CN112197262B - Intelligent control method for circulating fluidized bed coal-fired boiler - Google Patents
Intelligent control method for circulating fluidized bed coal-fired boiler Download PDFInfo
- Publication number
- CN112197262B CN112197262B CN202011150510.9A CN202011150510A CN112197262B CN 112197262 B CN112197262 B CN 112197262B CN 202011150510 A CN202011150510 A CN 202011150510A CN 112197262 B CN112197262 B CN 112197262B
- Authority
- CN
- China
- Prior art keywords
- boiler
- coal
- temperature
- control
- load
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
- F23C—METHODS OR APPARATUS FOR COMBUSTION USING FLUID FUEL OR SOLID FUEL SUSPENDED IN A CARRIER GAS OR AIR
- F23C10/00—Fluidised bed combustion apparatus
- F23C10/18—Details; Accessories
- F23C10/28—Control devices specially adapted for fluidised bed, combustion apparatus
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
- F23C—METHODS OR APPARATUS FOR COMBUSTION USING FLUID FUEL OR SOLID FUEL SUSPENDED IN A CARRIER GAS OR AIR
- F23C10/00—Fluidised bed combustion apparatus
- F23C10/18—Details; Accessories
- F23C10/20—Inlets for fluidisation air, e.g. grids; Bottoms
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
- F23L—SUPPLYING AIR OR NON-COMBUSTIBLE LIQUIDS OR GASES TO COMBUSTION APPARATUS IN GENERAL ; VALVES OR DAMPERS SPECIALLY ADAPTED FOR CONTROLLING AIR SUPPLY OR DRAUGHT IN COMBUSTION APPARATUS; INDUCING DRAUGHT IN COMBUSTION APPARATUS; TOPS FOR CHIMNEYS OR VENTILATING SHAFTS; TERMINALS FOR FLUES
- F23L9/00—Passages or apertures for delivering secondary air for completing combustion of fuel
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The application belongs to the technical field of boiler control, and discloses an intelligent control method of a circulating fluidized bed coal-fired boiler, which is a novel control method formed by combining advanced process control technologies of a DMC (dynamic matrix control) system based on PID (proportion integration differentiation) control, and can track the change of boiler efficiency, adjust a control strategy and realize long-period optimization of the system; according to working conditions, an optimal operation mode can be automatically selected, and single-furnace and multi-furnace linkage real-time optimal control is realized; automatically recognizing the coal quality change and the downstream demand change, quickly adjusting the load and stabilizing the steam quality.
Description
Technical Field
The application relates to the technical field of boiler control, in particular to an intelligent control method for a circulating fluidized bed coal-fired boiler.
Background
Circulating fluidized bed coal fired boilers are a complex control object of multiple input/multiple output non-linearity, strong coupling and large hysteresis, now usually by feedback based PID regulation, a manual open loop process. The operation of circulating fluidized beds often suffers from the following problems:
(1) Frequent change of coal quality
The variation of the coal type and the co-firing of the boiler fuel are common phenomena, and even one type of coal, the volatile matter and the heat value thereof are not constant, and also vary with the storage time, weather conditions and the like. So that the coal quality change always exists objectively. The change of the heat value of the coal cannot be measured in real time, the change of the heat value of the coal can rapidly cause the change of a hearth temperature field, the combustion efficiency of the boiler is fluctuated, and the coal consumption is increased; in addition, steam quality fluctuation is caused, and the production operation of a downstream device is influenced.
(2) Fast dynamic change of boiler
The boiler production process is rapid and dynamic, strong coupling exists between control parameters, the weight of a control target is different under different working conditions, the operation means is limited, the production control needs to adapt to the rapid change of the process and the change of the importance of the control target, and the load change is responded rapidly so as to meet the requirements of steam users and save energy fully. The traditional DCS control mode based on feedback and feedforward control cannot fundamentally solve the requirements of quick response and global optimization, and fluctuation, difference and benefit loss caused by manual operation intervention process are necessarily caused.
(3) Random variation of load
Because the influence of environmental factors such as downstream device operation reasons and climate, weather, temperature difference between day and night and the like, and the change of heat demand that oil changes and cause, the demand of boiler downstream to steam is not constant, and boiler self fluctuation that factor such as coal quality change arouses, the boiler needs the random adjustment load in order to satisfy the demand, because intervention of human factor, the unavoidable two following problems that exist: firstly, the attention is not enough, the adjustment is not timely, and the best opportunity is missed; secondly, the regulation level is limited, the optimal combustion efficiency is difficult to maintain, and the steam quality fluctuation and the coal consumption are high, so that the downstream operation is influenced.
(4) Boiler intermodal optimization space
As a shared project, large enterprises supply steam by adopting a plurality of boilers. In addition to load adjustment of each boiler, in order to meet the requirement of great change of steam consumption, a spare boiler is usually built in enterprises and started and stopped according to the requirement. The start-stop process may not meet the requirements of sudden changes in steam usage in time, and due to different performance and load constraints, load distribution among multiple boilers may have unreasonable conditions such as low efficiency and energy waste. The method is faster, stable and efficient, on the basis of accurate energy balance in the whole plant, a boiler with good performance is selected as a load follow-up system, and the load is controlled by the intelligent APC, so that the load is adjusted at any time according to the requirement, and other boilers run at full load.
