservation and carbon reduction are realized.
Preferably, the boiler combustion-pollutant treatment system layer consists of a low-carbon/zero-carbon fuel mixing system, a boiler combustion system, a denitration device, an electrostatic dust collection device/low-temperature electric dust collection device/electric bag composite dust collection device, a wet desulfurization device and a wet electrostatic dust collection device;
Preferably, the intelligent and accurate cascade regulation layer consists of a boiler combustion prediction control module, a denitration device prediction control module, an electric dust collector prediction control module, a wet desulphurization device prediction control module, a wet electric dust collector prediction control module and a full-flow cascade collaborative optimization control module;
Preferably, the boiler combustion system controlled variables include: the controlled variables of the flue gas treatment device comprise total exhaust port particle concentration, total exhaust port SO 2 concentration/desulfurization device outlet SO 2 concentration, total exhaust port/denitration device outlet NO x concentration, ammonia escape concentration and total exhaust port SO 3 concentration.
The method for intelligently regulating pollution reduction and carbon reduction in the whole flow of combustion-pollutant treatment comprises the following steps:
Based on pollutant generation and removal mechanisms, operation parameters affecting target controlled variables are analyzed and determined, the affecting parameters of the target controlled variables are used as input variables, the target controlled variables are used as output variables, each controlled variable prediction model of the boiler combustion-flue gas treatment system is constructed, each controlled variable is respectively predicted, predicted values of each controlled variable are obtained in advance, a control module of the target controlled variables is constructed according to prediction results, control strategies of a boiler combustion system and a flue gas treatment device are formulated, key affecting parameters affecting the target controlled variables are adjusted in advance, and the accurate and stable control of the target controlled variables is realized.
Preferably, the boiler oxygen content, furnace outlet pressure and NOx concentration collaborative prediction control process is as follows:
Step Sa1, using a coal feeding amount, a primary fan frequency and a secondary fan frequency as input variables, using oxygen content as output variables, and utilizing a step response vector of the input variables to predict the change of the oxygen content in real time, so as to establish an oxygen content concentration prediction model;
The oxygen content concentration prediction model expression is as follows:
Wherein: The total predicted value of the oxygen content of P time domain lengths in the future is not corrected at the moment k; The total predicted values of the oxygen content of P time domain lengths under the influence of the coal supply amount, the primary fan frequency and the secondary fan frequency are respectively uncorrected, and P is the rolling optimization time domain length; u (k-1) is the value of N time domain lengths before the k moment of each variable, and N is the model time domain; Δu (k) is a control increment predicted value of each variable in k time domain length to M time instants in future, and M is a control time domain length; a 0 and A are dynamic matrixes of various variables, and describe the influence of various input variables on the system response.
Step Sa2, for correcting errors caused by model mismatch and environmental interference, the model predicted value is corrected by using real-time information, and the feedback correction process is as follows:
wherein: y c (k) is the total predicted value after k time correction; h is a feedback correction coefficient; y (k) and y c (k) are respectively the current measured value and the predicted value of the oxygen content at the moment k.
Step Sa3, the difference between the oxygen content target value and the predicted value is minimized to optimize the control, and the k time optimizing performance index is expressed as follows in a vector form:
J(k)＝[Yc(k)-Yr(k)]TQ[Yc(k)-Yr(k)]+ΔU(k)TRΔU(k)
Wherein: j (k) is an optimization objective function; y r (k) is a target controlled variable control target value; q, R are the target controlled variable prediction error weight matrix and the key parameter control weight matrix respectively.
And (3) making:
Step Sa4, determining a control increment of the secondary air blower according to the target value of the oxygen content, realizing stable control of the oxygen content, performing real-time feedback correction according to the actual measurement value of the oxygen content, and outputting an optimized control increment expression as follows:
ΔU(k)＝(ATQA+R)-1ATQ{Yr(k)-A0U(k-1)-H[y(k)-yc(k)]}
Step Sa5, taking the future frequency regulation and control instruction of the secondary air blower output in the step S4 as the input quantity of the hearth negative pressure control module; under the dynamic working condition, the relation between the primary fan frequency variation and the secondary fan frequency variation and the induced draft fan frequency is analyzed by establishing a linear regression model, and the induced draft fan frequency is adjusted in advance; under the static working condition, the least square method is utilized to conduct parameter identification on the PID controller, the initial parameter value of the PID controller is determined, a draught fan control instruction is given in advance, and the negative pressure stability of the hearth is ensured.
Further preferably, PSO or WOA optimization algorithm is adopted to determine the step response parameters of the predictive control module for identification.
Preferably, the denitration outlet NOx concentration and ammonia slip concentration prediction control process is as follows:
Step Sb1, taking the load of a boiler, the coal feeding amount, the air quantity and the flue gas temperature as input variables, taking the NOx concentration at the outlet of a hearth, namely the inlet of a denitration device, as output variables, and establishing a partition-based sectional denitration device inlet NOx concentration prediction model;
further preferably, the expression of the denitration prediction model, which is the concentration of NOx at the outlet of the furnace, is as follows:
Wherein: For the time k, P time domain length hearths in future an uncorrected total predicted value of outlet NOx concentration; The predicted values of the NOx concentration of the outlet of the hearth with P time domain lengths under the influence of the coal supply quantity, the air quantity and the flue gas temperature are respectively uncorrected, and P is the rolling optimized time domain length; u (k-1) is the value of N time domain lengths before the k moment of each variable, and N is the model time domain; Δu (k) is a control increment predicted value of each variable in k time domain length to M time instants in future, and M is a control time domain length; a 0 and A are dynamic matrixes of various variables, and describe the influence of various input variables on the system response.
In step Sb2, to correct errors caused by model mismatch and environmental interference, the model predicted value is corrected by using real-time information, and the feedback correction process is as follows:
Wherein: y N (k) is the total predicted value after k time correction; h is a feedback correction coefficient; and y (k) and y N (k) are respectively the actual measurement value and the predicted value of the current furnace outlet NOx concentration at the moment k, namely the NOx concentration at the inlet of the denitration device.
Step Sb3, calculating a difference value between a predicted result of the NOx concentration at the inlet of the denitration device and a controlled target in a certain period of time in the future in order to correct the stable deviation of the NOx concentration model at the inlet of the denitration device, and correcting by adopting a specific coefficient to realize real-time and accurate prediction of the NOx concentration at the outlet of a hearth, namely the NOx concentration at the inlet of the denitration device;
step Sb4, taking the boiler load, the operating temperature of a denitration region, the ammonia injection flow and the NOx concentration at the inlet of the denitration device as input variables, and taking the NOx concentration at the outlet of the denitration device as output variables, and establishing a prediction control model based on the NOx concentration at the outlet of the partition and segmentation denitration device;
and step Sb5, adding an inlet NOx concentration predicted value, namely a furnace outlet NOx predicted value, into a full-working-condition multi-parameter denitration device prediction control module as a feedforward prediction, further outputting an optimized set value of ammonia injection flow, calculating an opening value of an ammonia injection regulating valve by an intelligent advanced controller according to deviation between a measured value of the ammonia injection flow and the optimized value, formulating a full-working-condition multi-parameter coordination-cascade intelligent advanced control strategy to realize stable control of concentration of a denitration outlet NO x and ammonia escape, and carrying out real-time feedback correction according to an actual measured value of concentration of a denitration outlet NO x and the ammonia escape.
