CN112197262A - Intelligent control method for coal-fired boiler of circulating fluidized bed - Google Patents
Intelligent control method for coal-fired boiler of circulating fluidized bed Download PDFInfo
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- CN112197262A CN112197262A CN202011150510.9A CN202011150510A CN112197262A CN 112197262 A CN112197262 A CN 112197262A CN 202011150510 A CN202011150510 A CN 202011150510A CN 112197262 A CN112197262 A CN 112197262A
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- 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
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- 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
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- 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
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- 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]
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Abstract
The utility model belongs to the technical field of boiler control, and discloses an intelligent control method for a coal-fired boiler of a circulating fluidized bed, which is a novel control method combined by combining the advanced process control technology of a DMC (dynamic matrix control) system based on PID control, can track the change of boiler efficiency, adjust the control strategy and realize the long-period optimization of the system; the optimization operation mode can be automatically selected according to the working condition, and the real-time optimization control of single-furnace and multi-furnace linkage is realized; the coal quality change and the downstream demand change are automatically identified, the load is quickly adjusted, and the steam quality is stabilized.
Description
Technical Field
The application relates to the technical field of boiler control, in particular to an intelligent control method for a coal-fired boiler of a circulating fluidized bed.
Background
Circulating fluidized bed coal fired boilers are complex control targets of multiple input/multiple output non-linearity, strong coupling and large hysteresis, and are now a manual open loop process, usually by feedback-based PID regulation. The following problems are often encountered in operating a circulating fluidized bed:
(1) frequent change of coal quality
Coal type change and co-combustion of boiler fuel are common phenomena, and even one kind of coal, the volatile matter and the heat value are not constant, and change along with storage time, weather conditions and the like. Therefore, coal quality changes always exist objectively. The change of the heat value of the coal cannot be measured in real time, and the change of the heat value can quickly cause the change of a hearth temperature field, so that the combustion efficiency of a boiler fluctuates, and the coal consumption is increased; in addition, the quality of the steam fluctuates, which affects the production operation of downstream devices.
(2) Rapid dynamic change of boiler
The boiler production process is rapid and dynamic, strong coupling exists among 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, the load change is rapidly responded, the requirement of a steam user is met, and energy is fully saved. The traditional DCS control mode based on feedback and feedforward control cannot fundamentally meet the requirements of quick response and global optimization, and inevitably causes fluctuation, difference and benefit loss caused by manual operation intervention process.
(3) Random variation of load
Because the influence of environmental factor such as low reaches device operation reason and weather, the difference in temperature round the clock to and the change of the heat demand that the oil change arouses, the demand of boiler low reaches is not constant to steam, and the boiler self that causes in addition factors such as coal quality change is undulant, and the load needs random adjustment to the boiler in order to satisfy the demand, because the intervention of human factor, can inevitably have following two problems: firstly, attention is not enough, adjustment is not timely, and the best opportunity is missed; secondly, the regulation level is limited, the optimal combustion efficiency is difficult to maintain, the steam quality fluctuation is large, the coal consumption is high, and the downstream operation is influenced.
(4) Boiler combined transport optimization space
As a common project, large enterprises adopt multi-boiler combined transportation for steam supply. Except for the load adjustment of each boiler, in order to meet the requirement of large change of steam consumption, an enterprise generally establishes a standby boiler and starts and stops the boiler according to the requirement. The start-stop process may not meet the requirement of sudden change of steam consumption in time, and due to different performance and load constraints, load distribution among multiple boilers may have unreasonable conditions of low efficiency, energy waste and the like. The method is faster, more stable and more efficient, on the basis of accurate energy balance of the whole plant, a boiler with good performance is selected as a load 'follow-up system', the load is adjusted at any time according to the requirement under the control of the intelligent APC, 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, if the operation of temperature reduction water is not fine enough, the trend of rising or falling of steam temperature is rapidly suppressed by adopting a method of adding water in excess in the traditional manual operation, then the process tends to be stable through several times of adjustment back, and the excessive adjustment mode inevitably causes heat loss; in addition, because of safety considerations, an operator usually controls the oxygen content of the boiler in a higher range, and during dynamic load adjustment, in order to meet the requirements, the operation action of the oxygen content is larger, so that 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, load change and the like and energy waste caused by high concentration of tail oxygen and the like existing in a circulating fluidized bed in the background technology, the intelligent control system solution developed based on the DMC (dynamic matrix control) system advanced process control technology enables a coal-fired steam boiler to respond in an optimal mode and at the fastest speed in the dynamic process of dealing with coal quality change, load adjustment and the like through single-furnace intelligent control and multi-furnace intelligent control optimization, replaces an operator to carry out system operation by an intelligent control system based on a model, overcomes the problems of hysteresis and fluctuation of a conventional regulation mode based on feedback, enables the boiler to keep higher combustion efficiency all the time, reduces steam coal consumption, optimizes load adjustment distribution through dynamic judgment of boiler performance difference, considers respective furnace temperature constraint to limit the regulation range of each boiler, ensure safety and prolong the service life of the boiler.