(5) Heat loss during combustion
In the combustion process of the boiler, a large amount of space for optimizing and utilizing heat energy exists, for example, the operation of reducing the temperature is not fine enough, the traditional manual operation usually adopts a method of adding and reducing the water excessively to quickly press the rising or falling trend of the steam temperature, then the process is stabilized by a plurality of times of callback, and the excessive adjustment mode inevitably causes heat loss; in addition, for safety reasons, operators generally control the oxygen content of the boiler in a higher range, and in the process of dynamic load adjustment, the oxygen content is operated to meet the requirements, so that the operation action is large, and a large amount of heat is lost along with the flue gas.
Disclosure of Invention
Aiming at the problems of fluctuation of dynamic process control such as coal quality change and load change and energy waste caused by higher tail oxygen concentration in a circulating fluidized bed in the background technology, the intelligent control system solution developed based on the advanced process control technology of a DMC (dynamic matrix control) system is used for responding to the dynamic process such as coal quality change and load adjustment in an optimal mode and at the fastest speed through single-furnace intelligent control and multi-furnace intelligent control optimization, and the intelligent control system replaces an operator to perform system operation based on a model, so that the problems of hysteresis and fluctuation of a conventional regulation mode based on feedback are overcome, the boiler always keeps higher combustion efficiency, steam coal consumption is reduced, the regulation amplitude of each boiler is limited by optimizing load adjustment distribution and considering respective furnace temperature constraint, the safety is ensured, and the service life of the boiler is prolonged.
In order to achieve the above purpose, the invention adopts the following technical scheme:
an intelligent control method for a circulating fluidized bed coal-fired boiler, comprising the following steps:
setting a basic load, performing coal type compensation by the intelligent controller through the DCS according to the basic load, and then performing coal feeding quantity setting and coal feeding speed setting by the intelligent controller;
the intelligent controller performs gain compensation through the DCS according to the coal feeding quantity setting and the coal feeding speed setting;
the intelligent controller sends the set coal feeding amount and the set coal feeding speed to the DCS, the DCS controls the coal feeder through the received coal feeding amount and the received coal feeding speed, and when the boiler system works, the DCS collects and stores real-time data of combustion parameters and fan parameters of the boiler system; and
the intelligent controller sets the main steam pressure and flow of the boiler according to the real-time data of the combustion parameters and the fan parameters and the historical data within a certain time range, and performs multiple optimization control when the boiler operates.
Further, the combustion parameters comprise fluidized bed temperature and boiler furnace temperature, and the fan parameters comprise wind speeds, running time and wind quantity of a primary fan and a secondary fan.
Further, the plurality of optimization controls include:
multi-furnace optimization control, coal quality change response optimization control, main steam temperature and hearth temperature optimization control, oxygen content optimization control and coal feeder optimization control.
Further, the specific method for optimizing and controlling the multiple furnaces comprises the following steps:
the standby furnace signals, the real-time values of the running efficiency of each furnace and the optimal load interval of each furnace are introduced as input signals, the use/standby conditions of the boilers are identified and judged through a model, the load adjustment of the master boiler and the slave boiler is timely determined, and the load can be adjusted as soon as possible on the premise of ensuring safety all the time.
Further, the specific method for optimizing and controlling the coal quality change response comprises the following steps:
the coal quality change is reflected on the temperature and the oxygen concentration of a controlled variable hearth, the project indirectly adopts a method of accumulated deviation correction between the prediction of the controlled variable model and the actual value, the intelligent controller judges the fluctuation of the heat value through the accumulated error between the prediction of the hearth temperature and the oxygen concentration and the actual value, the gain of the coal supply to the pressure is properly adjusted when the heat value is increased, and the gain of the coal supply to the pressure is reduced when the heat value is reduced, so that the boiler is maintained to always operate under the optimal condition.
Further, the specific method for optimally controlling the main steam temperature and the hearth temperature comprises the following steps:
and (3) establishing a model of the relationship among the pressure of the primary air, the water supply, the temperature of the hearth and the temperature of the primary steam, and adjusting related control variables such as the pressure of the primary air, the water supply flow or a temperature controller of the primary desuperheating water according to different conditions, so that the pressure of the primary steam can be kept to fluctuate within a small range, the temperature of the hearth can be controlled within a reasonable range, and the use of the desuperheating water can be reduced. The conversion efficiency of the boiler is improved, and the energy consumption is reduced. Meanwhile, as the temperature of the main steam tends to be constant, the consumption of the temperature-reducing water is reduced, so that the steam temperature generated by the boiler is reduced, the reasonable temperature of the hearth is maintained, the production of oxynitride is reduced, and the heat dissipated to the surrounding air by the boiler is reduced. Thus, the heat efficiency of the boiler can be improved, and the condition that coking is easy to form due to high furnace temperature can be reduced.
Further, the specific method for optimizing and controlling the oxygen content comprises the following steps:
the steady-state boiler load is stable, a model of the relationship between the secondary air and the oxygen content is built, and the oxygen content in the boiler is controlled at a target value by adjusting the secondary air.
Further, the specific method for optimizing and controlling the oxygen content comprises the following steps:
the dynamic boiler load changes, a model related to the load, primary air, secondary air and oxygen content is built, and the oxygen content in the boiler is optimized by adjusting the primary air and the secondary air. Due to the control of the oxygen concentration, the goal is to approach the lower limit. Thus, the oxygen concentration at the furnace outlet is reduced, thereby reducing the total amount of excess air in the furnace. The air quantity in the main combustion zone is reduced as much as possible, so that the main combustion becomes a rich combustion zone. Meanwhile, the air quantity of the burnout area is increased, so that coal dust is burnout, the generation of nitrogen-oxygen compounds is reduced, the temperature of flue gas is reduced, the heat loss taken away by the flue gas is reduced, and the efficiency of the boiler is improved.
