CN110598966A - Method for grading equipment in post-processing facility based on risk guidance - Google Patents
Method for grading equipment in post-processing facility based on risk guidance Download PDFInfo
- Publication number
- CN110598966A CN110598966A CN201910243536.9A CN201910243536A CN110598966A CN 110598966 A CN110598966 A CN 110598966A CN 201910243536 A CN201910243536 A CN 201910243536A CN 110598966 A CN110598966 A CN 110598966A
- Authority
- CN
- China
- Prior art keywords
- equipment
- decision
- risk
- post
- processing facility
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000012805 post-processing Methods 0.000 title claims abstract description 32
- BTCSSZJGUNDROE-UHFFFAOYSA-N gamma-aminobutyric acid Chemical compound NCCCC(O)=O BTCSSZJGUNDROE-UHFFFAOYSA-N 0.000 claims abstract description 28
- 238000004458 analytical method Methods 0.000 claims abstract description 22
- 238000003672 processing method Methods 0.000 claims abstract description 7
- 238000011156 evaluation Methods 0.000 claims description 20
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000004140 cleaning Methods 0.000 claims description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 3
- 238000013517 stratification Methods 0.000 claims 2
- 238000012545 processing Methods 0.000 abstract description 5
- 238000010586 diagram Methods 0.000 description 5
- 238000012423 maintenance Methods 0.000 description 4
- 239000011159 matrix material Substances 0.000 description 3
- 238000000275 quality assurance Methods 0.000 description 3
- 238000012958 reprocessing Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000002301 combined effect Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000005202 decontamination Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 239000002915 spent fuel radioactive waste Substances 0.000 description 1
- 230000002195 synergetic effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Computational Mathematics (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Probability & Statistics with Applications (AREA)
- Algebra (AREA)
- Artificial Intelligence (AREA)
- Operations Research (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a method for grading equipment in a post-processing facility guided by risks, which comprises the following steps that (1) after an expert j selects the equipment, a decision factor i is identified and a decision level is constructed by using a level analysis and processing method; (2) constructing a Bayesian confidence network model by the expert j, and judging to obtain P (C)ij|Si) And calculating the safety importance P (S) of each decision factor of the target component by using the Bayesian belief networkiHi); (3) obtaining evidence E by an expertijAnd updating P (S) using the evidenceiHi); (4) obtaining the weight w of the decision factor i by applying hierarchical analysis processingi(ii) a (5) Deriving a total probability P (S) based on probability theory and deterministic theoryTHi) and finally classified. The method provided by the invention can provide a basis for grading the post-treatment facilities.
Description
Technical Field
The invention belongs to the technical field of probability safety evaluation of spent fuel post-processing facilities, and particularly relates to a risk-guided classification method for equipment in a post-processing facility.
Background
Currently, the american Nuclear Regulatory Commission (NRC) is dedicated to enforce the management of Risk-oriented (RI) based on Probabilistic Security Assessment (PSA). The safety importance Ranking (RISSC) of RIs is used for management and application of RI, and RISSC ranks structures, systems or components (SSCs) into two or more groups according to their safety importance using the view of determinism and probability theory. The process generally comprises three steps, as shown in FIG. 1. The first and second steps are performed by the working team who develops the RISSC and the third step is performed by a group of experts (or a comprehensive decision-making group). The two steps performed by the work team are classified as an initial grading process; the end steps performed by the expert group are grouped into a comprehensive decision making process.
In the first step, by using the point of view of probability theory, components are classified into two or more stages according to their relative importance to the Core Damage Frequency (CDF). Generally, cut set importance (Fussell-Vesely, FV for short) and Risk Achieveentworth (RAW for short) are used as measures of quantitative security importance. In the second step, the results of the first step are supplemented by qualitative evaluations based on determinism points of view. In the third step, the expert group uses the ideas compiled in the initial ranking process in combination with other theoretic ideas to discuss and mediate the ideas and make final decisions (rankings) qualitatively.
The decision of expert group is made through discussion and opinion mediation, and the traditional method has the following advantages: various opinions of experts are considered and there may be synergistic effects through discussion and opinion mediation. However, due to the qualitative and linear decision making process, the conventional method has the following disadvantages: (1) the time and labor consumption is serious; (2) when the expert opinions are left, the final decision is difficult to achieve; (3) since the linear patterns have overlapping processes (perform two levels-first, by the engineer; second, by the expert group.)
