CN112731873A - Coordination control method for blast furnace combined cycle system and post-combustion carbon capture system - Google Patents
Coordination control method for blast furnace combined cycle system and post-combustion carbon capture system Download PDFInfo
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Abstract
The invention provides a coordination control method of a blast furnace combined cycle system and a burnt carbon capture system, which can enhance the load tracking capability of the blast furnace combined cycle system on the premise of generating less influence on the burnt carbon capture system. The coordinated control method of the invention adjusts the steam extraction flow of the reboiler in advance through the deviation of the system output power and the set value, and adjusts the opening of the blast furnace gas bypass valve and CO through the deviation of the steam extraction flow of the reboiler and the set value2The flow of the barren solution is adjusted by the deviation of the capture rate and a set value, so that the output power and CO are controlled by the blast furnace gas combined circulation system and the carbon capture system after combustion in a mutually coordinated and matched manner2The collection rate. The method of the invention improves the load response speed of the blast furnace combined cycle system and enhances the operation flexibility on the premise of having little influence on the carbon capture system after combustion.
Description
Technical Field
The invention belongs to the technical field of thermal control, and particularly relates to a coordination control method of a blast furnace combined cycle system and a carbon capture system after combustion.
Background
Under the situation that climate change situation is getting more severe, reducing carbon emission has become a key problem for sustainable development of human society and economy. The steel industry, as a typical energy-intensive industry, is the key industry for greenhouse gas emissions, accounting for about 6% of global greenhouse gas emissions. The carbon emission of iron and steel enterprises in China accounts for 13% -15% of the total national emission, and the emission reduction of the industry is reluctant. Under the background that renewable energy sources are not fully deployed, a Carbon Capture and Storage (CCS) technology is the only effective way for realizing decarburization in the steel industry at present.
Blast Furnace Gas (BFG) is the byproduct Gas with the highest yield in steel plants, and is also the largest carbon emission source, and how to reasonably utilize BFG and reduce CO2 emission is the current research focus. The Blast Furnace Gas-based Gas-steam Combined Cycle (BFG-CCGT) is a way of efficiently utilizing Gas resources, avoids heat exchange loss of a boiler compared with the traditional Gas-fired boiler power generation, and has the thermoelectric conversion efficiency of 40-45% under the condition of no external heat supply. In the aspect of Carbon emission reduction, Post-combustion Carbon Capture (PCC) based on chemisorption is the most mature technology due to the advantages of low cost and easy modification. Kevin et al, "carbon capture and utilization in the iron and steel industry: in the chemical engineering challenge and opportunity (Carbon capture and optimization in the steel industry: charles and opportunities for chemical engineering) "it is pointed out that the integration of a PCC system into a BFG-CCGT is more favorable for CO in consideration of the dispersed arrangement of a blast furnace and the secondary Carbon emission of the smoke discharged by a generator set2Large scale concentrated capture.
The integrated system has a complex coupling relationship between variables, and has a high requirement on the peak regulation capability of the system under the condition that renewable energy sources such as wind, light and the like are greatly accessed in the future, so that a coordination control method which effectively enhances the load tracking performance of a power plant and realizes the flexible coordination of the relationship between electricity and carbon of the system on the premise of generating less influence on a carbon capture system is necessary.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the coordination control method of the blast furnace combined cycle system and the burnt carbon capture system is provided, and the load tracking capability of the blast furnace combined cycle system can be enhanced on the premise of generating small influence on the burnt carbon capture system.
In order to solve the technical problem, the invention provides a coordination control method of a blast furnace combined cycle system and a carbon capture system after combustion, which comprises the following steps:
step 10), acquiring a set value of steam extraction flow of a reboiler;
step 30), adjusting the opening of a blast furnace gas bypass valve by adopting a first controller according to the deviation between the steam extraction flow of the reboiler and the set value of the steam extraction flow of the reboiler; adjusting the opening degree of the inlet guide vane by adopting a second controller according to the deviation between the gas turbine exhaust temperature of the blast furnace combined cycle system and the gas turbine exhaust temperature set value; adjusting the steam extraction flow of the reboiler by adopting a third controller according to the deviation of the system output power of the blast furnace combined cycle system and a power set value; CO from post-combustion carbon capture system2Adjusting the flow of the barren solution by adopting a fourth controller according to the deviation of the capture rate and the capture rate set value; the steam extraction flow of the reboiler finally reaches the steam extraction flow set value of the reboiler, so that the temperature of the reboiler is in an allowable range.