Further, the concrete method for optimizing and controlling the coal feeder comprises the following steps:
and establishing a model related to primary air, secondary air, the speed of the coal feeder, main steam pressure, load and hearth temperature, maintaining a reasonable load, pressure and hearth temperature range of the boiler, and adjusting the speed of the coal feeder to balance the coal feeding speed of the coal feeder.
Further, the method further comprises:
the method comprises the steps of running a monitoring system, establishing a timer, and monitoring communication between the DCS and an upper computer through the timer; when the communication is normal, resetting the timer in each operation period of the advanced control system of the upper computer, so that the timer is operated repeatedly; if the communication is interrupted, the reset signal of the upper computer cannot be issued, and the timer is stopped or exceeds a certain range finally, so that the communication interruption is indicated, and a corresponding mark is given.
The optimal state of the system operation is ensured, and the application adopts an Aspen DMCplus multivariate model predictive control. The multivariable model predictive control operation principle is that the oxygen content of flue gas, the pressure of a hearth, the pressure difference of the hearth, the pressure of a steam pipe network, the temperature of a lower layer of a bed temperature, the flow of primary air, a primary temperature-reducing valve position and a secondary temperature-reducing valve position are used as controlled variables; the variable frequency output of the primary fan, the variable frequency output of the secondary fan, the variable frequency output of the induced draft fan, the variable frequency output of the coal feeder, the variable frequency output of the slag extractor and the temperature setting of the primary desuperheating water are used as operation variables; taking the total coal yield, primary fan current and secondary fan current as feedforward variables; predicting the change of a controlled variable from the current time to the steady state time, using the current value of the controlled variable, the current value of the operating variable, the current value of the feedforward variable and the predicted value of the controlled variable in the upper period as input data of a prediction model to obtain the predicted value of the controlled variable, obtaining the operating variable according to the predicted value of the controlled variable as output data, and obtaining the actual value of the controlled variable according to the operating variable. And comparing the current actual value of the controlled variable with the predicted value, and correcting the predicted result by using the deviation of the current actual value and the predicted value. Thus, the predicted value of each period is ensured to be corrected, and feedback information is provided for the intelligent controller. The intelligent controller determines the effect of the changes in the manipulated and feedforward variables on the controlled variable and continues to correct the predicted value. And the intelligent controller obtains a series of predictive correction values of each controlled variable in the whole steady state time according to the calculation result of the predictive model.
And calculating to obtain target values of the operation variable and the controlled variable by taking the predicted correction value of the controlled variable, the upper limit value and the lower limit value of the operation variable, the upper limit value and the lower limit value of the controlled variable, the feasibility stage objective function in the model, the economic optimization stage objective function and the set value of the operation variable as input data of steady-state optimization control.
At each sampling moment, the intelligent controller calculates the control action of the operation variable so as to minimize the error between the controlled variable and the expected value in a certain time interval in the future, and realizes the dynamic optimization control of the multivariable model predictive control.
Compared with the prior art, the beneficial effects of this application are:
the application does not need to change any setting and control loop in the original DCS in the implementation process. Under steady-state conditions (i.e. stable load and stable downstream consumption), the boiler is enabled to stably run by reasonably controlling the operation variables such as coal feeding amount, primary air, secondary air and the like. When the boiler load changes, the intelligent controller can automatically identify (such as through a certain key point steam pressure change trend or other key points), the intelligent controller can adjust the boiler with excellent preferential selection performance (judging the superiority through the boiler heat efficiency, wherein the heat efficiency parameters can be calculated from the original DCS data, and some systems also directly provide the heat efficiency parameters), and other boiler loads remain stable. And (3) performing multi-furnace optimal control, coal quality change response optimal control, main steam temperature and hearth temperature optimal control, oxygen content optimal control and coal feeder optimal control through an intelligent controller.
The novel control method based on PID control and combined with advanced process control technology of DMC (dynamic matrix control) system can track the change of boiler efficiency, adjust control strategy and realize long-period optimization of the system; according to working conditions, an optimal operation mode can be automatically selected, and single-furnace and multi-furnace linkage real-time optimal control is realized; automatically recognizing the coal quality change and the downstream demand change, quickly adjusting the load and stabilizing the steam quality.
The intelligent control system solution developed based on the advanced process control technology of the DMC (dynamic matrix control) system enables the coal-fired steam boiler to respond in an optimal mode and at the fastest speed in the dynamic process of coping with coal quality changes, load adjustment and the like through single-boiler intelligent control and multi-boiler intelligent control optimization, replaces operators with the intelligent control system to operate the system based on a model, overcomes the problems of hysteresis and fluctuation of a conventional adjustment mode based on feedback, enables the boiler to always maintain higher combustion efficiency, reduces steam coal consumption, optimizes load adjustment distribution through dynamic judgment of boiler performance differences, limits the adjustment amplitude of each boiler by considering respective furnace temperature constraint, ensures safety and prolongs the service life of the boiler.
