Sulfur dioxide concentration calculation method and device, electronic equipment and storage medium
By extracting the parameters of flue gas and slurry in the limestone-gypsum wet flue gas desulfurization system, and calculating the sulfur dioxide concentration using particle swarm optimization algorithm, the problem of inefficiency of automated control loops in the existing technology is solved, and ultra-low sulfur dioxide emissions are achieved and system optimization.
Patent Information
- Application Number
- CN202411721604.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-28
- Publication Date
- 2025-02-14
AI Technical Summary
In the prior art, the control circuit of the limestone-gypsum wet flue gas desulfurization system has low degree of automation, low control accuracy and generalization ability, and it is difficult to achieve optimal control, resulting in low sulfur dioxide emissions.
By extracting the raw flue gas flow, raw flue gas sulfur dioxide concentration, limestone slurry flow and limestone slurry fineness from the flue gas to be identified, and using the particle swarm optimization algorithm to identify the parameter set, input it to the pre-constructed net flue gas sulfur dioxide concentration control model , output the sulfur dioxide concentration in the flue gas to be identified.
The precise adjustment of the automatic control circuit of the wet desulfurization system has been achieved, the control circuit has been optimized, and the precise control of limestone materials has been accurately controlled, so as to achieve ultra-low sulfur dioxide emissions and quality improvement and efficiency improvement.
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Figure CN119446331A_ABST
Abstract
Description
Technical Field
[0001] The embodiments of the present application relate to the field of industrial control technology, and in particular to a method, device, electronic device and storage medium for calculating sulfur dioxide concentration. Background Art
[0002] Limestone-gypsum wet flue gas desulfurization is a highly efficient desulfurization technology widely used in coal-fired power plants and other industrial facilities. The technology uses limestone slurry to absorb sulfur dioxide in the flue gas and eventually produces gypsum as a by-product.
[0003] Since the limestone-gypsum wet flue gas desulfurization system has the characteristics of nonlinearity and large delay, it is difficult to obtain an accurate system model based on mechanism analysis, and the formulated sulfur dioxide control scheme has poor robustness. The control loops of the desulfurization systems of many thermal power units still use manual control methods, which wastes a lot of manpower and material resources, has a low degree of automation, and has low control accuracy and generalization ability, making it difficult to achieve optimal control of the desulfurization system. Summary of the invention
[0004] The present application provides a sulfur dioxide concentration calculation method, device, electronic equipment and storage medium, which can provide a basis for the precise adjustment of the automatic control loop of the net flue gas sulfur dioxide concentration of wet desulfurization, which is conducive to optimizing the control loop, so as to accurately control the limestone material and achieve ultra-low sulfur dioxide emissions and improve quality and efficiency.
[0005] In a first aspect, an embodiment of the present application provides a method for calculating sulfur dioxide concentration, the method comprising:
[0006] Extracting the original flue gas flow, original flue gas sulfur dioxide concentration, limestone slurry flow and limestone slurry fineness from the flue gas to be identified;
[0007] Identify, by particle swarm optimization algorithm, a first parameter set corresponding to the original flue gas flow rate, a second parameter set corresponding to the original flue gas sulfur dioxide concentration, a third parameter set corresponding to the limestone slurry flow rate, and a fourth parameter set corresponding to the limestone slurry fineness;
[0008] Inputting the raw flue gas flow rate, the raw flue gas sulfur dioxide concentration, the limestone slurry flow rate and the limestone slurry fineness into a pre-constructed net flue gas sulfur dioxide concentration control model respectively;
[0009] The concentration of sulfur dioxide in the flue gas to be identified is outputted through the clean flue gas sulfur dioxide concentration control model based on the first parameter set, the second parameter set, the third parameter set and the fourth parameter set.
[0010] In a second aspect, the embodiment of the present application further provides a sulfur dioxide concentration calculation device, the device comprising: an extraction module, an identification module, an input module and a calculation module; wherein:
[0011] The extraction module is used to extract the original flue gas flow rate, the original flue gas sulfur dioxide concentration, the limestone slurry flow rate and the limestone slurry fineness from the flue gas to be identified;
[0012] The identification module is used to identify the first parameter set corresponding to the original flue gas flow rate, the second parameter set corresponding to the original flue gas sulfur dioxide concentration, the third parameter set corresponding to the limestone slurry flow rate, and the fourth parameter set corresponding to the limestone slurry fineness through a particle swarm optimization algorithm;
[0013] The input module is used to input the original flue gas flow rate, the original flue gas sulfur dioxide concentration, the limestone slurry flow rate and the limestone slurry fineness into a pre-constructed net flue gas sulfur dioxide concentration control model respectively;
[0014] The calculation module is used to output the concentration of sulfur dioxide in the flue gas to be identified based on the first parameter set, the second parameter set, the third parameter set and the fourth parameter set through the net flue gas sulfur dioxide concentration control model.
