Buried water supply pipeline full life cycle risk optimization method considering carbon emission

Abstract

The invention relates to the technical field of green and low-carbon development of infrastructures, and particularly discloses a buried water supply pipeline full-life-cycle risk optimization method considering carbon emission, and the method comprises the steps: S1, collecting carbon emission data, and constructing a carbon emission evaluation data set; s2, constructing a carbon emission model and a risk assessment model based on the carbon emission assessment data set; s3, calculating the full life cycle risk of the buried water supply pipeline based on the risk assessment model and the carbon emission model; and S4, optimizing the full-life-cycle risk of the buried water supply pipeline by using an NSGA-II optimization method to complete the full-life-cycle risk optimization of the buried water supply pipeline considering the carbon emission.

Description

Technical Field

[0001] The present invention relates to the technical field of green and low-carbon development of infrastructure, and in particular to a method for optimizing the risk of buried water supply pipelines throughout their life cycle taking carbon emissions into consideration. Background Art

[0002] As a significant area of ​​energy consumption and carbon emissions, infrastructure requires crucial carbon emissions management throughout its lifecycle. However, carbon emissions management for buried water supply pipelines, a crucial component of infrastructure, has yet to fully address all aspects of their lifecycle and struggles to adapt to the diverse range of materials and construction methods. Furthermore, existing methods fail to fully balance the optimal relationship between carbon emissions and risks, lacking systematicity and comprehensiveness. Therefore, developing a systematic and comprehensive approach to optimizing carbon emissions and risks throughout the entire lifecycle of buried water supply pipelines is crucial for promoting green and low-carbon infrastructure development. Summary of the Invention

[0003] To address the above issues, the present invention proposes a risk optimization method for the entire life cycle of buried water supply pipelines taking carbon emissions into consideration. The method specifically includes:

[0004] Step S1: Collect carbon emission data to construct a carbon emission assessment data set;

[0005] Step S2: constructing a carbon emission model and a risk assessment model based on the carbon emission assessment dataset;

[0006] Step S3: Calculate the full life cycle risk of the buried water supply pipeline based on the risk assessment model and the carbon emission model;

[0007] Step S4: Use the NSGA-II optimization method to optimize the full life cycle risk of the buried water supply pipeline, and complete the full risk optimization life cycle risk optimization of the buried water supply pipeline considering carbon emissions.

[0008] Optionally, in step S2, the risk assessment model is constructed by:

[0009] Use FMEA method to identify pipeline failure modes and calculate risk priority number;

[0010] Based on the risk priority number, the pipeline failure probability and loss situation are estimated in combination with the QRA method;

[0011] A risk assessment model is obtained based on the pipeline failure probability and the loss condition.

[0012] Optionally, in step S2, the content of the carbon emission model specifically includes:

[0013] ;

[0014] in, is the total carbon emissions; E SC is the total carbon emissions during the production phase, E YS is the total carbon emissions during the transportation phase, E SA is the total carbon emissions during the construction and installation phase, E YW is the total carbon emissions during the operation and maintenance phase, E BF is the total carbon emissions at the end-of-life stage.

[0015] Optional, E SC The specific content is:

[0016] ;

[0017] Among them, AD 1i is the consumption of the i-th raw material, EF 1i is the carbon emission factor of the i-th raw material, AD 1j is the consumption of the jth energy, EF 1j is the carbon emission factor of the jth energy source, and n is the number of types of raw materials or energy consumed.

[0018] Optional, E YS The specific content is:

[0019] ;

[0020] Among them, L i is the transportation distance of the i-th type of transportation equipment, Q si is the carbon emission per unit mass per kilometer of the i-th type of transportation equipment, Q d is the fuel consumption of loading and unloading equipment per unit mass of cargo, and EF2 is the carbon emission factor of fuel.

[0021] Optional, E SA The specific content is:

[0022] ;

[0023] Among them, H 3i is the working time of the i-th type of construction equipment, P 3i is the power of the i-th construction equipment, EF 3i is the carbon emission factor of the i-th type of construction equipment, M 3j is the consumption of the jth type of construction material, EF 3j is the carbon emission factor of the jth construction material, L 3k is the working hours of the kth type of workers, EF 3k is the average hourly carbon emission factor of the kth type of worker, m is the number of types of construction equipment, and p is the number of worker categories.

