1. An intelligent control method for purifying and recycling furnace flue gas is characterized in that: the method comprises the following specific steps:

1) establishing an RD valve control system model based on sliding mode variable structure control;

2) the method comprises the steps of identifying flue gas parameters at a furnace mouth on line to obtain an input value, transmitting the input value to a PLC (programmable logic controller), converting the input value into an actual differential pressure value by the PLC, and obtaining an opening set value of an RD valve by the actual differential pressure value through an RD valve control system model;

3) and transmitting the opening set value of the RD valve to an actuating mechanism to automatically adjust the differential pressure control loop.

2. The intelligent control method for purifying and recycling the furnace flue gas as claimed in claim 1, wherein: in the step 1), an impulse response method is used for identifying experiments, a parameter estimation model based on a least square method is established, and an object transfer function model is obtained.

3. The intelligent control method for purifying and recycling the furnace flue gas as claimed in claim 2, characterized in that: the RD valve control system model in the step 1) is an object transfer function model, and specifically comprises the following steps:

4. the intelligent control method for purifying and recycling the furnace flue gas as claimed in claim 3, wherein: converting the object transfer function into a state space, wherein the expression is as follows:

C＝[1 0](ii) a In the formula, X is an input state quantity, and Y is an output state quantity.

5. The intelligent control method for purifying and recycling the furnace flue gas as claimed in claim 1, wherein: the input value in the step 2) is the air pressure at the furnace mouth.

6. The intelligent control method for purifying and recycling the furnace flue gas as claimed in claim 1, wherein: and (3) adopting a fuzzy RBF neural network to identify a mathematical model of the furnace mouth differential pressure and the CO concentration on line.

7. The utility model provides a converter gas cleaning retrieves intelligent control system which characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,

the differential pressure adjusting device comprises an adjuster and a nonlinear compensator, wherein the adjuster obtains a set value through calculation and transmits the set value to the nonlinear compensator;

the smoke gas flow regulating unit comprises an actuating mechanism and an RD valve, the electric signal of the nonlinear compensator is compared with the difference value of the position feedback signal of the RD valve, and then the difference value is input into the actuating mechanism, and the opening degree of the RD valve is regulated by the actuating structure;

a controller converting the measured value into an actual differential pressure value;

and the differential pressure transmitter transmits the air pressure electric signal of the measured value to the controller, and the controller transmits the actual differential pressure value to the regulator.

8. The intelligent control system for purifying and recycling converter flue gas according to claim 7, characterized in that: the device also comprises a first angular displacement sensor, and the opening value of the RD valve is obtained and transmitted to the controller.

9. The intelligent control system for purifying and recycling converter flue gas according to claim 7, characterized in that: the smoke transmission pipeline is connected with the differential pressure transmitter through a pressure guide pipe.

10. The intelligent control system for purifying and recycling converter flue gas according to claim 7, characterized in that: the flue gas transmission pipeline is characterized by further comprising a fan, the regulator is connected with the fan, and the fan is arranged at a furnace mouth of the flue gas transmission pipeline.

Background

The information in this background section is only for enhancement of understanding of the general background of the invention and is not necessarily to be construed as an admission or any form of suggestion that this information forms the prior art that is already known to a person of ordinary skill in the art.

The Recovery of Converter flue Gas by an Oxygen Converter Gas Recovery (OG) method is a key technology in Converter steelmaking technology, and plays an important role in realizing negative energy steelmaking, reducing production cost, saving energy, protecting environment and improving economic benefits of enterprises.

At present, a converter flue gas purification and recovery system mostly adopts a PID control algorithm, an electro-hydraulic valve is controlled through hydraulic servo amplification according to a converter mouth differential pressure signal, and the opening degree of an RD valve is controlled through a hydraulic execution mechanism, so that the differential pressure of a converter mouth smoke hood is adjusted.

Because converter gas cleaning retrieves control system is big inertia, big hysteresis, nonlinear complex system, and the parameter is changeable, and it is great to carry out accurate quick control degree of difficulty to it, after the operation of several years, because the uncertainty of system parameter change and operating mode for the control parameter of original design no longer adapts to actual system, and the control effect can not satisfy actual requirement, and the regulating effect is unsatisfactory, and response speed is slower, causes the fluctuation of furnace mouth flue gas pressure great, and the furnace mouth appears a large amount of flames and emits the phenomenon outward. The skirt cover can be damaged, the flue gas purification effect is poor, the environment is polluted due to the large amount of overflow of the flue gas, the flue gas purification and dust removal effects of the converter are seriously influenced, the recovery rate and the heat productivity of the converter gas are reduced, and the purposes of optimizing and controlling the production process and improving the energy-saving and emission-reducing effects of the gas recovery process are difficult to achieve. With the continuous progress of control technology, various intelligent control technologies such as adaptive control, parameter self-tuning control, neural network and the like are gradually mature and are applied to actual control systems more and more. Meanwhile, with the development of society, the requirements for energy conservation, environmental protection, efficiency improvement and consumption reduction are higher and higher.

