Optimization Method, Optimization Device and Optimization System for Mixing Refrigerant Ratio Parameters

Abstract

The present application provides an optimization method, an optimization device, and an optimization system for the mixing refrigerant ratio parameters. The method includes: under actual working conditions, obtaining the parameter values of the natural gas to be liquefied, where the parameter values of the natural gas to be liquefied are within a first value range; when both the parameter values and a second value range are constraint conditions of the specific power consumption estimation function, using a non-linear optimization algorithm to solve the minimum output value of the specific power consumption estimation function to obtain the minimum value of the compressor specific power consumption, where the second value range is a preset value range of the mixing refrigerant ratio parameters, and the specific power consumption estimation function is obtained by fitting using a fitting algorithm; determining the optimal ratio parameter values according to the minimum value of the compressor specific power consumption. This method solves the problem in the prior art that when determining the optimal ratio parameters of the mixing refrigerant, when the natural gas parameter conditions change, it is necessary to continuously manually modify the parameters of the natural gas liquefaction process model, resulting in low optimization efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of natural gas liquefaction processes. Specifically, it relates to an optimization method for mixed refrigerant ratio parameters, an optimization device, a computer-readable storage medium, a processor, and an optimization system. Background Art

[0002] In the process flow of producing LNG by mixed refrigerant refrigeration liquefaction, the raw gas pressure, the temperature of the raw gas after precooling, the subcooling temperature, the mixed refrigerant circulation pressure, and the content of each component of the mixed refrigerant all have important effects on the separation efficiency of the process. When the key parameters in the process change, only by adjusting the circulation amount of the mixed refrigerant, the heat and cold loads in the LNG process cannot reach a better matching degree. As the medium and carrier of cold energy in the refrigeration liquefaction process, the composition ratio of the mixed refrigerant directly affects the overall energy consumption of the process. When the cold quantity provided by the mixed refrigerant is less than the cold quantity required by the LNG process, the product yield will decrease. When the cold quantity provided by the mixed refrigerant is greater than the cold quantity required by the LNG process, the cold quantity carried by the refrigerant is surplus, and the energy consumption of the device increases. Therefore, the composition ratio of the mixed refrigerant should be adjusted according to the composition, pressure of the main process raw gas, and the different process flows. An appropriate mixed refrigerant ratio can not only significantly reduce the energy consumption of the device, improve the product yield, but also greatly reduce the production cost and enhance the competitiveness of the enterprise.

[0003] The disadvantages of the existing mixed refrigerant ratio optimization methods are as follows:

[0004] 1. When the gas quality conditions or composition conditions (proportion of each component) of the raw gas change in the existing technology, the optimized result of the refrigerant ratio cannot be directly given. The existing technology needs to build a simulated process flow in HYSYS according to the new gas quality conditions or composition conditions to calculate and obtain the optimized result of the mixed refrigerant ratio. The existing technology needs to reset the refrigeration temperature of the expected mixed working medium, the high and low end pressures of the compressor, and all available working media for traversal calculation to obtain the mixed refrigerant ratio result.

[0005] 2. When optimizing in the existing technology, it is necessary to manually adjust the parameters in the LNG liquefaction process model established by process simulation software such as HYSYS multiple times and record the simulation results each time to optimize the mixed refrigerant ratio, resulting in low efficiency of the ratio optimization calculation.

[0006] 3. In the existing technology, a recursive algorithm is used to traverse all working media to obtain all feasible mixed refrigerants composed of different working media, and based on this, a genetic algorithm is used to optimize the refrigerant ratio calculation. Traversing all possible working medium compositions will increase the calculation amount.

[0007] The above information disclosed in the background section is only used to enhance the understanding of the background of the technology described herein. Therefore, the background section may contain certain information that does not constitute the prior art known in the country to those skilled in the art. Summary of the Invention

[0008] The main purpose of the present application is to provide an optimization method, an optimization device, a computer-readable storage medium, a processor, and an optimization system for the mixing refrigerant ratio parameters, so as to solve the problem in the prior art that when the natural gas parameter conditions change, the parameters of the natural gas liquefaction process model need to be continuously modified manually when determining the optimal ratio parameters of the mixing refrigerant, resulting in low optimization efficiency.

[0009] According to an aspect of an embodiment of the present application, an optimization method for the mixing refrigerant ratio parameters is provided. The mixing refrigerant is used to liquefy the natural gas to be liquefied. The method includes: under actual working conditions, obtaining the parameter value of the natural gas to be liquefied, where the parameter value is within a first value range; when both the parameter value and a second value range are constraint conditions of the specific power consumption estimation function, using a non-linear optimization algorithm to solve the minimum output value of the specific power consumption estimation function to obtain the minimum value of the compressor specific power consumption. The second value range is a preset value range of the mixing refrigerant ratio parameter value. The specific power consumption estimation function is obtained by fitting multiple sets of data to be fitted using a fitting algorithm. Each set of the data to be fitted includes: a first parameter to be fitted, a second parameter to be fitted, and the corresponding compressor specific power consumption. The first parameter to be fitted is within the first value range, and the second parameter to be fitted is within the second value range; determining the optimal ratio parameter value according to the minimum value of the compressor specific power consumption.

[0010] Optionally, before using the non-linear optimization algorithm to solve the minimum output value of the specific power consumption estimation function to obtain the minimum value of the compressor specific power consumption, the method further includes: within the first value range, using the Latin hypercube sampling method to perform sampling to obtain multiple first parameters to be fitted, and within the second value range, using the Latin hypercube sampling method to perform sampling to obtain the second parameters to be fitted corresponding to each of the first parameters to be fitted.

[0011] Optionally, before using the non-linear optimization algorithm to solve the minimum output value of the specific power consumption estimation function to obtain the minimum value of the compressor specific power consumption, the method further includes: inputting each of the first parameters to be fitted and the corresponding second parameters to be fitted into a natural gas liquefaction process model to obtain multiple compressor specific power consumptions. The natural gas liquefaction process model is a PID model established using a process simulation software according to the natural gas liquefaction process. The PID model includes multiple devices for the natural gas liquefaction process.

[0012] Optionally, before obtaining the minimum value of the compressor specific power consumption by solving the minimum output value of the specific power consumption estimation function using a non - linear optimization algorithm, the method further includes: fitting each group of the data to be fitted using the fitting algorithm to obtain the specific power consumption estimation function.