Preferably, the total exhaust particulate matter and SO 3 concentration predictive control process is as follows:
Step Sc1, based on the generation mechanism of particulate matters and SO 3 in the boiler combustion process and the arrangement and particle size distribution characteristics of slag and flue gas, establishing a flue gas particulate matter and SO 3 generation concentration prediction model, and realizing the prediction of the concentration and mass distribution of the particulate matters and SO 3 at the inlet of the dust removal device;
sc2, based on an electric field corona discharge mechanism in the electrostatic dust collector and a particle and SO 3 charge migration mechanism, turbulent flow, backflow multi-physical field strengthening particle and SO 3 trapping mechanism in the wet desulphurization device, and a strengthening mechanism of condensation, agglomeration, charge and migration of fine particles and SO 3 in the wet electrostatic dust collector, SO as to construct a mechanism model for predicting the concentration and mass distribution of the whole process of removing the particle and SO3 at the inlet and outlet of the dust collector, the wet desulphurization device and the wet electrostatic dust collector;
Step Sc3, correcting a mechanism model of the prediction of the concentration and the mass distribution of the particulate matters and SO 3 removal overall process established in the step Sc1 and the step Sc2 based on actual operation data, and correcting a mechanism model established by a mechanism model of the particulate matters and SO 3 concentration prediction model cooperatively driven by establishing a process mechanism and multi-station sectional machine learning;
preferably, the data correction model construction method comprises a parameter identification method based on a gradient descent+particle swarm algorithm and a long-term and short-term memory neural network algorithm based on an attention mechanism;
Sc4, a process mechanism established based on the step and multi-station sectional machine learning collaborative driven particulate matter and SO 3 concentration prediction model, adopting the energy consumption of an electrostatic precipitator/a low-temperature electrostatic precipitator/an electric bag composite precipitator and the energy consumption of a wet electrostatic precipitator, the total exhaust particulate matter and SO 3 concentration as intelligent regulation targets, constructing an energy consumption and particulate matter and SO 3 concentration emission optimization problem, and solving by adopting a particle swarm algorithm or a particle swarm-gradient descent algorithm according to the characteristics of the optimization problem, thereby obtaining the optimal secondary voltage setting mode of the electrostatic precipitator/the low-temperature electrostatic precipitator/the electric bag composite precipitator and the wet electrostatic precipitator under different operation conditions, and constructing the intelligent regulation strategy of different types of power supplies of the particulate matter electrostatic removal device in the electric field and the partition chamber region.
Further preferably, the coal-fired flue gas particulate matter/SO 3 generation-removal model relates to a denitration device, an electric dust collector/low-temperature electric dust collector/electric bag composite dust collector, a wet electrostatic precipitator and a wet desulfurization cooperative dust collector;
further preferably, the energy consumption optimization problem is expressed as follows:
Wherein n is the total number of electric fields of the electrostatic precipitator/the low-temperature electrostatic precipitator/the electric bag composite precipitator; w f is the power of the f-th electric field; m is the total electric field number of the wet electrostatic precipitator; w i is the power of the ith electric field; and c limit is the predicted value and limit of total exhaust particulate matter/SO 3 emission concentration; u f,min and U f,max are the minimum and maximum secondary voltages of the f-th electric field; u i,min and U i,max are the minimum and maximum secondary voltages of the ith electric field.
Further preferably, in the process of constructing the intelligent regulation strategy, in order to ensure the stability and convergence of the algorithm, the rolling optimization strategy is implemented by predicting the variation trend of the controlled variable in a period of time, and in addition, the prediction model is required to be corrected online according to the actual running condition of the site, which specifically comprises the following steps:
Step Sc401, setting constraint conditions of the particulate matters/SO 3 of the concentration of the outlet pollutants;
Step Sc402, in order to ensure the relative stability of the concentration of the outlet pollutants, to cope with the rapid abrupt change of working conditions, adding an error between a reference track line and a predicted value in an optimization target, wherein the reference track is set to be smaller than an outlet concentration limit value in the above formula;
step Sc403, on the basis of step Sc402, in order to ensure that the control process can converge to the vicinity of the target value, a terminal error needs to be added in the optimization target;
in step Sc404, in the feedback correction module of the conventional model prediction control, the model is usually corrected by taking the actual value of the outlet particulate matter concentration measured at the next time as the error of the model. In this context, since the outlet contaminant concentration marker measurement itself has some noise, a moving average error is employed as the error of the model, where the window length of the moving average is the same as the predicted time domain.
Preferably, the total discharge SO 2 concentration prediction control process is as follows:
Sd1, based on SO 2 in the generation of the boiler combustion process and SO 2/SO3 conversion mechanism in the denitration device, establishing a flue gas SO 2 at the inlet of the desulfurization device based on a machine learning algorithm to generate a concentration prediction model;
Sd2, constructing a multi-absorbent SO 2 removal process mechanism model based on a multi-absorbent SO 2 removal process principle in the desulfurization device;
Sd3, performing correction parameter identification on a coal-fired flue gas SO 2 generation-removal mechanism model by adopting a PSO algorithm based on historical operation data, and further constructing a SO 2 removal process data correction model based on an LSTM network;
Sd4, generating and removing a model based on the SO 2 of the coal-fired flue gas established in the step, adopting the concentration of the outlet SO 2 and the desulfurization operation pH as intelligent regulation targets, constructing an optimization problem, and solving by adopting an enumeration method, a particle swarm algorithm or a particle swarm-gradient descent algorithm according to the characteristics of the optimization problem, thereby obtaining an optimal circulating pump of the wet desulfurization device and a frequency regulation strategy thereof under different operation conditions;
Further preferably, the desulfurization device optimization problem is expressed as follows:
min cost(sA,sB,sC,sD...sn)＝sApA+sBpB+sCpC+sDpD+…+snpn
Wherein s A、sB、sC、sD...sE is the running state of the circulating pump A, B, C, D, n, and when the circulating pump is started, the running state is 1; when the circulation pump is turned off, the operation state is 0.p A、pB、pC、pD…pn is the rated power of the circulation pump A, B, C, D … n. c outlet is the actual outlet SO 2 concentration, and c outlet,target is the target outlet SO 2 concentration.
Further preferably, in the process of constructing the intelligent regulation strategy, in order to ensure the stability and convergence of the algorithm, the rolling optimization strategy is implemented by predicting the variation trend of the controlled variable in a period of time, and in addition, the prediction model is required to be corrected online according to the actual running condition of the site, which specifically comprises the following steps:
Step Sc401, setting constraints on the outlet contaminant concentration SO 2, is expressed as follows:
Wherein r p,i is the number of circulating pumps and the frequency weight coefficient; p (t+i) is the sum of the slurry amounts of the circulating pumps; r e,j is an superscalar weight coefficient; sgn (·) is a sign function; w limit (t+j) is the upper emission limit of the outlet contaminant concentration SO 2; m is a control time domain; p is the prediction time domain;
In step Sc402, in order to ensure the relative stability of the outlet pollutant concentration, to cope with the rapid abrupt change of the working condition, an error between the reference trajectory and the predicted value is added to the optimization target, and the reference trajectory is set to be smaller than the outlet concentration limit value in the above formula, which is expressed as follows:
Wherein q j is a tracking weight coefficient; w t (t+j) is the emission target;
in step Sc403, in order to ensure that the control process can converge to the vicinity of the target value based on step Sc402, a terminal error needs to be added to the optimization target, so the final rolling optimization problem can be expressed as follows:
wherein min J (t) is an objective function at time t; q j is a tracking weight coefficient; w t (t+j) is the emission target; r p,i is the number of circulating pumps and the frequency weight coefficient; p (t+i) is the sum of the slurry amounts of the circulating pumps; r e,j is an superscalar weight coefficient; sgn (·) is a sign function; w limit (t+j) is the upper emission limit of the outlet contaminant concentration SO 2; v f (·) is the terminal error function; χ (P) is a control amount calculated when predicting the last P moments of the time domain; χ s is the control after steady state optimization; y s is the outlet concentration after steady state optimization.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
(1) Based on an accurate prediction model of each controlled variable of the boiler combustion system, a predicted value of the controlled variable is obtained in advance, a control strategy of the boiler combustion system is formulated by combining the predicted value, key equipment adjustment of the combustion system is performed in advance, adjustment lag time is shortened, unit generated energy/steam coal consumption is reduced, and pollution and carbon reduction of a source are realized; based on an accurate prediction model of a target controlled variable of a pollutant removal system, the change of the pollutant concentration at the outlet of a boiler is predicted in advance, the intelligent accurate regulation and control of multiple device key parameter steps of the whole-flow multi-device in the boiler combustion-flue gas treatment process are combined, the ultra-high precision clamping control of the pollutant emission concentration under variable load/fuel is realized, the material energy consumption of a pollutant removal device is reduced in the whole-flow manner, and the effective reduction of CO 2 emission is realized cooperatively.