Drawings
FIG. 1 is a chart of I-MR (single value range control) of drum pressure by date before and after application of a 75t/h coal-fired steam boiler in example 1 of the present invention;
FIG. 2 is a chart of I-MR (single value range control) of drum liquid level by date before and after application of example 1 of the present invention to a 75t/h coal-fired steam boiler;
FIG. 3 is a graph of I-MR (single value range control) of negative pressure according to date at the front and rear outlets of a 75t/h coal-fired steam boiler of example 1 of the present invention;
FIG. 4 is a graph of I-MR (single value range control) of oxygen content by date before and after application of a 75t/h coal-fired steam boiler in accordance with example 1 of the present invention;
FIG. 5 is a schematic diagram of a single-furnace model matrix of an intelligent controller of a single-furnace intelligent control system of a circulating fluidized bed coal-fired boiler;
FIGS. 6-7 are diagrams of multivariate predictive model control logic of the present application;
FIG. 8 is a schematic diagram of the topology of the intelligent control system of the present application;
fig. 9 is a diagram showing a connection relationship between a conventional circulating fluidized bed coal-fired steam boiler DCS and the intelligent control process system of the present application.
Detailed Description
For a better understanding of the technical solution of the present invention, the technical solution of the present invention will be further described with reference to fig. 1 to 9 and examples. The manner of carrying out the invention includes, but is not limited to, the following examples, which are intended to illustrate the invention, but are not intended to limit the scope thereof. The technical means used in the examples are conventional means well known to those skilled in the art unless otherwise indicated. The test methods in the following examples are conventional methods unless otherwise specified.
Example 1
An intelligent control method for a circulating fluidized bed coal-fired boiler comprises the following specific steps:
the first step: setting a basic load, performing coal type compensation by the intelligent controller through the DCS according to the basic load, and then performing coal feeding quantity setting and coal feeding speed setting by the intelligent controller;
and a second step of: the intelligent controller performs gain compensation through the DCS according to the coal feeding quantity setting and the coal feeding speed setting;
and a third step of: the intelligent controller sends the set coal feeder parameters to the DCS through OPC (a communication protocol), and the DCS controls the coal feeder through the given coal feeder parameters. When the boiler system works, the DCS collects and stores real-time data of combustion parameters and fan parameters, wherein the combustion parameters comprise fluidized bed temperature and boiler furnace temperature, and the fan parameters comprise wind speeds, running time and wind volumes of the primary fan and the secondary fan.
Fourth step: the intelligent controller sets the main steam pressure and flow of the boiler according to the combustion parameters and the fan parameters. And (3) performing multiple optimizations when the boiler is in operation. The multiple optimizations include: multi-furnace optimization control, coal quality change response optimization control, main steam temperature and hearth temperature optimization control, oxygen content optimization control and coal feeder optimization control.
The intelligent controller communicates with the DCS through OPC (a communication protocol), the intelligent controller invokes real-time data in a DCS database through OPC and processes the real-time data, the processed result is sent to the DCS through OPC, and the DCS optimizes and adjusts a boiler system.
The steam coal consumption before and after the coal-fired steam boiler of 75t/h is shown in Table 1, and the drum pressure, drum liquid level, outlet negative pressure and oxygen content before and after the coal-fired steam boiler is used are respectively shown in figures 1-4 according to the I-MR (single value range control chart) of the date.
TABLE 1
As can be seen from table 1, the use of the control method of the present invention can significantly reduce steam coal consumption; as can be seen from fig. 1, the pressure movement of the steam drum is extremely reduced from 0.2305 to 0.1157 by 50% after the invention is put into use; as can be seen from FIG. 2, the liquid level movement of the steam drum is extremely reduced from 13.3 to 9.4 by 30% after the invention is put into use; as can be seen from FIG. 3, the negative pressure movement range of the outlet after the application of the invention is reduced from 148 to 80 by 45%; as can be seen from FIG. 4, the oxygen content movement range after the application of the invention is reduced from 1.23 to 0.739 by 40%; therefore, the invention is put into use, can ensure that the boiler always maintains higher combustion efficiency, reduces steam coal consumption, optimizes load adjustment and distribution by dynamically judging the performance difference of the boiler, takes account of respective furnace temperature constraint to limit the adjustment range of each boiler, ensures safety and prolongs the service life of the boiler.
The control target of the method is to balance the temperature of the hearth by changing the feeding amount of coal, the feeding amount of primary air, the feeding amount of secondary air and the like when the external load and the coal quality are changed, so that the pressure of the steam drum is maintained to be stable, when a plurality of boilers run, the boilers are preferentially judged through a model, and the master-slave boilers are distinguished according to the regulation reaction speed of the boilers. The safety, economy and stability of the boiler operation are ensured in the whole process. Therefore, the combustion process control of the circulating fluidized bed boiler is mainly controlled at two points, namely, the energy supply, namely, the heat provided by fuel combustion, is required to be suitable for the steam load of the boiler. Secondly, the pressure control of the steam pipe network is realized mainly by the same pressure as the pressure of the steam drum. Controlled variables of the system in the combustion process are: the flue gas oxygen content, the furnace pressure, the furnace temperature, the pipe network steam pressure, the primary air flow, the primary temperature-reducing valve position and the secondary temperature-reducing valve position; the operating variables are: primary fan variable frequency output, secondary fan variable frequency output, induced draft fan variable frequency output, coal feeder variable frequency output and primary temperature reduction water setting; the feedforward variables are: total coal yield. Which correspond to different optimization controls, respectively. The method comprises the steps of multi-furnace optimization control, coal quality change response optimization control, main steam temperature and hearth temperature optimization control, steam pipe network pressure optimization control, oxygen content optimization control and coal feeder optimization control. Different coupling relations exist between the two, if the primary air quantity is changed, the furnace temperature is changed, the main steam pressure is influenced, and the like, and the relation among the above variables is controlled by a control model in the combustion process.