Therefore, it is necessary to invent a method for classifying equipment in a risk-oriented post-processing facility to solve the above problems.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a risk-oriented equipment classification method in a post-processing facility, which enables experts to utilize hierarchical analysis processing and Bayesian confidence network to participate early and provides a basis for classifying the post-processing facility so as to reduce the operation and maintenance cost of relevant equipment.
In order to achieve the above purposes, the invention adopts the technical scheme that:
a method of ranking equipment in a risk-indexed post-processing facility, the method comprising:
(1) after the expert j selects equipment, identifying a decision factor i and constructing a decision level by using a level analysis processing method;
(2) constructing a Bayesian confidence network model by the expert j, and judging to obtain P (C)ij|Si) And calculating the safety importance P (S) of each decision factor of the target component by using the Bayesian belief networki＝Hi)；
(3) Obtaining evidence E by an expertijAnd updating P (S) using the evidencei＝Hi)；
(4) Obtaining weight w of decision factor i by using hierarchical analysis processing methodi；
(5) Deriving a total probability P (S) based on probability theory and deterministic theoryTHi) and finally classified.
Further, the step (1) specifically includes:
after the target equipment is selected, the decision factor i of the probability theory of the equipment is based on the fault tree analysis of all accidents related to the equipment, namely the accident Risk Risk; the decision factor i of the device determinism is based on qualitative evaluation factors: the critical safety functions and the specific normal operating functions, i.e. the qualitative evaluation QA.
Further, the qualitative evaluation QA includes: QA1The equipment is required to maintain the integrity of the corresponding containment; QA2The equipment is required for cleaning or reducing the radioactivity released; QA3Auxiliary systems (water supply, power supply, compressed air) require the device.
Further, the constructing of the bayesian belief network model by the expert j in the step (2) specifically includes:
creating a Bayesian belief network model, the Bayesian belief network model containing a parent variable SiAnd the subvariable Cij，SiDecision factors i representing the target component fall into Hi (high security importance rating) or Lo (low security importance rating), CijThe decision factor i representing the target component falls into a decision criterion (c) based on the judgment of expert j1,c2,…,cmM is the total number of criteria for decision factor i).
Further, the decision criteria of the risk of accident in the probability theory in the post-processing facility RISSC specifically include:
consequences of the accident>108MBq，0<The frequency of occurrence is less than or equal to 10-9a-1When the decision criterion is c4；
Consequences of the accident>108MBq，10-9a-1<The frequency of occurrence is less than or equal to 10-6a-1When the decision criterion is c2；
Consequences of the accident>108MBq，10-6a-1<When the frequency occurs, the decision criterion is c1；
When 0 is present<Accident consequence is less than or equal to 108MBq，0<The frequency of occurrence is less than or equal to 10-9a-1When the decision criterion is c6；
When 0 is present<Accident consequence is less than or equal to 108MBq，10-9a-1<The frequency of occurrence is less than or equal to 10-6a-1When the decision criterion is c5；
When 0 is present<Accident consequence is less than or equal to 108MBq，10-6a-1<When the frequency occurs, the decision criterion is c3。
Further, decision criteria c for qualitative evaluation in the reprocessing facilities RISSC1-c4The method specifically comprises the following steps:
c1: the device is directly required and no other device performs the same or similar functions;
c2: the device is directly required and other devices are still available to perform the same or similar functions;
c3: the device is indirectly required (e.g., the device may affect the behavior of a person or the device is directly required by the device, etc.);
c4: this component is not required at all.
Further, in the step (2), the expert j evaluates and obtains P (C)ij|Si) And calculating the safety importance P (S) of each decision factor of the target component by using the Bayesian belief networkiHi) specifically includes:
evaluation of P (S) by expert ji|Cij) And P (C)ij)；
According to P (S) by Bayes' theoremi|Cij) Calculate P (C)ij|Si)；
Calculating P (S)iHi), the calculation formula is
P(Si＝Hi)＝P(Si＝Hi|Ci1＝c1)P(Ci1＝c1)+…+P(Si＝Hi|Ci1＝cm)P(Ci1＝cm)。
Further, the calculation formula of the total probability in the step (5) is as follows:
P(ST＝Hi)＝wPP(SPT＝Hi)+wDP(SDT＝Hi)，
wherein, P (S)PTHi) is the classification probability based on PSA; p (S)DTHi) is a ranking probability derived based on qualitative assessment of determinism; w is aP、wDAre respectively P (S)PTHi) and P (S)DTHi).
