Population selection method and electronic device for optimizing natural gas liquefaction process parameters

Abstract

The application discloses a population selection method and electronic equipment for optimizing natural gas liquefaction process parameters. According to the application, when a population is screened, a preliminary search is conducted in a large sample phase space, as many samples in the phase space as possible are covered under the premise that calculation conditions are allowed, then the number of samples in the phase space is reduced through appropriate grouping rules, new populations are formed, meanwhile, the diversity of each variable in the population is preserved as much as possible, then the new population numbers generated by each grouping rule are respectively optimized, and a global optimal solution is searched. The technical scheme of the application can cover as many points in the phase space as possible, preserve the uniqueness of each point in the population, especially the uniqueness of strong correlation variables of a target function, and achieve the purpose of greatly reducing the calculation amount.

Description

Technical Field

[0001] The present invention belongs to the technical field of natural gas liquefaction, and more particularly, relates to a population selection method and electronic equipment for optimizing natural gas liquefaction process parameters. Background Art

[0002] Natural gas liquefaction is a core technology for energy companies, and achieving low energy consumption and low costs is a key goal of natural gas liquefaction technology. LNG production is affected by ambient temperature, source gas pressure, and processing volume, requiring adjustments to process parameters such as the mixed refrigerant formula and compressor operating parameters. Optimizing process parameters for such complex systems can be reduced to a multi-degree-of-freedom, multi-constraint optimization problem. Conventional operating condition analysis methods or classical gradient-based optimization methods are often helpless for systems with such multiple degrees of freedom (e.g., potentially more than 10). The development of modern artificial intelligence optimization methods has provided an effective means for optimizing natural gas liquefaction process parameters.

[0003] Conventional algorithms have significant limitations when optimizing complex systems with multiple variables and constraints, and they tend to converge to local optima. Genetic algorithms (GAs), however, originate from computer simulations of biological systems. These algorithms use digital strings to represent chromosomes, similar to natural evolutionary processes such as natural selection, mutation, and crossover. They simulate the basic evolutionary process of organisms through genetic operators such as selection, crossover, and mutation. Fitness functions are used to represent the quality of the solutions implied by chromosomes. Through continuous "generational" evolution, the average fitness of the population is improved, and the fitness function guides the evolutionary direction of the population, thereby ensuring that the solutions represented by the optimal individuals approach the global optimal solution. Unlike classical optimization algorithms, which rely on gradient information, genetic algorithms are intelligent. After determining the encoding scheme, fitness function, and genetic operators, they use information gained during evolution to organize their search. This self-organizing and adaptive nature enables them to automatically discover environmental features and patterns in response to changing circumstances. Traditional algorithms, on the other hand, require a comprehensive description of the problem and an explanation of the appropriate approaches to address specific problems.

[0004] Genetic algorithms operate on a swarm basis, requiring an initial population of several initial solutions. The quality of this initial population significantly impacts the algorithm's computational performance and efficiency. For multivariable systems, a larger initial population, the greater the number of entities contained in the phase space, increases the likelihood of finding the global optimum. However, this also results in a higher computational load.

[0005] Since the optimization of the natural gas liquefaction process requires the simultaneous iteration of genetic algorithms and complex process simulation programs, the amount of calculation is huge. How to reduce the amount of calculation while seeking the global optimal solution is an important issue in lowering the application difficulty threshold. Summary of the Invention

[0006] In view of this, an embodiment of the present invention provides a population selection method for optimizing natural gas liquefaction process parameters, which can generate a suitable initial population for a genetic algorithm, significantly reducing the amount of computation while seeking a global optimal solution.