The application adopts Aspen DMCplus multivariable model predictive control. The multivariable predictive control takes the oxygen content of flue gas, the pressure of a hearth, the pressure difference of the hearth, the pressure of a steam pipe network, the temperature of the lower layer of the bed temperature, the flow of primary air, the valve position of primary temperature-reducing water and the valve position of secondary temperature-reducing water as controlled variables; the variable frequency output of the primary fan, the variable frequency output of the secondary fan, the variable frequency output of the induced draft fan, the variable frequency output of the coal feeder, the variable frequency output of the slag extractor and the temperature setting of the primary desuperheating water are used as operation variables; the total coal yield, primary fan current and secondary fan current are used as feedforward variables. Predicting the change of a controlled variable from the current time to the steady state time, using the current value of the controlled variable, the current value of the operating variable, the current value of the feedforward variable and the predicted value of the controlled variable in the upper period as input data of a prediction model to obtain the predicted value of the controlled variable, obtaining the operating variable according to the predicted value of the controlled variable as output data, and obtaining the actual value of the controlled variable according to the operating variable. And comparing the current actual value of the controlled variable with the predicted value, and correcting the predicted result by using the deviation of the current actual value and the predicted value. Thus, the predicted value of each period is ensured to be corrected, and feedback information is provided for the intelligent controller. The intelligent controller determines the effect of the changes in the manipulated and feedforward variables on the controlled variable and continues to correct the predicted value. And the intelligent controller obtains a series of predictive correction values of each controlled variable in the whole steady state time according to the calculation result of the predictive model.
And calculating to obtain target values of the operation variable and the controlled variable by taking the predicted correction value of the controlled variable, the upper limit value and the lower limit value of the operation variable, the upper limit value and the lower limit value of the controlled variable, the feasibility stage objective function in the model, the economic optimization stage objective function and the set value of the operation variable as input data of steady-state optimization control.
At each sampling moment, the intelligent controller calculates the control action of the operation variable so as to minimize the error between the controlled variable and the expected value in a certain time interval in the future, and realizes the dynamic optimization control of the multivariable model predictive control. Table 2 is the model structure of the intelligent control system of the circulating fluidized bed coal-fired boiler, and FIG. 5 is a matrix of the model of the controller of the intelligent control system of the circulating fluidized bed coal-fired boiler.
Table 2 model structure of single-boiler intelligent control system of circulating fluidized bed coal-fired boiler
The coal quality change responds to the optimization control, the coal quality change can be firstly represented on the change of the temperature and the oxygen concentration of the controlled variable hearth, the intelligent controller judges the fluctuation of the heat value through predicting the model hearth temperature and the accumulated error between the oxygen concentration prediction and the actual value (namely through the real-time data change trend), the gain of the coal feeding to the pressure is properly adjusted when the heat value is increased, the gain of the coal feeding to the pressure is reduced when the heat value is reduced, and therefore the stability of the hearth temperature and the steam drum pressure of the boiler is maintained, and the boiler is always in the optimal running state.
The main steam temperature and the hearth temperature are optimally controlled, and the main steam temperature is a very important parameter which influences the power generation efficiency of the steam turbine and the protection of equipment. The temperature control of the current steam is to adjust the temperature of the main steam by adjusting the valves of the first-stage desuperheating water and the second-stage desuperheating water. The desuperheating water may cause a decrease in boiler efficiency. Meanwhile, the coal feeding amount, the air quantity and the water feeding amount can influence the temperature of the main steam. The coal feeding quantity and the air quantity change mainly react on the temperature change of the hearth at first, and the influence on the temperature of main steam is lagged.
The primary air pressure variable frequency output, the water supply valve position and the primary temperature reduction valve position are used as operation variables, the hearth temperature and the temperature of main steam are used as multivariable prediction control models related to controlled variables, and related operation variables such as the pressure of the primary air, the water supply flow or a temperature controller of the primary temperature reduction water are automatically adjusted according to different conditions, so that the pressure of the main steam is kept to fluctuate within a small range, the hearth temperature is controlled within a reasonable range, and the use of the temperature reduction water is reduced. The conversion efficiency of the boiler is improved, and the energy consumption is reduced. Meanwhile, the temperature of the main steam tends to be constant, so that the consumption of the heat-reducing water is reduced. This also reduces the temperature of the steam produced by the boiler while maintaining a reasonable temperature in the furnace.
The oxygen content is optimally controlled, and the oxygen content is controlled by the change of the boiler load, the change of the coal quality, the change of primary air and the change of secondary air. The level of oxygen affects the efficiency of the boiler. The oxygen content requirements for combustion are different in steady state and dynamic conditions, and combustion needs to be higher when the load is increased, so that the oxygen content control needs to be targeted at different target values under different working conditions. When the coal-fired steam boiler is in stable operation (model automatic identification), the load is stable and unchanged, the oxygen content in the boiler is controlled at a target value by adjusting the secondary air through the control of a predictive model related to the secondary air and the oxygen content. When the boiler is in a dynamic process of load change (model automatic identification), the primary air and the secondary air are controlled and regulated through the prediction models related to the load, the primary air, the secondary air and the oxygen content, so that the stable and optimal control of the oxygen content of the boiler is realized, and the current load change is matched with the oxygen content.
