Variable working condition screw unit convenient for performance prediction

Abstract

The invention relates to the field of compressors, in particular to a variable-working-condition screw unit facilitating performance prediction. The screw unit comprises an air inlet buffer tank, an air filter, an air inlet valve, a screw compressor, an oil-gas separator, a first minimum pressure valve, an air cooler, an oil-water separator, a steam-water separator, an exhaust buffer tank and a second minimum pressure valve which are sequentially connected in the air flowing direction. In the oil way conveying direction, an oil outlet of the oil-gas separator, the temperature control valve, the oil filter and an oil inlet of the screw compressor are sequentially connected to form a closed circulating oil way, and an oil cooler for forced convection heat exchange of lubricating oil is further installed on the downstream portion of the temperature control valve. According to the method, performance prediction of the variable-working-condition screw unit is realized.

Description

[0001] This application is a divisional application of a Chinese patent application with the application number 202111655526.X, the application date of December 31, 2021, and the invention title of "A Performance Prediction Method, Electronic Device and Storage Medium for a Variable Condition Screw Unit". Technical Field

[0002] The present invention relates to the field of compressors, and specifically to a variable condition screw unit facilitating performance prediction. Background Art

[0003] Screw air compressor units are one of the most common mechanical devices in industries such as electric power, chemical fiber, and rail transit. With the continuous change of their application scope and scenarios, air compressor units often exhibit different working characteristics. Taking the subway in rail transit as an example, due to the short distance between stations, the braking system, air suspension device, door control, etc. of the train work frequently, the gas consumption increases. When the pressure in the air storage tank at the rear end of the air compressor unit is lower than 750 kpa, the air compressor unit starts to supplement air to the storage tank. When the pressure in the air storage tank reaches 900 kpa, the air compressor unit stops operating. This makes the air compressor unit work with variable pressure ratio and frequent start-stop characteristics. In addition, the train operates in different regions (plain, plateau) and in different seasons, and the intake temperature and pressure of the screw unit also vary within a wide range, making the screw unit always in an unsteady working mode. The unsteady working conditions present completely different performances from the steady-state operating conditions. The mismatch between the design of the screw air compressor and the actual application conditions will bring a series of faults such as lubricating oil emulsification, carbonization, high exhaust temperature, insufficient exhaust volume, and even cause ice blockage of the heat exchanger. These faults will pose a serious threat to the safe operation of rail transit. Therefore, to ensure that the screw air compressor can work stably under corresponding working conditions, an effective performance prediction method for the screw air compressor unit must be proposed according to the working characteristics of the air compressor unit when designing the screw unit. Then, based on the prediction of the performance of the air compressor unit, the optimization and improvement of the air compressor unit can be completed.

[0004] Currently, the methods for predicting the performance of screw machines in the industry are generally theoretical calculations based on the mathematical model of the steady-state operation of the unit, and the independent variables are relatively single (such as only changing the intake temperature or intake pressure, etc.). This prediction method does not consider the coupling of intake temperature, pressure, variable exhaust pressure, and frequent start-stop conditions together, and the calculation results have a large error and cannot be applied in engineering. And it is very complex to couple the intake temperature, intake pressure, exhaust pressure, and frequent start-stop of the compressor together. It is very difficult to predict the performance of the air compressor using the above method, so it needs to be solved urgently. Summary of the Invention

[0005] In order to avoid and overcome the technical problems existing in the prior art, the present invention provides a variable-condition screw unit that facilitates performance prediction. The present invention realizes the performance prediction of the variable-condition screw unit.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A variable-condition screw unit that facilitates performance prediction, the screw unit includes an intake buffer tank, an air filter, an intake valve, a screw compressor, an oil-gas separator, a first minimum pressure valve, an air cooler, an oil-water separator, a steam-water separator, an exhaust buffer tank, and a second minimum pressure valve connected in sequence along the gas flow direction; along the oil pipeline conveying direction, the oil outlet of the oil-gas separator, a temperature control valve, an oil filter, and the oil inlet of the screw compressor are connected in sequence to form a closed-loop oil circuit, and an oil cooler for forced convection heat exchange of the lubricating oil is also installed downstream of the temperature control valve;

[0008] When performing performance prediction on the screw unit, determine the operating condition variables that affect the performance of the screw unit, assuming a total of m, denoted as x i (i = 1, 2, 3,..., m), where the mth operating condition variable x m is defined as the unit operation rate, and its expression is:

[0009]

[0010] t1 represents the total startup duration of the screw unit within one startup and shutdown cycle, and t2 represents the total shutdown duration of the screw unit within one startup and shutdown cycle, that is, the startup time interval, with the unit of s;

