Combined desulfurization optimization operation method of circulating fluidized bed boiler and circulating fluidization system

Abstract

The invention belongs to the technical field of electric digital data processing, and particularly relates to a combined desulfurization optimized operation method of a circulating fluidized bed boiler and a circulating fluidization system.The combined desulfurization optimized operation method of the circulating fluidized bed boiler comprises the steps that the fitness of each particle is calculated; obtaining a historical optimal cost vector and a population minimum cost vector; calculating position vectors of the particles after iteration; according to the position vectors of the particles after iteration, obtaining operation parameters of the corresponding circulating fluidized bed boiler, the input amount of lime in the boiler, the input amount of a tail desulfurizer and operation parameters of a denitration device; according to the method, the optimal solution of each parameter can be found through the particle swarm optimization, the effect of coupling in-boiler desulfurization, tail desulfurization and denitration of the boiler is fully considered in calculation, and meanwhile, the emission of sulfur dioxide and nitrogen oxide reaches the standard, and economic operation is achieved; and the requirement for continuous optimization of the combustion efficiency and nitrogen oxide control of the circulating fluidized bed boiler is met.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of digital data processing, and particularly relates to a machine learning method, and in particular to a combined desulfurization optimization operation method for a circulating fluidized bed boiler and a circulating fluidized system. BACKGROUND

[0002] In the field of sulfur dioxide control of a circulating fluidized bed boiler, it is difficult for a traditional single method to achieve an ultra-low emission target: on the one hand, in-furnace desulfurization plays a core role, and a desulfurization reaction is achieved in the furnace by quantitatively spraying limestone powder into the combustion zone, the particles of which are violently turbulent in the fluidized boiling to achieve the in-furnace desulfurization reaction (such as a main reaction path CaCO3+SO2+1 / 2O2→CaSO4+CO2), and the conversion efficiency is as high as 90% in an optimal temperature range of 850-950℃ (covering more than 80% of SO2 generation load), and more importantly, the carrying effect of circulating ash (residence time > 100 seconds) forms a self-strengthening desulfurization surface renewal mechanism, which significantly reduces the final residual sulfur content; on the other hand, to deal with the residual SO2 escape under high-sulfur coal or load fluctuation, an external desulfurization module captures the free SO2 in the flue gas (reaction formula Ca(OH)2+SO2→CaSO3+H2O) through an external semi-dry desulfurization or wet desulfurization.

[0003] However, the in-furnace desulfurization is affected by the operation parameters of the boiler, and the input of limestone in the furnace also affects the generation of nitrogen oxides, thereby causing pressure on the denitration system, and in addition, the calcium oxide that does not undergo complete reaction in the furnace also enters the tail desulfurization system, which affects the tail desulfurization system.

[0004] Therefore, it is urgent to develop a new combined desulfurization optimization operation method for a circulating fluidized bed boiler and a circulating fluidized system, so as to solve the technical problems of low combustion efficiency and inaccurate control of nitrogen oxides caused by adjusting the in-furnace desulfurization and external desulfurization parameters according to the sulfur content in the fuel.

[0005] It should be noted that the above information disclosed in the background section of the present application is only used to understand the background of the present application, and therefore, the above description is not considered to constitute prior art information. SUMMARY

[0006] The embodiments of the present disclosure at least provide a combined desulfurization optimization operation method for a circulating fluidized bed boiler and a circulating fluidized system.

[0007] In a first aspect, the embodiments of the present disclosure provide a combined desulfurization optimization operation method of a circulating fluidized bed boiler, which comprises the following steps: S1, based on a particle swarm algorithm, an upper computer obtains particles according to a load, a bed temperature, primary and secondary air matching, a bed pressure, an oxygen content, a suspension section differential pressure, an in-furnace lime input amount, a tail section desulfurizing agent input amount and a denitration agent injection flow rate of a denitration device of the circulating fluidized bed boiler to form a particle swarm; S2, the upper computer calculates an adaptability of each particle according to a target function, and the adaptability is a cost value; S3, the adaptability of each particle is compared with a value of a historically best cost to make the upper computer obtain a historically best cost vector by traversing the adaptability of each particle; S4, the adaptability of each particle is compared with a minimum cost to make the upper computer obtain a population minimum cost vector by traversing the adaptability of each particle; S5, the upper computer calculates a position vector and a velocity vector of a particle after iteration according to a velocity vector iteration formula, a position vector iteration formula, the historically best cost vector and the population minimum cost vector; S6, when the particle after iteration meets an iteration number, the upper computer obtains the load, the bed temperature, the primary and secondary air matching, the bed pressure, the oxygen content, the suspension section differential pressure, the in-furnace lime input amount, the tail section desulfurizing agent input amount and the denitration agent injection flow rate of the denitration device of the circulating fluidized bed boiler according to the position vector of the particle after iteration, and calculates a cost value according to the target function, and executes S8; S7, when the particle after iteration does not meet the iteration number, the upper computer executes S2; and S8, the upper computer sets an operation parameter of the circulating fluidized bed boiler, the in-furnace lime input amount, the tail section desulfurizing agent input amount and an operation parameter of the denitration device according to the load, the bed temperature, the primary and secondary air matching, the bed pressure, the oxygen content, the suspension section differential pressure, the in-furnace lime input amount, the tail section desulfurizing agent input amount and the denitration agent injection flow rate of the denitration device of the circulating fluidized bed boiler obtained by the particle after iteration.