In order to achieve the purpose, the invention adopts the technical scheme that:
an intelligent control method for a circulating fluidized bed coal-fired boiler comprises the following steps:
setting a basic load, performing coal type compensation by the intelligent controller through a DCS according to the basic load, and then setting a coal feeding amount and a coal feeding speed by the intelligent controller;
the intelligent controller performs gain compensation through DCS according to the coal feeding amount 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 the DCS acquires and stores real-time data of combustion parameters and fan parameters of the boiler system when the boiler system works; 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 items of optimization control during the operation of the boiler.
Further, the combustion parameters comprise the temperature of a fluidized bed layer and the temperature of a boiler hearth, and the fan parameters comprise the wind speeds, the running time and the wind amounts of a primary fan and a secondary fan.
Further, the multiple optimization controls comprise:
the method comprises the following steps of 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 the optimal control of the multiple furnaces comprises the following steps:
introducing a standby boiler signal, a real-time value of the operation efficiency of each boiler and an optimal load interval of each boiler as input signals, identifying and judging the use/standby condition of the boiler through a model, and determining a main boiler and a slave boiler to carry out load regulation in time, so that the load can be regulated quickly as soon as possible on the premise of ensuring safety all the time.
Further, the specific method for response optimization control of coal quality change comprises the following steps:
the change of coal quality can be reflected on the temperature and oxygen concentration of a controlled variable hearth in the first time, the project indirectly adopts a method of correcting the accumulative deviation of the prediction and the actual value of a controlled variable model, the intelligent controller judges the fluctuation of a heat value through the accumulative error between the prediction and the actual value of the temperature and the oxygen concentration of the hearth, 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 kept to operate under the optimal condition all the time.
Further, the specific method for optimally controlling the main steam temperature and the furnace temperature comprises the following steps:
establishing a model of correlation between primary air pressure, feed water, hearth temperature and main steam temperature, adjusting the primary air pressure, feed water flow or related manipulated variables such as a temperature controller of primary desuperheating water and the like according to different conditions, keeping the pressure of the main steam fluctuating within a small range, controlling the hearth temperature within a reasonable range, and reducing the use of desuperheating water. 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 use amount of the desuperheating water is reduced, so that the temperature of the steam generated by the boiler can be reduced, a reasonable temperature is maintained in a hearth, the production of oxynitride is reduced, and the heat dissipated to the ambient air by the boiler is reduced. Thus, the thermal efficiency of the boiler can be improved, and the situation that coking is easily formed due to high temperature of the hearth can be reduced.
Further, the specific method for the optimal control of the oxygen content comprises the following steps:
and (3) stable load of the boiler, establishing a model of correlation between secondary air and oxygen content, and controlling the oxygen content in the boiler to be a target value by adjusting the secondary air.
Further, the specific method for the optimal control of the oxygen content comprises the following steps:
and (3) dynamically changing the load of the boiler, establishing a model related to the load, the primary air, the secondary air and the oxygen content, and optimizing the oxygen content in the boiler by adjusting the primary air and the secondary air. Due to the control of the oxygen concentration, the target is the lower limit approach. Thus, the oxygen concentration at the furnace exit 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 pulverized coal is burnt out, the generation of oxynitride is reduced, the temperature of flue gas is reduced, the heat loss brought away by the flue gas is reduced, and the efficiency of the boiler is improved.