[0011] When any operating condition variable x i and x j (i ≠ j) have no interaction and the operating condition variable and the response value are in a linear relationship, the response value can be obtained by the following formula:

[0012]

[0013] where is the response value, x i is the value of the x i operating condition variable during the test, and b i is the coefficient to be determined;

[0014] When any operating condition variable x i and x j (i ≠ j) have an interaction and the operating condition variable and the response value are in a quadratic relationship, the response value can be obtained by the following formula:

[0015]

[0016] where is the response value, xi For the value of the xth i operating condition variable during the test, b i and b ii are the coefficients to be determined. Since the cross-term x i x j is considered, that is, any two are selected from the m operating condition variables for combination;

[0017] When it is uncertain whether there is an interaction between the operating condition variables, the interaction between x i and x j (i≠j) is used to determine the number of tests. The coefficients of the equation total ;

[0018] According to the range [a, b] of the operating condition variable x i , the level variable x ik (k = 1, 2, 3,..., n) is determined;

[0019]

[0020] where x ik represents the value taken by the operating condition variable x i at the kth test, and n is the evenly divided number of tests;

[0021] Before shutdown, when the data collected by the screw compressor unit tends to be stable, continuous sampling is started, and the most stable set of data is taken as the final value of this test; the test data of the discrete points obtained from the test are subjected to regression analysis, and the mathematical relationships between the temperature, pressure, and flow parameters of each point of the screw air compressor unit and the operating condition variables are fitted, so as to obtain the expression of all position parameters of the screw compressor unit under all operating conditions, and complete the performance prediction of the screw compressor unit under different operating conditions.

[0022] As a further solution of the present invention: a coarse adjustment valve and a fine adjustment valve are provided at the air inlet end of the intake buffer tank to achieve fine adjustment of the intake pressure; a second minimum pressure valve is provided at the air outlet end of the exhaust buffer tank to achieve fine adjustment of the exhaust pressure; the motor provides power for the screw compressor; the screw compressor is arranged in a high and low temperature environmental chamber to achieve precise adjustment of the intake temperature; the operation rate is adjusted through the plc outside the high and low temperature environmental chamber.

[0023] As a still further solution of the present invention: flow sensors are provided at the upstream end of the oil cooler, the downstream end of the oil filter, and the downstream end of the steam-water separator; a temperature sensor is provided at the air inlet of the intake buffer tank, a pressure sensor is provided inside the intake buffer tank, a temperature sensor is provided at the downstream end of the screw compressor, and a temperature sensor and a pressure sensor are provided at the oil inlet of the screw compressor.

[0024] As a further solution of the present invention: a pressure sensor is provided inside the oil-gas separator, temperature sensors and pressure sensors are provided at the downstream ends of the first minimum pressure valve and the air compressor, a pressure sensor is provided at the downstream end of the oil filter, and temperature sensors and pressure sensors are provided at the upstream and downstream ends of the oil cooler.

[0025] As a further solution of the present invention: the coefficients to be determined should be selected such that the sum of squared deviations is minimized, that is

[0026]

[0027] To minimize the sum of squared deviations, according to the principle of minimum value in mathematical analysis:

[0028]

[0029] Simplifying the above formula gives the following normal equations:

[0030] l 11 b1 + l 12 b2 +... + l 1m b m = l 1y

[0031] l 21 b1 + l 22 b2 +... + l 2m b m = l 2y

[0032] …

[0033] l m1 b1 + l m2 b2 +... + l mm b m = l my

[0034]

[0035] The relevant parameters in the formula are calculated as follows:

[0036]

[0037] In the formula, x ik represents the value taken by the operating condition variable x i at the k-th test, and y k represents the test result of the response value y at the k-th test.

[0038] As a further solution of the present invention: the calculated coefficients b to be determined i are substituted into the equation, and the regression equation is obtained. The significance test of the entire regression equation is carried out by using the F-test;

[0039] The total variation sum of squares can be decomposed into the sum of the regression sum of squares U and the residual sum of squares Q:

[0040] L yy = U + Q

[0041] Total variation sum of squares:

[0042]

[0043] Regression sum of squares:

[0044]

[0045] Residual standard deviation:

[0046] S 2 = Q / (n - m - 1)

[0047] Perform a test on the total regression equation:

[0048]

[0049] In order to characterize the linear influence of each operating condition variable in all operating condition variables x1, x2,..., x m on y in total, conduct a partial regression sum of squares and its significance test;

[0050] Partial regression sum of squares P i :

[0051]

[0052] where C ii is the i-th element on the diagonal of the inverse matrix of the coefficient matrix (l ij ) m×m of the regression normal equation;

[0053] The larger the partial regression sum of squares P i , the more obvious the influence of x i on y. Conduct an F i value test on each operating condition variable x i :

[0054]

[0055] Any operating condition variable with a small partial regression sum of squares P i and an insignificant F i test should be removed from the regression equation;

[0056] After removing x i , the relationship between the coefficients of the new regression equation and the original regression coefficient b i is:

[0057]

[0058] Where C ij is the coefficient matrix of the regression normal equation (l ij ) m×m The inverse matrix C=(C ij ) is the element in the i-th row and j-th column.

