CN117973875A - Fuzzy inference system-based risk assessment modeling method for ore pulp water delivery system - Google Patents
Fuzzy inference system-based risk assessment modeling method for ore pulp water delivery system Download PDFInfo
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Abstract
The invention relates to the technical field of ore pulp pipeline risk assessment, and discloses a modeling method for risk assessment of an ore pulp water delivery system based on a fuzzy inference system, which comprises the following steps: and establishing a comprehensive parameter list of the failure result of the water pipeline, modeling the water pipeline in the ore pulp system by adopting fuzzy logic, verifying the established model, and evaluating the risk of the water pipeline. The fuzzy reasoning system-based risk assessment modeling method for the ore pulp water delivery system fills the gap between data by using the fuzzy reasoning system, and improves the accuracy of the model; according to the invention, economic, social, environmental, operation characteristics, maintenance complexity and other factors are comprehensively considered, so that the solving result is more fit with engineering reality, and various risk types of the pipeline can be clearly distinguished.
Description
Technical Field
The invention relates to the technical field of risk assessment of ore pulp pipelines, in particular to a risk assessment modeling method of an ore pulp water delivery system based on a fuzzy reasoning system.
Background
Most areas rich in mineral resources are distributed in remote mountainous areas or remote areas, which are relatively far from the main urban areas. Thus, the transportation problem becomes particularly important when transporting these mineral resources from the mining site to the processing plant or other site. Conventional newly built railway, highway and other transportation trunks require huge investments, lengthy construction periods, and often are accompanied by serious environmental damage. This has led to an increasing need for transportation methods that seek more cost-effective and environmentally friendly characteristics.
The pulp pipeline system is gradually exposed to the head angle due to the unique advantages, however, the failure of the water pipeline in the system often leads to the stagnation of the whole workflow, so that the accurate risk assessment of the water pipeline is very important. When pipes fail, the costs associated with repairing such pipe failures are very high, including not only replacement of the pipe, but also many other indirect costs, such as loss of property due to flooding, and disruption of critical facilities such as mining companies, steel smelters, etc.
In recent years, technicians put forward several risk assessment models of water pipelines, although good results are achieved, gaps still exist between data, and the overall influence of faults and related complexity is not considered, so that the accuracy of identification of the risk assessment models under partial conditions is low.
Disclosure of Invention
(One) solving the technical problems
Aiming at the defects of the prior art, the invention provides a fuzzy reasoning system-based risk assessment modeling method for an ore pulp water delivery system, which has the advantages of accurate identification and the like and solves the technical problems.
(II) technical scheme
In order to achieve the above purpose, the present invention provides the following technical solutions: a fuzzy reasoning system-based ore pulp water delivery system risk assessment modeling method comprises the following steps:
s1, establishing a comprehensive parameter list of the failure result of a water pipeline;
S2, modeling a water conveying pipeline in the ore pulp system by adopting fuzzy logic;
S3, verifying the established model;
and S4, evaluating the risk of the water conveying pipeline.
As a preferred embodiment of the present invention, the comprehensive parameter list includes economic impact, environmental impact, operation characteristics, social impact and update complexity,
Parameters in the economic impact include water cost loss, system update loss, and system maintenance cost;
Parameters in the environmental impact include costs incurred to the surrounding environment and the likelihood of occurrence of natural disasters;
parameters in the operating characteristics include financial impact, available labor, pipe diameter, system shut down time, and pipe pressure;
parameters in the social impact include traffic impact, water quality impact, financial loss costs and customer satisfaction;
parameters in the update complexity include terrain impact and pipe depth.
As a preferable technical scheme of the invention, the specific process of the step S2 is as follows:
S2.1, determining a membership function used.
S2.2, establishing a fuzzy rule base according to the membership function.
S2.3, mapping the given clear quantitative input to the output fuzzy set according to the membership function and the rule.
S2.4, combining all output fuzzy sets into one fuzzy set according to the aggregation operation by utilizing a summation operator, wherein the fuzzy set is used for representing all truncated values in the output membership function.
S2.5, converting the fuzzy output set into clear ratings by a defuzzification method of an area gravity center method, wherein the range of the fuzzy output set is within [0,5 ].
S2.6, defining the rating in [0,5 ].
