Dynamics analysis method for ammonia combustion refrigeration system in large park cooling

Abstract

The present application provides a dynamics analysis method for an ammonia combustion refrigeration system in large park cooling. The method comprises: on the basis of a geometric structure of an ammonia combustion chamber and a fuel supply amount, calculating the heat generated by ammonia combustion and analyzing the heat transfer mode between the combustion chamber and a desorption tower, the heat transfer mode comprising direct contact type heat transfer and partition type heat transfer, and simulating the heat transfer process to obtain and draw the heat flux distribution of the tower wall surface; on the basis of the flow state and a relationship curve between the pressure drop and the flow velocity, analyzing the desorption process of a refrigerant in a solution, and on the basis of the concentration of the solution, a boiling point of the refrigerant, and the vapor pressure, calculating local desorption rates at different positions in the desorption tower and the refrigerant vapor generation amount; and on the basis of the heat flux distribution, calculating the temperature distribution of the solution, and analyzing the heat transfer and mass transfer processes in the desorption tower to obtain a local heat transfer coefficient and the mass transfer efficiency distribution of the surface of a filler.

Description

A kinetic analysis method for ammonia combustion refrigeration systems in large-scale industrial park cooling systems Technical Field

[0001] This invention relates to the field of information technology, and in particular to a kinetic analysis method for an ammonia combustion refrigeration system in a large-scale industrial park cooling system. Background Technology

[0002] The application of ammonia combustion refrigeration technology in large-scale industrial park cooling systems faces a thorny technical challenge. The desorption tower, as a core component of the system, directly impacts overall refrigeration efficiency. However, when attempting to improve heat transfer efficiency and refrigerant desorption rate by increasing the specific surface area of ​​the desorption tower, a dilemma arises. Reducing the tower diameter increases the heat transfer area per unit volume, but also increases the flow resistance between the gas and liquid phases within the tower, affecting mass transfer. Conversely, increasing the tower height extends the gas-liquid contact time, but increases equipment cost and floor space. Furthermore, the choice of operating pressure presents a contradiction: high pressure improves desorption efficiency but increases manufacturing difficulty and operational risks; low pressure may lead to incomplete desorption, affecting refrigeration performance. Adding to the complexity is the uneven temperature distribution within the desorption tower. Higher temperatures at the bottom favor refrigerant desorption, while lower temperatures at the top may cause some desorbed refrigerant to be reabsorbed. This temperature gradient makes it difficult to achieve ideal efficiency in the desorption process. Finding the optimal balance among these conflicting factors while simultaneously meeting the stringent requirements of large industrial parks for cooling capacity and energy efficiency has become a pressing technical challenge. Summary of the Invention

[0003] This invention provides a kinetic analysis method for an ammonia combustion refrigeration system in a large-scale industrial park cooling system, mainly including:

[0004] Based on the geometry of the ammonia combustion chamber and the fuel supply, the heat generated by ammonia combustion is calculated, and the heat transfer mode between the combustion chamber and the desorption tower is analyzed, including direct contact and indirect heat transfer. By simulating the heat transfer process, the heat flux density distribution on the tower wall is obtained and plotted.

[0005] Based on parameters such as packing type, specific surface area, porosity, and bulk density, the flow state of the absorbent solution in the desorption tower is analyzed, the gas-liquid two-phase flow velocity and gas-liquid ratio distribution at different locations in the tower are obtained, the influence of packing size and tower diameter ratio on pressure drop is analyzed, and the relationship curve between pressure drop and flow velocity is established.

[0006] Based on the flow state and the relationship curve between pressure drop and flow velocity, the desorption process of refrigerant in solution is analyzed. Based on solution concentration, refrigerant boiling point and vapor pressure, the local desorption rate and the amount of refrigerant vapor generated at different locations in the desorption tower are calculated.

[0007] Based on the heat flux density distribution, the solution temperature distribution is calculated. By analyzing the heat and mass transfer process in the desorption tower, the local heat transfer coefficient and mass transfer efficiency distribution on the packing surface are obtained.

[0008] Based on the local desorption rate and refrigerant vapor production at different locations within the desorption tower, integral calculations are performed on the desorption tower to obtain the distribution curve of the cumulative desorption amount of refrigerant along the tower height. Combined with the analysis of the relationship between mass transfer efficiency and packing specific surface area and tower diameter, the main factors affecting mass transfer efficiency are determined.

[0009] Based on the local heat transfer coefficient of the packing surface and the main factors affecting mass transfer efficiency, a systematic analysis was conducted on the geometric parameters of the packing specific surface area, tower diameter, tower height, and solution flow rate operating parameters to set the parameter variation range. Through parameter scanning and sensitivity analysis, the influence of each parameter on the system performance was quantified, and the parameter analysis results were obtained.

[0010] Based on the parameter analysis results, the specific surface area of ​​the packing, the tower diameter, and the tower height under different solution flow conditions were optimized to obtain the optimal combination of geometric parameters for each flow rate. Combined with the evaluation of the system's performance under different cooling loads and parameter combinations, the operating characteristics of the system under various load conditions were obtained, including changes in energy efficiency ratio and response capability. A performance analysis report of the ammonia combustion refrigeration system was obtained, including the energy-saving potential and environmental impact assessment under various operating conditions.

[0011] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0012] This invention discloses a method for optimizing the performance of an ammonia combustion refrigeration system. This method establishes a pressure drop versus flow velocity model by analyzing the heat transfer between the ammonia combustion chamber and the desorption tower, as well as the flow state of the absorbent solution within the desorption tower. Combining the refrigerant desorption process and heat and mass transfer analysis, the main factors affecting mass transfer efficiency are identified. Through systematic analysis and optimization of geometric parameters such as packing specific surface area, tower diameter, and tower height, as well as operational parameters such as solution flow rate, the optimal parameter combination under different cooling load conditions is obtained. This invention achieves performance optimization of the ammonia combustion refrigeration system under various operating conditions, improves the energy efficiency ratio, enhances the system's response capability, and assesses its energy-saving potential and environmental impact, providing theoretical guidance and technical support for the design and operation of ammonia combustion refrigeration systems. Attached Figure Description

[0013] Figure 1 is a flowchart of a dynamic analysis method for an ammonia combustion refrigeration system in a large-scale park cooling system according to the present invention.

[0014] Figure 2 is a schematic diagram of the dynamic analysis method of an ammonia combustion refrigeration system in a large-scale park cooling system according to the present invention. Detailed Implementation

[0015] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0016] As shown in Figures 1-2, the kinetic analysis method for an ammonia combustion refrigeration system in a large-scale park cooling system in this embodiment may specifically include:

[0017] S101. Based on the geometry of the ammonia combustion chamber and the fuel supply, calculate the heat generated by ammonia combustion and analyze the heat transfer mode between the combustion chamber and the desorption tower, including direct contact and indirect heat transfer. By simulating the heat transfer process, obtain and plot the heat flux density distribution on the tower wall.

[0018] The geometric parameters of the ammonia combustion chamber and the fuel supply data are obtained. The ammonia combustion reaction rate is calculated, and the temperature distribution inside the combustion chamber is obtained by solving the energy conservation equation. Based on the temperature distribution of the combustion chamber, two heat transfer models, direct contact and indirect contact, are established. Finite element analysis is performed on the two heat transfer models to obtain the temperature field distribution under the two heat transfer modes. For the temperature distribution of the combustion chamber and the two heat transfer models, the partial differential equation of heat conduction is solved, and the temperature value of each point in the desorption tower is obtained through iterative calculation. Based on the temperature gradient data of the desorption tower, the heat flux density value of each point on the tower wall is calculated using Fourier's law of heat conduction. A continuous heat flux density distribution function is generated using a bilinear interpolation algorithm. If the continuous heat flux density distribution function is generated, a heat map of the heat flux density distribution on the tower wall is drawn.

[0019] Specifically, based on the geometric parameters of the ammonia combustion chamber and fuel supply data, the Arrhenius equation was used to calculate the ammonia combustion reaction rate, and the temperature distribution within the combustion chamber was obtained by combining this with the energy conservation equation. For heat transfer between the combustion chamber and the desorption tower, finite element analysis was performed using COMSOL Multiphysics software. Two heat transfer models were established: direct contact and indirect contact. A convection heat transfer model was used for the direct contact model, and a conduction model was used for the indirect contact model. The temperature field distribution under both heat transfer modes was obtained. Based on the combustion chamber temperature distribution and the two heat transfer models, the Runge-Kutta method was implemented using MATLAB programming to solve the partial differential equations of heat conduction. The combustion chamber temperature distribution and the heat transfer model results were used as boundary conditions. The temperature values ​​at various points in the desorption tower were obtained through iterative calculation, and a temperature gradient cloud map of the desorption tower was plotted. Based on the temperature gradient data of the desorption tower, the heat flux density values ​​at various points on the tower wall were calculated using Fourier's law of heat conduction. A continuous heat flux density distribution function was generated using a bilinear interpolation algorithm, and a thermodynamic map of the heat flux density distribution on the tower wall was plotted using Python's Matplotlib library. The ammonia combustion chamber is designed as a cylindrical structure with a diameter of 0.5 meters and a height of 1.2 meters, and a fuel supply of 10 kg / h. The ammonia combustion reaction rate is calculated using the Arrhenius equation, with an activation energy of 60 kJ / mol and a pre-exponential factor of 1.2 × 10⁶ s⁻¹. Combining the energy conservation equation, the temperature distribution within the combustion chamber is obtained through discretization using the finite difference method, with the highest temperature at the center reaching 1200℃. Two heat transfer models are constructed using COMSOL Multiphysics software: direct contact and indirect contact. The direct contact model employs a convective heat transfer model with a heat transfer coefficient of 500 W / (m²·K); the indirect contact model uses a conductive model with stainless steel as the heat transfer wall material and a thermal conductivity of 16 W / (m·K). The temperature field distributions under both heat transfer methods are obtained: 800℃ at the inlet of the desorption tower for the direct contact model and 750℃ for the indirect contact model. Based on the above results, the fourth-order Runge-Kutta method was implemented using MATLAB programming to solve the two-dimensional partial differential equation of heat conduction. The grid size was set to 0.01 meters, the time step to 0.1 seconds, and 5000 iterations were performed to obtain the temperature values ​​at various points in the desorption tower. The built-in function `contourf` was used to plot the temperature gradient contour map of the desorption tower, with a temperature range from 400℃ to 800℃. Based on the temperature gradient data, Fourier's law of heat conduction was applied to calculate the heat flux density at various points on the tower wall, with a thermal conductivity of 5 W / (m·K). A bilinear interpolation algorithm was used with 100×100 interpolation points to generate a continuous heat flux density distribution function. Finally, the `pcolormesh` function from Python's Matplotlib library was used to plot the heat flux density distribution thermogram of the tower wall, with a heat flux density range from 5000 W / m² to 25000 W / m², and the "hot" color mapping method was selected.

[0020] S102. Based on the parameters of packing type, specific surface area, porosity and bulk density, analyze the flow state of the absorbent solution in the desorption tower, obtain the gas-liquid two-phase flow velocity and gas-liquid ratio distribution at different positions in the tower, analyze the influence of packing size and tower diameter ratio on pressure drop, and establish the relationship curve between pressure drop and flow velocity.

[0021] The desorption tower packing type, specific surface area, porosity, and bulk density parameters are obtained. Based on these parameters, the Stichlmair two-phase flow pressure drop model is used to calculate the pressure gradient at different locations within the desorption tower. The pressure distribution within the desorption tower is obtained through numerical integration, and the gas-liquid two-phase velocity distribution within the desorption tower is determined by combining the continuity equation. A two-dimensional axisymmetric model of the desorption tower is established, and the porous medium parameters of the desorption tower packing region are set. These porous medium parameters include porosity, inertial drag coefficient, and viscous drag coefficient calculated from the packing characteristics. The flow field information within the desorption tower is obtained, and the gas-liquid two-phase velocity and gas-liquid ratio distribution are extracted from the flow field information. The gas-liquid ratio distribution is characterized to determine the flow state of the solution within the desorption tower. If the flow state of the solution within the desorption tower meets preset conditions, the desorption tower pressure drop data is recorded. The relationship curve between the pressure drop data and the flow velocity is fitted using the least squares method to obtain the power function relationship between pressure drop and flow velocity.

[0022] Specifically, based on the packing type, specific surface area, porosity, and bulk density parameters, the pressure gradient at different locations within the desorption tower is calculated using the Stichlmair two-phase flow pressure drop model. The pressure distribution within the tower is obtained through numerical integration, and the gas-liquid two-phase velocity distribution is solved using the continuity equation. A two-dimensional axisymmetric model of the desorption tower is established using the computational fluid dynamics software FLUENT. Porous medium parameters for the packing region are set, including porosity, inertial drag coefficient, and viscous drag coefficient calculated from the packing characteristics. The absorbent solution properties and inlet boundary conditions are input, and the Navier-Stokes equations are solved to obtain the flow field information within the tower. The gas-liquid two-phase velocity and gas-liquid ratio distribution are extracted from the flow field data. MATLAB interpolation functions are used to generate a gas-liquid ratio contour map within the tower. The K-means clustering algorithm is used to identify features of the gas-liquid ratio distribution to determine the solution flow state. The calculation process was automated using Python scripts. FLUENT's parametric research function was used to perform batch calculations for different packing sizes and column diameter ratios, recording pressure drop data. The least squares method was used to fit the pressure drop versus flow rate curve, obtaining a power function relationship between pressure drop and flow rate. Simultaneously, multiple regression analysis was employed to establish a model relating pressure drop to packing size, column diameter ratio, and flow rate. In the implementation process, ceramic Raschig ring packing with a specific surface area of ​​350 m² was initially selected. 2 / m 3 It has a porosity of 0.75 and a bulk density of 650 kg / m³. 3The pressure gradient within the desorption tower was calculated using the Stichlmair two-phase flow pressure drop model. The tower was 10 meters high and divided into 100 computational cells, each with a pressure drop of approximately 20 Pa. The pressure distribution within the tower was obtained through trapezoidal numerical integration, with the pressure at the bottom of the tower being 1.2 bar. Using the continuity equation, the apparent velocity of the gas phase was calculated to be 2 m / s, and that of the liquid phase to be 0.01 m / s. A two-dimensional axisymmetric model of the desorption tower was established in FLUENT, with a tower diameter of 1 meter. The porous media parameters in the packing region were set, with an inertial drag coefficient of 3.5 and a viscous drag coefficient of 1.2 × 10⁻⁶. 5 m -2 The density of the input absorbent solution is 1200 kg / m³. 3 With a viscosity of 0.002 Pa·s, the flow field distribution inside the column was obtained. The flow field data was interpolated using the MATLAB `griddata` function to generate a 100×100 grid of gas-liquid ratio isopleths. K-means clustering was used, with three cluster centers, to identify the characteristics of the gas-liquid ratio distribution, determining the flow states in the top, middle, and bottom regions of the column as film flow, transition flow, and bubbling flow, respectively. The `parameterStudy` module in Python was used to perform a parameterized study with packing sizes of 10-50 mm and column diameter ratios of 0.1-0.5, recording pressure drop data at 10 flow rates for each parameter group. The `polyfit` function in NumPy was used to fit the power function relationship between pressure drop and flow rate, with an exponent of approximately 1.8. Finally, the `OLS` function in the `statsmodels` library was used for multiple regression analysis to establish a model of the relationship between pressure drop and packing size, column diameter ratio, and flow rate, with a coefficient of determination R0. 2 It reached 0.95.

[0023] S103. Based on the flow state and the relationship curve between pressure drop and flow velocity, analyze the desorption process of refrigerant in solution. Based on solution concentration, refrigerant boiling point and vapor pressure, calculate the local desorption rate and the amount of refrigerant vapor generated at different locations in the desorption tower.

[0024] The system receives a model building request carrying structural parameters of the desorption tower, the request being sent by the FLUENT software; it builds a coupled fluid flow and heat transfer model within the desorption tower based on the request, the model being used to solve for the temperature distribution and velocity field within the tower; it obtains the activity calculation results of the refrigerant in the liquid phase, the activity calculation results being obtained from the NRTL activity coefficient model; it solves for the vapor pressure of the refrigerant at different temperatures and pressures based on the activity calculation results; it calculates the gas-liquid interface mass transfer coefficient, the mass transfer coefficient being used to solve the mass transfer equation of the desorption process using the finite difference method; it calculates the cumulative desorption amount at each cross section within the tower, the cumulative desorption amount being used to calculate the amount of refrigerant vapor generated using the principle of mass conservation; it sets the differential equation dM / dz=f(z,M,C), where M is the vapor generation amount, z is the tower height, and C is the solution concentration; it solves the differential equation, the solution of which is used to obtain the distribution of refrigerant vapor generation along the tower height direction.

[0025] Specifically, based on the flow state and the relationship between pressure drop and velocity, a coupled fluid flow and heat transfer model within the desorption tower was established using the computational fluid dynamics software FLUENT. Inlet boundary conditions and wall heat transfer conditions were set, and a structured mesh generation method with a mesh size of 100×500 was employed to obtain the temperature distribution and velocity field within the tower. The NRTL activity coefficient model was used to calculate the refrigerant activity in the liquid phase, and the vapor pressure of the refrigerant at different temperatures and pressures was solved using the equation of state. The local driving force at various locations within the tower was obtained through interpolation. The gas-liquid interface mass transfer coefficient was calculated using the Onda correlation, considering packing characteristic parameters such as specific surface area and porosity. Combining the local driving force and mass transfer coefficient, the mass transfer equation of the desorption process was solved using the finite difference method to obtain the local desorption rate at different locations within the tower. The cumulative desorption rate at each cross-section within the tower was calculated using numerical integration. The refrigerant vapor production rate was calculated using the principle of mass conservation. A differential equation was established: dM / dz = f(z,M,C), where M is the vapor production rate, z is the tower height, and C is the solution concentration. The ode45 function in MATLAB was used to solve this differential equation, obtaining the distribution of refrigerant vapor production along the tower height. The plot function was then used to plot the curve of refrigerant vapor production as a function of tower height. In implementation, based on the previously obtained flow state and pressure drop-velocity relationship curves, a desorption tower model was established in FLUENT. The tower was 10 meters high and 1 meter in diameter. The inlet boundary conditions were set as follows: solution velocity 0.5 m / s, temperature 80℃; gas velocity 2 m / s, temperature 70℃. Using a 100×500 structured mesh, the temperature distribution within the tower was found to be 70-85℃, liquid phase velocity 0.3-0.6 m / s, and gas phase velocity 1.8-2.5 m / s. The activity of refrigerant, such as R134a, in the liquid phase was calculated using the NRTL activity coefficient model, with an activity coefficient range of 0.8–1.2. Combined with the Peng-Robinson equation of state, the vapor pressure range was calculated to be 0.5–1.5 MPa. Local driving forces at 100 locations within the column were obtained using cubic spline interpolation. The gas-liquid interface mass transfer coefficient was calculated using the Onda correlation, with a packing specific surface area of ​​250 m². 2 / m 3 With a porosity of 0.95, the mass transfer coefficient ranged from 0.001 to 0.005 m / s. Using the finite difference method with a central difference scheme and a grid number of 1000, the mass transfer equation was solved, and the local desorption rate was calculated to be 0.01–0.05 kg / (m²). 3•s). The cumulative desorption rate was calculated using the trapezoidal integral method, yielding a total desorption rate of 180 kg / h at the top of the column. A differential equation was set up: dM / dz = 0.05 * (1 - M / 180) * (1 - z / 10), where M is the steam generation rate (kg / h) and z is the column height (m). The equation was solved using the ode45 function in MATLAB, with an initial value of M(0) = 0, to obtain the steam generation rate at 10 points along the column height. Finally, a curve was plotted, with the horizontal axis representing the column height (0-10m) and the vertical axis representing the steam generation rate (0-180 kg / h). The curve was S-shaped, with the fastest growth occurring in the middle of the column.

[0026] S104. Based on the heat flux density distribution, calculate the solution temperature distribution. By analyzing the heat and mass transfer process in the desorption tower, obtain the local heat transfer coefficient and mass transfer efficiency distribution on the packing surface.

[0027] A three-dimensional temperature field numerical model of the desorption tower was established, including inlet temperature and wall heat flux boundary conditions. The solution temperature distribution was obtained using this numerical model. The physical properties of the solution and refrigerant were acquired, and a coupled heat and mass transfer model within the desorption tower was constructed using FLUENT based on these properties. The coupled model was used to simulate gas-liquid two-phase flow. During the simulation, the Lee model was used for the gas-liquid phase mass transfer term, and the RNGk-ε model was selected for the turbulence model. The velocity and concentration fields on the packing surface were obtained based on the simulation results. The temperature gradient and heat flux density on the packing surface were obtained, and the local heat transfer coefficient was calculated based on these values. The relationship between the local heat transfer coefficient and the Reynolds number and Prandtl number was obtained using the least squares method. The actual and ideal mass flux on the packing surface were obtained, and the local mass transfer efficiency was calculated based on these values.

[0028] Specifically, based on the heat flux density distribution, the energy equation is discretized using the finite volume method, and a three-dimensional temperature field numerical model of the desorption tower is established. A hexahedral structured mesh with a mesh size of 100×100×200 is used. Inlet temperature and wall heat flux boundary conditions are set, and the solution temperature distribution is obtained through iterative solution using the SIMPLE algorithm. A coupled heat and mass transfer model of the desorption tower is constructed using the computational fluid dynamics software FLUENT. The physical properties of the solution and refrigerant are set, and an Euler-Euler two-fluid model is used to simulate the gas-liquid two-phase flow. The Lee model is used for the gas-liquid mass transfer term, and the RNGk-ε model is selected for the turbulence model. The velocity and concentration fields on the packing surface are obtained by solving the model. Based on the temperature gradient and heat flux density on the packing surface, the local heat transfer coefficient h is calculated using the modified Newton's law of cooling q = h(Tw - Tb), where q is the heat flux density, Tw is the wall temperature, and Tb is the bulk temperature. The relationship between the local heat transfer coefficient h and the Reynolds number Re and Prandtl number Pr is obtained by fitting the data using the least squares method.

[0029] h = a·Re b ·Pr c

[0030] 'a' is the proportionality constant in the formula, and 'b' is the exponent of the Reynolds number ReRe, representing the influence of the Reynolds number on the heat transfer coefficient. By comparing the actual mass transfer flux on the packing surface with the ideal mass transfer flux, the local mass transfer efficiency is calculated. A three-layer BP neural network algorithm is used to establish a model relating the mass transfer efficiency to the packing geometry, fluid properties, and operating conditions. The input layer contains 10 nodes, the hidden layer contains 20 nodes, and the output layer contains 1 node. The Levenberg-Marquardt algorithm is used to train the network, and the contour function in MATLAB is used to plot the distribution contours of the heat transfer coefficient and mass transfer efficiency. In the implementation process, based on the previously obtained heat flux density distribution data, a three-dimensional temperature field model of the desorption tower is constructed using the finite volume method. The tower is 10 meters high and 1 meter in diameter, and a 100×100×200 hexahedral structured mesh is used. The inlet temperature is set at 80℃, and the wall heat flux density ranges from 5000 to 25000 W / m2. 2 The SIMPLE algorithm was iterated 500 times, and the convergence criterion was 10. -6 The solution temperature distribution range was found to be 70-90℃. An Euler-Euler two-fluid model was established in FLUENT, with the solution density set to 1200 kg / m³. 3 Viscosity 0.002 Pa·s, refrigerant density 4 kg / m³ 3 Viscosity 1.2×10 -5 Pa·s. The Lee model coefficients were set to 0.1, and the RNG k-ε model constant Cμ was taken as 0.0845. The solution yielded a packing surface velocity field ranging from 0.1 to 1.0 m / s and a concentration field ranging from 0.1 to 0.5 kg / m³. 3 Based on the temperature gradient and heat flux density of the packing surface, the local heat transfer coefficient is calculated, ranging from 500 to 2000 W / (m²). 2 ·K). Using the least squares method, the relation h = 0.023Re was obtained. 0 · 8 Pr 0 · 4 By comparing the actual mass transfer flux with the ideal mass transfer flux, the local mass transfer efficiency ranged from 0.6 to 0.9. A three-layer backpropagation (BP) neural network was constructed, with 10 nodes in the input layer including parameters such as packing specific surface area, porosity, and liquid-to-gas ratio; 20 nodes in the hidden layer; and 1 node in the output layer representing the mass transfer efficiency. The network was trained using 100 sets of experimental data with a learning rate of 0.01, and after 1000 training iterations, the mean squared error was reduced to 0.001. Finally, the heat transfer coefficient and mass transfer efficiency distribution contour plots were plotted using the `contourf` function in MATLAB, with the horizontal and vertical axes representing the tower diameter and tower height, and the color range set to blue to red.

[0031] S105. Based on the local desorption rate and refrigerant vapor generation at different locations within the desorption tower, perform integral calculations on the desorption tower to obtain the distribution curve of the cumulative desorption amount of refrigerant along the tower height. Combined with the analysis of the relationship between mass transfer efficiency and packing specific surface area and tower diameter, determine the main factors affecting mass transfer efficiency.

[0032] Data on local desorption rates and refrigerant vapor generation at different locations within the desorption tower are obtained. Based on this data, the trapezoidal numerical integration method is used to perform integral calculations on the desorption tower to obtain the cumulative desorption distribution of the refrigerant. Cubic spline interpolation is performed on the cumulative desorption data to generate a distribution curve of the cumulative desorption along the tower height. The average and standard deviation of the local mass transfer efficiency are calculated. If the average plus or minus twice the standard deviation exceeds a preset threshold, the optimal mass transfer region with the highest desorption efficiency is determined. A model is established using multiple regression analysis to establish the relationship between mass transfer efficiency and the specific surface area of ​​the packing and the tower diameter, with mass transfer efficiency as the dependent variable and the specific surface area of ​​the packing and the tower diameter as independent variables. The importance of each factor to the mass transfer efficiency is evaluated, and the importance ranking of each factor is determined by calculating the average reduction in impurity.

[0033] Specifically, based on the local desorption rate and refrigerant vapor production data at different locations within the desorption tower, the trapezoidal numerical integration method is used to perform integral calculations on the desorption tower, accumulating the local desorption amounts along the tower height to obtain the cumulative desorption distribution of the refrigerant. MATLAB's spline function is used to perform cubic spline interpolation on the cumulative desorption data, generating a high-precision distribution curve of the cumulative desorption along the tower height. This curve is then plotted using the plot function, with the tower height on the x-axis and the cumulative desorption amount on the y-axis. Combining the mass transfer efficiency distribution data, the moving window method is used to calculate the mean and standard deviation of the local mass transfer efficiency. The window size is set to 10% of the tower height, and the sliding step size is 20% of the window size. By setting a threshold (mean plus or minus twice the standard deviation), the optimal mass transfer region with the highest desorption efficiency is determined, and this region is marked on the cumulative desorption distribution curve. A model was established using multiple regression analysis to model the relationship between mass transfer efficiency and the specific surface area of ​​the packing material and the tower diameter. A stepwise regression algorithm was used to screen for significant variables, with the F-test critical value (α = 0.05) used for inclusion and exclusion. Multicollinearity was diagnosed by calculating the variance expansion factor (VIF), and variables with a VIF greater than 10 were excluded. The main factors affecting mass transfer efficiency were identified by comparing standardized regression coefficients. A random forest algorithm was used to evaluate the importance of each factor to mass transfer efficiency, with 100 trees and a minimum leaf node sample size of 5. The importance of each factor was ranked by calculating the reduction in average impurity, validating the results of the multiple regression analysis. In the implementation, integral calculations were first performed on the desorption tower. The tower was 10 meters high and divided into 100 calculation units, with the local desorption rate of each unit ranging from 0.01 to 0.05 kg / (m³).3 The cumulative desorption distribution was obtained by numerical integration using the trapezoidal rule, with a cumulative desorption rate of 180 kg / h at the top of the column. MATLAB's spline function was used to perform cubic spline interpolation on these 100 data points, generating 1000 interpolation points and forming a smooth cumulative desorption distribution curve. A moving window method was applied to the mass transfer efficiency data, with a window size of 1 meter and a sliding step size of 0.2 meters. The calculated local mass transfer efficiency average was 0.75, and the standard deviation was 0.08. A threshold of 0.59-0.91 (mean plus or minus twice the standard deviation) was set, determining the optimal mass transfer region to be located at a column height of 3.5-6.5 meters. Multiple regression analysis was performed, with independent variables including the packing specific surface area (200-400 m²). 2 / m 3 The column diameter ranged from 0.5 to 2.0 m, with mass transfer efficiency as the dependent variable. In stepwise regression, the critical value for the F-test was set to 4.0, and after screening, only the packing specific surface area and column diameter were retained. The calculated VIF values ​​were all less than 5, indicating no severe multicollinearity. Standardized regression coefficients showed that the packing specific surface area (0.72) had a greater impact on mass transfer efficiency than the column diameter (-0.45). A random forest algorithm with 100 trees and a minimum leaf node sample size of 5 was used to assess factor importance. The results showed that the importance score for packing specific surface area was 0.68, and for column diameter, it was 0.32, validating the regression analysis results.

[0034] S106. Based on the local heat transfer coefficient of the packing surface and the main factors affecting mass transfer efficiency, a systematic analysis is conducted on the geometric parameters of the packing specific surface area, tower diameter, tower height, and solution flow rate operating parameters to set the parameter variation range. Through parameter scanning and sensitivity analysis, the influence of each parameter on the system performance is quantified to obtain the parameter analysis results.

[0035] Obtain uniformly distributed sample points generated by the Latin hypercube sampling method; construct a simplified model of the desorption tower based on the sample points, wherein the simplified model uses a second-order polynomial to fit the relationship between system performance and parameters; determine the importance ranking of each parameter in the Pareto plot, and if the importance of a parameter exceeds a preset threshold, it is identified as a key parameter; calculate the sensitivity coefficient for the key parameter, wherein the sensitivity coefficient is obtained by the central difference method; perform multi-objective optimization using the NSGA-II algorithm, wherein the multi-objective optimization combines three performance indicators: total heat transfer, desorption efficiency, and pressure drop, to obtain the Pareto optimal solution set, and select the optimal parameter combination from the Pareto optimal solution set.

[0036] Specifically, based on the local heat transfer coefficient of the packing surface and the main factors affecting mass transfer efficiency, a parameter space was established, setting the variation range of geometric parameters such as packing specific surface area, tower diameter, and tower height, as well as solution flow rate operating parameters. The Latin hypercube sampling method was implemented using the pyDOE library to generate 100 uniformly distributed sample points. A simplified model of the desorption tower was constructed using the response surface methodology, and the relationship between system performance and parameters was fitted using a second-order polynomial. Parameter scanning was implemented using a Python script to obtain system performance indicators such as total heat transfer, desorption efficiency, and pressure drop under different parameter combinations. Data processing was performed on the parameter scanning results, and analysis of variance was conducted using the statsmodels library to calculate the F-value and p-value of each parameter. Pareto charts were plotted to visually display the importance ranking of each parameter, identifying the key parameters affecting system performance. For the key parameters, local sensitivity analysis was performed, using the central difference method to calculate the sensitivity coefficient with a step size of 1% of the parameter range. The contour function in MATLAB was used to plot two-dimensional sensitivity contour plots to quantify the impact of parameter changes on system performance. Simultaneously, the Morris screening method was implemented using the SALib library for global sensitivity analysis to verify the results of the local sensitivity analysis. Finally, the NSGA-II algorithm was used for multi-objective optimization, considering three performance indicators: total heat transfer, desorption efficiency, and pressure drop, to obtain the Pareto optimal solution set, from which the best parameter combination was selected. During implementation, the parameter variation range was initially set to 200-400 m² of the packing specific surface area. 2 / m 3 The tower diameter is 0.5-2.0m, the tower height is 5-15m, and the solution flow rate is 1000-5000 kg / h. 100 Latin hypercube sample points are generated using the `lhs` function from the pyDOE library. A second-order polynomial response surface model is constructed to fit the system performance indices, such as the total heat transfer Q = a0 + a1x1 + a2x2 + a3x3 + a4x4 + a12x1x2 + ... + a11x1 2 +..., where x1-x4 are four parameters. Through parameter scanning, the performance index range was obtained as follows: total heat transfer 50-200kW, desorption efficiency 60-90%, and pressure drop 0.5-5kPa / m. Analysis of variance was performed using the OLS function of statsmodels.api to calculate the F-value and p-value. The results showed that the probability p-values ​​for the packing specific surface area and solution flow rate were <0.05, indicating they were significant parameters. Local sensitivity analysis was performed on these two key parameters using the central difference method with a step size of 1% of the parameter range. The calculated sensitivity coefficient was 0.5kW / (m²) for total heat transfer versus packing specific surface area. 2 / m 3The solution flow rate was 0.03 kW / (kg / h). Global sensitivity analysis was performed using the Morris function from the SALib library, and the results verified the accuracy of the local analysis. Finally, multi-objective optimization was performed using the NSGA-II algorithm with a population size of 100 and 500 iterations, resulting in 20 Pareto optimal solutions. From these, the optimal parameter combination was selected, which yielded a total heat transfer of 180 kW, a desorption efficiency of 85%, and a pressure drop of 2 kPa / m². The optimal parameter combination was a packing specific surface area of ​​350 m². 2 / m 3 The tower has a diameter of 1.2m, a height of 10m, and a solution flow rate of 3500kg / h.

[0037] S107. Based on the parameter analysis results, optimize the packing specific surface area, tower diameter, and tower height parameters under different solution flow conditions to obtain the optimal geometric parameter combination for each flow rate. Combined with the evaluation of the system's performance under different cooling loads and parameter combinations, obtain the system's operating characteristics under various load conditions, including changes in energy efficiency ratio and response capability, and obtain a performance analysis report of the ammonia combustion refrigeration system, including energy saving potential and environmental impact assessment under various operating conditions.

[0038] The specific surface area of ​​the packing material, the tower diameter, and the tower height are obtained, and multi-objective optimization is performed on these parameters. Based on the optimized geometric parameter combination, a simplified dynamic model of the ammonia combustion refrigeration system is constructed. The optimized geometric parameter combination is input, different cooling load conditions are set, and a system of differential equations is solved, recording the energy efficiency ratio, cooling capacity, and response time performance indicators. The polyfit function from the NumPy library is used for polynomial fitting to obtain the energy efficiency ratio variation curve. If the energy efficiency ratio variation curve meets preset conditions, the optimal operating load range of the system is determined. Environmental impact indicators such as carbon emissions and energy consumption are calculated. A fuzzy comprehensive evaluation model is constructed, and a triangular membership function is set. The system performance under different operating conditions is quantitatively evaluated using the fuzzy weighted average method. The degree of influence of different parameters on system performance is determined. If the degree of influence exceeds a preset threshold, the robustness of the optimization results is verified.

[0039] Specifically, based on the parameter analysis results, the DEAP library was used to implement the NSGA-II algorithm for multi-objective optimization of packing specific surface area, tower diameter, and tower height parameters under different solution flow rates. A population size of 100, crossover probability of 0.9, mutation probability of 0.1, and 500 iterations were set to obtain the Pareto optimal solution set for each flow rate. From this set, the geometric parameter combination with the best overall performance was selected. A simplified dynamic model of the ammonia combustion refrigeration system was constructed using Python's scipy.integrate library. The optimized geometric parameter combination was input, and different cooling load conditions were set. The odeint function was used to solve the differential equations, simulating the system's operation under partial load conditions. Performance indicators such as energy efficiency ratio, cooling capacity, and response time were recorded. Based on the simulation results, the NumPy polyfit function was used for polynomial fitting to establish a model relating system performance to cooling load, obtaining the energy efficiency ratio variation curve. The optimal operating load range of the system was determined by calculating the first derivative of the energy efficiency ratio, and the step response method was used to evaluate the system's dynamic response capability. Based on system performance data, OpenLCA software was used for life cycle assessment to calculate environmental impact indicators such as carbon emissions and energy consumption. A fuzzy comprehensive evaluation model was constructed, and a triangular membership function was set. The fuzzy weighted average method was used to quantitatively evaluate the system performance under different operating conditions, generating a performance analysis report for the ammonia combustion refrigeration system. The SALib library was used to implement Sobol sensitivity analysis to assess the impact of different parameters on system performance and verify the robustness of the optimization results.

[0040] In the implementation process, the NSGA-II algorithm was first implemented using the DEAP library to optimize five flow points within the solution flow range of 1000-5000 kg / h. Taking a flow rate of 3000 kg / h as an example, after 500 iterations, the Pareto optimal solution set was obtained, from which a packing material with a specific surface area of ​​350 m² was selected. 2 / m 3 The optimal combination of a tower diameter of 1.2m and a tower height of 10m was determined. Subsequently, a simplified dynamic model was constructed using the scipy.integrate library, setting the cooling load range to 20-100kW with a step size of 10kW. The odeint function was used to solve the differential equations, obtaining data points on the energy efficiency ratio as a function of load. A fourth-order polynomial was fitted using the NumPy polyfit function, achieving a goodness of fit R0. 2 The efficiency ratio reaches 0.99, resulting in the energy efficiency ratio curve COP = -2e-7Q. 4 +5e-5Q 3 -0.004Q 2+0.15Q+1.2, where Q is the cooling load. The optimal operating load range was determined to be 60-80kW through differentiation. Life cycle assessment was performed using OpenLCA software, calculating that under rated operating conditions, the carbon emissions per 1kWh of cooling are 0.5kg CO2 equivalent, and the energy consumption is 1.8kWh. A fuzzy comprehensive evaluation model was constructed, setting three evaluation indicators: energy efficiency ratio, response time, and environmental impact. A triangular membership function was used with weights of 0.4, 0.3, and 0.3, respectively. The fuzzy weighted average method yielded a comprehensive system performance score of 0.85. Finally, Sobol sensitivity analysis was performed using the SALib library, calculating the first-order sensitivity indices of packing specific surface area, tower diameter, and tower height on system performance to be 0.45, 0.30, and 0.25, respectively, validating the rationality of the optimization results.

[0041] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the concept of this application. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A kinetic analysis method for an ammonia combustion refrigeration system in a large-scale industrial park cooling system, characterized in that, The method includes: Based on the geometry of the ammonia combustion chamber and the fuel supply, the heat generated by ammonia combustion is calculated, and the heat transfer mode between the combustion chamber and the desorption tower is analyzed, including direct contact and indirect heat transfer. By simulating the heat transfer process, the heat flux density distribution on the tower wall is obtained and plotted. Based on parameters such as packing type, specific surface area, porosity, and bulk density, the flow state of the absorbent solution in the desorption tower is analyzed, the gas-liquid two-phase flow velocity and gas-liquid ratio distribution at different locations in the tower are obtained, the influence of packing size and tower diameter ratio on pressure drop is analyzed, and the relationship curve between pressure drop and flow velocity is established. Based on the flow state and the relationship curve between pressure drop and flow velocity, the desorption process of refrigerant in solution is analyzed. Based on solution concentration, refrigerant boiling point and vapor pressure, the local desorption rate and the amount of refrigerant vapor generated at different locations in the desorption tower are calculated. Based on the heat flux density distribution, the solution temperature distribution is calculated. By analyzing the heat and mass transfer process in the desorption tower, the local heat transfer coefficient and mass transfer efficiency distribution on the packing surface are obtained. Based on the local desorption rate and refrigerant vapor production at different locations within the desorption tower, integral calculations are performed on the desorption tower to obtain the distribution curve of the cumulative desorption amount of refrigerant along the tower height. Combined with the analysis of the relationship between mass transfer efficiency and packing specific surface area and tower diameter, the main factors affecting mass transfer efficiency are determined. Based on the local heat transfer coefficient of the packing surface and the main factors affecting mass transfer efficiency, a systematic analysis was conducted on the geometric parameters of the packing specific surface area, tower diameter, tower height, and solution flow rate operating parameters to set the parameter variation range. Through parameter scanning and sensitivity analysis, the influence of each parameter on the system performance was quantified, and the parameter analysis results were obtained. Based on the parameter analysis results, the specific surface area of ​​the packing, the tower diameter, and the tower height under different solution flow conditions were optimized to obtain the optimal combination of geometric parameters for each flow rate. Combined with the evaluation of the system's performance under different cooling loads and parameter combinations, the operating characteristics of the system under various load conditions were obtained, including changes in energy efficiency ratio and response capability. A performance analysis report of the ammonia combustion refrigeration system was obtained, including the energy-saving potential and environmental impact assessment under various operating conditions.

2. The method according to claim 1, wherein, The process involves calculating the heat generated by ammonia combustion based on the geometry of the ammonia combustion chamber and the fuel supply, and analyzing the heat transfer mechanisms between the combustion chamber and the desorption tower, including direct contact and indirect heat transfer. By simulating the heat transfer process, the heat flux density distribution on the tower wall is obtained and plotted, including: Obtain the geometric parameters of the ammonia combustion chamber and the fuel supply data, calculate the ammonia combustion reaction rate, and solve the temperature distribution in the combustion chamber by combining the energy conservation equation; Based on the combustion chamber temperature distribution, two heat transfer models, direct contact and indirect wall, were established. Finite element analysis was performed based on the two heat transfer models to obtain the temperature field distribution under the two heat transfer modes. For the temperature distribution in the combustion chamber and two heat transfer models, the partial differential equation of heat conduction is solved, and the temperature values ​​at each point in the desorption tower are obtained through iterative calculation. Based on the temperature gradient data of the desorption tower, Fourier's law of heat conduction is applied to calculate the heat flux density at each point on the tower wall. A continuous heat flux density distribution function is generated using a bilinear interpolation algorithm. Once the continuous heat flux density distribution function is generated, a heat flux density distribution thermogram of the tower wall is plotted.

3. The method according to claim 1, wherein, Based on parameters such as packing type, specific surface area, porosity, and bulk density, the flow state of the absorbent solution in the desorption tower is analyzed. The gas-liquid two-phase flow velocity and gas-liquid ratio distribution at different locations within the tower are obtained. The influence of packing size and tower diameter ratio on pressure drop is analyzed, and a pressure drop versus flow velocity curve is established, including: The parameters of desorption tower packing type, specific surface area, porosity and bulk density are obtained. Based on the parameters, the Stichlmair two-phase flow pressure drop model is used to calculate the pressure gradient at different locations in the desorption tower. The pressure distribution inside the desorption tower was obtained by numerical integration, and the gas-liquid two-phase velocity distribution inside the desorption tower was determined by combining the continuity equation. A two-dimensional axisymmetric model of the desorption tower is established, and the porous media parameters of the packing region of the desorption tower are set. The porous media parameters include porosity, inertial resistance coefficient and viscous resistance coefficient calculated from the packing characteristics. Obtain flow field information inside the desorption tower, and extract the gas-liquid two-phase flow velocity and gas-liquid ratio distribution from the flow field information; The gas-liquid ratio distribution is characterized to determine the flow state of the solution inside the desorption tower; If the flow state of the solution inside the desorption tower meets the preset conditions, then record the pressure drop data of the desorption tower. By fitting the pressure drop data and flow velocity curves using the least squares method, a power function relationship between pressure drop and flow velocity is obtained.

4. The method according to claim 1, wherein, The process of refrigerant desorption in solution is analyzed based on the flow state and the relationship curve between pressure drop and flow velocity. Based on solution concentration, refrigerant boiling point, and vapor pressure, the local desorption rate and refrigerant vapor generation at different locations within the desorption tower are calculated, including: Receive a model building request carrying structural parameters of the desorption tower, the model building request being sent by the FLUENT software; Based on the model establishment request, a coupled model of fluid flow and heat transfer inside the desorption tower is established. The model is used to solve the temperature distribution and velocity field inside the tower. The activity calculation results of the refrigerant in the liquid phase are obtained, and the activity calculation results are obtained by the NRTL activity coefficient model; The vapor pressure of the refrigerant at different temperatures and pressures is calculated based on the activity calculation results. Calculate the mass transfer coefficient at the gas-liquid interface, which is used to solve the mass transfer equation of the desorption process using the finite difference method. The cumulative desorption amount at each cross section inside the tower is calculated, and the cumulative desorption amount is used to calculate the amount of refrigerant vapor generated using the principle of mass conservation. Set up the differential equation dM / dz=f(z,M,C), where M is the steam generation rate, z is the tower height, and C is the solution concentration; Solve the differential equation, and the solution of the differential equation is used to obtain the distribution of refrigerant vapor production along the height of the tower.

5. The method according to claim 1, wherein, The process involves calculating the solution temperature distribution based on the heat flux density distribution, and analyzing the heat and mass transfer processes within the desorption tower to obtain the local heat transfer coefficient and mass transfer efficiency distribution on the packing surface, including: A three-dimensional temperature field numerical model is established inside the desorption tower, the numerical model including inlet temperature and wall heat flux boundary conditions; The solution temperature distribution was obtained using the numerical model described above. Obtain the physical property parameters of the solution and refrigerant, and construct a coupled heat and mass transfer model in the desorption tower using FLUENT based on the physical property parameters; The coupled model was used to simulate gas-liquid two-phase flow. During the simulation, the mass transfer term between the gas and liquid phases was simulated using the Lee model, and the turbulence model was selected from the RNGk-ε model. The velocity field and concentration field on the filler surface are obtained based on the simulation results; Obtain the temperature gradient and heat flux density on the surface of the packing material, and calculate the local heat transfer coefficient based on the temperature gradient and heat flux density; The relationship between the local heat transfer coefficient and the Reynolds number and Prandtl number was obtained by fitting the least squares method. Obtain the actual and ideal mass transfer flux on the packing surface, and calculate the local mass transfer efficiency based on the actual and ideal mass transfer flux.

6. The method according to claim 1, wherein, The desorption tower is calculated by integrating the local desorption rates and refrigerant vapor production at different locations within the tower to obtain the cumulative desorption rate distribution curve along the tower height. Combined with analysis of the relationship between mass transfer efficiency and packing specific surface area and tower diameter, the main factors affecting mass transfer efficiency are determined, including: Obtain data on local desorption rates and refrigerant vapor generation at different locations within the desorption tower; Based on the data, the trapezoidal numerical integration method is used to perform integral calculations on the desorption tower to obtain the cumulative desorption distribution of the refrigerant; The cumulative desorption data is subjected to cubic spline interpolation to generate a distribution curve of cumulative desorption along the column height; Calculate the average value and standard deviation of the local mass transfer efficiency. If the average value plus or minus twice the standard deviation exceeds a preset threshold, then the region with the highest desorption efficiency is determined as the optimal mass transfer region. A model was established using multiple regression analysis to model the relationship between mass transfer efficiency and packing specific surface area and tower diameter, with mass transfer efficiency as the dependent variable and packing specific surface area and tower diameter as independent variables. Assess the importance of each factor to mass transfer efficiency, and determine the importance ranking of each factor by calculating the average reduction in impurity.

7. The method according to claim 1, wherein, Based on the local heat transfer coefficient of the packing surface and the main factors affecting mass transfer efficiency, a systematic analysis is conducted on the geometric parameters of the packing specific surface area, tower diameter, and tower height, as well as the solution flow rate operating parameters. This analysis aims to define the parameter variation range and quantify the impact of each parameter on system performance through parameter scanning and sensitivity analysis, yielding parameter analysis results, including: Obtain uniformly distributed sample points generated by the Latin hypercube sampling method; A simplified model of the desorption tower is constructed based on sample points. The simplified model uses a second-order polynomial to fit the relationship between the system performance and parameters. Determine the importance ranking of each parameter in the Pareto chart. If the importance of a parameter exceeds a preset threshold, it is identified as a critical parameter. The sensitivity coefficient is calculated for the key parameters, and the sensitivity coefficient is obtained by the central difference method; The NSGA-II algorithm is used for multi-objective optimization, which combines three performance indicators: total heat transfer, desorption efficiency, and pressure drop, to obtain a Pareto optimal solution set. The optimal parameter combination is then selected from the Pareto optimal solution set.

8. The method according to claim 1, wherein, Based on the parameter analysis results, the specific surface area of ​​the packing, the tower diameter, and the tower height under different solution flow rates are optimized to obtain the optimal geometric parameter combination for each flow rate. Combined with the evaluation of the system's performance under different cooling loads and parameter combinations, the system's operating characteristics under various load conditions are obtained, including changes in energy efficiency ratio and response capability. This results in a performance analysis report for the ammonia combustion refrigeration system, including energy-saving potential and environmental impact assessments under various operating conditions. Obtain the parameters of packing specific surface area, tower diameter, and tower height, and perform multi-objective optimization on these parameters; Based on the optimized combination of geometric parameters, the dynamic model of the ammonia combustion refrigeration system is simplified. Input the optimized combination of geometric parameters, set different cooling load conditions, solve the differential equation system, and record the performance indicators of energy efficiency ratio, cooling capacity and response time. The polyfit function from the NumPy library is used to perform polynomial fitting to obtain the energy efficiency ratio change curve; If the energy efficiency ratio change curve meets the preset conditions, then the optimal operating load range of the system is determined; Calculate environmental impact indicators such as carbon emissions and energy consumption; Construct a fuzzy comprehensive evaluation model and set a triangular membership function; The system performance under different operating conditions is quantitatively evaluated using the fuzzy weighted average method; Determine the degree of impact of different parameters on system performance; If the degree of influence exceeds a preset threshold, the robustness of the optimization results is verified.

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