Method for determining operating parameters of compressor units

Abstract

The present application discloses a method for determining the operating parameters of a compressor unit, the method comprising: obtaining the constraints of a genetic algorithm, the constraints comprising: the operating parameters of each of the compressors are within the operating parameter range corresponding to the compressor, and when the pressure ratio of each of the compressors is equal to the preset pressure ratio, the total flow of the compressor unit obtained according to the operating parameter combination and the total flow calculation model of the compressor unit is greater than or equal to the preset flow threshold. The total power calculation model of the compressor unit is determined as the fitness function of the genetic algorithm; the genetic algorithm is used to iteratively optimize the operating parameter combination of the compressor unit according to the constraints and the fitness function to obtain a target operating parameter combination, so that the compressor unit can operate according to the target operating parameter combination. This method can quickly and accurately determine the optimal operating parameters of each compressor in the compressor unit.

Description

Technical Field

[0001] The present application belongs to the technical field of compressors, and specifically relates to a method for determining operating parameters of a compressor unit. Background Art

[0002] Compressors are common high-energy-consuming equipment in the industrial field. In actual applications, in order to meet the large flow demand for compressors, multiple compressors can be connected in parallel to obtain a parallel-connected compressor unit. On the basis of meeting the flow and pressure ratio requirements, the operating parameters of each compressor in the compressor unit can be adjusted to make the compressor unit operate at minimum power, thereby achieving energy saving effects.

[0003] In related technologies, the operating parameters of each compressor in a compressor unit are typically adjusted gradually based on flow rate and pressure ratio constraints to produce multiple combinations of compressor operating parameters. The total power of the compressor unit is calculated for each combination. The minimum total power is then selected from the multiple total powers, allowing each compressor in the compressor unit to operate according to the operating parameters of the combination corresponding to the minimum total power.

[0004] However, the methods in the related art suffer from low processing efficiency and are unable to quickly obtain the optimal combination. Furthermore, by gradually adjusting the operating parameters of each compressor in the compressor unit, the number of possible combinations is limited. The combination with the lowest total power selected from these limited combinations may not be the optimal solution that meets the flow rate and pressure ratio requirements, as well as the minimum total power requirement. In other words, the related art lacks a method that can quickly and accurately obtain the optimal operating parameter combination for a compressor unit. Summary of the Invention

[0005] The present application aims to provide a method for determining the operating parameters of a compressor unit, at least to solve the problem in the prior art of lacking a method for quickly and accurately obtaining the optimal operating parameter combination of the compressor unit.

[0006] In order to solve the above technical problems, this application is implemented as follows:

[0007] An embodiment of the present application provides a method for determining operating parameters of a compressor unit, wherein the compressor unit includes a plurality of compressors connected in parallel; the method includes:

[0008] Obtaining constraints for the genetic algorithm, the constraints including: an operating parameter of each of the compressors being within an operating parameter range corresponding to the compressor, and a total flow rate of the compressor group obtained according to an operating parameter combination and a total flow rate calculation model of the compressor group being greater than or equal to a preset flow rate threshold when a pressure ratio of each of the compressors is equal to a preset pressure ratio, the operating parameter combination including a plurality of operating parameters having a one-to-one correspondence with the compressors;

[0009] Determining a total power calculation model of the compressor group as the fitness function of the genetic algorithm; the total power calculation model is used to calculate the total power of the compressor group according to the operating parameter combination;

[0010] A genetic algorithm is used to iteratively optimize the operating parameter combination of the compressor group according to the constraint conditions and the fitness function to obtain a target operating parameter combination for the compressor group to operate according to the target operating parameter combination.

[0011] In summary, in this embodiment, the genetic algorithm's constraints are that the operating parameters of each compressor are within the corresponding operating parameter range, and that, when the pressure ratio of each compressor is equal to a preset pressure ratio, the total flow rate of the compressor group, obtained based on the operating parameter combination and the total flow rate calculation model of the compressor group, is greater than or equal to a preset flow rate threshold. The fitness function of the genetic algorithm is determined based on the total power calculation model of the compressor group. Using the genetic algorithm, the operating parameter combination of the compressor group is iteratively optimized based on the aforementioned constraints and the fitness function to obtain a target operating parameter combination, so that the compressor group can operate according to the target operating parameter combination. Using the genetic algorithm with the aforementioned constraints and fitness function, the optimal operating parameters (i.e., the target operating parameter combination) with the minimum total power can be quickly and accurately obtained, provided that the operating parameters of each compressor in the compressor group are within the corresponding operating parameter range, the compressor pressure ratio meets the user's pressure ratio requirement (i.e., is equal to the preset pressure ratio), and the total flow rate of the compressor group meets the user's required flow rate (i.e., the total flow rate is greater than or equal to the preset flow rate threshold). BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a flowchart of a method for determining operating parameters of a compressor unit provided in an embodiment of the present application;

[0013] Figure 2 is a flowchart of another method for determining operating parameters of a compressor unit provided in an embodiment of the present application;

[0014] Figure 3 Schematic diagram of the flow-pressure ratio performance curve of compressor 1 in the compressor unit provided in an embodiment of the present application;

[0015] Figure 4 Schematic diagram of the flow-pressure ratio performance curve of compressor 2 in the compressor unit provided in an embodiment of the present application;

[0016] Figure 5 Schematic diagram of flow-power performance curve of compressor 1 in the compressor unit provided in an embodiment of the present application;

[0017] Figure 6 Schematic diagram of the flow-power performance curve of compressor 2 in the compressor unit provided in an embodiment of the present application;

[0018] Figure 7 This is a flowchart of the steps of another method for determining operating parameters of a compressor unit provided in an embodiment of the present application;

[0019] Figure 8 This is a performance curve diagram provided by an embodiment of the present application;

[0020] Figure 9 This is a flowchart of the steps of another method for determining the operating parameters of a compressor unit provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0022] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein, and that the objects distinguished by "first," "second," and the like are generally of the same type, and do not limit the number of objects; for example, the first object can be one or more. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects connected are in an "or" relationship.

[0023] Energy conservation and consumption reduction are gaining increasing attention in energy-intensive sectors. Compressors are common high-energy-consuming equipment in the industrial sector. Improperly set compressor operating parameters can lead to significant energy consumption during operation. In practical applications, adjusting the compressor to its lowest power setting, while still meeting flow and pressure ratio requirements, is one of the primary means of achieving energy-efficient operation.

[0024] A compressor system typically includes components such as the compressor, oil system, and control system. The compressor is the primary energy consumer in the system, and optimizing the energy-efficient operation of the compressor system can be considered equivalent to optimizing the energy-efficient operation of the compressor itself. When the demand for compressor flow is high and a single compressor cannot meet the high flow demand, multiple compressor units can be operated in parallel, resulting in a parallel-connected compressor unit. The adjustable operating parameters of each compressor in the unit can be combined in different ways, allowing for different flow rates to be allocated to each compressor unit while meeting the pressure ratio requirements. The power consumed by the compressor may also vary under different flow rates and adjustable operating parameters.

[0025] In the related art, in order to find the adjustable parameter combination that minimizes the operating power of the compressor unit, the adjustable operating parameters of each compressor are typically gradually adjusted based on flow and pressure ratio limits. The total power of the compressor unit corresponding to each adjustable operating parameter combination is calculated, and the combination with the lowest total power is selected for operation of the compressor unit according to the selected combination. However, this method of gradually adjusting the adjustable operating parameters of each compressor unit results in a limited number of combinations, making it impossible to calculate all combinations. It is also impossible to achieve both short calculation time and optimal solutions. Furthermore, the selected combination is the optimal combination among the limited number of combinations at which adjustments are made. This optimal combination is a local optimal combination, not a global optimal combination.

[0026] Furthermore, in related technologies, in order to find the adjustable parameter combination with the lowest operating power of multiple units, the adjustable parameters of each unit are often gradually adjusted according to the flow and pressure ratio limitations to obtain multiple adjustable operating parameter combination schemes, and the multiple adjustable operating parameter combination schemes are calculated separately. According to the calculation results, the operating parameter combination scheme with the lowest total power is selected from the multiple adjusted operating parameter combination schemes.

[0027] For example, the compressor unit includes two compressors (compressor 1 and compressor 2), and the operating parameter is the speed. According to the method of the related art, the speed of the compressor can be adjusted within the adjustable range of the compressor speed according to the adjustment step of 5%. For example, within the adjustable range of [n 1,min ,n 1,max ] to adjust the speed of compressor 1, the adjustment step is Among them, n 1,min and n 1,max are the minimum and maximum values ​​of the adjustable range of compressor 1 respectively. 2,min ,n 2,max ] within the operating parameter range of the compressor 2, and the adjustment step is Among them, n 2,min and n 2,maxThey are the minimum and maximum values ​​of the adjustable range of compressor 2 respectively.