[0007] According to one aspect of the present invention, a population selection method for optimizing natural gas liquefaction process parameters is provided, characterized in that the method comprises:

[0008] Step 1: determining the optimization objective function according to the process objectives of natural gas liquefaction;

[0009] Step 2: Use the process parameters to be optimized as the independent variables of the genetic algorithm and set the constraint relationship between the independent variables;

[0010] Step 3: Set the upper and lower bounds of each independent variable and generate multiple monomers Z based on the set upper and lower bounds. 0,i , forming the original population G0, i=1,2,…I, I represents the number of monomers in the original population G0;

[0011] Step 4: Perform the first round of genetic algorithm iteration based on the original population G0 to optimize the objective function. The number of iterations does not exceed the preset number of iterations, and obtain multiple monomers Z after iteration. i , i=1,2,…I, constituting the population G;

[0012] Step 5: Set multiple grouping rules. For each grouping rule, group multiple monomers Z in the population G according to the grouping rule. i Divide into M groups, and extract one monomer from each group into the initial population G corresponding to the grouping rule 0,n , n=1…N, N represents the number of grouping rules;

[0013] Step 6: Based on the initial population G under each grouping rule 0,n Perform genetic algorithm iterations of the optimization objective function respectively to obtain the optimal value of the optimization objective function under each grouping rule, as well as the monomer corresponding to the optimal value;

[0014] Step 7: Select a global optimal value from the optimal values ​​of the optimization objective function under each grouping rule, and determine the value of the independent variable of the monomer corresponding to the global optimal value as the value of the process parameter to be optimized.

[0015] Optionally, the process target is the energy consumption of the natural gas liquefaction unit, the operating cost of the natural gas liquefaction unit, or the capital expenditure of the natural gas liquefaction unit.

[0016] Optionally, the process parameters to be optimized include some or all of the following: flow rates of different components in the mixed refrigerant, inlet and outlet pressures of each section of the mixed refrigerant compressor, and outlet temperatures of each section of the cold box.

[0017] Optionally, in step 3, multiple monomers Z are generated based on the upper and lower boundaries set. 0,i include:

[0018] For each independent variable, randomly generate individual Z between the upper and lower boundaries of the independent variable o,i The value of the independent variable in .

[0019] Optionally, in step 4, the first round of genetic algorithm iteration ends when any one of the following conditions is met:

[0020] The number of iterations reaches the preset number of iterations;

[0021] Although the number of iterations has not reached the preset number of iterations, the population after this iteration meets the preset termination condition.

[0022] Optionally, the preset termination condition is:

[0023] The number of monomers in the population after this iteration that meet the constraint conditions exceeds a preset threshold.

[0024] Optionally, in step 5, the grouping rules include some or all of the following:

[0025] Calculate the Z of each monomer in the population G i The distance between the lower boundary and the Z of each monomer is calculated according to the distance. i Sorting, dividing the sorted monomers into M groups in sequence with 1 / M monomers in each group;

[0026] Calculate the Z of each monomer in the population G i The distance between the upper boundary and the Z of each monomer is calculated according to the distance. i Sorting, dividing the sorted monomers into M groups in sequence with 1 / M monomers in each group;

[0027] Calculate the Z of each monomer in the population G i The variance of the lower and upper boundaries is calculated based on the size of the variance. i Sorting, dividing the sorted monomers into M groups in sequence with 1 / M monomers in each group.

[0028] Optionally, in step 5, one monomer is drawn from each group into the initial population G in the following manner: 0,n:

[0029] When the proportion of monomers in the group that meet the constraints is less than the preset proportion, the probability of extracting monomers is adjusted so that the ratio of the probability of extracting monomers that meet the constraints to the probability of extracting monomers that do not meet the constraints is greater than the preset probability ratio, and then randomly extract monomers from the group to enter the initial population G 0,n ;

[0030] When the proportion of monomers in the group that meet the constraints is greater than or equal to the preset proportion, monomers are randomly drawn from the group and enter the initial population G. 0,n .

[0031] Optionally, the preset proportion is e / (1+e), where e is a natural constant; and the preset probability ratio is e.

[0032] According to another aspect of the present invention, an electronic device is provided, comprising:

[0033] a memory storing executable instructions;

[0034] A processor runs the executable instructions in the memory to implement the population selection method for optimizing natural gas liquefaction process parameters as described above.