As a further improvement of the embodiment of the present invention, the step 10) specifically includes:
step 101) acquiring all-working-condition steady-state operation data of a blast furnace combined cycle system and a carbon capture system, wherein the operation data comprises blast furnace gas components of the blast furnace combined cycle system, system output power of the blast furnace combined cycle system, gas turbine exhaust temperature of the blast furnace combined cycle system, and CO of the carbon capture system after combustion2The capture rate, the reboiler temperature and the reboiler extraction flow rate; the collected operation data is divided into training data and test data;
102) mixing blast furnace gas components of the blast furnace combined cycle system, system output power of the blast furnace combined cycle system, gas turbine exhaust temperature of the blast furnace combined cycle system and CO of a carbon capture system after combustion2The capture rate and the reboiler temperature are used as input variables of the BP neural network model, the steam extraction flow of the reboiler is used as an output variable of the BP neural network model, the BP neural network model is trained by adopting training data, and the BP neural network model is verified by adopting test data;
and 103) collecting blast furnace gas components of the current blast furnace combined circulation system, and calculating by using the BP neural network model to obtain a reboiler steam extraction flow set value by combining a power set value, a gas turbine exhaust temperature set value, a capture rate set value and a reboiler optimal temperature value.
As a further improvement of the embodiment of the present invention, the first controller, the second controller, and the third controller are all PI controllers.
As a further improvement of the embodiment of the present invention, between the step 10) and the step 30), there is further included:
and 20) optimizing parameters of the first controller, the second controller and the third controller based on a multi-objective genetic algorithm to meet the preset control quantity constraint, so as to obtain the optimal parameters of the first controller, the second controller and the third controller.
As a further improvement of the embodiment of the present invention, the preset control amount constraint is as shown in formula (1):
in the formula u1Indicates the opening degree, Deltau, of the blast furnace gas bypass valve1Represents the variation of the opening of the gas bypass valve of the blast furnace2Representing inlet guide vane opening, Δ u2Representing the variation of the opening of the inlet guide vanes, u3Denotes the reboiler extraction flow, Δ u3Represents the variation of the extraction flow of the reboiler u4Denotes the lean flow rate, Δ u4Indicates the amount of change in the lean liquid flow rate; u. of1minIndicates the control quantity u1Lower limit value u1maxIndicates the control quantity u1Upper limit value, Δ u1minIndicates the control quantity u1Lower limit of rate of change, Δ u1maxIndicates the control quantity u1Upper limit of rate of change, u2minIndicates the control quantity u2Lower limit value u2maxIndicates the control quantity u2Upper limit value, Δ u2minIndicates the control quantity u2Lower limit of rate of change, Δ u2maxIndicates the control quantity u2Variations inUpper limit of rate, u3minIndicates the control quantity u3Lower limit value u3maxIndicates the control quantity u3Upper limit value, Δ u3minIndicates the control quantity u3Lower limit of rate of change, Δ u3maxIndicates the control quantity u3Upper limit of rate of change, u4minIndicates the control quantity u4Lower limit value u4maxIndicates the control quantity u4Upper limit value, Δ u4minIndicates the control quantity u4Lower limit of rate of change, Δ u4maxIndicates the control quantity u4An upper limit value of the rate of change.
As a further improvement of the embodiment of the present invention, the step 20) specifically includes:
step 201) generating an initial population:
generating initial populations of n individuals which are uniformly distributed by taking a preset parameter range as a constraint, wherein the initial population of each individual represents a group of initial parameters; the initial parameters comprise parameters of a first controller, parameters of a second controller and parameters of a third controller; n represents an integer greater than 0;
step 202) selecting a parent individual:
setting an objective function as shown in equations (2) and (3):
expression (2) represents the tracking performance for the set value, and expression (3) represents the control overshoot;
in the formula, r1Indicates the power set point, r2Indicating the set value of the exhaust gas temperature of the combustion engine, y1Representing the system output power, y, of the combined cycle system of the blast furnace2The method comprises the steps of (1) representing the gas turbine exhaust temperature of a blast furnace combined cycle system, wherein n represents the number of sampling points; r is1bIndicating the power set point after change, r2bIndicating a changed exhaust gas temperature set value, alpha1Representing the proportion of a system output power control target in an objective function, alpha, of a blast furnace combined cycle system2Representing the proportion of a combustion engine exhaust temperature control target of a blast furnace combined cycle system in a target function;
selecting m parent individuals in an initial population by adopting a championship competition ordering method based on non-dominated ordering and crowded distance calculation; m represents an integer greater than 0;
step 203), crossing and varying parent individuals to generate new individuals to obtain a new population;
wherein, the cross formula is shown as formula (4):
child parent1+ rand Ratio (parent2-parent1) formula (4)
In the formula, child represents a child individual, parent1 and parent2 represent a parent individual, rand represents a random number between 0 and 1, and Ratio represents a crossover operator;
step 204) combining the new population and the current population into an expanded population, and calculating the grades and crowding distances of all individuals;
step 205) eliminating the individuals with high level and small crowding distance in the expanded population by a pruning algorithm, and pruning the individuals into a new population of n individuals, wherein the parameters corresponding to the individuals have better load tracking performance compared with the previous generation;
step 206) judging whether the termination condition is met, if not, repeating the steps 32 to 36;
step 207) obtaining preferred parameters of the first controller, the second controller and the third controller.