Disclosure of Invention

Aiming at the problems in the prior art, the invention aims to provide an intelligent control system and a control method for purifying and recycling converter flue gas. By combining a multi-mode variable structure intelligent control method, the intelligent regulation algorithm of the venturi orifice RD valve can be better solved; the improvement of the control stability and the response speed of the smoke at the furnace mouth solves the problems of the response hysteresis and the instability of the RD valve.

In order to solve the technical problems, the technical scheme of the invention is as follows:

in a first aspect, the invention provides an intelligent control method for purifying and recycling converter flue gas, which comprises the following specific steps:

1) establishing an RD valve control system model based on sliding mode variable structure control;

2) the method comprises the steps of identifying flue gas parameters at a furnace mouth on line to obtain an input value, transmitting the input value to a PLC (programmable logic controller), converting the input value into an actual differential pressure value by the PLC, and obtaining an opening set value of an RD valve by the actual differential pressure value through an RD valve control system model;

3) and transmitting the opening set value of the RD valve to an actuating mechanism to automatically adjust the differential pressure control loop.

The system model established based on the sliding mode variable structure improves the response speed of the system, and avoids the phenomena that the flue gas pressure fluctuation at the furnace mouth is large and a large amount of flame emits outwards.

And identifying the furnace mouth differential pressure by adopting a nonlinear PID neural network through establishing a system model of the furnace mouth differential pressure. The dynamic characteristic of the change of the furnace mouth differential pressure can be mastered, and the stability of the output value is better than that of the output value obtained by the conventional PID control method (namely the conventional proportional-integral-derivative control method).

In a second aspect, the invention provides an intelligent control system for purifying and recycling converter flue gas, comprising,

the differential pressure adjusting device comprises an adjuster and a nonlinear compensator, wherein the adjuster obtains a set value through calculation and transmits the set value to the nonlinear compensator;

the smoke gas flow regulating unit comprises an actuating mechanism and an RD valve, the electric signal of the nonlinear compensator is compared with the difference value of the position feedback signal of the RD valve, and then the difference value is input into the actuating mechanism, and the opening degree of the RD valve is regulated by the actuating structure;

a controller converting the measured value into an actual differential pressure value;

and the differential pressure transmitter transmits the air pressure electric signal of the measured value to the controller, and the controller transmits the actual differential pressure value to the regulator.

The differential pressure value is obtained through the controller, the set value is obtained through calculation of the regulator, and then the opening degree of the RD valve of the smoke opening is realized through the cooperation of signal conversion, an actuating mechanism and the like. Thereby realizing the accurate control of the flue gas at the furnace mouth.

One or more technical schemes of the invention have the following beneficial effects:

(1) aiming at the problems in the converter flue gas purification control process, the system analyzes the process of converter flue gas purification and coal gas recovery, innovatively applies the sliding mode variable structure intelligent control technology with strong robustness to the flue gas purification recovery system, designs and develops a furnace mouth pressure control model, develops a corresponding control algorithm, and designs and realizes a control system based on approach rate and interference observation compensation. Compared with the conventional PID control, the slip-form control has the advantages that the differential pressure fluctuation of the furnace mouth is obviously reduced, particularly, the furnace has violent reaction in a converting period and the gas production rate is increased steeply, and the differential pressure fluctuation of the furnace mouth is not changed greatly due to timely control of the flue gas flow, so that the coal gas recovery process is stabilized, and the recovery amount is increased.

(2) In the invention, a parameter estimation model based on a least square method is created, and a mathematical model of a furnace mouth differential pressure control system is identified, so that the control precision is improved by nearly 3 times, and the combustion rate of the gas at the furnace mouth is controlled within 10%.

(3) The fuzzy RBF neural network is used for identifying a mathematical model between the furnace mouth pressure difference and the CO concentration on line, the set value of a pressure difference control loop is adjusted in real time according to the identification model, the control system tracks the set value, and the furnace mouth pressure difference is controlled to be close to the set value, so that the effects of obviously improving the CO concentration and the coal gas recovery quality are achieved, the CO concentration of the recovered coal gas and the heat value of the recovered coal gas are improved, and the fuzzy RBF neural network is put into practical application, wherein the two indexes are respectively improved to be about 1680kCal/m3 and 55 percent from about 1450kCal/m3 and 47 percent.