[0015] In a third aspect, an embodiment of the present application provides an electronic device, including:
[0016] one or more processors;
[0017] a memory for storing one or more programs,
[0018] When the one or more programs are executed by the one or more processors, the one or more processors implement the sulfur dioxide concentration calculation method described in any embodiment of the present application.
[0019] In a fourth aspect, an embodiment of the present application provides a storage medium on which a computer program is stored, and when the program is executed by a processor, the method for calculating sulfur dioxide concentration described in any embodiment of the present application is implemented.
[0020] The embodiment of the present application proposes a sulfur dioxide concentration calculation method, device, electronic device and storage medium, firstly extracting the original flue gas flow, original flue gas sulfur dioxide concentration, limestone slurry flow and limestone slurry fineness from the flue gas to be identified respectively; then identifying the first parameter set corresponding to the original flue gas flow, the second parameter set corresponding to the original flue gas sulfur dioxide concentration, the third parameter set corresponding to the limestone slurry flow and the fourth parameter set corresponding to the limestone slurry fineness through the particle swarm optimization algorithm; then inputting the original flue gas flow, the original flue gas sulfur dioxide concentration, the limestone slurry flow and the limestone slurry fineness into a pre-constructed net flue gas sulfur dioxide concentration control model respectively; finally, outputting the concentration of sulfur dioxide in the flue gas to be identified based on the first parameter set, the second parameter set, the third parameter set and the fourth parameter set through the net flue gas sulfur dioxide concentration control model. That is to say, in the technical solution of the present application, the first parameter set corresponding to the original flue gas flow rate, the second parameter set corresponding to the original flue gas sulfur dioxide concentration, the third parameter set corresponding to the limestone slurry flow rate, and the fourth parameter set corresponding to the limestone slurry fineness can be identified by the particle swarm optimization algorithm, so as to calculate the concentration of sulfur dioxide in the flue gas to be identified. In the prior art, the control loop of the desulfurization system of many thermal power units still adopts a manual control method, which wastes a lot of manpower and material resources, has a low degree of automation, and has low control accuracy and generalization ability, making it difficult to achieve optimal control of the desulfurization system. Therefore, compared with the prior art, the sulfur dioxide concentration calculation method, device, electronic device and storage medium proposed in the embodiment of the present application can provide a basis for the precise adjustment of the automatic control loop of the net flue gas sulfur dioxide concentration of wet desulfurization, which is conducive to optimizing the control loop, so that the limestone material can be accurately controlled to achieve ultra-low sulfur dioxide emissions and improve quality and efficiency; and the technical solution of the embodiment of the present application is simple and convenient to implement, easy to popularize, and has a wider range of applications. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] Figure 1 A schematic diagram of a flow chart of a method for calculating sulfur dioxide concentration provided in one embodiment of the present application;
[0022] Figure 2 A schematic diagram of a flow chart of a method for calculating sulfur dioxide concentration provided in another embodiment of the present application;
[0023] Figure 3 A schematic diagram of a flow chart of a method for calculating sulfur dioxide concentration provided in yet another embodiment of the present application;
[0024] Figure 4 A schematic diagram of the structure of a clean flue gas sulfur dioxide concentration control model provided in an embodiment of the present application;
[0025] Figure 5 A schematic diagram of the structure of a sulfur dioxide concentration calculation device provided in one embodiment of the present application;
[0026] Figure 6 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION
[0027] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for ease of description, only the parts related to the present application, rather than all structures, are shown in the accompanying drawings.
[0028] Figure 1 This is a flow chart of a method for calculating sulfur dioxide concentration provided by an embodiment of the present application. The method can be executed by a sulfur dioxide concentration calculation device or electronic device. The device or electronic device can be implemented by software and / or hardware. The device or electronic device can be integrated in any intelligent device with network communication function. Figure 1 As shown, the sulfur dioxide concentration calculation method may include the following steps:
[0029] S101. Extracting the original flue gas flow rate, the original flue gas sulfur dioxide concentration, the limestone slurry flow rate and the limestone slurry fineness from the flue gas to be identified.
