A process safety analysis system for lithium battery separator production

Abstract

The present invention discloses a process safety analysis system for lithium battery separator production, belonging to the technical field of lithium battery separator production, and comprising a data application module and a process analysis module; the data application module is used for analyzing the reference safety assessment data of the production process to determine a process reference model; and establishing a hidden danger database according to the reference safety assessment data; the hidden danger database is used for storing each reserved hidden danger and the corresponding process variation range of each reserved hidden danger, and the reserved hidden danger is a safety hidden danger caused by changing the production process; the process analysis module is used for managing the changed production process, identifying the changed production process and marking it as an evaluation process; comparing the evaluation process with the initial production process to determine the change process, 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 separator production, and specifically relates to a process safety analysis system for lithium battery separator production. Background Technique

[0002] With the rapid development of the new energy industry, as a core component of it, lithium batteries have an expanding production scale and an increasingly wide range of application fields. However, the production process of lithium batteries involves complex chemical reactions and physical changes, and there are relatively high safety risks, such as fires, explosions, and leakage of toxic gases. Therefore, ensuring the safety of the lithium battery production process is of crucial importance.

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

[0004] Although enterprise employees are familiar with the production process, they may lack in the professionalism and systematicness of safety analysis, and it is difficult to comprehensively and accurately identify all potential safety hazards. In addition, the adjustment of the production process may introduce new risk factors, and the production process changes without professional evaluation may exacerbate the safety risks, leading to the occurrence of safety accidents.

[0005] Therefore, in view of the safety analysis requirements during the dynamic adjustment process of the lithium battery production process, it is particularly important to develop an efficient and economical safety analysis system; 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 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 through the following technical solutions:

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

[0009] The data application module is used to analyze the benchmark safety assessment data of the production process to determine the process benchmark model; and establish a hidden danger database based on the benchmark safety assessment data, and the hidden danger database is used to store each reserved hidden danger and the corresponding process change range of each reserved hidden danger, and the reserved hidden danger is a safety hidden danger caused by changing the production process.

[0010] Further, the method for setting the process reference model includes:

[0011] Obtain the initial production process; establish a production process model according to the initial production process;

[0012] Obtain the reference safety assessment data, determine each potential safety hazard and the corresponding triggering conditions for each potential safety hazard according to the reference safety assessment data, identify the corresponding process links in the production process model according to each triggering condition, and mark them as triggering links;

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

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

[0015] Further, the expression of the triggering judgment model is:

[0016]

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

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

[0019] Further, the method for adjusting the process reference model according to the changed process includes:

[0020] Judge whether the changed process belongs to the triggering link;

[0021] When the changed process does not belong to the triggering link, perform production process adjustment, that is, adjust the original production process model; input the changed process into the potential hazard database for matching. When a reserved potential hazard is matched, mark the corresponding reserved potential hazard as a potential safety hazard, determine the corresponding triggering conditions for each potential safety hazard, mark the triggering link according to the triggering conditions, and configure the corresponding triggering judgment model;

[0022] When the changed process belongs to the triggering link, perform production process adjustment, and adjust the triggering link and the triggering judgment model according to the changed process.

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

[0024] Obtain production simulation data, identify each potential safety hazard in the trigger link corresponding to the change process, perform simulation verification on each potential safety hazard through the 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 potential hazard database for matching to determine candidate potential hazards, perform simulation verification on the candidate potential hazards through the production simulation data, obtain the corresponding simulation verification results, determine the corresponding trigger conditions according to the simulation verification results, mark the candidate potential hazards with trigger conditions as potential safety hazards, and configure the trigger judgment model according to the trigger conditions.

[0026] Further, the method for risk assessment according to the process change model includes:

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

[0028] Analyze the production simulation data through the trigger judgment model corresponding to each reference potential hazard to determine the probability change value of the corresponding reference potential hazard occurring after the process change;

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

[0030] Unify the occurrence probability of each derivative potential hazard and the probability change value of the reference potential hazard as factor values; unify the derivative potential hazards and the reference potential hazards as factor items;

[0031] Identify the safety impacts when each factor item occurs, and set the corresponding weight coefficients for each factor item according to each safety impact;

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

[0033] In the formula: 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 the natural constant; μi is the factor value of the corresponding factor item;

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

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

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

[0037] Real-time obtain the monitoring data and corresponding monitoring results of lithium battery separator production, and mark the corresponding monitoring results as verification standards;

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

[0039] Analyze the monitoring simulation results and verification standards through a preset verification model to obtain the corresponding verification results.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] Through the mutual cooperation between the data application module and the process analysis module, the safety analysis of the production process of lithium battery separators is realized, especially the continuous safety assessment of the production process is realized, and the problem that many small, medium and micro enterprises are inconvenient to conduct risk assessment after changing the production process is solved; the real-time monitoring and automatic safety assessment of the production process changes are realized, and timely and accurate safety risk warnings and improvement suggestions are provided for enterprises, so as to effectively reduce the safety risks in the production process of lithium batteries and ensure production safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