Further, the specific method for the optimized control of the coal feeder comprises the following steps:
establishing a model of correlation between primary air, secondary air, the speed of a coal feeder, main steam pressure, load and hearth temperature, maintaining reasonable load, pressure and hearth temperature ranges of a boiler, and adjusting the speed of the coal feeder to balance the coal feeding speed of the coal feeder.
Further, the method further comprises:
operating the monitoring system, establishing a timer, and monitoring communication between the DCS and the upper computer through the timer; when the communication is normal, the upper computer advanced control system resets the timer in each operation period, so that the timer continuously operates in cycles; if the communication is interrupted, the reset signal of the upper computer cannot be sent, the timer will finally stop or exceed a certain range, so as to indicate the communication interruption and give a corresponding mark.
And ensuring the optimal state of system operation, and adopting an Aspen DMCplus multivariate model for prediction 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 bed temperature lower layer, primary air flow, a primary desuperheating water valve position and a secondary desuperheating water valve position are used as controlled variables; setting primary fan frequency conversion output, secondary fan frequency conversion output, induced fan frequency conversion output, coal feeder frequency conversion output, slag extractor frequency conversion output and primary desuperheating water temperature as operation variables; taking the total coal feeding amount, the primary fan current and the secondary fan current as feedforward variables; and predicting the change of the controlled variable from the current time to the steady-state time, taking the current value of the controlled variable, the current value of the manipulated variable, the current value of the feedforward variable and the predicted value of the controlled variable in the last period as input data of a prediction model to obtain the predicted value of the controlled variable, obtaining the manipulated variable as output data according to the predicted value of the controlled variable, and obtaining the actual value of the controlled variable according to the manipulated variable. And comparing the current actual value with the predicted value of the controlled variable, and using the deviation of the current actual value and the predicted value to correct the predicted result. Therefore, the predicted value of each period is guaranteed to be corrected, and feedback information is provided for the intelligent controller. The intelligent controller determines the effect of the changes in the manipulated variables and the feedforward variables on the controlled variables and continues to correct the predicted values. And the intelligent controller obtains a series of predicted corrective values of each controlled variable within the whole steady-state time according to the calculation result of the prediction model.
And calculating to obtain target values of the manipulated variables and the controlled variables by taking the predicted corrected values of the controlled variables, the upper and lower limit values of the manipulated variables, the upper and lower limit values of the controlled variables, the feasibility stage objective function in the model, the economic optimization stage objective function and the set values of the manipulated variables as input data of steady-state optimization control.
At each sampling moment, the intelligent controller calculates the control action of the operating variable so as to minimize the error of the controlled variable from the expected value in a certain time interval in the future, and the dynamic optimization control of the multivariable model prediction control is realized.
Compared with the prior art, the beneficial effect of this application is:
the method and the device do not need to change any setting and control loop in the original DCS in the implementation process. Under the steady state condition (namely, the load is stable, the downstream dosage is stable), the boiler stably operates by reasonably controlling the operation variables such as the coal feeding quantity, the primary air, the secondary air and the like. When the load of the boiler changes, the intelligent controller can automatically recognize (such as through a steam pressure change trend of a certain key point or other points), the intelligent controller can preferably select the boiler with excellent performance to adjust (judge that the boiler has the superiority through heat efficiency, the heat efficiency parameter can be calculated from the original DCS data, and some systems also directly provide the heat efficiency parameter), and the loads of other boilers are kept stable. And performing 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 through an intelligent controller.
The novel control method is formed by combining the advanced process control technology of a DMC (dynamic matrix control) system based on PID control, can track the change of boiler efficiency, adjusts a control strategy and realizes long-period optimization of the system; the optimization operation mode can be automatically selected according to the working condition, and the real-time optimization control of single-furnace and multi-furnace linkage is realized; the coal quality change and the downstream demand change are automatically identified, the load is quickly adjusted, and the steam quality is stabilized.
The intelligent control system solution is developed based on a DMC (dynamic matrix control) system advanced process control technology, through single-furnace intelligent control and multi-furnace intelligent control optimization, a coal-fired steam boiler can respond in an optimal mode and at the fastest speed in dynamic processes of dealing with coal quality change, load adjustment and the like, an intelligent control system replaces an operator to carry out system operation based on a model, the problems of hysteresis and fluctuation of a conventional adjustment mode based on feedback are solved, the boiler always keeps high combustion efficiency, steam coal consumption is reduced, load adjustment distribution is optimized through dynamic judgment of boiler performance difference, furnace temperature constraints of the boiler are considered, adjustment amplitude of each boiler is limited, safety is guaranteed, and service life of the boiler is prolonged.