(2) The invention reduces the unit steam coal consumption and the pollutant discharge amount by regulating and controlling the key parameters of the coupled pollutant removal device through the key parameters of the boiler combustion system, realizes the reduction of the unit steam/degree electric coal consumption by more than 1.5 percent, the fluctuation of the total discharge particle concentration is less than or equal to +/-0.2 mg/m 3、SO2 concentration fluctuation less than or equal to +/-0.5 mg/m 3、NOx concentration fluctuation less than or equal to +/-0.5 mg/m 3, realizes the ultra-high precision clamping control of the pollutant discharge concentration under the working conditions of load and fuel change, cooperatively realizes the cascade pollution reduction and carbon reduction cooperative optimization of the whole flow, wherein the total discharge concentration of SO 3 is less than or equal to 1mg/m 3, the total discharge concentration of mercury/arsenic/lead/cadmium/chromium is less than 20 mug/m 3, and simultaneously the overall electric consumption of the flue gas treatment system is reduced by more than 20 percent, the cooperative energy conservation and carbon reduction, the ammonia water consumption is reduced by more than 20 percent, the limestone consumption is reduced by more than 5 percent, and the effective reduction of CO 2 discharge is realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow diagram of a full-flow intelligent regulation pollution-reduction and carbon-reduction system for combustion-pollutant treatment provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of overall optimization of multi-pollutant co-removal for a boiler-ultra-low emission system according to an embodiment of the present invention;
FIG. 3 is a logic diagram of the coordinated control of the controlled variables of the oxygen content of the boiler and the outlet pressure of the furnace according to the embodiment of the invention;
FIG. 4 is a comparison of actual operation effects of a cooperative control model provided by an embodiment of the present invention and an original control;
Fig. 5 is a schematic diagram of a full-working-condition multi-parameter coordination prediction control strategy of a denitration device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a full-flow intelligent regulation pollution-reduction carbon-reduction method and system for combustion-pollutant treatment, which are used for respectively predicting each controlled variable through a prediction model of each controlled variable, making a control strategy of a boiler combustion system, performing equipment regulation in advance, shortening the regulation lag time and playing the effects of energy conservation and emission reduction.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1
As shown in fig. 1-2, the present invention provides a combustion-pollutant treatment full-process intelligent regulation pollution-reducing and carbon-reducing system, which comprises: a boiler combustion-pollutant treatment system layer, a knowledge-data coupling modeling layer, a model parameter identification optimizing layer and a cascade intelligent accurate regulation layer;
The flow of the combustion-pollutant treatment full-flow intelligent regulation pollution-reducing and carbon-reducing system is as follows, a knowledge-data coupling modeling layer is used for establishing a boiler combustion-pollutant treatment full-flow multi-section pollutant concentration generation and step removal mechanism model based on a pollutant generation and removal mechanism of a full-flow device in a boiler combustion-pollutant treatment system layer, and a knowledge-data fusion-driven multi-section pollutant concentration prediction model is further established by combining historical operation data and advanced experience knowledge in the boiler combustion-pollutant treatment system layer;
In the model parameter identification optimization layer, taking a target controlled variable as an intelligent regulation target, constructing an optimization problem, carrying out model parameter identification and optimization solution by adopting a PSO algorithm, a WOA algorithm, a particle swarm-gradient descent algorithm and an enumeration algorithm according to the characteristics of the optimization problem, and simultaneously carrying out rolling optimization solution on the optimization solution parameters by combining offline excavation and online iteration, thereby establishing a key parameter prediction model driven by the process mechanism of the boiler combustion-flue gas treatment process and multi-working-condition segmentation machine learning in a cooperative manner;
The boiler outlet and the multi-section pollutant concentration of the pollutant removal system are accurately predicted through a cascade intelligent accurate regulation layer, the corresponding relation between the pollutant discharge concentration of the outlet of different pollutant treatment devices and the key regulation parameters is obtained through analyzing the boiler low-carbon/zero-carbon fuel blending combustion quantity under different working conditions (high load, medium load, low load, abrupt ascending load, abrupt descending load and the like), the frequency of a boiler section fan (a primary fan, a secondary fan and a draught fan), the ammonia injection quantity in different areas of a denitration device, the operation secondary voltages of different electric fields of an electric dust collector, different circulating pump frequencies of slurry of a wet desulphurization device and the key regulation parameters of slurry pH of the circulating pump are combined to achieve the cascade emission reduction effect of particulate matters, SO 3 and heavy metal multi-pollutants along the whole flue gas flow, and the pollutant removal multi-device control strategy with the multi-objective cooperation of energy consumption, material consumption and pollutant discharge is established, SO that the whole flow pollutant cascade optimal control and the cooperation energy conservation and carbon reduction are realized.
The boiler combustion-pollutant treatment system layer consists of a low-carbon/zero-carbon fuel blending system, a boiler combustion system, a denitration device, an electrostatic dust collection device, a low-temperature electric dust collection device/electric bag composite dust collection device, a wet desulfurization device and a wet electrostatic dust collection device;
The step intelligent accurate regulation and control layer consists of a boiler combustion prediction control module, a denitration device prediction control module, an electric dust collector prediction control module, a wet desulphurization device prediction control module, a wet electric dust collector prediction control module and a full-flow step collaborative optimization control module;
the controlled variables of the boiler combustion system include: the controlled variables of the flue gas treatment device comprise total discharge port particulate matter concentration, total discharge port SO 2 concentration, namely desulfurization device outlet SO 2 concentration, total discharge port, namely denitration device outlet NO x concentration, ammonia escape concentration and total discharge port SO 3 concentration.
Example two
The invention also provides a combustion-pollutant treatment whole-flow intelligent regulation pollution-reduction and carbon-reduction method, which comprises the following steps:
Based on a pollutant generation and removal mechanism, analyzing and determining operation parameters affecting target controlled variables, taking the influence parameters of the target controlled variables as input variables and the target controlled variables as output variables, and constructing each controlled variable prediction model of the boiler combustion-flue gas treatment system; and respectively predicting each controlled variable, obtaining the predicted value of each controlled variable in advance, constructing a control module of the target controlled variable according to the predicted result, formulating a control strategy of a boiler combustion system and a flue gas treatment device, and adjusting key influence parameters influencing the target controlled variable in advance to realize the accurate and stable control of the target controlled variable. Meanwhile, the ultralow emission system of the coal-fired power plant generally comprises a boiler combustion system, a catalytic denitration device SCR, an electrostatic precipitator ESP, a wet desulphurization device WFGD and wet electrostatic precipitator WESP removing equipment. In the operation process, each system has the synergistic effect of different degrees aiming at NOx, SO 2、PM、SO3 and Hg pollutants, SO that on the basis of considering the synergistic effect of removal, a multi-objective synergistic boiler combustion-pollutant removal system regulation strategy with energy consumption, material consumption and pollutant emission is established, overall optimization of the whole operation process of the ultra-low emission system is realized, and guidance is provided for the operation of key parameters of key equipment for boiler combustion and pollutant removal.