[0028] Thus, we can get 20 2 = 400 speed combination schemes of two compressors and the corresponding performance curves of each scheme. After obtaining the 400 speed combination schemes, the 400 speed combination schemes are constrained and judged according to the constraints. Among them, the constraints include flow distribution constraints and pressure ratio constraints. The flow distribution constraint means that when the two compressors in the compressor unit are running under the speed combination scheme, the total flow of the compressor unit (that is, the sum of the flow of the two compressors) is greater than the total target flow required by the user (for example, 4.5m 3 / s). The pressure ratio constraint refers to the requirement that the pressure ratio of the two compressors in the compressor unit meet the required total pressure ratio (for example, the required total pressure ratio is 3) when operating under the speed combination scheme. For each speed combination scheme that meets the constraint, the total power of the compressor unit is calculated and the schemes are sorted by total power. The speed combination scheme with the lowest total power of the compressor unit is obtained, and the two compressors in the compressor unit can operate at the speed of this combination scheme.

[0029] According to the method of the related art, the accuracy of the calculation result depends on the size of the adjustment step. Specifically, the larger the adjustment step, the fewer the adjustment schemes, the less the calculation time, but the more difficult it is to obtain the optimal result; the smaller the adjustment step, the more the adjustment schemes, the longer the calculation time, and the closer the result is to the optimal result. However, as the number of compressors in the parallel compressor unit increases, the adjusted operating parameter combination schemes will increase exponentially, and correspondingly, the calculation time will also increase exponentially. At the same time, there is no correlation between the various adjustment schemes, which will result in the adjustment scheme that has been analyzed not being able to be used as a reference for the next adjustment scheme, and the current operating parameter combination scheme cannot be determined based on the analysis results of the previous set of operating parameter combination schemes, which will also lead to long calculation time. In the related art, there is a lack of a method that can quickly and accurately obtain the optimal operating parameter combination of the compressor unit.

[0030] In response to the problems existing in the related art, an embodiment of the present application provides a method for determining the operating parameters of a compressor unit, including: obtaining the constraints of a genetic algorithm, the constraints including: the operating parameters of each compressor are within the operating parameter range corresponding to the compressor, and when the pressure ratio of each compressor is equal to the preset pressure ratio, the total flow of the compressor unit obtained according to the operating parameter combination and the total flow calculation model of the compressor unit is greater than or equal to the preset flow threshold, the operating parameter combination includes multiple operating parameters, and the operating parameters and the compressors have a one-to-one correspondence; the total power calculation model of the compressor unit is determined as the fitness function of the genetic algorithm; the total power calculation model is used to calculate the total power of the compressor unit according to the operating parameter combination; the genetic algorithm is used to iteratively optimize the operating parameter combination of the compressor unit according to the constraints and the fitness function to obtain a target operating parameter combination for the compressor unit to operate according to the target operating parameter combination. Based on the aforementioned constraints and fitness function, the genetic algorithm can be used to quickly and accurately determine the optimal operating parameter combination that meets the constraints.

[0031] The following further illustrates the method for determining the operating parameters of the compressor unit of the present application with reference to the accompanying drawings and specific embodiments.

[0032] Figure 1 1 is a flowchart of a method for determining operating parameters of a compressor unit provided in an embodiment of the present application. In this embodiment, the compressor unit includes a plurality of compressors connected in parallel. Figure 1 As shown, the method may include:

[0033] Step 101, obtaining the constraints of the genetic algorithm, the constraints including: the operating parameters of each compressor are within the operating parameter range corresponding to the compressor, and, when the pressure ratio of each compressor is equal to the preset pressure ratio, the total flow of the compressor group obtained according to the operating parameter combination and the total flow calculation model of the compressor group is greater than or equal to the preset flow threshold.

[0034] Specifically, the operating parameter combination includes multiple operating parameters, and the operating parameters correspond to compressors one by one. For example, the operating parameters may include: compressor speed, compressor inlet guide vane angle, or other adjustable operating parameters that affect compressor power.

[0035] The compressor's operating parameter range consists of the minimum and maximum operating parameter values. Multiple performance curves can be obtained, showing how the pressure ratio changes with flow rate when the compressor operates according to different operating parameters. Each performance curve corresponds to each operating parameter. If the performance curve trend is such that the pressure ratio decreases with increasing flow rate, each performance curve has a minimum flow rate value and a maximum pressure ratio corresponding to that minimum flow rate value.

[0036] The compressor may be a centrifugal compressor, an axial flow compressor, or other compressors. Further, when the compressor is a centrifugal compressor, the performance curve is a centrifugal compressor performance curve; when the compressor is an axial flow compressor, the performance curve is an axial flow compressor performance curve.

[0037] Furthermore, the performance curve may be a performance curve provided by the compressor manufacturer when the compressor leaves the factory, or a performance curve obtained after a performance test of the compressor.

[0038] A performance curve is obtained, where the pressure ratio is equal to the preset pressure ratio at the minimum flow rate. The operating parameter corresponding to this performance curve is determined as the minimum value of the operating parameter in the operating parameter range. The maximum operating parameter that can ensure the safe operation of the compressor is determined as the maximum value of the operating parameter in the operating parameter range.

[0039] For example, the total flow calculation model of the compressor unit is as follows:

[0040]

[0041] Among them, q tot is the total flow of the compressor unit, q i is the flow rate of the i-th compressor, g i (X i ,q i ) is the flow calculation function of the i-th compressor, X i is the operating parameter of the i-th compressor, ε i is the pressure ratio of the i-th compressor, and n is the total number of compressors in the compressor group.

[0042] According to the total flow calculation model, the total flow of the compressor group is equal to the sum of the flow rates of each compressor. The compressor flow rate is a quantity related to operating parameters and the pressure ratio. Therefore, when the pressure ratio is fixed (for example, the pressure ratio is equal to the preset pressure ratio), the compressor flow rate is a quantity related to the operating parameters. Therefore, when performing iterative optimization using the genetic algorithm, the total flow rate of the compressor group can be obtained by substituting the operating parameters during the iterative process into the total flow calculation model.

[0043] Step 102: Determine the total power calculation model of the compressor group as the fitness function of the genetic algorithm.

[0044] Specifically, the total power calculation model is used to calculate the total power of the compressor unit according to a combination of operating parameters.

[0045] For example, the total power calculation model of the compressor group is as follows:

[0046]

[0047] Among them, P tot is the total power of the compressor unit, p i is the power of the i-th compressor, h i (X i ,q i ) is the power calculation function of the i-th compressor, X i is the operating parameter of the i-th compressor, q i is the flow rate of the i-th compressor, and n is the total number of compressors in the compressor group.

[0048] According to the total power calculation model, the total power of a compressor unit is equal to the sum of the power of each compressor in the unit. Compressor power is a quantity related to operating parameters and flow rate, and flow rate is a quantity related to the compressor pressure ratio and operating parameters. When performing iterative optimization using a genetic algorithm, when the compressor pressure ratio equals the preset pressure ratio, the compressor power can be calculated based on the compressor operating parameters, flow rate, and the power calculation function of the compressor power.

[0049] Step 103 : Using a genetic algorithm, according to the constraints and the fitness function, iteratively optimize the operating parameter combination of the compressor unit to obtain a target operating parameter combination, so that the compressor unit can operate according to the target operating parameter combination.

[0050] In each iteration of optimization, individuals inherited from the individuals obtained in the previous iteration are selected and adjusted to obtain new individuals for this iteration. One individual corresponds to one operating parameter combination.

[0051] The constraints of the genetic algorithm include: the operating parameters of each compressor are within the corresponding operating parameter range of the compressor. Therefore, when adjusting the individuals, it is necessary to ensure that the operating parameters of each compressor in the new individuals are within the corresponding operating parameter range of the compressor.

[0052] The genetic algorithm's constraints also include the following: when the pressure ratio of each compressor is equal to a preset pressure ratio, the total flow rate of the compressor group, obtained based on the operating parameter combination and the compressor group's total flow rate calculation model, is greater than or equal to a preset flow rate threshold. Therefore, during each iteration, after obtaining a new individual, it is necessary to ensure that the pressure ratio of the compressors in the compressor group is equal to the preset pressure ratio, and that when the compressor group operates according to the operating parameter combination corresponding to the new individual, the total flow rate of the compressor group, obtained based on this operating parameter combination and the compressor group's total flow rate calculation model, is greater than or equal to the preset flow rate threshold.