Drawings
FIG. 1 is a graph showing the time-by-date I-MR (single value range control) of the drum pressures before and after a certain 75t/h coal-fired steam boiler using the embodiment 1 of the present invention;
FIG. 2 is a graph showing the time-by-date I-MR (single value range control chart) of drum liquid levels before and after a certain 75t/h coal-fired steam boiler according to example 1 of the present invention is put into operation;
FIG. 3 is a schematic view showing the time-by-date I-MR (single value range control chart) of the negative pressure at the front and rear outlets of a 75t/h coal-fired steam boiler using the embodiment 1 of the present invention;
FIG. 4 is a graph of the before and after oxygen content by date I-MR (single value range control chart) for a 75t/h coal-fired steam boiler using example 1 of the present invention;
FIG. 5 is 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 illustrate multivariable predictive model control logic of the present application;
FIG. 8 is a schematic diagram of a topology of an intelligent control system of the present application;
fig. 9 is a connection diagram of a conventional coal-fired steam boiler DCS of a circulating fluidized bed and the intelligent control process system of the present application.
Detailed Description
In order to better understand the technical solution of the present invention, the technical solution of the present invention will be further described with reference to the accompanying drawings 1-9 and the examples. The mode for carrying out the present invention includes, but is not limited to, the following examples, which are provided to illustrate the present invention but not to limit the scope of the present invention. Unless otherwise specified, the technical means used in the examples are conventional means well known to those skilled in the art. 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 is as follows: setting a basic load, performing coal type compensation by the intelligent controller through a DCS according to the basic load, and then setting a coal feeding amount and a coal feeding speed by the intelligent controller;
the second step is that: the intelligent controller performs gain compensation through DCS according to the coal feeding amount setting and the coal feeding speed setting;
the third step: the intelligent controller sends the set coal feeder parameters to the DCS through OPC (OLE for process control) 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, the combustion parameters comprise fluidized bed layer temperature and boiler hearth temperature, and the fan parameters comprise wind speed, running time and wind quantity of a primary fan and a secondary fan.
The 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 performing multiple optimization during the operation of the boiler. The multiple optimization includes: the method comprises the following steps of 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 of the application is communicated with the DCS through OPC (OLE for process control) and calls real-time data in a DCS database through the OPC and processes the real-time data, processed results are sent to the DCS through the OPC, and the DCS is used for optimizing and adjusting a boiler system.
The steam coal consumption before and after the 75t/h coal-fired steam boiler is put into use is shown in a table 1, and the drum pressure, the drum liquid level, the outlet negative pressure and the oxygen content before and after the boiler is put into use are respectively shown in a table 1-a table 4 according to the I-MR (single-value extreme 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 figure 1, the drum pressure movement after the invention is put into use is reduced to 0.1157 from 0.2305, which is reduced by 50%; as can be seen from figure 2, the movement range of the liquid level of the steam drum is reduced from 13.3 to 9.4 after the steam drum is put into use, and the movement range is reduced by 30 percent; as can be seen from FIG. 3, the movement range of the negative pressure at the outlet is reduced from 148 to 80 percent after the device is put into use, and the movement range is reduced by 45 percent; as can be seen from FIG. 4, the oxygen content shift range of the invention is reduced from 1.23 to 0.739, which is 40% lower; therefore, the invention can keep the boiler with higher combustion efficiency all the time, reduce the steam coal consumption, optimize the load adjustment distribution through the dynamic judgment of the boiler performance difference, limit the adjustment range of each boiler by considering the respective furnace temperature constraint, ensure the safety and prolong the service life of the boiler.