[0035] According to the above embodiment of the present invention, a large-scale original population G0 can be generated to cover as many points in the phase space as possible; then, a finite number of genetic algorithm iterations are performed on the original population. Since the number of iterations is limited, a small amount of computation can be guaranteed even though the population is large; then, the population G updated after the finite number of iterations is grouped according to different grouping rules, and monomers are extracted from each group to form an initial population G0,n under different grouping rules. At this time, the size of each initial population is much smaller than the original population G0, and the uniqueness of each point in the population is fully retained; then, a genetic algorithm iteration is performed based on each initial population separately. Since the size of the initial population is significantly smaller than the original population, the amount of computation can be significantly reduced; then, the optimal values ​​obtained based on different initial populations are comprehensively considered to find the global optimal value, and the value of the independent variable of the monomer corresponding to the global optimal value is determined as the optimized natural gas liquefaction process parameter.

[0036] According to the present invention, while fully covering the points in the phase space, the uniqueness of different populations is retained as much as possible, and the amount of calculation is significantly reduced, thereby improving the calculation efficiency.

[0037] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0039] Figure 1 A flow chart of a population selection method for optimizing natural gas liquefaction process parameters according to one embodiment of the present invention is shown. DETAILED DESCRIPTION

[0040] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0041] Figure 1 A population selection method for optimizing natural gas liquefaction process parameters according to an embodiment of the present invention is shown. The method includes steps 1 to 7.

[0042] Step 1: Determine the optimization objective function based on the process objectives of natural gas liquefaction.

[0043] In some examples, the energy consumption of the natural gas liquefaction plant can be used as the process target; in other examples, the operating cost (also known as OPEX) of the natural gas liquefaction plant can be used as the process target; in other examples, the capital expenditure (also known as CAPEX) of the natural gas liquefaction plant can be used as the process target.

[0044] Step 2: Use the process parameters to be optimized as the independent variables of the genetic algorithm and set the constraint relationship between the independent variables.

[0045] There are many process parameters related to the process objectives, such as the outlet temperature of the water cooler or air cooler, the flow rate of different components in the mixed refrigerant (such as the percentage molar flow rate and total flow rate of single components such as nitrogen, methane, C2, C3, C4, and C5), the inlet and outlet pressures of each section of the mixed refrigerant compressor, the outlet temperature of each section of the cold box, and other factors affecting liquefaction.

[0046] Based on experience, process parameters that are strongly correlated with the process objectives can be selected from a large number of process parameters as the process parameters to be optimized in the present invention. In a preferred embodiment according to the present invention, some or all of the following process parameters can be selected as independent variables of monomers: the flow rates of different components in the mixed refrigerant (such as nitrogen, methane, carbon 2, carbon 3, carbon 4, carbon 5, etc.), the inlet and outlet pressures of each section of the mixed refrigerant compressor, and the outlet temperatures of each section of the cold box. After in-depth research, the inventors found that these process parameters are strongly correlated with the process objectives. Using them as independent variables of population monomers can reduce the amount of calculation while ensuring the optimization effect and improve the calculation efficiency.

[0047] Constraints between independent variables can be set based on experience and device requirements / indicators.

[0048] Step 3: Set the upper and lower bounds of each independent variable and generate multiple monomers Z based on the set upper and lower bounds. 0,i , constituting the original population G0, i = 1, 2,…I, where I represents the number of monomers in the original population G0.

[0049] Assume that the number of independent variables contained in each monomer is L, then any independent variable is represented by J l , l=1,2,…,L, can be used for any independent variable J l Set its upper boundary J l,lb and the lower boundary J l,hb , and then generate each monomer Z based on the upper and lower boundaries 0,i The independent variable J in l , so that its value is at the corresponding upper boundary J l,lb and the lower boundary J l,hb between.

