Chemical process safety early warning method and system based on dynamic risk factor assessment

Abstract

The invention discloses a chemical process safety early warning method and system based on dynamic risk factor assessment, and belongs to the technical field of industrial safety. The method comprises the steps that S100, a static model is constructed, key risk factors are identified through accident tree analysis, and the weights of the key risk factors are determined through an analytic hierarchy process; and S200, performing dynamic risk assessment, establishing mapping between risk factors and real-time data, converting the real-time data into risk factor state probability through a quantization function, calculating a comprehensive dynamic risk value in combination with weights, and performing early warning. According to the method, the problems of static lag and strong subjectivity of a traditional safety assessment method are effectively solved, real-time, quantitative, dynamic and prospective early warning of the safety risk in the chemical process is realized, and the intelligent level of safety production management is remarkably improved.

Description

TECHNICAL FIELD

[0001] The application discloses a chemical process safety early warning method and system based on dynamic risk factor evaluation, and belongs to the technical field of industrial safety. The method comprises the following steps: S100, static model construction, key risk factors are identified through fault tree analysis, and weights of the key risk factors are determined by using an analytic hierarchy process; S200, dynamic risk assessment, a mapping between risk factors and real-time data is established, real-time data is converted into risk factor state probability through a quantitative function, a comprehensive dynamic risk value is calculated by combining the weights, and early warning is performed. The application effectively solves the problems of static lag, strong subjectivity of a traditional safety evaluation method, realizes real-time, quantitative, dynamic and forward-looking early warning of safety risks of a chemical process, and significantly improves the intelligent level of safety production management. BACKGROUND

[0002] The safety of a chemical production process, especially a process involving dangerous chemicals (such as chloroethylene in PVC production), is crucial. Traditional safety evaluation methods are mostly based on static and periodic inspection and evaluation, and have the following significant shortcomings: 1. Static lag: the evaluation result reflects the safety state at a historical or fixed time point, and cannot capture the risk fluctuations caused by dynamic changes in equipment state, environmental parameters, personnel operation, etc.

[0003] 2. Subjective experience: risk assessment relies heavily on expert scoring and historical statistical data, and the accuracy and reliability of the evaluation result decrease when there is a lack of data or when facing new working conditions.

[0004] 3. Lack of systematic quantification: traditional methods cannot systematically quantify the comprehensive influence of various risk factors (such as equipment failure, human error, and environmental changes) on the overall safety level and their interactions.

[0005] 4. Inadequate response: it cannot provide immediate early warning when the risk level begins to rise, and can only discover problems after an accident occurs or during regular inspections, missing the best opportunity for prevention.

[0006] Therefore, there is an urgent need in the field for a chemical process safety early warning method that can achieve real-time, dynamic, quantitative and systematic comprehensive evaluation. SUMMARY

[0007] The application aims to provide a chemical process safety early warning method and system based on dynamic risk factor evaluation to solve the problems of evaluation static lag, strong subjective dependence, lack of systematic quantification and inadequate response in the prior art.

[0008] To achieve the above-mentioned purpose, the application adopts the following technical solutions: In a first aspect, the present invention provides a chemical process safety early warning method based on dynamic risk factor assessment, executed by a computing device, comprising the following steps: S100: Static risk model construction phase.

[0009] S101: Risk Factor Identification: For the target chemical process unit, through hazard and operability analysis, identify all potential basic events that may lead to top-end events (such as leaks or explosions) to form an initial risk factor set.

[0010] S102: Fault Tree Modeling and Importance Analysis: Based on the initial risk factor set, construct a fault tree model describing the logic of the top event occurrence. Perform qualitative analysis on the fault tree to calculate the structural importance or probabilistic importance of each basic event.

[0011] S103: Screening of key risk factors: Based on the importance calculation results, key risk factors with importance higher than a preset threshold are screened from the initial risk factor set as core indicators for subsequent hierarchical analysis.

[0012] S104: Construction of Hierarchical Analysis Model: Establish a hierarchical analysis structure model including a target layer, a constraint layer, and an indicator layer; wherein, the target layer is the overall risk level of the process unit, the constraint layer is several major factor categories that lead to risk (such as natural weather, human operation, equipment and instruments, safety alarms, etc.), and the indicator layer is composed of the key risk factors screened in S103.

[0013] S105: Weight Calculation: Construct the judgment matrix for each level of the hierarchical analysis model. By calculating the eigenvectors of the matrices, determine the weights of each constraint layer factor relative to the target layer, and the local weights of each indicator layer risk factor relative to its respective constraint layer. Finally, obtain the global weight of each key risk factor through weighted synthesis. Perform a consistency check on all judgment matrices to ensure logical rationality.

[0014] S200: Dynamic risk assessment and early warning stage.

[0015] S201: Real-time data acquisition and mapping: Establish the mapping relationship between the key risk factors and real-time data sources; the real-time data sources include, but are not limited to: process sensor data (pressure, temperature, flow rate, liquid level), equipment status data, environmental monitoring data (ambient temperature, wind speed), personnel positioning and operation log data.

[0016] S202: Risk Factor State Probability Quantification: Based on the real-time data, dynamically calculate the current state probability or risk contribution value of each key risk factor through a predefined quantification function or rule.

[0017] For continuous sensor data, a bias-based function is used for quantization. For example, for the pressure risk factor, its state probability P_pressure = k * ( (P_current - P_operational) / (P_max - P_operational) )^n, where k and n are adjustment coefficients.

[0018] The probability of equipment failure is dynamically adjusted by combining its runtime with the basic failure rate model.

[0019] For human operational factors, a semi-quantitative assessment is conducted based on a behavioral safety database and real-time operation records.

[0020] For environmental factors, values ​​are assigned based on the comparison between real-time monitoring data and preset thresholds.

[0021] S203: Dynamic risk value calculation: Combine the dynamic state probabilities of each key risk factor obtained in S202 with their corresponding global weights obtained in S105, and use a weighted summation method or fuzzy comprehensive evaluation method to calculate the current comprehensive dynamic risk value R_dynamic = Σ (W_i * P_i), where W_i is the global weight of the i-th risk factor and P_i is its current state probability.

[0022] S204: Risk Level Determination and Early Warning: The calculated dynamic risk value is compared with a preset risk level threshold range to determine the current risk level (e.g., extremely low, low, medium, high, extremely high). When the risk level exceeds the preset safety threshold, an early warning message is automatically generated and issued.

[0023] S300: Visualization and Feedback. The dynamic risk values, risk levels, and the contribution of each risk factor are displayed in real time in a graphical manner (such as risk cloud maps, dashboards, and trend curves), and risk source analysis functions are provided to managers.

[0024] In a second aspect, the present invention provides a chemical process safety early warning system based on dynamic risk factor assessment, comprising at least one processor and a memory, wherein the memory stores computer program instructions, and when the program instructions are executed by the processor, the system is configured to perform the method described in any one of the first aspects.

[0025] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in any one of the first aspects.

[0026] Beneficial effects Compared with the prior art, the present invention has the following significant advantages: 1. Achieved a leap from static to dynamic: By integrating real-time operational data, environmental data, and personnel behavior data into the risk assessment model, dynamic updates of safety risks were achieved at the minute or even second level, greatly improving the timeliness of risk perception.

[0027] 2. Objective Quantification of Assessment Results: Weights are determined based on fault tree importance and analytic hierarchy process, reducing subjective arbitrariness; real-time data is quantified into risk probabilities through predefined mathematical functions, making the assessment results more scientific, objective, and comparable.

[0028] 3. Strong systematicity and clear root causes: Through fault tree and hierarchical analysis models, it systematically covers various risk factors from equipment, process to people, environment and management, and can clearly show the contribution of each factor to the overall risk, which facilitates targeted risk management.

[0029] 4. Strong early warning and foresight: It can issue early warnings before the quantitative changes in risk level accumulate and before a qualitative change (accident) occurs, thus winning valuable time for taking preventive measures and realizing the transformation from "post-event emergency response" to "pre-event early warning".

[0030] 5. High flexibility and adaptability: The method is not dependent on specific hardware and can be deployed on existing industrial control and information management platforms. The model and parameters can be adjusted and optimized according to different process units, exhibiting broad applicability. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the basic data preparation process for the chemical process safety dynamic assessment and early warning method of this invention.

[0032] Figure 2 This is a schematic diagram of the overall process of the chemical process safety dynamic assessment and early warning method of the present invention. Detailed Implementation

[0033] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0034] Example 1: This example uses a polymerization process unit of a chemical enterprise as the application scenario.

[0035] S100: Static risk model construction phase.

[0036] S101: Through analysis, 45 potential basic events were identified, including "Safety device failure (X22)", "Valve failure (X13)", "Pressure gauge failure (X15)", "Power alarm failure (X7)", and "Manual verification / inspection error (X11)".

[0037] S102: Construct as follows Figure 2The shown fault tree for the polymerization process has "polymerization process leak and explosion" as the top event. The structural importance of each basic event is calculated, for example, I[X22]=9.28, I[X13]=4.73, I[X15]=4.61.

[0038] S103: Select 18 basic events with a structural importance greater than 0.5 as key risk factors, such as "safety device failure", "valve failure", "pressure gauge failure", etc.

[0039] S104: Establish a hierarchical analysis model. The target layer is "aggregation process risk level," and the constraint layer includes six categories: natural weather (A1), human operation (A2), metering devices (A3), valve devices (A4), media factors (A5), and safety alarms (A6). The indicator layer consists of the aforementioned 18 key risk factors, each belonging to its corresponding constraint layer.

[0040] S105: By constructing a judgment matrix and performing a consistency check, the weight vectors of each constraint layer are calculated as [0.0588, 0.0979, 0.0979, 0.2880, 0.1695, 0.2880], and the global weights of each key risk factor are further calculated (for example, the global weight of "safety device failure" is 0.0948).

[0041] S200: Dynamic risk assessment and early warning stage.

[0042] S201: Establish data mapping. For example, "Pressure gauge failure (X15)" is mapped to the real-time data of pressure sensor PT-101 and the instrument's operating time; "High ambient temperature (X12)" is mapped to the real-time data of ambient temperature sensor TE-001.

[0043] S202: Risk Factor State Probability Quantification. For "Pressure Gauge Failure", its state probability P_15 = Base Failure Rate λ (based on instrument model and age) + Correction Term Δ (based on recent fluctuations of the instrument). For "High Ambient Temperature", its state probability P_12 = (T_current - T_normal) / (T_max - T_normal), taking 1 when the result is greater than 1. For "Polymerization Reactor Water Injection Flow Control Valve Failure (X31)", its state probability P_31 = |F_current - F_setpoint| / F_setpoint, exceeding a certain threshold indicates an increased risk.

[0044] S203: The system performs a calculation every minute. It reads the real-time state probabilities P_i of all key risk factors, combines them with their global weights W_i, and calculates the current dynamic risk value R_dynamic. For example, at a certain moment, R_dynamic is calculated to be 0.35.

[0045] S204: Based on preset risk level standards (e.g., 0.0-0.13 is extremely low risk, 0.13-0.29 is low risk, 0.29-0.48 is medium risk, 0.48-0.71 is high risk, and 0.71-1.0 is extremely high risk), the current risk level is determined to be "medium risk". If R_dynamic exceeds 0.48, the system will automatically send a "high risk" warning to the central control room and relevant personnel, prompting them to pay attention.

[0046] S300: On the factory's intelligent management platform, the R_dynamic value and risk level of each process unit in the whole plant are displayed in real time in the form of dashboards and regional risk cloud maps. Operators can click on any unit to see which specific risk factors contributed to the main risks, so as to carry out precise handling.

[0047] This embodiment demonstrates that the method described in this invention can effectively achieve dynamic, quantitative, and forward-looking early warning of chemical process safety.

[0048] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for early warning of chemical process safety based on dynamic risk factor assessment, characterized in that, The method is executed by a computing device and includes the following steps: S100: Static risk model construction stage, based on the target process unit, a static risk assessment basic model is constructed; S200: Dynamic risk assessment and early warning stage, based on the static risk model and real-time data, performs dynamic risk calculation and early warning; The S100 static risk model construction phase includes: S101: Risk factor identification step, identifying all potential basic events in the process unit that may lead to the top event, forming an initial risk factor set; S102: Fault tree modeling and importance analysis steps, constructing a fault tree model with the top event as the root, and calculating the structural importance or probabilistic importance of each basic event in the initial risk factor set; S103: Key risk factor screening step, based on the importance calculation results, key risk factors are screened from the initial risk factor set; S104: Hierarchical analysis model construction steps, establishing a hierarchical analysis structure model including a target layer, a constraint layer and an indicator layer, wherein the indicator layer is composed of the key risk factors; S105: Weight calculation step, by constructing and calculating the judgment matrix, determine the weight of the constraint layer relative to the target layer, and the local weight of the key risk factor relative to its respective constraint layer, and synthesize the global weight of each key risk factor. The S200 dynamic risk assessment and early warning phase includes: S201: Real-time data acquisition and mapping step, establishing the mapping relationship between the key risk factors and the real-time data source; S202: Risk factor state probability quantification step, which dynamically calculates the current state probability of each key risk factor based on the real-time data and through predefined quantification rules or functions; S203: Dynamic risk value calculation step, combining the current state probability of each key risk factor with its global weight to calculate the current comprehensive dynamic risk value of the process unit; S204: Risk level determination and early warning step, compare the current comprehensive dynamic risk value with the preset risk level threshold, determine the current risk level, and generate early warning information when the risk level exceeds the safety threshold.

2. The method according to claim 1, characterized in that, In step S102, the fault tree model connects the top event with each basic event through logic gates, and the calculation of the structural importance is based on the minimum cut set of the fault tree.

3. The method according to claim 1, characterized in that, In step S105, a consistency check is performed on each judgment matrix, and the weight calculation result is adopted only if the consistency ratio meets the requirements.

4. The method according to claim 1, characterized in that, In step S202, for continuous data from process sensors, the quantization function is a nonlinear function based on the degree to which the data deviates from its normal operating range.

5. The method according to claim 1, characterized in that, In step S202, the calculation of the state probability of the equipment instrument failure combines the basic failure rate model of the equipment and a dynamic correction factor based on the runtime.

6. The method according to claim 1, characterized in that, In step S202, the state probability of risk factors related to personnel operation is evaluated based on the behavioral safety database and real-time operation records.

7. The method according to claim 1, characterized in that, In step S203, the comprehensive dynamic risk value is calculated using the weighted summation model R_dynamic = Σ (W_i * P_i), where W_i is the global weight of the i-th key risk factor and P_i is its current state probability.

8. The method according to claim 1, characterized in that, The method further includes: S300: Visualization and feedback step, which graphically displays the current comprehensive dynamic risk value, risk level and contribution of each key risk factor.

9. A chemical process safety early warning system based on dynamic risk factor assessment, comprising at least one processor and a memory, wherein the memory stores computer program instructions, and when the program instructions are executed by the processor, the system is configured to perform the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 8.

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