[0013] Optionally, before obtaining the minimum value of the compressor specific power consumption by solving the minimum output value of the specific power consumption estimation function using a non - linear optimization algorithm, the method further includes: establishing the PID model on the process simulation software according to the natural gas liquefaction process; configuring the parameters of the equipment in the PID model according to the actual working conditions to obtain the natural gas liquefaction process model.

[0014] Optionally, determining the optimal ratio parameter value according to the minimum value of the compressor specific power consumption includes: taking the minimum value of the compressor specific power consumption as the output value of the specific power consumption estimation function, and using the non - linear optimization algorithm to inversely solve the input value of the specific power consumption estimation function to obtain the optimal ratio parameter value.

[0015] According to another aspect of the embodiments of the present application, there is also provided an optimization device for the mixing refrigerant ratio parameters. The mixing refrigerant is used to liquefy the natural gas to be liquefied. The device includes: an acquisition unit that acquires the parameter value of the natural gas to be liquefied under actual working conditions, and the parameter value is within a first value range; a calculation unit that, when both the parameter value and a second value range are constraint conditions of the specific power consumption estimation function, uses a non - linear optimization algorithm to solve the minimum output value of the specific power consumption estimation function to obtain the minimum value of the compressor specific power consumption. The second value range is a preset value range of the mixing refrigerant ratio parameter value. The specific power consumption estimation function is obtained by fitting multiple groups of data to be fitted using a fitting algorithm. Each group of the data to be fitted includes: a first parameter to be fitted, a second parameter to be fitted, and the corresponding compressor specific power consumption. The first parameter to be fitted is within the first value range, and the second parameter to be fitted is within the second value range; a determination unit that determines the optimal ratio parameter value according to the minimum value of the compressor specific power consumption.

[0016] According to still another aspect of the embodiments of the present application, there is also provided a computer - readable storage medium. The computer - readable storage medium includes a stored program, wherein the program executes any one of the above - mentioned methods.

[0017] According to yet another aspect of the embodiments of the present application, there is also provided a processor. The processor is used to run a program, wherein when the program runs, it executes any one of the above - mentioned methods.

[0018] According to one aspect of the embodiments of the present application, an optimization system for the mixing refrigerant ratio parameters is further provided, including: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include those for executing any one of the above methods.

[0019] In the above optimization method for the mixing refrigerant ratio parameters, first, under actual working conditions, parameter values of the natural gas to be liquefied are obtained, and the parameter values are within a first value range; then, when both the parameter values and a second value range are constraint conditions of the specific power consumption estimation function, a non-linear optimization algorithm is used to solve the minimum output value of the specific power consumption estimation function to obtain the minimum value of the compressor specific power consumption. The second value range is a preset value range of the mixing refrigerant ratio parameters, and the specific power consumption estimation function is obtained by fitting multiple groups of data to be fitted using a fitting algorithm. Each group of the data to be fitted includes: a first parameter to be fitted, a second parameter to be fitted, and the corresponding compressor specific power consumption. The first parameter to be fitted is within the first value range, and the second parameter to be fitted is within the second value range; finally, an optimal ratio parameter value is determined according to the minimum value of the compressor specific power consumption. The specific power consumption estimation function of this method is a non-linear mapping relationship between the mixing refrigerant ratio parameter value, the parameter value of the natural gas to be liquefied, and the compressor specific power consumption obtained by using a fitting algorithm. That is, when the value of the parameter of the natural gas to be liquefied in the actual working condition changes, there is no need to establish a new model. By using the new parameter value and the preset value range of the mixing refrigerant ratio parameters as constraint conditions and using a non-linear optimization algorithm to solve the specific power consumption estimation function, the minimum value of the corresponding compressor specific power consumption can be obtained. This method solves the problem in the prior art that when determining the optimal mixing refrigerant ratio parameters, when the natural gas parameter conditions change, it is necessary to continuously manually modify the parameters of the natural gas liquefaction process model, resulting in low optimization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings forming a part of this application are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0021] Figure 1 The flowchart of the optimization method for the mixing refrigerant ratio parameters according to an embodiment of the present application is shown;

[0022] Figure 2 The flowchart of the optimization method for the mixing refrigerant ratio parameters according to a specific embodiment of the present application is shown;

[0023] Figure 3The figure shows a schematic diagram of an optimization device for the mixing refrigerant ratio parameters according to an embodiment of the present application. Detailed implementation manners

[0024] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0025] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances for the embodiments of the present application described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0027] It should be understood that when an element (such as a layer, film, region, or substrate) is described as being "on" another element, the element can be directly on the other element, or there may also be an intermediate element. Moreover, in the specification and claims, when an element is described as "connected" to another element, the element can be "directly connected" to the other element, or "connected" to the other element through a third element.

[0028] As described in the background art, in the prior art, when determining the optimal mixing refrigerant ratio parameters, when the natural gas parameter conditions change, it is necessary to continuously manually modify the parameters of the natural gas liquefaction process model, resulting in low optimization efficiency. To solve the above problems, in a typical implementation manner of the present application, an optimization method, an optimization device, a computer-readable storage medium, a processor, and an optimization system for the mixing refrigerant ratio parameters are provided.

[0029] According to an embodiment of the present application, an optimization method for the mixing refrigerant ratio parameters is provided.

[0030] Figure 1It is a flowchart of an optimization method for the mixing refrigerant ratio parameters according to an embodiment of the present application. As Figure 1 shown, the method includes the following steps:

[0031] Step S101, under actual working conditions, obtain the parameter values of the above-mentioned liquefied natural gas to be liquefied, and the above-mentioned parameter values are within the first value range;

[0032] Step S102, when both the above-mentioned parameter values and the second value range are constraint conditions of the specific power consumption estimation function, use a non-linear optimization algorithm to solve the minimum output value of the above-mentioned specific power consumption estimation function, and obtain the minimum value of the compressor specific power consumption. The above-mentioned second value range is the preset value range of the mixing refrigerant ratio parameter values. The above-mentioned specific power consumption estimation function is obtained by using a fitting algorithm to fit multiple groups of data to be fitted. Each group of the above-mentioned data to be fitted includes: a first parameter to be fitted, a second parameter to be fitted, and the corresponding above-mentioned compressor specific power consumption. The above-mentioned first parameter to be fitted is within the above-mentioned first value range, and the above-mentioned second parameter to be fitted is within the above-mentioned second value range;

[0033] Step S103, determine the optimal ratio parameter value according to the minimum value of the above-mentioned compressor specific power consumption.