[0030] This step can be achieved in the following ways: 1) Original flue gas flow rate: use an orifice flowmeter, installed in the straight pipe section before the flue gas enters the absorption tower. 2) Original flue gas sulfur dioxide concentration: use an ultraviolet absorption analyzer, installed in the straight pipe section before the flue gas enters the absorption tower. 3) Limestone slurry flow rate: use an electromagnetic flowmeter, installed at the outlet of the slurry pump or in the straight pipe section before entering the absorption tower. 4) Limestone slurry fineness: use a laser particle size analyzer, and regularly take samples from the slurry delivery pipeline for analysis.
[0031] S102, identifying, by particle swarm optimization algorithm, a first parameter set corresponding to the original flue gas flow rate, a second parameter set corresponding to the original flue gas sulfur dioxide concentration, a third parameter set corresponding to the limestone slurry flow rate, and a fourth parameter set corresponding to the limestone slurry fineness.
[0032] The first parameter set in the embodiment of the present application may include: K 11 , T 11 , τ 11 , T 12 ; The second parameter set may include: K 21 , T 21 , τ 21 , T 22 ; The third parameter set may include: K 31 , T 31 , τ 31 , T 32 ; The fourth parameter set may include: K 41, T 41 , τ 41 , T 42 .
[0033] S103, inputting the original flue gas flow rate, the original flue gas sulfur dioxide concentration, the limestone slurry flow rate and the limestone slurry fineness into a pre-constructed net flue gas sulfur dioxide concentration control model respectively.
[0034] S104. Outputting the concentration of sulfur dioxide in the flue gas to be identified based on the first parameter set, the second parameter set, the third parameter set and the fourth parameter set through the net flue gas sulfur dioxide concentration control model.
[0035] In this step, the weighted value of the original flue gas flow rate, the weighted value of the original flue gas sulfur dioxide concentration, the weighted value of the limestone slurry flow rate and the weighted value of the limestone slurry fineness can be calculated based on the first parameter set, the second parameter set, the third parameter set and the fourth parameter set respectively through the first calculation sub-model, the second calculation sub-model, the third calculation sub-model and the fourth calculation sub-model in the net flue gas sulfur dioxide concentration control model; then, according to the original flue gas flow rate, the original flue gas sulfur dioxide concentration, the limestone slurry flow rate and the limestone slurry fineness, as well as the weighted value of the original flue gas flow rate, the weighted value of the original flue gas sulfur dioxide concentration, the weighted value of the limestone slurry flow rate and the weighted value of the limestone slurry fineness, the concentration of sulfur dioxide in the flue gas to be identified is calculated.
[0036] The sulfur dioxide concentration calculation method proposed in the embodiment of the present application first extracts the original flue gas flow, the original flue gas sulfur dioxide concentration, the limestone slurry flow and the limestone slurry fineness in the flue gas to be identified; then, a first parameter set corresponding to the original flue gas flow, a second parameter set corresponding to the original flue gas sulfur dioxide concentration, a third parameter set corresponding to the limestone slurry flow and a fourth parameter set corresponding to the limestone slurry fineness are identified through a particle swarm optimization algorithm; then, the original flue gas flow, the original flue gas sulfur dioxide concentration, the limestone slurry flow and the limestone slurry fineness are respectively input into a pre-constructed net flue gas sulfur dioxide concentration control model; finally, the net flue gas sulfur dioxide concentration control model is used to output the concentration of sulfur dioxide in the flue gas to be identified based on the first parameter set, the second parameter set, the third parameter set and the fourth parameter set. That is to say, in the technical solution of the present application, the first parameter set corresponding to the original flue gas flow rate, the second parameter set corresponding to the original flue gas sulfur dioxide concentration, the third parameter set corresponding to the limestone slurry flow rate, and the fourth parameter set corresponding to the limestone slurry fineness can be identified by the particle swarm optimization algorithm, so as to calculate the concentration of sulfur dioxide in the flue gas to be identified. In the prior art, the control loop of the desulfurization system of many thermal power units still adopts a manual control method, which wastes a lot of manpower and material resources, has a low degree of automation, and has low control accuracy and generalization ability, making it difficult to achieve optimal control of the desulfurization system. Therefore, compared with the prior art, the sulfur dioxide concentration calculation method proposed in the embodiment of the present application can provide a basis for the precise adjustment of the automatic control loop of the net flue gas sulfur dioxide concentration of wet desulfurization, which is conducive to optimizing the control loop, so that the limestone material can be accurately controlled to achieve ultra-low sulfur dioxide emissions and improve quality and efficiency; and the technical solution of the embodiment of the present application is simple and convenient to implement, easy to popularize, and has a wider range of applications.