[0024] Optional, E YW The specific content is:

[0025] ;

[0026] Among them, E 4i is the carbon emission factor of the i-th type of construction equipment, MA 4j is the average annual consumption of maintenance materials, EF 4i is the carbon emission factor of the i-th type of construction equipment, EF 4j is the carbon emission factor of the jth construction material, T 4k is the average annual transport volume of the kth mode of transportation, EF 4k is the carbon emission factor of the kth mode of transportation.

[0027] Optional, E BF The specific content is:

[0028] ;

[0029] Among them, W 5i is the total amount of type i waste material, EF 5i is the carbon emission factor of the i-th type of waste material, T 5j is the transportation volume of the jth type of waste material, EF 5j is the carbon emission factor of the jth mode of transportation.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] The present invention aims to propose a buried water supply pipeline optimization method based on a full life cycle perspective, construct a carbon emission optimization model and a risk optimization model, and use a multi-objective optimization algorithm to achieve a dynamic balance among carbon emissions, risks and costs.

[0032] This method can provide scientific support and decision-making basis for the planning, design, operation and maintenance of water supply pipelines. Through a multi-objective optimization algorithm, it achieves a dynamic balance between carbon emissions, risks, and costs to improve the sustainability, safety, and economic efficiency of water supply pipelines. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0034] Figure 1 A flowchart of a method for optimizing the risk of a buried water supply pipeline throughout its life cycle taking carbon emissions into consideration according to an embodiment of the present invention;

[0035] Figure 2 This is a full life cycle optimization framework diagram of a buried water supply pipeline according to an embodiment of the present invention;

[0036] Figure 3 This is a framework diagram of a carbon emission assessment model according to an embodiment of the present invention;

[0037] Figure 4 This is a schematic diagram of risk analysis according to an embodiment of the present invention;

[0038] Figure 5 Schematic diagram of the Pareto front and optimized solution distribution according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] Example

[0041] A risk optimization method for the entire life cycle of buried water supply pipelines considering carbon emissions, such as Figure 1 As shown, the method includes:

[0042] Step S1: Collect carbon emission data to construct a carbon emission assessment dataset. This data is integrated across the entire pipeline lifecycle, including planning and design, construction, operation and maintenance, aging assessment, renewal and decommissioning, and environmental data, to construct a pipeline carbon emission model and risk assessment model. Through literature research, carbon emission data for different materials, production processes, transportation methods, and construction methods is systematically collected. Secondly, field surveys are conducted to obtain first-hand data from actual engineering projects, focusing on the carbon emission characteristics during the construction and operation phases.

[0043] Furthermore, through expert interviews and in-depth discussions with industry experts, we gained professional insights into the carbon emission characteristics and optimization strategies of different materials and construction methods. Ultimately, we integrated data resources from the China Life Cycle Database (CLCD) and Switzerland's Ecoinvent to construct an accurate and comprehensive carbon emission assessment dataset, providing data support for the optimization model.

[0044] Step S2: Constructing a carbon emission model and a risk assessment model based on the carbon emission assessment dataset. Constructing a carbon emission model and a risk assessment model respectively to quantify carbon emissions, risk factors, and cost consumption.

[0045] Carbon emission model: covers the calculation of the carbon footprint of the entire life cycle of materials production, transportation, construction, operation, maintenance and decommissioning.

[0046] Risk Optimization Model:

[0047] ;

[0048] Where x is the design variable (pipe material, construction method, etc.), f(x) is the total risk function, P 0i is the probability of occurrence of the i-th failure mode, C 0i is the loss of the ith failure mode, C m is the maintenance cost caused by pipeline failure, and n is the number of types of consumed raw materials or energy.

[0049] Risk Assessment: Calculate the pipeline risk priority number (RPN) through pipeline failure mode analysis (FMEA), and combine it with quantitative risk assessment (QRA) to quantify the failure probability and accident consequences. Using the potential failure mode and effects analysis (FMEA) method, identify pipeline failure modes and calculate the risk priority number (RPN). Based on this RPN, combine it with the quantitative risk analysis (QRA) method to estimate the pipeline failure probability and loss scenario. A risk assessment model is then developed based on this pipeline failure probability and loss scenario.

[0050] Step S3: Calculate the risk of the entire life cycle of the buried water supply pipeline based on the risk assessment model and the carbon emission model. Figure 2-Figure 3 As shown. The optimization objectives are risk optimization and carbon emission minimization. Cost analysis: This covers the full life cycle risk calculations for construction, operation, maintenance, and decommissioning.

[0051] NSGA-II algorithm: Use non-dominated sorting genetic algorithm (NSGA-II) to achieve multi-objective optimization. The specific steps are as follows Figure 5 As shown in the figure, specifically: 1. Initialize the population: randomly generate multiple optimization schemes, each corresponding to different pipeline design parameters. 2. Non-dominated sorting: sort the schemes according to carbon emissions, risk and cost targets, and screen out high-quality solutions. 3. Congestion calculation: ensure the diversity of optimization solutions and improve the quality of solutions. 4. Selection, crossover and mutation: generate new optimization schemes and continuously optimize the objective function. 5. Iterative optimization: continue to calculate until convergence to obtain the optimal Pareto front solution set. The NSGA-II algorithm sets the population size to 100, the iteration number to 500, the crossover probability to 0.9, and the mutation probability to 0.1.

[0052] Result Output: Output the Pareto optimal solution for decision makers to select, conduct sensitivity analysis, identify key influencing factors, optimize design parameters, and improve the scientific nature of decision making. Risk Model: Utilize the potential failure mode and effects analysis (FMEA) method combined with quantitative risk analysis (QRA) to establish a risk assessment model based on failure probability and consequential loss.

[0053] FMEA analysis: Identify the failure modes of the pipeline, as shown in Table 1, and calculate the RPN value (Risk Priority Number).

[0054] ;

[0055] S (Severity): The impact of the failure on the system and environment (1-10 points); O (Occurrence): The probability of the failure occurring (1-10 points); D (Detection): The difficulty of detecting the failure (1-10 points).

[0056] Table 1

[0057]

[0058] The failure modes of pipelines are defined here as 1. physical failure, 2. chemical failure, 3. mechanical failure, and 4. operational failure.

[0059] QRA calculation: estimate the failure probability and accident consequence loss. Failure probability:

[0060] ;

[0061] The calculated values ​​of failure probability are shown in Table 2:

[0062] Table 2

[0063]

[0064] The accident consequences are calculated as:

[0065] ;

[0066] Among them, C economic Economic losses; C fatality Casualties: C environment For environmental impact.

[0067] The accident consequence costs of different failure modes are shown in Table 3:

[0068] Table 3

[0069]

[0070] Logistic regression modeling: Predict whether a pipeline is in a high-risk state.

[0071] ;

[0072] Among them, the β regression coefficient can measure the impact of each variable on risk: If it is larger, it means that RPN has a greater impact on risk. Reflects the importance of failure probability. Shows the contribution of accident consequences to risk.

[0073] Carbon emission model: Quantifies carbon emissions during material production, transportation, construction, operation, maintenance, and decommissioning based on the ISO 14067 standard.

[0074] The carbon emission model specifically includes:

[0075] ;

[0076] Among them, E SC is the total carbon emissions during the production phase, E YS is the total carbon emissions during the transportation phase, E SA is the total carbon emissions during the construction and installation phase, E YW is the total carbon emissions during the operation and maintenance phase, E BF is the total carbon emissions at the end-of-life stage.

[0077] Production stage: Mainly consider raw material consumption and energy consumption: E SC The specific content is:

[0078] ;

[0079] Among them, E SC is the total carbon emissions during the production phase, AD 1i is the consumption of the i-th raw material, EF 1i is the carbon emission factor of the i-th raw material, AD 1j is the consumption of the jth energy, EF 1j is the carbon emission factor of the jth energy source, and n is the number of types of raw materials or energy consumed.

[0080] Transportation stage: The transportation stage mainly considers transportation equipment and loading and unloading equipment: E YS The specific content is:

[0081] ;

[0082] Among them, L i is the transportation distance of the i-th type of transportation equipment, Q si is the carbon emission per unit mass per kilometer of the i-th type of transportation equipment, Q d is the fuel consumption of loading and unloading equipment per unit mass of cargo, and EF2 is the carbon emission factor of fuel.

[0083] Construction and installation phase: Mainly consider equipment usage, material consumption and labor input: E SA The specific content is:

[0084] ;

[0085] Among them, H 3iis the working time of the i-th type of construction equipment (hours), P 3i is the power of the i-th type of construction equipment (kW), EF 3i is the carbon emission factor of the i-th type of construction equipment (kgCO2 / kWh), M 3j is the consumption of the jth type of construction material (tons or cubic meters), EF 3j is the carbon emission factor of the jth construction material (kgCO2 / unit mass), L 3k is the working hours of the kth category worker (hours), EF 3k is the average hourly carbon emission factor of the kth type of worker (kgCO2 / hour), m is the number of types of construction equipment, and p is the number of worker categories.

[0086] E YW Total carbon emissions during operation and maintenance: E YW The specific content is:

[0087] ;

[0088] Among them, E 4i is the carbon emission factor of the i-th type of construction equipment (kWh or L), MA 4j is the average annual consumption of maintenance materials, EF 4i is the carbon emission factor of the i-th type of construction equipment (tons or cubic meters), EF 4j is the carbon emission factor of the jth construction material (kgCO2 / kWh or kgCO2 / L), T 4k is the average annual transport volume of the kth mode of transport (person-km or equipment-km), EF 4k is the carbon emission factor of the kth mode of transportation (kgCO2 / km).

[0089] Scrap stage: mainly consider material removal, transportation and processing: E BF The specific content is:

[0090] ;

[0091] Among them, W 5i is the total amount of waste material of type i (tons), EF 5i is the carbon emission factor of the i-th waste material (kgCO2 / ton), T 5j is the transportation volume of the jth type of waste material (tons·km), EF 5j is the carbon emission factor of the j-th mode of transport (kgCO2 / ton·km).

[0092] The total carbon emissions are obtained by integrating the carbon emissions at each stage of the pipeline's entire life cycle .

[0093] Step S4: Use the NSGA-II optimization method to optimize the full life cycle risk of the buried water supply pipeline, and complete the full life cycle risk optimization of the buried water supply pipeline considering carbon emissions.

[0094] After the model is built, it needs to be applied and verified: for different pipeline materials, the carbon emission data of different pipeline materials are collected and summarized as shown in Table 4:

[0095] Table 4

[0096]

[0097] Failure mode (external corrosion) and risk data are shown in Table 5:

[0098] Table 5

[0099]

[0100] risk assessment:

[0101] ①FMEA analysis:

[0102] RPN 铸铁管 =250, RPN 球墨铸铁管 =216, RPN PE管 =120;

[0103] ②QRA calculation

[0104] The failure probabilities of cast iron pipes, ductile iron pipes, and PE pipes are 5%, 2%, and 3% respectively. The consequences of the accidents are shown in Table 6 and Figure 4 .

[0105] Table 6

[0106]

[0107] The mnrfit function is used in Matlab to perform parameter fitting and the β parameters are as follows:

[0108] ;

[0109] Conclusion: PE pipes, while minimizing carbon emissions, present a slightly higher risk. Ductile iron pipes offer a compromise, balancing both low carbon emissions and low risk. This example only considers three materials and ignores construction methods, resulting in fewer Pareto frontier points. Using a weighted approach to assign weights to carbon emissions yields a decision-making approach. When considering multiple combinations of materials and construction methods, decision makers can use the NSGA-II algorithm to select the appropriate option on the Pareto frontier.

[0110] Full life cycle optimization: Comprehensively consider the carbon emissions, risks, and costs of buried water supply pipelines from material production to scrapping to avoid systemic problems caused by local optimization.

[0111] Risk collaborative optimization: Quantitatively evaluate pipeline failure risks based on the FMEA+QRA method and incorporate them into the cost optimization model to provide a more scientific basis for decision-making.

[0112] Intelligent multi-objective optimization algorithm: Using the NSGA-II optimization method, it automatically searches for the optimal balance point between different objectives, improving the adaptability and intelligence of the solution.

[0113] Applicable to a variety of pipeline materials and construction conditions: The optimized framework can be applied to different materials (such as cast iron pipes, ductile iron pipes, PE pipes) and different construction methods (excavation, trenchless).

[0114] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A method for optimizing the risk of a buried water supply pipeline throughout its life cycle considering carbon emissions, characterized in that: The method comprises: Step S1: Collect carbon emission data to construct a carbon emission assessment data set; Step S2: constructing a carbon emission model and a risk assessment model based on the carbon emission assessment dataset; Step S3: Calculate the full life cycle risk of the buried water supply pipeline based on the risk assessment model and the carbon emission model; Step S4: Use the NSGA-II optimization method to optimize the full life cycle risk of the buried water supply pipeline, and complete the full risk optimization life cycle risk optimization of the buried water supply pipeline considering carbon emissions.

2. The method for optimizing the risk of the entire life cycle of buried water supply pipelines considering carbon emissions according to claim 1 is characterized in that: In step S2, the risk assessment model is constructed by: Use FMEA method to identify pipeline failure modes and calculate risk priority number; Based on the risk priority number, the pipeline failure probability and loss situation are estimated in combination with the QRA method; A risk assessment model is obtained based on the pipeline failure probability and the loss condition.

3. The method for optimizing the risk of the entire life cycle of buried water supply pipelines considering carbon emissions according to claim 2 is characterized in that: In step S2, the content of the carbon emission model specifically includes: ； in, is the total carbon emissions; E SC is the total carbon emissions during the production phase, E YS is the total carbon emissions during the transportation phase, E SA is the total carbon emissions during the construction and installation phase, E YW is the total carbon emissions during the operation and maintenance phase, E BF is the total carbon emissions at the scrapping stage.

4. The method for optimizing the risk of the entire life cycle of buried water supply pipelines considering carbon emissions according to claim 3 is characterized in that: E SC The specific content is: ； Among them, AD 1i is the consumption of the i-th raw material, EF 1i is the carbon emission factor of the i-th raw material, AD 1j is the consumption of the jth energy, EF 1j is the carbon emission factor of the jth energy source, and n is the number of types of raw materials or energy consumed.

5. The method for optimizing the risk of the entire life cycle of buried water supply pipelines considering carbon emissions according to claim 4 is characterized in that: E YS The specific content is: ； Among them, L i is the transportation distance of the i-th type of transportation equipment, Q si is the carbon emission per unit mass per kilometer of the i-th type of transportation equipment, Q d is the fuel consumption of loading and unloading equipment per unit mass of cargo, and EF2 is the carbon emission factor of fuel.

6. The method for optimizing the risk of the entire life cycle of buried water supply pipelines considering carbon emissions according to claim 5 is characterized in that: E SA The specific content is: ； Among them, H 3i is the working time of the i-th type of construction equipment, P 3i is the power of the i-th construction equipment, EF 3i is the carbon emission factor of the i-th type of construction equipment, M 3j is the consumption of the jth type of construction material, EF 3j is the carbon emission factor of the jth construction material, L 3k is the working hours of the kth type of workers, EF 3k is the average hourly carbon emission factor of the kth type of worker, m is the number of types of construction equipment, and p is the number of worker categories.

7. The method for optimizing the risk of the entire life cycle of buried water supply pipelines considering carbon emissions according to claim 6 is characterized in that: E YW The specific content is: ； Among them, E 4i is the carbon emission factor of the i-th type of construction equipment, MA 4j is the average annual consumption of maintenance materials, EF 4i is the carbon emission factor of the i-th type of construction equipment, EF 4j is the carbon emission factor of the jth construction material, T 4k is the average annual transport volume of the kth mode of transportation, EF 4k is the carbon emission factor of the kth mode of transportation.

8. The method for optimizing the risk of the entire life cycle of buried water supply pipelines considering carbon emissions according to claim 7 is characterized in that: E BF The specific content is: ； Among them, W 5i is the total amount of type i waste material, EF 5i is the carbon emission factor of the i-th type of waste material, T 5j is the transportation volume of the jth type of waste material, EF 5j is the carbon emission factor of the jth mode of transportation.

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