And (3) optimally controlling the coal feeder, establishing a model related to primary air, secondary air, the speed of the coal feeder, the slag discharge amount, main steam pressure, load and hearth temperature, and automatically adjusting the coal feeding amount of the coal feeder according to the load requirement to maintain the boiler within a reasonable pressure and hearth temperature range.
As shown in fig. 6 and 7, which are multivariable predictive model control automatic control logic diagrams, the specific steps are as follows:
taking the oxygen content of flue gas, the pressure of a hearth, the pressure difference of the hearth, the pressure of a steam pipe network, the lower layer temperature of bed temperature, the flow of primary air, a primary temperature-reducing valve position and a secondary temperature-reducing valve position as controlled variables; the variable frequency output of the primary fan, the variable frequency output of the secondary fan, the variable frequency output of the induced draft fan, the variable frequency output of the coal feeder, the variable frequency output of the slag extractor and the temperature setting of the primary desuperheating water are used as operation variables; the total coal yield, primary fan current and secondary fan current are used as feedforward variables. Predicting the change of a controlled variable from the current time to the steady state time, using the current value of the controlled variable, the current value of the operating variable, the current value of the feedforward variable and the predicted value of the controlled variable in the upper period as input data of a prediction model to obtain the predicted value of the controlled variable, obtaining the operating variable according to the predicted value of the controlled variable as output data, and obtaining the actual value of the controlled variable according to the operating variable. And comparing the current actual value of the controlled variable with the predicted value, and correcting the predicted result by using the deviation of the current actual value and the predicted value. Thus, the predicted value of each period is ensured to be corrected, and feedback information is provided for the intelligent controller. The intelligent controller determines the effect of the changes in the manipulated and feedforward variables on the controlled variable and continues to correct the predicted value. And the intelligent controller obtains a series of predictive correction values of each controlled variable in the whole steady state time according to the calculation result of the predictive model.
And calculating to obtain target values of the operation variable and the controlled variable by taking the predicted correction value of the controlled variable, the upper limit value and the lower limit value of the operation variable, the upper limit value and the lower limit value of the controlled variable, the feasibility stage objective function in the model, the economic optimization stage objective function and the set value of the operation variable as input data of steady-state optimization control.
At each sampling moment, the intelligent controller calculates the control action of the operation variable so as to minimize the error between the controlled variable and the expected value in a certain time interval in the future, and realizes the dynamic optimization control of the multivariable model predictive control.
The working principle of the embodiment of the application provides a complete tool set by means of a DMC (dynamic matrix control) system so as to meet the design requirement of an intelligent control system of the coal-fired boiler. The intelligent control system developed on the basis integrates deep understanding of the flow process into an intelligent controller, a human brain mechanism is embedded into the system, so that an APC controller which can only realize stable operation originally has the function of process optimization, is the unity of an optimal process engineer and an optimal operator, not only carries out 24X 7 annual non-break nursing on the device, but also carries out 24X 7 annual non-break process optimization on the device, thereby carrying out global dynamic prediction and optimization control on the future state of the production device in real time by means of a multi-objective and multi-variable model based on the control technology of model prediction, and improving and stabilizing the product quality, improving the yield, reducing the consumption and creating benefits on the basis of ensuring the safety.
The invention realizes the intelligent control of the circulating fluidized bed coal-fired boiler: firstly, according to the working condition and load change of the boiler, and according to the set model of the intelligent controller, a certain boiler or a plurality of boilers are judged by self to be regulated preferentially, namely, as the artificial judgment method, the boiler to be regulated and other boilers are regulated to maintain stable load, and then the control is carried out according to a single boiler controller. In the working process, the control loop of the original boiler is not changed, only reasonable and appropriate operation of the boiler is realized, and the working condition judgment can be carried out in advance according to the change trend of each parameter, so that the hysteresis regulation of the original control method is overcome.
In view of the foregoing, it should be appreciated that any combination of the various embodiments of the invention can be made without departing from the spirit of the invention; within the scope of the technical idea of the invention, any combination of various simple modifications and different embodiments of the technical proposal without departing from the inventive idea of the invention should be within the scope of the invention.