[0050] In one embodiment, for monomer Z 0,i Each independent variable J l , can be found at the upper boundary J of the independent variable l,lb and the lower boundary J l,hb Each monomer Z is randomly generated o,i The random generation method ensures that the population randomly covers points in the phase space, reducing deviations and limitations introduced by human intervention.

[0051] Let the upper bounds of all independent variables form a vector J lb ={J 1,lb ,J 2,lb ,…J L,lb}, the lower boundary forms the vector J hb ={J 1,hb ,J 2,hb ,…J L,hb}, can be found in J lb ≤Z 0,i ≤J hb Under the condition of 0,i .

[0052] Step 4: Perform the first round of genetic algorithm iteration based on the original population G0 to optimize the objective function. The number of iterations does not exceed the preset number of iterations, and obtain multiple monomers Z after iteration. i , i=1,2,…I, constituting the population G.

[0053] According to the technical solution of the present invention, in step 4, a limited number of first round genetic iterative algorithms can be performed on the original population G0, which not only ensures that the population covers as much phase space as possible but also limits the computational complexity of the genetic algorithm iteration.

[0054] In some embodiments, the first round of genetic algorithm iteration may end when any one of the following conditions is met:

[0055] The number of iterations reaches the preset number of iterations;

[0056] Although the number of iterations has not reached the preset number of iterations, the population after this iteration meets the preset termination condition.

[0057] For example, if the preset number of iterations is 5, then when the number of iterations reaches 5, or although the number of iterations has not reached 5 but the updated population after this iteration - which can be the 1st, 2nd, 3rd, or 4th iteration - has met specific conditions, the first round of genetic algorithm iteration is ended.

[0058] In some embodiments, the preset condition is that the number of monomers in the updated population satisfying the constraint condition after the current iteration exceeds a preset threshold. Those skilled in the art may design this preset threshold as needed. In one example, the preset threshold may be set to three times the number of independent variables L.

[0059] Step 5: Set multiple grouping rules. For each grouping rule, group multiple monomers Z in the population G according to the grouping rule. i Divide into M groups, and extract one monomer from each group into the initial population G corresponding to the grouping rule 0,n , n=1…N, N represents the number of grouping rules.

[0060] In some implementations, the grouping rules include some or all of the following:

[0061] Grouping rule 1. Calculate the individual Z in population G i The distance between the lower boundary and the Z of each monomer is calculated according to the distance. i Sorting, dividing the sorted monomers into M groups in sequence with 1 / M monomers in each group;

[0062] Grouping rule 2: Calculate the individual Z in population G i The distance between the upper boundary and the Z of each monomer is calculated according to the distance. i Sorting, dividing the sorted monomers into M groups in sequence with 1 / M monomers in each group;

[0063] Grouping rule 3: Calculate the individual Z in population G i The variance of the lower and upper boundaries is calculated based on the size of the variance. iSorting, dividing the sorted monomers into M groups in sequence with 1 / M monomers in each group.

[0064] The above grouping rule 1 is used as an example. The following formula can be used to calculate the value of each monomer Z in the population G: i Distance S from the lower boundary l,i , i=1,2,…I:

[0065] S l,i =∑ l＝1,…,L |J l -J l,lb |,

[0066] Among them, |J l -J l,lb | represents monomer Z i The independent variable J l The value of the variable is the same as the lower bound J l,lb The absolute value between.

[0067] For all monomers Z i According to the corresponding distance S l,i Sort from large to small or from small to large, and sort the sorted monomer Z i Sequentially divide the monomers into M groups with I / M monomers in each group. For example, if I is 1000 and M is 50, then for the sorted monomers, divide the first 20 monomers into one group, the next 20 monomers into one group, and so on.

[0068] Take grouping rule 2 as an example. Each monomer Z can be calculated based on the following formula i Distance S from the upper boundary h,i , i=1,2,…I:

[0069] S h,i =∑ l＝1,…,L |J l -J l,hb |,

[0070] Among them, |J l -J l,hb | represents monomer Z i The independent variable J l The value of the upper bound J of the independent variable l,hb The absolute value between.