As shown in fig. 2, as a preferred embodiment, the following is implemented for a certain circulating fluidized bed boiler oxygen content, furnace outlet pressure and NOx predictive control:
step Sa1, taking the coal feeding amount, the primary fan frequency and the secondary fan frequency as input variables, taking the boiler oxygen content as output variables, and utilizing a step response vector of the input variables to predict the change of the oxygen content in real time, so as to establish an oxygen content concentration prediction model;
The oxygen content concentration prediction model expression is as follows:
Wherein: The total predicted value of the oxygen content of P time domain lengths in the future is not corrected at the moment k; The total predicted values of the oxygen content of P time domain lengths under the influence of the coal supply amount, the primary fan frequency and the secondary fan frequency are respectively uncorrected, and P is the rolling optimization time domain length; u (k-1) is the value of N time domain lengths before the k moment of each variable, and N is the model time domain; Δu (k) is a control increment predicted value of each variable in k time domain length to M time instants in future, and M is a control time domain length; a 0 and A are dynamic matrixes of various variables, and describe the influence of various input variables on the system response.
Step Sa2, to correct errors caused by model mismatch and environmental interference, the model predicted value is corrected by using real-time information, and the feedback correction process is as follows:
wherein: y c (k) is the total predicted value after k time correction; h is a feedback correction coefficient; y (k) and y c (k) are respectively the current measured value and the predicted value of the oxygen content at the moment k.
Step Sa3, the difference between the oxygen content target value and the predicted value is minimized to optimize the control, and the k time optimizing performance index is expressed as follows in a vector form:
J(k)＝[Yc(k)-Yr(k)]TQ[Yc(k)-Yr(k)]+ΔU(k)TRΔU(k) (6)
Wherein: j (k) is an optimization objective function; y r (k) is a target controlled variable control target value; q, R are the target controlled variable prediction error weight matrix and the key parameter control weight matrix respectively.
And (3) making:
Step Sa4, determining a control increment of the secondary air blower according to the target value of the oxygen content, realizing stable control of the oxygen content, performing real-time feedback correction according to the actual measurement value of the oxygen content, and outputting an optimized control increment expression as follows:
ΔU(k)＝(ATQA+R)-1ATQ{Yr(k)-A0U(k-1)-H[y(k)-yc(k)]} (8)
Step Sa5, taking the future frequency regulation and control instruction of the secondary air blower output in the step S4 as the input quantity of the hearth negative pressure control module; under the dynamic working condition, the relation between the primary fan frequency variation and the secondary fan frequency variation and the induced draft fan frequency is analyzed by establishing a linear regression model, and the induced draft fan frequency is adjusted in advance; under the static working condition, the least square method is utilized to conduct parameter identification on the PID controller, the initial parameter value of the PID controller is determined, a draught fan control instruction is given in advance, and the negative pressure stability of the hearth is ensured.
Under steady-state working conditions, when the output hearth outlet pressure is in a set range, the frequency of the induced draft fan is not required to be regulated, and when the hearth outlet pressure exceeds the set range, the frequency of the induced draft fan is corrected through PID. The correction formula is as follows:
Wherein: u (k) is the frequency variation of the output induced draft fan of the PID controller; e (k) is the difference between the target and measured furnace exit pressure values.
And under the dynamic working condition, determining the frequency regulation increment of the induced draft fan by fitting and analyzing the frequency relation of the primary air fan, the secondary air fan and the induced draft fan. The fitting formula is as follows:
ΔL Guiding device ＝0555 18ΔL Once-through +0.602 37ΔL Secondary time +-0001 35L Guiding device +0.013 63
wherein: Δl Once-through is the primary fan frequency variation; Δl Secondary time is the secondary fan frequency variation; l Guiding device is the actual measurement value of the frequency of the induced draft fan.
As shown in fig. 3, a graph is shown which compares the actual operation effect of the cooperative control based on the oxygen content and the furnace outlet pressure with the original control. Under the original control, the fluctuation range of the oxygen content of the flue gas is 1.8-3.0%, and the distribution is discrete; under the cooperative prediction control of the invention, the fluctuation range of the oxygen content is 2.3% -2.8%, the distribution is more concentrated, and the deviation between the measured value of the oxygen content and the set target value is within +/-0.25%.
The boiler prediction control module is used for predicting the frequency regulation quantity of the secondary fan to be output in the future, and the frequency of the induced draft fan is cooperatively controlled in advance by combining the actual working condition of boiler operation. Under the original control, the fluctuation range of the outlet pressure of the hearth is-180 Pa to +105Pa, and the standard deviation is 44.61Pa; under the collaborative prediction control of the invention, the fluctuation range of the hearth outlet pressure is-110 Pa to-10 Pa, the standard deviation is 12.86Pa, wherein 99% of the actual hearth outlet pressure value is within the range of +/-45 Pa of the set target value. Compared with the original control, under cooperative control, the secondary air blower and the induced draft fan have the characteristic of small-amplitude multi-frequency regulation, the regulation is more timely, the oxygen content and the fluctuation amplitude of the pressure at the outlet of the hearth are reduced, and the problem of current rush caused by the large-amplitude regulation of the air blower can be avoided; the original content of the concentration of NOx, SO 2, the concentration of particulate matters and the concentration of heavy metal pollutants at the outlet of the hearth is further reduced under the cooperative control.
Compared with the original control, under cooperative control, the unit steam yield and coal consumption of the boiler are saved by more than 1.6%, and the unit steam yield and the power consumption of the fan are reduced by more than 2%.
As shown in fig. 4, as a preferred embodiment, the denitration outlet NOx concentration and ammonia slip controlled variable predictive control process is as follows:
Step Sb1, taking the load of a boiler, the coal feeding amount, the air quantity and the flue gas temperature as input variables, taking the NOx concentration at the outlet of a hearth, namely the inlet of a denitration device, as output variables, and establishing a partition-based sectional denitration device inlet NOx concentration prediction model;
the furnace outlet NOx concentration prediction model expression is as follows:
Wherein: For the time k, P time domain length hearths in future an uncorrected total predicted value of outlet NOx concentration; The predicted values of the NOx concentration of the outlet of the hearth with P time domain lengths under the influence of the coal supply quantity, the air quantity and the flue gas temperature are respectively uncorrected, and P is the rolling optimized time domain length; u (k-1) is the value of N time domain lengths before the k moment of each variable, and N is the model time domain; Δu (k) is a control increment predicted value of each variable in k time domain length to M time instants in future, and M is a control time domain length; a 0 and A are dynamic matrixes of various variables, and describe the influence of various input variables on the system response.
And step Sb2, correcting the module predicted value by using real-time information, wherein the feedback correction process is as follows:
Wherein: y N (k) is the total predicted value after k time correction; h is a feedback correction coefficient; and y (k) and y N (k) are respectively the actual measurement value and the predicted value of the current furnace outlet NOx concentration at the moment k, namely the NOx concentration at the inlet of the denitration device.
And step Sb3, calculating a difference value between a denitration device inlet NOx concentration prediction result and a controlled target at a certain time period in the future in order to correct stable deviation of denitration device inlet NOx concentration model prediction, and correcting by adopting a specific coefficient to realize real-time accurate prediction of the furnace outlet NOx concentration, namely the denitration device inlet NOx concentration.