(4) On the basis of realizing micro-differential pressure control and improving the stability of the recovery process, the invention provides an intelligent optimization control method based on CO concentration improvement, so that a large amount of CO reburning and surge in the smelting process are avoided, the molten steel smelting process is stabilized, the carbon content and the temperature of the target molten steel are reached as far as possible at the blowing end point, the blowing hit rate is improved, the smelting period is shortened, and the consumption is reduced; thereby improving the CO concentration by more than 10 percent.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the invention and not to limit the invention.

FIG. 1 is a schematic structural diagram of a converter flue gas purification recovery micro-differential pressure intelligent control device of the present invention;

FIG. 2 is a comparison of the pressure difference trend of a sliding mode control furnace mouth;

FIG. 3 is a comparison graph of the pressure difference trend of a neural network PID control furnace mouth;

FIG. 4 is an optimized control scheme for increasing CO concentration;

FIG. 5 is an OG system overall monitoring screen;

the device comprises a differential pressure adjusting device 1, a flue gas flow adjusting unit 2, a differential pressure transmitter 3, a differential pressure transmitter 4, a regulator 5, a nonlinear compensator 6, an actuating mechanism 7, an RD valve 8, a flue gas transmission pipeline 9, a fan 10, a first angular displacement sensor 11, a second angular displacement sensor 12 and a display instrument.

Detailed Description

It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.

In a first aspect, the invention provides an intelligent control method for purifying and recycling converter flue gas, which comprises the following specific steps:

1) establishing an RD valve control system model based on sliding mode variable structure control;

2) the method comprises the steps of identifying flue gas parameters at a furnace mouth on line to obtain an input value, transmitting the input value to a PLC (programmable logic controller), converting the input value into an actual differential pressure value by the PLC, and obtaining an opening set value of an RD valve by the actual differential pressure value through an RD valve control system model;

3) and transmitting the opening set value of the RD valve to an actuating mechanism to automatically adjust the differential pressure control loop.

The system model established based on the sliding mode variable structure improves the response speed of the system, and avoids the phenomena that the flue gas pressure fluctuation at the furnace mouth is large and a large amount of flame emits outwards.

Through the establishment of a system model of the furnace mouth differential pressure, the dynamic characteristic of the change of the furnace mouth differential pressure can be mastered, and the stability of the output value obtained by the method is better than that obtained by a conventional PID control method (namely a conventional proportional-integral-derivative control method).

In some embodiments of the present invention, in step 1), an impulse response method identification experiment is used, and a parameter estimation model based on a least square method is established to obtain an object transfer function model.

The method for measuring the impulse response curve comprises the following steps: during converter blowing, the upper computer sends ｃA command to set the P-A to ｃA certain position. The micro differential pressure of the furnace mouth measured by the micro differential pressure transmitter is in a pressure range of-20 to 20Pa, after the system enters a stable state, the upper computer sends an instruction to artificially increase the set value by a range, and then the set value is immediately returned to the original set point, so that the interference on the system is completed. During the process, the data acquisition system records the change data of the valve opening and the furnace mouth pressure at the same time.

After the impulse response curve is obtained, the impulse response curve can be converted into a step response curve of the system through a linear system superposition principle, the dynamic characteristic of the system can be analyzed through the step response curve, and parameters in a system transfer function are obtained through a least square method.

The impulse response method is a method of representing a change characteristic of a differential pressure by using a time function.

In some embodiments of the present invention, the RD valve control system model in step 1) is an object transfer function model, specifically:

a least squares based identification of a parameter estimation model is created. And combining the mechanism modeling and the experimental modeling to obtain a hybrid model of the system. And estimating the structural parameters of the model by using an experimental modeling method. The resulting object transfer function is identified.

The invention utilizes the sliding mode variable structure control method to dynamically acquire, analyze and identify data of the furnace mouth differential pressure so as to overcome the uncertainty of the system and enhance the anti-interference capability. The control rate parameter is optimized, the control precision is improved, and the dynamic response quality of the control system is improved. The system can move up and down along the specified state track with small amplitude and high frequency, thereby achieving good dynamic quality.

In some embodiments of the invention, the object transfer function is converted into a state space, where the expression is:

C＝[1 0](ii) a In the formula, X is an input state quantity, and Y is an output state quantity.

A sliding mode variable structure control strategy with strong robustness is adopted to overcome the uncertainty of the system and enhance the anti-interference capability. For the 'buffeting' problem generated in sliding mode control application, the design solutions of a quasi-sliding mode controller, a sliding mode controller based on an approximation rule and a sliding mode controller based on interference observation compensation are discussed, and the effectiveness of the method is proved through simulation research.