The control objective of the application is to balance the temperature of a hearth by changing the supply quantity of coal supply quantity, primary air, secondary air and the like when external load and coal quality change, so as to maintain stable pressure of a steam drum, preferentially judge a boiler passing model when multiple boilers run, and distinguish a master boiler from a slave boiler according to the regulation reaction speed of the boilers. The safety, the economical efficiency and the stability of the operation of the boiler are ensured in the whole process. Therefore, the control of the combustion process of the circulating fluidized bed boiler is mainly controlled by two points, namely the supply of energy, namely the heat provided by the combustion of fuel is required to be suitable for the steam load of the boiler. And secondly, the pressure control of the steam pipe network is realized mainly by the pressure of the steam drum. The controlled variables of the system in the combustion process are: the system comprises a flue gas oxygen content, a hearth pressure, a hearth temperature, a pipe network steam pressure, a primary air flow, a primary desuperheating water valve position and a secondary desuperheating water valve position; the manipulated variables are: primary fan frequency conversion output, secondary fan frequency conversion output, induced draft fan frequency conversion output, coal feeder frequency conversion output and primary desuperheating water setting; the feed forward variables are: the total coal amount. They correspond to different optimization controls, respectively. Namely, multi-furnace optimization control, coal quality change response optimization control, main steam temperature and hearth temperature optimization control, pressure optimization control of a steam pipe network, oxygen content optimization control and coal feeder optimization control. Different coupling relations exist among the two variables, if the primary air quantity is changed, the temperature of a hearth can be changed, the main steam pressure is influenced, and the like, and the relation among the variables is well controlled through a control model in the combustion process.
The application adopts Aspen DMCplus multivariate model prediction control. The multivariable predictive control takes the oxygen content of the flue gas, the pressure of a hearth, the pressure difference of the hearth, the pressure of a steam pipe network, the temperature of a bed temperature lower layer, the primary air flow, the primary desuperheating water valve position and the secondary desuperheating water valve position as controlled variables; setting primary fan frequency conversion output, secondary fan frequency conversion output, induced fan frequency conversion output, coal feeder frequency conversion output, slag extractor frequency conversion output and primary desuperheating water temperature as operation variables; the total coal feeding amount, the primary fan current and the secondary fan current are used as feedforward variables. And predicting the change of the controlled variable from the current time to the steady-state time, taking the current value of the controlled variable, the current value of the manipulated variable, the current value of the feedforward variable and the predicted value of the controlled variable in the last period as input data of a prediction model to obtain the predicted value of the controlled variable, obtaining the manipulated variable as output data according to the predicted value of the controlled variable, and obtaining the actual value of the controlled variable according to the manipulated variable. And comparing the current actual value with the predicted value of the controlled variable, and using the deviation of the current actual value and the predicted value to correct the predicted result. Therefore, the predicted value of each period is guaranteed to be corrected, and feedback information is provided for the intelligent controller. The intelligent controller determines the effect of the changes in the manipulated variables and the feedforward variables on the controlled variables and continues to correct the predicted values. And the intelligent controller obtains a series of predicted corrective values of each controlled variable within the whole steady-state time according to the calculation result of the prediction model.
And calculating to obtain target values of the manipulated variables and the controlled variables by taking the predicted corrected values of the controlled variables, the upper and lower limit values of the manipulated variables, the upper and lower limit values of the controlled variables, the feasibility stage objective function in the model, the economic optimization stage objective function and the set values of the manipulated variables as input data of steady-state optimization control.
At each sampling moment, the intelligent controller calculates the control action of the operating variable so as to minimize the error of the controlled variable from the expected value in a certain time interval in the future, and the dynamic optimization control of the multivariable model prediction control is realized. Table 2 shows the model structure of the intelligent control system of the circulating fluidized bed coal-fired boiler, and fig. 5 shows a single-furnace model matrix of the controller of the intelligent control system of the single-furnace 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 optimization control, the coal quality change can be the first time body on the change of the controlled variable hearth temperature and the oxygen concentration, the intelligent controller judges the fluctuation of the heat value through the accumulated error (namely the real-time data change trend) between the prediction of the model hearth temperature and the oxygen concentration and the actual value, the gain of the coal supply to the pressure is properly increased 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 hearth temperature and the steam pocket pressure are kept stable, 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 and influences the power generation efficiency of the steam turbine and the protection of equipment. The current temperature control of the steam is to adjust the temperature of the main steam by adjusting valves of the first-stage temperature-reducing water and the second-stage temperature-reducing water. Desuperheating water can cause a reduction in boiler efficiency. Meanwhile, the coal feeding amount, the air quantity and the water feeding amount also influence the temperature of the main steam. The coal feeding amount and the air volume change are mainly reflected on the temperature change of a hearth, and the influence on the temperature of main steam is lagged.
The multi-variable predictive control model takes primary air pressure variable frequency output, a water supply valve position and a primary desuperheating water valve position as operation variables, takes the temperature of a hearth and the temperature of main steam as related controlled variables, automatically adjusts the pressure of the primary air, the water supply flow or the temperature controller of the primary desuperheating water and other related manipulated variables according to different conditions, enables the pressure of the main steam to fluctuate within a small range, controls the temperature of the hearth within a reasonable range, and reduces the use of desuperheating water. 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 use amount of the temperature-reducing water is reduced. This also reduces the temperature of the steam generated by the boiler while maintaining a reasonable temperature in the furnace.
Oxygen content optimization control, changes of boiler load, coal quality, primary air and secondary air influence the control of oxygen content. The level of oxygen content affects the efficiency of the boiler. The oxygen content requirements of combustion are different between steady-state and dynamic states, and the combustion needs to be higher when the load is increased, so that the oxygen content control target needs to be set to 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 secondary air is controlled by a prediction model related to the oxygen content, and the oxygen content in the boiler is controlled at a target value by adjusting the secondary air. When the boiler is in a dynamic process of load change (model automatic identification), the primary air and the secondary air are controlled and adjusted through a prediction model associated with the load, the primary air, the secondary air and the oxygen content, the stable and optimized control of the oxygen content of the boiler is realized, and the current load change is matched with the oxygen content.
The method comprises the steps of coal feeder optimization control, building a model of correlation of primary air, secondary air, coal feeder speed, slag discharge amount, main steam pressure, load and hearth temperature, and automatically adjusting the coal feeding amount of the coal feeder to maintain a boiler in a reasonable pressure and hearth temperature range according to the load requirement.
As shown in fig. 6 and 7, which are multivariable predictive model control automatic control logic diagrams, the specific steps are as follows:
the oxygen content of the 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 bed temperature, primary air flow, a primary desuperheating water valve position and a secondary desuperheating water valve position are used as controlled variables; setting primary fan frequency conversion output, secondary fan frequency conversion output, induced fan frequency conversion output, coal feeder frequency conversion output, slag extractor frequency conversion output and primary desuperheating water temperature as operation variables; the total coal feeding amount, the primary fan current and the secondary fan current are used as feedforward variables. And predicting the change of the controlled variable from the current time to the steady-state time, taking the current value of the controlled variable, the current value of the manipulated variable, the current value of the feedforward variable and the predicted value of the controlled variable in the last period as input data of a prediction model to obtain the predicted value of the controlled variable, obtaining the manipulated variable as output data according to the predicted value of the controlled variable, and obtaining the actual value of the controlled variable according to the manipulated variable. And comparing the current actual value with the predicted value of the controlled variable, and using the deviation of the current actual value and the predicted value to correct the predicted result. Therefore, the predicted value of each period is guaranteed to be corrected, and feedback information is provided for the intelligent controller. The intelligent controller determines the effect of the changes in the manipulated variables and the feedforward variables on the controlled variables and continues to correct the predicted values. And the intelligent controller obtains a series of predicted corrective values of each controlled variable within the whole steady-state time according to the calculation result of the prediction model.
And calculating to obtain target values of the manipulated variables and the controlled variables by taking the predicted corrected values of the controlled variables, the upper and lower limit values of the manipulated variables, the upper and lower limit values of the controlled variables, the feasibility stage objective function in the model, the economic optimization stage objective function and the set values of the manipulated variables as input data of steady-state optimization control.
At each sampling moment, the intelligent controller calculates the control action of the operating variable so as to minimize the error of the controlled variable from the expected value in a certain time interval in the future, and the dynamic optimization control of the multivariable model prediction control is realized.
The working principle of the embodiment of the application provides a complete set of tools by means of a DMC (dynamic matrix control) system so as to meet the design requirements of the intelligent control system of the coal-fired boiler. The intelligent control system developed on the basis is characterized in that the deep understanding of the process technology is integrated in the intelligent controller, a human brain mechanism is embedded in the system, so that an APC controller which can only realize stable operation originally has a technology optimization function, and is a unified body of an optimal process engineer and an optimal operator, the device is not only cared continuously for 24 x 7 all the year, but also is subjected to continuous technology optimization for 24 x 7 all the year, and therefore, based on the control technology of model prediction, global dynamic prediction and optimization control are carried out on the future state of the production device in real time by means of a multi-objective and multi-variable model, on the basis of ensuring safety, the product quality is improved and stabilized, the yield is improved, the consumption is reduced, and benefits are created.
The invention realizes the intelligent control of the circulating fluidized bed coal-fired boiler as follows: firstly, a certain boiler or a plurality of boilers is/are judged and preferentially adjusted according to the working condition and load change of the boiler and the set model of the intelligent controller, namely, the boiler to be adjusted and the other boilers are adjusted to maintain stable load as the manual judgment method, and then the control is carried out according to a single boiler controller. In the working process, the control loop of the original boiler cannot be changed, only the reasonable and appropriate operation of the boiler is realized, and the working condition can be judged in advance according to the variation trend of each parameter, so that the lag adjustment of the original control method is overcome.
In summary, any combination of the various embodiments of the present invention without departing from the spirit of the present invention should be considered as the disclosure of the present invention; within the scope of the technical idea of the invention, any combination of various simple modifications and different embodiments of the technical solution without departing from the inventive idea of the present invention shall fall within the protection scope of the present invention.
Claims (10)
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 a DCS according to the basic load, and then setting a coal feeding amount and a coal feeding speed by the intelligent controller;
the intelligent controller performs gain compensation through DCS according to the coal feeding amount 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 the DCS acquires and stores real-time data of combustion parameters and fan parameters of the boiler system when the boiler system works; and
the intelligent controller sets the main steam pressure and flow of the boiler according to the real-time data and historical data of the combustion parameters and the fan parameters, and performs multiple items of optimization control during the operation of the boiler.
2. The method of claim 1, wherein the combustion parameters include fluidized bed temperature, boiler furnace temperature, and the fan parameters include primary fan and secondary fan wind speed, run time, and wind volume.
3. The method of claim 1, wherein the plurality of optimization controls comprises:
the method comprises the following steps of 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.
4. The method of claim 3, wherein the specific method for the optimal control of the multiple furnaces comprises: introducing a standby boiler signal, a real-time value of the operating efficiency of each boiler and an optimal load interval of each boiler as input signals, identifying and judging the use/standby condition of the boiler through a model, and determining a main boiler and a slave boiler to carry out load regulation in time.
5. The method of claim 3, wherein the specific method of coal quality change response optimization control comprises:
the intelligent controller judges the fluctuation of the heat value through the accumulated error between the prediction of the temperature and the oxygen concentration of the hearth and the actual value, and adjusts the medium feeding gain according to the heat value.
6. The method of claim 3, wherein the specific method for the optimal control of the main steam temperature and the furnace temperature comprises the following steps:
establishing a model of correlation between primary air pressure, feed water, hearth temperature and main steam temperature, adjusting the primary air pressure, feed water flow or related manipulated variables such as a temperature controller of primary desuperheating water and the like according to different conditions, keeping the pressure of the main steam fluctuating within a small range, controlling the hearth temperature within a reasonable range, and reducing the use of desuperheating water.
7. The method of claim 3, wherein the specific method for the optimal control of the oxygen content comprises:
and (3) stable load of the boiler, establishing a model of correlation between secondary air and oxygen content, and controlling the oxygen content in the boiler to be a target value by adjusting the secondary air.
8. The method of claim 3, wherein the specific method for the optimal control of the oxygen content comprises:
and (3) dynamically changing the load of the boiler, establishing a model related to the load, the primary air, the secondary air and the oxygen content, and optimizing the oxygen content in the boiler by adjusting the primary air and the secondary air.
9. The method of claim 3, wherein the specific method of optimizing control of the coal feeder comprises:
establishing a model of correlation between primary air, secondary air, the speed of a coal feeder, main steam pressure, load and hearth temperature, maintaining reasonable load, pressure and hearth temperature ranges of a boiler, and adjusting the speed of the coal feeder to balance the coal feeding speed of the coal feeder.
10. The method of claim 1, further comprising:
operating the monitoring system, establishing a timer, and monitoring communication between the DCS and the upper computer through the timer; when the communication is normal, the timer runs repeatedly and continuously; when the communication is interrupted, the timer gives a corresponding flag.
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