As a preferred technical solution of the present invention, the verification process in step S3 specifically includes the following steps:
S3.1, carrying out band-pass test on the model, and checking model behaviors of three wave bands to represent the best, medium and worst conditions; if the parameters represent the best case, the model should give the best output;
S3.2, estimating a first-order sensitivity index and a total sequence sensitivity index based on sensitivity analysis of global variance.
As a preferred embodiment of the present invention, the membership function in step S2.1The expression is as follows:
Wherein, Representing the domain of discussionIs a combination of the two elements,Representation ofIs a set of random variations in the (c),Representation ofIn order to getAs a fuzzy set of elastic boundaries,In order to obscure the number of experiments,Representing a limit operation.
As a preferred technical scheme of the present invention, the specific process in the step S2.2 is as follows:
S2.2.1, introducing a comprehensive parameter list established in the S1;
S2.2.2, weighting each parameter in the comprehensive parameter list, and the specific expression is as follows:
Wherein, Representing parameters/>Membership degree of/>Representing a binary set.
S2.2.3, using the IF-THEN rule to show the dependency relationship among 16 parameters in the step S1, and completing the establishment of the fuzzy rule base.
As a preferred embodiment of the present invention, the transfer function in step S2.5The expression is as follows:
Wherein, Representation of parameters/>Membership functions of (a) are provided.
As a preferred embodiment of the present invention, the step S2.6 determines the rating in [0,5] in the step S2.5, and the specific expression is as follows:
Wherein, Representing the rating.
As a preferred embodiment of the present invention, the first-order sensitivity index expression in the step S3.2 is as follows:
Wherein, Representing sensitivity,/>Representing the output of the model function,/>Is the decomposition variance of the Sobol operator.
As a preferred embodiment of the present invention, the expression of the total sequence sensitivity index in the step S3.2 is as follows:
Wherein, Representing the overall sequential sensitivity index,/>For/>Is input,/>Condition expectations representing contributions of respective inputs to output variances,/>Represented as the variance of the outputs of the respective input pairs.
Compared with the prior art, the invention provides a fuzzy reasoning system-based risk assessment modeling method for an ore pulp water delivery system, which has the following beneficial effects:
The invention fills the gap between data by using a fuzzy reasoning system. Firstly, the influence of a plurality of risk factors on failure results is synthesized, and the risk condition of the system is comprehensively evaluated; then, processing the mutual relation between the uncertainty and the subjective information through fuzzy logic; finally, an IF-THEN is used for constructing a rule base among various parameters, so that the accuracy of the model is improved; factors such as economy, society, environment, operation characteristics, maintenance complexity and the like are comprehensively considered, so that a solving result is more fit with engineering reality; the invention develops a comprehensive parameter list capable of comprehensively reflecting the failure result of the water pipe, uses the IF-THEN rule to express the dependency relationship among the selected parameters, builds 381 fuzzy rules in total, and can clearly distinguish the types of various risks of the pipe through the comprehensive fuzzy rules.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a schematic diagram of a parameter framework of the present invention;
FIG. 3 is a comparative schematic diagram of experimental results of the present invention;
fig. 4 is a flow chart of step S2 of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention; all other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to further verify the accuracy of the method of the invention, an analysis is performed by taking a mining company as an example; the mining company is provided with a pulp water delivery pipeline, and the company can provide engineering knowledge to develop and verify data of the proposed fuzzy inference system and related parameters such as materials, diameters, pipeline identification numbers and the like, so as to provide related parameters for the whole water delivery pipeline; the failure rating results of 2340 pipe sections obtained from the water pipe of the mining company are connected to the whole data set for verifying the accuracy of the method according to the present invention, and the specific process is shown in fig. 1-4, and includes the following steps:
S1, establishing a comprehensive parameter list of the failure result of the water pipeline, wherein the comprehensive parameter list comprises economic influence, environmental influence, operation characteristics, social influence and update complexity, and the comprehensive parameter list comprises the following components:
parameters in economic impact include water cost loss, system update loss, and system maintenance cost;
Parameters in the environmental impact include costs incurred to the surrounding environment and the likelihood of natural disasters occurring;
Parameters in the operating characteristics include financial impact, available labor, pipe diameter, system shut down time, and pipe pressure;
Parameters in social impact include traffic impact, water quality impact, financial loss costs and customer satisfaction;
Parameters in update complexity include terrain impact and pipe depth.