The interference observation compensation sliding mode variable structure control technology is applied to a converter mouth micro-differential pressure control system, so that control rate parameters are optimized, control precision is improved, and dynamic response quality of the control system is improved. The system can move up and down along the specified state track with small amplitude and high frequency, thereby achieving good dynamic quality. And (4) implementing a variable-structure sliding mode control scheme by taking the furnace mouth differential pressure as a control target, and putting the variable-structure sliding mode control scheme into production and application.

In some embodiments of the invention, the input value in step 2) is the gas pressure at the furnace mouth.

In some embodiments of the invention, an online identification of the furnace mouth differential pressure is performed using a nonlinear PID neural network identifier.

On the basis of adopting the nonlinear PID neural network identifier to carry out online identification on the controlled object, the system has self-adaptability by adjusting the weight of the neural network controller in real time, thereby achieving the effective micro-differential pressure control effect.

The optimal control parameters of the objects in different stages are determined by tracking and identifying the control objects in real time by utilizing the self-learning capability of the neural network, and the control parameters of the PID controller are adjusted according to the optimal control parameters, so that the optimal control parameters and the optimal control performance are realized in the whole control stage.

The defects that the conventional PID control method is difficult to adapt to the variable system parameters and the disturbance suppression capacity is poor are overcome. The optimal control parameters of the objects in different stages are determined by tracking and identifying the control objects in real time by utilizing the self-learning capability of the neural network, and the control parameters of the PID controller are adjusted according to the optimal control parameters, so that the optimal control parameters and the optimal control performance are realized in the whole control stage, and a better control effect is achieved.

In some embodiments of the invention, a fuzzy RBF neural network is used to identify a mathematical model of the furnace mouth differential pressure and CO concentration online. And (3) identifying a mathematical model of differential pressure and CO concentration in the smelting process of the converter on line by adopting a fuzzy RBF neural network, wherein TDL represents a delay line with a tap. The online optimization part utilizes the obtained model to find the optimal set value of the differential pressure control loop on line so as to achieve the process index that the CO concentration (namely the heat value) is the highest. The control system automatically adjusts the differential pressure control loop according to the set value of online adjustment, thereby achieving the effect of improving the CO concentration and improving the CO concentration by more than 10 percent.

In a second aspect, the invention provides an intelligent control system for purifying and recycling converter flue gas, comprising,

the differential pressure adjusting device comprises an adjuster and a nonlinear compensator, wherein the adjuster obtains a set value through calculation and transmits the set value to the nonlinear compensator;

the smoke gas flow regulating unit comprises an actuating mechanism and an RD valve, the electric signal of the nonlinear compensator is compared with the difference value of the position feedback signal of the RD valve, and then the difference value is input into the actuating mechanism, and the opening degree of the RD valve is regulated by the actuating structure;

a controller converting the measured value into an actual differential pressure value;

and the differential pressure transmitter transmits the air pressure electric signal of the measured value to the controller, and the controller transmits the actual differential pressure value to the regulator.

In some embodiments of the present invention, a first angular displacement sensor is further included, and the RD valve opening value is obtained and transmitted to the controller.

In some embodiments of the invention, the device further comprises a flue gas transmission pipeline, and the flue gas transmission pipeline is connected with the differential pressure transmitter through a pressure guide pipe.

In some embodiments of the invention, the flue gas treatment device further comprises a fan, the regulator is connected with the fan, and the fan is arranged at the furnace mouth of the flue gas transmission pipeline.

The invention will be further illustrated by the following examples

Example 1

As shown in fig. 1, an intelligent control system for purifying and recycling converter flue gas comprises,

the differential pressure adjusting device 1 comprises an adjuster 4 and a nonlinear compensator 5, wherein the adjuster 4 obtains a set value through calculation and transmits the set value to the nonlinear compensator 5;

the smoke flow adjusting unit 2 comprises an actuating mechanism 6 and an RD valve 7, the electric signal of the nonlinear compensator 5 is compared with the difference value of the RD valve position feedback signal and then is input into the actuating mechanism 6, and the actuating mechanism 6 adjusts the opening degree of the RD valve;

a controller converting the measured value into an actual differential pressure value;

differential pressure transmitter 3, differential pressure transmitter 3 transmits the atmospheric pressure electrical signal of measurement to the controller, and the controller transmits the actual differential pressure value to regulator 4.