And step Sb4, taking the boiler load, the operating temperature of the denitration region, the ammonia injection flow and the NOx concentration at the inlet of the denitration device as input variables, and taking the NOx concentration at the outlet of the denitration device as output variables, and establishing a prediction control model based on the NOx concentration at the outlet of the denitration device in a zoned and segmented manner.
And step Sb5, adding an inlet NOx concentration predicted value as a feedforward prediction into a denitration device prediction control module, outputting an optimized set value of ammonia injection flow, calculating an ammonia injection regulating valve opening value by an advanced controller according to deviation between a measured value of the ammonia injection flow and the optimized value, formulating an all-working-condition multi-parameter coordination-cascade advanced control strategy to realize stable control of the concentration of NO x at a denitration outlet and ammonia escape, and carrying out real-time feedback correction according to the measured values of the concentration of NO x at the denitration outlet and the ammonia escape.
As a preferred embodiment, the total exhaust particulate matter and SO 3 concentration predictive control process is as follows:
step Sc1, based on the generation mechanism of particulate matters and SO 3 in the boiler combustion process and the arrangement and particle size distribution characteristics of slag and flue gas, establishing a flue gas particulate matter and SO 3 generation concentration prediction model, and realizing the prediction of the concentration and mass distribution of the particulate matters and SO 3 at the inlet of the dust removal device;
Sc2, based on an electric field corona discharge mechanism in the electrostatic dust collector and a particle and SO 3 charge migration mechanism, turbulent flow, backflow multi-physical field strengthening particle and SO 3 trapping mechanism in the wet desulphurization device, and a strengthening mechanism of condensation, agglomeration, charge and migration of fine particles and SO 3 in the wet electrostatic dust collector, SO as to construct a mechanism model for predicting the concentration and mass distribution of the whole process of removing the particle and SO 3 from the inlet and outlet of the dust collector, the wet desulphurization device and the wet electrostatic dust collector;
Step Sc3, correcting a mechanism model for predicting the concentration and mass distribution of the particulate matters and SO 3 removal overall process established in the step Sc1 and the step Sc2 based on actual operation data, and establishing a particulate matter and SO 3 concentration prediction model driven by the process mechanism and multi-station sectional machine learning in a cooperative manner;
the data correction model construction method comprises a parameter identification method based on a gradient descent+particle swarm algorithm and a long-term and short-term memory neural network algorithm based on an attention mechanism;
Sc4, a process mechanism established based on the step and multi-station sectional machine learning collaborative driven particulate matter and SO 3 concentration prediction model is adopted, the energy consumption of an electrostatic precipitator/a low-temperature electrostatic precipitator/an electric bag composite precipitator and the concentration of SO 3 at the total discharge port are taken as intelligent regulation targets, the energy consumption and the concentration emission optimization problem of particulate matter and SO 3 are constructed, and then a particle swarm algorithm or a particle swarm-gradient descent algorithm is adopted to solve according to the characteristics of the optimization problem, SO that the optimal secondary voltage setting mode of the electrostatic precipitator/the low-temperature electrostatic precipitator/the electric bag composite precipitator and the wet electrostatic precipitator under different operation conditions is obtained, and the intelligent regulation strategy of different types of power supplies of the particulate matter electrostatic removal device in the sub-electric field and sub-chamber region is constructed;
The energy consumption optimization problem is expressed as follows:
Wherein n is the total number of electric fields of the electrostatic precipitator/the low-temperature electrostatic precipitator/the electric bag composite precipitator; w f is the power of the f-th electric field; m is the total electric field number of the wet electrostatic precipitator; w i is the power of the ith electric field; And c limit is the predicted value and the limit value of the total exhaust particulate matter/SO 3 emission concentration; u f,min and U f,max are the minimum and maximum secondary voltages of the f-th electric field; u i,min and U i,max are the minimum and maximum secondary voltages of the ith electric field.
In the process of constructing the intelligent regulation strategy, in order to ensure the stability and convergence of the algorithm, the rolling optimization strategy is implemented by predicting the change trend of the controlled variable in a period of time, and in addition, the prediction model is required to be corrected on line according to the actual running condition of the site.
The invention is applied to the electrostatic dust collector, and the comparison result shows that: in the manual control, in order to ensure that the outlet concentration is stable and reaches the standard, each electric field basically operates at the highest operating voltage. At this time, the voltage and current of each electric field are controlled by flashover. At this time, not only the energy consumption of the electrostatic dust collector is overall higher, but also the frequent flashover can generate great harm to the operation safety of the dust collector, and the anode plate can be seriously damaged. In addition, the occurrence of flashovers is often faced with momentary voltage drops and current rises, which also lead to unstable outlet particulate concentrations. When the manual control is performed, the fluctuation of the outlet concentration of the dust removing device can reach +/-5 mg/m 3. After the intelligent regulation and control mode is adopted, the energy consumption of the dust removing device is obviously reduced to 200-800kW under similar working conditions. Meanwhile, as the operating voltage is far away from the flashover voltage, the flashover times of each electric field are reduced to 0 under intelligent regulation and control. Meanwhile, the voltage and the current of each electric field are more stable, so that the outlet concentration is also more stable, and the concentration is only +/-2 mg/m 3.
By further applying the full-flow step collaborative optimization control module, the power comparison effect before and after optimization shows that the total concentration of the discharged particles and sulfur trioxide is lower than 1mg/m 3, the fluctuation of the discharged concentration of the particles and sulfur trioxide is less than or equal to +/-0.2 mg/m 3, and meanwhile, the operation energy consumption can be reduced by 20% after the full-flow step collaborative optimization control module is applied to comparison experience, and the maximum power operation can be reduced by 42.0%; because the operating voltage is far away from the flashover voltage, the flashover frequency of each electric field is reduced to 0 under intelligent regulation, which means that the voltage and the current of each electric field are more stable.
As a preferred embodiment, the total discharge SO 2 concentration controlled variable predictive control process is as follows:
Sd1, based on SO 2 in the generation of the boiler combustion process and SO 2/SO3 conversion mechanism in the denitration device, establishing a flue gas SO 2 at the inlet of the desulfurization device based on a machine learning algorithm to generate a concentration prediction model;
Sd2, constructing a multi-absorbent SO 2 removal process mechanism model based on a multi-absorbent SO 2 removal process principle in the desulfurization device;
Sd3, performing correction parameter identification on a coal-fired flue gas SO 2 generation-removal mechanism model by adopting a PSO algorithm based on historical operation data, and further constructing a SO 2 removal process data correction model based on an LSTM network;
Sd4, generating and removing a model based on the SO 2 of the coal-fired flue gas established in the step, adopting the concentration of the outlet SO 2 and the desulfurization operation pH as intelligent regulation targets, constructing an optimization problem, and solving by adopting an enumeration method, a particle swarm algorithm or a particle swarm-gradient descent algorithm according to the characteristics of the optimization problem, thereby obtaining an optimal circulating pump of the wet desulfurization device and a frequency regulation strategy thereof under different operation conditions;
Further preferably, the desulfurization device optimization problem is expressed as follows:
min cost(sA,sB,sC,sD...sn)＝sApA+sBpB+sCpC+sDpD+…snpn
Wherein s A、sB、sC、sD...sE is the running state of the circulating pump A, B, C, D, n, and when the circulating pump is started, the running state is 1; when the circulation pump is turned off, the operation state is 0.p A、pB、pC、pD…pn is the rated power of the circulation pump A, B, C, D … n. c outlet is the actual outlet SO 2 concentration, and c outlet,target is the target outlet SO 2 concentration.