[0059] As a further solution of the present invention: during sampling, let the sampling frequency be f, continuously sample 3t points, divide the 3t points into three groups of data, denoted as d1, d2, and d3, and denote d i =[x i1 , x i2 , x i3 ... x it , i = 1, 2, 3, and process each group of data respectively in the following way:

[0060]

[0061] If where i = 1 or 2 or 3, then it is considered that the d i -th group of data is relatively stable, and take the weighted average value of the d i -th group of data as the final value of this test.

[0062] As a further solution of the present invention: according to the determination of the number of tests, select the uniform experimental design table and then arrange the test according to the corresponding usage table.

[0063] As a further solution of the present invention: the total number of tests n should satisfy n>z + 1.

[0064] Compared with the prior art, the beneficial effects of the present invention are:

[0065] 1. The present invention analyzes the operating condition variables and parameters that affect the performance of the screw compressor unit, creatively couples multiple operating condition variables that affect the performance of the air compressor unit, selects the optimal uniform test and combines with sensors to complete the acquisition of each operating condition variable and response data, and collects relatively stable data, ensuring the accuracy of the data; through the fitting process of the test data, the mathematical relationships between the temperature, pressure, flow rate and other parameters of each measuring point of the screw air compressor unit and the operating condition variables are obtained, so as to accurately complete the performance prediction of the screw compressor unit under different operating conditions.

[0066] 2. During the prediction process of the present invention, the significance test of the entire regression equation was carried out through the F-test to determine the significance level of the regression equation for prediction. At the same time, the partial regression sum of squares and its significance test were carried out to determine the influence degree of each factor on the response value. The establishment of the screw compressor unit provided strong support for the subsequent pre-test. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 is a schematic structural diagram of the present invention.

[0068] In the figure:

[0069] 1. Inlet buffer tank; 101. Coarse adjustment valve; 102. Fine adjustment valve;

[0070] 2. Air filter; 3. Inlet valve; 4. Screw compressor; 401. Motor;

[0071] 5. Oil-gas separator; 6. First minimum pressure valve; 7. Air cooler; 8. Oil-water separator;

[0072] 9. Steam-water separator; 10. Exhaust buffer tank; 11. Second minimum pressure valve;

[0073] 12. Temperature control valve; 13. Oil filter; 14. Oil cooler. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0074] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0075] Please refer to Figure 1 , in the embodiment of the present invention, a variable-condition screw compressor unit for facilitating performance prediction includes the following steps when performing performance prediction:

[0076] S1. Build a screw compressor unit and install temperature sensors, pressure sensors, and flow sensors at predetermined positions within the screw compressor unit.

[0077] The screw unit consists of an intake buffer tank 1, an air filter 2, an intake valve 3, a screw compressor 4, an oil-gas separator 5, a first minimum pressure valve 6, an air cooler 7, an oil-water separator 8, a steam-water separator 9, an exhaust buffer tank 10, and a second minimum pressure valve 11, which are connected in sequence along the gas flow direction. The intake end of the intake buffer tank is provided with a coarse adjustment valve 101 and a fine adjustment valve 102 in parallel. Through the coarse adjustment and fine adjustment of the two valves, the intake pressure of the intake buffer tank is accurately controlled; the outlet end of the exhaust buffer tank is provided with a second minimum pressure valve to achieve fine adjustment of the exhaust pressure.

[0078] Along the oil delivery direction, the oil outlet of the oil-gas separator 5, a temperature control valve 12, an oil filter 13, and the oil inlet of the screw compressor 4 are connected in sequence to form a circulating oil circuit. The operation of the screw compressor 4 is powered by a motor 401 or other driving components. The screw compressor 4 is arranged in a high and low temperature environmental chamber to achieve accurate adjustment of the intake temperature; the operation rate is adjusted through PLC settings outside the high and low temperature environmental chamber.

