CN111611691B - Multi-target optimization control method for predicting and controlling desulfurization system based on multi-mode model - Google Patents
Multi-target optimization control method for predicting and controlling desulfurization system based on multi-mode model Download PDFInfo
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Abstract
The invention relates to a multi-target optimization control method for a desulfurization system based on multi-mode model predictive control. The multi-target optimization control method for controlling the desulfurization system based on the multi-mode model prediction comprises the following steps: the method comprises the steps of taking historical data of a desulfurization system and a host system recorded by a DCS control system as a data source for process characteristic analysis, and establishing a database; normalizing the database; establishing an absorption tower model, a slurry pool model, a desulfurization efficiency model and other necessary models and connecting units; identifying a steady-state point linear state space; future system dynamics are predicted based on the linear state space model. According to the multi-target optimization control method for the desulfurization system based on the multi-mode model predictive control, provided by the invention, the multi-target steady state optimization, uncertainty compensation and nonlinear predictive control are combined, so that the multi-target real-time optimization control of the desulfurization system is achieved.
Description
Technical Field
The invention relates to the fields of optimization control, model predictive control and multi-objective optimization of a desulfurization system, in particular to a multi-objective optimization control method for the desulfurization system based on multi-mode model predictive control.
Background
At present, the world energy shortage and environmental pollution problems are increasingly concerned. The flue gas of the coal-fired power plant contains a large amount of pollutants such as SO 2, carbon monoxide, hydrocarbon, oxynitride, dust and the like, and the emission amount must be controlled by adopting a corresponding technology. In order to treat the atmospheric pollution generated by the emission of the flue gas of the coal-fired power plant, the national institutes of ordinary use meeting deeply discusses the environmental protection policy of comprehensively implementing the ultralow emission of the coal-fired power plant and enhancing the energy conservation and emission reduction work, and the meeting finally decides the environmental protection policy. The annular emission No. 164 issued by the three units of the national development and reform committee, the environmental protection department and the energy bureau is about the notice of the comprehensive implementation of the ultralow emission and energy saving reformation working scheme of the coal-fired power plant, and the notice clearly proposes that the domestic coal-fired power plant is required to realize ultralow emission through reformation on the premise of technical feasibility (namely, the emission concentration of smoke dust, SO 2 and nitrogen oxides is not higher than 10 mg/cubic meter respectively under the condition of the reference oxygen content of 6%). In order to achieve increasingly strict flue gas emission indexes, the flue gas desulfurization process adopted by coal-fired power plants worldwide is mainly a limestone-gypsum wet desulfurization process, and compared with other flue gas desulfurization technologies, the limestone (lime) -gypsum wet desulfurization process has the advantages of mature technology, high desulfurization efficiency and the like. However, as the time of the desulfurization system increases, the product gypsum of the limestone (lime) -gypsum wet process is easy to adhere to the inner wall of the spray tower and each spray layer, and is not easy to clean, so that the conditions that the operation performance of the desulfurization device is obviously reduced and the desulfurization efficiency can not meet the national standard requirements in actual operation are caused, and the conditions provide great challenges for the real-time operation and control level of the desulfurization system.
The desulfurization system of the coal-fired power plant is a complex coupling system with a large-range variable working condition, the conditions of large-range change of sulfur content of flue gas, change of sulfur content of coal and the like often occur in the actual operation process, and the requirement on multi-target real-time optimization of the desulfurization system is high. In this demand context, predictive control is evolving. The method meets the actual requirements of industrial process control to the greatest extent, has good comprehensive control effect, and can be quickly applied to the field of industrial control once proposed. With the rapid development of computer technology, the application field of predictive control is rapidly expanding to numerous engineering fields including power plant control.
The existing predictive control system also has the problems of incapability of real-time prediction and poor accuracy in practical application.