As a further improvement of the embodiment of the present invention, the preset parameter range is as shown in formula (5):
in the formula, P1Representing the P parameter, I, of the first controller1Denotes the I parameter, P, of the first controller2Representing the P parameter, I, of the second controller2Denotes the I parameter, P, of the second controller3P parameter, I, representing the third controller3Indicating a third controllerThe I parameter of (1); p1minDenotes the lower limit of the first parameter, P1maxDenotes the upper limit value of the first parameter, I1minDenotes the lower limit of the second parameter, I1maxDenotes the upper limit of the second parameter, P2minDenotes the lower limit of the third parameter, P2maxRepresents the upper limit value of the third parameter, I2minDenotes the lower limit of the fourth parameter, I2maxDenotes the upper limit value of the fourth parameter, P3minDenotes the lower limit of the fifth parameter, P3maxRepresents the upper limit value of the fifth parameter, I3minDenotes the lower limit value of the sixth parameter, I3maxAnd represents the sixth parameter upper limit value.
As a further improvement of the embodiment of the present invention, the termination condition is that a preset maximum algebra is reached or an error satisfies a condition.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
the embodiment of the invention provides a coordination control method of a blast furnace combined cycle system and a carbon capture system after combustion, which comprises the steps of adjusting the steam extraction flow of a reboiler in advance through the deviation of the output power of the blast furnace combined cycle system and a set value, adjusting the opening of a gas bypass valve of a blast furnace through the deviation of the steam extraction flow of the reboiler and the set value, and adjusting the opening of a CO (carbon monoxide) bypass valve through the deviation of the steam extraction flow2The flow of the barren solution is adjusted by the deviation of the capture rate and the set value, so that the output power of the blast furnace gas combined cycle system and the CO of the carbon capture system after combustion are controlled by the blast furnace gas combined cycle and the carbon capture system after combustion in a mutually coordinated and matched manner2The collection rate. Because the time scale difference between the blast furnace combined cycle system and the burnt carbon capture system is large, the steam extraction flow of the reboiler has a quick influence on the power of the blast furnace combined cycle system, the influence on the burnt carbon capture system is slow, a part of steam is lent from the burnt carbon capture system to preferentially adjust the power of the unit, the load is returned after the load adjustment is finished, and the CO of the burnt carbon capture system is subjected to CO return2The capture rate has less influence. Compared with the conventional method that the opening of the blast furnace gas bypass valve is adjusted through the deviation of the output total power and the set value, and the steam extraction flow of the reboiler is adjusted through the deviation of the reboiler temperature and the set value, the method provided by the embodiment of the invention can improve the combined circulation system of the blast furnace on the premise of having small influence on the post-combustion carbon capture systemThe system load response speed is increased, and the operation flexibility is enhanced.
Drawings
FIG. 1(a) is a block flow diagram of a method for coordinated control of a combined blast furnace circulation system and a post-combustion carbon capture system in accordance with an embodiment of the present invention;
FIG. 1(b) is a schematic structural diagram of a combined blast furnace circulation system and a post-combustion carbon capture system according to an embodiment of the present invention;
FIG. 2 is a diagram of a BP neural network model structure in the method according to the embodiment of the present invention;
FIG. 3 is a Pareto frontier chart of an optimal solution set obtained by optimizing PI parameters through a multi-objective genetic algorithm in the method of the embodiment of the invention;
FIG. 4(a) is a graph illustrating the variation of the output power of the controlled quantity system during the variation of the power setpoint and the exhaust temperature setpoint of the combustion engine according to the exemplary method and the conventional control method of the present invention;
FIG. 4(b) is a graph of the change in engine exhaust temperature during a change in a power set point and an engine exhaust temperature set point for an embodiment of the present invention and a conventional control method;
FIG. 4(c) is a graph illustrating CO during a change in power setpoint and engine exhaust temperature setpoint, according to an exemplary method of the present invention and a conventional control method2A change curve of the trapping rate;
FIG. 4(d) is a graph showing the reboiler temperature profile during a change in the power setpoint and the turbine exhaust temperature setpoint for an exemplary method and conventional control method of the present invention;
FIG. 5(a) is a graph showing the variation of the steam extraction rate of the reboiler during the variation of the power setpoint and the exhaust temperature setpoint of the gas turbine according to the exemplary method and the conventional control method of the present invention;
FIG. 5(b) is a graph showing the change in lean flow rate during a change in power set point and engine exhaust temperature set point for an exemplary method of the present invention and a conventional control method;
FIG. 5(c) is a graph showing the variation of the opening of the blast furnace gas bypass valve during the variation of the power set point and the set point of the exhaust temperature of the gas turbine according to the method of the embodiment of the present invention and the conventional control method;
FIG. 5(d) is a graph showing the variation of the inlet guide vane opening during the variation of the power set point and the exhaust temperature set point for a combustion engine according to the method of the present invention and the conventional control method.
Detailed Description
The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.