[0079] An oil cooler 14 is installed downstream of the temperature control valve 12 to conduct forced convection heat transfer on the lubricating oil. There are bolt holes on the valve cover of the temperature control valve 12, and adjustable hexagon socket head cap screws are installed on the bolt holes. A spring that abuts against the valve core is also arranged in the valve cavity of the temperature control valve 12. When adjusting the stroke of the valve core of the temperature control valve, the screw moves towards the inside of the valve cavity, thereby pushing the valve core to move. The movement of the valve core causes the cut-off element to reduce the orifice connecting the oil filter and the valve cavity, increasing the resistance of the lubricating oil in the valve cavity to enter the oil filter, and thereby forcing the lubricating oil to enter the oil cooler with relatively less resistance for forced convection heat transfer.

[0080] Each sensor in the screw unit is set at the corresponding position. Among them, flow sensors are arranged at the upstream end of the oil cooler 14, the downstream end of the oil filter 13, and the downstream end of the steam-water separator 9.

[0081] A temperature sensor is arranged at the intake port of the intake buffer tank 1, a pressure sensor is arranged inside the intake buffer tank 1, a temperature sensor is arranged at the downstream end of the screw compressor 4, and temperature sensors and pressure sensors are arranged at the oil inlet of the screw compressor 4.

[0082] A pressure sensor is arranged inside the oil-gas separator 5, temperature sensors and pressure sensors are arranged at the downstream ends of the first minimum pressure valve 6 and the air compressor 7, a pressure sensor is arranged at the downstream end of the oil filter 13, and temperature sensors and pressure sensors are arranged at the upstream and downstream ends of the oil filter 14.

[0083] The flow rate of the screw unit of the present invention is 1 Nm 3 / min, and the rated exhaust pressure is 0.9 MPa. Its flow rate is collected through an LUGB type vortex flowmeter and an NI 9234 board.

[0084] Combined with the working characteristics of the screw compressor unit, the working condition variables affecting the screw compressor unit are determined to be the intake pressure x1, the intake temperature x2, the discharge pressure x3, and the operation rate x4.

[0085] S2. Determine the working condition variables (factors) affecting the performance of the screw compressor unit. Assume there are a total of m, denoted as x i (i = 1, 2, 3,..., m), where the mth working condition variable x m is defined as the operation rate of the unit, and its expression is:

[0086]

[0087] t1 represents the total startup duration of the screw compressor unit within one start-stop cycle, and t2 represents the total shutdown duration (startup time interval) of the screw compressor unit within one start-stop cycle, with the unit of s;

[0088] S3. When there is no interaction between any factors x i and x j (i ≠ j) and the working condition variables and the response value are linearly related, the response value can be obtained from the following formula:

[0089]

[0090] where is the response value, x i is the value of the x i working condition variable during the test, and b i is the coefficient to be determined;

[0091] When there is an interaction between any factors x i and x j (i ≠ j) and the working condition variables and the response value are quadratic-related, the response value can be obtained from the following formula:

[0092]

[0093] where is the response value, x i is the value of the x i working condition variable during the test, b i and b ii are the coefficients to be determined. Since the cross-term x i x j is considered, so (that is, any two are selected from m factors for combination);

[0094] When it is not certain whether there is an interaction between factors, taking the interaction between x i and x j (i ≠ j) to determine the number of tests, the coefficients of the equation total piece

[0095] For the operating conditions parameters, there are intake pressure x1, intake temperature x2, exhaust pressure x3 and operation rate x4. If it is possible to estimate the equation coefficients, the total number of tests n should satisfy n > 14 + 1 = 15. Therefore, the number of tests n is taken as 16.

[0096] In step S3, the equation coefficients should be selected to minimize the sum of squared deviations, that is

[0097]

[0098] To minimize the sum of squared deviations, according to the minimum value principle in mathematical analysis:

[0099]

[0100] Simplifying the above formula gives the following normal equations:

[0101] l 11 b1 + l 12 b2 +... + l 1m b m = l 1y

[0102] l 21 b1 + l 22 b2 +... + l 2m b m = l 2y

[0103] …

[0104] l m1 b1 + l m2 b2 +... + l mm b m = l my

[0105]

[0106] The relevant parameters in the formula are calculated as follows:

[0107]

[0108] In the formula, x ik represents the value taken by factor x i at the k-th test, and y k represents the test result of the response value y at the k-th test.

[0109] The calculated equation coefficient b i is substituted into the equation in step S3, and then the regression equation is obtained. The significance test of the entire regression equation is carried out using the F-test.