[0037] Figure 2 A flow chart of a method for calculating sulfur dioxide concentration provided in another embodiment of the present application. The above technical solution is further optimized and expanded, and can be combined with the above optional implementation methods. Figure 2 As shown, the sulfur dioxide concentration calculation method may include the following steps:
[0038] S201. Extract the original flue gas flow rate, original flue gas sulfur dioxide concentration, limestone slurry flow rate and limestone slurry fineness from the flue gas to be identified.
[0039] S202, identifying, by particle swarm optimization algorithm, a first parameter set corresponding to the original flue gas flow rate, a second parameter set corresponding to the original flue gas sulfur dioxide concentration, a third parameter set corresponding to the limestone slurry flow rate, and a fourth parameter set corresponding to the limestone slurry fineness.
[0040] In this step, each parameter of the first parameter set, the second parameter set, the third parameter set and the fourth parameter set can be used as a particle to construct a particle swarm; then a position and a speed are randomly assigned to each particle in the particle swarm in the search space; if the particle swarm does not meet the stop update condition, the position and speed of each particle in the particle swarm are updated, and the above operation is repeated until the particle swarm meets the stop update condition, and the optimal solution for each particle in the particle swarm is obtained.
[0041] S203, inputting the original flue gas flow rate, the original flue gas sulfur dioxide concentration, the limestone slurry flow rate and the limestone slurry fineness into a pre-constructed net flue gas sulfur dioxide concentration control model respectively.
[0042] S204. Calculate the weighted value of the original flue gas flow rate, the weighted value of the original flue gas sulfur dioxide concentration, the weighted value of the limestone slurry flow rate and the weighted value of the limestone slurry fineness based on the first parameter set, the second parameter set, the third parameter set and the fourth parameter set respectively through the first calculation sub-model, the second calculation sub-model, the third calculation sub-model and the fourth calculation sub-model in the net flue gas sulfur dioxide concentration control model.
[0043] In this step, the original flue gas flow rate can be expressed as x mgas (s), the weighted value of the original flue gas flow can be expressed as G 11 (s); the original flue gas sulfur dioxide concentration can be expressed as x cso2 (s), the weighted value of the original flue gas sulfur dioxide concentration can be expressed as G 21 (s); the limestone slurry flow rate can be expressed as x mcaco3 (s), the weighted value of limestone slurry flow can be expressed as G 31 (s); the fineness of limestone slurry can be expressed as x fcaco3 (s), the weighted value of limestone slurry fineness can be expressed as G 41 (s).
[0044] S205. Calculate the concentration of sulfur dioxide in the flue gas to be identified based on the original flue gas flow rate, the original flue gas sulfur dioxide concentration, the limestone slurry flow rate and the limestone slurry fineness, as well as the weighted value of the original flue gas flow rate, the weighted value of the original flue gas sulfur dioxide concentration, the weighted value of the limestone slurry flow rate and the weighted value of the limestone slurry fineness.
[0045] In this step, the concentration y of sulfur dioxide in the flue gas to be identified can be calculated according to the following formula: so2 (s): y so2 (s) = G 11 (s)x mgas (s)+G 21 (s)x cso2 (s)+G 31(s)x mcaco3 (s)+G 41 (s)x fcaco3 (s).