Disclosure of Invention
The invention provides a multi-target optimization control method for controlling a desulfurization system based on multi-mode model prediction, which can control the desulfurization system based on multi-mode model prediction in real time;
the invention provides a multi-target optimization control method for controlling a desulfurization system based on multi-mode model prediction, which aims to solve the problems that an existing desulfurization system model cannot be predicted in real time and is poor in accuracy.
The invention provides a multi-target optimization control method for controlling a desulfurization system based on multi-mode model prediction, which comprises the following steps:
Step one: establishing a database according to historical data of the desulfurization system and the host system;
Step two: normalizing the database, and analyzing the normalized data;
Step three: establishing a model of an absorption tower, establishing a model of a slurry pond, establishing a model of desulfurization efficiency and establishing a dynamic mathematical model of a desulfurization system based on a mass conservation law and an energy conservation law;
Step four: the model established in the third step is converted into a linear state space model, and a system steady-state point is determined according to the linear state space model;
Step five: predicting future system dynamics through a linear state space model;
step six: future system dynamic solution multi-constraint open loop optimization based on prediction and rolling calculation are carried out.
In the first step, the historical data of the desulfurization system and the host system recorded by the DCS control system are used as data sources for process characteristic analysis, a database is established in a standard OPC mode supporting an OPC protocol, and the database is continuously expanded through real-time data acquired by the DCS control system, so that physical quantities generated by operation of the desulfurization system and the host system are acquired.
In the second step, the database recorded in the step 1 is used as a data source, the data is normalized, the dimension is reduced by adopting a kernel principal component analysis method, the correlation degree of the normalized data is analyzed and identified, SO that the relevant physical quantity which has a larger influence on the concentration of the flue gas SO 2 at the outlet of the absorption tower and the pH value of the main body of the slurry pool is identified, and the principal component proportion calculation is carried out according to the preset total proportion of a plurality of principal components on the physical quantity which has a larger influence on the classification effect.
Wherein, in the third step:
The method for establishing the model of the absorption tower is established by adopting a method for calculating parameters closely related to the desulfurization efficiency of the desulfurization system by calculating mass transfer coefficients of spray liquid drops and SO 2 in a gas phase region of the absorption tower, the drop time of the liquid drops and the change of the ion concentration in the liquid drops along with the height, and calculating related parameters in the gas phase region;
the method comprises the steps of establishing a model of a slurry pond by establishing a chemical reaction model containing hydrogen ions, hydroxyl ions, sulfate ions, sulfite ions, hydrogen sulfate ions, sulfite ions, calcium ions and gypsum reactants, calculating the change of the concentration of each reactant of the slurry pond along with the change of each condition of the slurry pond by combining the influence of liquid drops returned to the slurry pond by an absorption tower on the concentration of each reactant of the slurry pond, the influence of the slurry pumped from the slurry pond by a circulating slurry pump set on the concentration of each reactant of the slurry pond, the influence of gypsum slurry pumped from the slurry pond by a slurry discharge pump on the concentration of each reactant of the slurry pond, and the influence of fresh limestone slurry supplied to the slurry pond by a slurry pump on the concentration of each reactant of the slurry pond;
The model for establishing the desulfurization efficiency is established by adopting the principle of conservation of mass in a gas phase region, taking the amount of oxidized air, the evaporation amount of a slurry pool and the amount of inlet smoke as input and the amount of smoke at the outlet of an absorption tower as output, and calculating the concentration of SO 2 at the outlet of the absorption tower by combining the absorption rate, the diameter of liquid drops and the concentration parameters inside the liquid drops of SO 2 of the absorption tower model
The establishing of the dynamic mathematical model of the desulfurization system based on the law of conservation of mass and the law of conservation of energy comprises the following steps: calculating dynamic characteristics of an absorption tower, calculating dynamic characteristics of a slurry tank, calculating desulfurization efficiency and calculating pH value of the slurry tank; imitates the change of desulfurization efficiency under the working conditions of different flue gas flow rates, inlet SO 2 concentration, liquid-gas ratio and slurry pool pH value.