Further, said P (S)PTHi) is calculated as:
wherein, P (S)nHi) (N1, 2.., N) is the probability of the system belonging to Hi from the nth risk of accident.
Further, said P (S)DTHi) is calculated as:
wherein, w1n、w2nQA for qualitative assessment of the nth accident1、QA2Weight value in qualitative assessment, w2N+1For qualitative evaluation of QA3Weight values in qualitative assessments.
Further, the classification criteria in step (5) are as follows:
when the total probability is more than 0 and less than or equal to 0.35, the equipment has low safety importance of Lo;
when the total probability is less than or equal to 0.65 and less than 0.35, the equipment has potential safety importance of Po;
when 0.65< total probability ≦ 1, the device is of high safety importance for Hi.
The method has the advantages that the method enables experts to participate in early stage by utilizing hierarchical analysis processing and Bayesian confidence network, and provides basis for grading post-processing facilities so as to reduce the operation and maintenance cost of relevant equipment.
Drawings
FIG. 1 is a schematic flow diagram of one embodiment of a conventional RISSC method;
FIG. 2 is a schematic flow diagram of a RISSC method based on hierarchical analysis and processing and Bayesian belief networks;
FIG. 3 is a schematic diagram of a hierarchy of decisions of the post-processing facility RISSC based on a hierarchy analysis process;
FIG. 4 is a schematic diagram of a Bayesian belief network model for decision factor i;
fig. 5 is a schematic diagram of decision criteria for accident risk in the aftertreatment facility RISSC.
Detailed Description
In order to make the technical problems solved, the technical solutions adopted, and the technical effects achieved by the present invention clearer, the technical solutions of the embodiments of the present invention will be further described in detail with reference to the accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Referring to fig. 1, fig. 1 is a schematic flow chart of an embodiment of the method of the present invention.
A method of ranking equipment in a risk-indexed post-processing facility, comprising the steps of:
step 101: after the expert j selects the equipment, the decision factor i is identified and a decision level is constructed by using a level analysis processing method.
After the expert j (j is 1,2, …, n, n is the total number of experts) selects the target device, the decision factor i of the probability theory of the device is based on the fault tree analysis of all accidents involved by the device, namely the accident Risk; the decision factor i of the device determinism is based on qualitative evaluation factors: the critical safety functions and the specific normal operating functions, i.e. the qualitative evaluation QA.
In particular, the RISSC implements information using probabilistic and deterministic theory. Therefore, available probabilistic or deterministic information should be identified. The importance analysis of PSA is information as a probability theory; qualitative evaluations of functional attributes based on SSCs are used as information for determinism.
For systems or devices of the post-processing facility, it is often not difficult to determine abnormal events (initial events) that may occur, such as nuclear criticality, explosions, leaks, etc. But the cause and progression of the event is difficult to determine. From the PSA's perspective, these identified possible exceptional events are the top events in the fault tree analysis. After determining the top event, reasoning finds out the various causes (failure modes) that cause the result, and a result-to-cause algorithm is used. Decision factors of the probability theory of the post-processing facility RISSC are analyzed by importance, which is derived from independent fault tree analysis of all accidents involved by the target device. For example, a facility may be involved in three types of accidents, critical, fire and leakage, and the combined effects of all three types of accidents should be considered in the RISSC for that facility. In the view of probability theory, the importance of PSA analysis is mainly used as FV importance and RAW importance.
The decision factors of the determinism of the aftertreatment facility RISSC are based on the following qualitative evaluations: (1) critical safety functions-ensuring the integrity of the corresponding containment; ensuring the ability to prevent or mitigate the consequences of an accident that may lead to potential off-site exposure; (2) specific normal function-can result in an originating event or a production outage.
TABLE 1 decision factors for the aftertreatment facility RISSC
As shown in table 1, after the target device is selected by the expert j (j ═ 1,2, …, n, n is the total number of experts), the decision factor i of the target device post-processing facility RISSC is determined according to the above method (i ═ 1, 2. Specifically, the qualitative evaluation QA includes: QA1The equipment is required to maintain the integrity of the corresponding containment; QA2The equipment is required for cleaning or reducing the radioactivity released; QA3Auxiliary systems (water supply, power supply, compressed air) require the device.
Analytic Hierarchy Process (AHP) is a tool that helps decision makers to perform multi-criteria decision analysis. Saaty has been widely used in a variety of decision making areas since its introduction in the last 70 th century. In the present invention, the decision problem is the RISSC. As shown in fig. 3, the decision problem is decomposed layer by layer into interrelated decision elements, which the AHP builds the RISSC hierarchically.