[0046] The sulfur dioxide concentration calculation method proposed in the embodiment of the present application first extracts the original flue gas flow, the original flue gas sulfur dioxide concentration, the limestone slurry flow and the limestone slurry fineness in the flue gas to be identified; then, a first parameter set corresponding to the original flue gas flow, a second parameter set corresponding to the original flue gas sulfur dioxide concentration, a third parameter set corresponding to the limestone slurry flow and a fourth parameter set corresponding to the limestone slurry fineness are identified through a particle swarm optimization algorithm; then, the original flue gas flow, the original flue gas sulfur dioxide concentration, the limestone slurry flow and the limestone slurry fineness are respectively input into a pre-constructed net flue gas sulfur dioxide concentration control model; finally, the net flue gas sulfur dioxide concentration control model is used to output the concentration of sulfur dioxide in the flue gas to be identified based on the first parameter set, the second parameter set, the third parameter set and the fourth parameter set. That is to say, in the technical solution of the present application, the first parameter set corresponding to the original flue gas flow rate, the second parameter set corresponding to the original flue gas sulfur dioxide concentration, the third parameter set corresponding to the limestone slurry flow rate, and the fourth parameter set corresponding to the limestone slurry fineness can be identified by the particle swarm optimization algorithm, so as to calculate the concentration of sulfur dioxide in the flue gas to be identified. In the prior art, the control loop of the desulfurization system of many thermal power units still adopts a manual control method, which wastes a lot of manpower and material resources, has a low degree of automation, and has low control accuracy and generalization ability, making it difficult to achieve optimal control of the desulfurization system. Therefore, compared with the prior art, the sulfur dioxide concentration calculation method proposed in the embodiment of the present application can provide a basis for the precise adjustment of the automatic control loop of the net flue gas sulfur dioxide concentration of wet desulfurization, which is conducive to optimizing the control loop, so that the limestone material can be accurately controlled to achieve ultra-low sulfur dioxide emissions and improve quality and efficiency; and the technical solution of the embodiment of the present application is simple and convenient to implement, easy to popularize, and has a wider range of applications.
[0047] Figure 3 A flow chart of a method for calculating sulfur dioxide concentration provided in another embodiment of the present application. The above technical solution is further optimized and expanded, and can be combined with the above optional implementation methods. Figure 3 As shown, the sulfur dioxide concentration calculation method may include the following steps:
[0048] S301. Extract the original flue gas flow rate, original flue gas sulfur dioxide concentration, limestone slurry flow rate and limestone slurry fineness from the flue gas to be identified.
[0049] S302, taking each parameter of the first parameter set corresponding to the original flue gas flow, the second parameter set corresponding to the original flue gas sulfur dioxide concentration, the third parameter set corresponding to the limestone slurry flow, and the fourth parameter set corresponding to the limestone slurry fineness as a particle to construct a particle group.
[0050] The first parameter set in the embodiment of the present application may include: K 11 , T 11 , τ 11 , T 12 ; The second parameter set may include: K 21 , T 21 , τ 21 , T 22 ; The third parameter set may include: K 31 , T 31 , τ 31 , T 32 ; The fourth parameter set may include: K 41 , T 41 , τ 41 , T 42 Therefore, the particle swarm can include: K 11 , T 11 , τ 11 , T 12 , K 21 , T 21 , τ 21 , T 22 , K 31 , T 31 , τ 31 , T 32 , K 41 , T 41 , τ 41 , T 42 .
[0051] S303, randomly assigning a position and a speed to each particle in the particle swarm in the search space.
[0052] S304: If the particle swarm does not meet the stop updating condition, the position and velocity of each particle in the particle swarm are updated, and the above operation is repeated until the particle swarm meets the stop updating condition, and the optimal solution for each particle in the particle swarm is obtained.
[0053] In this step, the global optimal position and the historical optimal position of each particle in the particle swarm can be calculated; then, according to the global optimal position and the historical optimal position of each particle in the particle swarm, the position and speed of each particle in the particle swarm are updated. Specifically, the position and speed of each particle in the particle swarm, as well as the global optimal position and the historical optimal position of each particle in the particle swarm can be input into a pre-set first update model, and the position of each particle in the particle swarm is output through the first update model. The position and speed of each particle in the particle swarm can also be input into a pre-set second update model, and the speed of each particle in the particle swarm is output through the second update model.
[0054] Particle Swarm Optimization (PSO) is a global optimization algorithm based on swarm intelligence, which is used to solve continuous nonlinear optimization problems. PSO searches for the optimal solution by simulating the movement patterns of bird flocks or fish schools, where each individual is called a "particle" and all particles search together in the solution space. The basic concepts in the particle swarm optimization algorithm can include: particles, positions, speeds, historical optimal positions, and global optimal positions. Particles: In PSO, each solution is regarded as a particle in the search space. Position: The position of each particle represents a possible solution to the problem. Speed: The speed of each particle determines how it moves in the search space. Historical optimal position (pbest): The best position experienced by each particle. Global optimal position (gbest): The best position experienced by the entire group. In some variants, a local optimal position (lbest) can also be defined, which is the best position of a group of neighboring particles.