[0034] In the above method for optimizing the mixing refrigerant ratio parameters, first, under actual working conditions, the parameter values of the natural gas to be liquefied are obtained, and the parameter values are within the first value range; then, when both the parameter values and the second value range are constraints of the specific power consumption estimation function, a non-linear optimization algorithm is used to solve the minimum output value of the specific power consumption estimation function to obtain the minimum value of the compressor specific power consumption. The second value range is the preset value range of the mixing refrigerant ratio parameters, and the specific power consumption estimation function is obtained by fitting multiple groups of data to be fitted using a fitting algorithm. Each group of the data to be fitted includes: a first parameter to be fitted, a second parameter to be fitted, and the corresponding compressor specific power consumption. The first parameter to be fitted is within the first value range, and the second parameter to be fitted is within the second value range; finally, the optimal ratio parameter value is determined according to the minimum value of the compressor specific power consumption. The specific power consumption estimation function of this method is a non-linear mapping relationship between the mixing refrigerant ratio parameter value, the parameter value of the natural gas to be liquefied, and the compressor specific power consumption obtained by using a fitting algorithm. That is, when the value of the parameter of the natural gas to be liquefied changes in the actual working condition, there is no need to establish a new model. By using the new parameter value and the preset value range of the mixing refrigerant ratio parameter as constraints and using a non-linear optimization algorithm to solve the specific power consumption estimation function, the minimum value of the corresponding compressor specific power consumption can be obtained. This method solves the problem in the prior art that when determining the optimal mixing refrigerant ratio parameters, when the natural gas parameter conditions change, it is necessary to continuously manually modify the parameters of the natural gas liquefaction process model, resulting in low optimization efficiency.

[0035] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0036] In an optional embodiment of the present application, before using a non-linear optimization algorithm to solve the minimum output value of the specific power consumption estimation function to obtain the minimum value of the compressor specific power consumption, the method further includes: within the first value range, sampling is performed using the Latin hypercube sampling method to obtain multiple first parameters to be fitted, and within the second value range, sampling is performed using the Latin hypercube sampling method to obtain the second parameters to be fitted corresponding to each of the first parameters to be fitted. In this embodiment, the mixing refrigerant ratio parameters include the proportion X of each refrigerant in the mixing refrigerant i , the parameter values of the natural gas to be liquefied include the temperature T, pressure P of the natural gas quality condition to be liquefied, and the proportion (Z1,..., Z n ) of each gas in the natural gas to be liquefied, and the second value range includes the proportion X of each refrigerant iThe upper and lower limits. The first value range includes the temperature T, pressure P of the liquefied natural gas quality conditions set according to the actual situation, and the proportions (Z1,..., Z n ) of each gas in the liquefied natural gas, and the upper and lower limits of the possible change ranges. The proportion X i of each refrigerant can be represented by the inequality x i,min ≤X i ≤x i,m , where x i，min is the lower limit of the proportion X i of each refrigerant, and x i，max is the upper limit of the proportion X i of each refrigerant. The value range of the temperature T can be represented by the inequality t i,min ≤T i ≤t i,max , where t i，min is the lower limit of the temperature T, and t i，max is the upper limit of the temperature T. The value range of the pressure P can be represented by the inequality p i,min ≤P i ≤p i,max , where p i，min is the lower limit of the pressure P, and p i，max is the upper limit of the pressure P. The value range of the proportion Z i of each gas can be represented by the inequality z i,min ≤Z i ≤z i,max , where z i，min is the lower limit of the proportion Z i of each gas, and z i，max is the upper limit of the proportion Z i of each gas. Set the proportion X i of each refrigerant, and the temperature T, pressure P of the liquefied natural gas quality conditions, and the proportions (Z1,..., Z n ) of each gas in the liquefied natural gas all follow a uniform distribution. As Figure 2 shown, N stratified samplings are performed using the Latin hypercube sampling method within the set first value range and second value range to obtain an N*M parameter matrix. The sum of the proportions of all refrigerants is 1, and the sum of the proportions of all gases in the liquefied natural gas is 1. The parameter matrix includes multiple parameter combinations {X1,..., X i , T, P, Z1,..., Z n}, and each parameter combination {X1,..., X i , T, P, Z1,..., Z n} contains the above first parameter to be fitted {X1,..., X i} and the corresponding second parameter to be fitted {T, P, Z1,..., Z n}(There are a total of M parameters. Among them, sampling is performed within the first value range, that is, only considering the possible values of the temperature T, pressure P of the liquefied natural gas quality conditions and the proportions (Z1,..., Z n ) in the actual situation. Sampling is performed within the second value range, that is, only considering the possible values of the proportions X i ) of each refrigerant in the mixed refrigerant in the actual situation, which improves the calculation speed in the optimization process and further improves the optimization efficiency of the mixed refrigerant ratio parameter values.

[0037] In an optional embodiment of the present application, before using the non-linear optimization algorithm to solve the minimum output value of the above specific power consumption estimation function and obtaining the minimum value of the compressor specific power consumption, the above method further includes: inputting each of the above first parameters to be fitted and the corresponding second parameters to be fitted into the natural gas liquefaction process model to obtain multiple compressor specific power consumptions. The natural gas liquefaction process model is a PID model established using a process simulation software according to the natural gas liquefaction process. The PID model includes multiple devices for the natural gas liquefaction process. In this embodiment, as Figure 2 shown, using Python software, the N parameter combinations {X1,..., X i , T, P, Z1,..., Z n} obtained by the Latin hypercube sampling method are respectively substituted into the natural gas liquefaction process model, that is, the LNG liquefaction process model for calculation, to obtain the compressor power consumption and the liquefied natural gas production corresponding to each parameter combination, and calculate the ratio of the compressor power consumption to the liquefied natural gas production to obtain the corresponding compressor specific power consumption y, and then obtain the result vector {y1,..., y n}, and then obtain each group of data to be fitted. Each group of data to be fitted includes a parameter combination and the corresponding compressor specific power consumption.