[0053] If, within the operating parameter range, after adjusting the individuals selected from the individuals in the previous iteration, the total flow rate corresponding to the new individuals obtained is less than the preset flow rate threshold, individuals with high fitness (i.e., low total power) and corresponding total flow rates greater than or equal to the preset flow rate threshold can be selected from the individuals obtained from the previous iteration, and the newly selected individuals are used to replace the new individuals with total flow rates less than the preset flow rate. In this way, it is ensured that all new individuals can meet the following constraint: when the pressure ratio of each compressor is equal to the preset pressure ratio, the total flow rate of the compressor group obtained according to the operating parameter combination and the total flow rate calculation model of the compressor group is greater than or equal to the preset flow rate threshold.

[0054] The preset pressure ratio and preset flow rate thresholds are the pressure ratio and flow rate values ​​that the user needs the compressor to provide. For example, the user needs a pressure ratio of 3 and a total flow rate of 4.5m 3 / s. In each iteration, it is necessary to ensure that for each new individual in the iteration, when the pressure ratio of each compressor is set to 3, the total flow rate of each compressor in the compressor group is greater than or equal to 4.5m 3 / s.

[0055] Furthermore, multiple selection operations are performed on the individuals obtained from the previous iterative optimization. During each selection operation, the probability of an individual being selected is inversely proportional to the value of the individual's fitness function. In this embodiment, the fitness function is a total power calculation model for the compressor unit. The fitness function value is the total power of the compressor unit, and the smaller the fitness function value, the higher the individual's fitness.

[0056] Correspondingly, during each selection operation, the probability of an individual being selected is inversely proportional to the total power of the compressor unit when the compressor unit operates according to the operating parameter combination corresponding to the individual. In other words, the smaller the total power corresponding to the individual, the higher the probability of the individual being selected.

[0057] The target operating parameter combination obtained is used to operate the compressor unit according to the target operating parameter combination. For example, the compressor unit includes compressor 1 and compressor 2, and the operating parameter is speed. The target operating parameter combination includes speed n1 of compressor 1 and speed n2 of compressor 2. After determining the target operating parameter combination, compressor 1 operates at speed n1 and compressor 2 operates at speed n2 to ensure that the total flow rate of the compressor unit meets user requirements, the pressure ratio of each compressor meets user requirements, and the speed of each compressor is within the corresponding speed range of the compressor, so that the total power of the compressor unit is minimized.

[0058] In this embodiment, the genetic algorithm's constraints are that the operating parameters of each compressor are within the corresponding operating parameter range, and that, when the pressure ratio of each compressor is equal to a preset pressure ratio, the total flow rate of the compressor group, obtained based on the operating parameter combination and the total flow calculation model of the compressor group, is greater than or equal to a preset flow threshold. The total power calculation model of the compressor group is determined as the fitness function of the genetic algorithm. Using the genetic algorithm, the operating parameter combination of the compressor group is iteratively optimized based on the aforementioned constraints and fitness function to obtain a target operating parameter combination, so that the compressor group can operate according to the target operating parameter combination. Using the genetic algorithm with the aforementioned constraints and fitness function, the optimal operating parameters (i.e., the target operating parameter combination) with the minimum total power can be quickly and accurately obtained, based on the operating parameters of each compressor in the compressor group being within the corresponding operating parameter range, the compressor pressure ratio meeting the user's pressure ratio requirement for the compressor (i.e., being equal to the preset pressure ratio), and the total flow rate of the compressor group meeting the user's required flow rate (i.e., the total flow rate being greater than or equal to the preset flow threshold).

[0059] Figure 2 This is a flowchart of another method for determining the operating parameters of a compressor unit provided in an embodiment of the present application, with reference to Figure 2 , the method may include the following steps:

[0060] Step 201, obtaining the constraints of the genetic algorithm, the constraints including: the operating parameters of each compressor are within the operating parameter range corresponding to the compressor, and, when the pressure ratio of each compressor is equal to the preset pressure ratio, the total flow of the compressor group obtained according to the operating parameter combination and the total flow calculation model of the compressor group is greater than or equal to the preset flow threshold.

[0061] The operating parameter combination includes multiple operating parameters, and the operating parameters correspond to the compressors one by one.

[0062] The method of this step has been described in the aforementioned step 101 and will not be repeated here.

[0063] Step 202: Determine the total power calculation model of the compressor group as the fitness function of the genetic algorithm.

[0064] The total power calculation model is used to calculate the total power of the compressor group according to the combination of operating parameters. The method of this step has been described in the above step 102 and will not be repeated here.

[0065] Step 203: During each iteration, multiple selection operations are performed on the multiple first operating parameter combinations obtained in the previous iteration to obtain multiple target first operating parameter combinations.

[0066] The first operating parameter combination includes a plurality of first operating parameters corresponding one-to-one to the compressors.

[0067] In each selection operation, the probability of the first operating parameter combination being selected is negatively correlated with the first total power of the compressor group corresponding to the first operating parameter combination.

[0068] The total power calculation model of the compressor unit is determined as the fitness function of the genetic algorithm, and the total power is the fitness function value of the genetic algorithm. The smaller the total power, the higher the fitness of the first operating parameter combination, and the higher the quality of the first operating parameter combination.

[0069] Step 204 : For each target first operating parameter combination, adjust the first operating parameter in the target first operating parameter combination according to the constraint conditions of the genetic algorithm to obtain a second operating parameter combination.

[0070] The second operating parameters in the second operating parameter combination are all within the operating parameter range of the compressor corresponding to the second operating parameters.

[0071] Each second operating parameter combination has a corresponding second total power. Specifically, based on the second operating parameter of each compressor in the second operating parameter combination, the compressor power of each compressor is obtained, and the compressor powers of the compressors in the compressor group are summed to obtain the total power of the compressor group.

[0072] For example, step 204 may include the following sub-steps:

[0073] Sub-step 2041 , combining target first operating parameter combinations in pairs to obtain a plurality of parameter combination pairs.

[0074] The data structure of the target first operating parameter combination is a string structure. For example, multiple first operating parameters are concatenated and encoded to obtain the target first operating parameter of the string structure.

[0075] The target first operating parameter combination may be randomly assigned to obtain a plurality of parameter combination pairs.

[0076] Sub-step 2042: within the parameter combination interval, swap the characters of the first preset data bits of the two target first operating parameter combinations in the parameter combination pair to obtain two third operating parameter combinations.

[0077] The first preset data bit may be one or more, and the second preset data bit may be randomly selected.

[0078] Specifically, in the parameter combination pair, the characters of the first target first operating parameter combination in the first preset data and the characters of the second target first operating parameter combination in the first preset data are obtained, and these two characters are exchanged to obtain two third operating parameter combinations.

[0079] For example, the target first operating parameter combination is binary string structure data, one of the target first operating parameter combinations is 0000100100001001, and the other target first operating parameter combination is 0000101000011011. The first preset data is the third data bit arranged from left to right. In "0000100000011001", the character of the seventh data bit is "0", and in "0000101000011011", the character of the seventh data bit is "1". By exchanging these two characters, the two third operating parameter combinations obtained are "0000101100001001" and "0000100100001011".

[0080] For another example, determine the crossover bit in two string structure data, determine the two data bits before and after the crossover bit as the first preset data bits, and for each string structure data, exchange the characters of the two first preset data bits to obtain two third operating parameter combinations.

[0081] Sub-step 2043: within the parameter combination interval, changing the character of the second preset data bit of the third operating parameter combination to obtain a fourth operating parameter combination, and determining the fourth operating parameter combination as the second operating parameter combination.

[0082] The second preset data bit may be one or more, and the second preset data bit may be randomly selected.

[0083] For example, if the target first operating parameter is a string structure data in binary format, the character of the second preset data bit can be inverted to obtain the fourth operating parameter combination. Specifically, the inversion process is to change the character "0" to "1" and the character "1" to "0".

[0084] For example, the third operating parameter is "0000100100001011", and the second preset data bit is the sixth data bit arranged from left to right. Then the character of the second preset data bit is inverted, and the fourth operating parameter combination obtained is "00001101000011011".

[0085] For example, the parameter combination intervals in sub-steps 2042 and 2043 can be obtained according to the following sub-steps:

[0086] Sub-step A1: acquiring a first upper limit value and a first lower limit value of the first operating parameter according to an operating parameter range of the compressor corresponding to the first operating parameter.

[0087] The first operating parameter combination includes multiple first operating parameters, and the first operating parameters correspond one-to-one to the compressors in the compressor group. The method for obtaining the operating parameter interval of the compressor corresponding to the first operating parameter can refer to the description of the operating parameter interval in the above step 101 and is not repeated here.