[0008] In an optional embodiment, the load is L; the bed temperature is T; the primary and secondary air matching is A k , and k takes 1, 2 or 3; the bed pressure is B k , and k takes a positive integer; the oxygen content is OX k , and k takes a positive integer; the suspension section differential pressure is DP k , and k takes 1 or 2; the in-furnace lime input amount is C k , and k takes a positive integer; the tail section desulfurizing agent input amount is G k , and k takes a positive integer; the denitration agent injection flow rate is Q k , and k takes a positive integer; and the particle i is (L, T, A k , B k , OX k , DP k , C k , G k , Q k ).

[0009] In an alternative embodiment, the objective function is configured to calculate the total cost of boiler combustion and pollution control, i.e., the objective function V = f(L, T, A k ,B k ,OX k ,DP k ,C k ,G k ,Q k ); the host computer performs deep learning on the historical operation data of the boiler through a neural network-based deep learning module and a data prediction module to obtain a correlation model between the operation parameters of the boiler and the cost, and predicts the cost of the boiler under different operation parameters through the data prediction module, i.e., through the neural network-based deep learning module in the host computer, the historical operation data of the boiler is analyzed periodically for self-learning to obtain a correlation model between the cost V and the operation parameters (L, T, A k ,B k ,OX k ,DP k ,C k ,G k ,Q k ) of the boiler; and through the data prediction module in the host computer, the correlation model trained by the deep learning module is called, and the operation parameters (L, T, A k ,B k ,OX k ,DP k ,C k ,G k ,Q k ) of each particle i provided in the particle swarm algorithm are predicted to obtain the fitness of the particle.

[0010] In an alternative embodiment, the step S3 comprises: traversing the fitness of each particle; comparing the fitness of each particle with the value of the historically achieved best cost; when the fitness of the particle i is less than the value of the historically achieved best cost, the position vector of the particle i is replaced by the historical best cost vector P besti .

[0011] In an alternative embodiment, the step S4 comprises: traversing the fitness of each particle; comparing the fitness of each particle with the value of the minimum cost; when the fitness of the particle i is less than the value of the minimum cost, the position vector of the particle i is replaced by the population minimum cost vector G best .

[0012] In an alternative embodiment, the step S5 comprises: the velocity vector iteration formula is V i+1 =wV i +c1r1(P besti -X i)+c2r2(G best -X i The iterative formula for the position vector is X. i+1 =X i +V i+1 Where w represents the weights of the particle swarm optimization algorithm, and V... i Let X be the velocity vector of particle i. i Let V be the position vector of particle i, c1 and c2 be learning factors, r1 and r2 be random probability values, and V be the position vector of particle i. i+1 Let X be the velocity vector of particle (i+1). i+1 Let be the position vector of particle (i+1).

[0013] In an optional implementation, step S6 includes: the host computer determining whether particle (i+1) satisfies the iteration count; when the host computer determines that the particle satisfies the iteration count after iteration, the host computer determines the iteration count based on the position vector X. i+1 Obtain the corresponding circulating fluidized bed boiler's load L, bed temperature T, and primary and secondary air matching A. k Bed pressure B k Oxygen content (OX) k , suspending differential pressure DP k The amount of limestone added to the furnace is C. k The amount of desulfurizing agent added at the tail end is G. k The denitrification agent injection flow rate Q of the denitrification unit k The cost value V is calculated based on the objective function; the host computer executes step S8.

[0014] In a second aspect, the embodiments of the present disclosure further provide a circulating fluidized system, comprising: a host computer, a circulating fluidized bed boiler, and a denitration device; wherein based on a particle swarm algorithm, the host computer acquires particles according to a load of the circulating fluidized bed boiler, a bed temperature, a primary and secondary air matching, a bed pressure, an oxygen content, a suspended section differential pressure, an in-furnace lime input amount, a tail section desulfurizer input amount, and a denitration agent injection flow rate of the denitration device to form a particle swarm; the host computer calculates a fitness of each particle according to a target function, and the fitness is a cost value; the fitness of each particle is compared with a value of a historically best cost to enable the host computer to acquire a historically best cost vector by traversing the fitness of each particle; the fitness of each particle is compared with a minimum cost to enable the host computer to acquire a population minimum cost vector by traversing the fitness of each particle; the host computer calculates a position vector and a velocity vector of a particle after iteration according to a velocity vector iteration formula, a position vector iteration formula, the historically best cost vector, and the population minimum cost vector; when the particle after iteration meets an iteration number, the host computer acquires the load of the circulating fluidized bed boiler, the bed temperature, the primary and secondary air matching, the bed pressure, the oxygen content, the suspended section differential pressure, the in-furnace lime input amount, the tail section desulfurizer input amount, and the denitration agent injection flow rate of the denitration device according to the position vector of the particle after iteration, and calculates a cost value according to the target function, and executes step S8; when the particle after iteration does not meet the iteration number, the host computer executes step S2; and the host computer sets an operation parameter of the circulating fluidized bed boiler, the in-furnace limestone input amount, the tail section desulfurizer input amount, and an operation parameter of the denitration device according to the load of the circulating fluidized bed boiler, the bed temperature, the primary and secondary air matching, the bed pressure, the oxygen content, the suspended section differential pressure, the in-furnace lime input amount, the tail section desulfurizer input amount, and the denitration agent injection flow rate of the denitration device acquired by the particle after iteration.

[0015] In an optional embodiment, the host computer is adapted to set the operation parameter of the circulating fluidized bed boiler, the in-furnace limestone input amount, the tail section desulfurizer input amount, and the operation parameter of the denitration device by using the circulating fluidized bed boiler combined desulfurization optimization operation method as described above.

[0016] In a third aspect, the embodiments of the present disclosure further provide a non-transitory readable storage medium having a program / instruction stored thereon, wherein the program / instruction is executed by a processor to implement the steps of the circulating fluidized bed boiler combined desulfurization optimization operation method as described above.

[0017] The beneficial effect of the present application is that the present application can find the optimal solution of each parameter through the particle swarm algorithm by constructing the particle group composed of the load of the circulating fluidized bed boiler, the bed temperature, the primary and secondary air matching, the bed pressure, the oxygen content, the suspension section differential pressure, the lime input amount in the furnace, the tail desulfurizer input amount and the denitration device denitration agent injection flow, so as to realize the full consideration of the coupling effect of the furnace desulfurization, the tail desulfurization and the denitration in the calculation, and meet the sulfur dioxide and nitrogen oxide emission standard and economic operation, and meet the demand for continuous optimization of the combustion efficiency and nitrogen oxide control of the circulating fluidized bed boiler.

[0018] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application will be realized and attained by the structure particularly pointed out in the description and the drawings.

[0019] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the preferred embodiments are described in detail below, and the accompanying drawings are described as follows. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings without creative labor on the basis of these drawings.

[0021] Fig. 1 A flow chart of a combined desulfurization optimization operation method of a circulating fluidized bed boiler provided by the embodiment of the present disclosure is shown in the figure. Fig. 2 A flow chart of particle iteration provided by the embodiment of the present disclosure is shown in the figure. Fig. 3 A principle block diagram of a circulating fluidized system provided by the embodiment of the present disclosure is shown in the figure. DETAILED DESCRIPTION

[0022] In order to make the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the present application will be described clearly and completely in the following with reference to the drawings. Obviously, the described embodiments are some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0023] The terminology used herein is for the purpose of describing particular example configurations only and is not intended to be limiting. As used herein, the singular articles "a," "an," and "the" can be intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises," "comprising," "including," and "having" are inclusive and therefore specify the presence of stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order

[0024] As used herein, the phrases "in an embodiment," "according to an embodiment," "in some embodiments," and the like generally mean the particular feature, structure, or characteristic following the phrase can be included in at least one embodiment of the present disclosure. Thus, appearances of such phrases in various places throughout this specification are not necessarily intended to refer to the same embodiment. As used herein, the term "for example" or "e.g." means "for the purpose of example, illustration, and / or elucidation." Any implementation, aspect or design described herein as "example" or "exemplary" is not necessarily to be construed as preferred or advantageous over other implementations, aspects or designs.

[0025] Particle swarm optimization: is a random search algorithm based on group cooperation developed by simulating the foraging behavior of bird swarm. In particle swarm optimization, the solution of each optimization problem is a bird in the search space, called "particle", and the optimal solution of the problem corresponds to the "corn field" searched in the bird swarm. All particles have a position vector (the position of the particle in the solution space) and a speed vector (the direction and speed of the next flight), and the fitness value of the current position can be calculated according to the objective function, which can be understood as the distance from the "corn field". In each iteration, the examples in the population can not only learn from their own experience (historical position), but also learn from the "experience" of the best particle in the population, so as to determine how to adjust and change the direction and speed of flight in the next iteration. In this way, step by step iteration, the examples of the whole population will gradually tend to the optimal solution.

[0026] It is found through research that the current method for combined desulfurization of circulating fluidized bed boiler uses in-furnace desulfurization combined with tail-end semi-dry or wet desulfurization technology. According to the sulfur content in the coal, first, limestone powder is sprayed into the circulating fluidized bed furnace to realize the first step of dry desulfurization, then the sulfur dioxide concentration at the furnace outlet is determined, and finally the tail-end desulfurization device is used to control the final sulfur dioxide concentration to the concentration required by the environmental protection standard.

[0027] Although the related technology can achieve deep control of sulfur dioxide of circulating fluidized bed boiler, there are still problems unsolved. One of them is that it is one-sided to adjust the matching of desulfurization in the furnace and desulfurization outside the furnace only according to the sulfur content in the fuel, which cannot control more accurately and economically, and other operating parameters of boiler combustion need to be considered, including bed temperature, oxygen content, bed pressure, suspended section differential pressure, nitrogen oxide generation, etc.

[0028] Based on the above research, the embodiment of the present disclosure provides a circulating fluidized bed boiler combined desulfurization optimization operation method and a circulating fluidized system. The particle swarm algorithm can find the optimal solution of each parameter, realize the full consideration of the coupling effect of boiler in-furnace desulfurization, tail desulfurization and denitration in the calculation, and at the same time meet the requirements of sulfur dioxide and nitrogen oxide emission standard and economic operation, meet the needs of continuous optimization of circulating fluidized bed boiler combustion efficiency and nitrogen oxide control.

[0029] The defects of the above schemes are the results of the inventors after practice and careful research, therefore, the discovery process of the above problems and the solutions proposed by the present disclosure to solve the above problems in the following should be the contribution of the inventors to the present disclosure in the process of the present disclosure.

[0030] It should be noted that similar reference numerals and letters indicate similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0031] Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. In the case of no conflict, the following embodiments and features in the embodiments can be combined with each other.

[0032] As Figs. 1-3As shown, at least one embodiment provides a circulating fluidized bed boiler combined desulfurization optimization operation method, which comprises: step S1: based on a particle swarm algorithm, an upper computer obtains particles according to the load, bed temperature, primary and secondary air matching, bed pressure, oxygen content, suspension section differential pressure, in-furnace lime input amount, tail section desulfurizing agent input amount and denitration device denitration agent injection flow rate of the circulating fluidized bed boiler to form a particle swarm; step S2: the upper computer calculates the fitness of each particle according to a target function, and the fitness is a cost value; step S3: the fitness of each particle is compared with the value of the historically best cost to enable the upper computer to obtain a historically best cost vector by traversing the fitness of each particle; step S4: the fitness of each particle is compared with the minimum cost to enable the upper computer to obtain a population minimum cost vector by traversing the fitness of each particle; step S5: the upper computer calculates the position vector and velocity vector of the particle after iteration according to a velocity vector iteration formula, a position vector iteration formula, the historically best cost vector and the population minimum cost vector; step S6: when the particle after iteration meets the iteration number, the upper computer obtains the load, bed temperature, primary and secondary air matching, bed pressure, oxygen content, suspension section differential pressure, in-furnace lime input amount, tail section desulfurizing agent input amount and denitration device denitration agent injection flow rate of the corresponding circulating fluidized bed boiler according to the position vector of the particle after iteration, and calculates a cost value according to the target function, and step S8 is performed; step S7: when the particle after iteration does not meet the iteration number, step S2 is performed; step S8: the upper computer sets the operation parameters of the circulating fluidized bed boiler, in-furnace limestone input amount, tail section desulfurizing agent input amount and operation parameters of the denitration device according to the load, bed temperature, primary and secondary air matching, bed pressure, oxygen content, suspension section differential pressure, in-furnace lime input amount, tail section desulfurizing agent input amount and denitration device denitration agent injection flow rate of the circulating fluidized bed boiler obtained by the particle after iteration.

[0033] Specifically, the particle swarm algorithm can also attempt to be replaced by a genetic algorithm, an ant colony algorithm and the like. Taking the genetic algorithm as an example, both the particle swarm algorithm and the genetic algorithm are global optimization algorithms that attempt to simulate the adaptability of individual populations on the basis of natural characteristics, both start from randomly generating an initial population in a solution space, and then search in the global solution space, and the search focus is concentrated on the part with high performance. However, the genetic algorithm has strong global search capability and weak local search capability, and can only obtain a suboptimal solution but not an optimal solution.

[0034] In at least one embodiment, by constructing a particle group consisting of the load, bed temperature, primary and secondary air matching, bed pressure, oxygen content, suspended section differential pressure, in-furnace lime input, tail-end desulfurizer input, and denitration device denitration agent injection flow of the circulating fluidized bed boiler, the particle swarm algorithm can find the optimal solution of each parameter, fully considering the coupling effect of in-furnace desulfurization, tail-end desulfurization and denitration of the boiler in the calculation, while meeting the sulfur dioxide and nitrogen oxide emission standards and economic operation, meeting the demand for continuous optimization of the combustion efficiency and nitrogen oxide control of the circulating fluidized bed boiler.

[0035] In at least one embodiment, the load is L; the bed temperature is T, and k is a positive integer; the primary and secondary air matching is A k , and k is 1, 2, or 3; the bed pressure is B k , and k is a positive integer; the oxygen content is OX k , and k is 1 or 2; the suspended section differential pressure is DP k , and k is 1 or 2; the in-furnace limestone input is C k , and k is a positive integer; the tail-end desulfurizer input is G k , and k is a positive integer; the denitration agent injection flow is Q k , and k is a positive integer; and the particle i is (L, T, A k , B k , OX k , DP k , C k , G k , Q k ).

[0036] Specifically, the operating parameters of the circulating fluidized bed boiler are controlled and optimized, under the premise of meeting the actual production and environmental protection requirements, considering the coupling effect of in-furnace combustion efficiency, nitrogen oxide control and tail-end denitration equipment, and optimizing the boiler combustion efficiency and denitration agent use cost.

[0037] In at least one embodiment, the objective function is configured to calculate the total cost of boiler combustion and pollution control, i.e., the objective function V=f(L, T, A k , B k , OX k , DP k , C k , G k , Q k); the host computer performs deep learning on the historical operation data of the boiler based on a neural network deep learning module and a data prediction module to obtain a correlation model between the boiler operation parameters and the cost, and predicts the cost of the boiler under different operation parameters through the data prediction module, that is, through the neural network deep learning module in the host computer, the historical operation data of the boiler (preferably the historical data of the previous 30 days) is analyzed periodically (preferably every 3 days) to obtain the correlation model between the cost V and the boiler operation parameters (L, T, A k ,B k , OX k , DP k , C k , G k , Q k ); and through the data prediction module in the host computer, the correlation model trained by the deep learning module is called, and the boiler operation parameters (L, T, A k ,B k , OX k , DP k , C k , G k , Q k ) provided by each particle i in the particle swarm algorithm is predicted to obtain the fitness of the particle.

[0038] Specifically, the particle swarm algorithm is used to realize the deep control and optimization of the circulating fluidized bed sulfur dioxide and nitrogen oxides; considering the limestone consumption, tail desulfurizer consumption, and denitration agent consumption under the given sulfur dioxide and nitrogen oxides emission concentration requirements, the operation cost is minimized.

[0039] In at least one embodiment, referring to Fig. 2 , the step S3 comprises: traversing the fitness of each particle; comparing the fitness of each particle with the value of the historical best cost; when the fitness of the particle i is less than the value of the historical best cost, the position vector of the particle i is replaced by the historical best cost vector P besti .

[0040] Specifically, the fitness of each particle is traversed, the fitness of each particle is compared with the value of the historical best cost, if the fitness of the particle i is less than the value of the historical best cost, the position vector of the particle i is replaced by the historical best cost vector P besti , otherwise, the historical best cost vector P besti is not replaced.

[0041] In at least one embodiment, referring to Fig. 2Step S4 includes: traversing the fitness of each particle; comparing the fitness of each particle with the minimum cost value; when the fitness of particle i is less than the minimum cost value, replacing the position vector of particle i with the population minimum cost vector G. best .

[0042] Specifically, the fitness of each particle is iterated, and the fitness of all particles is selected. Each particle's fitness is compared with the minimum cost value. If the fitness of particle i is less than the minimum cost value, then the position vector of particle i is replaced with the vector G of the minimum cost of the population. best Otherwise, do not replace the population minimum cost vector G. best .

[0043] In at least one embodiment, step S5 includes: the velocity vector iteration formula is V i+1 =wV i +c1r1(P besti -X i )+c2r2(G best -X i The iterative formula for the position vector is X. i+1 =X i +V i+1 Where w represents the weights of the particle swarm optimization algorithm, and V... i Let X be the velocity vector of particle i. i Let V be the position vector of particle i, c1 and c2 be learning factors, r1 and r2 be random probability values, and V be the position vector of particle i. i+1 Let X be the velocity vector of particle (i+1). i+1 Let be the position vector of particle (i+1).

[0044] Specifically, V can be calculated using the velocity vector iteration formula and the position vector iteration formula. i+1 and X i+1 This allows us to find the parameters corresponding to particle (i+1).

[0045] In at least one embodiment, step S6 includes: the host computer determining whether the particle (i+1) satisfies the iteration count; when the host computer determines that the particle satisfies the iteration count after iteration, the host computer determines the iteration count based on the position vector X. i+1 Obtain the corresponding circulating fluidized bed boiler's load L, bed temperature T, and primary and secondary air matching A. k Bed pressure B k Oxygen content (OX) k , suspending differential pressure DP k The amount of limestone added to the furnace is C. k The amount of desulfurizing agent added at the tail end is G. k The denitrification agent injection flow rate Q of the denitrification unit kand the host computer calculates a cost value V according to the target function; the host computer executes step S8.

[0046] Based on the same technical concept, at least one embodiment further provides a circulating fluidized system, comprising: a host computer, a circulating fluidized bed boiler, a denitration device; wherein based on a particle swarm algorithm, the host computer acquires particles according to the load of the circulating fluidized bed boiler, the bed temperature, the primary and secondary air matching, the bed pressure, the oxygen content, the suspended section differential pressure, the lime input in the furnace, the tail section desulfurizer input, and the denitration agent injection flow of the denitration device to form a particle swarm; the host computer calculates the fitness of each particle according to a target function, and the fitness is a cost value; the fitness of each particle is compared with the value of the historically best cost to make the host computer acquire a historically best cost vector by traversing the fitness of each particle; the fitness of each particle is compared with the minimum cost to make the host computer acquire a population minimum cost vector by traversing the fitness of each particle; the host computer calculates the position vector and the velocity vector of the particles after iteration according to a velocity vector iteration formula, a position vector iteration formula, the historically best cost vector, and the population minimum cost vector; when the particles after iteration meet the iteration number, the host computer acquires the load of the circulating fluidized bed boiler, the bed temperature, the primary and secondary air matching, the bed pressure, the oxygen content, the suspended section differential pressure, the lime input in the furnace, the tail section desulfurizer input, and the denitration agent injection flow of the denitration device according to the position vector of the particles after iteration, and calculates a cost value according to the target function, and executes step S8; when the particles after iteration do not meet the iteration number, the host computer executes step S2; the host computer sets the operating parameters of the circulating fluidized bed boiler, the lime input in the furnace, the tail section desulfurizer input, and the operating parameters of the denitration device according to the load of the circulating fluidized bed boiler, the bed temperature, the primary and secondary air matching, the bed pressure, the oxygen content, the suspended section differential pressure, the lime input in the furnace, the tail section desulfurizer input, and the denitration agent injection flow of the denitration device acquired by the particles after iteration.

[0047] In at least one embodiment, the host computer is adapted to set the operating parameters of the circulating fluidized bed boiler, the lime input in the furnace, the tail section desulfurizer input, and the operating parameters of the denitration device by using the circulating fluidized bed boiler combined desulfurization optimization operation method as described above.

[0048] Based on the same technical concept, at least one embodiment further provides a non-transitory readable storage medium having a program / instruction stored thereon, wherein the program / instruction is executed by a processor to implement the steps of the circulating fluidized bed boiler combined desulfurization optimization operation method as described above.

[0049] In summary, the present application can find the optimal solution of each parameter through the particle swarm algorithm, and realize the full consideration of the coupling effect of the desulfurization in the boiler, the tail desulfurization and the denitration in the calculation, while meeting the emission standards of sulfur dioxide and nitrogen oxides and economic operation, meeting the demand for continuous optimization of the combustion efficiency and nitrogen oxides control of the circulating fluidized bed boiler.

[0050] The disclosures and other solutions, examples, embodiments, modules and functional operations described in this document can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structural equivalents of what is disclosed herein, or combinations of one or more of them. The disclosures and other embodiments can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a tangible and non-transitory computer readable medium for execution by, or to control the operation of, data processing apparatus. The computer readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter affecting a machine-readable propagated signal, or combinations of one or more of them. The term "data processing apparatus" or "data processing device" includes all apparatus, devices, and machines for processing data, including, for example, programmable processors, computers, or multiple processors or computers. In addition to hardware, the apparatus can also include code that creates an execution environment for the computer program, such as, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or combinations of one or more of them. The propagated signal is an artificially generated signal, such as, for example, a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information to be transmitted to a suitable receiver device.

[0051] A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, subprograms, or portions of code). A computer program can be deployed on one or more computers to perform the functions described, which can be located on a single site or distributed over multiple sites and interconnected through a communication network.

[0052] The processes and logic flows described in this document can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit), and / or devices can be implemented as special purpose logic circuitry, e.g., an FPGA or an ASIC.

[0053] Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0054] Although the present application file contains many details, it should not be construed as limiting the scope of any invention or claim in which it is embodied, but rather as a description of features that can be part of a specific embodiment of a specific invention. Some of the features described in this application file in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented separately or in any suitable subcombination. Moreover, although features can be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination and the claimed combination can be directed to a subcombination or variation of a subcombination.

[0055] Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring such an order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing can be advantageous. Moreover, the separation of various system components in the embodiments described in this application file should not be understood as requiring such separation in all embodiments.

[0056] Only a few implementations and examples are described and other implementations, enhancements and variations can be made based on what is described and illustrated in this application document.

[0057] A first component is directly coupled to a second component when there are no intervening components between the first component and the second component other than a wire, trace, or another medium. A first component is indirectly coupled to a second component when there are intervening components between the first component and the second component other than a wire, trace, or another medium. The term “coupled” and variations thereof include both direct and indirect coupling. Unless stated otherwise, the use of the term “about” means the range including the upper and lower 10% of the value.

[0058] While several embodiments are provided in the present disclosure, it should be understood that the disclosed system and method might be embodied in many other specific forms without departing from the spirit or scope of the present disclosure. The present examples are therefore to be considered as illustrative and not restrictive, and the intention is not to limit the concepts to the details presented, which can be modi ed. For example, the various elements or components can be combined or integrated in another system or certain features can be omitted, or not implemented.

[0059] In several embodiments provided herein, it will be understood that the disclosed devices and methods might be implemented in other ways. The device embodiments described above are merely illustrative for the possible implementations of the devices, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams can represent a module, a procedure, or a portion of code which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession might be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations thereof, can be implemented by a dedicated hardware-based system that carries out specified functions or combinations of functions or by a combination of dedicated hardware and computer instructions.

[0060] Also, the various embodiments partly and methods described and illustrated herein can be combined or integrated with other systems, modules, techniques, or methods without departing from the scope of the present disclosure. Other items shown or discussed as separate from other items or further combined can be combined or further combined. The examples, as described and illustrated, can be stored, distributed, or implemented in various ways. Bookkeeping information, other than that required in or by existing laws or regulations, should not be stored or collected unless allowed by the owners or users of the information. The size, capacity, speed, or position of the many of the elements can be changed as further example. The techniques are not inherently related to any particular electronic system, virtualization environment, hardware, software, or other systems. Various systems can be used with or images.

Claims

1. A method for optimizing the operation of a combined desulfurization system in a circulating fluidized bed boiler, characterized in that, include: Step S1: Based on the particle swarm optimization algorithm, the host computer obtains particles according to the load, bed temperature, primary and secondary air matching, bed pressure, oxygen content, differential pressure of the suspension section, lime input in the furnace, desulfurizing agent input at the tail end, and denitrification agent injection flow rate of the denitrification device of the circulating fluidized bed boiler, so as to form a particle swarm. Step S2: The host computer calculates the fitness of each particle according to the objective function, and the fitness is the cost value; Step S3: Iterate through the fitness of each particle and compare the fitness of each particle with the historical best cost value so that the host computer can obtain the historical best cost vector. Step S4: Iterate through the fitness of each particle and compare the fitness of each particle with the minimum cost so that the host computer can obtain the minimum cost vector of the population. Step S5: The host computer calculates the position vector and velocity vector of the particle after iteration based on the velocity vector iteration formula, the position vector iteration formula, the historical best cost vector, and the population minimum cost vector; Step S6: When the host computer determines that the particle meets the iteration number after iteration, it obtains the load, bed temperature, primary and secondary air matching, bed pressure, oxygen content, suspension section differential pressure, lime input in the furnace, tail desulfurizer input and denitrification agent injection flow rate of the corresponding circulating fluidized bed boiler according to the position vector of the particle after iteration, and calculates the cost value according to the objective function, and executes step S8. Step S7: If the host computer determines that the number of iterations is not met after the iteration, it executes step S2; Step S8: The host computer sets the operating parameters of the circulating fluidized bed boiler, the amount of limestone added in the furnace, the amount of desulfurizer added at the tail end, and the operating parameters of the denitrification device based on the load, bed temperature, primary and secondary air matching, bed pressure, oxygen content, differential pressure of the suspension section, amount of limestone added in the furnace, amount of desulfurizer added at the tail end, and the denitrification agent injection flow rate of the denitrification device obtained by the particles after iteration.

2. The optimized operation method for combined desulfurization of circulating fluidized bed boilers as described in claim 1, characterized in that, The load is L; The bed temperature is T; The primary and secondary winds are matched as A. k And k takes positive integer values; Bed pressure is B k And k takes positive integer values; Oxygen content is OX k And k takes positive integer values; The differential pressure of the suspension section is DP k And k takes positive integer values; The amount of limestone added to the furnace is C. k And k takes positive integer values; The amount of desulfurizing agent added at the tail end is G k And k takes positive integer values; The denitrification agent injection flow rate is Q k And k takes positive integer values; Particle i is (L,T,A) k B k OX k DP k C k G k Q k ).

3. The optimized operation method for combined desulfurization of circulating fluidized bed boilers as described in claim 2, characterized in that, The objective function is configured to calculate the total cost of boiler combustion and pollutant control, i.e., the objective function V=f(L,T,A) k B k OX k DP k C k G k Q k ), to obtain the fitness of the particle, and the fitness is the cost value; The host computer uses a neural network-based deep learning module and a data prediction module to perform deep learning on the historical boiler operating data to obtain a correlation model between boiler operating parameters and costs. The data prediction module then predicts the boiler costs under different operating parameters. The host computer uses a deep learning module based on neural networks to periodically perform self-learning analysis on historical boiler operating data to obtain the cost V and boiler operating parameters (L, T, A). k B k OX k DP k C k G k Q k The relationship model between ) and ; The data prediction module in the host computer calls the association model trained by the deep learning module, and uses the boiler operating parameters (L, T, A) provided by each particle i in the particle swarm optimization algorithm. k B k OX k DP k C k G k Q k Predictions are made to obtain the fitness of the particle.

4. The optimized operation method for combined desulfurization of circulating fluidized bed boilers as described in claim 3, characterized in that, Step S3 includes: Iterate through the fitness of each particle; Compare the fitness of each particle with the historical cost-effectiveness. When the fitness of particle i is less than the historical best cost, the position vector of particle i is replaced with the historical best cost vector P. besti .

5. The optimized operation method for combined desulfurization of circulating fluidized bed boilers as described in claim 4, characterized in that, Step S4 includes: Iterate through the fitness of each particle; Compare the fitness of each particle with the minimum cost value; When the fitness of particle i is less than the minimum cost value, the position vector of particle i is replaced with the population minimum cost vector G. best .

6. The optimized operation method for combined desulfurization of circulating fluidized bed boilers as described in claim 5, characterized in that, Step S5 includes: The velocity vector iteration formula is V i+1 =wV i +c1r1(P besti -X i )+c2r2(G best -X i ); The iterative formula for the position vector is X i+1 =X i +V i+1 ; Where w is the weight of the particle swarm optimization algorithm, and V i Let X be the velocity vector of particle i. i Let V be the position vector of particle i, c1 and c2 be learning factors, r1 and r2 be random probability values, and V be the position vector of particle i. i+1 Let X be the velocity vector of particle (i+1). i+1 Let be the position vector of particle (i+1).

7. The optimized operation method for combined desulfurization of circulating fluidized bed boilers as described in claim 6, characterized in that, Step S6 includes: The host computer determines whether particle (i+1) satisfies the required number of iterations; When the host computer determines that the particle has met the required number of iterations after iteration, the host computer uses the position vector X... i+1 Obtain the corresponding circulating fluidized bed boiler's load L, bed temperature T, and primary and secondary air matching A. k Bed pressure B k Oxygen content (OX) k , suspending differential pressure DP k The amount of limestone added to the furnace is C. k The amount of desulfurizing agent added at the tail end is G. k The denitrification agent injection flow rate Q of the denitrification unit k And calculate the cost value V according to the objective function; The host computer executes step S8.

8. A circulating fluidized bed system, characterized in that, include: Supervisory control and data acquisition (SCADA) system, circulating fluidized bed boiler, denitrification unit; in Based on the particle swarm optimization algorithm, the host computer obtains particles according to the load, bed temperature, primary and secondary air matching, bed pressure, oxygen content, differential pressure of the suspension section, lime input in the furnace, desulfurizing agent input at the tail end, and denitrifying agent injection flow rate of the denitrification device of the circulating fluidized bed boiler, so as to form a particle swarm. The host computer calculates the fitness of each particle based on the objective function, and the fitness is a cost value; Iterate through the fitness of each particle and compare the fitness of each particle with the historical best cost value so that the host computer can obtain the historical best cost vector. Iterate through the fitness of each particle and compare the fitness of each particle with the minimum cost so that the host computer can obtain the minimum cost vector of the population. The host computer calculates the position and velocity vectors of the particles after iteration based on the velocity vector iteration formula, the position vector iteration formula, the historical best cost vector, and the population minimum cost vector. When the host computer determines that the particle meets the iteration number after iteration, it obtains the load, bed temperature, primary and secondary air matching, bed pressure, oxygen content, suspension section differential pressure, lime input in the furnace, tail desulfurizer input and denitrification agent injection flow rate of the corresponding circulating fluidized bed boiler according to the position vector of the particle after iteration, and calculates the cost value according to the objective function, and executes step S8. If the host computer determines that the particle does not meet the required number of iterations after iteration, it executes step S2. The host computer sets the operating parameters of the circulating fluidized bed boiler, the amount of limestone added in the furnace, the amount of desulfurizer added at the tail end, and the operating parameters of the denitrification device based on the load, bed temperature, primary and secondary air matching, bed pressure, oxygen content, differential pressure of the suspension section, amount of limestone added in the furnace, amount of desulfurizer added at the tail end, and the denitrification agent injection flow rate of the denitrification device obtained by the particles after iteration.

9. The circulating fluidized bed system as described in claim 8, characterized in that, The host computer is suitable for setting the operating parameters of the circulating fluidized bed boiler, the amount of limestone added in the furnace, the amount of desulfurizing agent added at the tail end, and the operating parameters of the denitrification device using the combined desulfurization optimization operation method for circulating fluidized bed boilers as described in any one of claims 1-7.

10. A non-transitory readable storage medium storing a program / instruction thereon, characterized in that, When executed by the processor, the program / instruction implements the steps of the combined desulfurization optimization operation method for circulating fluidized bed boilers as described in any one of claims 1 to 7.

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