[0110] The total variable sum of squares can be decomposed into the regression sum of squares U and the residual sum of squares Q:

[0111] L yy = U + Q

[0112] Total variable sum of squares:

[0113]

[0114] Regression sum of squares:

[0115]

[0116] Residual standard deviation:

[0117] S 2 = Q / (n - m - 1)

[0118] Conduct a test on the total regression equation:

[0119]

[0120] Record the above calculation results in the following analysis of variance table 1

[0121]

[0122] If F ≥ F 0.01 (m, n - m - 1), it is considered that the regression is highly significant, denoted as **;

[0123] If F 0.05 (m, n - m - 1) ≤ F ≤ F 0.01 (m, n - m - 1), it is considered that the regression is significant, denoted as *;

[0124] If F 0.1 (m, n - m - 1) ≤ F ≤ F 0.05 (m, n - m - 1), it is considered that the regression significance is general, denoted as ⊙;

[0125] If F < F 0.1 (m, n - m - 1), it is considered that the regression is not significant, denoted as △.

[0126] To characterize the linear influence of each factor in all operating condition variables (factors) x1, x2,..., x m on y in total, conduct the partial regression sum of squares and its significance test.

[0127] Partial regression sum of squares P i :

[0128]

[0129] where Cii is the i-th element on the diagonal of the inverse matrix of the regression normal equation coefficient matrix (l ij ); m×m The larger the partial regression sum of squares P

[0130] is, the more obvious the influence of x i on y. Perform an F i value test for each factor x i : i Value test:

[0131]

[0132] Any factor with a small partial regression sum of squares P i and an insignificant F i test should be removed from the regression equation;

[0133] After removing x i , the relationship between the new regression equation coefficients and the original regression coefficients b i is:

[0134]

[0135] where C ij is the element in the i-th row and j-th column of the inverse matrix C = (C ij ) of the regression normal equation coefficient matrix (l m×m ). ij In the i-th row and j-th column of the inverse matrix C = (C

[0136] According to the determination of the number of test times in step S3, select the uniform experimental design table Then, arrange the experiments according to the corresponding usage table .

[0137] In this embodiment, select the uniform experimental design table Then, arrange the experiments according to the corresponding usage table .

[0138] The following is the uniform experimental design table 2:

[0139]

[0140]

[0141] The following is the usage table 3 of the uniform design table :

[0142]

[0143] Note: In the table, s represents the number of factors, and D represents the deviation characterizing the uniformity. The smaller its value, the better the uniformity.

[0144] Since there are 4 operating condition variables in this experiment, according to the usage table, the 1st, 4th, 5th, and 6th columns in the design table should be selected to arrange the experiment.

[0145] S4. According to the range [a, b] of the operating condition variable x i , determine the level variable x ik (k = 1, 2, 3, …, n)

[0146]

[0147] where x ik represents the value taken by the factor x i at the k-th experiment, and n is the number of evenly divided experiments;

[0148] For the operating condition variable intake air temperature x1 with a range of [-25, 50]°C, then x2 = [-25, -20, -15, …, 50]°C; for the operating condition variable intake air pressure x2 with a range of [79, 101] kPa, then x1 = [79, 80.47, 81.94, …, 101]

[0149] kPa;

[0150] For the operating condition variable exhaust pressure x3 with a range of [750, 900] kPa, x3 = [750, 760, 770, …, 900] kPa;

[0151] For the operating condition variable operating rate with a range of [10%, 40%], the shutdown time x4 within one start-stop cycle corresponds to [10% (1620 s), 12% (1320 s), 14% (1105.7), …, 40% (270 s)]. In the operating rate, the start-up time within one start-stop cycle is uniformly set to 180 s; Note: The time in the brackets is the shutdown time t2 within one start-stop cycle corresponding to the operating rate when the start-up time within one start-stop cycle is 180 s.

[0152] S5. Arrange the experiment. When the data collected by the screw compressor unit tends to be stable before shutdown, start continuous sampling, and take the most stable set of data as the final value of this experiment.

[0153] During sampling, set the sampling frequency as f, continuously sample 3t points, evenly divide the 3t points into three groups of data, denoted as d1, d2, and d3, and denote d i = [x i1 , x i2 , x i3 ……x it , i = 1, 2, 3. Process each group of data respectively in the following way:

[0154]

[0155] If where \(i = 1\) or \(2\) or \(3\), then the \(d\)th i group of data is considered relatively stable, and the weighted average value of the \(d\)th i group of data is taken as the final value of this experiment.

[0156] The final results of the data collected under each test condition after processing are shown in Table 4 below:

[0157]

[0158]

[0159] Note: The flow rates in the table have been converted to standard conditions through the ideal gas state equation.

[0160] The sum and average value of each variable are calculated and shown in Table 5 below.