[0071] For all monomers Z i According to the corresponding distance S h,i Sort from large to small or from small to large, and sort the sorted monomer Z i Sequentially divide the monomers into M groups with I / M monomers in each group. For example, if I is 1000 and M is 50, then for the sorted monomers, divide the first 20 monomers into one group, the next 20 monomers into one group, and so on.

[0072] Take grouping rule 3 as an example. Each monomer Z can be calculated based on the following formula i Variance S with the lower and upper boundaries D,i , i=1,2,…I:

[0073]

[0074] For all monomers Z i According to the corresponding distance S D,i Sort from large to small or from small to large, and sort the sorted monomer Z i Sequentially divide the monomers into M groups with I / M monomers in each group. For example, if I is 1000 and M is 50, then for the sorted monomers, divide the first 20 monomers into one group, the next 20 monomers into one group, and so on.

[0075] In some implementations, the grouping rules set may include two of the above three grouping rules, that is, N=2, for example 1 and 2, 1 and 3, 2 and 3, which is equivalent to grouping the population G twice and obtaining two grouping results; in some implementations, all three grouping rules 1, 2, and 3 may be included, that is, N=3, which is equivalent to grouping the population G three times and obtaining three grouping results.

[0076] Take N=3, i.e., the embodiment including all three grouping rules 1, 2, and 3 as an example. Using the above assumptions, let I be 1000 and M be 50. According to grouping rule 1, the 1000 monomers Z in the population G after the first round of genetic algorithm iteration can be i Divide into 50 groups, each group includes 20 monomers, and extract one monomer from each group into the initial population G 0,1 , G 0,1 The number of monomers in the population G is 50; according to grouping rule 2, the 1000 monomers Z in the population G after the first round of genetic algorithm iteration can be i Divide into 50 groups again, each group includes 20 monomers, and extract one monomer from each group into the initial population G 0,2 , G 0,2 The number of monomers in the population G is 50; according to grouping rule 3, the 1000 monomers Z in the population G after the first round of genetic algorithm iteration can be i Divide into 50 groups, each group includes 20 monomers, and extract one monomer from each group into the initial population G 0,3 , G 0,3 The number of monomers in is 50.

[0077] According to the present step, one monomer is extracted from each group to form an initial population, which not only preserves the uniqueness of the population (monomers in each range are extracted into the initial population), but also makes the population cover the entire phase space as much as possible, while significantly reducing the population size, such as the number of monomers in each initial population being 50 in the above example, which is significantly less than the number of monomers in the original population 1000, thereby significantly reducing the calculation amount of subsequent genetic algorithm iterations.

[0078] In some embodiments, the quality of the initial population G 0,n can be further improved by setting appropriate extraction rules. For example, one monomer can be extracted from each group into the initial population G 0,n in the following manner:

[0079] When the proportion of monomers satisfying the constraint condition in the group to the total number of monomers in the group is less than the preset proportion, the extraction probability of the monomers is adjusted so that the ratio of the probability of extracting monomers satisfying the constraint condition to the probability of extracting monomers not satisfying the constraint condition is greater than the preset probability ratio, and then monomers are randomly extracted from the group into the initial population G 0,n .

[0080] When the proportion of monomers satisfying the constraint condition in the group to the total number of monomers in the group is greater than or equal to the preset proportion, monomers are directly randomly extracted from the group into the initial population G 0,n .

[0081] In one embodiment, the above-mentioned preset proportion is e / (1+e), where e is a natural constant, and 1 / (1+e) can be approximately equal to 26.89%; and the preset probability ratio is e.

[0082] The above extraction methods will be illustrated by examples below.