[0055] Specifically, the particle swarm optimization algorithm may include the following operation steps: 1) Initialization: Randomly initialize the position and speed of a group of particles. 2) Evaluation: Calculate the fitness value of the current position of each particle. 3) Update the historical optimal position: If the current position of the particle is better than its historical optimal position, update the historical optimal position. 4) Determine the global optimal position: Select the best from the historical optimal positions of all particles as the global optimal position. 5) Update speed and position: Adjust the speed and position of each particle according to the historical optimal position and the global optimal position. 6) Termination condition: Repeat steps 2) to 5) until a predetermined termination condition is met, such as reaching the maximum number of iterations or finding a satisfactory solution.
[0056] S305. Calculate the weighted value of the original flue gas flow rate, the weighted value of the original flue gas sulfur dioxide concentration, the weighted value of the limestone slurry flow rate and the weighted value of the limestone slurry fineness based on the first parameter set, the second parameter set, the third parameter set and the fourth parameter set respectively through the first calculation sub-model, the second calculation sub-model, the third calculation sub-model and the fourth calculation sub-model in the net flue gas sulfur dioxide concentration control model.
[0057] In this step, the original flue gas flow rate can be expressed as x mgas (s), the weighted value of the original flue gas flow can be expressed as G 11 (s); the original flue gas sulfur dioxide concentration can be expressed as x cso2 (s), the weighted value of the original flue gas sulfur dioxide concentration can be expressed as G 21 (s); the limestone slurry flow rate can be expressed as x mcaco3 (s), the weighted value of limestone slurry flow can be expressed as G 31 (s); the fineness of limestone slurry can be expressed as xfcaco3 (s), the weighted value of limestone slurry fineness can be expressed as G 41 (s).
[0058] S306. Calculate the concentration of sulfur dioxide in the flue gas to be identified based on the original flue gas flow rate, the original flue gas sulfur dioxide concentration, the limestone slurry flow rate and the limestone slurry fineness, as well as the weighted value of the original flue gas flow rate, the weighted value of the original flue gas sulfur dioxide concentration, the weighted value of the limestone slurry flow rate and the weighted value of the limestone slurry fineness.
[0059] In this step, the concentration y of sulfur dioxide in the flue gas to be identified can be calculated according to the following formula: so2 (s): y so2 (s) = G 11 (s)x mgas (s)+G 21 (s)x cso2 (s)+G 31 (s)x mcaco3 (s)+G 41 (s)x fcaco3 (s).
[0060] Figure 4 This is a schematic diagram of the structure of a clean flue gas sulfur dioxide concentration control model provided in an embodiment of the present application. Figure 4 As shown, G 11 (s), G 21 (s), G 31 (s) and G 41 (s) are calculated by the first calculation sub-model, the second calculation sub-model, the third calculation sub-model and the fourth calculation sub-model respectively. Specifically,
[0061]
[0062] The sulfur dioxide concentration calculation method proposed in the embodiment of the present application first extracts the original flue gas flow, the original flue gas sulfur dioxide concentration, the limestone slurry flow and the limestone slurry fineness in the flue gas to be identified; then, a first parameter set corresponding to the original flue gas flow, a second parameter set corresponding to the original flue gas sulfur dioxide concentration, a third parameter set corresponding to the limestone slurry flow and a fourth parameter set corresponding to the limestone slurry fineness are identified through a particle swarm optimization algorithm; then, the original flue gas flow, the original flue gas sulfur dioxide concentration, the limestone slurry flow and the limestone slurry fineness are respectively input into a pre-constructed net flue gas sulfur dioxide concentration control model; finally, the net flue gas sulfur dioxide concentration control model is used to output the concentration of sulfur dioxide in the flue gas to be identified based on the first parameter set, the second parameter set, the third parameter set and the fourth parameter set. That is to say, in the technical solution of the present application, the first parameter set corresponding to the original flue gas flow rate, the second parameter set corresponding to the original flue gas sulfur dioxide concentration, the third parameter set corresponding to the limestone slurry flow rate, and the fourth parameter set corresponding to the limestone slurry fineness can be identified by the particle swarm optimization algorithm, so as to calculate the concentration of sulfur dioxide in the flue gas to be identified. In the prior art, the control loop of the desulfurization system of many thermal power units still adopts a manual control method, which wastes a lot of manpower and material resources, has a low degree of automation, and has low control accuracy and generalization ability, making it difficult to achieve optimal control of the desulfurization system. Therefore, compared with the prior art, the sulfur dioxide concentration calculation method proposed in the embodiment of the present application can provide a basis for the precise adjustment of the automatic control loop of the net flue gas sulfur dioxide concentration of wet desulfurization, which is conducive to optimizing the control loop, so that the limestone material can be accurately controlled to achieve ultra-low sulfur dioxide emissions and improve quality and efficiency; and the technical solution of the embodiment of the present application is simple and convenient to implement, easy to popularize, and has a wider range of applications.