An embodiment of the present invention provides a method for coordinated control of a combined cycle system of a blast furnace and a post-combustion carbon capture system, as shown in fig. 1(a) and 1(b), including:
and step 10) acquiring a set value of the steam extraction flow of the reboiler.
Step 30) according to the steam extraction flow u of the reboiler3Adjusting the opening u of a blast furnace gas bypass valve (BPV) by a first controller according to the deviation of the set value of the steam extraction flow of the reboiler1(ii) a According to the exhaust temperature y of the combustion engine of the blast furnace combined cycle system2And the deviation of the set value of the exhaust temperature of the combustion engine, and adjusting the opening u of an Inlet Guide Vane (IGV) by adopting a second controller2(ii) a According to the system output power y of the blast furnace combined cycle system1Deviation from the power set point, a third controller is adopted to adjust the steam extraction flow u of the reboiler3(ii) a CO from post-combustion carbon capture system2Trapping rate y3Adjusting lean solution flow u using a fourth controller in accordance with the deviation from the collection rate set value4. Extraction flow u through reboiler3And blast furnace gas bypass valve (BPV) opening u1And controlling the system output power of the blast furnace combined cycle system. By Inlet Guide Vane (IGV) opening u2Controlling combustion engine exhaust temperature y of blast furnace combined cycle system2. Through lean solution flow u4Controlling capture rate y of post-combustion carbon capture system3. The steam extraction of the reboiler participates in load regulation and then gradually returns to the steam extraction flow set value of the reboiler, so that the temperature y of the reboiler is ensured4Within the allowable range.
The method of the embodiment of the invention adjusts the steam extraction flow of the reboiler in advance through the deviation of the output power of the blast furnace combined cycle system and the set value, and adjusts the BPV opening and the CO opening degree through the deviation of the steam extraction flow of the reboiler and the set value2The difference between the capture rate and the set value regulates the lean solution flow, the steam extraction flow of the reboiler and the blast furnaceThe opening of the gas bypass valve controls the system output power of the blast furnace combined cycle system, the barren liquor flow controls the capture rate of the burnt carbon capture system, and the output power of the blast furnace combined cycle system and the CO of the burnt carbon capture system are controlled by the blast furnace gas combined cycle and the burnt carbon capture system in a mutually coordinated and matched manner2The collection rate. Because the time scale difference between the blast furnace combined cycle system and the burnt carbon capture system is large, the steam extraction flow of the reboiler has a quick influence on the power of the blast furnace combined cycle system, the influence on the burnt carbon capture system is slow, a part of steam is lent from the burnt carbon capture system to preferentially adjust the power of the unit, the load is returned after the load adjustment is finished, and the CO of the burnt carbon capture system is subjected to CO return2The capture rate has less influence. Compared with the conventional method that the BPV opening is adjusted through the deviation of the output total power and the set value, and the steam extraction flow of the reboiler is adjusted through the deviation of the reboiler temperature and the set value, the method provided by the embodiment of the invention can improve the load response speed of the blast furnace combined circulation system and enhance the operation flexibility on the premise of having little influence on the carbon capture system after combustion.
As a preferred example, the reboiler extraction flow rate set point is obtained by the following steps:
step 101) acquiring all-working-condition steady-state operation data of a blast furnace combined cycle system and a burnt carbon capture system, wherein the operation data comprises blast furnace gas components of the blast furnace combined cycle system, system output power of the blast furnace combined cycle system, gas turbine exhaust temperature of the blast furnace combined cycle system and CO of the burnt carbon capture system2The capture rate, the temperature of the reboiler and the steam extraction flow rate of the reboiler; the collected operation data is divided into training data and test data;
102) mixing blast furnace gas components of the blast furnace combined cycle system, system output power of the blast furnace combined cycle system, gas turbine exhaust temperature of the blast furnace combined cycle system and CO of a carbon capture system after combustion2The capture rate and the reboiler temperature are used as input variables of the BP neural network model, the reboiler steam extraction flow is used as an output variable of the BP neural network model, as shown in FIG. 2, the BP neural network model is trained by using training data, and the BP neural network model is verified by using test data；
And 103) collecting blast furnace gas components of the current blast furnace combined circulation system, and calculating by using a BP (back propagation) neural network model to obtain a reboiler steam extraction flow set value by combining a power set value, a gas turbine exhaust temperature set value, a capture rate set value and a reboiler optimal temperature value.
According to the method, according to the blast furnace gas components of the current blast furnace combined cycle system, the power set value, the gas turbine exhaust temperature set value, the capture rate set value and the reboiler optimal temperature value are combined, the reboiler steam extraction flow set value calculated by the established BP neural network model is closer to the actual system operation condition, and the reboiler temperature can be effectively ensured to be within the allowable range.
Preferably, the first controller, the second controller and the third controller are all PI controllers. The PI controller is simple in structure and control method and easy to realize in engineering.
As a preferred example, the method of this embodiment further includes: and optimizing parameters of the first controller, the second controller and the third controller based on a multi-objective genetic algorithm so as to meet the preset control quantity constraint and obtain the optimal parameters of the first controller, the second controller and the third controller.