Step 102: constructing a Bayesian confidence network model by the expert j, and judging to obtain P (C)ij|Si) And calculating the safety importance P (S) of each decision factor of the target component by using the Bayesian belief networki＝Hi)。
Bayesian Belief Networks (BBN) strongly aggregate different knowledge sources with various degrees of uncertainty in a rigorous and efficient mathematical manner.
Referring to FIG. 4, a Bayesian belief network model is created, the Bayesian belief network model including a parent variable SiAnd the subvariable CijAnd the directional edges between the parent and child variables. SiDecision factors i representing the target component fall into Hi (high security importance rating) or Lo (low security importance rating), CijThe decision factor i representing the target component falls into a decision criterion (c) based on the judgment of expert j1,c2,…,cmM is the total number of criteria for decision factor i).
Referring to fig. 5, fig. 5 is a decision criterion for accident risk in the aftertreatment facility RISSC. The consequences in the present invention take the most radioactivity that the target device may leak; for a single plant, the frequency of certain nuclear accidents is generally greater, which results from the failure rates (e.g., rupture and leakage of valves) of some components of the after-treatment plant being significantly higher than the failure rates of the same components of the nuclear plant, by about an order of magnitude. The decision criterion of the accident risk of the probability theory in the post-processing facility RISSC specifically comprises the following steps:
consequences of the accident>108MBq，0<The frequency of occurrence is less than or equal to 10-9a-1When the decision criterion is c4；
Consequences of the accident>108MBq，10-9a-1<The frequency of occurrence is less than or equal to 10-6a-1When the decision criterion is c2；
Consequences of the accident>108MBq，10-6a-1<When the frequency occurs, the decision criterion is c1；
When 0 is present<Accident consequence is less than or equal to 108MBq，0<The frequency of occurrence is less than or equal to 10-9a-1When the decision criterion is c6；
When 0 is present<Accident consequence is less than or equal to 108MBq，10-9a-1<The frequency of occurrence is less than or equal to 10-6a-1When the decision criterion is c5；
When 0 is present<Accident consequence is less than or equal to 108MBq，10-6a-1<When the frequency occurs, the decision criterion is c3。
Decision criterion c for qualitative evaluation in a reprocessing System RISSC1-c4The method specifically comprises the following steps:
c1: the device is directly required and no other device performs the same or similar functions;
c2: the device is directly required and other devices are still available to perform the same or similar functions;
c3: the device is indirectly required (e.g., the device may affect the behavior of a person or the device is directly required by the device, etc.);
c4: this component is not required at all.
In the present invention, the safety importance of each decision factor of a component is represented by its probability of falling into Hi, and is denoted as P (S)iHi). Expert j judges to obtain P (C)ij|Si) And calculating the safety importance P (S) of each decision factor of the target component by using the Bayesian belief networkiHi) specifically includes:
first, P (S) is evaluated by expert ji|Cij)。P(Si|Cij) Is easy to obtain because of the variable SiThere are only 2 states, Hi or Lo. For example, if P (S)i＝Hi|Cij＝c1) Estimated by an expert, P (S)i＝Lo|Cij＝c1) Can be selected from the expression 1-P (S)i＝Hi|Cij＝c1) Can be directly obtained. Thus, P (S)i|Cij) Can be evaluated by an expert. Note that when expert j evaluates P (S)i|Cij) It is not easy to put P (S)i|Cij) And evaluating to obtain a point value. Therefore, P (S)i|Cij) Is estimated as an interval value. As shown in table 2, the median of the interval values is used as a typical value, and if the median of the expert-based opinion interval values is not suitable, the expert may specify a suitable value.
TABLE 2 Interval values for expert estimation
Then, according to P (S) by Bayes' theoremi|Cij) Calculate P (C)ij|Si). The calculation formula is as follows:
it is noted that in the absence of the variable CijCan assume P (C)ij＝c1)＝,…,＝P(Cij＝cm)＝1/m。
Finally, the safety importance P (S) of each decision factor of the target component is calculatediHi), the calculation formula is:
P(Si＝Hi)＝P(Si＝Hi|Ci1＝c1)P(Ci1＝c1)+…+P(Si＝Hi|Ci1＝cm)P(Ci1＝cm)。
wherein, P (S)i＝Lo)＝1-P(Si＝Hi)。
Step 103: obtaining evidence E by an expertijAnd updating P (S) using the evidencei＝Hi)。
After selecting the target part, P (S) for each ii) Evidence of judgment E by expertijAnd (5) upgrading.