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

[0044] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0045] As Figure 1 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 said data application module is used to analyze the benchmark safety assessment data of the production process to determine the process benchmark model. The benchmark safety assessment data is the data obtained by corresponding experts through safety assessment and analysis of the production process set in the initial stage of lithium battery separator production, such as corresponding data on what potential safety hazards exist, the triggering conditions of the potential safety hazards, and the safety accidents corresponding to the potential safety hazards. And a hidden danger database is established based on the benchmark safety assessment data. The hidden danger database is used to store various potential safety hazards that may occur during subsequent changes to the production process, marked as reserved hidden dangers, and the process change ranges corresponding to each reserved hidden danger are set. Preferably, it is evaluated and set by corresponding experts, or the platform side can use its own resources to set up the hidden danger database, such as obtaining various potential safety hazards that may exist in lithium battery separator production, and then matching and screening according to the production process and benchmark safety assessment data to determine each reserved hidden danger and the corresponding process conditions, forming the process change range.

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

[0048] Obtain the production process in the initial state, marked 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, methods, etc. of the initial production process, generally a three-dimensional model;

[0049] Obtain the benchmark safety assessment data, determine each potential safety hazard and the triggering condition corresponding to each potential safety hazard according to the benchmark safety assessment data, and identify the corresponding process links in the production process model according to each triggering condition, marked as triggering links, that is, the process links with this triggering condition;

[0050] Configure the corresponding trigger judgment model in the production process model according to the benchmark safety assessment data and each triggering link, which is used to judge the triggering condition according to the production situation. The expression of the trigger judgment model is In the formula: s is the input data, and the input data is the production data of the corresponding triggering link, such as equipment parameters, employee operations and other related data; the output data is the trigger judgment value FA(s);

[0051] Obtain the historical production data corresponding to the initial production process, collect it according to the enterprise's historical production situation or employee status, etc., marked as production simulation data, and verify and adjust each trigger judgment model through the production simulation data until it matches the benchmark safety assessment data;

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

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

[0054] Perform risk assessment according to the process change model to obtain the corresponding risk assessment results.

[0055] The method for adjusting the process benchmark model according to the change process includes:

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

[0057] When the change process does not belong to the triggering link, adjust the production process, that is, adjust the original production process model; input the change process into the hidden danger database for matching. When a reserved hidden danger is matched, mark the corresponding reserved hidden danger as a safety hidden danger, and determine the triggering conditions corresponding to each safety hidden danger. Mark the triggering link according to the triggering conditions and configure the corresponding triggering judgment model;

[0058] When the change process belongs to the triggering link, adjust the production process and adjust the triggering link and the triggering judgment model according to the change process.

[0059] The method for adjusting the triggering link and the triggering judgment model according to the change process includes:

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

[0061] Input the change process into the hidden danger database for matching to determine the newly added safety hidden danger, mark it as a candidate hidden danger, and perform simulation verification on the candidate hidden danger through the production simulation data to obtain the corresponding simulation verification results, that is, whether there are conditions for potential hidden danger problems; determine the corresponding triggering conditions according to the simulation verification results, mark the candidate hidden danger with triggering conditions as a safety hidden danger, and configure the triggering judgment model according to the triggering conditions.

[0062] The method for performing risk assessment according to the process change model includes:

[0063] Compare the process change model with the process benchmark model to identify new potential safety hazards, mark the new potential safety hazards as derived hazards, and mark each potential safety hazard in the process benchmark model as a benchmark hazard.

[0064] Analyze the production simulation data through the trigger judgment model corresponding to each benchmark hazard to determine the change value of the probability of the corresponding benchmark hazard occurring after the process change, that is, the change in the probability before and after. Through simulation statistics, it can be obtained that it can be reduced or increased.

[0065] Analyze the production simulation data through the trigger judgment model corresponding to each derived hazard to determine the occurrence probability of each derived hazard.

[0066] Unify the occurrence probability of each derived hazard and the probability change value of the benchmark hazard and mark them as factor values; unify the derived hazards and benchmark hazards and mark them as factor items.

[0067] Identify the safety impact when each factor item occurs, which can be represented by losses; set corresponding weight coefficients for each factor item according to the proportion of each safety impact, and a certain factor item corresponding to a benchmark hazard will be designated as a benchmark for setting in advance.

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

[0069] In the formula: 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 the natural constant; μi is the factor value of the corresponding factor item.

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

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

[0072] The threshold X1 is set synchronously by experts during the initial production process assessment or by the platform party.

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

[0074] Simulate the monitoring data according to the process change model to obtain the monitoring simulation results, that is, carry out production simulation according to the monitoring data to produce the corresponding monitoring results, and mark them as the monitoring simulation results.