Further, the preset control amount constraint is as shown in formula (1):
in the formula u1Indicates the opening degree, Deltau, of the blast furnace gas bypass valve1Represents the variation of the opening of the gas bypass valve of the blast furnace2Representing inlet guide vane opening, Δ u2Representing the variation of the opening of the inlet guide vanes, u3Denotes the reboiler extraction flow, Δ u3Represents the variation of the extraction flow of the reboiler u4Denotes the lean flow rate, Δ u4Indicates the amount of change in the lean liquid flow rate; u. of1minIndicates the control quantity u1Lower limit value u1maxIndicates the control quantity u1Upper limit value, Δ u1minIndicates the control quantity u1The lower limit value of the rate of change,Δu1maxindicates the control quantity u1Upper limit of rate of change, u2minIndicates the control quantity u2Lower limit value u2maxIndicates the control quantity u2Upper limit value, Δ u2minIndicates the control quantity u2Lower limit of rate of change, Δ u2maxIndicates the control quantity u2Upper limit of rate of change, u3minIndicates the control quantity u3Lower limit value u3maxIndicates the control quantity u3Upper limit value, Δ u3minIndicates the control quantity u3Lower limit of rate of change, Δ u3maxIndicates the control quantity u3Upper limit of rate of change, u4minIndicates the control quantity u4Lower limit value u4maxIndicates the control quantity u4Upper limit value, Δ u4minIndicates the control quantity u4Lower limit of rate of change, Δ u4maxIndicates the control quantity u4An upper limit value of the rate of change.
In the method, the physical limits and the actual operating condition range of control elements such as a valve, a feed pump and the like in an actual system are considered, and the rate constraint and the upper and lower limit constraints of the control variable are set, so that the actual operating condition is better met, and the optimized controller parameters can be used in the actual system.
As a preferred example, the method for optimizing the parameters of the first controller, the second controller and the third controller based on the multi-objective genetic algorithm to satisfy the preset control constraints to obtain the preferred parameters of the first controller, the second controller and the third controller specifically includes:
step 201) generating an initial population:
generating initial populations of n individuals which are uniformly distributed by taking a preset parameter range as a constraint, wherein the initial population of each individual represents a group of initial parameters; the initial parameters comprise parameters of a first controller, parameters of a second controller and parameters of a third controller;
step 202) selecting a parent individual:
setting an objective function as shown in equations (2) and (3):
expression (2) represents the tracking performance for the set value, and expression (3) represents the control overshoot;
in the formula, r1Indicates the power set point, r2Indicating the set value of the exhaust gas temperature of the combustion engine, y1Representing the system output power, y, of the combined cycle system of the blast furnace2The method comprises the steps of (1) representing the gas turbine exhaust temperature of a blast furnace combined cycle system, wherein n represents the number of sampling points; r is1bIndicating the power set point after change, r2bIndicating a changed exhaust gas temperature set value, alpha1Representing the proportion of a system output power control target in an objective function, alpha, of a blast furnace combined cycle system2Representing the proportion of a combustion engine exhaust temperature control target of a blast furnace combined cycle system in a target function;
selecting m parent individuals in an initial population by adopting a championship competition ordering method based on non-dominated ordering and crowded distance calculation;
step 203), crossing and varying parent individuals to generate new individuals to obtain a new population;
wherein, the cross formula is shown as formula (4):
child parent1+ rand Ratio (parent2-parent1) formula (4)
In the formula, child represents a child individual, parent1 and parent2 represent a parent individual, rand represents a random number between 0 and 1, and Ratio represents a crossover operator;
step 204) combining the new population and the current population into an expanded population, and calculating the grades and crowding distances of all individuals;
step 205) eliminating the individuals with high level and small crowding distance in the expanded population by a pruning algorithm, and pruning the individuals into a new population of n individuals, wherein the parameters corresponding to the individuals have better load tracking performance compared with the previous generation;
step 206) judging whether the termination condition is met, if not, repeating the step 202 to the step 206;
step 207) obtaining preferred parameters of the first controller, the second controller and the third controller.
Further, the preset parameter range is as shown in formula (5):
in the formula, P1Representing the P parameter, I, of the first controller1Denotes the I parameter, P, of the first controller2Representing the P parameter, I, of the second controller2Denotes the I parameter, P, of the second controller3P parameter, I, representing the third controller3An I parameter representing a third controller; p1minDenotes the lower limit of the first parameter, P1maxDenotes the upper limit value of the first parameter, I1minDenotes the lower limit of the second parameter, I1maxDenotes the upper limit of the second parameter, P2minDenotes the lower limit of the third parameter, P2maxRepresents the upper limit value of the third parameter, I2minDenotes the lower limit of the fourth parameter, I2maxDenotes the upper limit value of the fourth parameter, P3minDenotes the lower limit of the fifth parameter, P3maxRepresents the upper limit value of the fifth parameter, I3minDenotes the lower limit value of the sixth parameter, I3maxAnd represents the sixth parameter upper limit value.
