Method, device and electronic equipment for optimizing mixture of cryogens

Abstract

This invention provides a method, apparatus, and electronic device for optimizing the refrigerant mix ratio, relating to the field of liquefied natural gas (LNG) production technology. The method includes: obtaining the set condition parameter range and feed gas change conditions of the LNG preparation process; constructing a mapping model based on the set condition parameter range using a multivariate adaptive spline regression method; the mapping model characterizes the mapping relationship between the refrigerant mix ratio, feed gas conditions, and compressor specific power consumption in LNG production; and solving for the minimum compressor specific power consumption of the mapping model under feed gas change conditions and set constraints using a nonlinear optimization method to obtain the optimized refrigerant mix ratio. This invention eliminates the need to rebuild the process simulation model or reconstruct the mathematical model when feed gas conditions change. By utilizing the multivariate adaptive regression spline method and nonlinear optimization method, it achieves automatic optimization of the refrigerant mix ratio, reducing computational load and improving computational efficiency.

Description

Technical Field

[0001] This invention relates to the field of liquefied natural gas production technology, and more specifically to a method for optimizing the proportion of mixed refrigerants, a device for optimizing the proportion of mixed refrigerants, electronic equipment, machine-readable storage media, and computer program products. Background Technology

[0002] With the increasing environmental problems caused by the use of fossil fuels such as coal and oil, the demand for natural gas, as a new type of high-quality clean fuel, is also growing rapidly. In the process of producing liquefied natural gas (LNG) through refrigeration and liquefaction using mixed refrigerants (hereinafter referred to as mixed refrigerants), the feed gas pressure, the temperature of the feed gas after precooling, the subcooling temperature, the circulating pressure of the mixed refrigerant, and the content of each component in the mixed refrigerant all have a significant impact on the process separation efficiency. When key parameters in the process change, simply adjusting the circulation volume of the mixed refrigerant cannot achieve a better match between the heat and cold loads in the LNG process. As the medium and carrier of cold energy in the refrigeration and liquefaction process, the composition ratio of the mixed refrigerant directly affects the overall energy consumption of the process. When the cooling capacity provided by the mixed refrigerant is less than the cooling capacity required by the LNG process, it will lead to a decrease in product yield; when the cooling capacity provided by the mixed refrigerant is greater than the cooling capacity required by the LNG process, the refrigerant carries excess cooling capacity, increasing the energy consumption of the unit. Therefore, the composition ratio of the mixed refrigerant should be adjusted according to the composition and pressure of the main process feed gas and the different process flows. A suitable refrigerant mix ratio can not only significantly reduce equipment energy consumption and increase product yield, but also greatly reduce production costs and enhance enterprise competitiveness.

[0003] In existing technologies, when the gas quality conditions (temperature, pressure) or composition conditions (proportion of each component) of the feedstock gas in the liquefied natural gas production process change, the optimization of the mixed refrigerant ratio requires the reconstruction of the process simulation model or mathematical model, resulting in a large amount of computation and low computational efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, and electronic device for optimizing the proportion of mixed refrigerants, in order to solve the problem of large computational load and low computational efficiency in the optimization process of mixed refrigerant proportions when the gas quality conditions (temperature, pressure) or composition conditions (proportion of each component) of the feedstock gas change in the liquefied natural gas production process.

[0005] To achieve the above objectives, embodiments of the present invention provide a method for optimizing the mixing ratio of a refrigerant, comprising: Obtain the set condition parameter range and feed gas change conditions for the liquefied natural gas preparation process: the set condition parameter range includes the parameter range of the mixed refrigerant ratio and the parameter range of the feed gas conditions; A mapping model is constructed based on a set range of conditional parameters using multivariate adaptive spline regression; the mapping model characterizes the mapping relationship between the mixed refrigerant ratio, the feed gas conditions, and the specific power consumption of the compressor in liquefied natural gas production; The minimum compressor specific power consumption of the mapping model is solved using a nonlinear optimization method under the conditions of changing raw gas and set constraints, and the optimized result of the mixed refrigerant ratio is obtained.