[0088] Sub-step A2: performing concatenation and encoding processing on the first lower limit values ​​of all first operating parameters to obtain the second lower limit value of the target first operating parameter combination.

[0089] For example, a compressor group includes multiple compressors, each having a unique number. The compressor numbers are sorted to obtain a sorting result. According to the sorting result, first lower limits of first operating parameters corresponding to the compressors are concatenated and encoded to obtain a second lower limit of a target first operating parameter combination.

[0090] For example, the compressor includes compressor 1 and compressor 2, and the operating parameter is the speed. The lower limit value of the speed of compressor 1 is 15, and the lower limit value of the speed of compressor 2 is also 15. These two lower limit values ​​of the speed are spliced ​​and converted into binary format, and the second lower limit value of the target first operating parameter combination is "0000111100001111".

[0091] Sub-step A3: performing concatenation and encoding processing on the first upper limit values ​​of all first operating parameters to obtain the second upper limit value of the target first operating parameter combination.

[0092] For example, the sorting result obtained in step A2 is obtained, and according to the sorting result, the upper limit values ​​of the first operating parameters corresponding to the compressors are concatenated and encoded to obtain the second upper limit value of the target first operating parameter combination.

[0093] For example, if the upper limit value of the speed of compressor 1 is 100 and the upper limit value of the speed of compressor 2 is 110, then these two upper limit values ​​of the speed are concatenated and converted into binary format to obtain the second upper limit value of the target first operating parameter combination as "011001000110 1110".

[0094] Sub-step A4: constructing a parameter combination interval according to the second lower limit value and the second upper limit value.

[0095] The upper limit of the parameter combination interval is the second upper limit, and the lower limit of the parameter combination interval is the second lower limit. For example, if the second lower limit is "0000111100001111" and the second upper limit is "0110 01000110 1110", then the parameter combination interval is [0000111100001111, 0110 010001101110].

[0096] Step 205: Obtain the compressor power corresponding to the second operating parameter.

[0097] The compressor power function of the compressor is related to the operating parameters and the pressure ratio. When the pressure ratio is equal to the preset pressure ratio, the preset pressure ratio and the second operating parameter are substituted into the power function g. i (X i ,ε i ), the compressor power corresponding to the second operating parameter can be obtained.

[0098] For example, step 205 may include the following sub-steps:

[0099] Sub-step 2051: obtaining a first performance curve of the compressor when it operates according to the second operating parameters.

[0100] The first performance curve is a curve showing how the pressure ratio changes with flow rate when the compressor operates according to the second operating parameter. For example, the first performance curve is a cubic spline curve.

[0101] For the same compressor, different second operating parameters correspond to different first performance curves. For example, Figure 3 Shown is the flow-pressure ratio performance curve of compressor 1, Figure 4 The flow-pressure ratio performance curve of compressor 2 is shown. Figure 3 In the equation, q1 represents the flow rate of compressor 1, and ε1 represents the pressure ratio of compressor 1; Figure 4 In , q2 represents the flow rate of compressor 2, and ε2 represents the pressure ratio of compressor 2. Figure 3 In the figure, the gray solid line is the first performance curve of compressor 1; Figure 4 In FIG, the gray solid line is the first performance curve of compressor 2.

[0102] Sub-step 2052: obtaining a target flow rate corresponding to a preset pressure ratio according to the first performance curve.

[0103] For example, the second operating parameter is the speed, refer to Figure 3 , the speed of the first compressor is n k The preset pressure ratio ε1 is equal to 3, according to the speed n k The first performance curve shows that the target flow rate of compressor 1 corresponding to the preset pressure ratio ε1 is 2.9m 3 / s. Figure 4 , the speed of the second compressor is n s The target flow rate of compressor 2 corresponding to the preset pressure ratio ε1 is 1.9m 3 / s.

[0104] Sub-step 2053: obtaining a second performance curve of the compressor when it operates according to the second operating parameters.

[0105] The second performance curve is a curve showing power variation with flow rate when the compressor operates according to the second operating parameter. For example, the second performance curve is a cubic spline curve.

[0106] For the same compressor, different second operating parameters correspond to different second performance curves. For example, Figure 5 Shown is the flow-power performance curve of compressor 1, Figure 6 The flow-power performance curve of compressor 2 is shown. The operating parameter is the speed n. Figure 5 In the equation, q1 represents the flow rate of compressor 1, and P1 represents the power of compressor 1; Figure 4 In , q2 represents the flow rate of compressor 2, and P2 represents the power of compressor 2. Figure 5 In the figure, the gray solid line is the second performance curve of compressor 1; Figure 6 In FIG, the gray solid line is the second performance curve of compressor 2.

[0107] Sub-step 2054: acquiring a target power corresponding to the target flow rate according to the second performance curve, and determining the target power as the compressor power corresponding to the second operating parameter.

[0108] For example, the target flow rate of compressor 1 obtained in step 2052 is 2.9m 3 / s, combined Figure 5 , the speed is equal to n k When, according to and n k The corresponding second performance curve and target flow rate can be used to obtain the compressor power of the compressor 1 corresponding to the second operating performance parameter, which is 1.8KW. Figure 6 , the speed is equal to n s When, according to and n s According to the corresponding second performance curve and target flow rate, it can be obtained that the compressor power of the compressor 2 corresponding to the second operating performance parameter is 1.28KW.

[0109] Step 206 : summing up the compressor powers corresponding to all the second operating parameters in the second operating parameter combination to obtain a second total power corresponding to the second operating parameter combination.

[0110] For example, in Figure 5 and Figure 6 In the embodiment shown, the compressor power of compressor 1 is 1.8KW, and the compressor power of compressor 2 is 1.28KW, then the second operating parameter combination (n k , n s )’s second total power is 3.08KW.

[0111] Step 207: Obtain the minimum second total power from multiple second total powers, and when the minimum second total power is less than or equal to a preset power threshold, or the number of iterations reaches a preset number threshold, determine the second operating parameter combination corresponding to the minimum second total power as the target operating parameter combination.

[0112] The preset power threshold can be set according to user needs. For example, when the energy consumption requirement for the compressor unit is relatively high, the preset power threshold can be set smaller. Conversely, the preset power threshold can be set larger.

[0113] The preset number threshold can be set according to user needs. For example, if the time requirement for determining the operating parameters of the compressor unit is high, the preset number can be set smaller. Otherwise, the preset number can be set larger.

[0114] In this embodiment, constraints for the genetic algorithm are obtained. These constraints include: the operating parameters of each compressor are within the corresponding operating parameter range, and when the pressure ratio of each compressor is equal to a preset pressure ratio, the total flow rate of the compressor group, obtained based on the operating parameter combination and the total flow rate calculation model of the compressor group, is greater than or equal to a preset flow rate threshold. In other words, the constraints include the operating parameter range constraint and the flow rate distribution constraint for each compressor in the compressor group.

[0115] The genetic algorithm's constraints are that the operating parameters of each compressor are within the corresponding operating parameter range, and that, when the pressure ratio of each compressor is equal to a preset pressure ratio, the total flow rate of the compressor group, obtained based on the operating parameter combination and the total flow rate calculation model of the compressor group, is greater than or equal to a preset flow rate threshold. The total power calculation model of the compressor group is used as the fitness function of the genetic algorithm. During each iteration, multiple selection operations are performed on the multiple first operating parameter combinations obtained in the previous iteration to obtain multiple target first operating parameter combinations. In each selection operation, the probability of a first operating parameter combination being selected is negatively correlated with the first total power of the compressor group corresponding to the first operating parameter combination. This is equivalent to using the total power of the compressor group as the fitness function value and optimizing the fitness corresponding to this fitness function value. Individuals (operating parameter combinations) in the iterative process are derived from individuals obtained in the previous iteration. Therefore, the operating parameter combinations in different iterations are related to each other, and the processing results of the previous iteration provide guidance for the acquisition of the operating parameter combinations of the current iteration. In other words, the operating parameter combinations of the current iteration can be optimized based on the processing results of the previous iteration. Compared with the processing method in the related art in which each operating parameter combination adjustment scheme is unrelated, this embodiment can improve the processing efficiency of determining the optimal operating parameter combination.

[0116] In each iteration, for each target first operating parameter combination, the first operating parameter in the target first operating parameter combination is adjusted according to the constraints of the genetic algorithm to obtain a second operating parameter combination. In this way, the resulting operating parameter combination can satisfy the operating parameter range constraints and the flow distribution constraints for each compressor during the iteration of the genetic algorithm.