The embodiment of the invention sets the parameter range, can accelerate the optimization speed and obtain the optimal controller parameter as soon as possible.
Further, the termination condition is that a preset maximum algebra is reached or the error meets the condition.
A specific example is provided below to verify the performance of the method of the present invention.
Example 1
And step 10) acquiring a set value of the steam extraction flow of the reboiler. The method specifically comprises the following steps:
step 101) input variable is BFG component u11System output power y1Exhaust gas temperature y of the gas turbine2、CO2Trapping rate y3And reboiler temperature y4The output variable is the steam extraction flow u of the reboiler3Collecting blast furnace combined cycle system andall-condition steady-state operation data of the carbon capture system after combustion;
step 102), selecting 70% of data for training a BP neural network model, and performing model verification on the rest 30% of data;
step 103) according to the current BFG component, the system output set value is 30.72MW, the gas turbine exhaust temperature set value is 600.04 ℃, and CO is carried out2The set value of the trapping rate is 55 percent, the optimum temperature of the reboiler is 390.4 ℃, and the set value of the steam extraction flow of the reboiler is calculated to be 17.14kg/s through a BP neural network model.
Step 20) optimizing the controller parameters based on the multi-objective genetic algorithm. The method specifically comprises the following steps:
step 201) with a preset parameter range as shown in the following formula as a constraint,
generating an initial random population of evenly distributed n-80 individuals, representing a set of initial parameter solutions, wherein each individual comprises an initial value comprising a PI parameter of a first PI controller, a PI parameter of a second PI controller and a PI parameter of a third PI controller;
step 202) the objective function is set to the formula (2) and the formula (3), in the formula (3), alpha1＝0.8，α20.2; selecting individuals in a population by adopting a championship sorting method based on non-dominated sorting and crowded distance calculation, namely randomly selecting m to 30 individuals from a mother population, optimally selecting the individuals with low grade and large crowded distance, and continuously repeating the steps until a new population reaches the original population scale, wherein the grade is determined based on the non-dominated sorting, and the smaller the target value is, the lower the grade is; the crowding distance represents the distance between each point and the adjacent point in the same level, and the bigger the crowding distance is, the better the diversity of the algorithm is;
step 203) according to formula (4), where Ratio is 0.9; crossing and varying parent individuals, randomly generating a self-adaptive direction relative to the success or failure of the previous generation by a variation algorithm, selecting the direction and the step length to meet a preset parameter range, and generating new individuals;
step 204) combining the new population and the current population into an expanded population, and calculating the grades and crowding distances of all individuals;
step 205) eliminating the individuals with high grade and small crowding distance in the expanded population by a pruning algorithm, and pruning the individuals into a new population with n equal to 80 individuals, wherein PI parameters corresponding to the individuals generally have better load tracking performance compared with the PI parameters of the previous generation;
step 206) judging whether the preset maximum algebra is reached or the error meets the condition, if not, repeating the step 202) -the step 206).
Step 207) of final normalization to obtain a Pareto (Pareto) front map, as shown in fig. 3. Each point in the pareto frontier graph can be an optimal point, the tracking performance and the overshoot are comprehensively considered, and the point with the minimum sum of the two indexes is selected to be a punctuation point in the graph. And satisfies a predetermined control constraint as follows:
obtaining parameters of a first PI controller: p1＝-1.79，I1-0.0004; parameters of the second PI controller: p2＝-0.22，I2-0.0064; parameters of the third PI controller: p3＝-1.95，I3-0.11. Setting parameters of a fourth PI controller: p4＝23.98，I4＝4.80。
Step 30) according to the steam extraction flow u of the reboiler3And adjusting the BPV opening u by adopting a first PI controller according to the deviation of the set value of the steam extraction flow of the reboiler1(ii) a According to the exhaust temperature y of the combustion engine of the blast furnace combined cycle system2And the deviation of the set value of the exhaust temperature of the gas turbine, and adjusting the opening u of the IGV by adopting a second PI controller2(ii) a According to the system output power y of the blast furnace combined cycle system1Regulating the steam extraction flow u of the reboiler by a third PI controller according to the deviation from the power set value3(ii) a CO from post-combustion carbon capture system2Trapping rate y3Adjusting lean solution flow u using a fourth PI controller in response to a deviation from the capture rate set point4。
Comparative example 1
The system output power of the blast furnace combined cycle system is controlled through the BPV opening degree, the gas turbine exhaust temperature of the blast furnace combined cycle system is controlled through the IGV opening degree, and the CO of the carbon capture system after combustion is controlled through the lean solution flow2And the capture rate is controlled by the reboiler steam extraction flow to control the reboiler temperature of the carbon capture system after combustion.
