Enterprise process safety performance evaluation method and system based on multi-system collaboration degree measurement model

Abstract

The invention discloses an enterprise process safety performance evaluation method and system based on a multi-system collaboration degree measurement model, and the method comprises the steps: carrying out the scoring of all indexes of process safety through a Delphi method, screening the indexes, and constructing a process safety performance index system; each index is collected, acquired and calculated; calculating the system order degree of sequence parameters of each subsystem of the process safety performance index system, weighting the sequence parameters of each subsystem by using an entropy weight method, and integrating by using a linear weighted summation method, so as to construct an order degree measurement model of each subsystem; and finally, establishing a process safety performance evaluation system collaboration degree measurement model by using the order degree of each subsystem, obtaining a collaboration degree value, and obtaining an evaluation result according to an evaluation rule. According to the method, a more scientific process safety performance evaluation index system is constructed, a multi-system collaborative degree measurement process safety performance evaluation model is established, and quantitative tracking and traceability analysis of a refinery enterprise process safety performance evaluation result are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of chemical industry safety, and particularly relates to a method and system for evaluating the process safety performance of enterprises based on a multi-system collaborative degree measurement model. Background Art

[0002] Since the BP refinery accident in 2006, the international chemical safety community has unanimously recognized that process safety key performance indicators (Process safety KPI) are the key means to improve production safety. Currently, international large petrochemical enterprises have established HSE quantitative indicator systems to dynamically display the effectiveness of safety management implementation, management defects, and the trend of work safety, including multiple professional safety indicator families such as equipment management and process operation.

[0003] In recent years, many work safety accidents in refining and chemical enterprises are related to the failure of safety risk control during the production operation of the device. The abnormal working conditions that may occur during the production operation of the device usually include process anomalies such as sharp fluctuations or even exceeding the standard of production operation indicators, process parameter alarms, and abnormal interlocks. To reduce the risks during the operation of chemical devices, it is necessary to define process safety indicators, quantitatively characterize the risk management level of the production operation process of chemical devices through process safety indicators, realize the evaluation and dynamic monitoring of the process safety performance of refining and chemical enterprises, improve the management level, and reduce the occurrence of accidents.

[0004] CN114662815A discloses a method and an evaluation system for process stability performance management of a refining enterprise, which are used to solve technical problems such as the relative characteristics of each indicator being not obvious and the poor comparability caused by unreasonable setting of performance indicators, differences in direct design levels of different types of devices, differences in equipment operation conditions, and differences in raw material sources. This method defines indicators related to process stability and provides a basis for subsequent performance evaluation calculations.

[0005] CN114139599A provides a chemical process safety evaluation model based on a multi-class support vector machine, which is used for the safety evaluation of the water coal slurry gasification process. This method uses the specific process parameter indicators of the water coal slurry process as the training set, and uses a multi-class support vector machine according to the training set to divide the process safety state hyperplane to determine the process safety state. This method is used to evaluate the process safety state of a specific process device, but it cannot characterize the overall process safety performance management level of the device and the enterprise.

[0006] CN110390453A discloses a process safety risk management system for petrochemical plants. Through this system, dynamic risk display and rectification tracking are realized, the integrity of process safety risk analysis for petrochemical plants and the work efficiency of analysis objects are improved, and it is beneficial to establish a risk database for process management and equipment integrity. This system is used to classify, count and display the results of process safety risk analysis of existing plants.

[0007] The above-mentioned process safety performance evaluation method mainly takes specific plant process parameters or process nodes as inputs, and conducts targeted process safety evaluation and risk analysis. However, for the overall process safety performance management of enterprises, there is still no relevant quantitative calculation method. It is necessary to provide a new method and system for quantitatively characterizing the process safety performance level of chemical plants in refining enterprises, helping enterprises analyze weak links in process safety management and reducing accidents.

[0008] Therefore, the present invention proposes a process safety performance evaluation method and system for refining enterprises based on a multi-system synergy measure model. By constructing a more scientific process safety performance evaluation index system, collecting fields from existing systems to automatically calculate index data, establishing a multi-system synergy measure process safety performance evaluation model, and accurately calculating the synergy index of each subsystem of the process safety index system, quantitative tracking and traceability analysis of the process safety performance evaluation results of refining enterprises are realized. Summary of the Invention

[0009] Aiming at the problems existing in the above-mentioned prior art, the present invention proposes a process safety performance evaluation method and system for enterprises based on a multi-system synergy measure model, which is reasonably designed, overcomes the deficiencies of the prior art, and has good effects.

[0010] In order to achieve the above object 1, the present invention adopts the following technical solutions:

[0011] A process safety performance evaluation method for enterprises based on a multi-system synergy measure model, comprising the following steps:

[0012] S1. Use the Delphi method to score each process safety index, screen the indexes, and construct a process safety performance index system;

[0013] S2. Collect, obtain and calculate each index;

[0014] S3. Calculate the system order degree of the order parameters of each subsystem of the process safety performance index system, use the entropy weight method to weight the order parameters of each subsystem and adopt the linear weighted summation method for integration, so as to construct an order degree measure model for each subsystem. Finally, use the order degree of each subsystem to establish a synergy measure model for the process safety performance evaluation system, obtain the synergy value and get the evaluation result according to the evaluation rules.