[0038] In an optional embodiment of the present application, before using the non-linear optimization algorithm to solve the minimum output value of the above specific power consumption estimation function and obtaining the minimum value of the compressor specific power consumption, the above method further includes: using the above fitting algorithm to fit each group of the above data to be fitted to obtain the above specific power consumption estimation function. In this embodiment, as Figure 2 shown, in Python software, using the fitting algorithm, that is, the multivariate adaptive regression spline method, to fit the result vector {y1,..., y n} and the parameter matrix N*M to obtain the non-linear mapping relationship between the compressor specific power consumption y and the proportions X i of each refrigerant, the temperature T, pressure P of the liquefied natural gas quality conditions and the proportions (Z1,..., Z n ) in the liquefied natural gas to be liquefied. Among the parameter combinations {X1,..., X i , T, P, Z1,..., Z n} In the case of changes, the parameter combination {X1, …, X i , T, P, Z1, …, Z n} is input into the specific power consumption estimation function, and the corresponding compressor specific power consumption y can be obtained, without the need to manually adjust the parameters of the natural gas liquefaction process model again according to the parameter combination {X1, …, X i , T, P, Z1, …, Z n}, calculate the ratio of the compressor power consumption to the liquefied natural gas production to obtain the compressor specific power consumption. Compared with the prior art's optimization method for the mixed refrigerant ratio parameters, in this application, since a specific power consumption estimation function is constructed, the calculation amount in the optimization process is reduced, and the optimization efficiency is improved.

[0039] In an optional embodiment of this application, when using a non-linear optimization algorithm to solve the minimum output value of the specific power consumption estimation function and obtain the minimum value of the compressor specific power consumption, the above method further includes: according to the above natural gas liquefaction process, establish the above PID model on the above process simulation software; according to the above actual working conditions, configure the parameters of the above equipment in the above PID model to obtain the above natural gas liquefaction process model. In this embodiment, as Figure 2 shown, according to the natural gas liquefaction process, that is, the LNG liquefaction process, use the Aspen Plus process simulation software to establish a PID model including all the equipment used in the LNG liquefaction process, and configure the parameters of each equipment according to the actual working conditions to obtain the natural gas liquefaction process model, that is, the LNG liquefaction process model, and simulate the entire LNG liquefaction process to ensure that the obtained compressor specific power consumption conforms to the actual situation, thereby ensuring the accuracy of the determined optimization result of the mixed refrigerant ratio.

[0040] In an optional embodiment of this application, determining the optimal ratio parameter value according to the minimum value of the compressor specific power consumption includes: taking the minimum value of the above compressor specific power consumption as the output value of the above specific power consumption estimation function, and using the above non-linear optimization algorithm to inversely solve the input value of the above specific power consumption estimation function to obtain the above optimal ratio parameter value. In this embodiment, after solving the minimum value of the compressor specific power consumption y, inversely solve to obtain the parameter combination {X1, …, X i , T, P, Z1, …, Z n} at this time, and then obtain the proportion X i of each refrigerant at this time. The proportion X i of each refrigerant corresponding at this time is the optimal ratio result. The temperature T, pressure P of the natural gas quality conditions to be liquefied and the proportion (Z1, …, Z n ) of each gas in the natural gas to be liquefied should be the temperature T, pressure P of the natural gas quality conditions to be liquefied and the proportion (Z1, …, Z n ) under the actual working conditions.

[0041] In an alternative embodiment of the present application, the specific power consumption estimation function established by the fitting algorithm, namely the multivariate adaptive regression spline method, is used as the objective function to be optimized, and its constraint conditions include: the sum of the proportions of all refrigerants X i is 1, and the value range of the proportion of each refrigerant X i is x i,min ≤ X i ≤ x i,max , the value range of the temperature T of the natural gas to be liquefied is t i,min ≤ T i ≤ t i,max , the value range of the pressure P is p i,min ≤ P i ≤ p i,max , the value range of the proportion Z i of each gas in the natural gas to be liquefied is z i,min ≤ Z i ≤ z i,max . After obtaining the objective function and the constraint conditions, the minimum value of the obtained objective function y is solved under the constraint conditions by using a non-linear optimization algorithm. At this time, the proportion X i of each refrigerant is the optimized mixing ratio result. It should be noted that when solving, the temperature T, pressure P of the natural gas to be liquefied and the proportion (Z1,..., Z n ) of each gas in the natural gas to be liquefied should be the temperature T, pressure P of the natural gas to be liquefied and the proportion (Z1,..., Z n ) under the actual working conditions, and compared with the existing optimization method for the mixing ratio parameters of the mixed refrigerant, when the temperature T, pressure P of the natural gas to be liquefied and the proportion (Z1,..., Z n ) change, it is necessary to manually adjust the parameters of the natural gas liquefaction process model according to the parameter combination {X1,..., X i , T, P, Z1,..., Z n}, calculate the ratio of the compressor power consumption to the liquefied natural gas production to obtain the compressor specific power consumption. In the present application, when the temperature T, pressure P of the natural gas to be liquefied and the proportion (Z1,..., Z n ) change, there is no need to rebuild the LNG liquefaction process model or reconstruct the mathematical function. The temperature T, pressure P of the natural gas to be liquefied and the proportion (Z1,..., Z n ) are directly modified, and the minimum value of the obtained objective function y is solved under the new constraint conditions by using a non-linear optimization algorithm. After reverse solution, the new optimized mixing ratio result can be obtained, which improves the calculation efficiency of the mixed refrigerant mixing ratio optimization process.

[0042] The embodiment of the present application further provides an optimization device for the mixing refrigerant ratio parameters. It should be noted that the optimization device for the mixing refrigerant ratio parameters in the embodiment of the present application can be used to execute the optimization method for the mixing refrigerant ratio parameters provided in the embodiment of the present application. The following introduces the optimization device for the mixing refrigerant ratio parameters provided in the embodiment of the present application.

[0043] Figure 3 It is a schematic diagram of the optimization device for the mixing refrigerant ratio parameters according to the embodiment of the present application. As Figure 3 shown, the device includes:

[0044] An acquisition unit 10, which acquires the parameter values of the above-mentioned liquefied natural gas to be liquefied under actual working conditions, and the above-mentioned parameter values are within the first value range;

[0045] A calculation unit 20, when both the above-mentioned parameter values and the second value range are constraint conditions of the specific power consumption estimation function, uses a non-linear optimization algorithm to solve the minimum output value of the above-mentioned specific power consumption estimation function, and obtains the minimum value of the compressor specific power consumption. The above-mentioned second value range is the preset value range of the mixing refrigerant ratio parameter values, and the above-mentioned specific power consumption estimation function is obtained by fitting multiple groups of data to be fitted using a fitting algorithm. Each group of the above-mentioned data to be fitted includes: a first parameter to be fitted, a second parameter to be fitted, and the corresponding above-mentioned compressor specific power consumption. The above-mentioned first parameter to be fitted is within the above-mentioned first value range, and the above-mentioned second parameter to be fitted is within the above-mentioned second value range;

[0046] A determination unit 30, which determines the optimal ratio parameter value according to the minimum value of the above-mentioned compressor specific power consumption.