And step four, determining a steady-state point of the DCS control system, taking the desulfurization system as a controlled object, taking fresh limestone slurry volume and circulating slurry volume of the desulfurization system as control volumes, taking the concentration of flue gas SO 2 at an outlet of an absorption tower and the pH value of a slurry pool main body as controlled volumes, identifying a transfer function model accurately reflecting the dynamic characteristics of the steady-state point of a prediction model of the desulfurization system by adopting a step identification method, and converting the transfer function model into a linear state space model. Wherein, in the step six, the rolling type calculation of open loop optimization is performed: and establishing model multi-constraint open-loop optimization of the absorption tower, wherein the first element of the obtained control sequence acts on the controlled object. At the next sampling moment, repeating the process, taking the new measured value as an initial condition for predicting the future dynamic state of the system at the moment, refreshing and optimizing, and re-solving.
The invention has the beneficial effects that: according to the multi-target optimization control method for the desulfurization system based on the multi-mode model predictive control, provided by the invention, the multi-target steady state optimization, uncertainty compensation and nonlinear predictive control are combined, so that the multi-target real-time optimization control of the desulfurization system is achieved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a multi-objective optimization control method for controlling a desulfurization system based on multi-modal model prediction;
FIG. 2 is a graph showing the effect of a desulfurization efficiency set point of a multi-objective optimization control method for predictive control of a desulfurization system based on a multi-modal model;
FIG. 3 is a graph showing the control effect of the pH value set value of the main body of the slurry tank based on the multi-mode model predictive control desulfurization system multi-objective optimization control method.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is apparent that the described embodiments are 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.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise. Furthermore, the terms "mounted," "connected," "coupled," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Referring to fig. 1 to 3, the multi-objective optimization control method for controlling a desulfurization system based on multi-modal model prediction includes the steps of:
Step one: establishing a database according to historical data of the desulfurization system and the host system;
Step two: normalizing the database, and analyzing the normalized data;
Step three: establishing a model of an absorption tower, establishing a model of a slurry pond, establishing a model of desulfurization efficiency and establishing a dynamic mathematical model of a desulfurization system based on a mass conservation law and an energy conservation law;
Step four: the model established in the third step is converted into a linear state space model, and a system steady-state point is determined according to the linear state space model;
Step five: predicting future system dynamics through a linear state space model;
step six: future system dynamic solution multi-constraint open loop optimization based on prediction and rolling calculation are carried out.
Specifically, in the first step, historical data of the desulfurization system and the host system recorded by the DCS control system is used as a data source for process characteristic analysis, a database is established in a standard OPC manner supporting an OPC protocol, and the database is continuously expanded by real-time data collected by the DCS control system, so that physical quantities generated by the operation of the desulfurization system and the host system are collected.
Specifically, in the second step, the database recorded in the step 1 is used as a data source, the data is normalized, the dimension is reduced by adopting a kernel principal component analysis method, the correlation degree of the normalized data is analyzed and identified, SO that the relevant physical quantity which has a larger influence on the concentration of the flue gas SO 2 at the outlet of the absorption tower and the pH value of the main body of the slurry pool is identified, and the main component ratio calculation is carried out according to the preset total ratio of a plurality of main components and the physical quantity which has a larger influence on the classification effect.
Specifically, in the third step:
And establishing a model of the absorption tower, and calculating parameters closely related to desulfurization efficiency of a desulfurization system by calculating mass transfer coefficients of spray liquid drops and SO 2 in a gas phase region of the absorption tower, drop time of the liquid drops and change of ion concentration in the liquid drops along with height, and calculating related parameters in the gas phase region.