S2, modeling a water pipeline in the ore pulp system by adopting fuzzy logic.
S2.1, determining a membership function used, wherein the specific expression is as follows:
Wherein, Representing the domain/>An inherent element of (1)/>Representation/>Is a set of random variations in the (c),Representation/>Middle and/>Fuzzy set as elastic boundary,/>The number of fuzzy experiments.
When (when)When the frequency is increased, the membership frequency tends to be stable, and the stable value of the membership frequency is called/>Pair/>The expression is as follows:
S2.2, developing a fuzzy rule base according to the membership function;
S2.2.1, introducing a comprehensive parameter list established in the S1;
S2.2.2, weighting each parameter in the comprehensive parameter list, and the specific expression is as follows:
Wherein, Representing parameters/>Membership degree of/>Representing a binary set.
S2.2.3, using the IF-THEN rule table, shows the dependency between the 16 parameters in step S1:
According to the dependency relationship, the establishment of a fuzzy rule base among 16 parameters is completed, and the specific process is as follows:
constructing rule bases in each influence module, obtaining the influence of the comprehensive parameters on the system according to the influence of each different factor on the system under each influence module, wherein table 1 is a fuzzy rule base in the economic influence module; the construction method of the complexity fuzzy rule base is similar to that of environmental influence, operation characteristics, social influence and update.
TABLE 1
And constructing a fuzzy rule base of the comprehensive parameter list according to the influence of each comprehensive parameter on the system, as shown in table 2.
TABLE 2
S2.3, mapping the given clear quantitative input to the output fuzzy set according to the membership function and the rule.
S2.4, combining all output fuzzy sets into one fuzzy set according to the aggregation operation by utilizing a summation (sum of rule output sets) operator, and representing all truncated values in the output membership function.
S2.5, converting the fuzzy output set into clear ratings by using a defuzzification method of an area gravity center method, wherein the range of the fuzzy output set is within [0,5], and the specific expression is as follows:
Wherein, Representation of parameters/>Membership functions of (a) are provided.
S2.6, determining the rating in [0,5] in the step S2.5, wherein the specific expression is as follows:
s3, verifying the established model, which comprises the following steps:
s3.1, carrying out band-pass test on the model, and checking model behaviors of three wave bands to represent the best, medium and worst conditions; if the parameters represent the best case, the model should give the best output.
S3.2, estimating a first-order sensitivity index and a total sequence sensitivity index in a 95% confidence interval based on sensitivity analysis of global variance:
S3.2.1, first order sensitivity index expression is as follows:
Wherein, Representing sensitivity,/>Representing the output of the model function,/>The decomposition variance of the Sobol operator;
s3.2.2, the expression of the total sequence sensitivity index is as follows:
Wherein, Representing the overall sequential sensitivity index,/>For/>Is input,/>Condition expectations representing contributions of respective inputs to output variances,/>Represented as the variance of the outputs of the respective input pairs.
S4, evaluating risks of the water conveying pipeline
Model results based on root mean square error) And mean absolute error (/ >)) Evaluating the scores; these are commonly used model verification indicators that can be estimated as follows:
Wherein, For/>Model output of the individual samples; /(I)Representing an actual result of the actual fault data; /(I)For/>Prediction error of individual samples; /(I)Is the number of samples used for verification; /(I)And/>Can range from a fraction of 0 to ≡, a lower value is desirable for both indicators; the above cases are obtained by using the model provided by the inventionAnd/>Score estimates of 0.96 and 0.76, respectively, indicate that the model results are acceptable.
FIG. 3 is a graph showing the results of predicting the accuracy of the samples used by fuzzy inference system modeling (FIS) and by failure probability modeling (LOF), respectively, wherein the accuracy of the prediction can reach 96% and is obviously higher than that of the samples used by the fuzzy inference system modeling (FIS) and the failure probability modeling (LOF).
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A fuzzy reasoning system-based ore pulp water delivery system risk assessment modeling method is characterized by comprising the following steps of: the method comprises the following steps:
s1, establishing a comprehensive parameter list of the failure result of a water pipeline;
S2, modeling a water conveying pipeline in the ore pulp system by adopting fuzzy logic;
S3, verifying the established model;
and S4, evaluating the risk of the water conveying pipeline.
2. The fuzzy inference system-based risk assessment modeling method for an ore pulp water delivery system, as claimed in claim 1, is characterized in that: the comprehensive parameter list includes economic impact, environmental impact, operational characteristics, social impact, and update complexity:
Parameters in the economic impact include water cost loss, system update loss, and system maintenance cost;
Parameters in the environmental impact include costs incurred to the surrounding environment and the likelihood of occurrence of natural disasters;
parameters in the operating characteristics include financial impact, available labor, pipe diameter, system shut down time, and pipe pressure;
parameters in the social impact include traffic impact, water quality impact, financial loss costs and customer satisfaction;
parameters in the update complexity include terrain impact and pipe depth.
3. The fuzzy inference system-based risk assessment modeling method for an ore pulp water delivery system, as claimed in claim 2, is characterized in that: the specific process of the step S2 is as follows:
S2.1, determining a membership function used;
S2.2, establishing a fuzzy rule base according to the membership function;
s2.3, mapping given clear quantitative input to an output fuzzy set according to membership functions and rules;
S2.4, combining all output fuzzy sets into one fuzzy set according to aggregation operation by utilizing a summation operator, wherein the fuzzy set is used for representing all truncated values in an output membership function;
S2.5, converting the fuzzy output set into clear ratings by a defuzzification method of an area gravity center method, wherein the range of the fuzzy output set is within [0,5 ];
S2.6, defining the rating in [0,5 ].
4. The fuzzy inference system-based risk assessment modeling method for an ore pulp water delivery system, as claimed in claim 1, is characterized in that: the verification process of step S3 specifically includes the following steps:
S3.1, carrying out band-pass test on the model, and checking model behaviors of three wave bands to represent the best, medium and worst conditions; if the parameters represent the best case, the model should give the best output;
S3.2, estimating a first-order sensitivity index and a total sequence sensitivity index based on sensitivity analysis of global variance.
5. A fuzzy inference system-based risk assessment modeling method for a pulp water delivery system as defined in claim 3, wherein: the membership function in step S2.1The expression is as follows:
；
Wherein, Representing the domain/>An inherent element of (1)/>Representation/>A randomly varying set of (1)/>Representation/>Middle and/>Fuzzy set as elastic boundary,/>For the number of fuzzy experiments,/>Representing a limit operation.
6. The fuzzy inference system-based risk assessment modeling method for an ore pulp water delivery system of claim 5, wherein the method comprises the following steps: the specific process in the step S2.2 is as follows:
S2.2.1, introducing a comprehensive parameter list established in the S1;
S2.2.2, weighting each parameter in the comprehensive parameter list, and the specific expression is as follows:
；
Wherein, Representing parameters/>Membership degree of/>Representing a binary set;
s2.2.3, using the IF-THEN rule to show the dependency relationship among 16 parameters in the step S1, and completing the establishment of the fuzzy rule base.
7. The fuzzy inference system-based risk assessment modeling method for an ore pulp water delivery system of claim 5, wherein the method comprises the following steps: the transfer function in said step S2.5The expression is as follows:
；
Wherein, Representation of parameters/>Membership functions of (a) are provided.
8. The fuzzy inference system-based risk assessment modeling method for an ore pulp water delivery system of claim 7, wherein the method comprises the following steps: the step S2.6 determines the rating in [0,5] in the step S2.5, and the specific expression is as follows:
；
Wherein, Representing the rating.
9. The fuzzy inference system-based risk assessment modeling method for an ore pulp water delivery system according to claim 4, wherein the modeling method is characterized by comprising the following steps: the first-order sensitivity index expression in the step S3.2 is as follows:
；
Wherein, Representing sensitivity,/>Representing the output of the model function,/>Is the decomposition variance of the Sobol operator.
10. The fuzzy inference system-based risk assessment modeling method for an ore pulp water delivery system of claim 9, wherein the method comprises the following steps: the expression of the total sequential sensitivity index in step S3.2 is as follows:
；
Wherein, Representing the overall sequential sensitivity index,/>For/>Is input,/>Condition expectations representing contributions of respective inputs to output variances,/>Represented as the variance of the outputs of the respective input pairs.
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