Dual-power generation device and efficiency optimization regulation and control method

Abstract

The invention discloses a dual-power power generation device which comprises a water container, and the water container is sequentially connected with a first heat exchanger, a first expansion machine, a first condenser, a water vat, a water pump and a water valve through pipelines and then returns to the water container. The organic working medium container is sequentially connected with a second heat exchanger, a second expansion machine, a second condenser, a liquid storage tank, a working medium pump and a working medium valve through pipelines and returns to the organic working medium container; the first expansion machine and the second expansion machine are both connected with a power generator and are powered by the power generator, the power generator is sequentially connected with a central control end and a flow velocity detection module, and the flow velocity detection module is connected with a water valve and a working medium valve; the water container and the organic working medium container are arranged in a heat exchange mode and are connected with the solar heat collection device. According to the dual-power intelligent power generation device, solar energy is fully utilized, and waste heat recovery and utilization are achieved through the ORC technology.

Description

Technical Field

[0001] The present invention relates to the field of power generation, and in particular to a dual-power power generation device and an efficiency optimization control method. Background Art

[0002] At present, solar power generation technology combines organic Rankine cycle (ORC) waste heat recovery technology to achieve the dual utilization of renewable energy and industrial waste heat, but in practical applications, it still faces the problem of insufficient utilization of solar energy resources. Especially when there is sufficient solar energy, if it cannot be fully absorbed and converted into electrical energy, it will lead to a large amount of energy waste, thereby reducing the economic benefits of power generation. Summary of the invention

[0003] Purpose of the invention: In order to overcome the shortcomings of the background technology, the first purpose of the present invention is to request the disclosure of a dual-power power generation device that combines solar energy and organic Rankine cycle (ORC) waste heat recovery technology to improve energy utilization efficiency;

[0004] The second purpose is to disclose an efficiency optimization control method based on the above-mentioned dual-power power generation device.

[0005] Technical solution: The dual-power generator disclosed in the present invention comprises:

[0006] A water container, wherein the water container is sequentially connected to the first heat exchanger, the first expander, the first condenser, the water cylinder, the water pump, and the water valve through a pipeline and then returns to the water container;

[0007] An organic working medium container, wherein the organic working medium container is sequentially connected to a second heat exchanger, a second expander, a second condenser, a liquid storage tank, a working medium pump, and a working medium valve through a pipeline and then returns to the organic working medium container;

[0008] The first expander and the second expander are both connected to a generator and powered by the generator. The generator is sequentially connected to a central control terminal and a flow rate detection module. The flow rate detection module is respectively connected to a water valve and a working fluid valve.

[0009] The water container and the organic working medium container are heat exchanged and are connected to a solar heat collecting device at the same time.

[0010] An efficiency optimization control method based on the above dual-power power generation device, the central control end includes a sand cat intelligent optimization algorithm and a CPU, the CPU controls the working fluid valve and the water valve based on the sand cat intelligent optimization algorithm to optimize the power generation efficiency of the system, including the following steps:

[0011] S1. Setting the objective function of power generation and flow rate;

[0012] S2, the initial water flow rate of the water valve and the working fluid valve and the initial flow rate of the working fluid;

[0013] S3, setting the maximum number of iterations and the objective function threshold, and randomly generating a set of random variables;

[0014] S4, calculate the sensitivity range of the search mechanism;

[0015] S5. Calculate the location of the exploration and development phases;

[0016] S6, introducing nonlinear optimization factors in S5;

[0017] S7, obtaining a new local optimal position in the search area;

[0018] S8, full search phase and local development, output maximum power generation efficiency.

[0019] Among them, the objective function in S1 is:

[0020] maxQ sum =q1+q2

[0021]

[0022] V w≤speed =a·e -mt

[0023] V g-speed = b·e -nt

[0024] Among them, Q sum is the total power generation, q1 and q2 are the power generated by waste heat recovery of water and organic working fluid respectively, and c water , c gz is the specific heat capacity of water and organic working fluid, ρ water , ρ gz is the density of water and organic working fluid, A is the flow area, that is, the cross-sectional area through which the fluid flows; V w-speed is the water velocity, V g-speed is the flow rate of the organic working fluid, t is the working time for heat exchange between water and organic working fluid, t 0. is the starting time of work, α and β are the corresponding heat-to-electricity conversion coefficients when water and organic working fluid participate in waste heat recovery, a is the flow rate value of the water valve at the initial time, m is the attenuation coefficient, which approximately expresses the relationship between pressure and flow rate as the water level drops; b is the flow rate value of the organic working fluid valve at the initial time, n is the attenuation coefficient, which approximately expresses the relationship between pressure and flow rate in the container as the organic working fluid decreases;

[0025] The constraints of the objective function are:

[0026]

[0027] Among them, t1 is any working time, which does not exceed the maximum time t of the total capacity of the organic working fluid. max ; A is the flow area, which shall not exceed the maximum cross-sectional area A allowed by the pipeline max ; α and β are decimals between 0 and 1.

[0028] Furthermore, S2 is specifically:

[0029] S0=S min +rand(S max -S min )

[0030] Among them, S0 is the position of the initial sand cat population, and the initial coordinate position of the sand cat corresponds to the initial flow velocity state value of the system; rand is a constant between 0 and 1, ranging from [0,1]; S max , S min are the upper and lower bounds of the problem to be solved.

[0031] Furthermore, in S3, during the initialization phase, a set of randomly distributed prey position matrices are generated and expressed as:

[0032]

[0033] Among them, Prey is the prey matrix, S mn is the n-th dimensional position of the m-th prey, m is the total number of prey, and n is the dimension of the problem to be solved.

[0034] Further, in S4: the prey search mechanism of the sand cat depends on low-frequency noise emission, which depends on the sensitivity range of the sand cat, that is, the coordinates of the final position of the sand cat represent the values ​​of the water velocity and the working flow rate, and the sand cat's search mechanism for prey search corresponds to the water velocity and the working fluid pump flow rate to be adjusted based on the maximum power generation efficiency. The sensitivity range calculation formula is as follows,

[0035]

[0036] Among them, t is the current iteration number, T is the maximum iteration number, S M The value is the inspiration value of the sand cat's auditory characteristics, r G is the sensitivity range of the sand cat.

[0037] Furthermore, the S5 calculation method is:

[0038] R is the parameter that controls the transition between exploration and development phases.

[0039] R = 2 × r G ×rand()-r G

[0040] Among them, rand() represents a random number between 0 and 1, r G is the sensitivity range;

[0041] In the search step, each sand cat position update is given a random position. Through this position, the search agent can explore new space in the search space. In order to avoid local optimality, each sand cat has a different sensitivity range. Its random update position is determined by the random number rand(), which represents a random number between 0 and 1.

[0042] x best =rand()×r G

[0043] Among them, r G is the sensitivity range of the sand cat, rand() represents a random number between 0 and 1, and x best Used for position updates during exploration or development phases.

[0044] Furthermore, in S6, a nonlinear optimization factor g is introduced in S5 so that x best Not being affected by the individual state of the sand cat means that the entire system workflow is not affected by the accuracy of the system equipment itself. The optimization formula is:

[0045]

[0046] Further, S7 is as follows: the sand cat updates its position according to the current optimal position of the population, its own position and sensitivity range, and then captures prey. The sand cat finds a new local optimal position in the search area, and obtains a new position between the current position and the prey, corresponding to the flow rate of the two working fluids from the current value to the theoretical optimal value. The movement of the sand cat corresponds to the adjustment of the flow rate. The position update formula at this stage is as follows:

[0047]

[0048] Among them, θ is the direction of the sand dune cat's movement, which is a random angle between 0 and 360, that is, the value of cos(θ) will be between 1 and -1. Each sand cat will randomly update its position in any direction. The sand cat intelligent optimization algorithm selects a random angle for each sand dune cat, x t+1 is the local information optimum.

[0049] Furthermore, S8 is specifically:

[0050] R is a parameter that controls the transition between exploration and development stages and is in the interval [-2r G ,2r G ],r GIn the iterative process, it decreases linearly from 2 to 0. When the random value of R is in [-1,1], the next position of the sand cat can be anywhere between its current position and the hunting position. If the R value is ≤1, it will be developed; otherwise, it will be forced to explore, that is:

[0051]

[0052] in, is the optimal value of the final position of the ith sand cat for final position development or exploration, which corresponds to the maximum power generation efficiency at this time. The horizontal and vertical coordinates of its position coordinates correspond to the optimal flow rates of the working fluid pump and the water pump when the power generation is maximum. The value of cos(θ) will be between 1 and -1, p is a random position, r2 is the operating coefficient for the exploration or utilization stage, and the sand cat is in the stage of searching for prey, that is, exploration. The different search radius of each sand cat avoids falling into the local optimum. The above formula represents the position update of each sand cat in the exploration and development stages. When |R|≤1, the sand cats are guided to attack their prey, that is, to determine the maximum power generation and then adjust the flow rate of the working fluid pump and the water pump; otherwise, the task of the sand cat is to find a new solution in the global area.

[0053] Beneficial effects: Compared with the prior art, the advantages of the present invention are:

[0054] 1. By using solar thermal collectors, solar energy can be effectively converted into electrical energy as the main power source of the system, making full use of renewable clean energy and improving the utilization rate of waste heat: ORC waste heat recovery device can recover low-temperature waste heat generated in industrial processes and convert it into electrical energy through ORC cycle, thus improving energy utilization efficiency and reducing energy waste;

[0055] 2. The data collection of the flow rate detection module and the intelligent control of the central control terminal ensure the stability and consistency of the environment and avoid fluctuations caused by human factors. At the same time, the system can automatically adjust according to different working flow rates and has strong adaptability;

[0056] 3. Combined with the SandCat intelligent optimization algorithm, the impact of system factors is reduced, so that the operating power of the control equipment is optimized, so that the waste heat can be fully recovered, the power generation efficiency is maximized, the traditional large-scale energy waste is avoided, and energy consumption and production costs are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the device of the present invention;

[0058] Figure 2 This is a schematic diagram of the efficiency optimization control of the present invention. DETAILED DESCRIPTION

[0059] like Figure 1The dual-power generating device shown comprises:

[0060] A water container 1, wherein the water container 1 is connected to a first heat exchanger 2, a first expander 3, a first condenser 4, a water cylinder 5, a water pump 6, and a water valve 7 in sequence through a pipeline and returns to the water container 1;

[0061] An organic working medium container 8, wherein the organic working medium container 8 is sequentially connected to a second heat exchanger 9, a second expander 10, a second condenser 11, a liquid storage tank 12, a working medium pump 13, and a working medium valve 14 through a pipeline and then returns to the organic working medium container 10;

[0062] The first expander 3 and the second expander 10 are both connected to a generator 15, and are powered by the generator 15. The generator 15 is connected to a central control terminal 16 and a flow rate detection module 17 in sequence, and the flow rate detection module 17 is connected to a water valve 7 and a working fluid valve 14 respectively;

[0063] The water container 1 and the organic medium container 8 are arranged for heat exchange and are connected to a solar heat collecting device 18 at the same time.

[0064] The main function of the solar thermal collector is to collect solar thermal energy and store the collected heat in water and organic working fluids. Due to the limitation of the water boiling point threshold, sometimes there is sufficient heat but it cannot be effectively collected. The heat exchanger inputs the heat source into the evaporator, so that the organic working fluid evaporates to produce high-pressure gas, which makes up for the limitation brought by the water boiling point threshold and realizes the efficient use of thermal energy. The high-pressure gas produced by evaporation drives the expander to expand, thereby generating work. The working principle of the expander is based on the organic Rankine cycle, which can convert thermal energy into mechanical energy. The work generated by the expander drives the generator to rotate, thereby generating electricity. This process makes full use of solar energy and realizes efficient energy conversion. The expanded low-pressure steam enters the condenser and condenses into liquid after cooling. This process is completed in the condenser, avoiding heat loss and ensuring the continuous and stable operation of the system. Finally, a water pump is used to pump the liquid stored in the water tank to the water container to start a new round of heat collection, making full use of solar energy and realizing efficient conversion and recovery of thermal energy.

[0065] like Figure 2 As shown, based on the above-mentioned efficiency optimization control method of the dual-power power generation device, the central control end includes the Sand Cat intelligent optimization algorithm and the CPU. The CPU controls the working fluid valve and the water valve based on the Sand Cat intelligent optimization algorithm to optimize the power generation efficiency of the system, including the following steps:

[0066] S1. Setting the objective function of power generation and flow rate;

[0067] maxQ sum =q1+q2

[0068]

[0069] V w≤speed =a·e -mt

[0070] V g-speed = b·e -nt

[0071] Among them, Q sum is the total power generation, q1 and q2 are the power generated by waste heat recovery of water and organic working fluid respectively, and c water , c gz is the specific heat capacity of water and organic working fluid, ρ water , ρ gz is the density of water and organic working fluid, A is the flow area, that is, the cross-sectional area through which the fluid flows; V w-speed is the water velocity, V g-speed is the flow rate of the organic working fluid, t is the working time for heat exchange between water and organic working fluid, t 0. is the starting time of work, α and β are the corresponding heat-to-electricity conversion coefficients when water and organic working fluid participate in waste heat recovery, a is the flow rate value of the water valve at the initial time, m is the attenuation coefficient, which approximately expresses the relationship between pressure and flow rate as the water level drops; b is the flow rate value of the organic working fluid valve at the initial time, n is the attenuation coefficient, which approximately expresses the relationship between pressure and flow rate in the container as the organic working fluid decreases;

[0072] The constraints of the objective function are:

[0073]

[0074] Among them, t1 is any working time, which does not exceed the maximum time t of the total capacity of the organic working fluid. max ; A is the flow area, which shall not exceed the maximum cross-sectional area A allowed by the pipeline max ; α and β are decimals between 0 and 1.

[0075] The sand cat swarm optimization algorithm is used to control the flow rate to optimize the overall power generation efficiency.

[0076] S2, initializing the location information of the sand cat population, that is, initializing the historical data of the system operation, that is, the initial water flow rate of the water valve and the working fluid valve and the initial flow rate of the working fluid;

[0077] S0=S min +rand(S max -S min )

[0078] Among them, S0 is the position of the initial sand cat population, and the initial coordinate position of the sand cat corresponds to the initial flow velocity state value of the system; rand is a constant between 0 and 1, ranging from [0,1]; S max , Smin are the upper and lower bounds of the problem to be solved.

[0079] S3, set the maximum number of iterations and the objective function threshold, and randomly generate a set of random variables,

[0080] In the initialization phase, a set of randomly distributed prey position matrices are generated as:

[0081]

[0082] Among them, Prey is the prey matrix, S mn is the n-th dimensional position of the m-th prey, m is the total number of prey, and n is the dimension of the problem to be solved.

[0083] S4. Searching for prey. The prey search mechanism of the sand cat relies on low-frequency noise emission, which depends on the sensitivity range of the sand cat. That is, the coordinates of the final position of the sand cat represent the values ​​of the water velocity and the working flow rate. The water velocity and the working fluid pump flow rate corresponding to the search mechanism of the sand cat for prey search should be adjusted based on the maximum power generation efficiency. The sensitivity range calculation formula is as follows;

[0084]

[0085] Among them, t is the current iteration number, T is the maximum iteration number, S M The value is the inspiration value of the sand cat's auditory characteristics, r G is the sensitivity range of the sand cat.

[0086] S5. Calculate the location of the exploration and development phases;

[0087] R is the parameter that controls the transition between exploration and development phases.

[0088] R = 2 × r G ×rand()-r G

[0089] Among them, rand() represents a random number between 0 and 1, r G is the sensitivity range;

[0090] In the search step, each sand cat position update is given a random position. Through this position, the search agent can explore new space in the search space. In order to avoid local optimality, each sand cat has a different sensitivity range. Its random update position is determined by the random number rand(), which represents a random number between 0 and 1.

[0091] x best =rand()×r G

[0092] Among them, r Gis the sensitivity range of the sand cat, rand() represents a random number between 0 and 1, and x best Used for position updates during exploration or development phases.

[0093] S6. Introduce the nonlinear optimization factor g in S5 so that x best Not being affected by the individual state of the sand cat means that the entire system workflow is not affected by the accuracy of the system equipment itself. The optimization formula is:

[0094]

[0095] By effectively optimizing the nonlinear factor g, the system is less affected by the accuracy of the equipment, the delay caused by the asynchrony of data transmission, and the error caused by signal transmission. At the same time, the interference and fluctuation caused by the robustness of the system are effectively reduced. Compared with the original algorithm, the anti-interference ability of the system is greatly increased, and the accuracy and stability of the system are effectively improved.

[0096] S7, attack phase. The sand cat updates its position according to the current optimal position of the population, its own position and sensitivity range, and then captures prey. The sand cat finds a new local optimal position in the search area, and the new position is between the current position and the prey, corresponding to the flow rate of the two working fluids from the current value to the theoretical optimal value. The movement of the sand cat corresponds to the adjustment of the flow rate. The position update formula in this stage is as follows.

[0097]

[0098] θ is the direction in which the sand cat moves, which is a random angle between 0 and 360. That is, the value of cos(θ) will be between 1 and -1, and each sand cat will randomly update its position in any direction. The sand cat intelligent optimization algorithm selects a random angle for each sand cat. t+1 is the local information optimum.

[0099] S8, full search phase and local development, output maximum power generation efficiency.

[0100] R is a parameter that controls the transition between exploration and development stages. R is an interval [-2r G ,2r G ],r G It decreases linearly from 2 to 0 during the iteration process. When the random value of R is in [-1,1], the next position of the sand cat can be anywhere between its current position and the hunting position. If the R value is ≤1, it will be developed: otherwise, it will be forced to explore. That is:

[0101]

[0102] is the optimal value of the final position of the i-th sand cat for location development or exploration, which corresponds to the maximum power generation efficiency at this time. The horizontal and vertical coordinates of its position coordinates correspond to the optimal flow rates of the working fluid pump and the water pump when the power generation is maximum. The value of cos(θ) will be between 1 and -1. p is a random position, and r2 is the operating coefficient for the exploration or utilization stage. When the sand cat is searching for prey (exploration), the different search radius of each sand cat avoids falling into the local optimum. The above formula represents the position update of each sand cat in the exploration and development stages. When |R|≤1, (R is a parameter that controls the transition between the exploration and development stages) the sand cats are guided to attack their prey, that is, to determine the maximum power generation and then adjust the flow rate of the working fluid pump and the water pump; otherwise, the task of the sand cat is to find a new solution in the global area.

Claims

1. A dual-power generating device, characterized in that: include: A water container (1), wherein the water container (1) is connected in sequence to a first heat exchanger (2), a first expander (3), a first condenser (4), a water cylinder (5), a water pump (6), and a water valve (7) through a pipeline and then returns to the water container (1); An organic working medium container (8), wherein the organic working medium container (8) is connected in sequence to a second heat exchanger (9), a second expander (10), a second condenser (11), a liquid storage tank (12), a working medium pump (13), and a working medium valve (14) through pipelines and returns to the organic working medium container (10); The first expander (3) and the second expander (10) are both connected to a generator (15) and powered by the generator (15); the generator (15) is connected in sequence to a central control terminal (16) and a flow rate detection module (17); and the flow rate detection module (17) is respectively connected to a water valve (7) and a working fluid valve (14); The water container (1) and the organic working medium container (8) are arranged for heat exchange and are simultaneously connected to a solar heat collection device (18).

2. The efficiency optimization control method of the dual-power generator according to claim 1 is characterized in that: The central control end includes the SandCat intelligent optimization algorithm and a CPU. The CPU regulates the working fluid valve and the water valve based on the SandCat intelligent optimization algorithm so that the system can achieve the optimal power generation efficiency, including the following steps: S1. Setting the objective function of power generation and flow rate; S2, the initial water flow rate of the water valve and the working fluid valve and the initial flow rate of the working fluid; S3, setting the maximum number of iterations and the objective function threshold, and randomly generating a set of random variables; S4, calculate the sensitivity range of the search mechanism; S5. Calculate the location of the exploration and development phases; S6, introducing nonlinear optimization factors in S5; S7, obtaining a new local optimal position in the search area; S8, full search phase and local development, output maximum power generation efficiency.

3. The efficiency optimization control method of the dual-power generator according to claim 2 is characterized in that: The objective function in S1 is: <h2 style=";text-align:left;direction:ltr">maxQ<h2 style=";text-align:left;direction:ltr"> sum <h2 style=";text-align:left;direction:ltr"> =q1+q2 V w-speed =a·e -mt V g-speed =b·e -nt Among them, Q sum is the total power generation, q1 and q2 are the power generated by waste heat recovery of water and organic working fluid respectively, and c water , c gz is the specific heat capacity of water and organic working fluid, ρ water , ρ gz is the density of water and organic working fluid, A is the flow area, that is, the cross-sectional area through which the fluid flows; V w-speed is the water velocity, V g-speed is the flow rate of the organic working fluid, t is the working time for heat exchange between water and organic working fluid, t 0. is the starting time of work, α and β are the corresponding heat-to-electricity conversion coefficients when water and organic working fluid participate in waste heat recovery, a is the flow rate value of the water valve at the initial time, m is the attenuation coefficient, which approximately expresses the relationship between pressure and flow rate as the water level drops; b is the flow rate value of the organic working fluid valve at the initial time, n is the attenuation coefficient, which approximately expresses the relationship between pressure and flow rate in the container as the organic working fluid decreases; The constraints of the objective function are: Among them, t1 is any working time, which does not exceed the maximum time t of the total capacity of the organic working fluid. max ; A is the flow area, which shall not exceed the maximum cross-sectional area A allowed by the pipeline max ; α and β are decimals between 0 and 1.

4. The efficiency optimization control method of the dual-power generator according to claim 2 is characterized in that: S2 is specifically: S0=S min +rand(S max -S min ) Among them, S0 is the position of the initial sand cat population, and the initial coordinate position of the sand cat corresponds to the initial flow velocity state value of the system; rand is a constant between 0 and 1, ranging from [0,1]; S max , S min are the upper and lower bounds of the problem to be solved.

5. The efficiency optimization control method of the dual-power generator according to claim 2 is characterized in that: In S3, during the initialization phase, a set of randomly distributed prey position matrices are generated and expressed as: Among them, Prey is the prey matrix, S mn is the n-th dimensional position of the m-th prey, m is the total number of prey, and n is the dimension of the problem to be solved.

6. The efficiency optimization control method of the dual-power generator according to claim 2 is characterized in that: In S4: The prey search mechanism of the sand cat relies on low-frequency noise emission, which depends on the sensitivity range of the sand cat, that is, the coordinates of the final position of the sand cat represent the values ​​of water velocity and working flow rate, and the sand cat's search mechanism for prey search corresponds to the water velocity and working fluid pump flow rate to be adjusted based on the maximum power generation efficiency. The sensitivity range calculation formula is as follows: Among them, t is the current iteration number, T is the maximum iteration number, S M The value is the inspiration value of the sand cat's auditory characteristics, r G is the sensitivity range of the sand cat.

7. The efficiency optimization control method of the dual-power generator according to claim 2 is characterized in that: The S5 calculation method is: R is the parameter that controls the transition between exploration and development phases. R=2×r G ×rand()-r G Among them, rand() represents a random number between 0 and 1, r G is the sensitivity range; In the search step, each sand cat position update is given a random position. Through this position, the search agent can explore new space in the search space. In order to avoid local optimality, each sand cat has a different sensitivity range. Its random update position is determined by the random number rand(), which represents a random number between 0 and 1. x best =rand()×r G Among them, r G is the sensitivity range of the sand cat, rand() represents a random number between 0 and 1, and x best Used for position updates during exploration or development phases.

8. The efficiency optimization control method of the dual-power generator according to claim 2 is characterized in that: In S6, a nonlinear optimization factor g is introduced in S5 so that x best Not being affected by the individual state of the sand cat means that the entire system workflow is not affected by the accuracy of the system equipment itself. The optimization formula is:

9. The efficiency optimization control method of the dual-power generator according to claim 2 is characterized in that: S7 is as follows: the sand cat updates its position according to the current optimal position of the population, its own position and sensitivity range, and then captures prey. The sand cat finds a new local optimal position in the search area, and the new position is between the current position and the prey, corresponding to the flow rate of the two working fluids from the current value to the theoretical optimal value. The movement of the sand cat corresponds to the adjustment of the flow rate. The position update formula at this stage is as follows: Among them, θ is the direction of the sand dune cat's movement, which is a random angle between 0 and 360, that is, the value of cos(θ) will be between 1 and -1. Each sand cat will randomly update its position in any direction. The sand cat intelligent optimization algorithm selects a random angle for each sand dune cat, x t+1 is the local information optimum.

10. The efficiency optimization control method of the dual-power generator according to claim 2 is characterized in that: S8 is specifically: R is a parameter that controls the transition between exploration and development stages and is in the interval [-2r G ,2r G ],r G In the iterative process, it decreases linearly from 2 to 0. When the random value of R is in [-1,1], the next position of the sand cat can be anywhere between its current position and the hunting position. If the R value is ≤1, it will be developed; otherwise, it will be forced to explore, that is: in, is the optimal value of the final position of the ith sand cat for final position development or exploration, which corresponds to the maximum power generation efficiency at this time. The horizontal and vertical coordinates of its position coordinates correspond to the optimal flow rates of the working fluid pump and the water pump when the power generation is maximum. The value of cos(θ) will be between 1 and -1. p is a random position, and r2 is the operating coefficient for the exploration or utilization stage. When the sand cat is searching for prey, that is, exploring, the different search radius of each sand cat avoids falling into the local optimum. The above formula represents the position update of each sand cat in the exploration and development stages. When |R||1, the sand cats are guided to attack their prey, that is, determine the maximum power generation and then adjust the flow rate of the working fluid pump and the water pump; otherwise, the task of the sand cat is to find a new solution in the global area.

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