The method comprises the steps of establishing a model of a slurry pond by establishing a chemical reaction model containing hydrogen ions, hydroxyl ions, sulfate ions, sulfite ions, hydrogen sulfate ions, sulfite ions, calcium ions and gypsum reactants, calculating the change of the concentration of each reactant of the slurry pond along with the change of each condition of the slurry pond by combining the influence of liquid drops returned to the slurry pond by an absorption tower on the concentration of each reactant of the slurry pond, the influence of the slurry pumped from the slurry pond by a circulating slurry pump set on the concentration of each reactant of the slurry pond, the influence of gypsum slurry pumped from the slurry pond by a slurry discharge pump on the concentration of each reactant of the slurry pond, and the influence of fresh limestone slurry supplied to the slurry pond by a slurry pump on the concentration of each reactant of the slurry pond;
The method is characterized in that a model for establishing desulfurization efficiency is established by adopting a principle of conservation of mass in a gas phase region, taking an oxidation air amount, an evaporation amount of a slurry pool and an inlet flue gas amount as inputs, taking an outlet flue gas amount of an absorption tower as outputs, and calculating the concentration of SO 2 in the outlet flue gas of the absorption tower by combining the absorption rate, the diameter of liquid drops and the concentration parameters in the liquid drops of SO 2 of the absorption tower model;
The establishing of the dynamic mathematical model of the desulfurization system based on the law of conservation of mass and the law of conservation of energy comprises the following steps: calculating dynamic characteristics of an absorption tower, calculating dynamic characteristics of a slurry tank, calculating desulfurization efficiency and calculating pH value of the slurry tank; imitates the change of desulfurization efficiency under the working conditions of different flue gas flow rates, inlet SO 2 concentration, liquid-gas ratio and slurry pool pH value.
Specifically, in the fourth step, a steady-state point of the DCS control system is determined, the desulfurization system is used as a controlled object, the fresh limestone slurry volume and the circulating slurry volume of the desulfurization system are used as controlled volumes, the concentration of flue gas SO 2 at the outlet of the absorption tower and the pH value of the slurry pool main body are used as controlled volumes, and a transfer function model accurately reflecting the dynamic characteristics of the steady-state point of the prediction model of the desulfurization system is identified by a step identification method and is converted into a linear state space model.
Specifically, in the fifth step, according to the current measurement information obtained by sampling, the state and control input of the DCS control system at the current sampling time are used as initial conditions of the linear state space model, substituted into the linear state space model, and the system state and output within a period of time are predicted.
Specifically, in the sixth step, the concentration of flue gas SO 2 at the outlet of the tracking absorption tower and the pH value of the main body of the slurry pond are taken as control targets, physical constraints of fresh limestone slurry amount and circulating slurry amount of the desulfurization system are taken as constraint conditions, mathematical expression of an objective function and constraint conditions in the optimization problem is determined, the multi-constraint open-loop optimization problem is solved by adopting an interior point method, and a control sequence within a period of time is predicted.
Specifically, in the step six, the rolling type calculation of open loop optimization is performed: and establishing model multi-constraint open-loop optimization of the absorption tower, enabling a first element of the obtained control sequence to act on a controlled object, repeating the process at the next sampling moment, and refreshing and optimizing and re-solving by using a new measured value as an initial condition for predicting future dynamics of the system at the moment.
The multi-target optimization control method for controlling the desulfurization system based on the multi-mode model prediction is subdivided into the following steps:
Step 1, data acquisition: the method comprises the steps of taking historical data of a desulfurization system and a host system recorded by a DCS control system as data sources for process characteristic analysis, establishing a database in a standard OPC mode supporting an OPC protocol, and continuously expanding the database through real-time data acquired by the DCS control system, so that physical quantities generated by operation of the desulfurization system and the host system are acquired.
Each physical quantity is data obtained by the desulfurization apparatus in actual operation of the desulfurization system and the host system, for example: the concentration of SO 2 in the flue gas at the outlet of the absorption tower and the pH of the main body of the slurry pool.
Step 2, correlation analysis: and (3) carrying out normalization processing on the database recorded in the step (1) by taking the database as a data source, adopting a kernel principal component analysis method to reduce the dimension, analyzing and identifying the correlation degree of normalized data SO as to identify the related physical quantity which has larger influence on the concentration (conversion) of the flue gas SO 2 at the outlet of the absorption tower and the pH value of the main body of the slurry pool, and carrying out principal component proportion calculation on the physical quantity which has larger influence on the classification effect according to the preset total proportion of a plurality of principal components.