[0161]

[0162] The cross product sum of each variable is shown in Table 6 (the cross product and sum value of the variables):

[0163]

[0164] The coefficients and constant term \(l\) of the normal equation ij and the total sum of squares of \(y\) \(l\) yy =\(l\) 55 are shown in Table 7 below:

[0165]

[0166] Then the expression of the normal matrix equation is

[0167]

[0168] Solving it: \(b1 = -4.6595\), \(b2 = 13.7632\), \(b3 = -0.3634\), \(b4 = -0.0303\)

[0169]

[0170] Substitute the calculated equation coefficients \(b\) i into it, and the regression equation is obtained:

[0171]

[0172] Use the F-test to conduct a significance test on this regression equation.

[0173] The total variation sum of squares can be decomposed into the sum of the regression sum of squares \(U\) and the residual sum of squares \(Q\):

[0174] L yy = U + Q

[0175] Total sum of squared deviations:

[0176]

[0177] Regression sum of squares:

[0178]

[0179] Residual standard deviation:

[0180] S 2 = Q / (n - m - 1)

[0181] Make a test on the total regression equation:

[0182]

[0183] Make a test on the total regression equation, and the calculation results are shown in Table 8 below:

[0184]

[0185] From the parameters in the table, by looking up the F-distribution table, F 0.01 (4, 11) = 5.67, F > F 0.01 (4, 11). The above regression equation is highly significant.

[0186] In order to describe the linear influence of each factor (independent variable) x1, x2,..., x m in the overall factors on y, a partial regression coefficient test is carried out;

[0187] Partial regression sum of squares P i :

[0188]

[0189] where C ii is the i-th element on the diagonal of the inverse matrix of the regression normal equation coefficient matrix (l ij ) m×m ;

[0190] Inverse matrix C of the regression normal equation coefficient matrix:

[0191]

[0192] From this, it is calculated that P1 = 127471.47, P2 = 110048.03, P3 = 3235.96, P4 = 1639.45

[0193] Partial regression sum of squares P iThe larger, the x i has a more obvious impact on y. For each factor x i conduct an F i value test:

[0194]

[0195] F1 = 209.21, F2 = 180.61, F3 = 5.31, F4 = 2.69. Looking up the F-distribution table for F 0.01 (4,11) = 5.67, F 0.05 (4,11) = 3.36, F 0.1 (4,11) = 2.86, F 0.2 (4,11) = 1.8.

[0196] F1 = 209.21 > F 0.01 (4,11) = 5.67, indicating that the intake air temperature has a highly significant impact on the exhaust gas flow (**);

[0197] F2 = 180.61 > F 0.01 (4,11) = 5.67, indicating that the intake air pressure has a highly significant impact on the exhaust gas flow (**);

[0198] F 0.01 (4,11) = 5.67 > F3 = 5.31 > F 0.05 (4,11) = 3.36, indicating that the exhaust gas pressure has a significant impact on the exhaust gas flow (*);

[0199] F 0.1 (4,11) = 2.86 > F4 = 2.69 > F 0.2 (4,11) = 1.8, indicating that the operating rate has a certain impact on the exhaust gas flow (△);

[0200] In this example, since each operating condition variable has an impact on the exhaust gas volume, no operating condition variable is excluded, and the above regression equation is valid.

[0201] Given any intake air pressure x1, intake air temperature x2, exhaust gas pressure x3, and operating rate (start-up time interval) x4, the flow rate of the screw compressor unit under this operating condition can be predicted.

[0202] Repeating the above steps can predict the temperature and pressure at any position within the screw compressor unit.

[0203] Another embodiment of this application is an electronic device.

[0204] The electronic device can be the movable device itself or a stand-alone device independent of it. The stand-alone device can communicate with the movable device to receive the input signals collected from them and send the selected target decision-making behaviors to them.

[0205] The electronic device includes one or more processors and a memory.

[0206] The processor can be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and can control other components in the electronic device to perform desired functions.

[0207] The memory can include one or more computer program products, and the computer program products can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory can include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory can include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions can be stored on the computer-readable storage medium, and the processor can run the program instructions to implement the prediction methods of various embodiments of the present application described above.

[0208] In one example, the electronic device can further include: an input device and an output device, and these components are interconnected through a bus system and / or other forms of connection mechanisms. For example, the input device can include various devices such as on-board diagnostic system (OBD), cameras, industrial cameras, etc. The input device can also include, for example, a keyboard, a mouse, etc. The output device can include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0209] In addition, according to specific application scenarios, the electronic device can further include any other appropriate components.

[0210] Another embodiment of the present application can also be a computer program product, which includes computer program instructions, and when the computer program instructions are run by the processor, the processor is caused to execute the steps in the decision-making behavior decision method according to various embodiments of the present application described in the above prediction method part of this specification.

[0211] The computer program product can be written in any combination of one or more programming languages to write program codes for performing the operations of the embodiments of the present application. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program codes can be executed completely on the user computing device, partially on the user device, executed as an independent software package, partially on the user computing device and partially on a remote computing device, or completely executed on a remote computing device or server.

[0212] In addition, an embodiment of the present application may also be a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are run by a processor, the processor is caused to execute the steps in the decision behavior decision method according to various embodiments of the present application described in the above prediction method part of this specification.

[0213] The computer-readable storage medium may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may, for example, include but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0214] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, benefits, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, benefits, effects, etc. are essential for each embodiment of the present application. In addition, the above-disclosed specific details are only for the purposes of illustration and facilitating understanding, rather than limitations, and the above details do not limit the present application to necessarily adopt the above specific details for implementation.

[0215] The block diagrams of the devices, apparatuses, equipment, and systems involved in the present application are only illustrative examples and do not intend to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open-ended words, meaning "including but not limited to", and can be used interchangeably with each other. The word "or" and "and" used herein refer to the word "and / or", and can be used interchangeably with each other, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to", and can be used interchangeably with each other.

[0216] It should also be noted that in the devices, equipment, and methods of the present application, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the present application.

[0217] The above description of the disclosed aspects enables any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

[0218] The above description has been given for purposes of illustration and description. In addition, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although several example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

[0219] The basic principles of this application have been described above in connection with specific embodiments. However, it should be noted that the advantages, benefits, effects, etc. mentioned in this application are only examples and not limitations, and it cannot be considered that these advantages, benefits, effects, etc. are essential for each embodiment of this application. Additionally, the above-disclosed specific details are only for illustrative and facilitating understanding purposes and are not limitations. These details do not limit this application to necessarily implementing with the above specific details.

[0220] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "comprising," "including," "having," etc. are open-ended terms, meaning "including but not limited to," and can be used interchangeably with each other. The word "or" and "and" used herein refer to the word "and / or" and can be used interchangeably with it, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to" and can be used interchangeably with it.

Claims

1. A variable-condition screw compressor unit facilitating performance prediction, characterized in that The screw compressor unit includes an intake buffer tank (1), an air filter (2), an intake valve (3), a screw compressor (4), an oil-gas separator (5), a first minimum pressure valve (6), an air cooler (7), an oil-water separator (8), a steam-water separator (9), an exhaust buffer tank (10), and a second minimum pressure valve (11) that are connected in sequence along the gas flow direction; along the oil delivery direction, the oil outlet of the oil-gas separator (5), a temperature control valve (12), an oil filter (13), and the oil inlet of the screw compressor (4) are connected in sequence to form a closed-loop oil circuit. An oil cooler (14) for forced convection heat exchange of the lubricating oil is also installed downstream of the temperature control valve (12); When performing performance prediction on a screw compressor unit, the operating condition variables that affect the performance of the screw compressor unit are determined. Suppose there are a total of m, denoted as x i (i = 1, 2, 3,..., m), where the mth operating condition variable x m is defined as the unit operation rate, and its expression is: t1 represents the total startup duration of the screw compressor unit within a startup and shutdown cycle, and t2 represents the total shutdown duration of the screw compressor unit within a startup and shutdown cycle, that is, the startup time interval, with the unit of s; When any operating condition variable x i and x j (i≠j) have no interaction and the operating condition variable and the response value are linearly related, the response value can be obtained by the following formula: wherein is the response value, x i is the value of the x i working condition variable during the test, b i is the coefficient to be determined; When any operating condition variable x i and x j (i≠j) have an interaction, and the operating condition variable and the response value have a quadratic relationship, the response value can be obtained by the following formula: Among them is the response value, x i is the value of the x-th i operating condition variable during the test, b i and b ii are the coefficients to be determined. Since the cross-term x i x j is considered, so that is, any two are selected from the m operating condition variables for combination; When it is uncertain whether there is an interaction between uncertain working condition variables, taking x i and x j (i≠j) having an interaction to determine the number of tests, the coefficients of the equation are a total of ; According to the operating condition variable x i within the range [a, b], determine the horizontal variable x ik (k = 1, 2, 3, …, n); where x ik represents the operating condition variable x i at the k-th test, and n is the number of equally divided tests; Before shutdown, continuous sampling starts when the data collected by the screw compressor unit tends to be stable, and the most stable set of data is taken as the final value of this test; the test data of the discrete points obtained from the test are subjected to regression analysis, and the mathematical relationships between the temperature, pressure, and flow parameters of each point of the screw air compressor unit and the operating condition variables are fitted, so as to obtain the expression of all position parameters under all operating conditions of the screw compressor unit, and the performance prediction of the screw compressor unit under different operating conditions is completed.

2. The variable-condition screw compressor unit facilitating performance prediction according to claim 1, wherein, A coarse adjustment valve (101) and a fine adjustment valve (102) are provided at the intake end of the intake buffer tank (1) to achieve fine adjustment of the intake pressure; a second minimum pressure valve (11) is provided at the outlet end of the exhaust buffer tank (10) to achieve fine adjustment of the exhaust pressure; a motor (401) provides power for the screw compressor (4); the screw compressor (4) is arranged in a high and low temperature environmental chamber to achieve precise adjustment of the intake temperature; the operation rate is adjusted through PLC settings outside the high and low temperature environmental chamber.

3. The variable operating condition screw compressor unit for facilitating performance prediction according to claim 2, characterized in that, Flow sensors are provided at the upstream end of the oil cooler (14), the downstream end of the oil filter (13), and the downstream end of the steam-water separator (9); a temperature sensor is provided at the intake port of the intake buffer tank (1), a pressure sensor is provided inside the intake buffer tank (1), a temperature sensor is provided at the downstream end of the screw compressor (4), and a temperature sensor and a pressure sensor are provided at the oil inlet of the screw compressor (4).

4. A variable-condition screw compressor unit facilitating performance prediction according to claim 2, characterized in that, A pressure sensor is provided inside the oil-gas separator (5), temperature sensors and pressure sensors are provided at the downstream ends of the first minimum pressure valve (6) and the air compressor (7), a pressure sensor is provided at the downstream end of the oil filter (13), and temperature sensors and pressure sensors are provided at the upstream and downstream ends of the oil cooler (14).

5. A variable operating condition screw unit facilitating performance prediction according to any one of claims 1 to 4, characterized in that The selection of the coefficients to be determined should minimize the sum of squared deviations, that is To minimize the sum of squared deviations, according to the principle of minimum value in mathematical analysis: Simplifying the above formula gives the following normal equations: l 11 b1 + l 12 b2 +... + l 1m b m = l 1y l 21 b1 + l 22 b2 +... + l 2m b m = l 2y … l m1 b1 + l m2 b2 +... + l mm b m = l my The relevant parameters in the formula are calculated as follows: where x ik represents the operating condition variable x i at the k-th test, and y k represents the test result of the response value y at the k-th test.

6. The variable operating condition screw unit facilitating performance prediction according to claim 5, characterized in that, The calculated coefficient b to be determined i Substitute it into the equation to obtain the regression equation, and use the F-test to conduct a significance test on the entire regression equation; The total variation sum of squares can be decomposed into the sum of the regression sum of squares U and the residual sum of squares Q: L yy = U + Q Total variation sum of squares: Regression sum of squares: Residual standard deviation: S 2 = Q / (n - m - 1) Perform a test on the total regression equation: In order to characterize the linear influence of each operating condition variable in all operating condition variables \(x_1, x_2, \ldots, x\) m on \(y\) in total, the partial regression sum of squares and its significance test are carried out; Partial regression sum of squares P i : where C ii is the i-th element on the diagonal of the inverse matrix of the regression normal equation coefficient matrix (l ij ); m×m ​ Partial regression sum of squares P i The larger it is, the more obvious the influence of x i on y. For each working condition variable x i conduct an F i value test: Any partial regression sum of squares P i that is small and for which the F i test is not significant should be removed from the regression equation; Eliminate x i After that, the coefficients of the new regression equation and and the original regression coefficient b i The relationship is as follows: where C ij is the coefficient matrix of the regression normal equation (l ij ), m×m and the inverse matrix C = (C ij ) is the element in the i-th row and j-th column.

7. A variable operating condition screw compressor unit facilitating performance prediction according to any one of claims 1 to 4, characterized in that, During sampling, set the sampling frequency as f, continuously sample 3t points, evenly divide the 3t points into three groups of data, denoted as d1, d2, and d3, and denote d i = [x i1 , x i2 , x i3 ……x it , i = 1, 2, 3. Process each group of data respectively in the following manner: If where i = 1 or 2 or 3, then the d-th i group of data is considered relatively stable, and the weighted average of the d-th i group of data is taken as the final value of this experiment.

8. A variable-condition screw compressor unit facilitating performance prediction according to any one of claims 1 to 4, characterized in that, Select the uniform experimental design table according to the determination of the number of trials Then, according to the corresponding usage table, arrange the experiments 9. A variable-condition screw compressor unit facilitating performance prediction according to any one of claims 1 to 4, characterized in that, The total number of tests n should satisfy n > z + 1.

Images