The following simulation experiments were performed for example 1 and comparative example 1: the carbon capture system of the gas turbine of the blast furnace gas combustion engine is stabilized at the system output power of 35.72MW, the combustion engine exhaust temperature of 595.04 ℃ and CO2At the working point with the capture rate of 50%, when t is 100s, the set value of the system output power is changed to 30.72MW, the set value of the exhaust temperature of the gas turbine is changed to 600.04 ℃ and the CO is changed2The collection rate set value was 55% and the reboiler extraction flow rate set value was 17.14kg/s, and a control simulation result was obtained.
As shown in fig. 4(a), 4(b), 4(c) and 4(d), in the figure, the improved control is example 1 and the conventional control is comparative example. It can be seen from the graph that the method of the embodiment of the invention has almost perfect tracking performance for the power set point and the exhaust temperature of the combustion engine rises faster than the conventional control method. And the temperature change of the reboiler is very small and can be almost ignored, thereby enhancing the load tracking capability and the operation flexibility of the system.
As shown in fig. 5(a), 5(b), 5(c) and 5(d), in the figure, the improved control is example 1 and the conventional control is comparative example. As can be seen from the figure, the method of the embodiment of the invention rapidly increases the steam extraction flow of the reboiler when the power set value is reduced so as to accelerate the load regulation; and simultaneously increasing the BPV opening to decrease the fuel amount, thereby decreasing the system output power. The IGV opening is decreased to decrease the compressor inlet air amount as the engine exhaust temperature set point increases, thereby increasing the exhaust temperature. Since the reboiler extraction flow rate "replaces" a portion of the BPV opening, the method of this embodiment controls a more gradual change in BPV opening than conventional control. After the load adjustment is finished, the steam extraction flow of the reboiler gradually returns to a set value to finish the appointed CO2The collection rate is required.
In summary, the method provided by the embodiment of the invention introduces auxiliary control of the steam extraction flow of the reboiler on the basis of the conventional blast furnace combined cycle control, provides a coordinated control method, optimizes the PI parameter of the controller through a multi-objective genetic algorithm, and enhances the load tracking capability of the system while finishing the set emission reduction target compared with the conventional control method.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are intended to further illustrate the principles of the invention, and that various changes and modifications may be made without departing from the spirit and scope of the invention, which is also intended to be covered by the appended claims. The scope of the invention is defined by the claims and their equivalents.
Claims (8)
1. A coordination control method for a blast furnace combined cycle system and a carbon capture system after combustion is characterized by comprising the following steps:
step 10), acquiring a set value of steam extraction flow of a reboiler;
step 30), adjusting the opening of a blast furnace gas bypass valve by adopting a first controller according to the deviation between the steam extraction flow of the reboiler and the set value of the steam extraction flow of the reboiler; adjusting the opening degree of the inlet guide vane by adopting a second controller according to the deviation between the gas turbine exhaust temperature of the blast furnace combined cycle system and the gas turbine exhaust temperature set value; adjusting the steam extraction flow of the reboiler by adopting a third controller according to the deviation of the system output power of the blast furnace combined cycle system and a power set value; CO from post-combustion carbon capture system2Adjusting the flow of the barren solution by adopting a fourth controller according to the deviation of the capture rate and the capture rate set value; the steam extraction flow of the reboiler finally reaches the steam extraction flow set value of the reboiler, so that the temperature of the reboiler is in an allowable range.
2. The method for coordinated control of a blast furnace combined cycle system and a post combustion carbon capture system as set forth in claim 1, wherein said step 10) specifically comprises:
step 101) acquiring all-working-condition steady-state operation data of a blast furnace combined cycle system and a carbon capture system, wherein the operation data comprises blast furnace gas components of the blast furnace combined cycle system, system output power of the blast furnace combined cycle system, gas turbine exhaust temperature of the blast furnace combined cycle system, and CO of the carbon capture system after combustion2The capture rate, the reboiler temperature and the reboiler extraction flow rate; the collected operation data is divided into training data and test data;
102) mixing blast furnace gas components of the blast furnace combined cycle system, system output power of the blast furnace combined cycle system, gas turbine exhaust temperature of the blast furnace combined cycle system and CO of a carbon capture system after combustion2The capture rate and the reboiler temperature are used as input variables of the BP neural network model, the steam extraction flow of the reboiler is used as an output variable of the BP neural network model, the BP neural network model is trained by adopting training data, and the BP neural network model is verified by adopting test data;
and 103) collecting blast furnace gas components of the current blast furnace combined circulation system, and calculating by using the BP neural network model to obtain a reboiler steam extraction flow set value by combining a power set value, a gas turbine exhaust temperature set value, a capture rate set value and a reboiler optimal temperature value.
3. The method of claim 1, wherein the first controller, the second controller, and the third controller are PI controllers.
4. The method of claim 3, further comprising, between the steps 10) and 30):
and 20) optimizing parameters of the first controller, the second controller and the third controller based on a multi-objective genetic algorithm to meet the preset control quantity constraint, so as to obtain the optimal parameters of the first controller, the second controller and the third controller.