The main components are components which have a great influence on the concentration (conversion) of SO 2 in the flue gas at the outlet of the absorption tower and the pH value of the main body of the slurry pool.
Based on-site operation data of desulfurization equipment, analyzing influences of flue gas flow rate, inlet SO 2 concentration, liquid-gas ratio and slurry tank pH value factors on desulfurization efficiency and system energy consumption, taking the flue gas flow rate, inlet SO 2 concentration, liquid-gas ratio and slurry tank pH value factors as linear related variable compression through a nuclear principal component analysis method, and changing the linear relationship into an uncorrelated variable. And projecting the related variables into another set of orthogonal spaces to obtain a set of new variables, wherein the new variables are characterized by having the largest variance (the variance reflects the degree of data difference, and the direction with the largest variance corresponds to the direction with the largest information quantity). And determining main factors influencing the desulfurization efficiency and the system energy consumption, and analyzing the change rule of the desulfurization efficiency and the system energy consumption under the influence of a single factor.
And judging the rule by a single factor, fixing other factors, and performing variance analysis.
Step 3, establishing an absorption tower model: establishing an absorption tower model according to parameters closely related to desulfurization efficiency of the desulfurization system, such as mass transfer coefficients of spray liquid drops and SO 2 in a gas phase region of the absorption tower, drop time of the liquid drops and change of ion concentration in the liquid drops along with height; and calculating relevant parameters of the SO 2 in the gas phase zone of the absorption tower through the absorption tower model.
The gas-liquid contact area of the absorption tower part in the absorption tower model is a plug flow reactor, and the gas concentration and the concentration of each substance in the liquid drop in the absorption tower are changed along with the height.
The absorber model is built as linear state space model data.
Step 4, a slurry pool model is established, and a chemical reaction model containing hydrogen ions, hydroxyl ions, sulfate ions, sulfite ions, hydrogen sulfate ions, hydrogen sulfite ions, calcium ions and gypsum reactants is established;
And (3) calculating the change of each reactant concentration of the slurry tank along with each condition by combining the influence of liquid drops returned to the slurry tank by the absorption tower on each reactant concentration of the slurry tank, the influence of slurry pumped from the slurry tank by the circulating slurry pump on each reactant concentration of the slurry tank, the influence of gypsum slurry pumped from the slurry tank by the slurry discharge pump on each reactant concentration of the slurry tank and the influence of fresh limestone slurry supplied to the slurry tank by the slurry supply pump on each reactant concentration of the slurry tank, wherein the full mixed flow reactor model is used as a supplement of a slurry tank model data model.
And 5, a desulfurization efficiency model is established, and the desulfurization efficiency model is formed by mixing and desulfurizing raw flue gas at the inlet of the absorption tower with air blown by the oxidation fan, then leading the mixed flue gas out of the absorption tower, and sending the mixed flue gas into chimney data.
The variables in the desulfurization efficiency model are the flow of the oxidation fan, the concentration of SO 2 in the outlet flue gas and the desulfurization efficiency. The desulfurization efficiency model takes conservation of mass in a gas phase zone as a principle, takes the amount of oxidized air, the evaporation amount of a slurry pool and the amount of inlet smoke as input, takes the amount of smoke at the outlet of an absorption tower as output, and calculates the concentration (conversion) of the smoke SO 2 at the outlet of the absorption tower by combining the absorption rate, the diameter of liquid drops and the concentration parameters inside the liquid drops of the SO 2 of the absorption tower model in the step 3.
Step 6, establishing other necessary models and connection units, wherein the other necessary models and connection units are as follows: the dynamic mathematical model of the desulfurization system based on the law of conservation of mass and the law of conservation of energy is established, comprises the calculation of dynamic characteristics of an absorption tower, the calculation of dynamic characteristics of a slurry pool, the calculation of desulfurization efficiency, the calculation of pH value of the slurry pool and necessary connection, and simulates the change of desulfurization efficiency under the working conditions of different flue gas flow rates, inlet SO 2 concentration, liquid-gas ratio and pH value of the slurry pool, and the model is established.
Step 7, steady-state point linear state space identification: firstly, determining a steady-state point of a system, taking a desulfurization system as a controlled object, taking fresh limestone slurry volume and circulating slurry volume of the desulfurization system as control volumes, taking the concentration (conversion) of flue gas SO 2 at an outlet of an absorption tower and the pH value of a slurry pool main body as controlled volumes, identifying a transfer function model accurately reflecting the dynamic characteristics of the steady-state point of a prediction model of the desulfurization system by adopting a step identification method, and converting the transfer function model into a linear state space model.
And establishing a blurring model based on a small deviation theory, calculating different working point line state space models by adopting a model simplification method such as Taylor expansion and the like, and blurring the different working point line state space models. The number of non-critical intermediate variables is reduced by adopting a centralized parameter method, and a steady-state point local linearization method is adopted to convert a partial differential equation into a normal differential equation near a steady-state point.
The established linear state space model can better reflect the dynamic characteristics of the desulfurization system.
Step 8, predicting future system dynamics based on the linear state space model: according to the current measurement information obtained by sampling, the state and control input of the desulfurization system at the current sampling moment are used as initial conditions of a linear state space model, substituted into the linear state space model and predicted to be the system state and output within a period of time. The input quantity of the defined model is the opening of a fresh limestone slurry regulating valve and the opening of a circulating slurry quantity regulating valve respectively, and the output quantity is the desulfurization efficiency of the absorption tower and the pH value of the slurry pool main body respectively. The objective function consists of two parts, namely actual value and set value deviation of the desulfurization system and prediction control output error weighting, and future dynamics are predicted based on the FGD mathematical model and the linearization model. The state prediction aims at predicting the change trend of each variable in a period of time in the future of the system through a previously established desulfurization system mathematical model based on the current running state and running parameters of the system, so as to provide system running state information for solving the multi-objective optimization problem.
Step 9, dynamically solving multi-constraint open-loop optimization based on a predicted future system; the method is characterized in that the concentration (conversion) of flue gas SO 2 at the outlet of the tracking absorption tower and the pH value of the main body of the slurry pool are taken as control targets, the physical constraints of fresh limestone slurry amount and circulating slurry amount of a desulfurization system are taken as constraint conditions, an interior point method is adopted to solve the multi-constraint open-loop optimization problem, and a control sequence in a period of time is predicted.
Step 10, open-loop optimized rolling calculation; and the first element of the obtained control sequence acts on the controlled object. At the next sampling moment, repeating the process, taking the new measured value as an initial condition for predicting the future dynamic state of the system at the moment, refreshing the optimization problem and solving again. The first control quantity obtained by solving is used as the control quantity output by the controller to be introduced into the desulfurization system in rolling optimization, and the next sampling time is entered. When the system is at a new sampling time, the newly measured state quantity is reintroduced into the controller and used as an output quantity in the prediction model to calculate the dynamic change of the desulfurization system in the prediction time domain corresponding to the next sampling time. The new predicted sequence is used to solve the objective function optimization problem to obtain a new set of control delta sequences.
The controller is a nonlinear model predictive controller.
