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

Abstract

The invention relates to the technical field of catalytic reforming, and discloses a performance evaluation method, device and equipment, a storage medium and a program product. The method is applied to a real-time optimization system, and comprises the following steps: determining a correlation between first data and a corresponding first evaluation index; the first data is a physical property factor before the system is put into use; the first evaluation index is aromatic hydrocarbon yield or gross profit of unit raw materials before use; collecting multiple groups of commissioning test data, wherein a single group of commissioning test data comprises second data after system commissioning and a corresponding second evaluation index; determining a first evaluation index corresponding to the second data as a blank control test index according to the correlation; and determining a performance evaluation result of the system according to the second evaluation index and the blank control test index. According to the method, the physical property factors before and after the system is put into use can be ensured to be the same, and the performance evaluation result of the real-time optimization system can be determined under the condition that the physical property factors are changed and the changes of the physical property factors are the same, so that the accuracy of performance evaluation is improved.

Description

Technical Field

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

[0002] Catalytic reforming unit is an oil refining unit that converts naphtha into aromatics or high-octane gasoline under the conditions of catalyst and high temperature. The main factor affecting the yield of aromatics 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 adjust the production operating conditions in a timely manner.

[0003] Real-time optimization technology is not aimed at devices or systems whose external factors remain unchanged, but at changes in external factors, timely optimizing and adjusting the production operation of the device to obtain better economic and technical indicators. Devices or systems whose external factors remain unchanged only need to be optimized once. It can be seen that real-time optimization is necessary only when external factors change; if external factors remain unchanged, real-time optimization technology is unnecessary. In order to cope with external factors that often change, such as raw materials and market prices, the performance evaluation of real-time optimization technology and real-time optimization systems has received unprecedented attention.

[0004] In conventional technology, the performance evaluation of real-time optimization systems usually compares the changes in performance indicators before and after the real-time optimization system is put into use, while the load and market price remain unchanged. However, the performance evaluation method of the real-time optimization system is less accurate.

[0005] Therefore, there is an urgent need for a performance evaluation scheme that can improve the accuracy of performance evaluation of real-time optimization systems. Summary of the invention

[0006] Therefore, the technical problem to be solved by the present invention 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 problems, the present invention provides a performance evaluation method, which is applied to a real-time optimization system. The real-time optimization system is used to optimize a catalytic reforming device, and the catalytic reforming device is used to convert crude oil into products. The performance evaluation method includes:

[0008] Determine a correlation between the first data and the 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 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;

[0010] Collect multiple groups of commissioning test data within a single assessment period, where each group of commissioning test data includes second data and corresponding second evaluation indicators; 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 indicator is the aromatic yield or the gross profit per unit of raw material after the real-time optimization system is put into use;

[0011] Determine the first evaluation index corresponding to the second data in each group of test data according to the correlation; use the first evaluation index corresponding to the second data in each group of test data as a blank control test index;

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

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

[0014] Acquire 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 within a preset time period;

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

[0016] In an optional implementation, determining the correlation between the first data and the corresponding first evaluation index includes:

[0017] Taking the first data as an independent variable and the corresponding first evaluation index as a dependent variable, constructing a functional relationship model between the first data and the corresponding first evaluation index;

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

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

[0020] In an optional embodiment, the method of determining 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 group of commissioning test data and the blank control test index comprises:

[0021] The performance evaluation result of the real-time optimization system is determined based on the mean value of the second evaluation index and the mean value of the blank control test index under the condition that the physical property factors are the same.

[0022] In an optional embodiment, determining the performance evaluation result of the real-time optimization system according to the mean value of the second evaluation index and the mean value of the blank control test index under the condition that the physical property factors are the same includes:

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

[0024] In a second aspect, the present invention provides a performance evaluation device, which is applied to a real-time optimization system, wherein the real-time optimization system is used to optimize a catalytic reforming device, wherein the catalytic reforming device is used to convert feedstock oil into products, and the performance evaluation device comprises:

[0025] A first processing module, used 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; 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] The second processing module is used to collect multiple groups of commissioning test data within a single assessment period, and each group of commissioning test data includes second data and corresponding second evaluation indicators; the second data is the mean value of the aromatic potential after the real-time optimization system is put into use, the variance of the aromatic potential, and the variance of the aromatic potential change rate; the second evaluation indicator is the aromatic yield or the gross profit per unit of raw material after the real-time optimization system is put into use;

[0028] The third processing module is used to determine the first evaluation index corresponding to the second data in each group of test data according to the correlation; and use the first evaluation index corresponding to the second data in each group of test data as a blank control test index;

[0029] The fourth processing module is used 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 group of test data and the blank control test index; the physical property factors include the mean value of aroma potential, the variance of aroma potential and the variance of aroma potential change rate.

[0030] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the performance evaluation method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0031] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a single computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the performance evaluation method of the first aspect or any corresponding embodiment thereof.

[0032] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the performance evaluation method of the first aspect or any corresponding embodiment thereof.

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

[0034] The technical solution of the embodiment of the present invention determines the correlation between the first data and the corresponding first evaluation index according to the mean value of the aromatic potential, the variance of the aromatic potential and the variance of the aromatic potential change rate, the aromatic yield or the gross profit per unit of raw material before the real-time optimization system is put into use. The mean value of the aromatic potential, the variance of the aromatic potential and the variance of the aromatic potential change rate, the aromatic yield or the gross profit per unit of raw material are collected after the real-time optimization system is put into use. The first evaluation index (blank control test index) corresponding to the second data can be determined according to the correlation determined by 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. Thus, when the physical property factors: the mean value of the aromatic potential, the variance of the aromatic potential and the variance of the aromatic potential change rate are the same, the evaluation index (blank control test index and the second evaluation index) before and after the real-time optimization system is put into use is determined, and when the physical property factors are the same, the performance evaluation result of the real-time optimization system is determined according to the evaluation index before and after the real-time optimization system is put into use. The physical property factors of the blank control test and the real-time optimization system test can be controlled to be the same. The performance evaluation results of the real-time optimization system can be determined under the condition that the physical property factors of the blank control test and the real-time optimization system test are controlled to be the same and have the same changes, thereby improving the accuracy of the performance evaluation results of the real-time optimization system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 is a flow chart of a performance evaluation method according to an embodiment of the present invention;

[0037] Figure 2 is a flow chart of another performance evaluation method according to an embodiment of the present invention;

[0038] Figure 3 is a schematic structural diagram of a performance evaluation device according to an embodiment of the present invention;

[0039] Figure 4 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0041] The performance evaluation of the real-time optimization system should be carried out by comparing the performance of the catalytic reforming unit before and after the real-time optimization system is put into use, when external factors such as raw material properties, load, price, etc. change and the changes in the blank control test and the real-time optimization system test are exactly the same, so as to evaluate the performance of the real-time optimization system.

[0042] In conventional technology, it is assumed that the physical properties of raw materials remain the same before and after the real-time optimization system is put into use. The load and market price remain unchanged, and the changes in evaluation indicators before and after the implementation of real-time optimization technology are compared. It is acceptable to assume that the load and market price remain unchanged, but it is impossible to keep the physical properties of raw materials consistent in actual production, and there is no need to deliberately keep the physical properties consistent in actual industry. It is actually impossible to keep the physical properties consistent before and after the real-time optimization system is put into use.

[0043] To this end, the embodiments of the present invention provide a performance evaluation method, device, equipment, storage medium and program product, which determine the physical property factors of the blank control test by calculation, thereby ensuring that the system evaluation is carried out before and after the implementation of the real-time optimization technology while the physical properties of the raw materials remain unchanged, to solve the above-mentioned problems.

[0044] According to an embodiment of the present invention, a performance evaluation method embodiment is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer device such as a set of computer executable instructions, and although a 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 It is a flowchart of a performance evaluation method according to an embodiment of the present invention.

[0046] like Figure 1 As shown, a performance evaluation method is provided in an embodiment of the present invention. The performance evaluation method is applied to a real-time optimization system. The real-time optimization system is used to optimize a catalytic reforming device. The catalytic reforming device is used to convert crude oil into products. The performance evaluation method is used to implement performance evaluation of the real-time optimization system.

[0047] The performance evaluation method includes:

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

[0049] In this embodiment, 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. 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 the present embodiment, aromatic potential is the abbreviation of aromatic potential content. The main physical property factor affecting the performance of the real-time optimization system is aromatic potential. The mean of aromatic potential, the variance of aromatic potential, the mean of aromatic potential change rate, and the variance of 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 aromatic potential change rate will be a very small value and can be ignored. For this reason, the mean, variance, and variance of aromatic potential change rate of aromatic potential are mainly collected, which correspond to whether the average physical property changes, how large the change range is, and the rate of change.

[0051] In this embodiment, determining the correlation between the first data and the corresponding first evaluation index in S101 specifically includes: taking the first data as an independent variable and the corresponding first evaluation index as a dependent variable, constructing 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, the corresponding first evaluation index, X represents the independent variable, which is a matrix composed of the mean value of the aromatic potential of the feedstock oil, the variance of the aromatic potential, and the variance of the aromatic potential change rate, and A represents the undetermined coefficient. According to the first data, A=(X T *X) -1 *(XT *Y), X T represents the transposition of the matrix, and the superscript -1 represents the inverse operation of the matrix. Thus, the specific expression of Y=X*A is obtained. The present invention evaluates the specific operation of the real-time optimization system, so the operation factor is not introduced into the functional relationship model.

[0053] In this embodiment, the calculation formula of the aromatics yield is: aromatics yield = ∑ (product mass flow rate * mass content of aromatics in the product) / mass flow rate of raw materials.

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

[0055] In this embodiment, the raw material test data in the calculation formula of aromatic yield and unit raw material gross profit, the yield data of various products, the test data of various products, and the aromatic potential of the raw oil, the aromatic potential change rate and other data 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 timely analyze the changes in the physical properties of the raw materials, the real-time optimization technology requires an online analyzer of the physical properties of the raw materials. For example, the catalytic reforming device requires a near-infrared analyzer or an online chromatograph to detect the components and their concentrations in the raw materials, and the analysis frequency is set as needed.

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

[0058] In this embodiment, a single 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. The second evaluation index is the aromatic yield or the gross profit per unit raw material after the real-time optimization system is put into use. The assessment period is the assessment period of the real-time optimization system.

[0059] In this embodiment, the assessment period is a preset performance evaluation time period, for example, one week or one month can be an assessment period. The performance of the real-time optimization system can be periodically evaluated with the duration of the assessment period as a period. Each set of commissioning test data is determined based on the data of a team. The time of a team is 8 hours or 12 hours. Taking into account that different teams may have different operating habits, the two teams are not considered in a set of commissioning test data, and a set of commissioning test data contains only one team. During an assessment period, the duration of the team is fixed. For example, during an assessment period, the duration of the team is 8 hours or 12 hours. In a team, the second data and the corresponding second evaluation index can be collected at a preset time interval. The preset time interval can be determined according to the analysis frequency of the online analyzer. For example, if the analysis frequency of the online analyzer is once every 5 minutes, the preset time interval can be set to 5 minutes.

[0060] The mean, variance, and variance of the rate of change of aroma potential need to be calculated through multiple data. If the fixed time interval is 5 minutes, a team has at least 96 sets of data, which is sufficient. As an example, each set of data can include data such as aroma potential and aroma potential change rate. The mean, variance, and variance of the aroma potential change rate of the 96 sets of data can be calculated to obtain the second data in a set of commissioning test data, and then the second data in each set of commissioning test data can be obtained. The method for determining the second evaluation index in each set of commissioning test data is similar to the method for determining the second data, which will not be repeated here.

[0061] In this embodiment, the real-time optimization system is put into use as an actual production process. Therefore, the aromatic potential, aromatic potential change rate, and data required for calculating the aromatic yield or gross profit per unit of raw material after the real-time optimization system is put into use can be directly obtained from the catalytic reforming device, for example, through an online analyzer, and do not need to be determined based on the above-mentioned correlation. The aromatic yield and gross profit per unit of raw material can be calculated based on the actual data after the real-time optimization system is put into use.

[0062] S103: Determine the first evaluation index corresponding to the second data in each group of test data according to the correlation relationship. Use the first evaluation index corresponding to the second data in each group of test data as the blank control test index.

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

[0064] S104: Determine, based on 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.

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

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

[0067] The performance evaluation result of the real-time optimization system is determined according to the mean of the second evaluation index and the mean of the blank control test index under the condition that the physical property factors are the same. As an example, the difference between the mean of all the second evaluation indexes and the mean of all the blank control test indexes is determined as the performance evaluation result of the real-time optimization system under the condition that the physical property factors are the same. 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, when the assessment period is one week and the shift is 8 hours, 21 groups of commissioning test data can be obtained, and the mean of all second evaluation indicators 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 determine the first evaluation indicator corresponding to the second data in the 21 groups of commissioning test data as the blank control test indicator. When the first evaluation indicator is the aromatics yield, 21 blank control test indicators are calculated, and the mean of the 21 blank control test indicators is calculated, for example, M. The performance evaluation result P of the real-time optimization system is NM.

[0069] The technical solution of the present invention is used to determine the performance of the operation before and after the real-time optimization system is put into use. Therefore, the raw materials must change during the assessment period, but it is necessary to ensure that the blank control test and the real-time optimization system test raw material changes are the same, that is, the factors of raw material changes need to be removed from the assessment indicators. Therefore, the present invention needs to give priority to determining the correlation between the first data (mean value of aromatic potential, variance of aromatic potential and variance of aromatic potential change rate) and the corresponding first evaluation index (aromatic yield or gross profit per unit raw material) before the real-time optimization system is put into use. Then obtain the second data (mean value of aromatic potential, variance of aromatic potential and variance of aromatic potential change rate) and the corresponding second evaluation index (aromatic yield or gross profit per unit raw material) after the real-time optimization system is put into use, and determine the first evaluation index corresponding to the second data before the real-time optimization system is put into use 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 used as a blank control test index, thereby ensuring that the blank control test used for evaluation and the real-time optimization system test raw material changes are the same. In the present invention, when the raw material properties are variable, the evaluation index of the blank control test can be calculated by the above method. Based on the above scheme, it can be ensured that the blank control test used for evaluation and the raw material changes of the real-time optimization system test are the same, thereby improving the accuracy and reliability of evaluating the performance of the real-time optimization system.

[0070] In the case where the blank control test cannot be carried out in the conventional technology, the technical solution of the present invention obtains the blank control test index of the blank control test by calculation, and it is not necessary to maintain the raw material properties before and after the performance evaluation to remain consistent, which is in fact difficult to remain consistent. Assuming that the raw materials processed after the real-time optimization system is put into use, if the evaluation index (aromatic yield or gross profit per unit raw material) that can be generated completely by manual operation is used as the evaluation index (blank control test index) before the real-time optimization system is put into use. In the present invention, the relationship between the first evaluation index under the blank control test and the raw material properties is obtained by regression based on historical production data. The technical solution of the present invention uses 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 as the performance evaluation result of the real-time optimization system, which realizes the quantification of the effect of the real-time optimization system, which is simpler and more convenient. When evaluating the effect of the real-time optimization system, there is no need to maintain the stability of the raw material properties, and there is no need to ensure that the physical property evaluation is consistent before and after.

[0071] Figure 2 It is a flowchart of another performance evaluation method according to an embodiment of the present invention.

[0072] like Figure 2As shown, a performance evaluation method is provided in an embodiment of the present invention. The performance evaluation method is applied to a real-time optimization system. The real-time optimization system is used to optimize a catalytic reforming device. The catalytic reforming device is used to convert crude oil into products. The performance evaluation method is used to implement performance evaluation of the real-time optimization system.

[0073] The performance evaluation method includes:

[0074] S201: Obtain historical production data before the real-time optimization system is put into use.

[0075] In this embodiment, the historical production data includes multiple groups of sample data, and a single group of sample data includes production data within a preset time period. In this embodiment, a single group of sample data is the production data of a team. According to the above scheme, a team is 8 hours or 12 hours. The production data includes the data required to determine the first data and the corresponding first evaluation index, for example, the raw material test data in the calculation formula of the aromatic yield and the gross profit per unit raw material, the yield data of various products, the test data of various products, and the aromatic potential of the crude oil, the aromatic potential change rate and other data can be obtained through an online analyzer. Before the real-time optimization system is put into use, the catalytic reforming unit is completely manually operated.

[0076] Under the condition that the raw material properties and their changes are the same, in the blank control test, the first evaluation index obtained by the experienced operator operating the catalytic reforming device is better, and the first evaluation index obtained by the inexperienced operator operating the catalytic reforming device is worse. However, the first evaluation index of the blank control test cannot take the best value or the worst value, but should take the average value.

[0077] To further improve the accuracy of historical production data, historical production data under different crude oil conditions before the real-time optimization system is put into use should be obtained. Historical production data under working conditions of different raw materials (that is, different means of aromatic potential, variances of aromatic potential, and variances of the rate of change of aromatic potential) are obtained. The correlation between the first data determined based on the historical production data under working conditions of different raw materials and the corresponding first evaluation index is more accurate, and can also adapt to the situation of raw material changes after the real-time optimization system is put into use.

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

[0079] In this embodiment, a set of first data and a corresponding first evaluation result are determined according to each set of sample data in the historical production data. A set of second data, that is, a mean of aromatic potential, a variance of aromatic potential, and a variance of aromatic potential change rate can be obtained by performing mean calculation and variance calculation according to a set of sample data, and an aromatic yield or a gross profit per unit of raw material can be obtained according to the calculation formula of the above aromatic yield and gross profit per unit of raw material. As an example, when the historical production data includes 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 relevant description of S101 above, which will not be repeated here.

[0081] S204: Collect multiple groups of commissioning test data within a single assessment period. For details, refer to the relevant description of S102 above, which will not be repeated here.

[0082] S205: Determine the first evaluation index corresponding to the second data in each group of test data according to the correlation. The first evaluation index corresponding to the second data in each group of test data is used as the blank control test index. For details, refer to the relevant 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 same physical property factors according to the second evaluation index in each group of commissioning test data and the blank control test index. For details, refer to the relevant 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 invention belong to the common knowledge of those skilled in the art. For example, the method of determining the first data, the second data, the first evaluation index, and the second evaluation index through a real-time database and an online analyzer is common knowledge in the art.

[0085] In the present embodiment, a performance evaluation device is also provided, and a single device is used to implement the above-mentioned embodiment and optional implementation mode, and the description that has been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware is also possible and conceived.

[0086] Figure 3 Schematic diagram of the structure of a performance evaluation device according to an embodiment of the present invention.

[0087] The present invention provides a performance evaluation device, which is applied to a real-time optimization system. The real-time optimization system is used to optimize a catalytic reforming device, which is used to convert crude oil into products, such as Figure 3 As shown, the performance evaluation device comprises:

[0088] The first processing module 11 is used to determine the 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] The second processing module 12 is used to collect multiple groups of commissioning test data within a single assessment period, and the single group of commissioning test data includes second data and corresponding second evaluation indicators. The second data is the mean value of the aromatic potential after the real-time optimization system is put into use, the variance of the aromatic potential, and the variance of the aromatic potential change rate. The second evaluation indicator is the aromatic yield or the gross profit per unit of raw material after the real-time optimization system is put into use.

[0091] The third processing module 13 is used to determine the first evaluation index corresponding to the second data in each group of test data according to the correlation relationship, and to use the first evaluation index corresponding to the second data in each group of test data as the blank control test index.

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

[0093] In an optional embodiment, the performance evaluation device further includes: a historical data acquisition module, which is used to acquire historical production data before the real-time optimization system is put into use before determining the correlation between the first data and the corresponding first evaluation index. The historical production data includes multiple groups of sample data, and a single group of sample data includes production data within a preset time period; the first data and the corresponding first evaluation result are determined based on the historical production data.

[0094] In an optional embodiment, the first processing module 12 includes:

[0095] The first processing unit is used to construct a functional relationship model between the first data and the corresponding first evaluation index by taking the first data as an independent variable and the corresponding first evaluation index as a dependent variable.

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

[0097] In an optional embodiment, the fourth processing module 14 is specifically used 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 mean value of the second evaluation index and the mean value of the blank control test index.

[0098] In an optional embodiment, 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 real-time optimization system under the condition that the physical factors are the same.

[0099] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

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

[0101] The embodiment of the present invention also provides a computer device having the above Figure 3 The performance evaluation device shown.

[0102] See also Figure 4 , Figure 4 Schematic diagram of the hardware structure of the computer device according to the embodiment of the present invention. Figure 4 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In an optional embodiment, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor device). Figure 4 A processor 10 is taken as an example.

[0103] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0104] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0105] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating device, an application required for at least one function. The data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage devices. In an optional embodiment, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

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

[0107] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0108] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc. Further, the storage medium can also include a combination of the above-mentioned types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0109] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0110] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A performance evaluation method, applied to a real-time optimization system, wherein the real-time optimization system is used to optimize a catalytic reforming device, wherein the catalytic reforming device is used to convert feedstock oil into products, characterized in that: include: Determine a 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 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; Collect multiple groups of commissioning test data within a single assessment period, where each group of commissioning test data includes second data and corresponding second evaluation indicators; 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 indicator is the aromatic yield or the gross profit per unit of raw material after the real-time optimization system is put into use; Determine the first evaluation index corresponding to the second data in each group of test data according to the correlation; use the first evaluation index corresponding to the second data in each group of test data as a blank control test index; The performance evaluation result of the real-time optimization system is determined according to the second evaluation index in each group of test data and the blank control test index under the condition that the physical property factors are the same; the physical property factors include the mean value of aroma potential, the variance of aroma potential and the variance of aroma potential change rate.

2. The method according to claim 1, characterized in that Before determining the correlation between the first data and the corresponding first evaluation index, the method further includes: Acquire 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 within a preset time period; The first data and the corresponding first evaluation result are determined according to the historical production data.

3. The method according to claim 1, characterized in that The determining of the correlation between the first data and the corresponding first evaluation index includes: Taking the first data as an independent variable and the corresponding first evaluation index as a dependent variable, constructing 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 a functional relationship model between the first data and the corresponding first evaluation index.

4. The method according to claim 3, characterized in that The functional relationship model is a linear relationship function or a nonlinear relationship function.

5. The method according to claim 1, characterized in that The method of determining 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 group of commissioning test data and the blank control test index comprises: The performance evaluation result of the real-time optimization system is determined based on the mean value of the second evaluation index and the mean value of the blank control test index under the condition that the physical property factors are the same.

6. The method according to claim 5, characterized in that The method of determining the performance evaluation result of the real-time optimization system based on the mean value of the second evaluation index and the mean value of the blank control test index under the condition that the physical property factors are the same includes: The difference between the mean of the second evaluation index and the mean of the blank control test index is determined as the performance evaluation result of the real-time optimization system under the condition that the physical factors are the same.

7. A performance evaluation device, applied to a real-time optimization system, wherein the real-time optimization system is used to optimize a catalytic reforming device, wherein the catalytic reforming device is used to convert feedstock oil into products, characterized in that: include: A first processing module, used 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 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; The second processing module is used to collect multiple groups of commissioning test data within a single assessment period, and each group of commissioning test data includes second data and corresponding second evaluation indicators; the second data is the mean value of the aromatic potential after the real-time optimization system is put into use, the variance of the aromatic potential, and the variance of the aromatic potential change rate; the second evaluation indicator is the aromatic yield or the gross profit per unit of raw material after the real-time optimization system is put into use; The third processing module is used to determine the first evaluation index corresponding to the second data in each group of test data according to the correlation; and use the first evaluation index corresponding to the second data in each group of test data as a blank control test index; The fourth processing module is used 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 group of test data and the blank control test index; the physical property factors include the mean value of aroma potential, the variance of aroma potential and the variance of aroma potential change rate.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the performance evaluation method according to any one of claims 1 to 6 by executing the computer instructions.

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

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the performance evaluation method according to any one of claims 1 to 6.

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