First, ifExpert 1 judges that the decision factor i of the target component falls into c1That is, P*(Ci1)＝(1，0，0，...)，CijIs denoted as P*(Cij) Or Eij(P*(Cij)≡Eij)，P*(Ci1) As an update P (S), (1, 0, 0.) (S)iHi) as follows:
P*(Si＝Hi)＝P(Si＝Hi|Ci1＝c1)P*(Ci1＝c1)+…+P(Si＝Hi|Ci1＝cm)P*(Ci1＝cm)
＝P(Si＝Hi|Ci1＝c1)P*(Ci1＝c1)
·.·P*(Ci1)＝(1，0，0，...)
known as P (S)i|Ci1) And P*(Ci1＝c1) When 1, P can be calculated*(SiHi), the probability updated by the evidence is denoted as P*. In pair P (S)i) If expert 2 determines that the decision factor i of the target component falls within c in the second upgrade of (2)2That is, P*(Ci2)＝(0，1，0，...)，P*(SiHi) is updated by Bayes theorem as follows:
wherein, P (C)i2＝c2|Si＝Hi)，P(Ci2＝c2|SiLo) and P*(SiHi) is known, and P*(Si＝Lo)＝1-P*(SiHi). Thus, P*(Si＝Hi|Ci2＝c2) Can be calculated. Second updated P (S)iHi) is P**(SiHi) may be substituted by P*(Si＝Hi|Ci2＝c2) Calculated as follows:
P**(Si＝Hi)＝P*((Si＝Hi|Ci2＝c1)P*(Ci2＝c1)+…+P*(Si＝Hi|Ci2＝cm)P*(Ci2＝cm)
＝P*(Si＝Hi|Ci2＝c2)P*(Ci2＝c2)
·.·P*(Ci2)＝(0，1，0，...)
similarly, for each decision factor i, P (S)iHi) may continue to be updated until all evidence of expert judgment is used. Thus, as expert judged evidence adds to the BBN model, a more reasonable posterior P (S)iHi).
Step 104: obtaining weight w of decision factor i by using hierarchical analysis processing methodi。
Firstly, obtaining input data of a given problem, namely a judgment matrix comprising pairwise comparison of decision elements of each layer; secondly, if all judgment matrixes are provided by different experts, all elements of the combined judgment matrix are obtained by the geometric mean value of the corresponding elements of all the matrixes; thirdly, estimating the relevant weight value of the decision factor by a Saaty characteristic value method of the joint matrix; finally, the weight value of each bottom-layer decision factor is accumulated by the weight of each level-related element.
For all the levels of the AHP, the formula for calculating the weight value wi of the decision factor i is as follows:
wherein, wu/u-1: the weight of element u relative to its associated nearest previous level element u-1; u. ofi: a decision factor i; v: the number of layers between decision factor i and the decision problem; ut: and (5) making a decision.
Step 105: deriving a total probability P (S) based on probability theory and deterministic theoryT＝Hi)，And finally classified.
The formula for calculating the total probability is as follows:
P(ST＝Hi)＝wPP(SPT＝Hi)+wDP(SDT＝Hi)
wherein, P (S)PTHi) is the classification probability based on PSA; p (S)DTHi) is a ranking probability derived based on qualitative assessment of determinism; wP、WDAre respectively P (S)PTHi) and P (S)DTHi).
The P (S)PTHi) is calculated as:
wherein, P (S)nHi) (N1, 2.., N) is the probability of the system belonging to Hi from the nth risk of accident.
The P (S)DTHi) is calculated as:
wherein, W1n、W2nQA for qualitative assessment of the nth accident1、QA2Weight value in qualitative assessment, W2N+1For qualitative evaluation of QA3Weight values in qualitative assessments.
The classification criteria are as follows:
when the total probability is more than 0 and less than or equal to 0.35, the equipment has low safety importance;
when the total probability is more than 0.35 and less than or equal to 0.65, the equipment has potential safety importance of Po;
when 0.65< total probability ≦ 1, the device is of high safety importance for Hi.
In one particular embodiment, the present invention is used to RISSC key equipment for the co-decontamination cycle section of a typical post-treatment facility.