[0047] In the above-mentioned optimization device for the mixing refrigerant ratio parameters, an acquisition unit acquires the parameter values of the natural gas to be liquefied under actual working conditions, and the parameter values are within a first value range; a calculation unit, when both the parameter values and a second value range are constraint conditions of a specific power consumption estimation function, uses a non-linear optimization algorithm to solve the minimum output value of the specific power consumption estimation function to obtain the minimum value of the compressor specific power consumption. The second value range is a preset value range of the mixing refrigerant ratio parameters, and the specific power consumption estimation function is obtained by fitting multiple groups of data to be fitted using a fitting algorithm. Each group of the data to be fitted includes: a first parameter to be fitted, a second parameter to be fitted, and the corresponding compressor specific power consumption. The first parameter to be fitted is within the first value range, and the second parameter to be fitted is within the second value range; a determination unit determines the optimal ratio parameter value according to the minimum value of the compressor specific power consumption. The specific power consumption estimation function of this device is a non-linear mapping relationship between the mixing refrigerant ratio parameter value obtained by using a fitting algorithm, the parameter value of the natural gas to be liquefied, and the compressor specific power consumption. That is, when the value of the parameter of the natural gas to be liquefied changes in the actual working condition, there is no need to establish a new model. By using the new parameter value and the preset value range of the mixing refrigerant ratio parameter as constraint conditions and using a non-linear optimization algorithm to solve the specific power consumption estimation function, the minimum value of the corresponding compressor specific power consumption can be obtained. This device solves the problem in the prior art that when determining the optimal mixing refrigerant ratio parameter, when the natural gas parameter condition changes, it is necessary to continuously manually modify the parameters of the natural gas liquefaction process model, resulting in low optimization efficiency.

[0048] In an alternative embodiment of the present application, the above-mentioned optimization device for the mixing refrigerant ratio parameters further includes a first sampling unit and a second sampling unit. The first sampling unit is used to sample within the first value range by using the Latin hypercube sampling method to obtain multiple first parameters to be fitted, and the second sampling unit is used to sample within the second value range by using the Latin hypercube sampling method to obtain the second parameters to be fitted corresponding to each of the first parameters to be fitted. In this embodiment, the mixing refrigerant ratio parameters include the proportion X of each refrigerant in the mixing refrigerant i , the parameter values of the natural gas to be liquefied include the temperature T, pressure P of the natural gas quality condition to be liquefied, and the proportion (Z1,..., Z n ) of each gas in the natural gas to be liquefied. The second value range includes the upper and lower limits of the proportion X i of each refrigerant, and the first value range includes the upper and lower limits of the possible change range of the temperature T, pressure P of the natural gas quality condition to be liquefied, and the proportion (Z1,..., Z n ) of each gas in the natural gas to be liquefied set according to the actual situation. The value range of the proportion X i of each refrigerant can be expressed by the inequality xi,min ≤X i ≤x i,max means that, where x i，min is the lower limit of the proportion X of each refrigerant, and x i is the upper limit of the proportion X of each refrigerant. The value range of the temperature T can be expressed by the inequality t i，max ≤T i ≤t i,min means that, where t i is the lower limit of the temperature T, and t i,max is the upper limit of the temperature T. The value range of the pressure P can be expressed by the inequality p i，min ≤P i，max ≤p i,min means that, where p i is the lower limit of the pressure P, and p i,max is the upper limit of the pressure P. The value range of the proportion Z of each gas i，min can be expressed by the inequality z i，max ≤Z i ≤z i,min means that, where z i is the lower limit of the proportion Z of each gas, and z i,max is the upper limit of the proportion Z of each gas. Set the proportion X of each refrigerant i，min , and the gas quality conditions of the liquefied natural gas to be liquefied, namely the temperature T, the pressure P, and the proportions of each gas in the liquefied natural gas to be liquefied (Z1,..., Z i ) all follow a uniform distribution. As i，max shown, in the set first value range and second value range, the Latin hypercube sampling method is used to perform N stratified samplings to obtain an N*M parameter matrix. Among them, the sum of the proportions of all refrigerants is 1, and the sum of the proportions of all gases in the liquefied natural gas to be liquefied is 1. The parameter matrix includes multiple parameter combinations {X1,..., X i , T, P, Z1,..., Z i}, and each parameter combination {X1,..., X n , T, P, Z1,..., Z i} contains a total of M parameters, including the above first parameters to be fitted {X1,..., X n} and the corresponding second parameters to be fitted {T, P, Z1,..., Z i}. Among them, sampling is performed within the first value range, that is, only considering the possible set values of the temperature T, the pressure P, and the proportions of each gas in the liquefied natural gas to be liquefied (Z1,..., Z n ) in the actual situation, and sampling is performed within the second value range, that is, only considering the proportion X of each refrigerant in the mixed refrigerant in the actual situation i} in the actual situation n} and the corresponding second parameters to be fitted {T, P, Z1,..., Z n} in the actual situation i ) in the actual situation, and sampling is performed within the second value range, that is, only considering the proportion X of each refrigerant in the mixed refrigerant in the actual situation iPossible set values improve the calculation speed during the optimization process and further enhance the optimization efficiency of the mixed refrigerant ratio parameters.

[0049] In an alternative embodiment of the present application, the optimization device for the mixed refrigerant ratio parameters further includes an execution unit. The execution unit is configured to input each of the first parameters to be fitted and the corresponding second parameters to be fitted into a natural gas liquefaction process model to obtain a plurality of the compressor specific power consumptions. The natural gas liquefaction process model is a PID model established using a process simulation software according to the natural gas liquefaction process, and the PID model includes a plurality of devices for the natural gas liquefaction process. In this embodiment, as Figure 2 shown, the N parameter combinations {X1,..., X i , T, P, Z1,..., Z n} obtained by the Latin hypercube sampling method are respectively substituted into the natural gas liquefaction process model, i.e., the LNG liquefaction process model, for calculation to obtain the compressor power consumption and the liquefied natural gas production corresponding to each parameter combination, and calculate the ratio of the compressor power consumption to the liquefied natural gas production to obtain the corresponding compressor specific power consumption y, and further obtain the result vector {y1,..., y n}, and thus obtain each group of data to be fitted. Each group of data to be fitted includes a parameter combination and the corresponding compressor specific power consumption.