[0117] Obtain the compressor power corresponding to the second operating parameter, sum up the compressor powers corresponding to all the second operating parameters in the second operating parameter combination, obtain the second total power corresponding to the second operating parameter combination, obtain the minimum second total power from the multiple second total powers, and when the minimum second total power is less than or equal to the preset power threshold, or the number of iterations reaches the preset number threshold, determine the second operating parameter combination corresponding to the minimum second total power as the target operating parameter combination. When the individual (i.e., the second operating parameter combination) with the best fitness (i.e., the minimum second total power) meets the requirements, or the number of iterations reaches the requirements, stop the iteration, and determine the second operating parameter combination corresponding to the minimum second total power as the target operating parameter combination. The target operating combination is to ensure that the target operating parameter combination meets the operating parameter interval constraint and the corresponding compressor flow meets the flow distribution constraint, and the total power of the compressor unit is minimized. The compressor unit operates based on the target operating parameters, and can ensure minimum energy consumption and achieve energy saving on the basis of meeting the flow requirements and pressure ratio requirements.

[0118] In summary, this embodiment optimizes and iterates the operating parameter combination based on the genetic algorithm by setting the constraint conditions and fitness function, and can quickly and accurately obtain the optimal operating parameters (i.e., the target operating parameter combination) with the minimum total power.

[0119] For example, in sub-step 2051, obtaining a first performance curve when the compressor operates according to the second operating parameter may include the following sub-steps:

[0120] Sub-step F1: obtaining a plurality of third performance curves of the compressor.

[0121] Specifically, the third performance curve corresponds to the preset operating parameters one-to-one. The third performance curve is a curve showing how the pressure ratio changes with flow rate when the compressor operates according to the corresponding preset operating parameters. For the same compressor, different preset operating parameters may correspond to different third performance curves.

[0122] for example, Figure 3 The third performance curve of the compressor 1 is shown, wherein there are five third performance curves, and each third performance curve has a corresponding operating parameter (for example, the rotation speed).

[0123] Sub-step F2: performing interpolation processing on each third performance curve to obtain a plurality of first flow values ​​sorted in order of flow magnitude.

[0124] Each first flow value has a corresponding arrangement sequence number, and the number of first flow values ​​obtained from each first performance curve is equal.

[0125] For example, for each third performance curve, an equal-number encrypted interpolation method can be used to obtain multiple first flow values. For example, after performing equal-number encrypted interpolation on each third performance curve, 10 data points can be obtained for each first performance curve, each data point including a first flow value and a first pressure ratio corresponding to the first flow value.

[0126] For example, refer to Figure 8 , Figure 8 10 data points are shown, and the first flow rates of the data points increase in the order of arrangement numbers 1, 2, ..., 10.

[0127] Sub-step F3, fitting multiple fourth performance curves based on the first flow values ​​with the same arrangement sequence in the multiple third performance curves, the first pressure ratios corresponding to the first flow values, and the preset operating parameters corresponding to each third performance curve.

[0128] Among them, the fourth performance curve and the arrangement sequence number correspond one to one.

[0129] For example, refer to Figure 8 According to the five data points with the arrangement number 1 (the five data points with the smallest first flow value on each third performance curve), the fourth performance curve with the arrangement number 1 is fitted; according to the five data points with the arrangement number 2 (the five data points with the smallest first flow value on each third performance curve), the fourth performance curve with the arrangement number 2 is fitted; and so on, a total of 10 fourth performance curves are fitted, and the arrangement numbers of the fourth performance curves are 1, 2, ..., 10.

[0130] The number of first data points is not limited to 10, and may be more. For example, if the first flow rates are sorted from smallest to largest, and there are 50 first flow rates, then in each first performance curve, the 50 first flow rates are numbered 1, 2, ..., 50.

[0131] For example, if there are five third performance curves, extract the first flow rate value with an arrangement number of 1 and the first pressure ratio corresponding to the first flow rate value from each of the third performance curves corresponding to each preset operating parameter. This yields five data points, each including the first flow rate value and the first pressure ratio corresponding to the first flow rate value. A cubic spline curve is fitted to these five data points to obtain the fourth performance curve with an arrangement number of 1.

[0132] Sub-step F4: interpolating the fourth performance curve to obtain a second flow value when the compressor operates according to the second operating parameter, and a second pressure ratio corresponding to the second flow value.

[0133] For example, interpolation processing is performed on each fourth performance curve to obtain a data point on the second performance curve where the operating parameter is equal to the second operating parameter.

[0134] Reference Figure 8 , the 10 data points are arranged in order from small to large flow rate, and the arrangement results of the 10 data points are obtained, and each data point has a corresponding arrangement sequence number. Further, in the same third performance curve, the arrangement sequence number of the data point increases with the increase of flow rate.

[0135] Get the first data point in each third performance curve, and get five data points. Perform cubic spline fitting based on these five data points to get Figure 8 The fourth performance curve 1 in the third performance curve is obtained. The second data point in each third performance curve is obtained, and five data points are obtained by performing a cubic spline fit based on these five data points. Figure 8 The fourth performance curve 2 in the figure is repeated. The curve fitting steps are repeated to obtain Figure 8 The 10 fourth performance curves are shown in FIG.

[0136] When performing iterative optimization of operating parameters according to the genetic algorithm, if the operating parameter of a compressor is the operating parameter X k For each fourth performance curve, obtain the data point with the minimum flow rate in the fourth performance curve and the data point with the maximum flow rate in the fourth performance curve. Interpolate the two data points on the fourth performance curve to obtain the value corresponding to the operating parameter X. k According to the corresponding data points of the plurality of fourth curves and the operating parameter X k The corresponding data points are fitted to obtain the first performance curve in the form of cubic spline.

[0137] For example, in the fourth performance curve 1, the minimum flow rate is q min , the maximum flow is q max . Minimum flow q min The corresponding operating parameter is X a , the maximum flow is q max The corresponding operating parameter is X e Obtain and run parameter X according to the following method k The corresponding flow rate q k :

[0138]

[0139] According to the flow rate qk And the fourth performance curve 1, the operating parameter X can be obtained k The data points on the fourth performance curve 1. And so on, we can get the operating parameters X k Data points on the 10th performance curve.

[0140] Sub-step F5: fitting a first performance curve of the compressor when it operates according to the second operating parameters based on the multiple second flow values ​​and the second pressure ratio corresponding to each second flow value.

[0141] The second flow rate value and the second pressure ratio corresponding to the second flow rate value constitute a data point on the second performance curve. Fitting multiple data points on the second performance curve can generate a first performance curve corresponding to the second operating parameter. Based on the first performance curve and the preset pressure ratio, the second flow rate value when the compressor operates according to the second operating parameter can be obtained.

[0142] Reference Figure 8 After obtaining 10 data points according to the method in step F4, a cubic spline fitting is performed on these 10 data points to obtain k The corresponding performance curve is the first performance curve in this embodiment.

[0143] For example, in sub-step 2053, obtaining a second performance curve when the compressor operates according to the second operating parameters may include the following steps:

[0144] Sub-step M1: obtaining a plurality of fifth performance curves of the compressor.

[0145] The fifth performance curve corresponds to the preset operating parameters one by one, and the fifth performance curve is a curve showing power variation with flow rate when the compressor operates according to the corresponding preset operating parameters.

[0146] For example, the fifth performance curve is a cubic spline curve. For example, the fifth performance curve can be as follows Figure 6 The black solid line is shown in .

[0147] Sub-step M2: performing interpolation processing on each fifth performance curve to obtain a plurality of third flow values ​​sorted in order of flow magnitude;

[0148] Each third flow rate value has a corresponding arrangement sequence number, and the number of third flow rate values ​​obtained from each fifth performance curve is equal.

[0149] The method of this step can refer to the processing method of the aforementioned step F2, and will not be repeated here.

[0150] Sub-step M3, fitting multiple sixth performance curves according to the third flow values ​​with the same arrangement sequence number in the multiple fifth performance curves, the first powers corresponding to the third flow values, and the preset operating parameters corresponding to the fifth performance curves.

[0151] The sixth performance curve corresponds to the arrangement number in one-to-one correspondence. The sixth performance curve is a cubic spline curve.

[0152] The method of this step can refer to the processing method of the aforementioned step F3 and will not be repeated here.

[0153] Sub-step M4: interpolating the sixth performance curve to obtain a fourth flow value when the compressor operates according to the second operating parameter, and a second power corresponding to the fourth flow value.

[0154] When performing iterative optimization of operating parameters according to the genetic algorithm, if the operating parameter of a compressor is the operating parameter X k For each sixth performance curve, obtain the data point with the minimum flow rate in the sixth performance curve and the data point with the maximum flow rate in the sixth performance curve. Interpolate the two data points on the sixth performance curve to obtain the value corresponding to the operating parameter X. k According to the corresponding data points of the plurality of sixth curves and the operating parameter X k The corresponding data points are fitted to obtain the second performance curve in the form of cubic spline.