The equipment of the post-treatment facility is originally classified completely according to the method of the determinism, and is divided into three safety levels of radiochemical safety level 1, radiochemical safety level 2 and radiochemical safety level 3 (the lower the level is, the more important the lower the level is), and the earthquake-resistant category or quality assurance level corresponding to the three safety levels is shown in the following table 3.
TABLE 3 mapping of radiochemical Security level to earthquake-resistance Categories and warranty grades
The total number of the devices in the analysis is 31, and the original grades belong to radiochemical safety level 2. The results of the RISSC show that there are 13 devices belonging to the Po class, which corresponds to the original class, i.e. the results of the RISSC are consistent with the results of the complete determinism; there are 18 devices belonging to class Lo, which lowers the security level compared to the original class, indicating the conservatism of the original class. The results of the RISSC are consistent with the RISSC experience of the nuclear power plant facility, and the correctness and applicability of the RISSC method herein are also laterally demonstrated. It should be noted that, for the original quality assurance grade of the effluent evaporator to be grade a, the result of RISSC belongs to grade Lo, and the quality assurance grade can be considered to be reduced, so as to reduce the related operation and maintenance cost.
Different from the prior art, the method for grading the equipment in the post-processing facility with the risk guide provided by the invention enables experts to utilize hierarchical analysis processing and Bayesian confidence network to participate early, and provides a basis for grading the post-processing facility so as to reduce the operation and maintenance cost of the relevant equipment.
It will be appreciated by persons skilled in the art that the method of the present invention is not limited to the examples described in the specific embodiments, and that the above detailed description is for the purpose of illustrating the invention only and is not intended to limit the invention. Other embodiments will be apparent to those skilled in the art from the following detailed description, which is intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims.
Claims (11)
1. A method for staging equipment in a risk-indexed post-processing facility, the method comprising:
(1) after the expert j selects equipment, identifying a decision factor i and constructing a decision level by using a level analysis processing method;
(2) constructing a Bayesian confidence network model by the expert j, and judging to obtain P (C)ij|Si) And calculating the safety importance P (S) of each decision factor of the target component by using the Bayesian belief networki＝Hi)；
(3) Obtaining evidence E by an expertijAnd updating P (S) using the evidencei＝Hi)；
(4) Obtaining weight w of decision factor i by using hierarchical analysis processing methodi；
(5) Deriving a total probability P (S) based on probability theory and deterministic theoryTHi) and finally classified.
2. The method for grading equipment in a risk-oriented post-processing facility according to claim 1, wherein the step (1) specifically comprises:
after the target equipment is selected, the decision factor i of the probability theory of the equipment is based on the fault tree analysis of all accidents related to the equipment, namely the accident Risk Risk; the decision factor i of the device determinism is based on qualitative evaluation factors: the critical safety functions and the specific normal operating functions, i.e. the qualitative evaluation QA.
3. The method of grading equipment in a risk-oriented post-processing facility according to claim 2,
the qualitative assessment QA includes: QA1The equipment is required to maintain the integrity of the corresponding containment; QA2The equipment is required for cleaning or reducing the radioactivity released; QA3Auxiliary systems (water supply, power supply, compressed air) require the device.
4. The method for ranking equipment in a risk guidance post-processing facility according to claim 1, wherein the constructing of the bayesian belief network model by expert j in step (2) specifically comprises:
creating a Bayesian belief network model, BayesThe Si-confidence network model contains a parent variable SiAnd the subvariable Cij，SiDecision factors i representing the target component fall into Hi (high security importance rating) or Lo (low security importance rating), CijThe decision factor i representing the target component falls into a decision criterion (c) based on the judgment of expert j1,c2,…,cmM is the total number of criteria for decision factor i).
5. The method for the stratification of equipment in a risk guidance post-processing facility according to claim 4, wherein the decision criteria for probability theory accident risk in the post-processing facility RISSC specifically comprises:
consequences of the accident>108MBq，0<The frequency of occurrence is less than or equal to 10-9a-1When the decision criterion is c4；
Consequences of the accident>108MBq，10-9a-1<The frequency of occurrence is less than or equal to 10-6a-1When the decision criterion is c2；
Consequences of the accident>108MBq，10-6a-1<When the frequency occurs, the decision criterion is c1；
When 0 is present<Accident consequence is less than or equal to 108MBq，0<The frequency of occurrence is less than or equal to 10-9a-1When the decision criterion is c6；
When 0 is present<Accident consequence is less than or equal to 108MBq，10-9a-1<The frequency of occurrence is less than or equal to 10-6a-1When the decision criterion is c5；
When 0 is present<Accident consequence is less than or equal to 108MBq，10-6a-1<When the frequency occurs, the decision criterion is c3。
6. The method for risk-oriented stratification of equipment in a post-processing facility according to claim 4, wherein the decision criterion c for qualitative evaluation in the post-processing facility RISSC1-c4The method specifically comprises the following steps:
c1: the equipment is directly required and is not held by other equipmentRow-like or level-like functionality;
c2: the device is directly required and other devices are still available to perform the same or similar functions;
c3: the device is indirectly required (e.g., the device may affect the behavior of a person or the device is directly required by the device, etc.);
c4: this component is not required at all.