[0006] Optionally, the step of constructing a mapping model based on a set range of conditional parameters using multivariate adaptive spline regression includes: The Latin hypercube sampling method is used to perform multiple stratified samplings based on the set condition parameter range to obtain a parameter combination matrix; the parameter combination matrix includes the parameter combination of the mixed refrigerant ratio and feed gas conditions obtained from each stratified sampling. The parameter combination matrix is ​​input into the liquefied natural gas process model to obtain the compressor specific power consumption corresponding to each parameter combination output by the liquefied natural gas process model. Using the multivariate adaptive spline regression method, a mapping model is constructed based on the parameter combination matrix and the compressor specific power consumption corresponding to each parameter combination.

[0007] Optionally, the mapping model is constructed based on a constant term, multiple basis functions, a weight coefficient corresponding to each basis function, the parameter combination, and the compressor specific power consumption corresponding to the parameter combination; the constant term, the multiple basis functions, and the weight coefficient corresponding to each basis function are positively correlated with the compressor specific power consumption.

[0008] Optionally, the mapping model is represented by the following formula: ; Where y represents the compressor's specific power consumption. β 0 represents a constant term. h m Let m be the basis function. β m Let represent the weight coefficient of the m-th basis function, M represent the total number of basis functions, and x represent the parameter combination of the mixed refrigerant ratio and the feed gas conditions.

[0009] Optionally, after obtaining the optimized refrigerant ratio by solving the minimum compressor specific power consumption of the mapping model using a nonlinear optimization method under the conditions of changing raw gas and set constraints, the method further includes: Based on the optimized refrigerant ratio, the refrigerant is adjusted online, and each component in the refrigerant is individually recovered.

[0010] Optionally, the set constraints include at least one of the following: component ratio constraints of the mixed refrigerant, temperature constraints of the feed gas, pressure constraints of the feed gas, and component ratio constraints of the feed gas.

[0011] On the other hand, embodiments of the present invention also provide a device for optimizing the mixing ratio of refrigerants, comprising: The acquisition module is used to acquire the set condition parameter range and feed gas change conditions of the liquefied natural gas preparation process: the set condition parameter range includes the parameter range of the mixed refrigerant ratio and the parameter range of the feed gas conditions; The module is used to construct a mapping model based on a set range of conditional parameters using multivariate adaptive spline regression. The mapping model represents the mapping relationship between the mixed refrigerant ratio, the feed gas conditions, and the specific power consumption of the compressor in liquefied natural gas production. The solution module is used to solve for the minimum compressor specific power consumption of the mapping model under the conditions of changing raw gas and set constraints using a nonlinear optimization method, so as to obtain the optimized result of the mixed refrigerant ratio.

[0012] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method for optimizing the ratio of mixed refrigerants.

[0013] On the other hand, the present invention also provides a machine-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for optimizing the ratio of mixed refrigerants.

[0014] On the other hand, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-mentioned method for optimizing the ratio of mixed refrigerants.

[0015] Through the above technical solution, this embodiment of the invention utilizes a multivariate adaptive spline regression method to construct a mapping model based on a set range of conditional parameters, representing the mapping relationship between the mixed refrigerant ratio, the feed gas conditions, and the compressor specific power consumption in liquefied natural gas production. Then, a nonlinear optimization method is used to solve for the minimum compressor specific power consumption of the mapping model under the changed feed gas conditions and set constraints, obtaining the optimized mixed refrigerant ratio result. This embodiment of the invention eliminates the need to rebuild the process simulation model or reconstruct the mathematical model when feed gas conditions change, and utilizes the multivariate adaptive regression spline method and nonlinear optimization method to achieve automatic adjustment of the mixed refrigerant ratio, reducing computational load and improving computational efficiency during the mixed refrigerant ratio optimization process.

[0016] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic flowchart of the method for optimizing the mixing ratio of refrigerants provided by the present invention; Figure 2 This is a schematic diagram illustrating the recovery of the mixed refrigerant provided by the present invention; Figure 3 This is a schematic diagram of the structure of the mixed refrigerant ratio optimization device provided by the present invention; Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0018] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0019] Method Implementation Examples Please refer to Figure 1 This invention provides a method for optimizing the ratio of mixed refrigerants, comprising: Step 100: Obtain the set condition parameter range and feed gas change conditions for the liquefied natural gas preparation process.

[0020] The liquefied natural gas (LNG) preparation process can be any existing LNG production process. In this embodiment of the invention, the LNG preparation process is illustrated using the Fischer-Tropsch synthesis tail gas liquefaction process as an example. An electronic device acquires the set condition parameter range and feed gas change conditions for the LNG preparation process. The set condition parameter range includes the parameter range for the mixed refrigerant ratio and the parameter range for the feed gas conditions. Specifically, the parameter range for the mixed refrigerant ratio can be the range of each refrigerant X in the mixed refrigerant. i The upper and lower limits of component proportions. The parameter range of feed gas conditions can include the parameter range of feed gas gas quality conditions and / or feed gas composition conditions. Specifically, the parameter range of feed gas quality conditions includes the upper and lower limits of the possible variations in feed gas temperature and / or feed gas pressure. The parameter range of feed gas composition conditions includes the proportions of each component Z1,…,Z… n The upper and lower limits of the possible range of variation. In one embodiment, in order to consider as many influencing factors as possible and improve the accuracy of the subsequent mapping model, the setting parameter range of the feed gas conditions includes the temperature (T), pressure (P), and proportions of each component Z1,…,Z of the feed gas. nThe upper and lower limits of the possible range of variation. Specifically, the temperature, pressure, and proportion of each component of the feed gas are set to follow a uniform distribution, resulting in the input parameter combination {X1,…,X...}. i ,T,P,Z1,…,Z n It contains M parameters. Changes in feed gas conditions can include changes in feed gas quality conditions (temperature, pressure) and / or feed gas composition conditions (component ratio).

[0021] Step 200: Construct a mapping model based on the set range of conditional parameters using the multivariate adaptive spline regression method.

[0022] The electronic device can perform multiple random samplings or stratified sampling based on the set condition parameter range obtained in step 100 to obtain multiple input parameter combinations (hereinafter referred to as parameter combinations). Each input parameter combination includes a parameter combination of the refrigerant ratio and the feed gas conditions. The electronic device then inputs the multiple parameter combinations into the constructed liquefied natural gas (LNG) process model to obtain the compressor specific power consumption corresponding to each parameter combination output by the LNG process model. The electronic device uses a multivariate adaptive spline regression method to construct a mapping model based on the multiple parameter combinations and the compressor specific power consumption corresponding to each parameter combination. The mapping model represents the mapping relationship between the refrigerant ratio, the feed gas conditions, and the compressor specific power consumption in LNG production. In other embodiments, the electronic device can also use other nonlinear modeling methods to construct the mapping relationship between the refrigerant ratio, the feed gas conditions, and the compressor specific power consumption in LNG production. For example, the electronic device can also use a multinomial regression method to construct a mapping model based on multiple parameter combinations and the compressor specific power consumption corresponding to each parameter combination.

[0023] Step 300: Using a nonlinear optimization method, solve for the minimum compressor specific power consumption of the mapping model under the conditions of changing raw gas and set constraints, and obtain the optimized result of the mixed refrigerant ratio.

[0024] The electronic device uses the mapping model constructed in step 200 as the objective function to be optimized. The constraints include at least one of the following: component proportion constraints of the mixed refrigerant, feed gas temperature constraints, feed gas pressure constraints, and feed gas component proportion constraints. In one embodiment, to consider as many factors as possible and obtain a more accurate optimized result for the mixed refrigerant ratio, the constraints include each refrigerant X in the mixed refrigerant. i The sum of the component proportions is 1; each refrigerant X in the mixed refrigerant i Constraints on the proportion of components ; Constraints on temperature and pressure of the feed gas; Proportion of each component in the feed gas Z1,…,Z nThe constraints are defined within the specified range. When the gas quality or composition conditions change, the raw material gas change conditions also serve as constraints for solving the minimum compressor specific power consumption of the mapping model, together with the set constraints, to solve the minimum compressor specific power consumption of the mapping model. When the gas quality or composition conditions change, the raw material gas change conditions obtained by modifying the temperature, pressure, or the proportion of each component of the raw material gas are directly substituted into the mapping model to solve the objective function to obtain the ratio optimization result. Electronic equipment can use nonlinear optimization methods such as genetic algorithms and simulated annealing algorithms to solve the minimum compressor specific power consumption of the mapping model under the raw material gas change conditions and set constraints to obtain the mixed refrigerant ratio optimization result.

[0025] This invention utilizes a multivariate adaptive spline regression method to construct a mapping model based on a set range of conditional parameters, representing the mapping relationship between the refrigerant mix ratio, the feed gas conditions, and the compressor specific power consumption in liquefied natural gas production. Then, a nonlinear optimization method is used to solve for the minimum compressor specific power consumption of the mapping model under changing feed gas conditions and set constraints, obtaining the optimized refrigerant mix ratio. This invention eliminates the need to rebuild the process simulation model or reconstruct the mathematical model when feed gas conditions change. Furthermore, by employing multivariate adaptive spline regression and nonlinear optimization methods, it achieves automatic optimization of the refrigerant mix ratio, reducing computational load and improving computational efficiency during the refrigerant mix ratio optimization process.

[0026] In other aspects of the embodiments of the present invention, the step of constructing a mapping model based on a set range of conditional parameters using the multivariate adaptive spline regression method includes: performing multiple stratified samplings based on the set range of conditional parameters using the Latin hypercube sampling method to obtain a parameter combination matrix; inputting the parameter combination matrix into a liquefied natural gas (LNG) process model to obtain the compressor specific power consumption corresponding to each parameter combination in the liquefaction output of the LNG process model; and constructing a mapping model based on the parameter combination matrix and the compressor specific power consumption corresponding to each parameter combination using the multivariate adaptive spline regression method.

[0027] To ensure that all value ranges for each parameter are uniformly covered, the electronic device can use the Latin hypercube sampling method to perform multiple stratified samplings based on the set parameter ranges, obtaining a parameter combination matrix. The parameter combination matrix includes the parameter combinations of the mixed refrigerant ratio and feed gas conditions obtained from each stratified sampling. For example, the parameter combination {X1,…,X...} i ,T,P,Z1,…,Z nThe system contains M parameters. In one embodiment, the electronic device performs N stratified samplings using Latin Hypercube Sampling (LHS) based on the set parameter distribution to obtain a parameter combination matrix N*M, where the sum of the proportions of each refrigerant in the mixed refrigerant is 1, and the sum of the proportions of each component in the feed gas composition is 1. Next, following the liquefaction process of liquefied natural gas (LNG), the electronic device can use Aspen Plus process simulation software to establish a process model of the LNG liquefaction process (hereinafter referred to as the LNG process model) to perform LNG liquefaction process calculations, ensuring error-free simulation calculations. The electronic device then obtains multivariate adaptive regression spline modeling data based on the parameter combination matrix and the LNG process model. For example, using an established liquefied natural gas (LNG) liquefaction process, electronic devices can utilize Python software to input the parameter combinations obtained by the Latin hypercube sampling method into the LNG process model for calculation. This yields the compressor power consumption and LNG production corresponding to each parameter combination. The compressor power consumption and LNG production are then converted into compressor specific power consumption y (the ratio of compressor power consumption to LNG production), resulting in a vector {y1,…,y}. n}, where y nThis represents the compressor specific power consumption corresponding to the nth parameter combination. In this embodiment of the invention, the multivariate adaptive spline regression method is used again to construct a mapping model based on the parameter combination matrix and the compressor specific power consumption corresponding to each parameter combination. Multivariate Adaptive Regression Splines (MARS) is a non-parametric, non-linear modeling method proposed by Friedman. It is an extended linear model that can include non-linearity and interactions. This method uses data-driven generation of a set of coefficients and corresponding basis functions to construct the relationship between independent and dependent variables. The MARS method is based on a "divide and conquer" strategy, which divides the entire input space into multiple regions, each with independent coefficients and basis functions. This makes the MARS method very suitable for processing high-dimensional data. The modeling process of the MARS method is similar to stepwise regression, consisting of a forward process and a backward process. The MARS method first calculates the mean of all output compressor specific power consumptions and uses it as a constant term in the model. Then, the coefficients and basis functions obtained after dividing the regions are added to the model; this is the forward process. The model obtained through the forward process may overfit. Therefore, the model is "pruned" through a backward process. The contribution of each basis function to the model is measured by calculating the Generalized Cross-Validation (GCV), and basis functions with small contributions are removed from the resulting model, yielding the final mapping model. The mapping model is constructed based on a constant term, multiple basis functions, the weight coefficients corresponding to each basis function, the parameter combinations, and the compressor specific power consumption corresponding to the parameter combinations; wherein the constant term, the multiple basis functions, and the weight coefficients corresponding to each basis function are positively correlated with the compressor specific power consumption. In one embodiment, the mapping model is expressed by the following formula: ; Where y represents the compressor's specific power consumption. β 0 represents a constant term. h m Let m be the basis function. β m Let represent the weight coefficient of the m-th basis function, M represent the total number of basis functions, and x represent the parameter combination of the refrigerant ratio and feed gas conditions. Therefore, in this embodiment of the invention, the result vector y and the parameter combination matrix N*M are used in Python software to establish a model between the compressor specific power consumption y and the refrigerant ratio, feed gas quality conditions, and composition condition parameters M.

[0028] In other aspects of the embodiments of the present invention, after solving the minimum compressor specific power consumption of the mapping model using a nonlinear optimization method under the conditions of changing raw gas and set constraints to obtain the optimized result of the mixed refrigerant ratio, the method further includes: performing online adjustment of the mixed refrigerant based on the optimized result of the mixed refrigerant ratio, and separately recovering each component in the mixed refrigerant.

[0029] Please refer to Figure 2The electronic equipment, based on the optimized refrigerant mix ratio, discharges 50% of the existing refrigerant in the system after it has been reheated to a gaseous phase in a cold box. Simultaneously, it automatically calculates the replenishment amount for each component and opens valves to add the calculated amounts of each component into the refrigerant buffer tank. This achieves online control of the refrigerant mix ratio. The discharged refrigerant enters the refrigerant recovery tank. The gaseous refrigerant is compressed to 1.5 MPa by the refrigerant recovery compressor and sent to the bottom of the first refrigerant recovery tower (or primary recovery tower) as a heat source for the reboiler. The cooled gaseous refrigerant exchanges heat with the reflux refrigerant from the top of the second refrigerant recovery tower (or secondary recovery tower), cooling it to -90°C. It then enters the primary refrigerant recovery separator (or primary separator) for gas-liquid separation, initially separating the difficult-to-liquefy nitrogen component from other components, reducing the processing load on subsequent systems. After passing through the primary refrigerant recovery separator, the gas phase is further cooled to -120°C using hydrogen-rich reflux gas from the Fischer-Tropsch synthesis tail gas to LNG unit before entering the secondary separator. The methane and nitrogen mixture obtained at the top of the secondary separator enters a pressure swing adsorption (PSA) or membrane separation unit to separate the methane and nitrogen. The separated nitrogen is directly discharged, while the resulting methane gas is compressed to 1.7 MPa by the unit's existing BOG compressor and further cooled using reflux fuel gas to obtain liquid methane, which is then sent to the LNG storage tank. The liquid phase from both the primary and secondary separators enters the refrigerant recovery tower (or primary recovery tower). At the top of the tower, nitrogen components are removed from the liquid phase. The condenser at the top of the tower uses reflux nitrogen gas as a cold source. Liquid methane is extracted from the methane enrichment zone in the middle of the tower and sent to the LNG storage tank. C1, C2, C3, and C5 components are obtained at the bottom of the refrigerant recovery tower, and the compressor outlet gas phase is used as a heat source at the bottom of the tower. The liquid phase from the bottom of the first refrigerant recovery tower enters the second refrigerant recovery tower (or secondary recovery tower). The top of the second refrigerant recovery tower uses a cold box with refluxed mixed refrigerant as a cold source, while the bottom uses low-pressure steam as a heat source. Liquid methane obtained at the top of the second refrigerant recovery tower is sent to a liquefied natural gas (LNG) storage tank. Liquid C2 component with a purity of 96.7% is obtained from the C2 component enrichment tray in the middle of the tower through side-stream liquid phase extraction and sent to a C2 storage tank. The liquid phase from the bottom of the second refrigerant recovery tower enters the third refrigerant recovery tower (or tertiary recovery tower). The top of the third refrigerant recovery tower uses liquid propylene as a cold source, while the bottom uses low-pressure steam as a heat source. Liquid C3 with a purity of 99.1% and liquid C5 with a purity of 98.9% are obtained at the top of the third refrigerant recovery tower and sent to their respective storage tanks.

[0030] This invention addresses the problems of existing methods for optimizing the mixed refrigerant ratio in liquefied natural gas (LNG) liquefaction processes by proposing an online adjustment and recovery method for the mixed refrigerant ratio. This method eliminates the need to rebuild the process simulation model or mathematical model when the gas quality or composition conditions change. Furthermore, by utilizing multivariate adaptive regression spline regression and nonlinear optimization methods, it achieves automatic optimization of the mixed refrigerant ratio, improving the computational efficiency of the optimization process. Simultaneously, based on the temperature of each material within the cold box, a reasonable mixed refrigerant recovery process is established to recover each component of the mixed refrigerant separately for subsequent use, improving refrigerant utilization, enhancing the compatibility between the refrigeration system and the process system, and reducing system energy consumption. In other words, this invention aims to solve the problem of existing technologies requiring the reconstruction of process simulation models or mathematical models when the gas quality (temperature, pressure) or composition (component ratios) changes, by utilizing multivariate adaptive spline regression and nonlinear optimization methods. It proposes a ratio optimization method with lower computational load and higher computational efficiency than existing technologies. Furthermore, based on the optimization results of the mixed refrigerant ratio, the system's mixed refrigerant composition is automatically adjusted online to improve the matching between the refrigeration system and the process system, and reduce the system's operating energy consumption.

[0031] Device Examples Please refer to Figure 3 On the other hand, embodiments of the present invention also provide a device for optimizing the mixing ratio of refrigerants, comprising: The acquisition module 301 is used to acquire the set condition parameter range and feed gas change conditions of the liquefied natural gas preparation process: the set condition parameter range includes the parameter range of the mixed refrigerant ratio and the parameter range of the feed gas conditions. Module 302 is used to construct a mapping model based on a set range of conditional parameters using a multivariate adaptive spline regression method; the mapping model represents the mapping relationship between the mixed refrigerant ratio, the feed gas conditions, and the specific power consumption of the compressor in liquefied natural gas production; The solver module 303 is used to solve the minimum compressor specific power consumption of the mapping model under the conditions of changing raw gas and set constraints using a nonlinear optimization method, so as to obtain the optimized result of the mixed refrigerant ratio.

[0032] Optionally, the step of constructing a mapping model based on a set range of conditional parameters using the multivariate adaptive spline regression method includes... The Latin hypercube sampling method is used to perform multiple stratified samplings based on the set condition parameter range to obtain a parameter combination matrix; the parameter combination matrix includes the parameter combination of the mixed refrigerant ratio and feed gas conditions obtained from each stratified sampling. The parameter combination matrix is ​​input into the liquefied natural gas process model to obtain the compressor specific power consumption corresponding to each parameter combination output by the liquefied natural gas process model. Using the multivariate adaptive spline regression method, a mapping model is constructed based on the parameter combination matrix and the compressor specific power consumption corresponding to each parameter combination.

[0033] Optionally, the mapping model is constructed based on a constant term, multiple basis functions, a weight coefficient corresponding to each basis function, the parameter combination, and the compressor specific power consumption corresponding to the parameter combination; the constant term, the multiple basis functions, and the weight coefficient corresponding to each basis function are positively correlated with the compressor specific power consumption.

[0034] Optionally, the mapping model is represented by the following formula: ; Where y represents the compressor's specific power consumption. β 0 represents a constant term. h m Let m be the basis function. β m Let represent the weight coefficient of the m-th basis function, M represent the total number of basis functions, and x represent the parameter combination of the mixed refrigerant ratio and the feed gas conditions.

[0035] Optionally, the device further includes: The recovery module is used to adjust the mixed refrigerant online based on the optimized ratio of the mixed refrigerant, and to recover each component in the mixed refrigerant individually.

[0036] Optionally, the set constraints include at least one of the following: component ratio constraints of the mixed refrigerant, temperature constraints of the feed gas, pressure constraints of the feed gas, and component ratio constraints of the feed gas.

[0037] The refrigerant mixing ratio optimization device includes a processor and a memory. The acquisition module 301, construction module 302, and solution module 303 are all stored as program units in the memory. The processor executes these program units stored in the memory to achieve the corresponding functions. The processor contains a kernel, which retrieves the corresponding program units from the memory. One or more kernels may be provided. The memory may include non-permanent memory in computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. The memory includes at least one memory chip.

[0038] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4As shown, the electronic device may include a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a refrigerant ratio optimization method. This method includes: obtaining the set condition parameter range and feed gas change conditions of the liquefied natural gas production process; the set condition parameter range includes the parameter range of the refrigerant ratio and the parameter range of the feed gas conditions; constructing a mapping model based on the set condition parameter range using a multivariate adaptive spline regression method; the mapping model characterizes the mapping relationship between the refrigerant ratio, the feed gas conditions, and the compressor specific power consumption in liquefied natural gas production; and solving the minimum compressor specific power consumption of the mapping model under the feed gas change conditions and set constraints using a nonlinear optimization method to obtain the refrigerant ratio optimization result.

[0039] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0040] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a machine-readable storage medium. When the computer program is executed by a processor, the computer can execute a method for optimizing the refrigerant ratio. The method includes: obtaining the set condition parameter range and feed gas change conditions of the liquefied natural gas (LNG) production process; the set condition parameter range includes the parameter range of the refrigerant ratio and the parameter range of the feed gas conditions; constructing a mapping model based on the set condition parameter range using a multivariate adaptive spline regression method; the mapping model characterizes the mapping relationship between the refrigerant ratio, the feed gas conditions, and the compressor specific power consumption in LNG production; and solving the minimum compressor specific power consumption of the mapping model under the feed gas change conditions and set constraints using a nonlinear optimization method to obtain the optimized refrigerant ratio result.

[0041] In another aspect, the present invention also provides a machine-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a method for optimizing the refrigerant ratio. This method includes: obtaining a set range of parameters for a liquefied natural gas (LNG) production process and parameters for changes in feed gas conditions; the set range of parameters includes a parameter range for the refrigerant ratio and a parameter range for the feed gas conditions; constructing a mapping model based on the set range of parameters using a multivariate adaptive spline regression method; the mapping model characterizing the mapping relationship between the refrigerant ratio, the feed gas conditions, and the specific power consumption of the compressor in LNG production; and solving for the minimum specific power consumption of the compressor in the mapping model under the feed gas change conditions and set constraints using a nonlinear optimization method to obtain the optimized refrigerant ratio result.

[0042] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0043] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0044] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the proportion of mixed refrigerants, characterized in that, include: Obtain the set condition parameter range and feed gas change conditions for the liquefied natural gas preparation process: the set condition parameter range includes the parameter range of the mixed refrigerant ratio and the parameter range of the feed gas conditions; A mapping model is constructed based on a set range of conditional parameters using the multivariate adaptive spline regression method. The mapping model characterizes the mapping relationship between the mixed refrigerant ratio, the feed gas conditions, and the specific power consumption of the compressor in liquefied natural gas production; The minimum compressor specific power consumption of the mapping model is solved using a nonlinear optimization method under the conditions of changing raw gas and set constraints, and the optimized result of the mixed refrigerant ratio is obtained.

2. The method for optimizing the proportion of mixed refrigerants according to claim 1, characterized in that, The method of constructing a mapping model based on a set range of conditional parameters using multivariate adaptive spline regression includes: The Latin hypercube sampling method is used to perform multiple stratified samplings based on the set condition parameter range to obtain a parameter combination matrix; the parameter combination matrix includes the parameter combination of the mixed refrigerant ratio and feed gas conditions obtained from each stratified sampling. The parameter combination matrix is ​​input into the liquefied natural gas process model to obtain the compressor specific power consumption corresponding to each parameter combination output by the liquefied natural gas process model. Using the multivariate adaptive spline regression method, a mapping model is constructed based on the parameter combination matrix and the compressor specific power consumption corresponding to each parameter combination.

3. The method for optimizing the proportion of mixed refrigerants according to claim 2, characterized in that, The mapping model is constructed based on a constant term, multiple basis functions, a weight coefficient corresponding to each basis function, a parameter combination, and the compressor specific power consumption corresponding to the parameter combination; the constant term, the multiple basis functions, and the weight coefficient corresponding to each basis function are positively correlated with the compressor specific power consumption.

4. The method for optimizing the proportion of mixed refrigerants according to claim 3, characterized in that, The mapping model is expressed by the following formula: ； Where y represents the compressor's specific power consumption. β 0 represents a constant term. h m Let m be the basis function. β m Let represent the weight coefficient of the m-th basis function, M represent the total number of basis functions, and x represent the parameter combination of the mixed refrigerant ratio and the feed gas conditions.

5. The method for optimizing the proportion of mixed refrigerants according to claim 1, characterized in that, After obtaining the optimized refrigerant ratio by solving the minimum compressor specific power consumption of the mapping model using a nonlinear optimization method under the changed feed gas conditions and set constraints, the process further includes: Based on the optimized refrigerant ratio, the refrigerant is adjusted online, and each component in the refrigerant is individually recovered.

6. The method for optimizing the proportion of mixed refrigerants according to any one of claims 1 to 5, characterized in that, The set constraints include at least one of the following: component ratio constraints of the mixed refrigerant, temperature constraints of the feed gas, pressure constraints of the feed gas, and component ratio constraints of the feed gas.

7. A device for optimizing the proportion of mixed refrigerants, characterized in that, include: The acquisition module is used to acquire the set condition parameter range and feed gas change conditions of the liquefied natural gas preparation process: the set condition parameter range includes the parameter range of the mixed refrigerant ratio and the parameter range of the feed gas conditions; The module is used to construct a mapping model based on a set range of conditional parameters using multivariate adaptive spline regression. The mapping model represents the mapping relationship between the mixed refrigerant ratio, the feed gas conditions, and the specific power consumption of the compressor in liquefied natural gas production. The solution module is used to solve for the minimum compressor specific power consumption of the mapping model under the conditions of changing raw gas and set constraints using a nonlinear optimization method, so as to obtain the optimized result of the mixed refrigerant ratio.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for optimizing the mixed refrigerant ratio as described in any one of claims 1 to 6.

9. A machine-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for optimizing the mixed refrigerant ratio as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for optimizing the mixed refrigerant ratio as described in any one of claims 1 to 6.

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