Claims (2)
1. An intelligent control method for a circulating fluidized bed coal-fired boiler is characterized by comprising the following steps:
setting a basic load, performing coal type compensation by the intelligent controller through the DCS according to the basic load, and then performing coal feeding quantity setting and coal feeding speed setting by the intelligent controller;
the intelligent controller performs gain compensation through the DCS according to the coal feeding quantity setting and the coal feeding speed setting;
the intelligent controller sends the set coal feeding amount and the set coal feeding speed to the DCS, the DCS controls the coal feeder through the received coal feeding amount and the received coal feeding speed, and when the boiler system works, the DCS collects and stores real-time data of combustion parameters and fan parameters of the boiler system; and
the intelligent controller sets the main steam pressure and flow of the boiler according to the real-time data and the historical data of the combustion parameters and the fan parameters, and performs multiple optimization control when the boiler operates;
wherein the plurality of optimization controls comprises:
multi-furnace optimization control, coal quality change response optimization control, main steam temperature and hearth temperature optimization control, oxygen content optimization control and coal feeder optimization control;
the specific method for optimizing and controlling the multiple furnaces comprises the following steps: introducing a standby furnace signal, real-time values of the running efficiency of each furnace and optimal load intervals of each furnace as input signals, identifying and judging the use/standby conditions of the boilers through a model, and timely determining the master-slave boilers to carry out load adjustment;
the concrete method for optimizing and controlling the coal quality change response comprises the following steps:
the intelligent controller judges the fluctuation of the heat value through the accumulated errors between the prediction of the temperature and the oxygen concentration of the hearth and the actual value, and adjusts the coal feeding gain according to the heat value;
the combustion parameters comprise fluidized bed temperature and boiler furnace temperature, and the fan parameters comprise wind speeds, running time and wind quantity of a primary fan and a secondary fan;
the specific method for optimally controlling the main steam temperature and the hearth temperature comprises the following steps:
establishing a model of the relationship among the pressure of primary air, water supply, the temperature of a hearth and the temperature of main steam, and adjusting the pressure of primary air, the water supply flow or the control variable related to a temperature controller of primary desuperheating water according to different conditions, so that the pressure of the main steam can be kept to fluctuate within a small range, the temperature of the hearth can be controlled within a reasonable range, and the use of the desuperheating water can be reduced;
the specific method for optimizing and controlling the oxygen content comprises the following steps:
the steady-state boiler load is stable, a model with the relationship between the secondary air and the oxygen content is built, and the oxygen content in the boiler is controlled at a target value by adjusting the secondary air;
the dynamic boiler load changes, a model related to the load, primary air, secondary air and oxygen content is built, and the oxygen content in the boiler is optimized by adjusting the primary air and the secondary air;
the concrete method for optimizing and controlling the coal feeder comprises the following steps:
and establishing a model related to primary air, secondary air, the speed of the coal feeder, main steam pressure, load and hearth temperature, maintaining the boiler within a reasonable load, pressure and hearth temperature range, and adjusting the speed of the coal feeder to balance the coal feeding speed of the coal feeder.
2. The method of claim 1, wherein the method further comprises:
the method comprises the steps of running a monitoring system, establishing a timer, and monitoring communication between the DCS and an upper computer through the timer; when the communication is normal, the timer runs continuously and repeatedly; when communication is interrupted, the timer gives a corresponding flag.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011150510.9A CN112197262B (en) | 2020-10-24 | 2020-10-24 | Intelligent control method for circulating fluidized bed coal-fired boiler |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011150510.9A CN112197262B (en) | 2020-10-24 | 2020-10-24 | Intelligent control method for circulating fluidized bed coal-fired boiler |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112197262A CN112197262A (en) | 2021-01-08 |
| CN112197262B true CN112197262B (en) | 2023-06-27 |
Family
ID=74011033
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011150510.9A Active CN112197262B (en) | 2020-10-24 | 2020-10-24 | Intelligent control method for circulating fluidized bed coal-fired boiler |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112197262B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113485499B (en) * | 2021-08-26 | 2022-11-01 | 润电能源科学技术有限公司 | Coal feeding regulation and control method for coal quality working condition change |
| CN114992629B (en) * | 2022-07-28 | 2023-05-30 | 清云智通(北京)科技有限公司 | Combustion control system and method for circulating fluidized bed boiler |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1916492A (en) * | 2005-11-11 | 2007-02-21 | 南京科远控制工程有限公司 | Method for controlling optimized burning in circulating fluid bed boiler |
| CN101713536A (en) * | 2009-12-03 | 2010-05-26 | 太原理工大学 | Control method of combustion system of circulating fluidized bed boiler |
| CN101788809A (en) * | 2009-08-17 | 2010-07-28 | 杭州和利时自动化有限公司 | Coordinated control system (CCS) of large-size circulating fluidized bed boiler (CFBB) unit |
| CN102425807A (en) * | 2011-11-23 | 2012-04-25 | 华北电力大学（保定） | Combustion feedforward and feedback composite optimization controlling method for pulverized coal fired boiler |
| CN102799778A (en) * | 2012-07-16 | 2012-11-28 | 杭州电子科技大学 | Method for optimizing load distribution of boiler |