7. The method for classifying equipment in a risk guidance post-processing facility according to claim 4, wherein the expert j in the step (2) judges and obtains P (C)ij|Si) And calculating the safety importance P (S) of each decision factor of the target component by using the Bayesian belief networkiHi) specifically includes:
evaluation of P (S) by expert ji|Cij) And P (C)ij)；
According to P (S) by Bayes' theoremi|Cij) Calculate P (C)ij|Si)；
Calculating P (S)iHi), the calculation formula is
P(Si＝Hi)＝P(Si＝Hi|Ci1＝c1)P(Ci1＝c1)+…+P(Si＝Hi|Ci1＝cm)P(Ci1＝cm)。
8. The method for ranking equipment in a risk guidance post-processing facility according to claim 1, wherein the calculation formula of the total probability in the step (5) is:
P(ST＝Hi)＝wPP(SPT＝Hi)+wDP(SDT＝Hi)，
wherein, P (S)PTHi) is the classification probability based on PSA; p (S)DTHi) is a ranking probability derived based on qualitative assessment of determinism; w is aP、wDAre respectively P (S)PTHi) and P (S)DTHi).
9. The method of grading equipment in a risk-oriented post-processing facility according to claim 8, wherein P (S)PTHi) is calculated as:
wherein, P (S)nHi) (N1, 2.., N) is the probability of the system belonging to Hi from the nth risk of accident.
10. The method of grading equipment in a risk-oriented post-processing facility according to claim 8, wherein P (S)DTHi) is calculated as:
wherein, w1n、w2nQA for qualitative assessment of the nth accident1、QA2Weight value in qualitative assessment, w2N+1For qualitative evaluation of QA3Weight values in qualitative assessments.
11. The method for classifying equipment in a risk-oriented post-processing facility according to claim 1, wherein the classification criteria in step (5) are as follows:
when the total probability is more than 0 and less than or equal to 0.35, the equipment has low safety importance of Lo;
when the total probability is less than or equal to 0.65 and less than 0.35, the equipment has potential safety importance of Po;
when 0.65< total probability ≦ 1, the device is of high safety importance for Hi.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910243536.9A CN110598966B (en) | 2019-03-28 | 2019-03-28 | Grading method of equipment in risk-guided post-processing facility |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910243536.9A CN110598966B (en) | 2019-03-28 | 2019-03-28 | Grading method of equipment in risk-guided post-processing facility |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110598966A true CN110598966A (en) | 2019-12-20 |
| CN110598966B CN110598966B (en) | 2024-01-26 |
Family
ID=68852486
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910243536.9A Active CN110598966B (en) | 2019-03-28 | 2019-03-28 | Grading method of equipment in risk-guided post-processing facility |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110598966B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112652415A (en) * | 2020-12-01 | 2021-04-13 | 中国辐射防护研究院 | Post-processing plant emergency state grading determination method based on characteristic parameter analysis |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2000041714A1 (en) * | 1999-01-19 | 2000-07-20 | Bristol-Myers Squibb Company | Method for determining care and prevention pathways for clinical management of wounds |
| CN102034023A (en) * | 2010-12-07 | 2011-04-27 | 上海交通大学 | Evidence theory-based multi-source information fusion risk analysis method |
| CN104899682A (en) * | 2015-05-19 | 2015-09-09 | 上海市建工设计研究院有限公司 | Evaluation method for construction risk of antiseepage waterproof curtain of deep foundation fit in coastal area |
| CN105069692A (en) * | 2015-07-14 | 2015-11-18 | 国家电网公司 | Accurate power grid safety risk assessment method |
| CN106295332A (en) * | 2016-08-30 | 2017-01-04 | 湖北工业大学 | Based on interval number and the Information Security Risk Assessment Methods of ideal solution |
| CN108898314A (en) * | 2018-06-29 | 2018-11-27 | 厦门大学 | One kind being based on BIM and quality Multi-source Information Fusion Construction Risk Assessment method |
-
2019
- 2019-03-28 CN CN201910243536.9A patent/CN110598966B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2000041714A1 (en) * | 1999-01-19 | 2000-07-20 | Bristol-Myers Squibb Company | Method for determining care and prevention pathways for clinical management of wounds |