Step 11, selecting a steady-state working condition under a large-scale variable working condition: and calculating linearity between the control quantity and the controlled quantity of the desulfurization system when the concentration (conversion) of SO2 in the flue gas at the outlet of the absorption tower is changed in a large range, namely, the maximum deviation between a calibration curve of the concentration (conversion) of SO 2 in the flue gas at the outlet of the absorption tower and a fitting straight line and the percentage of rated condition output, and selecting three groups of stable conditions with strong nonlinearity as characteristic conditions for designing the nonlinear model predictive controller. For example:
Table 1 three typical steady state points
Step 12, design of fuzzy rules and weights: and (3) adopting a relative error measurement model output between the model and the controlled object and the proximity degree of the controlled object, putting all output errors on the same magnitude, calculating, carrying out weighted calculation, designing a multi-mode fuzzy control rule of the desulfurization system based on the output weighting of the controller, and determining corresponding weights. The closer the sub-model and the controlled object are, the greater the proportion of the output of the sub-model corresponding to the sub-controller to the total control quantity is, and the greater the weight of the controller is. For example:
TABLE 2 seven typical conditions with greater dynamic characteristics
Step 13, designing a multi-mode-based nonlinear model predictive controller: and (3) according to the fuzzy rule and the weight design in the step (12), taking the concentration (conversion) of the flue gas SO 2 at the outlet of the absorption tower as a key variable for controlling the fuzzy rule switching, and designing a switching rule to realize the design of a nonlinear controller based on a multi-mode model predictive control desulfurization system multi-target optimization control method.
Basic design principle of multi-mode controller:
The basic design principle of the multi-mode controller is that signals output by different sub-controllers are weighted by a pre-designed fuzzy rule and then output as a unified control signal by tracking the real-time change of working conditions, so that the control characteristics of nonlinearity and large hysteresis of the desulfurization process are adapted. In order to realize multi-mode control, membership functions and corresponding fuzzy rules are introduced, and control amounts output by linear model predictive controllers designed by different steady-state points are integrated into a new control amount for controlling a desulfurization system.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (7)
1. The multi-target optimization control method for controlling the desulfurization system based on the multi-mode model prediction is characterized by comprising the following steps:
Step one: establishing a database according to historical data of the desulfurization system and the host system;
Step two: normalizing the database, and analyzing the normalized data;
Step three: establishing a model of an absorption tower, establishing a model of a slurry pond, establishing a model of desulfurization efficiency and establishing a dynamic mathematical model of a desulfurization system based on a mass conservation law and an energy conservation law;
Step four: the model established in the third step is converted into a linear state space model, and a system steady-state point is determined according to the linear state space model;
Step five: predicting future system dynamics through a linear state space model;
step six: dynamically solving multi-constraint open-loop optimization based on a predicted future system, and performing rolling calculation;
determining a steady-state point of a DCS control system, taking a desulfurization system as a controlled object, taking fresh limestone slurry volume and circulating slurry volume of the desulfurization system as control volumes, taking the concentration of flue gas SO 2 at an outlet of an absorption tower and the pH value of a slurry pool main body as controlled volumes, and further identifying a transfer function model accurately reflecting the dynamic characteristics of the steady-state point of a prediction model of the desulfurization system by adopting a step identification method, and converting the transfer function model into a linear state space model;
Step seven: measuring the approach degree of the linear state space model and the controlled object by adopting the relative error output between the linear state space model and the controlled object, putting all the output errors on the same magnitude for weighting calculation, designing a multi-mode fuzzy control rule of the desulfurization system based on the output weighting of the controller, and determining corresponding weights;
step eight: and taking the concentration of SO 2 in the flue gas at the outlet of the absorption tower as a key variable for controlling the switching of the fuzzy rule, designing the switching rule, and realizing the design of a nonlinear controller based on a multi-mode model predictive control desulfurization system multi-target optimization control method.
2. The multi-target optimization control method for controlling the desulfurization system based on the multi-mode model prediction according to claim 1, wherein in the first step, historical data of the desulfurization system and the host system recorded by the DCS control system are used as data sources for process characteristic analysis, a database is built by a standard OPC method supporting an OPC protocol, and the database is continuously expanded by real-time data collected by the DCS control system, so that each physical quantity generated by the operation of the desulfurization system and the host system is collected.