[0155] For example, there are 10 sixth performance curves fitted, and the arrangement numbers are 1, 2, ..., 10. In the sixth performance curve 1 (for example, the sixth performance curve can be Figure 6 In the sixth performance curve on the left, the minimum flow rate is q min , the maximum flow is q max . Minimum flow q min The corresponding operating parameter is X a , the maximum flow is q max The corresponding operating parameter is X e Obtain and run parameter X according to the following method k The corresponding flow rate q k :

[0156]

[0157] According to the flow rate q k And the sixth performance curve 1, the operating parameter X can be obtained k The data points on the sixth performance curve 1. Similarly, the operating parameters X are obtained respectively. k Data points on the 10th performance curve.

[0158] The method of this step can refer to the processing method of the aforementioned step F4, and will not be repeated here.

[0159] Sub-step M5: fitting a second performance curve of the compressor when it operates according to the second operating parameters based on the multiple fourth flow values ​​and the second power corresponding to each fourth flow value.

[0160] After obtaining 10 data points according to the method of step M4, a cubic spline fitting is performed on these 10 data points to obtain the value of X k The corresponding performance curve is the second performance curve in this embodiment.

[0161] For example, step 201 may include the following sub-steps:

[0162] Sub-step 2011: obtaining a target fourth performance curve with the smallest arrangement number from the plurality of fourth performance curves.

[0163] The target fourth performance curve with the smallest arrangement number is a fourth performance curve fitted according to the minimum first flow value and the first pressure ratio corresponding to the minimum first flow value in each third performance curve.

[0164] For example, in Figure 3 In the example, the target fourth performance curve with the smallest arrangement number is the fourth performance curve on the far left.

[0165] Sub-step 2012: acquiring a target operating parameter value corresponding to a preset pressure ratio according to the target second performance curve, and determining the target operating parameter value as a minimum operating parameter value.

[0166] For example, the operating parameter is the speed. Figure 3 , the preset pressure ratio is 3, the speed n corresponding to the first performance curve where the intersection of the target fourth performance curve and the preset pressure ratio is located 1，min , determined as the target operating parameter value corresponding to the preset pressure ratio.

[0167] Sub-step 2013: constructing an operating parameter range of the compressor based on the minimum operating parameter and the maximum operating parameter that meets the safe operating requirements of the compressor.

[0168] The maximum operating parameter value that meets the safe operating requirements of the compressor can be obtained based on the operating parameter data provided by the manufacturer when the compressor leaves the factory.

[0169] For example, in Figure 3 In the embodiment shown, the minimum operating parameter is n 1，min , the maximum operating parameter is n 1，max , the operating parameter range of compressor 1 is [n 1，min , n 1，max ].

[0170] For example, in Figure 4 In the embodiment shown, the minimum operating parameter is n 2，min , the maximum operating parameter is n 2，max , the operating parameter range of compressor 1 is [n 2，min , n 2，max ].

[0171] Figure 7 This is a flowchart of another method for determining the operating parameters of a compressor unit provided in an embodiment of the present application, with reference to Figure 7 , the method may include the following steps:

[0172] Step S1 , interpolating the performance curves of the compressors in the compressor group to obtain a first performance curve of the compressor pressure ratio varying with flow rate and a second performance curve of the compressor power varying with flow rate when the compressors operate according to different operating parameters.

[0173] For example, for each compressor, a fifth performance curve is obtained when the compressor operates according to preset performance parameters. The fifth performance curve is a cubic spline curve. The fifth performance curve data is encrypted, interpolated, and then fitted to obtain a second performance curve in the form of a cubic spline curve.

[0174] For example, the compressor performance curve data before interpolation includes m performance curves, where the jth curve contains r j After performing equal number of encrypted interpolation on the data points on the m performance curves, each of the m performance curves contains s data points, where s>r j .

[0175] There are m performance curves corresponding to m adjustable parameters, where the adjustable parameter corresponding to the jth curve is X j Interpolate the data points of the same order on the m performance curves to obtain the adjustable parameter X k Corresponding performance curve.

[0176] For example, the compressor group consists of n compressor groups connected in parallel, and the performance curve data corresponding to each compressor is interpolated.

[0177] For example, the compressor unit includes two compressors connected in parallel. According to the performance curve data of the compressors in the unit, the data in the performance curve are interpolated twice in combination with the cubic spline curve.

[0178] The performance curves include a first performance curve showing that the pressure ratio of each of the two compressors varies with flow rate, and a second curve showing that the power varies with flow rate.

[0179] By interpolating the data points corresponding to the same order on multiple performance curves, a performance curve corresponding to any speed between the maximum speed and the minimum speed can be obtained.

[0180] The following is an example of the method of this step, taking the third performance curve of the pressure ratio changing with flow rate as an example. Figure 8 Before interpolation, there are 5 third performance curve data, and the 5 third performance curves correspond to the operating parameters X a 、X b 、X c 、X d 、X e After performing encrypted interpolation on the data points on the five third performance curves, each of the five performance curves contains 10 data points. It should be noted that in actual applications, the number of data points obtained by encrypted interpolation is not limited to 10, and can be 50 or more.

[0181] Reference Figure 8 , the 10 data points are arranged in order from small to large flow rate, and the arrangement results of the 10 data points are obtained, and each data point has a corresponding arrangement sequence number. Further, in the same third performance curve, the arrangement sequence number of the data point increases with the increase of flow rate.

[0182] Get the first data point in each third performance curve, and get five data points. Perform cubic spline fitting based on these five data points to get Figure 8 The fourth performance curve 1 in the third performance curve is obtained. The second data point in each third performance curve is obtained, and five data points are obtained by performing a cubic spline fit based on these five data points. Figure 8 The fourth performance curve 2 in the figure is repeated. The curve fitting steps are repeated to obtain Figure 8 The 10 fourth performance curves are shown in FIG.

[0183] When performing iterative optimization of operating parameters according to the genetic algorithm, if the operating parameter of a compressor is the operating parameter X k For each fourth performance curve, obtain the data point with the minimum flow rate in the fourth performance curve and the data point with the maximum flow rate in the fourth performance curve. Interpolate the two data points on the fourth performance curve to obtain the value corresponding to the operating parameter X. k According to the corresponding data points of the plurality of fourth curves and the operating parameter X k The corresponding data points are fitted to obtain the first performance curve in the form of cubic spline.

[0184] For example, in the fourth performance curve 1, the minimum flow rate is q min , the maximum flow is q max . Minimum flow q min The corresponding operating parameter is X a, the maximum flow is q max The corresponding operating parameter is X e Obtain and run parameter X according to the following method k The corresponding flow rate q k :

[0185]

[0186] According to the flow rate q k And the fourth performance curve 1, the operating parameter X can be obtained k Data points on the fourth performance curve 1.

[0187] And so on, we can get the operating parameters X k The data points on the 5 fourth performance curves are fitted with cubic spline to obtain the value of X k The corresponding performance curve is the first performance curve in this embodiment.

[0188] The method for obtaining the second performance curve according to the fifth performance curve and the sixth performance curve can refer to the method for obtaining the first performance curve according to the third performance curve and the fourth performance curve, and will not be repeated here.

[0189] Step S2: establishing a calculation model for the parallel operation of the compressor units.

[0190] For example, the calculation model for parallel operation of compressor units is established as follows:

[0191]

[0192] Among them, q tot1 is the total flow of multiple compressor units required by the user, q i is the flow rate of unit i, p tot is the total power of multiple compressor units, p i is the power of the i-th unit, ε tot The total pressure ratio of multiple compressor units required by users, X i is the adjustable operating parameter of each machine, ε i is the pressure ratio of the i-th unit, n is the number of compressor units, and both i and n are integers greater than 1.

[0193] For example, if the compressor unit includes two compressors connected in parallel, the calculation model for the parallel operation of the compressor unit is:

[0194]

[0195] Among them, q tot1 is the total flow of multiple compressor units required by the user, p tot is the total power of multiple compressor units, ε totis the total pressure ratio of the multi-compressor unit required by the user, q1 is the flow rate of compressor 1, P1 is the power of compressor 1, n1 is the speed of compressor 1, ε1 is the pressure ratio of compressor 1, q2 is the flow rate of compressor 2, P2 is the power of compressor 2, n2 is the speed of compressor 2, and ε2 is the pressure ratio of compressor 2.