A first angular displacement sensor 10 is also included to obtain the RD valve opening value and communicate it to the controller. The differential pressure control device further comprises a second angular displacement sensor 11, the second angular displacement sensor 11 transmits signals to a display instrument 12, and the feedback opening degree value of the R-D valve plate is displayed remotely in the differential pressure control cabinet. In order to facilitate centralized operation and data management, all control parameters and running states of the system are transmitted to an upper computer by a PLC. Thus, the system not only can independently control and operate the R-D valve and related equipment, but also can display the equipment state and manually operate on a monitoring picture in a control room.

The device also comprises a flue gas transmission pipeline 8, and the flue gas transmission pipeline is connected with the differential pressure transmitter through a pressure guide pipe. The opening degree of the RD valve directly influences the smoke condition at the furnace mouth of the smoke transmission pipeline.

The flue gas treatment device further comprises a fan, the regulator is connected with the fan, and the fan 9 is arranged at a furnace mouth of the flue gas transmission pipeline.

In order to prevent the actuator from frequently acting and reduce the burden of the actuator, the opening of the R-D valve is fixed at a value and is not automatically adjusted in the time except the blowing period because no smoke is generated, and the fan 9 is kept in a low-speed operation state.

In the blowing period, since the R-D valve needs time for adjustment, the opening degree control signal is calculated once every 6 seconds and acts on the actuator to reduce the load of the actuator.

Example 2

The intelligent control method for purifying and recycling the converter flue gas by using the control system shown in figure 1 comprises the following steps:

(1) preprocessing field test data by using a Matlab simulation tool, and establishing a control system object transfer function model according to the preprocessed data:

converting the object transfer function model to a state space:

C＝[1 0]。

(2) and (3) conveying the furnace mouth differential pressure to a differential pressure transmitter through a pressure guide pipe, comparing a differential pressure value with a field atmospheric pressure signal, converting the differential pressure value into a 4-20 mA standard differential pressure electric signal DV, sending the measured value into a PLC (programmable logic controller) of an electric indoor control cabinet, converting the measured value into an actual differential pressure value in the PLC, comparing the actual differential pressure value with a differential pressure set value, generating a deviation signal delta E, and inputting the deviation signal delta E to a regulator.

(3) Adopting a fuzzy RBF neural network to identify a mathematical model of differential pressure and CO concentration in the smelting process of the converter on line;

(4) the regulator searches for the optimal set value of the differential pressure control loop through calculation of a state space model according to the deviation signal and converts the optimal set value into a 4-20 mA electric signal MV;

(5) the pressure regulator transmits the optimal set value of the furnace mouth differential pressure to the nonlinear compensator, the electric signal of the nonlinear compensator and the position signal of the angular displacement sensor 1 are transmitted to the controller, the difference values are compared and then input to the execution mechanism, and the execution mechanism adjusts the opening degree of the R-D valve, so that the furnace mouth differential pressure is adjusted.

The angular displacement sensor 1 is used to feed back the RD valve position signal.

In the embodiment 1, the regulator obtains a set value of a furnace mouth differential pressure by using a state space model, and obtains an accurate set value of the furnace mouth differential pressure by calculating based on a control model of a sliding mode variable structure, so that the control precision is improved by nearly 3 times, and the combustion rate of furnace mouth gas is controlled within 10%.

As shown in FIG. 2, it can be seen that the obtained furnace mouth differential pressure value is more stable by the control system model and the conventional PID control method, the fluctuation change is smaller in the process of time, the fluctuation range of the conventional PID control method is larger between 300 times and 500 times, and the fluctuation which is unstable up and down occurs at other times.

As shown in fig. 3, the nonlinear PID neural network control method of example 1 has a significantly reduced furnace-mouth differential pressure fluctuation compared to the conventional PID control method.

As shown in fig. 4, TDL represents a delay line with taps. And inputting the set differential pressure value into a mathematical model between the furnace mouth differential pressure and the CO concentration identified by the fuzzy RBF neural network to obtain a set differential pressure value. And (5) transmitting the set differential pressure value to an RD valve, and controlling the smoke gas volume at the furnace mouth to obtain the CO concentration. Then, the differential pressure value is identified on line by the fuzzy RBF neural network, and then the differential pressure value is compared with the CO concentration value, and the mathematical model is further updated.

Further, a PLC is used as a development platform to realize the network control function of the furnace mouth micro-differential pressure control system: the design, debugging and the like of the monitoring interface can be finished by using tools in the configuration software. Designing a plurality of monitoring pictures according to the process requirements of the OG system, wherein the monitoring pictures comprise: the OG system comprises a total monitoring picture (shown in figure 5), a gas cooling and collecting system, an OG fan gas path system, an OG fan oil path system, a gas recovery system and the like, and also comprises a real-time trend graph of various monitoring variables, an alarm display picture and the like.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.