3. The multi-objective optimization control method for the desulfurization system based on the multi-modal model prediction control according to claim 1, wherein in the second step, the database recorded in the first step is used as a data source, the database is normalized, a kernel principal component analysis method is adopted to reduce the dimension, the correlation degree of the normalized data is analyzed and identified, SO that the relevant physical quantity which has a large influence on the concentration of the flue gas SO 2 at the outlet of the absorption tower and the pH value of the main body of the slurry pool is identified, and the principal component proportion calculation is carried out on the physical quantity which has a large influence on the classification effect according to the preset total proportion of a plurality of principal components.
4. The multi-mode model predictive control desulfurization system multi-target optimization control method according to claim 1, wherein the model for establishing the absorption tower in the third step is established by calculating parameters closely related to desulfurization efficiency of the desulfurization system by calculating mass transfer coefficients of spray droplets and SO 2 in a gas phase region of the absorption tower, drop down time and ion concentration in the drop along with the change of height;
Establishing a model of a slurry pond, establishing a chemical reaction model containing hydrogen ions, hydroxyl ions, sulfate ions, sulfite ions, hydrogen sulfate ions, hydrogen sulfite ions, calcium ions and gypsum reactants, calculating the influence of the concentration of each reactant of the slurry pond along with the change of each condition of the slurry pond by combining the influence of liquid drops returned to the slurry pond by an absorption tower on the concentration of each reactant of the slurry pond, the influence of the slurry pumped from the slurry pond by a circulating slurry pump set on the concentration of each reactant of the slurry pond, the influence of gypsum slurry pumped from the slurry pond by a slurry discharge pump on the concentration of each reactant of the slurry pond, and the influence of fresh limestone slurry supplied to the slurry pond by a slurry pump on the concentration of each reactant of the slurry pond;
The method comprises the steps of establishing a desulfurization efficiency model, taking an oxidation air amount, a slurry pool evaporation amount and an inlet flue gas amount as inputs, taking an absorption tower outlet flue gas amount as outputs, and calculating the concentration of the absorption tower outlet flue gas SO 2 by combining the absorption rate, the droplet diameter and the droplet internal concentration parameters of SO 2 of the absorption tower model;
The establishing of the dynamic mathematical model of the desulfurization system based on the law of conservation of mass and the law of conservation of energy comprises the following steps: calculating dynamic characteristics of an absorption tower, calculating dynamic characteristics of a slurry tank, calculating desulfurization efficiency and calculating pH value of the slurry tank; the method simulates the change of desulfurization efficiency under the working conditions of different flue gas flow rates, inlet SO 2 concentration, liquid-gas ratio and slurry pond pH value, and establishes a dynamic mathematical model of the desulfurization system based on the law of conservation of mass and the law of conservation of energy based on the change condition.
5. The multi-target optimization control method for the desulfurization system based on the multi-mode model prediction control according to claim 1, wherein in the fifth step, according to the current measurement information obtained by sampling, the state and the control input of the DCS control system at the current sampling time are used as initial conditions of the linear state space model, substituted into the linear state space model, and the system state and the output within a period of time are predicted.
6. The multi-objective optimization control method for the desulfurization system based on the multi-modal model prediction control according to claim 1, wherein in the sixth step, the concentration of flue gas SO 2 at the outlet of the tracking absorption tower and the pH value of the main body of the slurry pond are taken as control targets, physical constraints of fresh limestone slurry volume and circulating slurry volume of the desulfurization system are taken as constraint conditions, mathematical expressions of objective functions and constraint conditions in the optimization problem are determined, the multi-constraint open-loop optimization problem is solved by adopting an interior point method, and a control sequence in a period of time is predicted.
7. The multi-modal model predictive control based multi-objective optimization control method for a desulfurization system according to claim 6, wherein the rolling calculation of the open loop optimization in the step six: and establishing model multi-constraint open-loop optimization of the absorption tower, enabling a first element of the obtained control sequence to act on a controlled object, repeating the process at the next sampling moment, and refreshing and optimizing and re-solving by using a new measured value as an initial condition for predicting future dynamics of the system at the moment.
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