[0063] Figure 5 This is a schematic diagram of the structure of a sulfur dioxide concentration calculation device provided in one embodiment of the present application. Figure 5 As shown, the sulfur dioxide concentration calculation device includes: an extraction module 501, an identification module 502, an input module 503 and a calculation module 504; wherein,
[0064] The extraction module 501 is used to extract the original flue gas flow rate, the original flue gas sulfur dioxide concentration, the limestone slurry flow rate and the limestone slurry fineness from the flue gas to be identified;
[0065] The identification module 502 is used to identify the first parameter set corresponding to the original flue gas flow rate, the second parameter set corresponding to the original flue gas sulfur dioxide concentration, the third parameter set corresponding to the limestone slurry flow rate and the fourth parameter set corresponding to the limestone slurry fineness through a particle swarm optimization algorithm;
[0066] The input module 503 is used to input the original flue gas flow rate, the original flue gas sulfur dioxide concentration, the limestone slurry flow rate and the limestone slurry fineness into a pre-constructed net flue gas sulfur dioxide concentration control model;
[0067] The calculation module 504 is used to output the concentration of sulfur dioxide in the flue gas to be identified based on the first parameter set, the second parameter set, the third parameter set and the fourth parameter set through the net flue gas sulfur dioxide concentration control model.
[0068] The above sulfur dioxide concentration calculation device can execute the method provided in any embodiment of the present application, and has the corresponding functional modules and beneficial effects of the execution method. For technical details not described in detail in this embodiment, please refer to the sulfur dioxide concentration calculation method provided in any embodiment of the present application.
[0069] Figure 6 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application. Figure 6 A block diagram of an exemplary electronic device suitable for implementing the embodiments of the present application is shown. Figure 6 The electronic device 12 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.
[0070] like Figure 6 As shown, the electronic device 12 is in the form of a general purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).
[0071] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.
[0072] The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.
[0073] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 6 not shown, usually called a "hard drive"). Although Figure 6 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical medium) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application.
[0074] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described herein.
[0075] The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), may communicate with one or more devices that enable a user to interact with the electronic device 12, and / or may communicate with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the electronic device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via the bus 18. It should be understood that although Figure 6 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.
[0076] The processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, such as implementing the sulfur dioxide concentration calculation method provided in the embodiment of the present application.
[0077] The embodiment of the present application also provides a computer storage medium.
[0078] The computer-readable storage medium of the embodiment of the present application can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, - but not limited to - a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive lists) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.
[0079] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.
[0080] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
[0081] Computer program code for performing the operation of the present application can be written in one or more programming languages or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).
[0082] The embodiment of the present application also provides a computer program product.
[0083] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer program products, which can include one or more computer programs, which can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor, which can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.
[0084] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.
Claims
1. A method for calculating sulfur dioxide concentration, characterized in that: The method comprises: Extracting the original flue gas flow, original flue gas sulfur dioxide concentration, limestone slurry flow and limestone slurry fineness from the flue gas to be identified; Identify, by particle swarm optimization algorithm, a first parameter set corresponding to the original flue gas flow rate, a second parameter set corresponding to the original flue gas sulfur dioxide concentration, a third parameter set corresponding to the limestone slurry flow rate, and a fourth parameter set corresponding to the limestone slurry fineness; Inputting the raw flue gas flow rate, the raw flue gas sulfur dioxide concentration, the limestone slurry flow rate and the limestone slurry fineness into a pre-constructed net flue gas sulfur dioxide concentration control model respectively; The concentration of sulfur dioxide in the flue gas to be identified is outputted through the clean flue gas sulfur dioxide concentration control model based on the first parameter set, the second parameter set, the third parameter set and the fourth parameter set.
