Performance evaluation method, device, equipment, storage medium and program product

Abstract

The present application relates to catalytic reforming technical field, disclose a kind of performance evaluation method, device, equipment, storage medium and program product.The method is applied to real-time optimization system, comprising: determining the correlation between first data and corresponding first evaluation index;First data is the physical factor before system commissioning;First evaluation index is the aromatic yield before commissioning or gross profit per unit of raw material;Collect multiple sets of commissioning test data, and a single set of commissioning test data includes second data and corresponding second evaluation index after system commissioning;Determine the first evaluation index corresponding to the second data according to the correlation, as blank control test index;Determine the performance evaluation result of system according to second evaluation index and blank control test index.The present application can guarantee that the physical factor is same before and after system commissioning, and the change of physical factor is same in the case of physical factor change, to determine the performance evaluation result of real-time optimization system, improve the accuracy of performance evaluation.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of catalytic reforming, and in particular to a performance evaluation method, device, equipment, storage medium and program product. BACKGROUND

[0002] The catalytic reforming device is a refining device for converting naphtha into aromatic hydrocarbons or high-octane gasoline under the conditions of a catalyst and high temperature. The main influencing factor affecting the yield of aromatic hydrocarbons or high-octane gasoline is the aromatic potential content in the raw material. When the aromatic potential fluctuates greatly, real-time optimization technology is needed to timely adjust the production operation conditions.

[0003] Real-time optimization technology is not for a device or system with constant external factors, but for changes in external factors, timely optimization and adjustment of device production operations, so as to obtain better economic and technical indicators. The device or system with constant external factors only needs to be optimized once. Therefore, the external factors change, and real-time optimization is necessary; the external factors do not change, and real-time optimization technology is unnecessary. In order to cope with the frequently changing external factors such as raw materials and market prices, the performance evaluation of real-time optimization technology and real-time optimization system has never been more important.

[0004] The performance evaluation of the real-time optimization system in the conventional technology is usually performed under the condition that the load and market price are constant, and the change of the performance indicators before and after the real-time optimization system is put into use is compared. However, the performance evaluation method of the real-time optimization system has low accuracy.

[0005] Therefore, there is an urgent need for a performance evaluation scheme capable of improving the accuracy of performance evaluation of a real-time optimization system. SUMMARY

[0006] Therefore, the technical problem to be solved by the present application is to overcome the problem of low accuracy of the performance evaluation method of the real-time optimization system in the related art.

[0007] In order to solve the above technical problem, the present application provides a performance evaluation method applied to a real-time optimization system, wherein the real-time optimization system is used for optimizing a catalytic reforming device, the catalytic reforming device is used for converting raw oil into products, and the performance evaluation method comprises the following steps:

[0008] determining the correlation between first data and a corresponding first evaluation index;

[0009] The first data is the mean value of the aromatic potential, the variance of the aromatic potential and the variance of the change rate of the aromatic potential before the real-time optimization system is put into use; and the first evaluation index is the aromatic hydrocarbon yield or the gross profit per unit of raw material before the real-time optimization system is put into use.

[0010] The single evaluation period is collected in multiple groups of commissioning test data, and a single group of commissioning test data includes second data and corresponding second evaluation indexes; the second data is the mean of the aromatic potential, the variance of the aromatic potential and the variance of the aromatic potential change rate after the real-time optimization system is put into use; and the second evaluation index is the aromatic yield or the gross profit per unit of raw material after the real-time optimization system is put into use.

[0011] According to the correlation, the first evaluation index corresponding to the second data in each group of commissioning test data is determined; and the first evaluation index corresponding to the second data in each group of commissioning test data is taken as a blank control test index.

[0012] According to the second evaluation index in each group of commissioning test data and the blank control test index, the performance evaluation result of the real-time optimization system under the condition that the physical property factors are the same is determined; and the physical property factors include the mean of the aromatic potential, the variance of the aromatic potential and the variance of the aromatic potential change rate.

[0013] In an optional implementation, before the correlation between the first data and the corresponding first evaluation index is determined, the method further includes:

[0014] Obtaining historical production data before the real-time optimization system is put into use; the historical production data includes multiple groups of sample data, and a single group of sample data includes production data in a preset time period;

[0015] According to the historical production data, the first data and the corresponding first evaluation result are determined.

