Optimization Method for Variable Load Operation of Forced Circulation Steam Generation System and Related Equipment

Abstract

The present invention belongs to the field of new energy power generation systems, and discloses an optimization method for variable load operation of a forced circulation steam generation system and related equipment. By establishing a numerical calculation model of a shell-and-tube evaporator and a hydrodynamic balance model of the forced circulation system, and taking high evaporation capacity, evaporator efficiency, and minimum cost as optimization objectives, a multi-objective optimization model is constructed in combination with the changes in molten salt flow rate and circulating water flow rate. The Pareto domination method is used to solve the model, which can output the Pareto optimal solution set, and thus an optimized operation plan for the forced circulation system under different loads can be formulated according to these optimal solutions. Using this method not only helps to achieve higher evaporator efficiency and evaporation capacity, but also can effectively reduce pump power loss, achieving the key goal in the operation of a solar thermal power station, that is, the balance between high efficiency and low loss, providing strong support for the stable operation and economic benefits of the solar thermal power station.

Description

Technical Field

[0001] The present invention belongs to the field of new energy power generation systems, specifically relates to the field of steam generation systems, and particularly relates to an optimization method for variable load operation of a forced circulation steam generation system and related equipment. Background Art

[0002] As an environmentally friendly new energy power generation system, a solar thermal power station uses a large-scale array of mirrors to collect solar thermal energy, provides steam through a heat exchange device, and combines the process of a traditional steam turbine generator to achieve the purpose of power generation. In addition, in a solar thermal power station equipped with long-term molten salt energy storage, the molten salt heated by solar energy can be stored in a huge container and can still drive steam turbine power generation for several hours after sunset. It has rapid response and synchronous support capabilities, and has the ability to participate in aspects such as active power frequency regulation, reactive voltage control, and low-frequency oscillation suppression of the power grid. It can provide inertia support for the power system, is a stable and controllable zero-carbon power source, and is also a flexible power source with the same endowment as conventional thermal power for peak shaving and frequency modulation. Among them, a steam generation system with high-temperature molten salt is often equipped in a solar thermal power station to generate water vapor for subsequent steam turbine power generation and drive the steam turbine to do work. Using a forced circulation system with a circulation pump can achieve the purpose of rapid peak shaving by adjusting the pump power, which is an effective means for operating under variable load conditions.

[0003] During operation, increasing the output capacity of the forced circulation pump can accelerate the water circulation flow rate, increase the heat transfer coefficient, and enhance the evaporator efficiency, but it will increase the pump power loss; if the output of the forced circulation pump is reduced, although the pump power loss can be reduced, it will lead to a decrease in the water circulation flow rate, a decrease in the heat transfer coefficient, and a decrease in the evaporator efficiency; at the same time, the simultaneous change of the molten salt flow rate and the circulation flow rate will also affect the evaporation capacity of the steam generation system. Therefore, reasonably selecting the output power of the forced circulation pump to achieve higher evaporator efficiency, lower pump power loss, higher evaporation capacity or a set evaporation capacity is a crucial operation goal in the operation of a solar thermal power station.

[0004] It can be seen that the existing operation mode of the forced circulation pump cannot effectively regulate the molten salt flow rate and the circulation flow rate, and thus cannot take into account both the efficiency of the high evaporator and the energy consumption at the same time. Summary of the Invention

[0005] The purpose of the present invention is to provide an optimization method for variable load operation of a forced circulation steam generation system and related equipment to solve the technical problem that the existing operation mode of the forced circulation pump cannot effectively regulate the molten salt flow rate and the circulation flow rate, and thus cannot take into account both the efficiency of the high evaporator and the energy consumption at the same time.

[0006] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0007] An optimization method for variable load operation of a forced circulation steam generation system includes:

[0008] Establish a numerical calculation model of a shell-and-tube evaporator and a hydrodynamic balance model of a forced circulation system;

[0009] According to the numerical calculation model of the shell-and-tube evaporator and the hydrodynamic balance model of the forced circulation system, and taking high evaporation capacity, evaporator efficiency, and minimum cost as the optimization objectives, and the molten salt flow rate and circulating water flow rate as variables, establish a multi-objective optimization model;

[0010] Use the Pareto dominance method to solve the multi-objective optimization model, output the Pareto optimal solution set, so as to obtain an optimized variable load operation plan for the forced circulation system according to the Pareto optimal solution set.

[0011] Furthermore, the numerical calculation model of the shell-and-tube evaporator is used to solve the molten salt temperature, dryness, and density of wet steam at the evaporator outlet, and is constructed based on the effectiveness-number of transfer units method; among them, the effectiveness-number of transfer units method is used to calculate the heat transfer process of the evaporator where phase change occurs, and the specific formula is as follows:

[0012] ε=1-exp(-NTU)

[0013] In the formula, ε is the evaporator effectiveness, and NTU is the number of transfer units of the evaporator;

[0014]

[0015] In the formula, U is the total heat transfer coefficient of the evaporator, A is the heat transfer area of the evaporator, m o is the molten salt mass flow rate, c p,o is the specific heat capacity of the molten salt;

[0016]

[0017] In the formula, h o is the heat transfer coefficient on the molten salt side, R o,f is the fouling thermal resistance on the molten salt side, d o is the outer pipe diameter, d i is the inner pipe diameter, R i,f is the fouling thermal resistance on the wet steam side, h i is the heat transfer coefficient on the wet steam side.

[0018] Furthermore, establish the hydrodynamic balance model of the forced circulation system by making the resistance and driving force equal, and the specific formula is as follows:

[0019] ΔP i +ΔP f,H =ΔP g +ΔP i,pump

[0020] In the formula, ΔP iis the frictional pressure loss generated inside the steam generator, ΔP f,H is the frictional pressure loss generated in the circulation path; ΔP g is the driving force generated by the density difference between the water with zero mass gas content and the wet steam with mass gas content in the riser and downcomer, ΔP i,pump is the driving force provided by the forced circulation pump;

[0021] ΔP g =(ρ in -ρ out )gh

[0022] In the formula, ρ in is the density of the wet steam at the inlet of the evaporator, ρ out is the density of the wet steam at the outlet of the evaporator, g is the acceleration due to gravity, and h is the height difference between the evaporator and the steam drum.

[0023] Furthermore, the multi-objective optimization model includes an objective function and constraint conditions. Among them, the constraint conditions are that the adjustment ranges of the molten salt flow rate and the wet steam flow rate, and the molten salt pressure drop in the shell side and the wet steam pressure drop in the tube side shall not be higher than the preset pressure. The specific expressions of the objective function and the constraint conditions are as follows:

[0024]

[0025] In the formula, C total is the cost, Q steam is the evaporation capacity;

[0026] In the constraint conditions, ΔP o is the molten salt pressure drop in the shell side, ΔP i is the wet steam pressure drop in the tube side;

[0027] Q steam =m i x out

[0028] In the formula, x out is the dryness at the outlet of the evaporator;

[0029] The cost includes the cost C in of purchasing the evaporator and the operating cost C op of the power loss of the molten salt pump and the circulation pump;

[0030] C total =C in +C op

[0031]

[0032] In the formula, a 1 ,a 2 ,a 3are coefficients; n y is the system operation time; c el is the electricity cost; n a is the annual inflation rate; η is the pump efficiency; p i and p o are the pumping powers of the forced circulation pump and the molten salt respectively:

[0033] p i = m i ΔP i,pump / ρ in / η

[0034] p o = m o ΔP o / ρ o / η

[0035] In the formula, ρ o is the molten salt density.

[0036] Furthermore, in the multi-objective optimization model, the calculation process of the optimization objectives is as follows:

[0037] According to the input variable factors of the molten salt mass flow rate and the water mass flow rate, calculate the heat transfer effect according to the shell-and-tube evaporator numerical calculation model, and obtain the dryness, density and pressure drop of the wet steam at the evaporator outlet. Calculate the evaporator efficiency according to the dryness of the wet steam;

[0038] Calculate the evaporation capacity according to the wet steam density, and calculate the driving force caused by the density difference between the riser and the downcomer according to the wet steam density;

[0039] Add the wet steam pressure drop and the frictional resistance loss generated in the circulation path to obtain the resistance loss of the evaporator system;

[0040] Calculate the driving force required by the forced circulation pump according to the forced circulation system hydrodynamic balance model, and further obtain the calculation of the forced circulation pump pumping power;

[0041] According to the pressure drop and pumping power on the molten salt side, combine the purchased evaporator cost to obtain the total cost;

[0042] Among them, the evaporation amount is the product of the wet steam circulation flow rate and the dryness at the evaporator outlet.

[0043] Furthermore, the steps of solving the multi-objective optimization model by using the Pareto domination method include:

[0044] Initialize the particle swarm, and screen the individual optimal and global optimal of the initial particle swarm;

[0045] Calculate each particle in the filtered particle swarm until the maximum number of iterations is reached, and output the Pareto optimal solution set; among them, the Pareto optimal solution set uses efficiency, cost, and evaporation as coordinate axes.

[0046] Further, the specific steps for initializing the particle swarm include:

[0047] Set the population size, spatial dimension, objective function dimension, and Pareto solution set size of the initial particle swarm;

[0048] Define the position boundary and velocity boundary;

[0049] Initialize the initial position and initial velocity of the particle swarm to obtain the initial information of the particle swarm;

[0050] Calculate the fitness corresponding to each particle;

[0051] Perform non-dominated sorting on the initialized particle swarm;

[0052] Put the fitness of the initialized particle swarm into the Pareto solution set. Compare each particle with other particles. If it is dominated by other particles, delete it from the Pareto solution set, then obtain the particle swarm of non-dominated solutions;

[0053] Calculate the crowding distance of all particle individuals using the density distance method; randomly select the global optimum from the top 20% of the solutions with larger crowding distances;

[0054] Record the global optimal position and fitness in the Pareto solution set, where the individual optimum is the initial value;

[0055] The specific steps for calculating each particle in the filtered particle swarm include:

[0056] Use the particle swarm method to update the velocity and position of the particles;

[0057] Use the inertia adaptive weight coefficient to control the degree of change in each iteration;

[0058] Judge whether the updated particle velocity and position are within the constraint conditions: If they exceed the constraint conditions, regenerate the particle velocity and position within the boundary again; if they are within the constraint conditions, proceed to the next step;

[0059] Calculate the fitness corresponding to each particle in the updated particle swarm;

[0060] Judge whether the calculated molten salt pressure drop and wet steam pressure drop are within the constraint boundary conditions: If they exceed the range, remove the particle from the Pareto solution set; if they meet the constraint conditions, then proceed to the next step;

[0061] Perform non-dominated sorting and crowding degree calculation on the updated particle swarm: Randomly select the global optimum from the top 20% of the solutions with a larger crowding distance, and record the individual optimum value and the swarm optimum value at the same time, and output the Pareto optimal solution set.

[0062] A variable load operation optimization system for a forced circulation steam generation system, comprising:

[0063] A first model establishment module, configured to establish a numerical calculation model of a shell-and-tube evaporator and a hydrodynamic balance model of a forced circulation system;

[0064] A second model establishment module, configured to establish a multi-objective optimization model based on the numerical calculation model of the shell-and-tube evaporator and the hydrodynamic balance model of the forced circulation system, with high evaporation capacity and evaporator efficiency, and minimum cost as the optimization objectives, and the molten salt flow rate and the circulating water flow rate as variables;

[0065] An operation optimization module, configured to solve the multi-objective optimization model by using the Pareto domination method, output the Pareto optimal solution set, and obtain a variable load operation optimization scheme for the forced circulation system according to the Pareto optimal solution set.

[0066] A device, comprising:

[0067] A memory, configured to store a computer program;

[0068] A processor, configured to implement the steps of the above-mentioned variable load operation optimization method for the forced circulation steam generation system when executing the computer program.

[0069] A computer-readable storage medium, the computer-readable storage medium stores a computer program, and the computer program is used to implement the steps of the above-mentioned variable load operation optimization method for the forced circulation steam generation system when executed by a processor.

[0070] Compared with the prior art, the present invention has the following beneficial effects:

[0071] The present invention provides an optimization method for variable load operation of a forced circulation steam generation system. This method establishes a numerical calculation model of a shell-and-tube evaporator and a hydrodynamic balance model of the forced circulation system, and constructs a multi-objective optimization model with high evaporation rate, evaporator efficiency, and minimum cost as the optimization objectives, combined with the changes in molten salt flow rate and circulating water flow rate; the Pareto domination method is used to solve this model, which can output the Pareto optimal solution set, and thus formulate an optimized operation plan for the forced circulation system under different loads according to these optimal solutions; the adoption of this method not only helps to achieve a higher evaporator efficiency and evaporation rate, but also can effectively reduce the pump power loss, achieving the key objectives in the operation of a solar thermal power station, that is, the balance between high efficiency and low loss, providing strong support for the stable operation and economic benefits of the solar thermal power station; the adoption of this method helps to select a suitable plan according to specific requirements in actual operation, realizing the efficient, economic, and stable operation of the forced circulation steam generation system under variable load.

[0072] Preferably, in the present invention, by using the effectiveness-number of transfer units method to construct the numerical calculation model of the shell-and-tube evaporator, the molten salt temperature at the outlet of the evaporator, the dryness and density of the wet steam can be accurately solved, providing accurate data support for the establishment of the optimization model, and helping to improve the accuracy and reliability of the optimization results.

[0073] Preferably, in the present invention, by establishing a hydrodynamic balance model of the forced circulation system based on the equality of resistance and driving force, the influences of various resistances and driving forces in the system can be comprehensively considered, providing important constraint conditions for the establishment of the optimization model, and helping to ensure the feasibility and stability of the optimization plan in actual operation.

[0074] Preferably, in the present invention, the multi-objective optimization model includes an objective function and constraint conditions. Among them, the constraint conditions consider the adjustment ranges of the molten salt flow rate and the wet steam flow rate, as well as the limitations of the molten salt pressure drop in the shell side and the wet steam pressure drop in the tube side, ensuring the safety and economy of the optimization plan in actual operation; the objective function comprehensively considers multiple objectives such as cost and evaporation rate, helping to maximize the overall benefit.

[0075] Preferably, in the present invention, the calculation process of the optimization objectives in the multi-objective optimization model includes the calculations of heat transfer effect, evaporation capacity, driving force, resistance loss, pumping power, and cost, which helps to ensure the accuracy and comprehensiveness of the optimization results, providing a reliable basis for actual operation.

[0076] Preferably, in the present invention, the Pareto domination method is used to solve the multi-objective optimization model. By initializing the particle swarm and performing the screening of individual optimal and global optimal, as well as subsequent iterative calculations, the Pareto optimal solution set can be output. In this way, a suitable plan can be selected according to specific requirements in actual operation, realizing the balance and optimization of multiple objectives.

[0077] Further preferably, in the present invention, the calculation steps of the initialized particle swarm and the screened particle swarm include setting initial parameters, defining boundary conditions, initializing positions and velocities, calculating fitness, non-dominated sorting, crowding distance calculation, etc.; these steps ensure the accuracy and effectiveness of the particle swarm optimization algorithm, providing a reliable method for solving the multi-objective optimization model; at the same time, by judging whether the updated particle velocities and positions are within the constraint conditions, and calculating the fitness corresponding to each particle in the updated particle swarm, the feasibility and economy of the optimization scheme are further ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 It is a schematic structural diagram of a forced circulation steam generation system provided by an embodiment of the present invention;

[0079] Figure 2 It is a flowchart of the operation of a multi-objective optimization model of a forced circulation steam generation system provided by an embodiment of the present invention;

[0080] Figure 3 It is a flowchart of a variable load operation optimization method for a forced circulation steam generation system provided by an embodiment of the present invention;

[0081] Figure 4 It is a flowchart of the operation of solving a multi-objective optimization model by the Pareto domination method provided by an embodiment of the present invention;

[0082] Figure 5 It is a multi-objective optimization model verification diagram of the Pareto curves of three standard functions provided by an embodiment of the present invention;

[0083] Figure 6 It is the optimal Pareto front solution set with high efficiency and low cost under different evaporation rates calculated by using the optimization method provided by an embodiment of the present invention;

[0084] Figure 7 It is a flowchart of a variable load operation optimization method for a forced circulation steam generation system provided by the present invention;

[0085] Figure 8 It is a schematic structural diagram of a variable load operation optimization system for a forced circulation steam generation system provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0086] Example 1

[0087] As described in the background art, during operation, increasing the output capacity of the forced circulation pump can accelerate the water circulation flow rate, increase the heat transfer coefficient, and enhance the evaporator efficiency. However, it will increase the pump power loss. If the output of the forced circulation pump is reduced, although the pump power loss can be decreased, it will lead to a reduction in the water circulation flow rate, a decrease in the heat transfer coefficient, and a reduction in the evaporator efficiency. At the same time, the simultaneous change of the molten salt flow rate and the circulation flow rate will also affect the evaporation capacity of the steam generation system. Therefore, reasonably selecting the output power of the forced circulation pump to achieve a higher evaporator efficiency, lower pump power loss, higher evaporation capacity, or a set evaporation capacity is a crucial operation goal in the operation of a solar thermal power plant.

[0088] To achieve the above object, the present invention provides an optimization method for variable load operation of a forced circulation steam generation system. This method is directed at the forced circulation steam generation system and calculates through a multi-objective optimization particle swarm algorithm to obtain a Pareto optimal solution set. During engineering operation, the molten salt flow rate and water flow rate can be regulated according to the Pareto optimal solution set when the evaporation capacity demand changes, so as to achieve the purpose of the lowest cost and the highest efficiency, providing a reference for the operation optimization of variable load flow regulation in a solar thermal power plant industry. Using this method can effectively solve the problem of simultaneously achieving high evaporator efficiency and low cost during variable load regulation.

[0089] Among them, the Pareto domination method, also known as the Pareto optimization algorithm and the Pareto front algorithm, is a multi-objective optimization algorithm that solves problems based on the Pareto optimal theory.

[0090] As Figure 1 shown, this embodiment provides the structure of the forced circulation steam generation system. Combining the components of this system structure, its specific working process is as follows:

[0091] The feed water enters the steam drum 1, and the water in the steam drum that has not undergone a phase change flows into the evaporator 4 along the downcomer 2. The stored high-temperature molten salt is used to heat the water in the evaporator to generate wet steam with a certain dryness. The wet steam flows into the steam drum along the riser 5. The water and steam are separated in the steam drum, and the steam enters the next superheater for further heating. The forced circulation pump 3 can control the water circulation flow rate in the circulation system.

[0092] Combined with the above working process, as Figure 2 shown, this embodiment provides an optimization method for variable load operation of a forced circulation steam generation system, including the following steps:

[0093] S1: Establish a numerical calculation model of the shell-and-tube evaporator and a hydrodynamic balance model of the forced circulation system;

[0094] S2: Based on the numerical calculation model of the shell-and-tube evaporator and the hydrodynamic balance model of the forced circulation system, with the goals of high evaporation rate, high evaporator efficiency, and minimum cost, and taking the molten salt flow rate and the circulating water flow rate as variables, establish a multi-objective optimization model including an objective function and constraint conditions;

[0095] S3: Use the Pareto domination method to solve the multi-objective optimization model, output the Pareto optimal solution set, and obtain an optimization plan for improving the high-efficiency operation of the forced circulation system under variable and low loads.

[0096] The establishment of the numerical calculation model of the shell-and-tube evaporator and the hydrodynamic balance model of the forced circulation system includes:

[0097] (1) Use the effectiveness-NTU method to establish a numerical calculation model of the shell-and-tube evaporator, and solve the molten salt temperature at the evaporator outlet, the dryness and density of the wet steam. Among them, the effectiveness-NTU calculation formula uses an empirical formula for phase change; the heat transfer coefficient on the shell side of the molten salt is calculated using the Bell-Delaware method; the phase change heat transfer coefficient inside the tube on the wet steam side is calculated using the Chen's method.

[0098] (2) Establish a hydrodynamic balance model of the forced circulation system by making the resistance and driving force equal. Among them, the resistance includes the frictional resistance loss generated inside the steam generator and the frictional resistance loss generated in the circulation path. The driving force includes the driving force generated by the density difference between the water with zero mass gas content and the wet steam with a certain mass gas content in the riser and downcomer, and the driving force provided by the forced circulation pump.

[0099] In addition, the frictional resistance loss of two-phase flow, the local pressure loss of two-phase flow, and the acceleration pressure drop need to be calculated for the frictional resistance loss generated inside the evaporator. The frictional resistance loss and local pressure loss of the riser and downcomer need to be calculated for the frictional resistance loss generated in the circulation path.

[0100] The establishment of the multi-objective particle swarm optimization model includes:

[0101] (1) Establish an objective function with the goals of high evaporator efficiency, high evaporation rate, and minimum cost. Among them, the evaporation rate is the product of the wet steam circulation flow rate and the dryness at the evaporator outlet. The cost includes the cost of purchasing the evaporator and the operating cost of the power loss of the molten salt pump and the circulation pump. The constraints are the adjustment ranges of the molten salt flow rate and the wet steam flow rate, and the shell-side molten salt pressure drop and the tube-side wet steam pressure drop should not be higher than 2 bar.

[0102] (2) The calculation process of the optimization objective is as follows: According to the input variable factors, i.e., the molten salt mass flow rate and the water mass flow rate, calculate the heat transfer effect according to the evaporator numerical calculation model described in S1, and obtain the dryness, density, and pressure drop of the wet steam at the evaporator outlet. Calculate the evaporator efficiency according to the dryness of the wet steam. Calculate the evaporation capacity according to the density of the wet steam. Calculate the driving force caused by the density difference between the riser and the downcomer according to the density of the wet steam. Add the pressure drop of the wet steam and the frictional resistance loss generated in the circulation path to obtain the resistance loss of the evaporator system. Calculate the driving force that the forced circulation pump needs to provide according to the forced circulation system hydrodynamic balance model described in S1, and further obtain the calculated pumping power of the forced circulation pump. On the other hand, after calculating the pressure drop and pumping power on the molten salt side, combine the purchased evaporator cost to obtain the total cost.

[0103] The process of solving using the Pareto domination method is as follows:

[0104] (1) Initialize the particle population, and screen the individual optimal and global optimal for the initial particle swarm. Set the number of initial populations, the space dimension, the objective function dimension, and the size of the Pareto solution set. Define the position boundary and the velocity boundary. Initialize the initial position and initial velocity of the particle swarm to obtain the initial information of the particle swarm. Calculate the fitness corresponding to each particle. Perform non-dominated sorting on the initialized population. Put the fitness of the initialized population into the Pareto solution set. Compare each particle with other particles. If it is dominated by other individuals, delete it from the Pareto solution set, and thus obtain the population of non-dominated solutions. Use the crowding distance method to calculate the crowding distance of all particle individuals. Randomly select the global optimal from the top 20% of the solutions with a larger crowding distance. Record the global optimal position and fitness in the Pareto solution set. The individual optimal is the initial value.

[0105] (2) Record the number of iterations, and start the calculation of step (3) within the number of iterations.

[0106] (3) For each particle, perform the following calculations: Update the velocity and position of the particle using the particle swarm method. Use the inertia adaptive weight coefficient to control the change degree of each iteration to avoid premature convergence or local optimization. Judge whether the updated particle velocity and position are within the constraint conditions (the set boundary range). If it exceeds the boundary range, regenerate the particle velocity and position within the boundary again. If it is within the boundary range, proceed to the next step. Calculate the fitness corresponding to each particle in the updated particle swarm. Judge whether the calculated molten salt pressure drop and wet steam pressure drop are within the constrained boundary conditions. If it exceeds the range, remove the particle from the Pareto solution set and do not consider it. If it meets the constraint conditions, proceed to the next step. Perform non-dominated sorting and crowding degree calculation on the updated population. Randomly select the global optimal from the top 20% of the solutions with a larger crowding distance. Record the individual optimal value and the group optimal value.

[0107] After the iterative calculation, a three-dimensional Pareto optimal solution set with effectiveness, cost, and evaporation rate as the coordinate axes is obtained. All points on this solution set are non-dominated solutions. During engineering operation, the molten salt flow rate and water flow rate can be regulated according to the Pareto optimal solution set when the evaporation rate demand changes, so as to achieve the purpose of the lowest cost and the highest effectiveness.

[0108] To facilitate the understanding of the optimization method provided in this embodiment, the preferred method will be further explained with reference to the accompanying drawings, specifically including:

[0109] In this embodiment, the establishment of the numerical calculation model of the shell-and-tube evaporator and the hydrodynamic balance model of the forced circulation system in S1 includes:

[0110] Establish the following numerical calculation model of the shell-and-tube evaporator:

[0111] Use the effectiveness-number of transfer units method (ε-NTU) to calculate the heat transfer process of the evaporator where phase change occurs.

[0112] ε = 1 - exp(-NTU)

[0113] where ε is the effectiveness of the evaporator and NTU is the number of transfer units of the evaporator.

[0114]

[0115] where U is the total heat transfer coefficient of the evaporator, A is the heat transfer area of the evaporator, m o is the mass flow rate of molten salt, c p,o is the specific heat capacity of molten salt.

[0116]

[0117] where h o is the heat transfer coefficient on the molten salt side, R o,f is the fouling thermal resistance on the molten salt side, d o is the outer pipe diameter, d i is the inner pipe diameter, R i,f is the fouling thermal resistance on the wet steam side, h i is the heat transfer coefficient on the wet steam side. The shell-side heat transfer coefficient on the molten salt side is calculated using the Bell-Delaware method; the in-tube phase change heat transfer coefficient on the wet steam side is calculated using the Chen method.

[0118] The total heat load Q ε-NTU is calculated as follows:

[0119] Q ε-NTU = ε·m o c p,o (T o,in - T i,in )

[0120] Among them, T o,in is the molten salt inlet temperature, and T i,in is the wet steam inlet temperature.

[0121] The outlet temperatures on both sides are calculated as follows:

[0122]

[0123] Among them, T o,out is the molten salt outlet temperature, h i,out is the enthalpy value of the wet steam outlet, and h i,in is the enthalpy value of the wet steam inlet. m i is the mass flow rate of the wet steam, and c p,i is the specific heat capacity of the wet steam. The dryness x of the wet steam outlet is obtained from the enthalpy value of the wet steam outlet out and the density ρ out .

[0124] The following hydrodynamic balance model of the forced circulation system is established by making the resistance and the driving force equal:

[0125] ΔP i + ΔP f,H = ΔP g + ΔP i,pump

[0126] Among them, ΔP i is the frictional resistance loss along the way generated inside the steam generator, and ΔP f,H is the frictional resistance loss along the way generated in the circulation path; ΔP g is the driving force generated by the density difference between the water with zero mass gas content and the wet steam with a certain mass gas content in the riser and downcomer, and ΔP i,pump is the driving force provided by the forced circulation pump.

[0127] ΔP g = (ρ in - ρ out )gh

[0128] Among them, ρ in is the density of the wet steam at the evaporator inlet, ρ out is the density of the wet steam at the evaporator outlet, g is the acceleration due to gravity, and h is the height difference between the evaporator and the steam drum.

[0129] The frictional resistance loss along the way generated inside the evaporator needs to calculate the two-phase frictional resistance loss, the two-phase local pressure loss, and the acceleration pressure drop. The frictional resistance loss along the way generated in the circulation path needs to calculate the frictional resistance loss and the local pressure loss of the riser and downcomer.

[0130] In the above S2, based on the numerical calculation model of the shell-and-tube evaporator and the hydrodynamic balance model of the forced circulation system established in S1, with the goals of high evaporator efficiency, high evaporation rate, and minimum cost, and taking the molten salt flow rate and the circulating water flow rate as variables, the specific process of establishing a multi-objective optimization model including the objective function and constraint conditions is as follows:

[0131] 1) With the goals of high evaporator efficiency, high evaporation rate, and minimum cost, the following objectives are established:

[0132]

[0133] Among them, C total is the cost, and Q steam is the evaporation rate. In the constraint conditions, ΔP o is the molten salt pressure drop in the shell side, and ΔP i is the wet steam pressure drop in the tube side.

[0134] Q steam = m i x out

[0135] Among them, x out is the dryness at the evaporator outlet.

[0136] The cost includes the cost C in of purchasing the evaporator and the operating cost C op of the power loss of the molten salt pump and the circulating pump.

[0137] C total = C in + C op

[0138]

[0139] Among them, the coefficients a 1 , a 2 , a 3 are 8500, 409, and 0.85 respectively; it is considered that the system runs for n y which is 10 years. The electricity cost c el is 0.15 $ / kW; the annual operating time is 4380 h; the annual inflation rate n a is 0.1; η is the pump efficiency, which is 70%. p i and p o are the pumping powers of the forced circulation pump and the molten salt respectively:

[0140] p i = m i ΔP i,pump / ρ in / η

[0141] p o= m o ΔP o / ρ o / η

[0142] where ρ o is the density of molten salt.

[0143] The flowchart for calculating the optimization objectives is as Figure 3 shown. According to the input variable factors of the molten salt mass flow rate and the water mass flow rate, the heat transfer effect is calculated based on the evaporator numerical calculation model described in S1, and the dryness, density, and pressure drop of the wet steam at the evaporator outlet are obtained. The evaporator efficiency is calculated based on the dryness of the wet steam. The evaporation capacity is calculated based on the density of the wet steam. The driving force caused by the density difference between the riser and downcomer is calculated based on the density of the wet steam. The resistance loss of the evaporator system is obtained by adding the pressure drop of the wet steam and the frictional resistance loss generated in the circulation path. The driving force ΔP i,pump that the forced circulation pump needs to provide is calculated based on the hydrodynamic balance model of the forced circulation system described in S1, and further the pumping power p i of the forced circulation pump is obtained. On the other hand, after calculating the pressure drop and pumping power on the molten salt side and combining with the purchased evaporator cost, the total cost is obtained, and thus the calculation results of the three optimization objectives are obtained.

[0144] In the above S3, the flowchart for solving the multi-objective optimization model in S2 using the Pareto domination method is as Figure 4 shown. The particle position is used to represent the optimization variables, the fitness is used to store the optimization objectives, and the particle velocity represents the search direction. Specifically, the process of using the Pareto domination method to solve the multi-objective optimization model in S2 for the optimization of the forced circulation steam generation system in a solar thermal power plant is as follows:

[0145] S31: Initialize the particle population to obtain the initial information of the population. Specifically, set the initial population size to 35, the space dimension to 2 (the number of optimization variables), the objective function dimension to 3 (the number of optimization objectives); the Pareto solution set size to 30 (the number of optimal solutions to be retained). Define the position boundary and velocity boundary. Initialize the initial position and initial velocity of the particle swarm to obtain the initial information of the particle swarm.

[0146] S32: Calculate the fitness corresponding to each particle in the particle swarm according to the calculation method described in S2.

[0147] S33: Perform non-dominated sorting on the initialized population. Put the fitness of the initialized population into the Pareto solution set. For each particle, compare it with other particles. If it is dominated by other individuals, delete it from the Pareto solution set, and thus obtain the population of non-dominated solutions.

[0148] S34: Calculate the crowding distance of all particle individuals using the crowding distance method.

[0149] S35: Randomly select the global optimum from the top 20% of the solutions with a large crowding distance. Record the position and fitness of the global optimum in the Pareto solution set. The individual optimum is used as the initial value.

[0150] S36: Record the number of iterations and start the iterative calculation.

[0151] S37: Perform the following calculations for each particle:

[0152] S371: Update the velocity and position of the particle. The position x of the particle at the (k + 1)-th step is calculated as follows: i k+1 It is calculated by:

[0153]

[0154] where x i k is the position of the i-th particle at the k-th step of the individual. v i k is the velocity of the i-th particle at the k-th step of the individual. The formula for the particle velocity v of the i-th particle at the iterative step (k + 1) is as follows:

[0155]

[0156] where p i,best is the best position in the history of the particle individual, and G best is the best position of the current population. c per and c en are the self-learning factor and the group learning coefficient respectively. r per and r en are random numbers between 0 and 1. w is the inertia adaptive weight coefficient, which controls the degree of change in each iteration and avoids premature convergence or local optimization. It is calculated by:

[0157]

[0158] where d is the dimension of the overall (the number of variable factors), which is 2 in this embodiment. w max and w min are the maximum and minimum values of the set inertia adaptive weight coefficient respectively. x max and x min are the maximum and minimum values of the set particle position respectively.

[0159] Judge whether the updated particle velocity and position are within the constraint conditions (the set boundary range). If it exceeds the boundary range, regenerate the particle velocity and position within the boundary again. If it is within the boundary range, proceed to the next step.

[0160] S372: Calculate the fitness corresponding to each particle in the updated particle swarm according to the calculation method described in S2. Determine the calculated ΔP o and ΔP i Are they within the constraint boundary conditions? If it exceeds the range, remove this particle from the Pareto solution set and do not consider it. If the constraint conditions are met, proceed to the next step.

[0161] S373: Perform non-dominated sorting on the updated population.

[0162] S374: Calculate the crowding degree of the updated population.

[0163] S375: Randomly select the global optimum from the top 20% of the solutions with a larger crowding distance.

[0164] S376: Record the individual optimum value and the group optimum value.

[0165] S38: Determine whether the iteration number has been reached. If it has been reached, end the calculation. If not, increment the iteration number by 1 and return to S36 for recalculation.

[0166] Model verification was carried out on the established multi-objective particle swarm iteration calculation model. In this embodiment, three standard functions applicable to the evaluation of multi-objective optimization schemes were calculated; the specific results are as Figure 5 shown. All functions have achieved excellent calculation accuracy and presented a more evenly distributed Pareto front curve.

[0167] In this embodiment, as Figure 6 shown, Figure 6 is the Pareto optimal solution set for multi-objective optimization results. Specifically, it is the Pareto front solution set of the evaporation rate Q steam , the evaporator efficiency ε, and the cost C total . It can be seen that the Pareto optimal solution set after optimization calculation forms an upward plane. All points on this solution set are non-inferior solutions, indicating that when the evaporation rate increases, it will be accompanied by a decrease in efficiency and an increase in cost; during engineering operation, the molten salt flow rate and water flow rate can be regulated according to the Pareto optimal solution set when the evaporation rate demand changes, so as to achieve the purpose of the lowest cost and the highest efficiency.

[0168] Embodiment 2

[0169] As Figure 7 shown, this embodiment provides a method for optimizing the variable load operation of a forced circulation steam generation system, including the following steps:

[0170] S1: Establish a numerical calculation model of a shell-and-tube evaporator and a hydrodynamic balance model of a forced circulation system;

[0171] S2: According to the numerical calculation model of the shell-and-tube evaporator and the hydrodynamic balance model of the forced circulation system, with high evaporation capacity, evaporator efficiency, and minimum cost as the optimization objectives, and the molten salt flow rate and circulating water flow rate as variables, a multi-objective optimization model is established.

[0172] S3: Use the Pareto dominance method to solve the multi-objective optimization model and output the Pareto optimal solution set, so as to obtain the variable load operation optimization scheme of the forced circulation system according to the Pareto optimal solution set.

[0173] As Figure 8 shown, this embodiment also provides a variable load operation optimization system for a forced circulation steam generation system, including: a first model establishment module for establishing a numerical calculation model of a shell-and-tube evaporator and a hydrodynamic balance model of a forced circulation system; a second model establishment module for establishing a multi-objective optimization model according to the numerical calculation model of the shell-and-tube evaporator and the hydrodynamic balance model of the forced circulation system, with high evaporation capacity, evaporator efficiency, and minimum cost as the optimization objectives, and the molten salt flow rate and circulating water flow rate as variables; an operation optimization module for using the Pareto dominance method to solve the multi-objective optimization model and output the Pareto optimal solution set, so as to obtain the variable load operation optimization scheme of the forced circulation system according to the Pareto optimal solution set.

[0174] The present invention also provides a device, including: a memory for storing a computer program; a processor for implementing the steps of the variable load operation optimization method of the forced circulation steam generation system when executing the computer program.

[0175] When the processor executes the computer program, it implements the above steps of variable load operation optimization of the forced circulation steam generation system, such as: establishing a numerical calculation model of a shell-and-tube evaporator and a hydrodynamic balance model of a forced circulation system; establishing a multi-objective optimization model according to the numerical calculation model of the shell-and-tube evaporator and the hydrodynamic balance model of the forced circulation system, with high evaporation capacity, evaporator efficiency, and minimum cost as the optimization objectives, and the molten salt flow rate and circulating water flow rate as variables; using the Pareto dominance method to solve the multi-objective optimization model and output the Pareto optimal solution set, so as to obtain the variable load operation optimization scheme of the forced circulation system according to the Pareto optimal solution set.

[0176] Alternatively, when the processor executes the computer program, it realizes the functions of each module in the above system. For example: a first model establishment module, which is used to establish a numerical calculation model of a shell-and-tube evaporator and a hydrodynamic balance model of a forced circulation system; a second model establishment module, which is used to establish a multi-objective optimization model based on the numerical calculation model of the shell-and-tube evaporator and the hydrodynamic balance model of the forced circulation system, with high evaporation capacity, evaporator efficiency, and minimum cost as the optimization objectives, and the molten salt flow rate and the circulating water flow rate as variables; an operation optimization module, which is used to solve the multi-objective optimization model by using the Pareto domination method, output the Pareto optimal solution set, and obtain the variable load operation optimization scheme of the forced circulation system according to the Pareto optimal solution set.

[0177] Exemplarily, the computer program can be divided into one or more modules / units. The one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of completing preset functions, and the instruction segments are used to describe the execution process of the computer program in the variable load operation optimization device of the forced circulation steam generation system. For example, the computer program can be divided into a first model establishment module, a second model establishment module, and an operation optimization module; the specific functions of each module are as follows: the first model establishment module is used to establish a numerical calculation model of a shell-and-tube evaporator and a hydrodynamic balance model of a forced circulation system; the second model establishment module is used to establish a multi-objective optimization model based on the numerical calculation model of the shell-and-tube evaporator and the hydrodynamic balance model of the forced circulation system, with high evaporation capacity, evaporator efficiency, and minimum cost as the optimization objectives, and the molten salt flow rate and the circulating water flow rate as variables; the operation optimization module is used to solve the multi-objective optimization model by using the Pareto domination method, output the Pareto optimal solution set, and obtain the variable load operation optimization scheme of the forced circulation system according to the Pareto optimal solution set.

[0178] The variable load operation optimization device of the forced circulation steam generation system can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The variable load operation optimization device of the forced circulation steam generation system may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above are examples of the variable load operation optimization device of the forced circulation steam generation system, and do not constitute a limitation on the variable load operation optimization device of the forced circulation steam generation system. It may include more components than the above, or combine some components, or different components. For example, the variable load operation optimization device of the forced circulation steam generation system may further include input / output devices, network access devices, a bus, etc.

[0179] The so-called processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor, etc. The processor is the control center for optimizing the variable load operation of the forced circulation steam generation system, and connects various parts of the equipment for optimizing the variable load operation of the forced circulation steam generation system through various interfaces and lines.

[0180] The memory can be used to store the computer programs and / or modules. The processor realizes various functions of the equipment for optimizing the variable load operation of the forced circulation steam generation system by running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory.

[0181] The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices.

[0182] The present invention also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for optimizing the variable load operation of a forced circulation steam generation system are realized.

[0183] If the modules / units integrated in the system for optimizing the variable load operation of the forced circulation steam generation system are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0184] Based on such understanding, all or part of the processes in the above-mentioned optimization method for variable load operation of the forced circulation steam generation system of the present invention can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned optimization method for variable load operation of the forced circulation steam generation system can be realized. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or preset intermediate form, etc.

[0185] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0186] It should be noted that the content included in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice within the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0187] Thus, it can be seen that the present invention provides an optimization method for variable load operation of a forced circulation steam generation system. Compared with the existing operation mode, it has the following advantages:

[0188] The present invention is based on a numerical calculation model of a shell-and-tube evaporator established by the effectiveness-heat transfer unit method with phase change and a hydrodynamic balance model of a forced circulation system established by equal resistance and driving force. Using the multi-objective particle swarm optimization method, with the molten salt flow rate and the water circulation flow rate as control variables, and the evaporator effectiveness, cost, and evaporator as optimization objectives, the forced circulation steam generation system operating under variable load conditions is optimized. The Pareto optimal solution set obtained by the calculation of the present invention can provide an optimal solution for regulating the molten salt flow rate and water flow rate when the evaporation demand changes during the operation of the peak shaving project, so as to achieve the purpose of the lowest cost and the highest effectiveness. Among them, the mathematical model established by the present invention can not only quickly calculate the working state of the circulation system, but also has high calculation accuracy for the phase change process. Secondly, the optimization method of the present invention does not require mechanical or manual experimental optimization according to experience, greatly reducing the optimization time and labor cost. At the same time, the multi-objective particle swarm optimization algorithm established by the present invention combines the inertia adaptive weight coefficient, non-dominated sorting, and crowding degree calculation to control the change degree of each iteration, avoid premature convergence or local optimization, and quickly search for a uniform and dispersed optimal solution set, improving the optimization efficiency of the system. Finally, the method of the present invention calculates three standard functions applicable to the evaluation of multi-objective optimization schemes. All functions have obtained excellent calculation accuracy and present a more evenly distributed Pareto front curve, verifying the accuracy of the method of the present invention.

[0189] The above embodiments are only one of the implementation manners capable of implementing the technical solution of the present invention. The scope of protection required by the present invention is not limited only by this embodiment, but also includes any changes, substitutions, and other implementation manners that are easily conceivable by those skilled in the art within the technical scope disclosed by the present invention.

Claims

1. A method for optimizing variable load operation of a forced circulation steam generation system, characterized in that: include: Establish the numerical calculation model of shell and tube evaporator and the hydrodynamic balance model of forced circulation system; Based on the numerical calculation model of shell and tube evaporator and the hydrodynamic balance model of forced circulation system, with high evaporation capacity, evaporator efficiency and minimum cost as optimization objectives, and molten salt flow rate and circulating water flow rate as variables, a multi-objective optimization model is established. The Pareto dominance method is used to solve the multi-objective optimization model and output the Pareto optimal solution set, so as to obtain the variable load operation optimization scheme of the forced circulation system according to the Pareto optimal solution set; The shell and tube evaporator numerical calculation model is used to solve the molten salt temperature at the evaporator outlet and the dryness and density of wet steam, and is constructed based on the efficiency-heat transfer unit method; wherein the efficiency-heat transfer unit method is used to calculate the heat exchange process of the evaporator with phase change, and the specific formula is as follows: ε=1-exp(-NTU) In the formula, ε is the evaporator efficiency, and NTU is the number of heat transfer units of the evaporator; Where U is the total heat transfer coefficient of the evaporator, A is the heat transfer area of ​​the evaporator, m o is the mass flow rate of molten salt, c p,o is the specific heat capacity of molten salt; In the formula, h o is the heat transfer coefficient on the molten salt side, R o,f is the fouling thermal resistance on the molten salt side, d o is the outer diameter, d i is the inner diameter, R i,f is the fouling thermal resistance on the wet steam side, h i is the heat transfer coefficient on the wet steam side; The hydrodynamic balance model of the forced circulation system is established by equating the resistance and driving force. The specific formula is as follows: ΔP i +ΔP f,H =ΔP g +ΔP i,pump Where ΔP i is the resistance loss along the steam generator, ΔP f,H is the resistance loss along the circulation path; ΔP g The driving force is generated by the density difference between water with zero mass gas content and wet steam with mass gas content in the riser and downcomer, ΔP i,pump Provide driving force for forced circulation pump; ΔP g =(ρ in -r out )gh In the formula, ρ in is the wet steam density at the evaporator inlet, ρ out is the wet steam density at the evaporator outlet, g is the acceleration of gravity, and h is the height difference between the evaporator and the steam drum; The multi-objective optimization model includes an objective function and constraints, wherein the constraints are the adjustment range of the molten salt flow rate and the wet steam flow rate, and the shell-side molten salt pressure drop and the tube-side wet steam pressure drop cannot be higher than the preset pressure. The specific expressions of the objective function and the constraints are as follows: In the formula, C total is the cost, Q steam is the evaporation amount; ΔP in the constraint condition o is the shell-side molten salt pressure drop, ΔP i is the wet steam pressure drop in the tube; Q steam =m i x out In the formula, x out is the evaporator outlet dryness; The cost includes the cost of purchasing the evaporator C in and the operating cost of the molten salt pump and circulating pump power loss C op ; C total =C in +C op In the formula, a1, a2, a3 are coefficients respectively; n y is the system running time; c el is the electricity fee; a is the annual inflation rate; η is the pump efficiency; p i and p o The pumping power of the forced circulation pump and the molten salt are: p i =m i ΔP i,pump / r in / or p o =m o ΔP o / r o / or In the formula, ρ o is the density of molten salt.

2. The method for optimizing variable load operation of a forced circulation steam generation system according to claim 1, characterized in that: in, The calculation process of the optimization objective in the multi-objective optimization model is as follows: According to the input variable factors, the mass flow rate of molten salt and the mass flow rate of water, the heat exchange effect is calculated according to the numerical calculation model of the shell and tube evaporator, and the wet steam dryness, density and pressure drop at the evaporator outlet are obtained; the evaporator efficiency is calculated according to the wet steam dryness; Calculate the evaporation capacity based on the wet steam density, and calculate the driving force caused by the density difference between the riser and downcomer based on the wet steam density; The resistance loss of the evaporator system is obtained by adding the wet steam pressure drop and the resistance loss along the circulation path; The driving force required to be provided by the forced circulation pump is calculated according to the hydrodynamic balance model of the forced circulation system, and the pumping power of the forced circulation pump is further calculated; The total cost is obtained based on the pressure drop and pumping power on the molten salt side and the cost of the purchased evaporator; Among them, the evaporation capacity is the product of the wet steam circulation flow rate and the evaporator outlet dryness.

3. The method for optimizing variable load operation of a forced circulation steam generation system according to claim 1, characterized in that: The step of using the Pareto dominance method to solve the multi-objective optimization model includes: Initialize the particle swarm and screen the initial particle swarm for individual optimality and global optimality; Calculate each particle in the screened particle group until the maximum number of iterations is reached, and output the Pareto optimal solution set; wherein the Pareto optimal solution set takes efficiency, cost and evaporation as coordinate axes.

4. The method for optimizing variable load operation of a forced circulation steam generation system according to claim 3, characterized in that: The specific steps of initializing the particle swarm include: Set the population size, space dimension, objective function dimension, and Pareto solution set scale of the initial particle swarm; Define position boundaries and velocity boundaries; Initializing the initial position and initial velocity of the particle swarm to obtain initial information of the particle swarm; Calculate the fitness corresponding to each particle; Perform non-dominated sorting on the initialized particle swarm; Put the fitness of the initialized particle swarm into the Pareto solution set, and compare each particle with other particles. If it is dominated by other particles, delete it from the Pareto solution set, and then get a particle swarm with non-dominated solutions; The crowding distance of all individual particles is calculated using the dense distance method; the global optimum is randomly selected from the top 20% solutions with the largest crowding distance; Record the global optimal position and fitness in the Pareto solution set, where the individual optimal value is the initial value; The specific steps of calculating each particle in the screened particle group include: Use particle swarm method to update particle velocity and position; Use the inertia adaptive weight coefficient to control the degree of change in each iteration; Determine whether the updated particle speed and position are within the constraints: if they exceed the constraints, regenerate the particle speed and position within the boundary; if they are within the constraints, proceed to the next step; Calculate the fitness of each particle in the updated particle swarm; Determine whether the calculated molten salt pressure drop and wet steam pressure drop are within the bounding conditions of the constraints: if they are beyond the range, remove the particle from the Pareto solution set; if they meet the constraints, proceed to the next step; Perform non-dominated sorting and crowding calculation on the updated particle swarm: randomly select the global optimum from the top 20% solutions with larger crowding distance, record the individual optimum and group optimum, and output the Pareto optimal solution set.

5. A forced circulation steam generation system variable load operation optimization system, used to implement the steps of the forced circulation steam generation system variable load operation optimization method according to any one of claims 1 to 4, characterized in that: include: The first model building module is used to build a numerical calculation model of a shell and tube evaporator and a hydrodynamic balance model of a forced circulation system; The second model building module is used to establish a multi-objective optimization model based on the shell and tube evaporator numerical calculation model and the forced circulation system hydrodynamic balance model, with high evaporation capacity, evaporator efficiency and minimum cost as optimization goals, and molten salt flow rate and circulating water flow rate as variables; The operation optimization module is used to solve the multi-objective optimization model using the Pareto dominance method and output the Pareto optimal solution set, so as to obtain the variable load operation optimization scheme of the forced circulation system according to the Pareto optimal solution set.

6. An electronic device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the variable load operation optimization method of the forced circulation steam generation system according to any one of claims 1 to 4 when executing the computer program.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it is used to implement the steps of the method for optimizing variable load operation of a forced circulation steam generation system according to any one of claims 1 to 4.

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