[0015] Further, each indicator is divided into five levels from 1 to 5 according to its importance, where 5 means very important, 4 means important, 3 means moderately important, 2 means unimportant, and 1 means very unimportant; the coordination of the evaluation results is represented by the coefficient of variation of the importance scores of each indicator. The coefficient of variation is the Kendall coefficient, and its formula is:

[0016]

[0017] where T represents the number of pairs of consistent elements in the importance score tables of different indicators;

[0018] U represents the number of pairs of inconsistent elements in the importance score tables of different indicators;

[0019] represents the number of pairwise combinations of all result samples;

[0020] According to the value of the Kendall coefficient, judge the degree of consistency of the importance judgment of the indicators. If 0 ≤ τ ≤ 0.2, the degree of consistency is poor; if 0.2 ≤ τ < 0.4, the degree of consistency is average; if 0.4 ≤ τ < 0.6, the degree of consistency is medium; if 0.6 ≤ τ < 0.8, the degree of consistency is strong; if 0.8 ≤ τ < 1, the degree of consistency is very strong;

[0021] Finally, screen the indicators based on the calculation results.

[0022] Further, the process safety performance indicator system includes a process indicator qualification rate subsystem, a process parameter alarm rate subsystem, a process parameter volatility subsystem, and a process automation rate subsystem. The indicators of the process indicator qualification rate subsystem include the process card qualification rate, the distillate outlet qualification rate, and the process anti-corrosion indicator qualification rate; the indicators of the process parameter alarm rate subsystem include the hourly average alarm rate, the 24-hour continuous alarm rate, and the 10-minute peak alarm rate; the indicators of the process parameter volatility subsystem include the enterprise process stability rate and the minimum stability rate of a single device; the indicators of the process automation rate subsystem include the automatic control rate, the effective application rate of APC, the interlock application rate, the number of control loops per capita, and the advanced control complexity index.

[0023] Further, S2 includes the following sub-steps:

[0024] S2.1. Acquisition and calculation of the process indicator qualification rate subsystem:

[0025] Taking the real-time database as the source, obtaining the chemical analysis data of the samples, and calculating the process card qualification rate, the distillate outlet qualification rate, and the process anti-corrosion indicator qualification rate on a monthly statistical basis. The calculation formula is:

[0026] x = (a / b) × 100%;

[0027] Among them, a is the number of qualified samples detected;

[0028] b is the total number of samples;

[0029] S2.2. Acquisition and calculation of the process parameter alarm rate subsystem:

[0030] Using the number of process alarms in the DCS system as the data source and the month as the statistical unit, the formula for calculating the average number of alarms is:

[0031]

[0032] The number of continuous alarms in 24 hours is the average number of alarms with an alarm duration of more than 24 hours, and the calculation formula is:

[0033]

[0034] The number of peak alarms in 10 minutes is the maximum number of alarms occurring within every 10 minutes, and the calculation formula is:

[0035] y3 = max(z1,...,z i ,...,z m );

[0036] Among them, z i is the number of alarms within any 10 minutes, and i ∈ (1, m);

[0037] S2.3. Acquisition and calculation of the process parameter volatility subsystem:

[0038] Discretize the real-time data of the device control indicators, and calculate the daily stability rate, monthly stability rate, and annual stability rate;

[0039] The formula for calculating the daily stability rate of a single device control indicator is:

[0040]

[0041] Among them, x is the real-time data of the device control indicator;

[0042] M is the number of all valid real-time data on the current day;

[0043] The formula for calculating the daily stability rate β of the device is:

[0044] β = ∑ω i × σ i ;

[0045] Among them, ω i is usually the weight corresponding to a single control indicator;

[0046] The formula for calculating the monthly stability rate α of the device is:

[0047] α = ∑β / d;

[0048] Where d is the number of days for statistics;

[0049] The minimum stable rate of a single device is:

[0050] γ = min(∑β / d);

[0051] The formula for calculating the daily stable rate of an enterprise is:

[0052] p = ∑α(β) / P;

[0053] The formula for calculating the monthly stable rate of an enterprise is:

[0054] p' = ∑α(β) / P;

[0055] Where P is the total number of devices in the enterprise;

[0056] S2.4, Acquisition and calculation of the process automation rate subsystem:

[0057] The indicators that this subsystem needs to acquire and calculate are the automatic control rate, the effective application rate of APC, and the interlock application rate. The data source is the real-time database, with a monthly statistical unit, and the data acquisition frequency is not less than 1 time / minute;

[0058] The formula for calculating the automatic control rate is:

[0059]

[0060] The formula for calculating the interlock application rate is:

[0061]

[0062] The formula for calculating the APC application rate is:

[0063] APC = MV × 0.5 + CV × 0.5;

[0064] Where MV is the effective application rate of MV, which is used to represent the ratio of the time when the operating variable actually applied by the APC controller plays a control role to the continuous production time of the device;

[0065] CV is the effective application rate of CV, which is used to represent the ratio of the time when the controlled variable actually applied by the APC controller plays a control role to the continuous production time of the device;

[0066] The formula for calculating CV is:

[0067]

[0068] The formula for calculating MV is:

[0069]

[0070] Among them, T is the continuous production time of the device.

[0071] Furthermore, S3 includes the following sub-steps:

[0072] S3.1. Calculation of the weight of the order parameter of each subsystem:

[0073] S3.2. Calculation of the order degree of each subsystem:

[0074] S3.3. Establish a synergy measurement model for the process safety performance evaluation system;

[0075] S3.4. Conduct evaluation and grading according to the scoring rules;

[0076] S3.5. Analysis of subsystem defects:

[0077] Furthermore, S3.1 includes the following steps:

[0078] First, since there are large differences in the units and value ranges of each index, the range transformation method is used to normalize the indexes. For the extremely large type of index, its calculation formula is:

[0079]

[0080] For the extremely small type of index, its calculation formula is:

[0081]

[0082] Through normalization, all index values are made dimensionless, and the distribution interval is [0, 1]. Finally, the standardized matrix is obtained where n represents the number of months, and m represents the number of evaluation indexes of each subsystem;

[0083] Secondly, calculate the entropy; the entropy of the j-th index among the m evaluation indexes of each subsystem is:

[0084]

[0085] In the formula,

[0086] Finally, calculate the entropy weight; the entropy weight of the j-th index is expressed as:

[0087]

[0088] In the formula, 0 ≤ w k ≤ 1 and

[0089] Calculate the weight of each index:

[0090] wij = w i w j ;

[0091] Wherein, w j is the weight of the i-th subsystem;

[0092] Furthermore, S3.2 includes the following steps:

[0093] For each subsystem X i , use the variable X ij , j ∈ {1, 2,..., m} to represent its corresponding order parameter, that is, the index. The upper and lower limits of the order parameter values are represented by α ij , β ij respectively; The positive index is the extremely large index, and the larger the index value, the higher the order degree of the subsystem; The reverse index is the extremely small index, and the smaller the index value, the higher the order degree of the subsystem. Therefore, the order degree of each subsystem order parameter X ij of the process safety performance evaluation system is defined as follows:

[0094]

[0095] Define the order degree measurement model of subsystem X i as:

[0096] 0 ≤ w ij ≤ 1 and

[0097] where μ(X ij ) ∈ [0, 1] is the order degree of the subsystem order parameter; μ(X i ) ∈ [0, 1], and the larger its value, the higher the order degree of subsystem X i .

[0098] Furthermore, S3.3 includes the following steps:

[0099] Assume that at the given initial time t0, the order degrees of the subsystems of the process safety performance evaluation system are respectively At another moment t1 of dynamic evolution, the order degrees of each subsystem at this time Then the process safety performance evaluation system is defined as follows:

[0100]

[0101] Wherein, k is the number of subsystems, and θ satisfies:

[0102]

[0103] When the order degree of all subsystems increases over time, θ = 1. When the order degree of one subsystem decreases over time, θ = -1. Therefore, the final calculated result shows that the value range of the synergy degree is ρ ∈ [-1, 1]. The larger the value, the better the coordinated development of the system and the better the evaluation grading.

[0104] Furthermore, in S3.4, to reasonably evaluate the synergy degree of the process safety performance evaluation system, according to the magnitude of the synergy degree value, it is divided into four categories: uncoordinated form, weak synergy form, general synergy form, and high-efficiency synergy form. The corresponding process safety performance management grading is red, orange, yellow, and blue respectively. When 0.80 ≤ ρ ≤ 1, it is in the high-efficiency synergy form, the system operation effect is significant, the synergy is high, and the synergy effect is high. When 0.60 ≤ ρ < 0.80, it is in the general synergy form, the system has just entered the benign motion stage, the synergy is relatively strong, and the operation effect is good. When 0.40 ≤ ρ < 0.60, it is in the weak synergy form, the synergy is relatively low, and the synergy effect is initially manifested. When -1 ≤ ρ < 0.40, it is in the uncoordinated form, the system operation is poor, the synergy is very low, and the synergy effect is not obvious.

[0105] Furthermore, in S3.5, the order degree of each index in the process safety performance evaluation system calculated is used to measure the contribution degree to the overall synergy degree of the process safety performance evaluation system. The product w of the system index order degree and the weight j μ i (X ij ) The larger it is, the greater the contribution of the order parameter X ij to the order degree of the subsystem. Therefore, each index in the system is sorted, and for the last five ranked indexes, that is, the indexes with lower contribution degrees and defects, alarm processing is carried out. This is convenient for enterprises to improve the indexes subsequently and enhance the process safety performance.

[0106] To achieve the above-mentioned purpose 2, the present invention adopts the following technical solutions:

[0107] An enterprise process safety performance evaluation system based on a multi-system synergy degree measurement model, adopting the above-mentioned enterprise process safety performance evaluation method based on a multi-system synergy degree measurement model, includes a process safety performance index library module, an index automatic collection module, and a process safety performance evaluation module;

[0108] The process safety performance index library module is used to score each index by using the Delphi method to construct a process safety performance index evaluation system. This evaluation system includes a total of four subsystems, namely the qualified rate of process indexes, the alarm rate of process parameters, the volatility of process parameters, and the process automation rate. Each subsystem contains multiple parameter indexes used to characterize its order degree;

[0109] The said index automatic acquisition module is used to realize the automatic acquisition, obtaining and calculation of each index within the process safety performance index system. According to the parameter indexes determined in the index evaluation system, data is automatically obtained from the process stability systems of each enterprise according to the affiliated level, year and month. After data cleaning, anomaly processing and interface docking with the organization, this data is stored in the form of a file. After verifying the validity of the data, the valid data is stored in the database;

[0110] The said process safety performance evaluation module is used to calculate the system order degree of the order parameters of each subsystem in the process safety performance evaluation system. The entropy weight method is used to weight the order parameters of each subsystem and the linear weighted summation method is used for integration to construct the order degree measurement model of each subsystem. Finally, the synergy degree measurement model of the process safety performance evaluation system is established by using the order degrees of each subsystem, and the synergy degree value is obtained and the evaluation result is obtained according to the evaluation rules. At the same time, the changes in the order degrees of each subsystem in different time periods are compared, and the development degree of the subsystem is judged according to the magnitude of the order degree, and then the specific indexes that need to be improved in each subsystem are determined to realize traceability analysis and improvement.

[0111] The beneficial technical effects brought by the present invention:

[0112] 1. At present, there is only the analysis of process safety indexes for specific processes or devices. However, for the evaluation of the overall process safety performance level of chemical plants in refining enterprises, there is no unified standard, and there is no clear system establishment and classification for the performance indexes of process safety. Based on process safety expertise, the present invention classifies the general indexes related to process safety into subsystems, and at the same time clarifies the specific indexes under each subsystem. When evaluating the process safety performance of different enterprises, the same standard is adopted, which helps to realize horizontal comparison.

[0113] 2. The present invention uses the synergy degree measurement model for evaluation, which not only considers the indexes of each subsystem, but more importantly, pays attention to the mutual influence and synergy among multiple subsystems, making the evaluation result more accurate and comprehensive. In addition, the present invention can also automatically collect and process data, greatly reducing the errors and uncertainties of manual measurement.

[0114] 3. On the time scale, through the present invention, the changes in the order degrees of each subsystem of the process safety of refining enterprises over time can be obtained, reflecting the progress of process index qualification, process alarm, process stability and process automation of the enterprise on a monthly or even annual basis, thus promoting the continuous development of process safety management. BRIEF DESCRIPTION OF THE DRAWINGS

[0115] Figure 1 is the flow chart of the enterprise process safety performance evaluation method in the present invention;

[0116] Figure 2Schematic diagram of the process safety performance index system in the present invention;

[0117] Figure 3 Schematic diagram of the enterprise process safety performance evaluation system in the present invention;

[0118] Figure 4 Schematic diagram of the index automatic acquisition module in the present invention; Detailed implementation manners

[0119] The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present disclosure. On the contrary, they are merely examples of methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0120] The terms used in the present disclosure are for the purpose of describing specific embodiments only and are not intended to limit the present disclosure. The singular forms "a", "the", and "said" used in the present disclosure and the appended claims are also intended to include the plural forms unless the context clearly dictates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0121] The following further illustrates the detailed implementation manners of the present invention in conjunction with specific embodiments:

[0122] An enterprise process safety performance evaluation method based on a multi-system coordination degree measurement model, as Figure 1 shown, includes the following steps:

[0123] S1. Use the Delphi method to score each process safety index, screen the indexes, and construct a process safety performance index system;

[0124] Each index is divided into five levels from 1 to 5 according to importance, where 5 is very important, 4 is important, 3 is of general importance, 2 is unimportant, and 1 is very unimportant; the coordination of the evaluation results is represented by the coefficient of variation of the importance scores of each index, and the coefficient of variation is the Kendall coefficient, and its formula is:

[0125]

[0126] where T represents the number of pairs of consistent elements in the importance score tables of different indexes;

[0127] U represents the number of inconsistent elements in the importance score tables of different indexes;

[0128] represents the number of pairwise combinations of all result samples;

[0129] According to the value of Kendall's coefficient, judge the degree of consistency in the importance judgment of indicators. If 0 ≤ τ ≤ 0.2, the degree of consistency is poor; if 0.2 ≤ τ < 0.4, the degree of consistency is average; if 0.4 ≤ τ < 0.6, the degree of consistency is medium; if 0.6 ≤ τ < 0.8, the degree of consistency is strong; if 0.8 ≤ τ < 1, the degree of consistency is very strong. As shown in the following table:

[0130] Table 1

[0131] Kendall coefficient Degree of consistency 0 ≤ τ ≤ 0.2 Poor 0.2 ≤ τ < 0.4 Average 0.4 ≤ τ < 0.6 Medium 0.6 ≤ τ < 0.8 Strong 0.8 ≤ τ < 1 Very strong

[0132] Finally, screen the indicators based on the calculation results. The indicator screening method is as follows:

[0133] (1) Threshold method: Determine the threshold through the mean, full score rate, and variation degree of importance scores, and delete the indicators below or above the threshold.

[0134] (2) According to the reciprocal of the mean of importance scores or a pre-determined value, delete the indicators below this value.

[0135] (3) Judge according to Kendall's coefficient. For example, delete the indicators with Kendall's coefficient < 0.4.

[0136] After 2 - 3 rounds of iterative calculations, the process safety performance indicator system was determined and constructed through the Delphi method. As Figure 2 shown, the indicator system includes a process index qualification rate subsystem, a process parameter alarm rate subsystem, a process parameter volatility subsystem, and a process automation rate subsystem. The indicators of the process index qualification rate subsystem include the process card qualification rate, the distillate outlet qualification rate, and the process anti-corrosion index qualification rate; the indicators of the process parameter alarm rate subsystem include the hourly average alarm rate, the 24-hour continuous alarm rate, and the 10-minute peak alarm rate; the indicators of the process parameter volatility subsystem include the enterprise process stability rate and the minimum stability rate of a single device; the indicators of the process automation rate subsystem include the automatic control rate, the effective application rate of APC, the interlock application rate, the number of control loops per capita, and the advanced control complexity index.

[0137] S2. Collect, obtain, and calculate each indicator;

[0138] S2 includes the following sub-steps:

[0139] S2.1. Collection and calculation of the process index qualification rate subsystem:

[0140] Taking the real-time database as the source, obtain the chemical analysis data of the samples, and calculate the process card qualification rate, the distillate outlet qualification rate, and the process anti-corrosion index qualification rate on a monthly statistical basis. The calculation formula is:

[0141] x = (a / b) × 100%;

[0142] Among them, a is the number of qualified samples detected;

[0143] b is the total number of samples;

[0144] S2.2. Acquisition and calculation of the process parameter alarm rate subsystem:

[0145] Taking the number of process alarms in the DCS system as the data source and the month as the statistical unit, the calculation formula for the average number of alarms is:

[0146]

[0147] The number of continuous alarms in 24 hours is the average number of alarms with an alarm duration of more than 24 hours. The calculation formula is:

[0148]

[0149] The number of peak alarms in 10 minutes is the maximum number of alarms occurring within every 10 minutes. The calculation formula is:

[0150] y3 = max(z1,..., z i ,..., z m );

[0151] Among them, z i is the number of alarms within any 10 minutes, and i ∈ (1, m);

[0152] S2.3. Acquisition and calculation of the process parameter volatility subsystem:

[0153] Discretize the real-time data of the device control index, and calculate the daily stability rate, monthly stability rate, and annual stability rate;

[0154] The calculation formula for the daily stability rate of a single device control index is:

[0155]

[0156] Among them, x is the real-time data of the device control index;

[0157] M is the number of all valid real-time data on the current day;

[0158] The calculation formula for the daily stability rate β of the device is:

[0159] β = ∑ω i × σ i ;

[0160] Among them, ω i is usually the weight corresponding to a single control index;

[0161] The calculation formula for the monthly stability rate α of the device is:

[0162] α = ∑β / d;

[0163] where d is the number of days for statistics;

[0164] The minimum stable rate of a single device is:

[0165] γ = min(Σβ / d)

[0166] The formula for calculating the daily stable rate of an enterprise is:

[0167] p = ∑α(β) / P;

[0168] The formula for calculating the monthly stable rate of an enterprise is:

[0169] p' = ∑α(β) / P;

[0170] where P is the total number of devices in the enterprise; usually, the control indicators of the devices in the enterprise are parameters such as temperature, pressure, and liquid level;

[0171] S2.4. Acquisition and calculation of the process automation rate subsystem:

[0172] The indicators that need to be acquired and calculated by this subsystem are the automatic control rate, the effective application rate of APC, and the interlock application rate. The data comes from the real-time database, with a monthly statistical unit, and the data acquisition frequency is not less than 1 time / minute;

[0173] The formula for calculating the automatic control rate is:

[0174]

[0175] The formula for calculating the interlock application rate is:

[0176]

[0177] The formula for calculating the APC application rate is:

[0178] APC = MV × 0.5 + CV × 0.5;

[0179] where MV is the effective application rate of MV, which is used to represent the ratio of the time when the operating variable actually applied by the APC controller plays a control role to the continuous production time of the device;

[0180] CV is the effective application rate of CV, which is used to represent the ratio of the time when the controlled variable actually applied by the APC controller plays a control role to the continuous production time of the device;

[0181] The formula for calculating CV is:

[0182]

[0183] The formula for calculating MV is:

[0184]

[0185] Among them, T is the continuous production time of the device.

[0186] S3. Calculate the system order degree of the order parameters of each subsystem of the process safety performance index system, assign weights to the order parameters of each subsystem using the entropy weight method and integrate them using the linear weighted summation method, so as to construct an order degree measurement model for each subsystem. Finally, use the order degrees of each subsystem to establish a measurement model for the synergy degree of the process safety performance evaluation system, obtain the synergy degree value, and obtain the evaluation result according to the evaluation rules.

[0187] S3 includes the following sub-steps:

[0188] S3.1. Calculation of the weights of the order parameters of each subsystem:

[0189] To avoid the influence of human factors, the entropy weight method is selected to assign weights to the indicators of each subsystem based on the existing indicator data. The specific steps are as follows:

[0190] First, since there are large differences in the units and numerical ranges of each indicator, the range transformation method is used to normalize the indicators. For extremely large indicators, its calculation formula is:

[0191]

[0192] For extremely small indicators, its calculation formula is:

[0193]

[0194] Through normalization, all indicator values are made dimensionless, and the distribution interval is [0, 1]. Finally, the standardized matrix is obtained where n represents the number of months, and m represents the number of evaluation indicators of each subsystem;

[0195] Secondly, calculate the entropy; the entropy of the jth indicator among the m evaluation indicators of each subsystem is:

[0196]

[0197] In the formula,

[0198] Finally, calculate the entropy weight; the entropy weight of the jth indicator is expressed as:

[0199]

[0200] In the formula, 0 ≤ w j ≤ 1 and

[0201] Calculate the weights of each indicator:

[0202] w ij = w i w j ;

[0203] Wherein, w j is the weight of the i-th subsystem;

[0204] S3.2. Calculation of the order degree of each subsystem:

[0205] For each subsystem X i , use the variable X ij , j ∈ {1, 2,..., m} to represent its corresponding order parameter, that is, the index. The order parameters of all subsystems form the evaluation word list of the process safety performance evaluation system. The upper and lower limits of the order parameter values are represented by α ij , β ij respectively; The positive index is the extremely large index, and the larger the index value, the higher the order degree of the subsystem; The reverse index is the extremely small index, and the smaller the index value, the higher the order degree of the subsystem. Therefore, the order degree of the order parameter X ij of each subsystem in the process safety performance evaluation system is defined as follows:

[0206]

[0207] The order degree of the subsystem is not only related to the order degree of each order parameter (index), but also related to their specific combination form, that is, the weight of each order parameter. The order degree of each subsystem can be obtained through the weighted sum of the order degrees of each order parameter. Therefore, the order degree measurement model of the subsystem X i is defined as:

[0208]

[0209] where μ(X ij ) ∈ [0, 1] is the order degree of the subsystem order parameter; μ(X i ) ∈ [0, 1], and the larger its value, the higher the contribution of the order degree of the subsystem X i .

[0210] S3.3. Establish a measurement model for the synergy degree of the process safety performance evaluation system;

[0211] Assume that at the given initial time t0, the order degrees of the subsystems of the process safety performance evaluation system are respectively At another moment t1 of dynamic evolution, the order degrees of each subsystem at this time Then the process safety performance evaluation system is defined as follows:

[0212]

[0213] where k is the number of subsystems;

[0214] θ satisfies:

[0215]

[0216] When the order degree of all subsystems increases over time, θ = 1; when the order degree of one subsystem decreases over time, θ = -1. Therefore, the value range of the synergy degree ρ in the final calculation result is ρ ∈ [-1, 1]. The larger the value, the better the collaborative development degree of the system and the better the evaluation grading.

[0217] S3.4. Evaluate and classify according to the scoring rules;

[0218] To reasonably evaluate the synergy degree of the process safety performance evaluation system, according to the magnitude of the synergy degree value, it is divided into four categories: non - collaborative form, weak - collaborative form, general - collaborative form, and highly - efficient collaborative form. The corresponding process safety performance management classifications are red, orange, yellow, and blue respectively; when 0.80 ≤ ρ ≤ 1, it is a highly - efficient collaborative form, the system operation effect is significant, the synergy is high, and the synergy effect is high; when 0.60 ≤ ρ < 0.80, it is a general - collaborative form, the system has just entered the benign movement stage, the synergy is relatively strong, and the operation effect is good; when 0.40 ≤ ρ < 0.60, it is a weak - collaborative form, the synergy is relatively low, and the synergy effect is initially manifested; when - 1 ≤ ρ < 0.40, it is a non - collaborative form, the system operation is poor, the synergy is very low, and the synergy effect is not obvious. The corresponding process safety performance management classifications are red, orange, yellow, and blue respectively, as shown in the following table:

[0219] Table 2 Synergy Degree Levels and Evaluation Criteria of Process Safety Performance Evaluation System

[0220]

[0221] S3.5. Sub - system defect analysis:

[0222] Use the order degree of each index in the calculated process safety performance evaluation system to measure the contribution degree to the overall synergy degree of the process safety performance evaluation system. The product w of the system index order degree and the weight j μ i (X ij ) is larger, indicating that the order parameter X ij has a greater contribution to the order degree of the sub - system. Therefore, sort each index in the system, and for the last five ranked indexes, that is, the indexes with lower contribution degrees and defects, carry out alarm processing to facilitate the enterprise to improve the indexes subsequently and enhance the process safety performance.

[0223] An enterprise process safety performance evaluation system based on a multi-system synergy measurement model, which adopts an enterprise process safety performance evaluation method based on a multi-system synergy measurement model as described above, as Figure 3 shown, includes a process safety performance index library module, an index automatic acquisition module, and a process safety performance evaluation module;

[0224] The process safety performance index library module is used to score each index by using the Delphi method to construct a process safety performance index evaluation system. This evaluation system includes a total of four subsystems, namely the qualified rate of process indexes, the alarm rate of process parameters, the volatility rate of process parameters, and the process automation rate. Each subsystem contains multiple parametric indexes used to characterize its order degree;

[0225] The index automatic acquisition module is used to realize the automatic acquisition, obtaining and calculation of each index in the process safety performance index system. According to the parametric indexes determined in the index evaluation system, data is automatically obtained from the process stability system (process KPI system) of each enterprise according to the affiliated level, year and month, as Figure 4 shown. After data cleaning, anomaly processing and interface docking with the organization, this data is stored in the form of a file. After verifying the validity of the data, the valid data is stored in the database;

[0226] The process safety performance evaluation module is used to calculate the system order degree of the order parameters of each subsystem in the process safety performance evaluation system, use the entropy weight method to assign weights to the order parameters of each subsystem and adopt the linear weighted summation method for integration to construct an order degree measurement model for each subsystem. Finally, a synergy measurement model for the process safety performance evaluation system is established by using the order degrees of each subsystem, and the synergy value is obtained and the evaluation result is obtained according to the evaluation rules. At the same time, compare the changes in the order degrees of each subsystem in different time periods, judge the development degree of the subsystem according to the size of the order degree, and then determine the specific indexes that need to be improved for each subsystem to realize traceability analysis and improvement.

[0227] The above describes the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and the equipment and structures not described in detail should be understood to be implemented in a common manner in the art; any person skilled in the art can make many possible changes and modifications to the technical solution of the present invention by using the methods and technical contents disclosed above without departing from the scope of the technical solution of the present invention, or modify it into an equivalent embodiment with equivalent changes, which does not affect the essence of the present invention. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the protection of the technical solution of the present invention.

Claims

1. An enterprise process safety performance evaluation method based on a multi-system synergy measurement model, characterized in that The following steps are involved: S1. Use the Delphi method to score various process safety indicators, screen the indicators, and build a process safety performance indicator system; S2, collect, obtain and calculate each indicator; S3. Calculate the system order of the order parameters of each subsystem of the process safety performance index system, use the entropy weight method to weight the order parameters of each subsystem and integrate them using the linear weighted summation method, so as to construct the order measurement model of each subsystem. Finally, use the order of each subsystem to establish the coordination measurement model of the process safety performance evaluation system, obtain the coordination value and get the evaluation result according to the evaluation rules.

2. The enterprise process safety performance evaluation method based on the multi-system synergy measurement model according to claim 1, wherein Each indicator is divided into five levels from 1 to 5 according to its importance, 5 is very important, 4 is important, 3 is moderately important, 2 is not important, and 1 is very unimportant; the coefficient of variation of the importance score of each indicator is used to express the coordination of the evaluation results. The coefficient of variation is the Kendall coefficient, and its formula is: Among them, T represents the number of pairs of elements with consistency in the importance score tables of different indicators, and U represents the number of pairs of elements with inconsistency in the importance score tables of different indicators. represents the number of pairwise combinations of all result samples; According to the value of Kendall coefficient, the consistency of the index importance judgment is judged. If 0≤τ≤0.2, the consistency is poor; if 0.2≤τ<0.4, the consistency is fair; if 0.4≤τ<0.6, the consistency is moderate; if 0.6≤τ<0.8, the consistency is strong; if 0.8≤τ<1, the consistency is very strong; Finally, the indicators are screened based on the calculation results.

3. A method for evaluating the process safety performance of an enterprise based on a multi-system synergy measurement model according to claim 1, characterized in that, The process safety performance indicator system includes a process indicator qualification rate subsystem, a process parameter alarm rate subsystem, a process parameter fluctuation rate subsystem and a process automation rate subsystem. The indicators of the process indicator qualification rate subsystem include the process card qualification rate, the distillation outlet qualification rate and the process corrosion protection index qualification rate; the indicators of the process parameter alarm rate subsystem include the hourly average alarm rate, the 24-hour continuous alarm rate and the 10-minute peak alarm rate; The indicators of the process parameter fluctuation rate subsystem include the enterprise process stability rate and the minimum stability rate of a single device; the indicators of the process automation rate subsystem include the automatic control rate, APC effective utilization rate, interlocking utilization rate, per capita control loop number, and advanced control complexity index.

4. A method for evaluating the process safety performance of an enterprise based on a multi-system synergy measurement model according to claim 1, characterized in that S2 includes the following sub-steps: S2.

1. Collection and calculation of process index qualification rate subsystem: The real-time database is used as the source to obtain the sample analysis data. The qualified rate of process cards, qualified rate of distillation outlets and qualified rate of process anti-corrosion indicators are calculated with the month as the statistical unit. The calculation formula is: x = (a / b) × 100%; Among them, a is the number of qualified samples, and b is the total number of samples; S2.2, Collection and calculation of process parameter alarm rate subsystem: Taking the number of process alarms in the DCS system as the data source and the month as the statistical unit, the calculation formula for the average number of alarms is: The number of continuous alarms in 24 hours is the average number of alarms with a duration of more than 24 hours. The calculation formula is: The 10-minute peak alarm number is the maximum number of alarms that occur every 10 minutes, and the calculation formula is: y3 = max(z1,..., z i ,..., z m ); where z i is the number of alarms within any 10 - minute period, and \(i\in(1,m)\); S2.3, Collection and calculation of process parameter fluctuation rate subsystem: Discretize the real-time data of the device control index and calculate the daily, monthly and annual stability rates; The daily stability rate calculation formula of a single control index of the device is: Among them, x is the real-time data of the device control index, and M is the number of all valid real-time data on the current day; The calculation formula for the daily stability rate β of the device is: β = ∑ω i × σ i ; Among them, ω i usually represents the weight corresponding to a single control index; The calculation formula for the monthly stability rate α of the device is: α = Σβ / d; Among them, d is the number of statistical days; The minimum stability rate of a single device is: γ = min(∑β / d); The calculation formula for the daily stability rate of the enterprise is: p = ∑α(β) / P; The calculation formula for the monthly stability rate of the enterprise is: p' = ∑α(β) / P; Among them, P is the total number of devices in the enterprise; S2.

4. Acquisition and calculation of the process automation rate subsystem: The indicators that need to be collected and calculated by this subsystem are the automatic control rate, the effective application rate of APC, and the interlock application rate. The data comes from the real-time database, with a monthly statistical unit, and the data acquisition frequency is not less than 1 time / minute; The calculation formula for the automatic control rate is: The calculation formula for the interlock application rate is: The calculation formula for the APC application rate is: APC = MV×0.5 + CV×0.5; Among them, MV is the effective application rate of MV, which is used to represent the ratio of the time when the operation variable actually applied by the APC controller plays a control role to the continuous production time of the device; CV is the effective application rate of CV, which is used to represent the ratio of the time when the controlled variable actually applied by the APC controller plays a control role to the continuous production time of the device; The calculation formula for CV is: The calculation formula for MV is: Among them, T is the continuous production time of the device.

5. The enterprise process safety performance evaluation method based on the multi-system synergy degree measurement model according to claim 1, characterized in that S3 includes the following sub-steps: S3.

1. Calculation of the order parameter weights of each subsystem: S3.

2. Calculation of the order degree of each subsystem: S3.

3. Establish a measure model for the synergy degree of the process safety performance evaluation system; S3.

4. Evaluate and classify according to the scoring rules; S3.

5. Analysis of subsystem defects.

6. The enterprise process safety performance evaluation method based on the multi-system synergy degree measurement model according to claim 5, wherein, S3.1 includes the following steps: First, use the range transformation method to normalize the indicators. For extremely large indicators, its calculation formula is: For extremely small indicators, its calculation formula is: After normalization, all index values are dimensionless, and the distribution range is [0, 1], and finally a standardized matrix is obtained. Where n represents the number of months, and m represents the number of evaluation indexes of each subsystem; Secondly, calculate entropy; the entropy of the j-th indicator among the m evaluation indicators of each subsystem is: In the formula, Finally, calculate the entropy weight; the entropy weight of the j-th indicator is expressed as: where 0 ≤ w j ≤ 1 and Calculate the weights of each indicator: w ij = w i w j ; where w j is the weight of the i-th subsystem.

7. A method for evaluating the enterprise process safety performance based on a multi-system coordination degree measurement model according to claim 5, characterized in that S3.2 includes the following steps: For each subsystem X i , the corresponding order parameter, i.e., index, is represented by the variable X ij , j ∈ {1, 2,..., m}, and the upper and lower limits of the order parameter values are represented by α ij , β ij respectively; the positive index is the extremely large type index, and the larger the index value, the higher the order degree of the subsystem; the reverse index is the extremely small type index, and the smaller the index value, the higher the order degree of the subsystem. Therefore, the order degree of the order parameter X ij of each subsystem in the process safety performance evaluation system is defined as follows: Define subsystem X i The order measure model of is as follows: 0 ≤ w ij ≤ 1 and Among them, μ(X ij ) ∈ [0, 1] is the order degree of the subsystem order parameter; μ(X i ) ∈ [0, 1], and the larger its value, the higher the order degree of the subsystem X i .

8. A method for evaluating the process safety performance of an enterprise based on a multi-system synergy measurement model according to claim 5, characterized in that, S3.3 includes the following steps: Suppose that at the given initial moment \(t_0\), the order degrees of the subsystems of the process safety performance evaluation system are respectively At another moment \(t_1\) of dynamic evolution, the order degrees of each subsystem at this time Then the process safety performance evaluation system is defined as follows: In the formula, k is the number of subsystems, and θ satisfies: When the order degree of all subsystems increases over time, θ = 1. When the order degree of one subsystem decreases over time, θ = -1. Therefore, the value range of the synergy degree of the final calculation result is ρ ∈ [-1, 1]. The larger the value, the better the collaborative development degree of the system and the better the evaluation classification.

9. The enterprise process safety performance evaluation method based on the multi-system coordination degree measurement model according to claim 5, characterized in that In S3.4, to reasonably evaluate the synergy degree of the process safety performance evaluation system, according to the size of the synergy degree value, it is divided into four categories: non-synergistic form, weakly synergistic form, generally synergistic form, and highly synergistic form. The corresponding process safety performance management classifications are red, orange, yellow, and blue respectively; when 0.80 ≤ ρ ≤ 1, it is a highly synergistic form; when 0.60 ≤ ρ < 0.80, it is a generally synergistic form; when 0.40 ≤ ρ < 0.60, it is a weakly synergistic form; when -1 ≤ ρ < 0.40, it is a non-synergistic form.

10. A method for evaluating the process safety performance of an enterprise based on a multi-system synergy measurement model according to claim 5, characterized in that, In S3.5, the contribution degree to the overall synergy degree of the process safety performance evaluation system is measured by using the order degree of each index in the calculated process safety performance evaluation system. The product w of the system index order degree and the weight j μ i (X ij ) The larger it is, the greater the contribution of the order parameter X ij to the order degree of the subsystem. Therefore, the indexes are sorted, and the indexes ranked in the last five, that is, the indexes with lower contribution degree and defects, are alarmed.

11. An enterprise process safety performance evaluation system based on a multi-system collaboration degree measurement model, characterized in that, An enterprise process safety performance evaluation method based on a multi-system synergy measurement model as described in any one of claims 1-10, comprising a process safety performance index library module, an index automatic acquisition module, and a process safety performance evaluation module; The process safety performance index library module is used to score each index using the Delphi method and construct a process safety performance index evaluation system. This evaluation system includes a total of four subsystems, namely the process index qualification rate, the process parameter alarm rate, the process parameter volatility rate, and the process automation rate. Each subsystem contains multiple parametric indicators used to characterize its orderliness; The index automatic acquisition module is used to achieve the automatic acquisition, obtaining, and calculation of each index in the process safety performance index system. According to the parametric indicators determined in the index evaluation system, data is automatically obtained from the process stability systems of each enterprise according to the hierarchy, year, and month. After data cleaning, anomaly processing, and interface docking with the organization, this data is stored in the form of a file. After verifying the validity of the data, the valid data is stored in the database; The process safety performance evaluation module is used to calculate the system orderliness of the order parameters of each subsystem in the process safety performance evaluation system, assign weights to the order parameters of each subsystem using the entropy weight method, and use the linear weighted summation method for integration to construct an orderliness measurement model for each subsystem. Finally, a synergy measurement model for the process safety performance evaluation system is established using the orderliness of each subsystem, and the synergy value is obtained and the evaluation result is obtained according to the evaluation rules.

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