[0016] In an optional implementation, the correlation between the first data and the corresponding first evaluation index is determined, including:

[0017] Taking the first data as the independent variable and the corresponding first evaluation index as the dependent variable, a function relationship model between the first data and the corresponding first evaluation index is constructed;

[0018] The correlation between the first data and the corresponding first evaluation index is represented by the function relationship model between the first data and the corresponding first evaluation index.

[0019] In an optional implementation, the function relationship model is a linear relationship function or a nonlinear relationship function.

[0020] In an optional implementation, according to the second evaluation index in each group of commissioning test data and the blank control test index, the performance evaluation result of the real-time optimization system under the condition that the physical property factors are the same is determined, including:

[0021] According to the mean value of the second evaluation index and the mean value of the blank control test index, the performance evaluation result of the real-time optimization system under the condition of the same physical factors is determined.

[0022] In an alternative embodiment, the performance evaluation result of the real-time optimization system under the condition of the same physical factors is determined according to the mean value of the second evaluation index and the mean value of the blank control test index, including:

[0023] The difference between the mean value of the second evaluation index and the mean value of the blank control test index is determined as the performance evaluation result of the real-time optimization system under the condition of the same physical factors.

[0024] In a second aspect, the present application provides a performance evaluation device applied to a real-time optimization system, wherein the real-time optimization system is used for optimizing a catalytic reforming device, and the catalytic reforming device is used for converting raw oil into products, and the performance evaluation device includes:

[0025] A first processing module is configured to determine the correlation between the first data and the corresponding first evaluation index;

[0026] The first data is the mean value of the aromatic potential, the variance of the aromatic potential and the variance of the aromatic potential change rate before the real-time optimization system is put into use; and the first evaluation index is the aromatic yield or the gross profit per unit of raw material before the real-time optimization system is put into use.

[0027] A second processing module is configured to collect multiple sets of commissioning test data in a single evaluation period, and each set of commissioning test data includes second data and a corresponding second evaluation index; the second data is the mean value of the aromatic potential, the variance of the aromatic potential and the variance of the aromatic potential change rate after the real-time optimization system is put into use; and the second evaluation index is the aromatic yield or the gross profit per unit of raw material after the real-time optimization system is put into use.

[0028] A third processing module is configured to determine the first evaluation index corresponding to the second data in each set of commissioning test data according to the correlation; and take the first evaluation index corresponding to the second data in each set of commissioning test data as a blank control test index.

[0029] A fourth processing module is configured to determine the performance evaluation result of the real-time optimization system under the condition of the same physical factors according to the second evaluation index in each set of commissioning test data and the blank control test index; and the physical factors include the mean value of the aromatic potential, the variance of the aromatic potential and the variance of the aromatic potential change rate.

[0030] In a third aspect, the present application provides a computer device, comprising a memory and a processor, which are communicatively connected with each other, and the memory stores computer instructions, and the processor executes the computer instructions to perform the performance evaluation method of the first aspect or any of the corresponding embodiments thereof.

[0031] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer instructions, and the computer instructions are used to make a computer perform the performance evaluation method of the first aspect or any of the corresponding embodiments thereof.

[0032] In a fifth aspect, the present application provides a computer program product, which comprises computer instructions, and the computer instructions are used to make a computer perform the performance evaluation method of the first aspect or any of the corresponding embodiments thereof.

[0033] The technical solution provided by the present application has the following technical effects:

[0034] The technical solution of the embodiment of the present application determines the correlation between the first data and the corresponding first evaluation index according to the mean of the aromatic potential, the variance of the aromatic potential and the variance of the change rate of the aromatic potential before the real-time optimization system is put into use, and the gross profit of the aromatic yield or the unit raw material. The mean of the aromatic potential, the variance of the aromatic potential and the variance of the change rate of the aromatic potential after the real-time optimization system is put into use are collected. The correlation between the first data and the corresponding first evaluation index can be determined according to the correlation between the data before the real-time optimization system is put into use, that is, the correlation between the first data and the corresponding first evaluation index to determine the first evaluation index (blank control test index) corresponding to the second data. Thus, in the case that the physical factors such as the mean of the aromatic potential, the variance of the aromatic potential and the variance of the change rate of the aromatic potential are the same, the evaluation indexes (blank control test index and second evaluation index) before and after the real-time optimization system is put into use are determined, and the performance evaluation result of the real-time optimization system is determined according to the evaluation indexes before and after the real-time optimization system is put into use in the case that the physical factors are the same. The physical factors of the blank control test and the test of the real-time optimization system put into use are controlled to be the same, and the performance evaluation result of the real-time optimization system is determined in the case that the physical factors of the blank control test and the test of the real-time optimization system put into use are controlled to be the same and change the same, thereby improving the accuracy of the performance evaluation result of the real-time optimization system. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the specific embodiments or related art, the following will briefly introduce the drawings needed to be used in the specific embodiments or related art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creating any inventive labor.