[0075] A corresponding verification model is established based on neural networks such as CNN networks or DNN networks, and a corresponding training set is established and trained manually. The training set includes input data and output data. The input data is the monitoring simulation results and verification criteria over a period of time; the output data is the verification result; through analysis by the verification model after successful training, the corresponding verification result is obtained.

[0076] The above formulas are all calculated by removing the dimension and taking their numerical values. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation 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 of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A process safety analysis system for the production of lithium battery separators, characterized in that, It includes a data application module and a process analysis module; The data application module is used to analyze the benchmark safety assessment data of the production process to determine the process benchmark model; And establish a hidden danger database based on the benchmark safety assessment data; The hidden danger database is used to store each reserved hidden danger and the corresponding process change range of each reserved hidden danger. The reserved hidden danger is a safety hidden danger caused by changing the production process; The process analysis module is used to manage the changed production process, identify the changed production process, and mark it as the evaluation process; Compare the evaluation process with the initial production process to determine the change process, and adjust the process benchmark model according to the change process to obtain the process change model; Conduct a risk assessment based on the process change model to obtain the corresponding risk assessment result; The method for conducting a risk assessment based on the process change model includes: Compare the process change model with the process benchmark model to determine the newly added safety hidden dangers, and mark the newly added safety hidden dangers as derivative hidden dangers; mark each safety hidden danger in the process benchmark model as a benchmark hidden danger; 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 occurring after the process change; Analyze the production simulation data through the trigger judgment model corresponding to each derivative hidden danger to determine the occurrence probability of each derivative hidden danger; Unify the occurrence probability of each derivative hidden danger and the probability change value of the benchmark hidden danger as the factor value; unify the derivative hidden danger and the benchmark hidden danger as the factor item; Identify the safety impact when each factor item occurs, and set the corresponding weight coefficient for each factor item according to each safety impact; According to the formula Calculate the corresponding risk increment value; In the formula: 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 the natural constant; μi is the factor value of the corresponding factor item; When the risk increase value is greater than the threshold X1, the evaluated risk is abnormal; When the risk increase value is not greater than the threshold X1, the evaluated risk is normal.

2. The process safety analysis system for lithium battery separator production according to claim 1, characterized in that, The setting method of the process benchmark model includes: Obtain the initial production process; establish a production process model according to the initial production process; Obtain the benchmark safety assessment data, determine each safety hidden danger and the corresponding triggering condition according to the benchmark safety assessment data, and identify the corresponding process link in the production process model according to each triggering condition, and mark it as the triggering link; Configure the corresponding trigger judgment model in the production process model according to the benchmark safety assessment data and each triggering link; Set the production simulation data, and verify and adjust each trigger judgment model through the production simulation data until it matches the benchmark safety assessment data; Mark the current production process model as the process benchmark model.

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

4. The process safety analysis system for lithium battery separator production according to claim 1, wherein The method for adjusting the process benchmark model according to the change process includes: Judge whether the change process belongs to the triggering link; When the change process does not belong to the triggering link, the production process is adjusted, that is, the original production process model is adjusted; the change process is input into the hidden danger database for matching. When a reserved hidden danger is matched, the corresponding reserved hidden danger is marked as a safety hidden danger; Determine the triggering conditions corresponding to each safety hidden danger, mark the triggering link according to the triggering conditions, and configure the corresponding triggering judgment model; When the change process belongs to the triggering link, the production process is adjusted, and the triggering link and the triggering judgment model are adjusted according to the change process.

5. The process safety analysis system for lithium battery separator production according to claim 4, characterized in that The method for adjusting the triggering link and the triggering judgment model according to the change process includes: Obtain production simulation data, identify each safety hidden danger corresponding to the triggering link of the change process, perform simulation verification on each safety hidden danger through the production simulation data, and obtain the corresponding simulation verification result; adjust the corresponding triggering conditions according to the simulation verification result, and adjust the configured triggering judgment model according to the adjusted triggering conditions; Input the change process into the hidden danger database for matching to determine the candidate hidden dangers, perform simulation verification on the candidate hidden dangers through the production simulation data, obtain the corresponding simulation verification result, determine the corresponding triggering conditions according to the simulation verification result, mark the candidate hidden dangers with triggering conditions as safety hidden dangers, and configure the triggering judgment model according to the triggering conditions.

6. The process safety analysis system for lithium battery separator production according to claim 1, characterized in that, It also includes a risk verification module, which is used to verify and analyze the evaluated risk assessment result; Real-time obtain the monitoring data and the corresponding monitoring results of the lithium battery separator production, and mark the corresponding monitoring results as the calibration standard; Simulate the monitoring data according to the process change model to obtain the monitoring simulation result; Analyze the monitoring simulation result and the calibration standard through a preset verification model to obtain the corresponding verification result.

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