| CN102034023A (en) * | 2010-12-07 | 2011-04-27 | 上海交通大学 | Evidence theory-based multi-source information fusion risk analysis method |
| CN104899682A (en) * | 2015-05-19 | 2015-09-09 | 上海市建工设计研究院有限公司 | Evaluation method for construction risk of antiseepage waterproof curtain of deep foundation fit in coastal area |
| CN105069692A (en) * | 2015-07-14 | 2015-11-18 | 国家电网公司 | Accurate power grid safety risk assessment method |
| CN106295332A (en) * | 2016-08-30 | 2017-01-04 | 湖北工业大学 | Based on interval number and the Information Security Risk Assessment Methods of ideal solution |
| CN108898314A (en) * | 2018-06-29 | 2018-11-27 | 厦门大学 | One kind being based on BIM and quality Multi-source Information Fusion Construction Risk Assessment method |
Non-Patent Citations (1)
| Title |
|---|
| 刘文生: "矿业投资风险决策的贝叶斯网络方法", 《化工矿物与加工》, no. 06, pages 30 - 33 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112652415A (en) * | 2020-12-01 | 2021-04-13 | 中国辐射防护研究院 | Post-processing plant emergency state grading determination method based on characteristic parameter analysis |
| CN112652415B (en) * | 2020-12-01 | 2022-10-21 | 中国辐射防护研究院 | Post-processing plant emergency state grading determination method based on characteristic parameter analysis |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110598966B (en) | 2024-01-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| MIRI et al. | Application of fuzzy fault tree analysis on oil and gas offshore pipelines | |
| CN106483947A (en) | Distribution Running State assessment based on big data and method for early warning | |
| Ha et al. | A method for risk-informed safety significance categorization using the analytic hierarchy process and bayesian belief networks | |
| CN104951588A (en) | Aided design method for mine ventilation systems | |
| CN113743750B (en) | Nuclear power plant process system process risk assessment system and method | |
| RU2549514C2 (en) | System for predicting and evaluating safety of hazardous production facility using integrated safety model | |
| Singh et al. | Modeling and measuring common cause failures in measurement of reliability of nuclear power plant systems | |
| Soga et al. | A systematic approach to estimate an inter-unit common-cause failure probability | |
| Mirghafouri et al. | Developing a method for risk analysis in tile and ceramic industry using failure mode and effects analysis by data envelopment analysis | |
| Nadjafi et al. | Dynamic fault tree analysis using fuzzy LU bounds failure distributions | |
| Wang | The effects of uncertainties in forced outage rates and load forecast on the loss-of-load probability (LOLP) | |
| CN110598966A (en) | Method for grading equipment in post-processing facility based on risk guidance | |
| Su et al. | Handling uncertainty in human cognitive reliability method for safety assessment based on DSET | |
| Feng | Survival signature-based reliability approach for complex systems susceptible to common cause failures | |
| Kumar | Reliability and probabilistic safety assessment in multi-unit nuclear power plants | |
| Zhang et al. | FailureMode and Effects Analysis Based on Z-Numbers and the GradedMean Integration Representation. | |
| Wang et al. | Arithmetic operations for LR mixed fuzzy random variables via mean chance measure with applications | |
| Ahmed et al. | An Integrated Approach for Failure Analysis of Natural Gas Transmission Pipeline. CivilEng 2021, 2, 87–119 | |
| Zubair et al. | Study on nuclear accident precursors using AHP and BBN, a case study of Fukushima accident | |
| Calley | PRA technology and regulatory perspectives P-111 | |
| Ju et al. | Research on verification technology of aircraft RMS based on MBSE | |
| Kelly | Impact of uncertainty on calculations for recovery from loss of offsite power | |
| Viktorova et al. | Software for Testability Analysis of Aviation Systems | |
| Tchórzewska-Cieślak | Characterization of risk function in the analysis and assessment of water supply systems safety | |
| CN116596137A (en) | Enterprise carbon performance failure risk assessment method, system and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |