CN108641733B - Closed-loop control method for concentration of CO in dry quenching circulating gas - Google Patents
Closed-loop control method for concentration of CO in dry quenching circulating gas Download PDFInfo
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- C—CHEMISTRY; METALLURGY
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Abstract
The invention discloses a closed-loop control method for the concentration of coke dry quenching circulating gas CO, which constructs a mathematical model of a closed-loop control system for the concentration of coke dry quenching circulating gas CO by analyzing the influence of key data such as circulating air quantity, an opening degree change value of an air inlet valve, a circulating gas CO concentration change value, system response time and the like on the concentration of coke dry quenching circulating gas CO, introduces a fuzzy PID control technology into the circulating gas CO concentration control system, develops an intelligent control system by taking the constructed mathematical model of the coke dry quenching circulating gas CO concentration control system based on mixed fuzzy PID as a core algorithm, thereby effectively changing the current situation that the traditional circulating gas CO concentration control mode needs to adopt manual control, realizing the automatic control of the system, controlling the CO concentration in a range near an ideal set value, the burning loss is reduced, the emission of CO2 is reduced, and the yield of dry quenching coke is improved, thereby realizing the control targets of yield increase, energy conservation and emission reduction.
Description
Technical Field
The invention relates to a closed-loop control method for the concentration of CO in dry quenching circulating gas.
Background
The coke dry quenching technology is a coke quenching method for cooling red coke by adopting inert gas, in the coke dry quenching process, the red coke is loaded from the top of a coke dry quenching furnace, low-temperature inert gas is blown into a red coke layer of a cooling chamber of the coke dry quenching furnace by a circulating fan to absorb sensible heat of the red coke, the cooled coke is dispatched from the bottom of the coke dry quenching furnace, high-temperature inert gas from an annular flue of the coke dry quenching furnace flows through a coke dry quenching boiler to carry out heat exchange, the boiler generates steam, the cooled inert gas is blown into the coke dry quenching furnace again by the circulating fan, and the inert gas is recycled in a closed system. The coke quality of the dry quenching is obviously improved compared with that of the wet quenching; the method has the advantages of fully utilizing the sensible heat of the red coke, saving energy and the like, so that the technology is expected to be popularized in the steel industry by more than 20 percent in five years in the future, and becomes one of the most important popularization technologies in the industry.
However, in the process of coke dry quenching production, a small amount of air is leaked into the negative pressure section of the gas circulation system, and the air contains 02And H20, CO is formed as it passes through the red coke layer, and the C0 concentration in the dry quenching furnace increases gradually as time progresses. When the dry quenching furnace is uncapped for coke charging, because the system is in a negative pressure section, part of air is sucked into the furnace and reacts with CO for combustion, so that the concentration of the CO is reduced; and after the red coke is filled, the concentration of CO in the furnace is rapidly increased by the CO pyrolyzed out due to the continuous separation of residual volatile components of the coke. When the concentration of CO exceeds the control standard, the furnace body has the danger of explosion, and a large amount of CO sprayed out during the coke charging process can poison people. Too low CO concentration increases coke burning and melting loss, reduces coke yield, and increases C02And (4) discharging. Therefore, it is important to control the CO concentration. However, because the concentration of CO is influenced by various factors, the whole production process is often complicated and changeable, and an accurate mathematical model is difficult to obtain, the conventional PID controller is difficult to adapt to the system, and the control performance index of the system is difficult to meet the requirement of production process control, so the CO concentration control mode in the existing dry quenching circulating gas is still a manual control mode, the system control precision is low, the burning loss rate is high, and C0 and CO are high in burning loss rate2The emission is also higher, and the human cost is also higher moreover, needs personnel to carry out corresponding operation according to the change of CO concentration at any time, has increased operating personnel's working strength. Chinese invention patent application no: 201710203619.6 discloses a method for controlling a dry quenching gas circulation system, which realizes automatic control by dividing the CO concentration into a plurality of sections and setting a corresponding control strategy on each section to ensure that the CO concentration falls in a safer section. However, since the dry quenching production process is very complicated and variable, and the control of the CO concentration is affected not only by the air introduction amount and the circulation gas air amount but also by multiple factors such as the coke charging process, it is difficult to obtain an accurate mathematical model, and thus the system can realize automatic control, but the system control accuracy is difficult to ensure.
Disclosure of Invention
Aiming at the defects of the prior art, the technical problems to be solved by the invention are as follows: the closed-loop control method for the concentration of the CO in the dry quenching circulating gas can improve the steady-state precision of a CO concentration control system and simultaneously avoid the phenomenon that the system greatly vibrates, thereby achieving the control purpose of improving the yield and reducing the emission of harmful substances.
The technical scheme adopted by the invention is as follows: a closed-loop control method for the concentration of CO in a dry quenching circulating gas comprises the following steps:
1) setting a picture set value C (set) of an ideal CO concentration value, namely CO concentration, through a monitoring picture, and then entering the step 2);
2) judging whether the automatic control mode of the concentration of the circulating gas CO is started or not, and if so, entering a step 3); if not, continuing to execute the step 2);
3) sampling a boiler inlet temperature signal, a circulating gas CO concentration actual value signal and a circulating air quantity signal, introducing an air valve opening degree signal, and then entering the step 4);
4) judging whether the inlet temperature of the boiler is higher than 960 ℃, if so, entering to execute the step 5); if not, entering to execute the step 6);
5) judging whether the actual value of the concentration of the CO in the circulating gas is larger than the set value of the picture, if so, reducing the concentration of the CO in the circulating gas in a mode of increasing the circulating air volume; then returning to the step 2); if not, entering to execute the step 6);
6) calculating a deviation value of the concentration of the CO in the circulating gas, judging whether the absolute value of the deviation value is larger than a switching set value mu, and if so, entering the step 7); if not, entering to execute the step 12); the specific structure of the deviation value algorithm of the circulating gas CO concentration is as follows:
e (k) ═ c (k) -c (set); wherein E (k): deviation value of circulating gas CO concentration at kth sampling time, c (k): actual value of the circulating gas CO concentration at the kth sampling time, c (set): a picture setting value of the concentration of the circulating gas CO;
7) converting the deviation value of the circulating gas CO concentration into a fuzzy subset of corresponding membership degree through a membership function, and then entering step 8);
8) taking the difference value of the concentration deviation values of the circulating gas CO in two continuous sampling periods as the variation rate of the concentration deviation value of the circulating gas CO, converting the difference value into a fuzzy subset of corresponding membership degrees through a membership function, and then entering the step 9); the specific structure of the deviation value change rate algorithm of the circulating gas CO concentration is as follows: v (k) ═ E (k) — E (k-1);
9) calculating a fuzzy control output value U determined by the deviation value of the CO concentration of the circulating gasDThen entering step 10);
10) calculating a fuzzy control output value U determined by the variation rate of the circulating gas CO concentration deviation valueGThen, go to step 11);
11) calculating a fuzzy control total output value U jointly determined by the circulating gas CO concentration deviation value and the circulating gas CO concentration deviation value change rateZThen put UZOutputting the given value of the opening degree of the valve for leading air to a valve controller, and finally returning to the step 2); the specific structure of the fuzzy control total output value is as follows: u shapeZ＝UD+UGIn the formula, UZ: the concentration of the circulating gas CO is subjected to fuzzy control on a total output value;
12): calculating an output value U (k) of a control algorithm of a system adjusted by a PID control algorithm, outputting the U (k) as a given value of the opening degree of an air introducing valve to a valve controller, and finally returning to the step 2); the specific structure of the output value of the PID control algorithm of the circulating gas CO concentration is
In the formula KD: a differential coefficient; kI: an integral coefficient; kp: a proportionality coefficient; u (k): and controlling the output value of the algorithm.
The further technical scheme of the invention is as follows: the specific structure of the algorithm for increasing the circulating air volume in the step 5) is as follows: f (k) ═ F (k-1) × 1.02; wherein, F (k): and circulating air volume.
The further technical scheme of the invention is as follows: in the closed-loop control method of the CO concentration, Siemens WINCC monitoring software is adopted to collect and record various related data.
Hair brushThe further technical scheme is as follows: step 7), the specific structure of the deviation value membership function algorithm of the circulating gas CO concentration is as follows:
in the formula, a: fractional difference of deviation value of CO concentration of circulating gas, NiIs a corresponding subset of the point values { -3a, -2a, -a, 0, a, 2a, 3a }, DiAnd the subset of the deviation value membership map { NB, NM, NS,0, PS, PM, PB } is the concentration of the circulating gas CO.
The further technical scheme of the invention is as follows: step 8) the specific structure of the membership function algorithm of the deviation value change rate of the circulating gas CO concentration is as follows:
wherein V (k): deviation value change rate of circulating gas CO concentration, b: fractional difference of deviation rate of CO concentration of circulating gas, LiIs a corresponding subset of the point values { -3b, -2b, -b, 0, b, 2b, 3b }, GiAnd (4) performing membership mapping subset { NBV, NMV, NSV,0, PSV, PMV, PBV } for change rate of CO concentration deviation value of circulating gas.
The further technical scheme of the invention is as follows: step 9) the specific structure of the fuzzy control output value of the circulating gas CO concentration deviation value is as follows:
in the formula of UD: fuzzy control output value, J, of deviation value of CO concentration of circulating gasiThe current weight coefficient of the cycle gas CO concentration deviation value fuzzy control is adopted.
The further technical scheme of the invention is as follows: step 10) the specific structure of the fuzzy control output value of the change rate of the concentration deviation value of the circulating gas CO is as follows:
in the formula of UG: fuzzy control output value, Q, of variation rate of CO concentration deviation value of circulating gasiThe coefficient is the current weight coefficient of the cycle gas CO concentration deviation value change rate fuzzy control.
The invention is provided withThe beneficial effects are that: the invention develops an intelligent control system by taking a mathematical model of a dry quenching circulating gas CO concentration control system based on mixed fuzzy PID (proportion integration differentiation) as a core algorithm through combining experimental research and theoretical analysis, thereby effectively solving the current situation that the traditional circulating gas CO concentration control mode needs to adopt manual control, realizing automatic control of the system, having higher control precision and higher system response speed, completely adapting to the requirements of load period and rapid change during coke charging, simultaneously reducing burning loss and reducing CO2The discharge of the coke dry quenching furnace improves the yield of the coke dry quenching furnace, thereby realizing the control targets of yield increase, energy conservation and emission reduction.
Drawings
FIG. 1 is an operational flow diagram of a closed loop control method of the dry quenching cycle gas CO concentration of the present invention;
FIG. 2 is a CO concentration curve for a conventional manual control;
FIG. 3 is a CO concentration curve controlled by the control method of the present invention.
Detailed Description
The technical characteristics of the closed loop control method for the dry quenching cycle gas CO concentration of the invention are further explained in the following by combining the attached drawings and the embodiment.
Example 1
A closed-loop control method for the concentration of CO in a dry quenching circulating gas comprises the following steps:
1) setting a picture set value C (set) of an ideal CO concentration value, namely CO concentration, through a monitoring picture, and then entering the step 2);
2) judging whether the automatic control mode of the concentration of the circulating gas CO is started or not, and if so, entering a step 3); if not, continuing to execute the step 2);
3) sampling a boiler inlet temperature signal, a circulating gas CO concentration actual value signal and a circulating air quantity signal, introducing an air valve opening degree signal, and then entering the step 4);
4) judging whether the inlet temperature of the boiler is higher than 960 ℃, if so, entering to execute the step 5); if not, entering to execute the step 6);
5) judging whether the actual value of the concentration of the CO in the circulating gas is larger than the set value of the picture, if so, reducing the concentration of the CO in the circulating gas in a mode of increasing the circulating air volume; then returning to the step 2); if not, entering to execute the step 6); the specific structure of the algorithm for increasing the circulating air volume is as follows:
f (k) ═ F (k-1) × 1.02; wherein, F (k): circulating air volume;
6) calculating a deviation value of the concentration of the CO in the circulating gas, judging whether the absolute value of the deviation value is larger than a switching set value mu, and if so, entering the step 7); if not, entering to execute the step 12); the specific structure of the deviation value algorithm of the circulating gas CO concentration is as follows:
e (k) ═ c (k) -c (set); wherein E (k): deviation value of circulating gas CO concentration at kth sampling time, c (k): actual value of the circulating gas CO concentration at the kth sampling time, c (set): a picture setting value of the concentration of the circulating gas CO;
7) converting the deviation value of the circulating gas CO concentration into a fuzzy subset of corresponding membership degree through a membership function, and then entering step 8); the specific structure of the deviation value membership function algorithm of the circulating gas CO concentration is as follows:
in the formula, a: fractional difference of deviation value of CO concentration of circulating gas, NiIs a corresponding subset of the point values { -3a, -2a, -a, 0, a, 2a, 3a }, DiA sub-set of degree-of-membership mapping { NB, NM, NS,0, PS, PM, PB } for the deviation value of the circulating gas CO concentration;
8) taking the difference value of the concentration deviation values of the circulating gas CO in two continuous sampling periods as the variation rate of the concentration deviation value of the circulating gas CO, converting the difference value into a fuzzy subset of corresponding membership degrees through a membership function, and then entering the step 9); the specific structure of the deviation value change rate algorithm of the circulating gas CO concentration is as follows: v (k) ═ E (k) — E (k-1); the specific structure of the membership function algorithm for the deviation value change rate of the circulating gas CO concentration is as follows:
wherein V (k): partial concentration of CO in the circulating gasDifference change rate, b: fractional difference of deviation rate of CO concentration of circulating gas, LiIs a corresponding subset of the point values { -3b, -2b, -b, 0, b, 2b, 3b }, GiThe change rate membership degree mapping subset { NBV, NMV, NSV,0, PSV, PMV, PBV } of the circulating gas CO concentration deviation value is adopted;
9) calculating a fuzzy control output value U determined by the deviation value of the CO concentration of the circulating gasDThen entering step 10); the specific structure of the fuzzy control output value of the concentration deviation value of the circulating gas CO is as follows:
in the formula of UD: fuzzy control output value, J, of deviation value of CO concentration of circulating gasiThe current weight coefficient is the cyclic gas CO concentration deviation value fuzzy control;
10) calculating a fuzzy control output value U determined by the variation rate of the circulating gas CO concentration deviation valueGThen, go to step 11); the specific structure of the fuzzy control output value of the change rate of the concentration deviation value of the circulating gas CO is as follows:
in the formula of UG: fuzzy control output value, Q, of variation rate of CO concentration deviation value of circulating gasiThe current weight coefficient is the cycle gas CO concentration deviation value change rate fuzzy control;
11) calculating a fuzzy control total output value U jointly determined by the circulating gas CO concentration deviation value and the circulating gas CO concentration deviation value change rateZThen put UZOutputting the given value of the opening degree of the valve for leading air to a valve controller, and finally returning to the step 2); the specific structure of the fuzzy control total output value is as follows: u shapeZ＝UD+UGIn the formula, UZ: the concentration of the circulating gas CO is subjected to fuzzy control on a total output value;
when the deviation value of the actual concentration of the circulating gas CO and the picture set value in the step 6) is larger than the switching set value mu, the system control quantity can be rapidly changed through the fuzzy reasoning method, so that the robustness of the control system and the system response speed are improved, and the control system is suitable for the requirement of load period and rapid change during the coke charging period.
12): calculating an output value U (k) of a control algorithm of a system adjusted by a PID control algorithm, outputting the U (k) as a given value of the opening degree of an air introducing valve to a valve controller, and finally returning to the step 2); the specific structure of the output value of the PID control algorithm of the circulating gas CO concentration is
In the formula KD: a differential coefficient; kI: an integral coefficient; kp: a proportionality coefficient; u (k): controlling an algorithm output value;
when the deviation value of the actual concentration of the CO in the circulating gas and the set value of the picture in the step 6) is smaller than the switching set value mu, the control quantity of the system can be adjusted through the PID control algorithm, so that the steady-state precision of the CO concentration control system is improved, and the phenomenon that the system vibrates greatly is avoided.
The research process of the invention is as follows: the intelligent controller for the concentration of the CO in the dry quenching circulating gas is designed by combining experimental research and theoretical analysis, constructing a mathematical model of a dry quenching circulating gas CO concentration control system based on a mixed fuzzy PID, taking the mathematical model as a core algorithm and taking a data set acquired by Siemens WINCC in real time as basic data. The controller can enable the system to rapidly change the control quantity of the system through a fuzzy reasoning method when the CO concentration deviation value is larger than a certain threshold value, thereby improving the robustness of the CO concentration control system and the response speed of the system, and enabling the system to adapt to the requirement of load period and rapid change during coke charging; when the deviation value of the CO concentration of the system is smaller than a certain threshold value, the system control quantity can be adjusted by switching to a PID control algorithm, so that the steady-state precision of the CO concentration control system is improved, and the phenomenon that the system vibrates greatly is avoided, so that the control purpose of improving the yield and reducing the emission of harmful substances is realized. The specific content analysis is as follows:
A. operation curve for researching dry quenching system program and circulating gas CO concentration
The direct application object of the invention is a dry quenching circulating gas CO concentration control system, which is equivalent to the 'system upgrade' of the original control system, so that the control concept and the related linkage of the original program are researched to avoid the phenomenon that the new system conflicts with a peripheral mechanical system and even cannot normally operate to influence the production. The purpose of researching the circulating gas CO concentration operation curve is to analyze the mutual influence among key data such as circulating air quantity, the opening degree change value of an air inlet valve, the circulating gas CO concentration change value, system response time and the like, and provide data support for model construction.
B. Intelligent controller for constructing circulating gas CO concentration
According to the method, Siemens WINCC monitoring software is used as a tool to track and record data of key data such as circulating air quantity, an opening degree change value of an air inlet valve, a circulating gas CO concentration change value and system response time in real time, the running track data of the circulating gas CO concentration change value is divided into 3 typical periodic actions, the acquired data is integrated with an application model construction theory, and a mathematical model based on a mixed fuzzy PID controller is constructed through MATLAB. By MATLAB simulation and verification, the influence of parameters of the intelligent controller on the inhibition/aggravation effect of the opening degree change value of the air inlet valve, the circulating gas CO concentration change value, the response time and other key data can be obtained. The experimental result shows that when the temperature of the boiler inlet is lower than 960 ℃, the method is more suitable for reducing the concentration of circulating gas CO by increasing the flow rate of the introduced air; when the temperature of the boiler inlet is higher than 960 ℃, the method is more suitable for reducing the concentration of the circulating gas CO by increasing the circulating air quantity; when the concentration of the circulating gas CO is controlled to be about 6 percent, the system can realize the working condition of relatively lowest dry quenching loss rate on the premise of ensuring the safety of equipment.
C. And constructing a dry quenching circulating gas CO concentration control system based on the mixed fuzzy PID.
The invention combines the scientific research of the two aspects and designs a dry quenching circulating gas CO concentration control system based on mixed fuzzy PID. The system introduces the fuzzy controller into the PID control system, and designs the two-dimensional fuzzy PID controller. The change rates of the circulating gas CO concentration deviation value and the circulating gas CO concentration deviation value become two input variables of the fuzzy PID controller, and an opening degree signal of the air valve is led in to be used as single output of the fuzzy PID. When the concentration deviation value of the circulating gas CO is larger than the switching set value mu, the control system increases the opening degree signal control quantity of the lead-in air valve by a fuzzy control method in order to overcome the slow change of the concentration of the circulating gas CO caused by inertia, and eliminates errors as much as possible, thereby improving the dynamic characteristic of the control system; when the deviation value of the concentration of the CO in the circulating gas is smaller than the switching set value mu, the control system needs to eliminate errors and consider the stability of the system to prevent overshoot and oscillation, and the opening degree signal control quantity of the air inlet valve is adjusted by switching to a PID control algorithm, so that the steady-state precision of the CO concentration control system is improved, the phenomenon that the system greatly oscillates is avoided, and the control purpose of improving the yield and reducing the emission of harmful substances is realized.