[0083] For a certain group, it can be determined whether each monomer in the group satisfies the constraint condition. If the quotient of the number of monomers satisfying the constraint condition in the group divided by the total number of monomers in the group is less than 26.89%, the extraction probability of the monomers in the group is adjusted, i.e. the probability of extracting monomers satisfying the constraint condition is increased, rather than extracting all monomers with equal opportunity, and the extraction probability is adjusted to ensure that the following formula is satisfied:

[0084]

[0085] Then monomers are randomly extracted from the group into the initial population G 0,n based on the adjusted extraction probability.

[0086] If the quotient of the number of monomers satisfying the constraint condition in the group divided by the total number of monomers in the group is greater than or equal to 26.89%, monomers are directly randomly extracted from the group into the initial population G 0,n .

[0087] Step 6, initial population G under each grouping rule 0,n Respectively, the genetic algorithm iteration of the optimization objective function is performed to obtain the optimal value of the optimization objective function under each grouping rule, and the monomer corresponding to the optimal value.

[0088] Taking the grouping rules including the above three grouping rules as an example, the genetic algorithm iteration of the optimization objective function can be performed based on the initial population G 0,1 , G 0,2 , and G 0,3 respectively. Since the scale of G 0,1 , G 0,2 , and G 0,3 is much smaller than the scale of the original population G0, compared with the genetic algorithm iteration based on the original population G0, the amount of calculation according to the present application is significantly reduced. The genetic algorithm iteration is performed based on the population G 0,1 to obtain the optimal value D1 of the optimization objective function and the monomer Z1 corresponding to the optimal value; the genetic algorithm iteration is performed based on the population G 0,2 to obtain the optimal value D2 of the optimization objective function and the monomer Z2 corresponding to the optimal value; and the genetic algorithm iteration is performed based on the population G 0,3 to obtain the optimal value D3 of the optimization objective function and the monomer Z3 corresponding to the optimal value.

[0089] Step 7, selecting the global optimal value from the optimal values of the optimization objective function under each grouping rule, and determining the value of the independent variable of the monomer corresponding to the global optimal value as the value of the process parameter to be optimized.

[0090] Continuing with the above example, suppose that the optimal values D1, D2, and D3 of the optimization objective function are obtained based on the above three grouping rules, then in step 7, the value that best meets the expectation is selected from D1, D2, and D3 as the global optimal value, and a person skilled in the art can select the global optimal value from D1, D2, and D3 according to common sense and experience, for example, if the process objective of the optimization objective function is the energy consumption of a natural gas liquefaction device, then the minimum value of D1, D2, and D3 can be selected as the global optimal value. Suppose that the value of D1 is the smallest among D1, D2, and D3, then the value of D1 is determined as the global optimal value, and the value of the independent variable in the monomer Z1 corresponding to the global optimal value is determined as the optimized natural gas liquefaction process parameter.

[0091] According to the above embodiments of the present application, at least the following beneficial effects are achieved:

[0092] (1) As many points in the phase space are covered as possible;

[0093] (2) The uniqueness of each point in the population is preserved as richly as possible;

[0094] (3) preserving as much uniqueness of strongly correlated variables in the population as possible;

[0095] (4) The computational complexity is significantly reduced.

[0096] While various embodiments of the present invention have been described above, the above descriptions are exemplary, non-exhaustive, and not intended to be limiting of the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or improvements to existing technologies, or to enable others skilled in the art to understand the embodiments disclosed herein.

[0097] An electronic device according to an embodiment of the present invention includes a memory and a processor.

[0098] The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc.

[0099] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the present invention, the processor is used to run the computer-readable instructions stored in the memory.

[0100] Those skilled in the art should understand that in order to solve the technical problem of how to obtain a good user experience, this embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the scope of protection of the present invention.

[0101] For detailed description of this embodiment, please refer to the corresponding description in the aforementioned embodiments, which will not be repeated here.