[0050] In an alternative embodiment of the present application, the optimization device for the mixed refrigerant ratio parameters further includes a fitting unit. The fitting unit is configured to fit each group of the data to be fitted using the fitting algorithm to obtain the specific power consumption estimation function. In this embodiment, as Figure 2 shown, in the Python software, using the fitting algorithm, i.e., the multivariate adaptive regression spline method, to fit the result vector {y1,..., y n} and the parameter matrix N*M to obtain the non-linear mapping relationship between the compressor specific power consumption y and the proportion X i of each refrigerant, the temperature T, pressure P of the natural gas quality conditions to be liquefied, and the proportion (Z1,..., Z n ) of each gas in the natural gas to be liquefied. When the parameter combination {X1,..., X i , T, P, Z1,..., Z n} changes, input the parameter combination {X1,..., X i , T, P, Z1,..., Z n} into the specific power consumption estimation function, and the corresponding compressor specific power consumption y can be obtained, without the need to calculate according to the parameter combination {X1,..., X i , T, P, Z1,..., Z n}Manually readjust the parameters of the natural gas liquefaction process model again, and calculate the ratio of the compressor power consumption to the liquefied natural gas production to obtain the compressor specific power consumption. Compared with the prior art's optimization method for the mixed refrigerant ratio parameters, in this application, since a specific power consumption estimation function is constructed, the calculation amount in the optimization process is reduced, and the optimization efficiency is improved.

[0051] In an alternative embodiment of the present application, the above-mentioned optimization device for the mixed refrigerant ratio parameters further includes a modeling unit and a configuration unit. The above-mentioned modeling unit is used to establish the above-mentioned PID model on the above-mentioned process simulation software according to the above-mentioned natural gas liquefaction process; the above-mentioned configuration unit is used to configure the parameters of the above-mentioned equipment in the above-mentioned PID model according to the above-mentioned actual working conditions to obtain the above-mentioned natural gas liquefaction process model. In this embodiment, as Figure 2 shown, according to the natural gas liquefaction process, that is, the LNG liquefaction process, use the Aspen Plus process simulation software to establish a PID model including all the equipment used in the LNG liquefaction process, and configure the parameters of each equipment according to the actual working conditions to obtain the natural gas liquefaction process model, that is, the LNG liquefaction process model, and simulate the entire LNG liquefaction process to ensure that the above-mentioned obtained compressor specific power consumption fits the actual situation, thereby ensuring the accuracy of the determined optimization result of the mixed refrigerant ratio.

[0052] In an alternative embodiment of the present application, the above-mentioned determination unit includes a calculation module. The above-mentioned calculation module is used to use the minimum value of the above-mentioned compressor specific power consumption as the output value of the above-mentioned specific power consumption estimation function, and use the above-mentioned non-linear optimization algorithm to inversely solve the input value of the above-mentioned specific power consumption estimation function to obtain the above-mentioned optimal ratio parameter value. In this embodiment, after solving the minimum value of the compressor specific power consumption y, inversely solve to obtain the parameter combination {X1,..., X i , T, P, Z1,..., Z n}, and then obtain the proportion X i of each refrigerant at this time. The proportion X i of each refrigerant corresponding at this time is the optimal ratio result. The temperature T, pressure P of the natural gas quality conditions to be liquefied and the proportion (Z1,..., Z n ) of each gas in the natural gas to be liquefied should be the temperature T, pressure P of the natural gas quality conditions to be liquefied and the proportion (Z1,..., Z n ) under the actual working conditions.

[0053] In an alternative embodiment of the present application, the specific power consumption estimation function established by the fitting algorithm, that is, the multivariate adaptive regression spline method, is used as the objective function to be optimized. Its constraint conditions include: the sum of the proportions of all refrigerants X i is 1, and the value range of the proportion of each refrigerant X i component is x i,min ≤ Xi ≤ x i,max , the value range of the temperature T of the liquefied natural gas quality condition to be liquefied is t i,min ≤ T i ≤ t i,max , the value range of the pressure P is p i,min ≤ P i ≤ p i,max , for the proportion Z of each gas in the liquefied natural gas to be liquefied i , the value range is z i,min ≤ Z i ≤ z i,max , after obtaining the objective function and constraint conditions, use the non - linear optimization algorithm to solve the minimum value of the obtained objective function y under the constraint conditions. At this time, the proportion X of each refrigerant i is the optimization result of the ratio. Note that when solving, the temperature T, pressure P of the liquefied natural gas quality condition to be liquefied and the proportion (Z1,..., Z n ) of each gas in the liquefied natural gas to be liquefied should be the temperature T, pressure P of the liquefied natural gas quality condition and the proportion (Z1,..., Z n ) under the actual working conditions. And compared with the optimization method of the mixed refrigerant ratio parameters in the prior art, when the temperature T, pressure P of the liquefied natural gas quality condition and the proportion (Z1,..., Z n ) change, it is necessary to manually adjust the parameters of the natural gas liquefaction process model according to the parameter combination {X1,..., X i , T, P, Z1,..., Z n}, calculate the ratio of the compressor power consumption to the liquefied natural gas production to obtain the compressor specific power consumption. In this application, when the temperature T, pressure P of the liquefied natural gas quality condition and the proportion (Z1,..., Z n ) change, there is no need to rebuild the LNG liquefaction process model or reconstruct the mathematical function. Directly modify the temperature T, pressure P of the liquefied natural gas quality condition to be liquefied and the proportion (Z1,..., Z n ), and use the non - linear optimization algorithm to solve the minimum value of the obtained objective function y under the new constraint conditions. After reverse solution, the new optimization result of the ratio can be obtained, which improves the calculation efficiency of the mixed refrigerant ratio optimization process.

[0054] The above - mentioned optimization device for the mixed refrigerant ratio parameters includes a processor and a memory. The above - mentioned acquisition unit, calculation unit, determination unit, etc. are all stored in the memory as program units, and the corresponding functions are realized by the processor executing the above - mentioned program units stored in the memory.

[0055] The processor contains a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and by adjusting the kernel parameters, the problem in the prior art that when the natural gas parameter conditions change, the parameters of the natural gas liquefaction process model need to be continuously modified manually when determining the optimal ratio parameters of the mixed refrigerant, resulting in low optimization efficiency, can be solved.