2. The method according to claim 1, characterized in that The first parameter set includes: K 11 , T 11 , τ 11 , T 12 ; The second parameter set includes: K 21 , T 21 , τ 21 , T 22 ; The third parameter set includes: K 31 , T 31 , τ 31 , T 32 ; The fourth parameter set includes: K 41 , T 41 , τ 41 , T 42 .
3. The method according to claim 2, characterized in that Outputting the concentration of sulfur dioxide in the flue gas to be identified based on the first parameter set, the second parameter set, the third parameter set, and the fourth parameter set through the net flue gas sulfur dioxide concentration control model includes: The first calculation submodel, the second calculation submodel, the third calculation submodel and the fourth calculation submodel in the net flue gas sulfur dioxide concentration control model respectively calculate the weighted value of the original flue gas flow rate, the weighted value of the original flue gas sulfur dioxide concentration, the weighted value of the limestone slurry flow rate and the weighted value of the limestone slurry fineness based on the first parameter set, the second parameter set, the third parameter set and the fourth parameter set; The concentration of sulfur dioxide in the flue gas to be identified is calculated based on the original flue gas flow rate, the original flue gas sulfur dioxide concentration, the limestone slurry flow rate and the limestone slurry fineness, as well as the weighted value of the original flue gas flow rate, the weighted value of the original flue gas sulfur dioxide concentration, the weighted value of the limestone slurry flow rate and the weighted value of the limestone slurry fineness.
4. The method according to claim 3, characterized in that The first parameter set corresponding to the original flue gas flow rate, the second parameter set corresponding to the original flue gas sulfur dioxide concentration, the third parameter set corresponding to the limestone slurry flow rate, and the fourth parameter set corresponding to the limestone slurry fineness are identified by a particle swarm optimization algorithm, including: Taking each parameter of the first parameter set, the second parameter set, the third parameter set and the fourth parameter set as a particle to construct a particle swarm; Randomly assigning a position and a velocity to each particle in the particle swarm in the search space; If the particle swarm does not meet the stop updating condition, the position and speed of each particle in the particle swarm are updated, and the above operation is repeatedly performed until the particle swarm meets the stop updating condition, and the optimal solution of each particle in the particle swarm is obtained.
5. The method according to claim 4, characterized in that The position and velocity of each particle in the particle group are updated, including: Calculating the global optimal position and the historical optimal position of each particle in the particle swarm; The position and speed of each particle in the particle swarm are updated according to the global optimal position and the historical optimal position of each particle in the particle swarm.
6. The method according to claim 5, characterized in that Updating the position of each particle in the particle group, including: The position and speed of each particle in the particle swarm, as well as the global optimal position and the historical optimal position of each particle in the particle swarm are input into a pre-set first update model, and the position of each particle in the particle swarm is output through the first update model.
7. The method according to claim 5, characterized in that Updating the velocity of each particle in the particle group, including: The position and speed of each particle in the particle group are input into a preset second update model, and the speed of each particle in the particle group is output through the second update model.
8. A sulfur dioxide concentration calculation device, characterized in that: The device comprises: an extraction module, an identification module, an input module and a calculation module; wherein, The extraction module is used to extract the original flue gas flow rate, the original flue gas sulfur dioxide concentration, the limestone slurry flow rate and the limestone slurry fineness from the flue gas to be identified; The identification module is used to identify the first parameter set corresponding to the original flue gas flow rate, the second parameter set corresponding to the original flue gas sulfur dioxide concentration, the third parameter set corresponding to the limestone slurry flow rate, and the fourth parameter set corresponding to the limestone slurry fineness through a particle swarm optimization algorithm; The input module is used to input the original flue gas flow rate, the original flue gas sulfur dioxide concentration, the limestone slurry flow rate and the limestone slurry fineness into a pre-constructed net flue gas sulfur dioxide concentration control model respectively; The calculation module is used to output the concentration of sulfur dioxide in the flue gas to be identified based on the first parameter set, the second parameter set, the third parameter set and the fourth parameter set through the net flue gas sulfur dioxide concentration control model.
9. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the sulfur dioxide concentration calculation method according to any one of claims 1 to 7.
10. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the sulfur dioxide concentration calculation method according to any one of claims 1 to 7 is implemented.