[0102] While various embodiments of the present invention have been described above, the above description is intended to be illustrative, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A population selection method for optimizing natural gas liquefaction process parameters, characterized in that: The method comprises: Step 1: determining the optimization objective function according to the process objectives of natural gas liquefaction; Step 2: Use the process parameters to be optimized as the independent variables of the genetic algorithm and set the constraint relationship between the independent variables; Step 3: Set the upper and lower bounds of each independent variable and generate multiple monomers based on the set upper and lower bounds. , forming the original population G 0 , i=1,2,…I, I represents the original population G 0 The number of monomers in Step 4, based on the original population G 0 Perform the first round of genetic algorithm iteration to optimize the objective function. The number of iterations does not exceed the preset number of iterations, and obtain multiple monomers after iteration. , i=1,2,…I, constituting the population G; Step 5: Set multiple grouping rules. For each grouping rule, group multiple monomers in the population G according to the grouping rule. Divide into M groups, and extract one monomer from each group into the initial population G corresponding to the grouping rule 0,n , n=1…N, N represents the number of grouping rules, where the grouping rules include some or all of the following: Calculate the number of monomers in the population G The distance between the lower boundary and the monomers is calculated based on the distance. Sorting, the sorted monomers are divided into M groups according to I / M; calculating the number of monomers in the population G The distance from the upper boundary is used to adjust the size of each monomer according to the distance. Sorting, the sorted monomers are divided into M groups according to I / M; calculating the number of monomers in the population G The variance with the lower and upper boundaries is calculated for each monomer according to the size of the variance. Sorting, dividing the sorted monomers into M groups in sequence with 1 / M monomers in each group; Step 6: Based on the initial population G under each grouping rule 0,n Perform genetic algorithm iterations on the optimization objective function respectively to obtain the optimal value of the optimization objective function under each grouping rule, as well as the monomer corresponding to the optimal value; Step 7: Select a global optimal value from the optimal values ​​of the optimization objective function under each grouping rule, and determine the value of the independent variable of the monomer corresponding to the global optimal value as the value of the process parameter to be optimized.

2. The method according to claim 1, characterized in that The process target is the energy consumption of the natural gas liquefaction unit, the operating cost of the natural gas liquefaction unit, or the capital expenditure of the natural gas liquefaction unit.

3. The method according to claim 1, characterized in that The process parameters to be optimized include part or all of the following: flow rates of different components in the mixed refrigerant, inlet and outlet pressures of each section of the mixed refrigerant compressor, and outlet temperatures of each section of the cold box.

4. The method according to claim 1, wherein In step 3, multiple monomers are generated based on the upper and lower boundaries set include: For each independent variable, randomly generate individual units between the upper and lower boundaries of the independent variable The value of the independent variable in .

5. The method according to claim 1, wherein In step 4, the first round of genetic algorithm iteration ends when any of the following conditions is met: The number of iterations reaches the preset number of iterations; Although the number of iterations has not reached the preset number of iterations, the population after this iteration meets the preset termination condition.

6. The method according to claim 5, characterized in that The preset termination conditions are: After this iteration, the number of monomers in the population that meet the constraints exceeds the preset threshold.

7. The method according to claim 1, characterized in that In step 5, one monomer is drawn from each group into the initial population G in the following manner: 0,n : When the proportion of monomers in the group that meet the constraints is less than the preset proportion, the probability of extracting monomers is adjusted so that the ratio of the probability of extracting monomers that meet the constraints to the probability of extracting monomers that do not meet the constraints is greater than the preset probability ratio, and then randomly extract monomers from the group to enter the initial population G 0,n ; When the proportion of monomers in the group that meet the constraints is greater than or equal to the preset proportion, monomers are randomly drawn from the group and enter the initial population G. 0,n .

8. The method according to claim 7, wherein: The preset ratio is e / (1+e), where e is a natural constant; The preset probability ratio is e.

9. An electronic device, characterized in that: The electronic device comprises: a memory storing executable instructions; A processor runs the executable instructions in the memory to implement the population selection method for optimizing natural gas liquefaction process parameters according to any one of claims 1 to 8.

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