[0036] Figure 1 is a flowchart of a performance evaluation method of an embodiment of the present application;

[0037] Figure 2 is a flowchart of another performance evaluation method of an embodiment of the present application;

[0038] Figure 3 is a structural diagram of a performance evaluation device of an embodiment of the present application;

[0039] Figure 4 is a hardware structural diagram of a computer device of an embodiment of the present application. DETAILED DESCRIPTION

[0040] To make the objectives, technical solutions, and advantages of embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0041] The performance evaluation of the real-time optimization system should be performed under the condition that the external factors such as the material property, load, and price change and the blank control test and the test of putting into use of the real-time optimization system change completely consistently, and the performance of the catalytic reforming device before and after putting into use of the real-time optimization system is compared, so as to evaluate the performance of the real-time optimization system.

[0042] In the conventional technology, it is assumed that the material property before and after putting into use of the real-time optimization system remains consistent. The load and market price remain unchanged, and the change of the evaluation index before and after implementation of the real-time optimization technology is compared. The assumption that the load and market price remain unchanged is acceptable, but the material property remains consistent, which cannot be realized in actual production, and it is also not required to deliberately keep the material property consistent before and after in actual industry. The material property before and after putting into use of the real-time optimization system remains consistent, which is actually also difficult to realize.

[0043] Therefore, an embodiment of the present application provides a performance evaluation method, device, equipment, storage medium, and program product, which determines the material property factor of the blank control test through calculation, so as to ensure that the system evaluation is performed under the condition that the material property remains unchanged before and after implementation of the real-time optimization technology, to solve the above problem.

[0044] According to an embodiment of the present application, a performance evaluation method embodiment is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer device such as a group of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0045] Figure 1 Figure 1 is a flowchart of a performance evaluation method according to an embodiment of the present application.

[0046] As shown in Figure 1 Figure 1, a performance evaluation method according to an embodiment of the present application is provided. The performance evaluation method is applied to a real-time optimization system, which is used to optimize a catalytic reforming device for converting raw oil into products. The performance evaluation method is used to evaluate the performance of the real-time optimization system.

[0047] The performance evaluation method includes the following steps.

[0048] S101: Determine the correlation between the first data and the corresponding first evaluation index.

[0049] In this embodiment, the first data is the mean of the aromatic potential, the variance of the aromatic potential, and the variance of the aromatic potential change rate before the real-time optimization system is put into use. The first evaluation index is the aromatic yield or the gross profit per unit of raw material before the real-time optimization system is put into use. In actual application, one of the aromatic yield or the gross profit per unit of raw material can be selected as the first evaluation index, or both the aromatic yield and the gross profit per unit of raw material can be selected as the first evaluation index.

[0050] In this embodiment, the aromatic potential is a short form of the aromatic potential content. The main physical property factor affecting the performance of the real-time optimization system is the aromatic potential. The mean of the aromatic potential, the variance of the aromatic potential, the mean of the aromatic potential change rate, and the variance of the aromatic potential change rate all have an impact. When the sampling time span is large and the number of samples is large, the mean of the aromatic potential change rate will be a very small value, which can be ignored. Therefore, the mean of the aromatic potential, the variance of the aromatic potential, and the variance of the aromatic potential change rate are mainly collected to determine whether the average physical property changes, how large the change is, and how fast the change is.

[0051] In this embodiment, the determination of the correlation between the first data and the corresponding first evaluation index in S101 specifically includes: taking the first data as the independent variable and the corresponding first evaluation index as the dependent variable to construct a functional relationship model between the first data and the corresponding first evaluation index. The correlation between the first data and the corresponding first evaluation index is represented by the functional relationship model between the first data and the corresponding first evaluation index. The functional relationship model is a linear relationship function or a nonlinear relationship function.

[0052] As an example, when the functional relationship model is a linear relationship function, the constructed functional relationship model can be Y = X * A. Y represents the dependent variable, i.e., the corresponding first evaluation index. X represents the independent variable, which is a matrix composed of the mean of the aromatic potential of the raw oil, the variance of the aromatic potential, and the variance of the aromatic potential change rate. A represents the undetermined coefficient. According to the first data, A = (X T *X) -1 *(XT *Y), X T The superscript -1 indicates the inverse operation of the matrix, representing the transpose of the matrix. This yields the specific expression for Y = X * A. This invention evaluates the specific operations of a real-time optimization system; therefore, operational factors are not introduced into the functional relationship model.

[0053] In this embodiment, the formula for calculating the aromatic yield is: Aromatic yield = ∑(Product mass flow rate * Aromatic content in the product) / Raw material mass flow rate.

[0054] In this embodiment, the formula for calculating the gross profit per unit of raw material is: Gross profit per unit of raw material = (∑(product mass flow rate * product price) - ∑(total raw material mass flow rate * raw material price) - ∑(utility usage * utility price)) / mass flow rate of raw material oil.

[0055] In this embodiment, the raw material test data, yield data of various products, test data of various products, and data such as the aromatic potential and aromatic potential change rate of the feed oil in the formula for calculating the aromatic yield and gross profit per unit of raw material can be obtained from the distributed control system and real-time database of the laboratory and catalytic reforming unit.

[0056] In this embodiment, in order to analyze the changes in the physical properties of raw materials in a timely manner, real-time optimization technology requires an online analyzer of the physical properties of raw materials. For example, a near-infrared analyzer or an online chromatograph is required for the catalytic reforming unit to detect the components and their concentrations in the raw materials, and the analysis frequency is set as needed.

[0057] S102: Collect multiple sets of operational test data within a single assessment period.

[0058] In this embodiment, the single-set commissioning test data includes second data and a corresponding second evaluation index. The second data consists of the mean, variance, and variance of the aromatic potential after the real-time optimization system is commissioned. The second evaluation index is the aromatic yield or gross profit per unit of raw material after the real-time optimization system is commissioned. The evaluation period is the evaluation period for the real-time optimization system.

[0059] In the embodiment, the evaluation period is a preset performance evaluation time period, for example, one week or one month can be taken as an evaluation period. The performance of the real-time optimization system can be periodically evaluated in cycles of the length of the evaluation period. Each group of commissioning test data is determined according to the data of one shift. The length of one shift is 8 hours or 12 hours. Considering that different shifts may have different operation habits, two shifts are not considered in one group of commissioning test data, and one group of commissioning test data only contains one shift. In one evaluation period, the length of the shift is fixed, for example, the length of the shift in one evaluation period is 8 hours or 12 hours. In one shift, the second data and the corresponding second evaluation index can be collected in a preset time interval. The preset time interval can be determined according to the analysis frequency of the online analyzer, for example, the analysis frequency of the online analyzer is 5 minutes, and the preset time interval can be set to 5 minutes.

[0060] The mean, variance and variance of the change rate of the aromaticity need to be calculated by multiple data. If the fixed time interval is 5 minutes, there are at least 96 groups of data in one shift, which is sufficient in quantity. As an example, each group of data can include aromaticity, aromaticity change rate and the like. The mean, variance and variance of the change rate of the aromaticity in 96 groups of data are calculated to obtain the second data in one group of commissioning test data, and then the second data in each group of commissioning test data is obtained. The determination method of the second evaluation index in each group of commissioning test data is similar to that of the second data, which will not be described here.

[0061] In the embodiment, the real-time optimization system is put into actual production process, therefore, the aromaticity, the aromaticity change rate after the real-time optimization system is put into use, and the data required for calculating the aromatic yield or the gross profit per unit of raw material can be directly obtained from the catalytic reforming device, for example, by the online analyzer, without the need to be determined according to the above-mentioned correlation. The aromatic yield and the gross profit per unit of raw material can be calculated according to the actual data after the real-time optimization system is put into use.

[0062] S103: determining the first evaluation index corresponding to the second data in each group of commissioning test data according to the correlation. The first evaluation index corresponding to the second data in each group of commissioning test data is taken as a blank control test index.

[0063] In the embodiment, the second data in each group of commissioning test data can be input into the functional relationship model to obtain the first evaluation index corresponding to the second data in each group of commissioning test data. As an example, the second data in each group of commissioning test data can be input into Y=X*A to obtain the first evaluation index corresponding to the second data in each group of commissioning test data.

[0064] S104: determining the performance evaluation result of the real-time optimization system under the condition of the same physical factors according to the second evaluation index in each group of commissioning test data and the blank control test index.

[0065] In this embodiment, the physical factors include the mean of the aromatic potential, the variance of the aromatic potential, and the variance of the change rate of the aromatic potential.

[0066] In this embodiment, the step S104 of determining the performance evaluation result of the real-time optimization system under the condition of the same physical factors according to the second evaluation index in each group of commissioning test data and the blank control test index specifically includes:

[0067] The performance evaluation result of the real-time optimization system under the condition of the same physical factors is determined according to the mean of the second evaluation index and the mean of the blank control test index. As an example, the difference between the mean of all second evaluation indexes and the mean of all blank control test indexes is determined as the performance evaluation result of the real-time optimization system under the condition of the same physical factors. The performance evaluation result of the real-time optimization system = the mean of the second evaluation index - the mean of the blank control test index.

[0068] As an example, in the case of a one-week evaluation period and an 8-hour shift, 21 groups of commissioning test data can be obtained. The mean of all second evaluation indexes in the 21 groups of commissioning test data is calculated, for example, N. The second data in the 21 groups of commissioning test data and the functional relationship model are used to determine the first evaluation index corresponding to the second data in the 21 groups of commissioning test data as the blank control test index. When the first evaluation index is the aromatic yield, 21 blank control test indexes are calculated. The mean of the 21 blank control test indexes is calculated, for example, M. The performance evaluation result P of the real-time optimization system = N - M.

[0069] The technical scheme of the present application is used to determine the performance of the operation before and after the real-time optimization system is put into use, therefore, the raw material must be changed during the evaluation period, but it is also necessary to ensure that the change of the raw material in the blank control test and the test of putting the real-time optimization system into use is the same, that is, it is necessary to exclude the factors of the change of the raw material from the evaluation index, therefore, the present application needs to determine the correlation between the first data (the mean value of the aromatic potential, the variance of the aromatic potential and the variance of the change rate of the aromatic potential) before the real-time optimization system is put into use and the corresponding first evaluation index (the aromatic hydrocarbon yield or the gross profit per unit of raw material). Then, the second data (the mean value of the aromatic potential, the variance of the aromatic potential and the variance of the change rate of the aromatic potential) after the real-time optimization system is put into use and the corresponding second evaluation index (the aromatic hydrocarbon yield or the gross profit per unit of raw material) are obtained, the first evaluation index corresponding to the second data before the real-time optimization system is put into use is determined according to the second data after the real-time optimization system is put into use and the above-mentioned correlation, the first evaluation index corresponding to the second data before the real-time optimization system is put into use is taken as the blank control test index, so as to ensure that the change of the raw material in the blank control test and the test of putting the real-time optimization system into use is the same. In the present application, in the case that the physical properties of the raw material change greatly, the evaluation index of the blank control test can be calculated by the above-mentioned method. Based on the above-mentioned scheme, it can be ensured that the change of the raw material in the blank control test and the test of putting the real-time optimization system into use is the same, so as to improve the accuracy and reliability of the evaluation of the performance of the real-time optimization system.

[0070] The technical scheme of the present application obtains the blank control test index of the blank control test by calculation in the case that the blank control test cannot be carried out in the conventional technology, and it is not necessary to maintain the consistency of the physical properties of the raw material before and after the performance evaluation, and in fact, it is also difficult to maintain the consistency. Assuming that the raw material processed after the real-time optimization system is put into use, if the evaluation index (the aromatic hydrocarbon yield or the gross profit per unit of raw material) generated by the manual operation is taken as the evaluation index (the blank control test index) before the real-time optimization system is put into use. In the present application, the relationship between the first evaluation index under the blank control test and the physical properties of the raw material is obtained by regression according to the historical production data. The difference between the evaluation index after the real-time optimization system is put into use and the evaluation index before the real-time optimization system is put into use is taken as the performance evaluation result of the real-time optimization system in the technical scheme of the present application, the effect of the real-time optimization system is quantified, and it is more simple and convenient. When the effect of the real-time optimization system is evaluated, it is not necessary to maintain the stability of the physical properties of the raw material, and it is not necessary to ensure the consistency before and after the evaluation of the physical properties.

[0071] Figure 2 It is a flowchart of another performance evaluation method of the embodiment of the present application.

[0072] As Figure 2As shown, the embodiment of the present application provides a performance evaluation method, which is applied to a real-time optimization system for optimizing a catalytic reforming device for converting raw oil into products, and is used for performance evaluation of the real-time optimization system.

[0073] The performance evaluation method comprises:

[0074] S201: Obtain historical production data before commissioning of the real-time optimization system.

[0075] In the embodiment, the historical production data comprises a plurality of sets of sample data, and each set of sample data comprises production data in a preset time period. In the embodiment, each set of sample data is production data of one shift, and according to the above scheme, one shift is 8 hours or 12 hours. The production data comprises data required for determining the first data and the corresponding first evaluation index, for example, raw material testing data in a formula for calculating the aromatic yield and the gross profit per unit of raw material, yield data of various products, testing data of various products, and data such as aromatic potential and aromatic potential change rate of raw oil, which can be obtained through an online analyzer. The catalytic reforming device is completely operated by manual operation before commissioning of the real-time optimization system.

[0076] In the case of the same raw material properties and changes, the first evaluation index obtained by an experienced operator operating the catalytic reforming device under a blank control test is better, and the first evaluation index obtained by an inexperienced operator operating the catalytic reforming device is worse. However, the first evaluation index of the blank control test cannot be the best value or the worst value, but should be the average value.

[0077] To further improve the accuracy of the historical production data, historical production data under different raw oils before commissioning of the real-time optimization system should be obtained. Historical production data under different raw materials (that is, different average values of aromatic potential, variance of aromatic potential, and variance of change rate of aromatic potential) should be obtained. The correlation between the first data and the corresponding first evaluation index determined based on the historical production data under different raw materials has higher accuracy, and can adapt to the case of raw material change after commissioning of the real-time optimization system.

[0078] S202: Determine the first data and the corresponding first evaluation result according to the historical production data.

[0079] In the embodiment, a set of first data and corresponding first evaluation results are determined according to each set of sample data in the historical production data. A set of second data, that is, a mean value of the aromaticity, a variance of the aromaticity and a variance of the aromaticity change rate, can be obtained by mean value calculation and variance calculation according to a set of sample data. An aromatic yield or a gross profit per unit of raw material can be obtained according to the calculation formula of the aromatic yield or the gross profit per unit of raw material. As an example, when the historical production data include 100 sets of sample data, 100 sets of first data and corresponding first evaluation results can be obtained.

[0080] S203: Determine the correlation between the first data and the corresponding first evaluation index. For details, refer to the related description of S101 above, which will not be repeated here.

[0081] S204: Collect multiple sets of commissioning test data in a single evaluation period. For details, refer to the related description of S102 above, which will not be repeated here.

[0082] S205: Determine the first evaluation index corresponding to the second data in each set of commissioning test data according to the correlation. The first evaluation index corresponding to the second data in each set of commissioning test data is used as a blank control test index. For details, refer to the related description of S103 above, which will not be repeated here.

[0083] S206: Determine the performance evaluation result of the real-time optimization system under the condition that the physical property factors are the same according to the second evaluation index and the blank control test index in each set of commissioning test data. For details, refer to the related description of S104 above, which will not be repeated here.

[0084] It should be noted that the contents not described in detail in the specification of the present application belong to the common knowledge of those skilled in the art, for example, the way of determining the first data, the second data, the first evaluation index and the second evaluation index through the real-time database and the online analyzer is the common knowledge in the art.

[0085] In the embodiment, a performance evaluation device is also provided, and a single device is used to implement the above-described embodiments and optional implementation manners, which have been described above. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware or a combination of software and hardware can also be implemented and conceived.

[0086] Figure 3 is a structural schematic diagram of the performance evaluation device of the embodiment of the present application.

[0087] The application provides a performance evaluation device, which is applied to a real-time optimization system, and is used for optimizing a catalytic reforming device. Figure 3 As shown in the figure, the performance evaluation device comprises:

[0088] A first processing module 11 is configured to determine a correlation between the first data and the corresponding first evaluation index.

[0089] The first data is the mean value of the aromatic potential, the variance of the aromatic potential and the variance of the aromatic potential change rate before the real-time optimization system is put into use. The first evaluation index is the aromatic yield or the gross profit per unit of raw material before the real-time optimization system is put into use.

[0090] A second processing module 12 is configured to collect multiple sets of commissioning test data in a single evaluation period, and each set of commissioning test data comprises second data and a corresponding second evaluation index. The second data is the mean value of the aromatic potential, the variance of the aromatic potential and the variance of the aromatic potential change rate after the real-time optimization system is put into use. The second evaluation index is the aromatic yield or the gross profit per unit of raw material after the real-time optimization system is put into use.

[0091] A third processing module 13 is configured to determine the first evaluation index corresponding to the second data in each set of commissioning test data according to the correlation. The first evaluation index corresponding to the second data in each set of commissioning test data is taken as a blank control test index.

[0092] A fourth processing module 14 is configured to determine the performance evaluation result of the real-time optimization system under the condition that the physical property factors are the same according to the second evaluation index in each set of commissioning test data and the blank control test index. The physical property factors include the mean value of the aromatic potential, the variance of the aromatic potential and the variance of the aromatic potential change rate.

[0093] In an alternative embodiment, the performance evaluation device further comprises a historical data acquisition module, which is configured to acquire historical production data before the real-time optimization system is put into use before the correlation between the first data and the corresponding first evaluation index is determined. The historical production data comprises multiple sets of sample data, and each set of sample data comprises production data in a preset time period; the first data and the corresponding first evaluation result are determined according to the historical production data.

[0094] In an alternative embodiment, the first processing module 12 comprises:

[0095] A first processing unit is configured to take the first data as the independent variable and the corresponding first evaluation index as the dependent variable, and construct a function relationship model between the first data and the corresponding first evaluation index.

[0096] The second processing unit is used to represent the correlation between the first data and the corresponding first evaluation index using a functional relationship model. In an optional implementation, the functional relationship model is a linear relationship function or a nonlinear relationship function.

[0097] In an optional implementation, the fourth processing module 14 is specifically used to determine the performance evaluation result of the system in real time under the condition that the physical property factors are the same, based on the mean of the second evaluation index and the mean of the blank control test index.

[0098] In an optional implementation, the fourth processing module 14 is specifically used to determine the difference between the mean of the second evaluation index and the mean of the blank control test index as the performance evaluation result of the system in real time under the same physical property factors.

[0099] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0100] In this embodiment, the performance evaluation device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0101] This invention also provides a computer device having the above-described features. Figure 3 The performance evaluation device shown.

[0102] Please see Figure 4 , Figure 4 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention, such as... Figure 4 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In an alternative implementation, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor device). Figure 4 Take a processor 10 as an example.

[0103] The processor 10 can be a central processing unit, a network processing unit, or a combination thereof. The processor 10 can further include a hardware chip. The hardware chip can be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device can be a complex programmable logic device, a field programmable logic device, a general array logic, or any combination thereof.

[0104] The memory 20 stores instructions executable by the at least one processor 10 for causing the at least one processor 10 to perform the methods illustrated in the above embodiments.

[0105] The memory 20 can include a program storage area and a data storage area. The program storage area can store programs required for operating the apparatus and at least one function. The data storage area can store data created according to the use of the computer device, and the like. In addition, the memory 20 can include a high-speed random access memory, and can further include a non-transitory memory such as at least one disk storage device, a flash memory device, or other non-transitory solid state storage device. In an alternative embodiment, the memory 20 can optionally include a memory disposed remotely from the processor 10, which can be connected to the computer device through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0106] The memory 20 can include a volatile memory, such as a random access memory. The memory can also include a non-volatile memory, such as a flash memory, a hard disk, or a solid state disk. The memory 20 can further include a combination of the above-mentioned types of memory.

[0107] The computer device further includes a communication interface 30 for communication of the computer device with other devices or communication networks.

[0108] The embodiments of the present application also provide a computer readable storage medium, and the method according to the embodiments of the present application can be implemented in hardware, firmware, or recorded in a storage medium, or be implemented as computer code stored in a remote storage medium or a non-transitory machine readable storage medium and stored in a local storage medium to be downloaded through a network, so that the method described herein can be processed by such software on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid state disk, etc. Further, the storage medium can also include a combination of the above-mentioned types of memories. It can be understood that the computer, processor, microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the method shown in the above embodiments.

[0109] Part of the present application can be applied as a computer program product, for example, computer program instructions, when executed by a computer, through the operation of the computer, the method and / or technical solutions according to the present application can be invoked or provided. Those skilled in the art should understand that the form of computer program instructions in a computer readable medium includes but is not limited to source files, executable files, installation package files, etc. Correspondingly, the way of executing computer program instructions by computer includes but is not limited to: the computer directly executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Here, the computer readable medium can be any available computer readable storage medium or communication medium accessible to the computer.

[0110] Although the embodiments of the present application are described in conjunction with the accompanying drawings, various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes fall within the scope defined by the appended claims.

Claims

1. A performance evaluation method applied to a real-time optimization system for optimizing a catalytic reformer for converting a feedstock oil into products, characterized in that, The method comprises the following steps: determining the correlation between the first data and the corresponding first evaluation index; the first data is the mean value of the aromatic potential, the variance of the aromatic potential and the variance of the aromatic potential change rate before the real-time optimization system is put into operation; the first evaluation index is the aromatic hydrocarbon yield or the gross profit per unit of raw material before the real-time optimization system is put into operation; collecting multiple sets of commissioning test data in a single evaluation period, and each set of commissioning test data comprising second data and a corresponding second evaluation index; the second data is the mean value of the aromatic potential, the variance of the aromatic potential and the variance of the aromatic potential change rate after the real-time optimization system is put into operation; the second evaluation index is the aromatic hydrocarbon yield or the gross profit per unit of raw material after the real-time optimization system is put into operation; determining the first evaluation index corresponding to the second data in each set of commissioning test data according to the correlation; and taking the first evaluation index corresponding to the second data in each set of commissioning test data as a blank control test index; determining the performance evaluation result of the real-time optimization system under the condition that the physical factors are the same according to the second evaluation index in each set of commissioning test data and the blank control test index; the physical factors include the mean value of the aromatic potential, the variance of the aromatic potential and the variance of the aromatic potential change rate.

2. The method of claim 1, wherein, Before determining the correlation between the first data and the corresponding first evaluation index, the method further comprises the following steps: obtaining historical production data before the real-time optimization system is put into operation; the historical production data comprises multiple sets of sample data, and each set of sample data comprises production data in a preset time period; determining the first data and the corresponding first evaluation index according to the historical production data.

3. The method of claim 1, wherein, The method for determining the correlation between the first data and the corresponding first evaluation index comprises the following steps: taking the first data as the independent variable and the corresponding first evaluation index as the dependent variable to construct a function relationship model between the first data and the corresponding first evaluation index; representing the correlation between the first data and the corresponding first evaluation index through the function relationship model between the first data and the corresponding first evaluation index.

4. The method of claim 3, wherein, The function relationship model is a linear relationship function or a nonlinear relationship function.

5. The method of claim 1, wherein, The method for determining the performance evaluation result of the real-time optimization system under the condition that the physical factors are the same according to the second evaluation index in each set of commissioning test data and the blank control test index comprises the following steps: determining the performance evaluation result of the real-time optimization system under the condition that the physical factors are the same according to the mean value of the second evaluation index and the mean value of the blank control test index.

6. The method of claim 5, wherein, The method for determining the performance evaluation result of the real-time optimization system under the condition that the physical factors are the same according to the mean value of the second evaluation index and the mean value of the blank control test index comprises the following steps: determining the performance evaluation result of the real-time optimization system under the condition that the physical factors are the same according to the difference between the mean value of the second evaluation index and the mean value of the blank control test index.

7. A performance evaluation device applied to a real-time optimization system for optimizing a catalytic reformer for converting a feedstock oil into products, characterized by, The method comprises the following steps: a first processing module is configured to determine the correlation between the first data and the corresponding first evaluation index; the first data is the mean value of the aromatic potential, the variance of the aromatic potential and the variance of the aromatic potential change rate before the real-time optimization system is put into operation; the first evaluation index is the aromatic hydrocarbon yield or the gross profit per unit of raw material before the real-time optimization system is put into operation; The second processing module is configured to collect multiple sets of commissioning test data in a single evaluation period, and each set of commissioning test data comprises second data and a corresponding second evaluation index; the second data comprises a mean value of aromatic potential, a variance of aromatic potential, and a variance of aromatic potential change rate after the real-time optimization system is put into use; and the second evaluation index comprises an aromatic yield or a gross profit per unit of raw material after the real-time optimization system is put into use. The third processing module is configured to determine, according to the correlation, a first evaluation index corresponding to the second data in each set of commissioning test data; and take the first evaluation index corresponding to the second data in each set of commissioning test data as a blank control test index. The fourth processing module is configured to determine, according to the second evaluation index in each set of commissioning test data and the blank control test index, a performance evaluation result of the real-time optimization system in a case where physical property factors are the same; and the physical property factors comprise the mean value of aromatic potential, the variance of aromatic potential, and the variance of aromatic potential change rate.

8. A computer device, comprising: The performance evaluation method comprises: a memory and a processor, which are in communication connection with each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the performance evaluation method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, and the computer instructions are used to make a computer execute the performance evaluation method according to any one of claims 1 to 6.

10. A computer program product, characterised in that, The computer instructions are used to make a computer execute the performance evaluation method according to any one of claims 1 to 6.

Images