| CN103870877A (en) * | 2014-03-28 | 2014-06-18 | 西安西热控制技术有限公司 | System and method for intelligently controlling boiler combustion based on neural network |
| CN104197324A (en) * | 2014-09-24 | 2014-12-10 | 北京中科润东节能技术有限公司 | Combustion optimization regulating and controlling method and device of fluidized bed boiler |
| CN106587168A (en) * | 2016-12-22 | 2017-04-26 | 泸天化（集团）有限责任公司 | Iron pyrite FeS2 biogenic stimulant and preparation method thereof |
| CN106765022A (en) * | 2016-12-30 | 2017-05-31 | 江苏和隆优化能源科技有限公司 | The many stove coordination optimizing control systems of many criterions based on boiler efficiency |
| CN107230024A (en) * | 2017-06-13 | 2017-10-03 | 中国大唐集团科学技术研究院有限公司华东分公司 | A kind of blending method of thermal power generation |
| CN110260356A (en) * | 2019-07-15 | 2019-09-20 | 白海波 | A kind of energy-saving control method of fluidized-bed combustion boiler |
| CN110794775A (en) * | 2019-10-16 | 2020-02-14 | 北京华远意通热力科技股份有限公司 | Multi-boiler load intelligent control system and method |
| CN110848733A (en) * | 2020-01-15 | 2020-02-28 | 南京科远智慧科技集团股份有限公司 | Combustion optimization method based on coal quality on-line monitoring |
| CN111765445A (en) * | 2020-07-01 | 2020-10-13 | 河北工业大学 | Boiler on-line combustion optimization control method and system and computer equipment |
-
2020
- 2020-10-24 CN CN202011150510.9A patent/CN112197262B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1916492A (en) * | 2005-11-11 | 2007-02-21 | 南京科远控制工程有限公司 | Method for controlling optimized burning in circulating fluid bed boiler |
| CN101788809A (en) * | 2009-08-17 | 2010-07-28 | 杭州和利时自动化有限公司 | Coordinated control system (CCS) of large-size circulating fluidized bed boiler (CFBB) unit |
| CN101713536A (en) * | 2009-12-03 | 2010-05-26 | 太原理工大学 | Control method of combustion system of circulating fluidized bed boiler |
| CN102425807A (en) * | 2011-11-23 | 2012-04-25 | 华北电力大学（保定） | Combustion feedforward and feedback composite optimization controlling method for pulverized coal fired boiler |
| CN102799778A (en) * | 2012-07-16 | 2012-11-28 | 杭州电子科技大学 | Method for optimizing load distribution of boiler |
| CN103870877A (en) * | 2014-03-28 | 2014-06-18 | 西安西热控制技术有限公司 | System and method for intelligently controlling boiler combustion based on neural network |
| CN104197324A (en) * | 2014-09-24 | 2014-12-10 | 北京中科润东节能技术有限公司 | Combustion optimization regulating and controlling method and device of fluidized bed boiler |
| CN106587168A (en) * | 2016-12-22 | 2017-04-26 | 泸天化（集团）有限责任公司 | Iron pyrite FeS2 biogenic stimulant and preparation method thereof |
| CN106765022A (en) * | 2016-12-30 | 2017-05-31 | 江苏和隆优化能源科技有限公司 | The many stove coordination optimizing control systems of many criterions based on boiler efficiency |
| CN107230024A (en) * | 2017-06-13 | 2017-10-03 | 中国大唐集团科学技术研究院有限公司华东分公司 | A kind of blending method of thermal power generation |
| CN110260356A (en) * | 2019-07-15 | 2019-09-20 | 白海波 | A kind of energy-saving control method of fluidized-bed combustion boiler |
| CN110794775A (en) * | 2019-10-16 | 2020-02-14 | 北京华远意通热力科技股份有限公司 | Multi-boiler load intelligent control system and method |
| CN110848733A (en) * | 2020-01-15 | 2020-02-28 | 南京科远智慧科技集团股份有限公司 | Combustion optimization method based on coal quality on-line monitoring |
| CN111765445A (en) * | 2020-07-01 | 2020-10-13 | 河北工业大学 | Boiler on-line combustion optimization control method and system and computer equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112197262A (en) | 2021-01-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101556038B (en) | Optimization control system for stable operation and economical combustion of circulating fluidized-bed boiler | |
| CN101713536B (en) | Control method of combustion system of circulating fluidized bed boiler | |
| CN101338892B (en) | Thermal power unit reheated steam temperature control method | |
| CN112197262B (en) | Intelligent control method for circulating fluidized bed coal-fired boiler | |
| CN107420874B (en) | Ultra-supercritical thermal generator set coordination control system | |
| US20110224830A1 (en) | Control system for operation of a fossil fuel power generating unit | |
| CN105627356A (en) | Combustion optimization control system of metallurgical gas boiler | |
| CN202032549U (en) | Header pressure coordination control system for thermal power plant boiler | |
| CN104197324B (en) | Fluidized-bed combustion boiler burning optimization adjustment control method and device | |
| CN110260356B (en) | Energy-saving control method of fluidized bed boiler | |
| CN102629131A (en) | Coordinative control method for pulse-mode regulation of condensate flow to participate in frequency modulation | |
| CN107368049A (en) | The control method of coal-supplying amount under unit varying duty based on Power Plant DCS System | |
| CN103423740A (en) | System and method for automatically controlling combustion process of circulating fluidized bed boiler | |
| CN111963265B (en) | Method for coordinated control optimization of combustion process and unit of boiler for power generation | |
| CN111045321B (en) | Method for coordinately controlling embedded internal model controller under deep peak regulation | |
| CN108549231B (en) | Coordinated control method of combined heat and power generation unit integrating heat supply steam extraction regulation | |
| CN115419478A (en) | Optimized control method for steel mill gas power generation | |
| CN109595586B (en) | Combustion optimization method and system for preventing high-temperature corrosion of boiler based on CO online detection | |
| CN105202519A (en) | Frequency and peak load modulation all condition coordination control method of heat supply unit | |
| CN110347097A (en) | A kind of setting based on the power station IGCC Automatic Generation Control | |
| CN211316135U (en) | Automatic power generation control system of circulating fluidized bed unit | |
| CN202512382U (en) | Chain boiler burning rolling self-optimization - proportion integration differentiation (PID) compound control system | |
| CN113485499B (en) | Coal feeding regulation and control method for coal quality working condition change | |
| CN108332424B (en) | Automatic control method for hot water boiler | |
| CN114992629B (en) | Combustion control system and method for circulating fluidized bed boiler |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |