Process safety analysis system for lithium battery diaphragm production

Abstract

The invention discloses a process safety analysis system for lithium battery diaphragm production, which belongs to the technical field of lithium battery diaphragm production and comprises a data application module and a process analysis module. The data application module is used for analyzing the reference safety evaluation data of the production process and determining a process reference model; establishing a hidden danger database according to the reference safety assessment data; the hidden danger database is used for storing each storage hidden danger and a process variation range corresponding to each storage hidden danger, and the storage hidden dangers are potential safety hazards caused by variation of a production process; the process analysis module is used for managing the changed production process, identifying the changed production process and marking the changed production process as an evaluation process; comparing the evaluation process with the initial production process, determining a change process, and adjusting the process reference model according to the change process to obtain a process change model; and performing risk assessment according to the process change model to obtain a corresponding risk assessment result.

Description

Technical Field

[0001] The present invention belongs to the technical field of lithium battery diaphragm production, and specifically is a process safety analysis system for lithium battery diaphragm production. Background Art

[0002] With the rapid development of the new energy industry, lithium batteries, as its core components, have been continuously expanded in production scale and their application fields have become increasingly extensive. However, the production process of lithium batteries involves complex chemical reactions and physical changes, and there are high safety risks, such as fire, explosion, and toxic gas leakage. Therefore, it is very important to ensure the safety of lithium battery production process.

[0003] At present, many large companies will hire professional teams to conduct detailed safety analysis and assessment in the early stage of lithium battery production to identify and control potential safety risks. However, for many small and medium-sized enterprises, due to limited costs, efficiency and professional resources, they often only conduct a one-time safety analysis and assessment when the production process is first written. With the operation of the production line and changes in market demand, the production process often needs to be continuously adjusted and optimized, but the safety analysis work at this time is often ignored or only completed by the company's employees themselves.

[0004] Although enterprise employees are familiar with the production process, they may lack professionalism and systematicness in safety analysis, making it difficult to fully and accurately identify all potential safety hazards. In addition, adjustments to production processes may introduce new risk factors, and changes to production processes that have not been professionally evaluated may increase safety risks and lead to safety accidents.

[0005] Therefore, it is particularly important to develop an efficient and economical safety analysis system to meet the safety analysis needs during the dynamic adjustment of the lithium battery production process. Based on this, the present invention provides a process safety analysis system for lithium battery separator production. Summary of the invention

[0006] In order to solve the problems existing in the above-mentioned solutions, the present invention provides a process safety analysis system for lithium battery separator production.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] A process safety analysis system for lithium battery separator production, comprising a data application module and a process analysis module;

[0009] The data application module is used to analyze the baseline safety assessment data of the production process and determine the process baseline model; and to establish a hidden danger database based on the baseline safety assessment data. The hidden danger database is used to store various reserve hidden dangers and the process variation range corresponding to each reserve hidden danger. The reserve hidden dangers are safety hazards caused by changes in the production process.

[0010] Furthermore, the method for setting the process benchmark model includes:

[0011] Obtain the initial production process; establish a production process model based on the initial production process;

[0012] Obtaining baseline safety assessment data, determining each safety hazard and the triggering condition corresponding to each safety hazard based on the baseline safety assessment data, identifying the corresponding process link in the production process model based on each triggering condition, and marking it as a triggering link;

[0013] Configure the corresponding trigger judgment model in the production process model according to the baseline safety assessment data and each trigger link; set the production simulation data, and verify and adjust each trigger judgment model through the production simulation data until it matches the baseline safety assessment data;

[0014] Mark the current production process model as the process baseline model.

[0015] Furthermore, the expression of the trigger judgment model is:

[0016]

[0017] In the formula: s is the input data, the input data is the production data of the corresponding trigger link, and the output data is the trigger judgment value FA(s).

[0018] The process analysis module is used to manage the modified production process, identify the changed production process, and mark it as an evaluation process; compare the evaluation process with the initial production process, determine the change process, adjust the process baseline model according to the change process, and obtain a process change model; perform risk assessment based on the process change model to obtain corresponding risk assessment results.

[0019] Furthermore, the method for adjusting the process benchmark model according to the change process includes:

[0020] Determine whether the change process belongs to the triggering link;

[0021] When the changed process does not belong to the trigger link, the production process is adjusted, that is, the original production process model is adjusted; the changed process is input into the hidden danger database for matching, and when the reserve hidden danger is matched, the corresponding reserve hidden danger is marked as a safety hidden danger, and the trigger conditions corresponding to each safety hidden danger are determined, and the trigger link is marked according to the trigger condition and the corresponding trigger judgment model is configured;

[0022] When the change process belongs to the trigger link, the production process is adjusted, and the trigger link and trigger judgment model are adjusted according to the change process.

[0023] Furthermore, the method for adjusting the triggering link and the triggering judgment model according to the change process includes:

[0024] Obtain production simulation data, identify the safety hazards of the triggering links corresponding to the change process, simulate and verify the safety hazards through production simulation data, and obtain the corresponding simulation verification results; adjust the corresponding trigger conditions according to the simulation verification results, and adjust the configured trigger judgment model according to the adjusted trigger conditions;

[0025] Input the change process into the hidden danger database for matching, determine the hidden dangers to be selected, simulate and verify the hidden dangers to be selected through production simulation data, obtain the corresponding simulation verification results, determine the corresponding trigger conditions according to the simulation verification results, mark the hidden dangers to be selected with the trigger conditions as safety hazards, and configure the trigger judgment model according to the trigger conditions.

[0026] Furthermore, the method of risk assessment based on the process change model includes:

[0027] Compare the process change model with the process baseline model to determine newly added safety hazards, and mark the newly added safety hazards as derivative hazards; mark each safety hazard in the process baseline model as a baseline hazard;

[0028] Analyze the production simulation data through the trigger judgment model corresponding to each benchmark hidden danger to determine the probability change value of the corresponding benchmark hidden danger after the process change;

[0029] Analyze the production simulation data through the trigger judgment model corresponding to each derivative hidden danger to determine the probability of occurrence of each derivative hidden danger;

[0030] The occurrence probability of each derived hazard and the probability change value of the baseline hazard are uniformly marked as factor values; the derived hazards and baseline hazards are uniformly marked as factor items;

[0031] Identify the safety impact of each factor item when it occurs, and set the corresponding weight coefficient of each factor item according to each safety impact;

[0032] According to the formula Calculate the corresponding risk increase value;

[0033] Where: PU is the risk increase value; δi is the weight coefficient of the corresponding factor item, i represents the corresponding factor item, i = 1, 2, ..., n, n is a positive integer; e is a natural constant; μi is the factor value of the corresponding factor item;

[0034] When the risk increase value is greater than the threshold value X1, the risk is assessed as abnormal;

[0035] When the risk increase value is not greater than the threshold value X1, the risk assessment is normal.

[0036] Furthermore, it also includes a risk verification module, which is used to verify and analyze the risk assessment results;

[0037] Obtain monitoring data and corresponding monitoring results of lithium battery separator production in real time, and mark the corresponding monitoring results as calibration standards;

[0038] Simulate the monitoring data according to the process change model to obtain the monitoring simulation results;

[0039] The monitoring simulation results and calibration standards are analyzed through the preset verification model to obtain the corresponding verification results.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] Through the mutual cooperation between the data application module and the process analysis module, it is possible to carry out safety analysis of the production process of lithium battery separators, especially to achieve continuous safety assessment of the production process, and solve the problem that many small and medium-sized enterprises are inconvenient to conduct risk assessment after changing their production processes; it can achieve real-time monitoring and automatic safety assessment of production process changes, and provide enterprises with timely and accurate safety risk warnings and improvement suggestions, thereby effectively reducing safety risks in the lithium battery production process and ensuring production safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only 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.

[0043] Figure 1 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0044] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] like Figure 1 As shown, a process safety analysis system for lithium battery separator production includes a data application module, a process analysis module and a risk verification module;

[0046] The data application module is used to analyze the baseline safety assessment data of the production process and determine the process baseline model; the baseline safety assessment data is the data of the safety assessment analysis of the production process set in the initial stage of lithium battery diaphragm production by the corresponding experts, such as the corresponding data on which safety hazards there are, the triggering conditions of the safety hazards, the safety accidents corresponding to the safety hazards, etc.; and a hazard database is established based on the baseline safety assessment data; the hazard database is used to store various safety hazards that may arise when the production process is subsequently changed, marked as reserve hazards, and the process variation range corresponding to each reserve hazard is set. It is best to be evaluated and set by the corresponding experts, or the platform can use its own resources to set up a hazard database, such as obtaining various safety hazards that may exist in the production of lithium battery diaphragms, and then matching and screening according to the production process and the baseline safety assessment data, determining each reserve hazard and the corresponding process conditions, and forming a process variation range.

[0047] The method of setting up the process benchmark model includes:

[0048] Obtain the production process in the initial state and mark it as the initial production process; establish a production process model based on the initial production process, which is a data model used to simulate the production process, method, etc. of the initial production process, generally a three-dimensional model;

[0049] Obtaining baseline safety assessment data, determining each safety hazard and the triggering condition corresponding to each safety hazard based on the baseline safety assessment data, identifying the corresponding process link in the production process model based on each triggering condition, and marking it as a triggering link, that is, a process link with the triggering condition;

[0050] According to the baseline safety assessment data and each trigger link, the corresponding trigger judgment model is configured in the production process model to judge the trigger conditions according to the production situation. The expression of the trigger judgment model is: Where: s is the input data, which is the production data of the corresponding trigger link, such as equipment parameters, employee operations and other related data; the output data is the trigger judgment value FA(s);

[0051] Obtain historical production data corresponding to the initial production process, collect data based on the company's historical production situation or employee status, mark it as production simulation data, and verify and adjust each trigger judgment model through the production simulation data until it matches the baseline safety assessment data;

[0052] Mark the current production process model as the process baseline model.

[0053] The process analysis module is used to manage the modified production process, identify the changed production process, and mark it as an evaluation process; compare the evaluation process with the initial production process, determine the change process, adjust the process benchmark model according to the change process, and obtain the process change model;

[0054] Conduct risk assessment based on the process change model and obtain corresponding risk assessment results.

[0055] Methods for adjusting the process baseline model according to the changing process include:

[0056] Determine whether the change process belongs to the triggering link;

[0057] When the changed process does not belong to the trigger link, the production process is adjusted, that is, the original production process model is adjusted; the changed process is input into the hidden danger database for matching, and when the reserve hidden danger is matched, the corresponding reserve hidden danger is marked as a safety hidden danger, and the trigger conditions corresponding to each safety hidden danger are determined, and the trigger link is marked according to the trigger condition and the corresponding trigger judgment model is configured;

[0058] When the change process belongs to the trigger link, the production process is adjusted, and the trigger link and trigger judgment model are adjusted according to the change process.

[0059] Methods for adjusting the triggering link and triggering judgment model according to the change process include:

[0060] Obtain production simulation data, identify the various safety hazards in the triggering links corresponding to the change process, and simulate and verify each safety hazard through production simulation data, that is, simulate according to the occurrence principle of each safety hazard, determine whether a safety accident will occur under the production simulation data, and obtain the corresponding simulation verification results; adjust the corresponding trigger conditions according to the simulation verification results, that is, determine the trigger conditions according to which situations in the simulation process lead to the occurrence of safety accidents, and compare and adjust them with the original trigger conditions after integration; adjust the configured trigger judgment model according to the adjusted trigger conditions; in actual applications, a corresponding simulation verification model can be established based on a neural network such as a CNN network or a DNN network, and a corresponding training set can be established manually according to the occurrence principle of the corresponding safety hazard for training. The training set includes input data and output data. The input data is the safety hazard, the original trigger condition, the trigger principle, and the production simulation data, and the output data is the simulation verification result;

[0061] The change process is input into the hidden danger database for matching, and the newly added safety hazards are determined and marked as potential hazards to be selected. The potential hazards to be selected are simulated and verified through production simulation data to obtain the corresponding simulation verification results, that is, whether the conditions for the potential hazards to occur are met; the corresponding trigger conditions are determined according to the simulation verification results, and the potential hazards to be selected that have the trigger conditions are marked as safety hazards, and the trigger judgment model is configured according to the trigger conditions.

[0062] Methods for risk assessment based on process change models include:

[0063] Compare the process change model with the process baseline model to determine newly added safety hazards, and mark the newly added safety hazards as derivative hazards; mark each safety hazard in the process baseline model as a baseline hazard;

[0064] The production simulation data is analyzed through the trigger judgment model corresponding to each benchmark hidden danger to determine the probability change value of the corresponding benchmark hidden danger after the process change, that is, the change before and after the probability of occurrence. The simulation statistics can be obtained, which can be reduced or increased;

[0065] Analyze the production simulation data through the trigger judgment model corresponding to each derivative hidden danger to determine the probability of occurrence of each derivative hidden danger;

[0066] The occurrence probability of each derived hazard and the probability change value of the baseline hazard are uniformly marked as factor values; the derived hazards and baseline hazards are uniformly marked as factor items;

[0067] Identify the safety impact of each factor item when it occurs, which can be represented by loss; set the corresponding weight coefficient for each factor item according to the weight of each safety impact, and pre-specify the factor item corresponding to a certain baseline hidden danger as the benchmark for setting;

[0068] According to the formula Calculate the corresponding risk increase value;

[0069] Where: PU is the risk increase value; δi is the weight coefficient of the corresponding factor item, i represents the corresponding factor item, i = 1, 2, ..., n, n is a positive integer; e is a natural constant; μi is the factor value of the corresponding factor item;

[0070] When the risk increase value is greater than the threshold value X1, the risk is assessed as abnormal;

[0071] When the risk increase value is not greater than the threshold value X1, the risk assessment is normal.

[0072] The threshold X1 is set by experts simultaneously when conducting the initial production process evaluation, or by the platform.

[0073] The risk verification module is used to verify and analyze the risk assessment results of the assessment, obtain the monitoring data of the lithium battery separator production and the corresponding monitoring results in real time, and determine the actual subsequent occurrence of the monitoring data; mark the corresponding monitoring results as the verification standard;

[0074] The monitoring data is simulated according to the process change model to obtain the monitoring simulation results, that is, the production simulation is performed according to the monitoring data to produce the corresponding monitoring results, which are marked as monitoring simulation results;

[0075] A corresponding verification model is established based on a neural network such as a CNN network or a DNN network, and a corresponding training set is established manually for training. The training set includes input data and output data. The input data is the monitoring simulation results and calibration standards within a period of time; the output data is the verification result; the corresponding verification result is obtained by analyzing the verification model after successful training.

[0076] The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data.

[0077] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A process safety analysis system for lithium battery separator production, It is characterized in that Includes data application module and process analysis module; The data application module is used to analyze the baseline safety assessment data of the production process and determine the process baseline model; and establish a hidden danger database based on baseline safety assessment data; The hidden danger database is used to store various reserve hidden dangers and the process variation range corresponding to each reserve hidden danger, and the reserve hidden danger is a safety hidden danger caused by the change of production process; The process analysis module is used to manage the modified production process, identify the modified production process, and mark it as an evaluation process; Compare the evaluation process with the initial production process, determine the change process, adjust the process baseline model according to the change process, and obtain the process change model; Conduct risk assessment based on the process change model and obtain corresponding risk assessment results.

2. A process safety analysis system for lithium battery separator production according to claim 1, It is characterized in that The method of setting up the process benchmark model includes: Obtain the initial production process; establish a production process model based on the initial production process; Obtaining baseline safety assessment data, determining each safety hazard and the triggering condition corresponding to each safety hazard based on the baseline safety assessment data, identifying the corresponding process link in the production process model based on each triggering condition, and marking it as a triggering link; Configure the corresponding trigger judgment model in the production process model according to the baseline safety assessment data and each trigger link; Set production simulation data, and verify and adjust each trigger judgment model through the production simulation data until it matches the baseline safety assessment data; Mark the current production process model as the process baseline model.

3. A process safety analysis system for lithium battery separator production according to claim 2, It is characterized in that The expression of the trigger judgment model is: In the formula: s is the input data, the input data is the production data of the corresponding trigger link, and the output data is the trigger judgment value FA(s).

4. A process safety analysis system for lithium battery separator production according to claim 1, It is characterized in that Methods for adjusting the process baseline model according to the changing process include: Determine whether the change process belongs to the triggering link; When the changed process does not belong to the triggering link, the production process is adjusted, that is, the original production process model is adjusted; the changed process is input into the hidden danger database for matching, and when the reserve hidden danger is matched, the corresponding reserve hidden danger is marked as a safety hidden danger; Determine the trigger conditions corresponding to each safety hazard, mark the trigger link according to the trigger conditions, and configure the corresponding trigger judgment model; When the change process belongs to the trigger link, the production process is adjusted, and the trigger link and trigger judgment model are adjusted according to the change process.

5. A process safety analysis system for lithium battery separator production according to claim 4, It is characterized in that Methods for adjusting the triggering link and triggering judgment model according to the change process include: Obtain production simulation data, identify the safety hazards of the triggering links corresponding to the change process, simulate and verify the safety hazards through production simulation data, and obtain the corresponding simulation verification results; adjust the corresponding trigger conditions according to the simulation verification results, and adjust the configured trigger judgment model according to the adjusted trigger conditions; Input the change process into the hidden danger database for matching, determine the hidden dangers to be selected, simulate and verify the hidden dangers to be selected through production simulation data, obtain the corresponding simulation verification results, determine the corresponding trigger conditions according to the simulation verification results, mark the hidden dangers to be selected with the trigger conditions as safety hazards, and configure the trigger judgment model according to the trigger conditions.

6. A process safety analysis system for lithium battery separator production according to claim 1, It is characterized in that Methods for risk assessment based on process change models include: Compare the process change model with the process baseline model to determine newly added safety hazards, and mark the newly added safety hazards as derivative hazards; mark each safety hazard in the process baseline model as a baseline hazard; Analyze the production simulation data through the trigger judgment model corresponding to each benchmark hidden danger to determine the probability change value of the corresponding benchmark hidden danger after the process change; Analyze the production simulation data through the trigger judgment model corresponding to each derivative hidden danger to determine the probability of occurrence of each derivative hidden danger; The occurrence probability of each derived hazard and the probability change value of the baseline hazard are uniformly marked as factor values; the derived hazards and baseline hazards are uniformly marked as factor items; Identify the safety impact of each factor item when it occurs, and set the corresponding weight coefficient of each factor item according to each safety impact; According to the formula Calculate the corresponding risk increase value; Where: PU is the risk increase value; δi is the weight coefficient of the corresponding factor item, i represents the corresponding factor item, i = 1, 2, ..., n, n is a positive integer; e is a natural constant; μi is the factor value of the corresponding factor item; When the risk increase value is greater than the threshold value X1, the risk is assessed as abnormal; When the risk increase value is not greater than the threshold value X1, the risk assessment is normal.

7. A process safety analysis system for lithium battery separator production according to claim 1, It is characterized in that It also includes a risk verification module, which is used to verify and analyze the risk assessment results; Obtain monitoring data and corresponding monitoring results of lithium battery separator production in real time, and mark the corresponding monitoring results as calibration standards; Simulate the monitoring data according to the process change model to obtain the monitoring simulation results; The monitoring simulation results and calibration standards are analyzed through the preset verification model to obtain the corresponding verification results.

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