In the process of constructing the intelligent regulation strategy, in order to ensure the stability and convergence of the algorithm, the rolling optimization strategy is implemented by predicting the change trend of the controlled variable in a period of time, and in addition, the prediction model is required to be corrected on line according to the actual running condition of the site, and the specific steps are as follows:
Step Sc401, setting constraints on the outlet contaminant concentration SO 2, is expressed as follows:
wherein r p,i is the number of circulating pumps and the frequency weight coefficient; p (t+i) is the sum of the slurry amounts of the circulating pumps; r e,j is an superscalar weight coefficient; sgn (·) is a sign function; w limit (t+kj) is the upper emission limit of the outlet contaminant concentration SO 2; m is a control time domain; p is the prediction time domain;
In step Sc402, in order to ensure the relative stability of the outlet pollutant concentration, to cope with the rapid abrupt change of the working condition, an error between the reference trajectory and the predicted value is added to the optimization target, and the reference trajectory is set to be smaller than the outlet concentration limit value in the above formula, which is expressed as follows:
Wherein q j is a tracking weight coefficient; w t (t+j) is the emission target;
in step Sc403, on the basis of step Sc402, in order to ensure that the control process can converge to the vicinity of the target value, a terminal error needs to be added to the optimization target, so the final rolling optimization problem can be expressed as follows:
wherein min J (t) is an objective function at time t; q j is a tracking weight coefficient; w t (t+j) is the emission target; r p,i is the number of circulating pumps and the frequency weight coefficient; p (t+i) is the sum of the slurry amounts of the circulating pumps; r e,j is an superscalar weight coefficient; sgn (·) is a sign function; w limit (t+j) is the upper emission limit of the outlet contaminant concentration SO 2; v f (·) is the terminal error function; χ (P) is a control amount calculated when predicting the last P moments of the time domain; χ s is the control after steady state optimization; y s is the outlet concentration after steady state optimization.
After the method is applied, when the concentration target of the SO 2 at the outlet is set to be 35mg/m 3, compared with the energy consumption before optimization, the energy consumption can be reduced by about 40%, the concentration of the SO 2 stably meets the target set value, the fluctuation is less than or equal to +/-0.5 mg/m 3, and the limestone consumption is reduced by more than 5%.
As a preferred implementation mode, based on the thought of intelligent and precise regulation and control of the steps of a full-flow multi-device of a boiler combustion-flue gas treatment process, a step collaborative optimization and variable working condition precise method of a boiler combustion-multi-pollutant removal system is further constructed, pollutant concentration distribution of each section and pollutant removal efficiency distribution of a key device are obtained, then ammonia water/urea spray gun valve regulating control instructions of a denitration device, multi-field multi-channel multi-type power supply control instructions of an electrostatic dust removal device, limestone slurry circulating pump control instructions of a desulfurization device and multi-field multi-channel multi-type power supply control instructions of a wet electrostatic dust removal device are given in advance along flue gas flow sections, the overall steam coal consumption and pollutant discharge amount are reduced through key parameter regulation and control of the boiler combustion system and coupling pollutant removal device key parameter regulation and control, high-precision clamping control of pollutant discharge concentration under variable load and variable fuel working conditions is realized, total heavy metal discharge concentration of SO 3 is less than or equal to 1mg/m 3, total discharge concentration of mercury/arsenic/lead/cadmium/chromium is less than 20 mu g/m 3, meanwhile, the overall power consumption of the system is reduced by more than 20%, energy conservation and carbon is reduced by the total power consumption of the system is reduced by more than 20%, the steps of the energy conservation and the total carbon consumption is reduced by 2 is reduced, the full-flow is reduced by the steps is reduced, and the effective reduction of the carbon consumption is realized.
Example III
In order to verify the effectiveness of the achievement of the invention, an application industry verification research of a combustion-pollutant treatment full-flow intelligent regulation pollution-reducing and carbon-reducing system is developed by taking a certain 220t/h thermoelectric unit as an object. The operation effects of the original control and the achievement of the invention are compared. After the invention is applied, compared with the original control, the unit steam yield and coal consumption of the boiler are saved by 0.38t/h after the invention is applied, and the power consumption of a unit steam yield fan (a primary fan, a secondary fan and a draught fan of a boiler section) is reduced by 17.73kWh; the nitrogen oxide removing device saves the ammonia water consumption by about 0.043t/h, reduces the air resistance by about 200Pa, and converts the air resistance into the power consumption of the induced draft fan by 24kWh/h; the electricity consumption of the particulate removal device is saved by about 26kW; the circulating pump of the sulfur oxide removal device saves about 70kW of electricity consumption, the oxidation fan saves about 18kW of electricity consumption, the system resistance is reduced by about 200Pa, and the conversion induced fan saves 24kW of electricity consumption. By counting the operation data of one year after the achievement of the invention is used, compared with the original control, the unit steam yield and coal consumption of the boiler are saved by more than 1.6%, the power consumption of a unit steam yield fan is reduced by more than 2%, the average consumption of denitration ammonia water is reduced by about 40%, the energy consumption of an electric bag dust removal system is reduced by about 35%, the energy consumption of a desulfurization circulating pump is reduced by about 25%, the energy consumption of an oxidation fan is reduced by about 30%, and the annual direct operation cost of a single set of ultra-low emission intelligent regulation and control system can be saved by about 380.88 ten thousands yuan.
TABLE 1 cost-effective measuring and calculating table for 220t/h application years of the present invention
In the aspect of environmental benefit, the invention achieves the aim of greatly reducing the material and energy consumption and achieving the aims of saving energy, reducing consumption and reducing emission while ensuring that the emission concentration of NOx, SO 2 and particulate matters stably reaches the ultra-low emission requirement in the whole period. Through long-term operation verification, the main pollutants are stable and ultralow in emission, the fluctuation range of the concentration of the pollutants at the outlet is obviously reduced, and the fluctuation range of the concentration of NOx at the outlet of the nitrogen oxide removal device and the fluctuation range of the concentration of SO 2 at the outlet of the sulfur oxide removal device are reduced by more than 70%. The single 220t/h boiler combustion system and the pollutant treatment system can save standard coal by about 2923t (calculated according to the 0.302gce/MWh folded coal standard coefficient), reduce carbon dioxide amount by about 8221t in measuring and calculating years, remarkably improve the operation stability and adjustability of the boiler combustion system and the pollutant treatment system, reduce the operation cost and realize the cooperative emission reduction of carbon dioxide.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the method disclosed in the embodiment, since it corresponds to the system disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (5)
1. The full-flow intelligent regulation pollution reduction and carbon reduction method for combustion-pollutant treatment is characterized by comprising the following steps of:
Based on a pollutant generation and removal mechanism, analyzing and determining operation parameters affecting target controlled variables, taking the influence parameters of the target controlled variables as input variables and the target controlled variables as output variables, and constructing each controlled variable prediction model of the boiler combustion-flue gas treatment system;
predicting each controlled variable respectively, and obtaining predicted values of each controlled variable in advance;
constructing a control module of a target controlled variable according to the prediction result, formulating a control strategy of a boiler combustion system and a flue gas treatment device, and adjusting key influence parameters influencing the target controlled variable in advance;