[0056] The memory may include non-permanent memory in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flashRAM), and the memory includes at least one memory chip.

[0057] An embodiment of the present application provides a storage medium, on which a program is stored, and when the program is executed by a processor, the above-mentioned optimization method for the ratio parameters of the mixed refrigerant is implemented.

[0058] An embodiment of the present application provides a processor, which is used to run a program. When the program runs, the above-mentioned optimization method for the ratio parameters of the mixed refrigerant is executed.

[0059] An embodiment of the present application provides an optimization system for the ratio parameters of the mixed refrigerant, including: one or more processors, a memory, and one or more programs. Among them, the one or more programs are stored in the memory and are configured to be executed by the one or more processors. The one or more programs include methods for executing any of the above. When the processor executes the program, at least the following steps are implemented:

[0060] Step S101, under the actual working conditions, obtain the parameter values of the natural gas to be liquefied, and the parameter values are within the first value range;

[0061] Step S102, when both the parameter values and the second value range are constraint conditions of the specific power consumption estimation function, use a non-linear optimization algorithm to solve the minimum output value of the specific power consumption estimation function to obtain the minimum value of the compressor specific power consumption. The second value range is the preset value range of the ratio parameters of the mixed refrigerant. The specific power consumption estimation function is obtained by using a fitting algorithm to fit multiple groups of data to be fitted. Each group of data to be fitted includes: a first parameter to be fitted, a second parameter to be fitted, and the corresponding compressor specific power consumption. The first parameter to be fitted is within the first value range, and the second parameter to be fitted is within the second value range;

[0062] Step S103, determine the optimal ratio parameter value according to the minimum value of the compressor specific power consumption.

[0063] The present application also provides a computer program product, which, when executed on a data processing device, is adapted to execute a program initialized with at least the following method steps:

[0064] Step S101: Under actual working conditions, obtain the parameter values of the above-mentioned liquefied natural gas to be liquefied, and the above parameter values are within a first value range;

[0065] Step S102: When both the above parameter values and a second value range are constraint conditions of a specific power consumption estimation function, use a non-linear optimization algorithm to solve the minimum output value of the above specific power consumption estimation function, and obtain the minimum value of the compressor specific power consumption. The above second value range is a preset value range of the mixing ratio parameter values of the above mixed refrigerant. The above specific power consumption estimation function is obtained by fitting multiple groups of data to be fitted using a fitting algorithm. Each group of the above data to be fitted includes: a first parameter to be fitted, a second parameter to be fitted, and the corresponding above compressor specific power consumption. The above first parameter to be fitted is within the above first value range, and the above second parameter to be fitted is within the above second value range;

[0066] Step S103: Determine the optimal mixing ratio parameter value according to the minimum value of the above compressor specific power consumption.

[0067] In the above embodiments of the present application, the descriptions of the respective embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0068] In several embodiments provided by the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are merely illustrative. For example, the above unit division can be a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of units or modules can be electrical or other forms.

[0069] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0070] In addition, in each embodiment of the present application, each functional unit may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0071] If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the above methods in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0072] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0073] 1), in the optimization method of the mixed refrigerant ratio parameter of the present application, first, under actual working conditions, the parameter values of the natural gas to be liquefied are obtained, and the parameter values are within the first value range; then, when both the parameter values and the second value range are constraints of the specific power consumption estimation function, a non-linear optimization algorithm is used to solve the minimum output value of the specific power consumption estimation function to obtain the minimum value of the compressor specific power consumption. The second value range is the preset value range of the ratio parameter values of the mixed refrigerant. The specific power consumption estimation function is obtained by fitting multiple groups of data to be fitted using a fitting algorithm. Each group of the data to be fitted includes: a first parameter to be fitted, a second parameter to be fitted, and the corresponding compressor specific power consumption. The first parameter to be fitted is within the first value range, and the second parameter to be fitted is within the second value range; finally, the optimal ratio parameter value is determined according to the minimum value of the compressor specific power consumption. The specific power consumption estimation function of this method is a non-linear mapping relationship between the ratio parameter values of the mixed refrigerant obtained by using a fitting algorithm, the parameter values of the natural gas to be liquefied, and the compressor specific power consumption. That is, when the value of the parameter of the natural gas to be liquefied in the actual working condition changes, there is no need to establish a new model. By using the new parameter value and the preset value range of the ratio parameter value of the mixed refrigerant as constraints and using a non-linear optimization algorithm to solve the specific power consumption estimation function, the minimum value of the corresponding compressor specific power consumption can be obtained. This method solves the problem in the prior art that when determining the optimal ratio parameter of the mixed refrigerant, when the natural gas parameter conditions change, it is necessary to continuously manually modify the parameters of the natural gas liquefaction process model, resulting in low optimization efficiency.

[0074] 2) In the optimization device for the mixing refrigerant ratio parameters of the present application, an acquisition unit acquires the parameter values of the natural gas to be liquefied under actual working conditions, and the parameter values are within the first value range; a calculation unit, when both the parameter values and the second value range are constraint conditions of the specific power consumption estimation function, uses a non-linear optimization algorithm to solve the minimum output value of the specific power consumption estimation function to obtain the minimum value of the compressor specific power consumption. The second value range is the preset value range of the mixing refrigerant ratio parameters, and the specific power consumption estimation function is obtained by fitting multiple groups of data to be fitted using a fitting algorithm. Each group of the data to be fitted includes: a first parameter to be fitted, a second parameter to be fitted, and the corresponding compressor specific power consumption. The first parameter to be fitted is within the first value range, and the second parameter to be fitted is within the second value range; a determination unit determines the optimal ratio parameter value according to the minimum value of the compressor specific power consumption. The specific power consumption estimation function of this device is a non-linear mapping relationship between the mixing refrigerant ratio parameter value, the parameter value of the natural gas to be liquefied, and the compressor specific power consumption obtained by using a fitting algorithm. That is, when the value of the parameter of the natural gas to be liquefied changes in the actual working condition, there is no need to establish a new model. By using the new parameter value and the preset value range of the mixing refrigerant ratio parameter value as constraint conditions and using a non-linear optimization algorithm to solve the specific power consumption estimation function, the minimum value of the corresponding compressor specific power consumption can be obtained. This device solves the problem in the prior art that when determining the optimal mixing refrigerant ratio parameters, when the natural gas parameter conditions change, it is necessary to continuously manually modify the parameters of the natural gas liquefaction process model, resulting in low optimization efficiency.

[0075] 3) The optimization system for the mixing refrigerant ratio parameters of the present application includes: one or more processors, a memory, and one or more programs. Among them, the one or more programs are stored in the memory and are configured to be executed by the one or more processors. The one or more programs include those for executing any one of the above methods. The specific power consumption estimation function of this system is a non-linear mapping relationship between the mixing refrigerant ratio parameter value, the parameter value of the natural gas to be liquefied, and the compressor specific power consumption obtained by using a fitting algorithm. That is, when the value of the parameter of the natural gas to be liquefied changes in the actual working condition, there is no need to establish a new model. By using the new parameter value and the preset value range of the mixing refrigerant ratio parameter value as constraint conditions and using a non-linear optimization algorithm to solve the specific power consumption estimation function, the minimum value of the corresponding compressor specific power consumption can be obtained. This system solves the problem in the prior art that when determining the optimal mixing refrigerant ratio parameters, when the natural gas parameter conditions change, it is necessary to continuously manually modify the parameters of the natural gas liquefaction process model, resulting in low optimization efficiency.

[0076] The above are only the preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. An optimization method for the mixing ratio parameters of a mixed refrigerant, characterized in that, A mixed refrigerant is used to liquefy natural gas to be liquefied. The method includes: Under actual working conditions, obtaining the parameter values of the natural gas to be liquefied, where the parameter values are within a first value range; When both the parameter values and a second value range are constraint conditions of a specific power consumption estimation function, using a non-linear optimization algorithm to solve for the minimum output value of the specific power consumption estimation function, obtaining the minimum value of the compressor specific power consumption. The second value range is a preset value range of the mixing ratio parameter values of the mixed refrigerant. The specific power consumption estimation function is obtained by fitting multiple groups of data to be fitted using a fitting algorithm. Each group of the data to be fitted includes: a first parameter to be fitted, a second parameter to be fitted, and the corresponding compressor specific power consumption. The first parameter to be fitted is within the first value range, and the second parameter to be fitted is within the second value range; Determining the optimal mixing ratio parameter value according to the minimum value of the compressor specific power consumption; Before using the non-linear optimization algorithm to solve for the minimum output value of the specific power consumption estimation function and obtaining the minimum value of the compressor specific power consumption, the method further includes: within the first value range, using the Latin hypercube sampling method to sample, obtaining multiple first parameters to be fitted, and within the second value range, using the Latin hypercube sampling method to sample, obtaining the second parameters to be fitted corresponding to each of the first parameters to be fitted; Before using the non-linear optimization algorithm to solve for the minimum output value of the specific power consumption estimation function and obtaining the minimum value of the compressor specific power consumption, the method further includes: inputting each of the first parameters to be fitted and the corresponding second parameters to be fitted into a natural gas liquefaction process model, obtaining multiple compressor specific power consumptions. The natural gas liquefaction process model is a PID model established using a process simulation software according to the natural gas liquefaction process. The PID model includes multiple devices for the natural gas liquefaction process; Before using the non-linear optimization algorithm to solve for the minimum output value of the specific power consumption estimation function and obtaining the minimum value of the compressor specific power consumption, the method further includes: according to the natural gas liquefaction process, establishing the PID model on the process simulation software; configuring the parameters of the devices in the PID model according to the actual working conditions, obtaining the natural gas liquefaction process model.

2. The method according to claim 1, characterized in that, Before using the non-linear optimization algorithm to solve for the minimum output value of the specific power consumption estimation function and obtaining the minimum value of the compressor specific power consumption, the method further includes: Using the fitting algorithm to fit each group of the data to be fitted, obtaining the specific power consumption estimation function.

3. The method according to claim 1, characterized in that Determining the optimal mixing ratio parameter value according to the minimum value of the compressor specific power consumption includes: Taking the minimum value of the compressor specific power consumption as the output value of the specific power consumption estimation function, and using the non-linear optimization algorithm to inversely solve for the input value of the specific power consumption estimation function, obtaining the optimal mixing ratio parameter value.

4. An optimization device for the mixing refrigerant ratio parameters, characterized in that, A mixed refrigerant is used to liquefy natural gas to be liquefied. The device includes: An acquisition unit, which under actual working conditions, acquires the parameter values of the natural gas to be liquefied, where the parameter values are within a first value range; A calculation unit, when both the parameter value and the second value range are constraint conditions of the specific power consumption estimation function, uses a non-linear optimization algorithm to solve the minimum output value of the specific power consumption estimation function, and obtains the minimum value of the compressor specific power consumption. The second value range is a preset value range of the mixing ratio parameter value of the mixed refrigerant. The specific power consumption estimation function is obtained by fitting multiple groups of data to be fitted using a fitting algorithm. Each group of the data to be fitted includes: a first parameter to be fitted, a second parameter to be fitted, and the corresponding compressor specific power consumption. The first parameter to be fitted is within the first value range, and the second parameter to be fitted is within the second value range; A determination unit, which determines the optimal mixing ratio parameter value according to the minimum value of the compressor specific power consumption; The optimization device for the mixing ratio parameters of the mixed refrigerant further includes a first sampling unit and a second sampling unit. The first sampling unit is used to sample within the first value range using the Latin hypercube sampling method to obtain multiple first parameters to be fitted. The second sampling unit is used to sample within the second value range using the Latin hypercube sampling method to obtain the second parameters to be fitted corresponding to each of the first parameters to be fitted, The optimization device for the mixing ratio parameters of the mixed refrigerant further includes an execution unit. The execution unit is used to input each of the first parameters to be fitted and the corresponding second parameter to be fitted into a natural gas liquefaction process model to obtain multiple compressor specific power consumptions. The natural gas liquefaction process model is a PID model established using a process simulation software according to the natural gas liquefaction process. The PID model includes multiple devices for the natural gas liquefaction process, The optimization device for the mixing ratio parameters of the mixed refrigerant further includes a modeling unit and a configuration unit. The modeling unit is used to establish the PID model on the process simulation software according to the natural gas liquefaction process; the configuration unit is used to configure the parameters of the devices in the PID model according to the actual working conditions to obtain the natural gas liquefaction process model.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 3.

6. A processor, characterized in that, The processor is used to run a program, wherein when the program runs, it executes the method according to any one of claims 1 to 3.

7. An optimization system for the mixing refrigerant ratio parameters, characterized in that, Comprising: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors. The one or more programs include those for executing the method according to any one of claims 1 to 3.

Images