5. The coordinated control method of the combined cycle system of a blast furnace and the post combustion carbon capture system as set forth in claim 4, wherein said preset control amount is constrained as shown in formula (1):
in the formula u1Indicates the opening degree, Deltau, of the blast furnace gas bypass valve1Represents the variation of the opening of the gas bypass valve of the blast furnace2Representing inlet guide vane opening, Δ u2Representing the variation of the opening of the inlet guide vanes, u3Denotes the reboiler extraction flow, Δ u3Represents the variation of the extraction flow of the reboiler u4Denotes the lean flow rate, Δ u4Indicates the amount of change in the lean liquid flow rate; u. of1minIndicates the control quantity u1Lower limit value u1maxIndicates the control quantity u1Upper limit value, Δ u1minIndicates the control quantity u1Lower limit of rate of change, Δ u1maxIndicates the control quantity u1Upper limit of rate of change, u2minIndicates the control quantity u2Lower limit value u2maxIndicates the control quantity u2Upper limit value, Δ u2minIndicates the control quantity u2Lower limit of rate of change, Δ u2maxIndicates the control quantity u2Upper limit of rate of change, u3minIndicates the control quantity u3Lower limit value u3maxIndicates the control quantity u3Upper limit value, Δ u3minIndicates the control quantity u3Lower limit of rate of change, Δ u3maxIndicates the control quantity u3Upper limit of rate of change, u4minIndicates the control quantity u4Lower limit value u4maxIndicates the control quantity u4Upper limit value, Δ u4minIndicates the control quantity u4Lower limit of rate of change, Δ u4maxIndicates the control quantity u4An upper limit value of the rate of change.
6. The coordinated control method of the combined cycle system of a blast furnace and the carbon capture system after combustion as set forth in claim 4, wherein the step 20) specifically comprises:
step 201) generating an initial population:
generating initial populations of n individuals which are uniformly distributed by taking a preset parameter range as a constraint, wherein the initial population of each individual represents a group of initial parameters; the initial parameters comprise parameters of a first controller, parameters of a second controller and parameters of a third controller; n represents an integer greater than 0;
step 202) selecting a parent individual:
setting an objective function as shown in equations (2) and (3):
expression (2) represents the tracking performance for the set value, and expression (3) represents the control overshoot;
in the formula, r1Indicates the power set point, r2Indicating the set value of the exhaust gas temperature of the combustion engine, y1Representing the system output power, y, of the combined cycle system of the blast furnace2The method comprises the steps of (1) representing the gas turbine exhaust temperature of a blast furnace combined cycle system, wherein n represents the number of sampling points; r is1bIndicating the power set point after change, r2bIndicating a changed exhaust gas temperature set value, alpha1Representing the proportion of a system output power control target in an objective function, alpha, of a blast furnace combined cycle system2Representing the proportion of a combustion engine exhaust temperature control target of a blast furnace combined cycle system in a target function;
selecting m parent individuals in an initial population by adopting a championship competition ordering method based on non-dominated ordering and crowded distance calculation; m represents an integer greater than 0;
step 203), crossing and varying parent individuals to generate new individuals to obtain a new population;
wherein, the cross formula is shown as formula (4):
child parent1+ rand Ratio (parent2-parent1) formula (4)
In the formula, child represents a child individual, parent1 and parent2 represent a parent individual, rand represents a random number between 0 and 1, and Ratio represents a crossover operator;
step 204) combining the new population and the current population into an expanded population, and calculating the grades and crowding distances of all individuals;
step 205) eliminating the individuals with high level and small crowding distance in the expanded population by a pruning algorithm, and pruning the individuals into a new population of n individuals, wherein the parameters corresponding to the individuals have better load tracking performance compared with the previous generation;
step 206) judging whether the termination condition is met, if not, repeating the steps 32 to 36;
step 207) obtaining preferred parameters of the first controller, the second controller and the third controller.
7. The method of claim 6, wherein the predetermined parameter range is as shown in equation (5):
in the formula, P1Representing the P parameter, I, of the first controller1Denotes the I parameter, P, of the first controller2Representing the P parameter, I, of the second controller2Denotes the I parameter, P, of the second controller3P parameter, I, representing the third controller3An I parameter representing a third controller; p1minDenotes the lower limit of the first parameter, P1maxDenotes the upper limit value of the first parameter, I1minDenotes the lower limit of the second parameter, I1maxDenotes the upper limit of the second parameter, P2minDenotes the lower limit of the third parameter, P2maxRepresents the upper limit value of the third parameter, I2minDenotes the lower limit of the fourth parameter, I2maxDenotes the upper limit value of the fourth parameter, P3minDenotes the lower limit of the fifth parameter, P3maxRepresents the upper limit value of the fifth parameter, I3minDenotes the lower limit value of the sixth parameter, I3maxAnd represents the sixth parameter upper limit value.
8. The coordinated control method of the combined cycle system of a blast furnace and the carbon capture system after combustion as set forth in claim 6, wherein the termination condition is that a preset maximum algebra is reached or that an error is satisfied.
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