Based on the synergistic effect of the removal equipment in the pollutant treatment system on NOx, SO 2、PM、SO3 and Hg pollutants, a boiler combustion-pollutant removal system regulation strategy with multi-objective synergistic effect of energy consumption, material consumption and pollutant emission is established, and the real-time adjustment and optimization of key parameters of key equipment for boiler combustion and pollutant removal are realized;
the controlled variables of the boiler combustion system include: boiler oxygen content, furnace outlet pressure, outlet pollutant concentration;
The controlled variables of the flue gas treatment device comprise total discharge port particulate matter concentration, total discharge port SO 2 concentration/desulfurization device outlet SO 2 concentration, total discharge port/denitration device outlet NO x concentration, ammonia escape concentration and total discharge port SO 3 concentration;
the boiler oxygen content, hearth outlet pressure and NOx concentration collaborative prediction control process comprises the following steps:
Step Sa1, using a coal feeding amount, a primary fan frequency and a secondary fan frequency as input variables, using oxygen content as output variables, and utilizing a step response vector of the input variables to predict the change of the oxygen content in real time, so as to establish an oxygen content concentration prediction model;
The oxygen content concentration prediction model expression is as follows:
Wherein: The total predicted value of the oxygen content of P time domain lengths in the future is not corrected at the moment k; the total predicted values of the oxygen content of P time domain lengths under the influence of the coal supply amount, the primary fan frequency and the secondary fan frequency are respectively uncorrected, and P is the rolling optimization time domain length; u (k-1) is the value of N time domain lengths before the k moment of each variable, and N is the model time domain; Δu (k) is a control increment predicted value of each variable in k time domain length to M time instants in future, and M is a control time domain length; a 0, A is the dynamic matrix of each variable, describing the influence of each input variable on the system response;
Step Sa2, for correcting errors caused by model mismatch and environmental interference, the model predicted value is corrected by using real-time information, and the feedback correction process is as follows:
Wherein: y c (k) is the total predicted value after k time correction; h is a feedback correction coefficient; y (k) and y c (k) are respectively the current oxygen content actual measurement value and the predicted value at the moment k;
Step Sa3, the difference between the oxygen content target value and the predicted value is minimized to optimize the control, and the k time optimizing performance index is expressed as follows in a vector form:
J(k)＝[Yc(k)-Yr(k)]TQ[Yc(k)-Yr(k)]+ΔU(k)TRΔU(k) (6)
Wherein: j (k) is an optimization objective function; y r (k) is a target controlled variable control target value; q, R are respectively a target controlled variable prediction error weight matrix and a key parameter control weight matrix;
and (3) making:
Step Sa4, determining a control increment of the secondary air blower according to the target value of the oxygen content, realizing stable control of the oxygen content, performing real-time feedback correction according to the actual measurement value of the oxygen content, and outputting an optimized control increment expression as follows:
ΔU(k)＝(ATQA+R)-1ATQ{Yr(k)-A0U(k-1)-H[y(k)-yc(k)]} (8)
Step Sa5, taking the future frequency regulation and control instruction of the secondary air blower output in the step S4 as the input quantity of the hearth negative pressure control module; under the dynamic working condition, the relation between the primary fan frequency variation and the secondary fan frequency variation and the induced draft fan frequency is analyzed by establishing a linear regression model, and the induced draft fan frequency is adjusted in advance; under the static working condition, the least square method is utilized to conduct parameter identification on the PID controller, the initial parameter value of the PID controller is determined, a draught fan control instruction is given in advance, and the negative pressure stability of the hearth is ensured;
Under the steady-state working condition, when the output hearth outlet pressure is in a set range, the frequency of the induced draft fan is not required to be regulated, and when the hearth outlet pressure exceeds the set range, the frequency of the induced draft fan is corrected through PID; the correction formula is as follows:
Wherein: u (k) is the frequency variation of the output induced draft fan of the PID controller; e (k) is the difference between the target value and the measured value of the furnace outlet pressure;
Under the dynamic working condition, the relationship among the frequencies of the primary fan, the secondary fan and the induced draft fan is analyzed by fitting, the frequency regulation increment of the induced draft fan is determined, and the fitting formula is as follows:
ΔL Guiding device ＝0.555 18ΔL Once-through +0.602 37ΔL Secondary time +-0.001 35L Guiding device +0.013 63
Wherein: Δl Once-through is the primary fan frequency variation; Δl Secondary time is the secondary fan frequency variation; l Guiding device is the actual measurement value of the frequency of the induced draft fan;
the NOx concentration and ammonia slip prediction control process of the outlet of the denitration device is as follows:
Step Sb1, taking the load of a boiler, the coal feeding amount, the air quantity and the flue gas temperature as input variables, taking the NOx concentration at the outlet of a hearth, namely the inlet of a denitration device, as output variables, and establishing a partition-based sectional denitration device inlet NOx concentration prediction model;
the furnace outlet NOx concentration prediction model expression is as follows:
Wherein: For the time k, P time domain length hearths in future an uncorrected total predicted value of outlet NOx concentration; The predicted values of the NOx concentration of the outlet of the hearth with P time domain lengths under the influence of the coal supply quantity, the air quantity and the flue gas temperature are respectively uncorrected, and P is the rolling optimized time domain length; u (k-1) is the value of N time domain lengths before the k moment of each variable, and N is the model time domain; Δu (k) is a control increment predicted value of each variable in k time domain length to M time instants in future, and M is a control time domain length; a 0, A is the dynamic matrix of each variable, describing the influence of each input variable on the system response;
In step Sb2, for correcting errors caused by model mismatch and environmental interference, the model predicted value is corrected by using real-time information, and the feedback correction process is as follows:
wherein: y N (k) is the total predicted value after k time correction; h is a feedback correction coefficient; y (k) and y N (k) are respectively the actual measurement value and the predicted value of the NOx concentration at the current hearth outlet at the moment k, namely the NOx concentration at the inlet of the denitration device;
Step Sb3, calculating a difference value between a predicted result of the NOx concentration at the inlet of the denitration device and a controlled target in a certain period of time in the future in order to correct the stable deviation of the NOx concentration model at the inlet of the denitration device, and correcting by adopting a coefficient to realize real-time and accurate prediction of the NOx concentration at the outlet of a hearth, namely the NOx concentration at the inlet of the denitration device;
step Sb4, taking the boiler load, the operating temperature of a denitration region, the ammonia injection flow and the NOx concentration at the inlet of the denitration device as input variables, and taking the NOx concentration at the outlet of the denitration device as output variables, and establishing a prediction control model based on the NOx concentration at the outlet of the partition and segmentation denitration device;
And step Sb5, adding an inlet NOx concentration predicted value, namely a furnace outlet NOx predicted value, into a denitration device prediction control module as a feedforward prediction, further outputting an optimized set value of ammonia injection flow, calculating an opening value of an ammonia injection regulating valve by an intelligent advanced controller according to deviation between a measured value of the ammonia injection flow and the optimized value, formulating a full-working-condition multi-parameter coordination-cascade intelligent advanced control strategy to realize stable control of denitration outlet NOx concentration and ammonia escape, and carrying out real-time feedback correction according to actual measurement values of denitration outlet NOx concentration and ammonia escape.
2. The full-flow intelligent regulation pollution abatement and carbon reduction method for combustion-pollutant treatment according to claim 1, wherein the total exhaust particulate matter and SO 3 concentration predictive control process is as follows:
Step Sc1, based on the generation mechanism of particulate matters and SO 3 in the boiler combustion process and the arrangement and particle size distribution characteristics of slag and flue gas, establishing a flue gas particulate matter and SO 3 generation concentration prediction model, and realizing the prediction of the concentration and mass distribution of the particulate matters and SO 3 at the inlet of the dust removal device;
sc2, based on an electric field corona discharge mechanism in the electrostatic dust collector and a particle and SO 3 charge migration mechanism, turbulent flow, backflow multi-physical field strengthening particle and SO 3 trapping mechanism in the wet desulphurization device, and a strengthening mechanism of condensation, agglomeration, charge and migration of fine particles and SO 3 in the wet electrostatic dust collector, SO as to construct a mechanism model for predicting the concentration and mass distribution of the whole process of removing the particle and SO3 at the inlet and outlet of the dust collector, the wet desulphurization device and the wet electrostatic dust collector;
Step Sc3, correcting a mechanism model of the prediction of the concentration and the mass distribution of the particulate matters and SO 3 removal overall process established in the step Sc1 and the step Sc2 based on actual operation data, and correcting a mechanism model established by a mechanism model of the particulate matters and SO 3 concentration prediction model cooperatively driven by establishing a process mechanism and multi-station sectional machine learning;
the data correction model construction method comprises a parameter identification method based on a gradient descent+particle swarm algorithm and a long-term and short-term memory neural network algorithm based on an attention mechanism;
Sc4, a process mechanism established based on the step and multi-station sectional machine learning collaborative driven particulate matter and SO 3 concentration prediction model, adopting the energy consumption of an electrostatic precipitator/a low-temperature electrostatic precipitator/an electric bag composite precipitator and the energy consumption of a wet electrostatic precipitator, the total exhaust particulate matter and SO 3 concentration as intelligent regulation targets, constructing an energy consumption and particulate matter and SO 3 concentration emission optimization problem, and solving by adopting a particle swarm algorithm or a particle swarm-gradient descent algorithm according to the characteristics of the optimization problem, thereby obtaining the optimal secondary voltage setting mode of the electrostatic precipitator/the low-temperature electrostatic precipitator/the electric bag composite precipitator and the wet electrostatic precipitator under different operation conditions, and constructing the intelligent regulation strategy of different types of power supplies of the particulate matter electrostatic removal device in the electric field and the partition chamber region.
3. The full-flow intelligent regulation pollution abatement and carbon reduction method for combustion-pollutant treatment according to claim 1, wherein the total discharge SO 2 concentration predictive control process is as follows:
Sd1, based on SO 2 in the generation of the boiler combustion process and SO 2/SO3 conversion mechanism in the denitration device, establishing a flue gas SO 2 at the inlet of the desulfurization device based on a machine learning algorithm to generate a concentration prediction model;
Sd2, constructing a multi-absorbent SO 2 removal process mechanism model based on a multi-absorbent SO 2 removal process principle in the desulfurization device;
Sd3, performing correction parameter identification on a coal-fired flue gas SO 2 generation-removal mechanism model by adopting a PSO algorithm based on historical operation data, and further constructing a SO 2 removal process data correction model based on an LSTM network;
And Sd4, generating and removing a model based on the SO 2 of the coal-fired flue gas established in the step, adopting the concentration of the outlet SO 2 and the desulfurization operation pH as intelligent regulation targets, constructing an optimization problem, and solving by adopting an enumeration method, a particle swarm algorithm or a particle swarm-gradient descent algorithm according to the characteristics of the optimization problem, thereby obtaining the optimal circulating pump of the wet desulfurization device and a frequency regulation strategy thereof under different operation conditions.
4. The method for intelligently controlling pollution reduction and carbon reduction by complete flow of combustion-pollutant treatment according to claim 3, wherein in order to ensure stability and convergence of an algorithm in the process of constructing an intelligent control strategy, a rolling optimization strategy is implemented by predicting a change trend of a controlled variable in a period of time, and in addition, on-line correction is required to be carried out on a prediction model according to actual running conditions of a site, and the specific steps are as follows:
Step Sc401, setting constraints on the outlet contaminant concentration SO 2, is expressed as follows:
Wherein r p,i is the number of circulating pumps and the frequency weight coefficient; p (t+i) is the sum of the slurry amounts of the circulating pumps; r e,j is an superscalar weight coefficient; sgn (·) is a sign function; w limit (t+j) is the upper emission limit of the outlet contaminant concentration SO 2; m is a control time domain; p is the prediction time domain;
In step Sc402, in order to ensure the relative stability of the outlet pollutant concentration, to cope with the rapid abrupt change of the working condition, an error between the reference trajectory and the predicted value is added to the optimization target, and the reference trajectory is set to be smaller than the outlet concentration limit value in the above formula, which is expressed as follows:
Wherein q j is a tracking weight coefficient; w t (t+j) is the emission target;
in step Sc403, on the basis of step Sc402, in order to ensure that the control process can converge to the vicinity of the target value, a terminal error needs to be added to the optimization target, so the final rolling optimization problem can be expressed as follows:
wherein minJ (t) is an objective function at time t; q j is a tracking weight coefficient; w t (t+j) is the emission target; r p,i is the number of circulating pumps and the frequency weight coefficient; p (t+i) is the sum of the slurry amounts of the circulating pumps; r e,j is an superscalar weight coefficient; sgn (·) is a sign function; w limit (t+j) is the upper emission limit of the outlet contaminant concentration SO 2; v f (·) is the terminal error function; χ (P) is a control amount calculated when predicting the last P moments of the time domain; χ s is the control after steady state optimization; y d is the outlet concentration after steady state optimization.
5. A system for implementing the combustion-pollutant abatement full-process intelligent regulation and control pollution abatement and carbon reduction as set forth in claims 1-4, said system comprising:
A boiler combustion-pollutant treatment system layer, a knowledge-data coupling modeling layer, a model parameter identification optimizing layer and a cascade intelligent accurate regulation layer;
The boiler combustion-pollutant treatment system layer consists of a low-carbon/zero-carbon fuel blending system, a boiler combustion system, a denitration device, an electrostatic dust collection device, a low-temperature electric dust collection device/electric bag composite dust collection device, a wet desulfurization device and a wet electrostatic dust collection device;
The step intelligent accurate regulation and control layer consists of a boiler combustion prediction control module, a denitration device prediction control module, an electric dust collector prediction control module, a wet desulphurization device prediction control module, a wet electric dust collector prediction control module and a full-flow step collaborative optimization control module;
The flow of the combustion-pollutant treatment full-flow intelligent regulation pollution-reducing and carbon-reducing system is as follows, a knowledge-data coupling modeling layer is used for establishing a boiler combustion-pollutant treatment full-flow multi-section pollutant concentration generation and step removal mechanism model based on a pollutant generation and removal mechanism of a full-flow device in a boiler combustion-pollutant treatment system layer, and a knowledge-data fusion-driven multi-section pollutant concentration prediction model is further established by combining historical operation data and advanced experience knowledge in the boiler combustion-pollutant treatment system layer;
In the model parameter identification optimization layer, taking a target controlled variable as an intelligent regulation target, constructing an optimization problem, carrying out model parameter identification and optimization solution by adopting a PSO algorithm, a WOA algorithm, a particle swarm-gradient descent algorithm and an enumeration algorithm according to the characteristics of the optimization problem, and simultaneously carrying out rolling optimization solution on the optimization solution parameters by combining offline excavation and online iteration, thereby establishing a key parameter prediction model driven by the process mechanism of the boiler combustion-flue gas treatment process and multi-working-condition segmentation machine learning in a cooperative manner;
The method comprises the steps of accurately predicting the concentration of multi-section pollutants of a boiler outlet and a pollutant removal system through a cascade intelligent accurate regulation layer, establishing a pollutant removal multi-device control strategy by combining the pollutant discharge concentration of the outlet of a different pollutant treatment device with the key regulation parameters under different working conditions, namely the boiler low-carbon/zero-carbon fuel blending combustion quantity, the fan frequency of a boiler section, the ammonia injection quantity of different areas of a denitration device, the operation secondary voltage of different electric field types of power supplies of an electric dust collector, different circulating pump combinations of slurry of a wet desulphurization device and the key regulation parameters of the circulating pump frequency and the slurry pH of the slurry, and realizing the full-process pollutant optimal control and the collaborative energy-saving carbon reduction of the full-process pollutants along the flue gas cascade.
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