Claims (3)
1. A closed-loop control method for the concentration of CO in a coke dry quenching circulating gas is characterized by comprising the following steps:
1) setting a picture set value C (set) of an ideal CO concentration value, namely CO concentration, through a monitoring picture, and then entering the step 2);
2) judging whether the automatic control mode of the concentration of the circulating gas CO is started or not, and if so, entering a step 3); if not, continuing to execute the step 2);
3) sampling a boiler inlet temperature signal, a circulating gas CO concentration actual value signal and a circulating air quantity signal, introducing an air valve opening degree signal, and then entering the step 4);
4) judging whether the inlet temperature of the boiler is higher than 960 ℃, if so, entering to execute the step 5); if not, entering to execute the step 6);
5) judging whether the actual value of the concentration of the CO in the circulating gas is larger than the set value of the picture, if so, reducing the concentration of the CO in the circulating gas in a mode of increasing the circulating air volume; then returning to the step 2); if not, entering to execute the step 6);
6) calculating a deviation value of the concentration of the CO in the circulating gas, judging whether the absolute value of the deviation value is larger than a switching set value mu, and if so, entering the step 7); if not, entering to execute the step 12); the specific structure of the deviation value algorithm of the circulating gas CO concentration is as follows: e (k) ═ c (k) -c (set); wherein E (k): deviation value of circulating gas CO concentration at kth sampling time, c (k): actual value of the circulating gas CO concentration at the kth sampling time, c (set): a picture setting value of the concentration of the circulating gas CO;
7) converting the deviation value of the circulating gas CO concentration into a fuzzy subset of corresponding membership degree through a membership function, and then entering step 8); the specific structure of the deviation value membership function algorithm of the circulating gas CO concentration is as follows:
in the formula, a: fractional difference of deviation value of CO concentration of circulating gas, NiIs a corresponding subset of the point values { -3a, -2a, -a, 0, a, 2a, 3a }, DiA sub-set of degree-of-membership mapping { NB, NM, NS,0, PS, PM, PB } for the deviation value of the circulating gas CO concentration;
8) taking the difference value of the concentration deviation values of the circulating gas CO in two continuous sampling periods as the variation rate of the concentration deviation value of the circulating gas CO, converting the difference value into a fuzzy subset of corresponding membership degrees through a membership function, and then entering the step 9); the specific structure of the deviation value change rate algorithm of the circulating gas CO concentration is as follows: v (k) ═ E (k) — E (k-1); the specific structure of the membership function algorithm for the deviation value change rate of the circulating gas CO concentration is as follows:
wherein V (k): deviation value change rate of circulating gas CO concentration, b: fractional difference of deviation rate of CO concentration of circulating gas, LiIs a corresponding subset of the point values { -3b, -2b, -b, 0, b, 2b, 3b }, GiThe change rate membership degree mapping subset { NBV, NMV, NSV, O, PSV, PMV, PBV } of the circulating gas CO concentration deviation value is adopted;
9) calculating a fuzzy control output value U determined by the deviation value of the CO concentration of the circulating gasDThen entering step 10); the specific structure of the fuzzy control output value of the concentration deviation value of the circulating gas CO is as follows:
in the formula of UD: fuzzy control output value, J, of deviation value of CO concentration of circulating gasiThe current weight coefficient is the cyclic gas CO concentration deviation value fuzzy control;
10) calculating a fuzzy control output value U determined by the variation rate of the circulating gas CO concentration deviation valueGThen, go to step 11); the specific structure of the fuzzy control output value of the change rate of the concentration deviation value of the circulating gas CO is as follows:
in the formula of UG: fuzzy control output value, Q, of variation rate of CO concentration deviation value of circulating gasiThe current weight coefficient is the cycle gas CO concentration deviation value change rate fuzzy control;
11) calculating a fuzzy control total output value U jointly determined by the circulating gas CO concentration deviation value and the circulating gas CO concentration deviation value change rateZThen put UZOutputting the given value of the opening degree of the valve for leading air to a valve controller, and finally returning to the step 2); the specific structure of the fuzzy control total output value is as follows: u shapeZ＝UD+UGIn the formula, UZ: the concentration of the circulating gas CO is subjected to fuzzy control on a total output value;
12): calculating an output value U (k) of a control algorithm of a system adjusted by a PID control algorithm, outputting the U (k) as a given value of the opening degree of an air introducing valve to a valve controller, and finally returning to the step 2); the specific structure of the output value of the PID control algorithm of the circulating gas CO concentration is
In the formula KD: a differential coefficient; kI: an integral coefficient; kp: a proportionality coefficient; u (k): and controlling the output value of the algorithm.
2. The closed-loop control method for the concentration of CO in the coke dry quenching circulating gas as claimed in claim 1, wherein Siemens WINCC monitoring software is adopted to collect and record various related data.
3. The closed-loop control method for the CO concentration of the coke dry quenching circulating gas as claimed in claim 1, wherein the specific structure of the algorithm for increasing the circulating air volume in the step 5) is as follows: f (k) ═ F (k-1) × 1.02; wherein, F (k): and circulating air volume.
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