[0196] In step S3, the total power calculation model of the compressor group is determined as the fitness function of the genetic algorithm, the adjustable operating parameters of the compressor are determined as the optimizable parameters of the genetic algorithm, the operating parameter range of the adjustable operating parameters of the compressor, and the flow distribution of each compressor in the compressor group are determined as the constraints of the genetic algorithm.

[0197] Specifically, the fitness function f is:

[0198]

[0199] For example, the compressor group consists of two compressors connected in parallel, and the fitness function is:

[0200] f=P1+P2

[0201] The constraints of the genetic algorithm include the flow distribution and operating parameter range of each compressor in the compressor group, specifically:

[0202]

[0203] where h i (X i ,q i ) is the power calculation function of the i-th compressor, g i (X i ,ε i ) is the flow constraint function of the i-th compressor, X i,min With X i,max are the lower limit and upper limit of the operating parameters corresponding to the i-th compressor respectively.

[0204] For example, if the compressor unit consists of two compressors connected in parallel, the constraints of the genetic algorithm are:

[0205]

[0206] Where g1(n1,ε1) is the flow constraint function of compressor 1, n 1,min With n 1,max is the lower and upper limit of the speed of compressor 1. g2(n2,ε2) is the flow constraint function of compressor 2, n 2,min With n 2,max They are the lower limit and upper limit of the rotation speed of the compressor 2 respectively.

[0207] Reference Figure 3 and Figure 4 The compressor unit consists of two parallel compressors: Figure 3 The corresponding compressor 1, and Figure 4 The pressure ratio of the corresponding compressor 2, the compressor group, is equal to the pressure ratio of each compressor, that is, the pressure ratio of the compressor group, compressor 1 and compressor 2 is 3.

[0208] According to the method of step S1, a first performance curve corresponding to any speed between the maximum speed and the minimum speed can be obtained. When the pressure ratio (for example, a preset pressure ratio) and the speed are both known, the flow rate of the compressor corresponding to the preset pressure ratio can be obtained based on the first performance cancellation of the compressor.

[0209] According to the constraints of the genetic algorithm, the sum of the flow rates of the two compressors must be greater than the total flow rate of the compressor units required (for example, 4.5m 3 / s).

[0210] The preset pressure ratio is equal to 3, and the pressure ratio decreases with the increase of flow rate. Then, on the first performance curve corresponding to the minimum speed, the pressure ratio of the maximum pressure ratio point is equal to 3.

[0211] Step S4, using a genetic algorithm to perform optimization calculation on the fitness function to obtain an operating parameter combination corresponding to the optimal fitness; during the optimization calculation process, the optimizable parameters are adjusted according to the constraints, and the total power of the compressor unit that is negatively correlated with the fitness is obtained according to the first performance curve and the second performance curve.

[0212] For example, the optimizable parameters (i.e., the operating parameters of the compressor) are encoded and an initial population is randomly generated. The initial population includes multiple individuals, each of which corresponds to an encoded operating parameter combination (i.e., the target first operating parameter combination in the aforementioned embodiment). The operating parameter combination is selected, crossover, and mutated. Individuals that do not meet the flow constraint conditions are removed from the multiple individuals. Then, individuals that meet the flow constraint conditions and have high fitness (corresponding to low total power of the compressor unit) are reselected from the previous generation population to ensure the number and quality of the population. The above iterative evolution process is repeated to finally obtain the optimizable parameters under the optimal fitness function.

[0213] Reference Figure 9 , step S4 may include the following sub-steps:

[0214] Sub-step S41, randomly generating a certain amount of initial string structure data, one initial string structure data corresponds to one individual, and multiple individuals constitute an initial population; the initial string structure data is a string structure representation of the operating parameter combination.

[0215] The solution data of the solution space is encoded to obtain the genotype string structure data in binary format in the genetic space. The solution data is the data corresponding to the operating parameter combination.

[0216] The solution data of the solution space is described as the genotype string structure data of the genetic space.

[0217] For example, a binary encoding method is used to convert the adjustable operating parameters of each machine in the multi-compressor group into string structure data in binary format.

[0218] The lower limit value of the operating parameter combination is encoded as the minimum value in the binary string structure data, and the upper limit value of the operating parameter combination is encoded as the maximum value in the binary string structure data.

[0219] Each individual corresponds to a combination of adjustable parameters of the compressor. According to step S1 , for each compressor, different operating parameter combinations correspond to different performance curves.

[0220] The set of all individuals is the population in this embodiment. When performing iterative optimization according to the genetic algorithm, the evolution iteration is performed with the initial population as the starting point.

[0221] The number of individuals can be set according to user needs. For example, when the accuracy of the genetic algorithm iteration result is required to be high, the number of individuals can be set to be larger; otherwise, it can be set to be smaller.

[0222] For example, a compressor unit consists of two compressors, and the operating parameter is speed. 50 individuals can be randomly generated, each corresponding to a combination of the two compressor speeds, and then evolutionary iterations can begin with the initial population as the starting point.

[0223] Sub-step S42: performing fitness evaluation on each individual in the population.

[0224] For example, the individual fitness function value is calculated based on the fitness function f. A smaller fitness function value indicates a higher individual fitness. In this embodiment, the fitness function f is a compressor unit total power model. The fitness function value is the total compressor unit power obtained based on the operating parameter combination and the compressor unit total power model. A smaller total power indicates a smaller fitness value corresponding to the operating parameter combination, and a higher fitness corresponding to the operating parameter combination.

[0225] Sub-step S43, judging whether the iteration termination rule is satisfied, if so, proceeding to step S44, otherwise proceeding to step S49.

[0226] The termination criterion may be: the fitness of the best individual in the population reaches the optimal state, or the number of iterations reaches a preset threshold.

[0227] Furthermore, if the fitness of the best individual in the current iterative population has reached the optimal situation, the optimization calculation is terminated. If the fitness of the best individual in the population has not yet reached the optimal situation, step S44 is entered to continue the selection, crossover and mutation calculation steps to obtain the next generation population.

[0228] Sub-step S44, performing a selection operation on the individuals obtained in the previous iteration according to the fitness corresponding to each individual obtained in the previous iteration; the fitness is negatively correlated with the total power of the compressor corresponding to the individual.

[0229] Specifically, from the population obtained in the previous iteration, superior individuals are selected with a certain probability to be passed down to the next generation. For example, a roulette wheel method can be used for genetic individual selection. Specifically, in each selection operation, the probability of an individual being selected is determined based on the proportion of the individual's fitness function value to the total fitness function value of the population.

[0230] For example, if the previous generation population includes 50 individuals, then 50 selection operations can be performed on these 50 individuals, selecting one individual each time. In each selection operation, the probability of an individual being selected is negatively correlated with the proportion of the individual's fitness function value to the total fitness function value of the population. It should be noted that among the 50 individuals selected based on this method, there may be duplicate individuals. For example, if the fitness function value of an individual (labeled as individual G) is relatively small, then its probability of being selected is relatively high, and individual G may be selected in multiple selection operations. Therefore, the 50 individuals finally selected include multiple individuals G.

[0231] Sub-step S45: performing a crossover operation on the selected individuals.

[0232] Specifically, multiple individuals are selected to form a population, and individuals are randomly selected from the population to be paired with each other. Whether to crossover is selected with a certain probability, and the data bit of the intersection point in the binary string structure data is randomly determined, and the data bits before and after the intersection point of the two individuals are exchanged.

[0233] Sub-step S46, performing mutation operation on the individuals after the crossover operation to obtain new individuals.

[0234] For each individual in the population, the value of a gene in that individual is changed with a certain probability. For example, in this embodiment, the individuals are represented in binary using binary encoding, so one gene corresponds to one data bit in the individual. Individuals can be mutated by negating their gene values. For example, if the gene value is 0, negating it yields a value of 1; for another example, if the gene value is 1, negating it yields a value of 0.

[0235] Sub-step S47: determine whether the new individual meets the constraints of the genetic algorithm. If yes, proceed to step S48; otherwise, return to step S42.

[0236] The constraints include the flow distribution constraints of each compressor in the compressor group and the adjustable range constraints of the operating parameters.

[0237] The flow distribution constraint means that the sum of the flow rates of each compressor in the compressor group is greater than the total target flow rate required by the user.

[0238] The adjustable operating parameter range constraint means that the adjustable range of each compressor's operating parameter within the compressor unit must not exceed the operating parameter interval formed by its upper and lower limits. During the iterative genetic algorithm process, the lower limit of the adjustable operating parameter is encoded as the minimum value in the binary string structure data, and the upper limit of the adjustable operating parameter is encoded as the maximum value in the binary string structure data, ensuring that all subsequent generations meet this constraint.

[0239] If the termination criteria are not met, the above steps of fitness evaluation, selection, crossover, mutation, and constraint determination are repeated. When the termination criteria are met, the optimal individual in the latest population is output, and the algorithm ends. The output results are the adjustable parameters of each unit in the multi-unit group.

[0240] Sub-step S48: replace the new individuals that do not meet the genetic algorithm constraints with the high-fitness individuals that meet the genetic algorithm constraints among the multiple individuals in the previous iteration, and then return to step S42.

[0241] Specifically, after the selection, crossover, and mutation operations, it is determined whether the population meets the flow distribution constraints, and high-fitness individuals that meet the constraints are selected from the previous generation population to replace the individuals that do not meet the constraints, and a new generation population is formed with other individuals that meet the constraints for the next round of fitness evaluation.

[0242] Step S49: output the optimal individual with the highest fitness.

[0243] For example, the compressor unit includes two compressors, and the operating parameter is the speed. Then the optimal individual output includes the speeds of the two compressors.

[0244] The fitness function is a calculation model for the total compressor power. The fitness function value is the total compressor power. The lower the fitness function value, the higher the fitness. Therefore, the optimal individual output in this step is the operating parameter combination that minimizes the total compressor power.

[0245] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0246] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for determining operating parameters of a compressor unit, characterized in that: The compressor unit includes a plurality of compressors connected in parallel; the method includes: Obtaining constraints for the genetic algorithm, the constraints including: an operating parameter of each of the compressors being within an operating parameter range corresponding to the compressor, and a total flow rate of the compressor group obtained according to an operating parameter combination and a total flow rate calculation model of the compressor group being greater than or equal to a preset flow rate threshold when a pressure ratio of each of the compressors is equal to a preset pressure ratio, the operating parameter combination including a plurality of operating parameters having a one-to-one correspondence with the compressors; Determining a total power calculation model of the compressor group as the fitness function of the genetic algorithm; the total power calculation model is used to calculate the total power of the compressor group according to the operating parameter combination; During each iteration, multiple selection operations are performed on multiple first operating parameter combinations obtained in the previous iteration to obtain multiple target first operating parameter combinations; the first operating parameter combinations include multiple first operating parameters corresponding one-to-one to the compressors; in each selection operation, the probability of the first operating parameter combination being selected is negatively correlated with the first total power of the compressor group corresponding to the first operating parameter combination; for each target first operating parameter combination, the first operating parameter in the target first operating parameter combination is adjusted according to the constraints of the genetic algorithm to obtain a second operating parameter combination; the second operating parameters in the second operating parameter combination are all within the operating parameter range of the compressor corresponding to the second operating parameter, and each second operating parameter combination has a corresponding second total power; Obtaining a minimum second total power from the plurality of second total powers, and, when the minimum second total power is less than or equal to a preset power threshold, or the number of iterations reaches a preset number threshold, determining the second operating parameter combination corresponding to the minimum second total power as a target operating parameter combination, so that the compressor unit operates according to the target operating parameter combination; The total power calculation model of the compressor unit is: in, is the total power of the compressor unit, is the power of the i-th compressor, is the power calculation function of the i-th compressor, is the operating parameter of the i-th compressor, is the flow rate of the i-th compressor, and n is the total number of compressors in the compressor group.

2. The method according to claim 1, characterized in that The data structure of the target first operating parameter combination is a string structure; the adjusting the first operating parameter in the target first operating parameter combination to obtain the second operating parameter combination includes: Combining the target first operating parameter combinations in pairs to obtain a plurality of parameter combination pairs; the data structure of the target first operating parameter combinations is a string structure; In the parameter combination interval, swapping characters of first preset data bits of two target first operating parameter combinations in the parameter combination pair to obtain two third operating parameter combinations; Within the parameter combination interval, the character of the second preset data bit of the third operating parameter combination is changed to obtain a fourth operating parameter combination, and the fourth operating parameter combination is determined as the second operating parameter combination.

3. The method according to claim 2, characterized in that Also includes: Obtaining a first upper limit value and a first lower limit value of the first operating parameter according to an operating parameter range of the compressor corresponding to the first operating parameter; performing concatenation and encoding processing on the first lower limit values ​​of all the first operating parameters to obtain a second lower limit value of the target first operating parameter combination; performing concatenation and encoding processing on the first upper limit values ​​of all the first operating parameters to obtain a second upper limit value of the target first operating parameter combination; The parameter combination interval is constructed according to the second lower limit value and the second upper limit value.

4. The method according to claim 1, wherein After adjusting the first operating parameter in the target first operating parameter combination to obtain the second operating parameter combination, the method further includes: obtaining a compressor power corresponding to the second operating parameter; The compressor powers corresponding to all the second operating parameters in the second operating parameter combination are summed to obtain the second total power corresponding to the second operating parameter combination.

5. The method according to claim 4, characterized in that The obtaining of the compressor power corresponding to the second operating parameter includes: Obtaining a first performance curve when the compressor operates according to the second operating parameters; the first performance curve is a curve of pressure ratio versus flow rate when the compressor operates according to the second operating parameters; According to the first performance curve, obtaining a target flow rate corresponding to a preset pressure ratio; Obtaining a second performance curve when the compressor operates according to the second operating parameters; the second performance curve is a curve of power variation with flow rate when the compressor operates according to the second operating parameters; According to the second performance curve, a target power corresponding to the target flow rate is obtained, and the target power is determined as the compressor power corresponding to the second operating parameter.

6. The method according to claim 5, characterized in that The obtaining of a first performance curve of the compressor when the compressor operates according to the second operating parameter includes: Acquire multiple third performance curves of the compressor; the third performance curves correspond one-to-one to preset operating parameters, and the third performance curves are curves showing changes in pressure ratio with flow rate when the compressor operates according to the corresponding preset operating parameters; For each of the third performance curves, interpolation processing is performed on the third performance curve to obtain a plurality of first flow values ​​sorted in order of flow magnitude; each of the first flow values ​​has a corresponding arrangement sequence number, and the number of first flow values ​​obtained from each of the third performance curves is equal; Fitting a plurality of fourth performance curves based on first flow values ​​with the same arrangement number in the plurality of third performance curves, first pressure ratios corresponding to the first flow values, and preset operating parameters corresponding to each of the third performance curves; wherein the fourth performance curves correspond to the arrangement number in a one-to-one manner; performing interpolation processing on the fourth performance curve to obtain a second flow value when the compressor operates according to the second operating parameter, and a second pressure ratio corresponding to the second flow value; A first performance curve of the compressor when operating according to the second operating parameters is fitted based on a plurality of second flow values ​​and a second pressure ratio corresponding to each of the second flow values.

7. The method according to claim 6, characterized in that The constraint conditions of the genetic algorithm are obtained, including: Obtaining a target fourth performance curve with the smallest arrangement number from the plurality of fourth performance curves; the target fourth performance curve with the smallest arrangement number is a second performance curve fitted based on the minimum first flow value and the first pressure ratio corresponding to the minimum first flow value in each of the third performance curves; acquiring a target operating parameter value corresponding to a preset pressure ratio according to the target fourth performance curve, and determining the target operating parameter value as a minimum operating parameter value; An operating parameter range of the compressor is constructed according to the minimum operating parameter and the maximum operating parameter that meets the safe operation requirement of the compressor.

8. The method according to claim 5, characterized in that The obtaining of a second performance curve of the compressor when the compressor operates according to the second operating parameter includes: Acquire multiple fifth performance curves of the compressor; the fifth performance curves correspond one-to-one to preset operating parameters, and the fifth performance curves are curves showing power variation with flow rate when the compressor operates according to the corresponding preset operating parameters; For each of the fifth performance curves, interpolation processing is performed on the fifth performance curve to obtain a plurality of third flow values ​​sorted in order of flow magnitude; each of the third flow values ​​has a corresponding arrangement sequence number, and the number of third flow values ​​obtained from each of the fifth performance curves is equal; Fitting a plurality of sixth performance curves based on the third flow values ​​with the same arrangement number in the plurality of fifth performance curves, the first powers corresponding to the third flow values, and the preset operating parameters corresponding to the fifth performance curves; the sixth performance curves and the arrangement numbers have a one-to-one correspondence; performing interpolation processing on the sixth performance curve to obtain a fourth flow value when the compressor operates according to the second operating parameter, and a second power corresponding to the fourth flow value; A second performance curve of the compressor when operating according to the second operating parameters is fitted based on a plurality of fourth flow values ​​and a second power corresponding to each of the fourth flow values.

9. The method according to claim 1, characterized in that The total flow calculation model of the compressor group is: in, is the total flow of the compressor unit, is the flow rate of the i-th compressor, is the flow calculation function of the i-th compressor, is the pressure ratio of the i-th compressor.

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