Method for optimizing structural design of bioretention facility taking carbon emissions into consideration

Abstract

A method for optimizing a structural design of a bioretention facility taking carbon emissions into consideration. A multi-objective evaluation system for a bioretention facility is constructed to perform comprehensive evaluation on a runoff control capability, carbon emissions during construction and operation, and construction investment costs; and an NSGA-II multi-objective intelligent optimization algorithm is used to optimize a structural design of the facility. The specific steps comprise: establishing a HYDRUS-1D model to calculate a runoff control capability; constructing a carbon emission accounting model to account for carbon emissions of a facility during material production, transportation, construction, and operation and maintenance; establishing an investment cost calculation method; and finally, by means of an optimization algorithm, obtaining structural design schemes having different preferences. The method can effectively balance a runoff regulation effect, carbon emissions and investment costs, can provide an optimal design scheme of a bioretention facility, and conforms to the concept of green and low-carbon development, thereby facilitating the improvement of the quality of an urban ecological environment, the deceleration of climate change and the protection of water resources.

Description

Structural design optimization method for bioretention facilities considering carbon emissions Technical Field

[0001] This invention belongs to the field of sponge city construction technology, and specifically relates to a method for optimizing the structural design of bioretention facilities that takes carbon emissions into account. Background Technology

[0002] Bioretention facilities are a key component of sponge city construction and are widely used globally in urban stormwater management, sponge city development, and urban ecological construction. They achieve comprehensive runoff control goals by utilizing plant, soil, and microbial systems to infiltrate and purify runoff. These facilities excel in controlling runoff, reducing construction and maintenance costs, and play a crucial role in improving rainwater infiltration efficiency.

[0003] Structurally, bioretention facilities typically include a water storage layer, a soil layer, a filler layer, and a drainage layer. The soil layer not only provides essential nutrients for vegetation but also stores and infiltrates rainwater through its porous structure. The filler layer, through appropriate filler materials and particle sizes, optimizes water flow paths and improves treatment efficiency to meet the water purification and rainwater retention requirements of the rainwater system. The rational design of these structures and the selection of materials are key factors in controlling construction and maintenance costs, and also directly affect the facility's operational efficiency and effectiveness.

[0004] With the rapid development of urbanization, traditional rigid drainage systems can no longer meet the dual demands of environmental and ecological balance and low-carbon development. The optimized design of bioretention facilities, especially considering carbon reduction and sustainability, is particularly important. This requires that the planning and design of facility structures not only consider their stormwater treatment functions but also comprehensively take into account their costs and environmental benefits.

[0005] Therefore, existing technologies, when designing bioretention facilities, only focus on runoff control capacity, without simultaneously considering carbon emissions and construction investment costs during the construction and operation of the facilities, resulting in deficiencies in both environmental and economic benefits. Summary of the Invention

[0006] The technical problem to be solved by this invention is to provide a structural design optimization method for bioretention facilities that takes carbon emissions into account. By constructing a multi-objective evaluation system for bioretention facilities, the method comprehensively considers the runoff control capacity, carbon emissions during construction and operation, and investment costs of the facilities. The method uses the NSGA-II multi-objective intelligent optimization algorithm to optimize the hierarchical structural design of the facilities, thereby guiding the structural design of bioretention facilities.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0008] A method for optimizing the structural design of bioretention facilities considering carbon emissions, comprising the following steps:

[0009] 1) Construct a multi-objective evaluation system for bioretention facilities to comprehensively evaluate the facilities' runoff control capacity, carbon emissions during construction and operation, and construction investment costs. The multi-objective evaluation system for bioretention facilities includes multiple indicators such as total runoff control rate, peak flow reduction rate, total carbon emissions, and construction investment costs.

[0010] 2) Construct a HYDRUS-1D model and set initial parameters such as soil layer thickness and filler layer thickness according to the local sponge facility planning and design documents to simulate the runoff regulation capacity of the bioretention facility under different rainfall scenarios.

[0011] 3) Construct a carbon emission accounting model to calculate the carbon emissions generated during the construction and operation of the facility. The accounting model covers multiple stages, including material production, material transportation, construction, operation and maintenance.

[0012] 4) Establish an investment cost calculation model to calculate the construction cost of the facility under different structural design parameters;

[0013] 5) The NSGA-II multi-objective optimization algorithm was used to optimize the structural design parameters (including soil layer thickness and filler layer thickness) of the bioretention facility. The objective functions included maximizing runoff regulation capacity, minimizing carbon emissions, and minimizing investment costs. The Pareto front method was used to obtain the optimal solution set during the optimization process.

[0014] 6) Obtain the Pareto front solution set, perform multi-objective trade-offs and preference classification on each design scheme in the solution set, and obtain the optimal structural design scheme suitable for different needs.

[0015] Preferably, the runoff control capacity includes the total runoff control rate and the peak flow reduction rate, and the runoff control capacity is calculated by simulating the facility performance under rainfall scenarios using the HYDRUS-1D model. The design rainfall scenarios include design rainfall under different return periods (1a, 5a, 10a, 20a, 50a, 100a).

[0016] Preferably, the carbon emission accounting model covers the life cycle stages including material production, material transportation, construction, and operation and maintenance, and also considers the carbon sink effect achieved through plant photosynthesis. The calculation formula is as follows:

[0017] (1) Material production stage: The formula for calculating carbon emissions generated during the raw material production process is as follows:

[0018] In the formula, InCE material The carbon emissions generated during the material production process are expressed in kg CO2; mi represents the i-th type of building material; Mmi t represents the amount of the i-th type of building material used; EF mi denoted as the carbon emission factor produced by the i-th type of building material, kg CO2 / t.

[0019] (2) Material Transportation Stage: The carbon emissions generated during material transportation are related to the transportation volume, transportation distance, and energy type of the transportation vehicle. The calculation formula is as follows:

[0020] In the formula, InCE transport Represents the carbon emissions during the transportation of building materials, kg CO2; D mi EF represents the transportation distance of the i-th type of building material, in km; tr-mi denoted by , which represents the carbon emission factor of the transportation vehicle for the i-th type of building material, kg CO2 / (t·km).

[0021] (3) Construction phase: During the construction process, carbon emissions are generated due to the energy consumption of construction equipment. The calculation formula is as follows:

[0022] In the formula, InCE construction Carbon emissions during the construction process, expressed in kg CO2; T i,j,k V represents the number of machine shifts used per unit of work for the i-th type of project, the j-th type of construction equipment, and the k-th type of energy; i m represents the quantity of work for the i-th type of project. 3 ;R j Energy consumption per unit shift of the j-th type of construction equipment, kg / shift or kWh / shift; EF k The carbon emission factor representing the kth energy type is kg CO2 / kg or kg CO2 / kwh.

[0023] (4) Operation and Maintenance Phase: Indirect carbon emissions generated by equipment energy consumption during the operation or maintenance of the facility are calculated using the following formula:

[0024] In the formula, InCE operation InCE maintenance These represent carbon emissions generated by equipment energy consumption during operation and maintenance, respectively, in kg CO2 and E. i,k EF represents the energy consumption of the i-th type of equipment and the k-th type of energy, expressed in kg or kWh; k The carbon emission factor representing the kth energy type is kg CO2 / kg or kg CO2 / kwh.

[0025] Runoff Reduction: During operation, the sponge city infrastructure reduces the amount of water entering the stormwater pipes through runoff reduction, thereby reducing the energy consumption of downstream stormwater pumping stations. The calculation formula is as follows: Q control =q×Area×α×T (7)

[0026] In the formula, CR raincontrol ρ represents the carbon emission reduction achieved through runoff reduction during operation, expressed in kg CO2; ρ represents the density of water, expressed in kg / m³. 3 g represents gravitational acceleration, m / s² 2 h represents the average head of the downstream pumping station, in meters; Q control Represents the reduction in stormwater runoff, m 3 η represents the operating efficiency of the downstream pumping station, %; EF E Carbon emission factor representing the region's electricity consumption, kg CO2 / kWh; q representing the region's average annual rainfall, mm; Area representing the catchment area, m². 2 ; α represents the annual runoff volume control rate; T represents the number of years of operation, a.

[0027] Green space carbon sequestration: During facility operation, green plants can act as carbon sinks through photosynthesis. Different types of vegetation are suitable for planting at different soil depths and infill materials. The types of green plants considered here mainly include herbaceous plants, shrubs, and trees. The calculation formula is as follows:

[0028] In the formula, CR green Area represents the carbon sink generated through green space sequestration, expressed in kg CO2; i represents the i-th facility capable of carbon sequestration; Area i The area of ​​facility i, in m 2 ;EF * i The carbon sequestration rate of facility i, kg CO2 / (m 2 ·a); T i a represents the number of years that facility i has been in operation.

[0029] Preferably, the objective function is solved using the NSGA-II optimization algorithm, which includes steps such as random population generation, crossover and mutation, and non-dominated sorting. During the optimization process, the weights can be adjusted to balance the objectives, and the optimal solution set is finally obtained through non-dominated sorting.

[0030] The objective functions include: maximizing runoff regulation capacity as the first objective function, minimizing the total carbon emissions from bioretention facilities as the second objective function, and minimizing investment costs as the third objective function.

[0031] The first objective function is: max f HYDRUS-1D(X1,X2); where X1 and X2 are the soil layer thickness and filler layer thickness of the bioretention facility, respectively, f HYDRUS-1D (X1,X2) represents the runoff regulation capacity of the bioretention facility calculated using the HYDRUS-1D model under this structural design; the second objective function is minf CE (X1,X2), where f CE (X1,X2) represents the total carbon emissions of the bioretention facility under this structural design; the third objective function is min f CI (X1,X2), where f CI (X1,X2) represents the investment cost of the bioretention facility under this structural design.

[0032] The present invention can achieve the following beneficial effects:

[0033] This invention constructs a multi-objective evaluation system for bioretention facilities and applies a multi-objective optimization calculation method to balance runoff regulation capacity, carbon emissions, and investment costs. It obtains the optimal structural design that maintains low carbon emissions and low costs while achieving high runoff regulation effect. This invention not only maximizes the runoff regulation effect of bioretention facilities, but also improves the environmental and economic benefits of the facilities by considering the carbon emission level and investment cost of the facilities. Attached Figure Description

[0034] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0035] Figure 1 is a flowchart of the present invention;

[0036] Figure 2 is the accounting framework for the carbon emission accounting model of the bioretention facility constructed in this invention;

[0037] Figure 3 shows the heavy rain process under six different rainfall return periods;

[0038] Figure 4 is a structural diagram of a typical bioretention facility;

[0039] Figure 5 shows the Pareto front solution for NSGA-II. Detailed Implementation

[0040] Example 1

[0041] Overview of Study Area Examples

[0042] Wuhan, located in the middle reaches of the Yangtze River, receives an average annual rainfall of 1200-1400 mm. In recent years, with rapid urbanization, the surface impermeability has increased dramatically. Coupled with the impact of climate change and frequent extreme rainfall events, the risk of urban flooding has become increasingly prominent. In 2015, Wuhan was selected as one of the first batch of national pilot cities for sponge city construction. Bioretention facilities, as one of the core measures of sponge city construction, have been widely used in the central urban area. However, in actual operation, it has been found that existing facilities suffer from insufficient standardization in structural design and declining long-term operational efficiency, particularly lacking scientific basis in aspects such as filler layer thickness and material ratio. Meanwhile, as a national low-carbon pilot city, Wuhan is actively promoting the construction of green infrastructure. Research shows that green infrastructure such as bioretention facilities can not only effectively control stormwater runoff processes but also achieve negative carbon emissions throughout the entire life cycle through vegetation carbon sequestration and indirect reduction of drainage system energy consumption, providing important support for sustainable urban development. Therefore, the method proposed in this invention performs multi-objective optimization design of bioretention facilities based on local rainfall conditions and emission factor data in Wuhan City, providing a low-carbon emission reduction and cost-effective structural design scheme for the construction and renovation of green facilities in sponge cities. This helps to enhance the drainage and flood control capabilities and carbon emission reduction benefits of the facilities, and is of great significance for improving urban resilience and promoting low-carbon urban development.

[0043] A method for optimizing the structural design of bioretention facilities considering carbon emissions, as shown in Figure 1 of the specification (this flowchart illustrates the core steps of the method for optimizing the structural design of bioretention facilities considering carbon emissions, with a clear logical sequence, unfolding from model building to optimization solution), includes the following steps:

[0044] Step 1: Establish a HYDRUS-1D model. Based on the local planning and design documents or the facility structure and filler type of the actual research object, set the initial parameter values ​​of the model to obtain the reference model and run it.

[0045] The process of building the HYDRUS-1D model in this step is as follows:

[0046] (1) Set the basic model information such as time step, geometric dimensions, and iteration method; set the upper and lower boundary conditions of the model. In this example, the upper boundary condition is set as the atmospheric boundary condition, the input is the rainfall in each time period, and the lower boundary condition is the free drainage interface, which corresponds to the drainage layer of the bioretention facility.

[0047] (2) Divide the soil profile and set the soil hydraulic parameters. The main objects of the simulation are the soil layer and filler layer of the bioretention facility. The profile structure is set according to the facility structure, i.e. the layer thickness, and the soil hydraulic parameters are set according to the type of soil and filler.

[0048] (3) Design rainfall: Design rainfall for different return periods (1a, 5a, 10a, 20a, 50a, 100a) based on different rainstorm formulas for different regions.

[0049] (4) Run the HYDRUS-1D model and calibrate and adjust the soil hydraulic parameters based on the model running results.

[0050] Step 1 involves modeling by running the Hydrus-1d 4.xx modeling software on a computer or by calling the Python third-party library phydrus.

[0051] Step 2: Establish a carbon emission accounting model for the bioretention facility, as shown in Figure 2 of the instruction manual (the framework illustrates the carbon emission and carbon sink sources throughout the entire life cycle of the bioretention facility, divided into carbon emission items and carbon sink items). Carbon emission sources include energy consumption for building material generation during the design and construction phase, energy consumption for building material transportation during the material transportation phase, energy consumption for construction equipment during the construction phase, energy consumption for operating equipment and carbon emissions generated from pollutant degradation during the operation and maintenance phase, energy consumption for dismantling equipment during the dismantling phase, and energy consumption for recycling equipment during the recycling phase. Carbon sink sources include carbon sequestration in green spaces, runoff reduction, rainwater purification, and rainwater utilization during the operation and maintenance phase, and emission reduction through recycling during the recycling phase. The carbon emission sources throughout the entire life cycle can be selected based on application requirements and available data. In this invention, the multi-objective optimization calculation process only considers carbon emission and carbon sink sources related to the facility's structural design. Specific calculation methods and formulas are detailed in the instruction manual. This step 2 involves writing Python code to complete the carbon emission accounting module.

[0052] Step 3: Establish a function for calculating the investment cost of bioretention facilities. The amount of materials used in facilities varies under different structural design parameters, which affects their investment cost.

[0053] The facility investment cost is calculated in this step as follows:

[0054] In the formula, CI represents the investment cost of constructing the bioretention facility, in yuan; c i m represents the unit price of the i-th material; i This represents the amount of the i-th material used.

[0055] Step 3 involves writing Python code on a computer to complete the cost calculation module.

[0056] Step 4: Establish an evaluation function for the runoff control capacity of bioretention facilities, where V in Q in These represent the total inflow and peak value during the inflow process; V out Q outThese represent the total outflow and peak value of the outflow process, respectively. The outflow process of the bioretention facility was simulated using the HYDRUS-1D model, and these values ​​were calculated. v w q These are the weighting coefficients for total runoff reduction and peak flow reduction, respectively.

[0057] Step 4 involves writing Python code on a computer to complete the performance evaluation module.

[0058] Step 5: Establish the NSGA-II optimization framework. The NSGA-II optimization framework is optimized using a genetic algorithm. Its main steps include individual generation, crossover, mutation, and selection until a convergent Pareto front solution is obtained.

[0059] Step 5 involves writing Python code on a computer or calling the Python third-party library deap to complete the NSGA-II optimization calculation module.

[0060] Step 6: Obtain the Pareto front solution based on the convergence results of the NSGA-II optimization algorithm, and analyze the trade-offs between optimal runoff control performance, minimum carbon emissions, and minimum cost under different preferences. The convergence results in this example are shown in Table 5 below.

[0061] Step 6 involves analyzing the results using a computer.

[0062] The process of substituting data in steps 1-6 is as follows:

[0063] Step 1: HYDRUS-1D Model Construction and Design of Rainstorm Input

[0064] According to the "Design Guidelines for Sponge City Construction in Wuhan," the typical structure of a bioretention facility is shown in Table 1. Using this structural design as the initial condition, a HYDRUS-1D model of the soil layer and filler layer was established. Its structure is shown in Figure 4 of the instruction manual (this figure is a schematic diagram of the cross-sectional structure of the bioretention facility, showing the thickness and material composition of each functional layer). Specific details are as follows: During the division of the soil profile, since the filler layer thickness is twice the soil layer thickness, to avoid oversimplification and neglecting the heterogeneity of the filler medium at different depths, the filler layer was divided into two layers, resulting in a total of three layers for modeling. The depth of the entire soil column was set to 900 mm, with three soil texture types and three layers. The vertical tilt angle of the longitudinal axis was 0°. The entire soil profile was discretized into 90 grids. Coordinates 0 to 300 represent the soil layer, coordinates 310 to 600 represent the upper filler layer, and coordinates 610 to 900 represent the lower filler layer. The coordinates of the sub-regions correspond consistently. The model parameter settings for the soil layer and filler layer are shown in Table 2. The aquifer is generalized as an atmospheric boundary condition with a surface layer, which is set as the upper boundary condition for water transport. Simultaneously, the maximum thickness of the surface water layer before surface runoff in the model is set to 300 mm. Considering that in reality, the facility's interior is connected to the filler layer by a gravel drainage layer, and since the particle size of the drainage layer is much larger than that of the filler layer, its lower boundary can be approximated as a free-flow boundary condition. Therefore, the lower boundary condition in the model is set as a free drainage interface. The initial soil moisture content is used as the initial condition. Considering that during the actual operation of the facility, shallow soil moisture will evaporate more than deep soil moisture, and deep soil moisture content will be less, the upper boundary moisture content of the entire soil column is set to 12%, the lower boundary moisture content to 20%, and the soil grid in between is linearly uniformly distributed.

[0065] Table 1 Typical Structures of Bioretention Facilities in Wuhan

[0066] Table 2 Model Parameter Settings

[0067] The design of the inflow intensity references the storm intensity calculation formula for short-duration rainstorms in Wuhan City in the "Planning and Design Standard for Drainage and Flood Control Systems in Wuhan City," and calculates the rainwater flow rate using a deductive formula method. Two-hour rainfall events under different return periods (P = 1a, 5a, 10a, 20a, 50a, 100a) of the Chicago rainfall pattern design are used as the model rainfall input. The designed rainfall events are shown in Figure 3 of the instruction manual (this figure shows the 2-hour rainstorm events in Wuhan City under different rainfall return periods (1 year, 5 years, 10 years, 20 years, 50 years, 100 years), with the horizontal axis representing time (minutes) and the vertical axis representing rainfall intensity (mm / h)), encompassing the design rainfall events under six return periods.

[0068] In the formula: q is the design rainfall intensity, L / (s×hm) 2 Q is the calculated rainfall flow rate, L / s; P is the rainfall return period, taken as P = 1a, 5a, 10a, 20a, 50a, 100a; t is the rainfall duration, min; The comprehensive runoff coefficient is taken as 0.9; F is the catchment area, hm². 2 .

[0069] Step 1 involves using the Hydrus-1d 4.xx modeling software on a computer or calling the third-party Python library phydrus to complete the modeling, and then writing Python code to design the rainstorm module.

[0070] Step 2: Life Cycle Carbon Emission Accounting

[0071] A carbon emission accounting model for bioretention facilities is established. During construction and operation, bioretention facilities generate direct or indirect carbon emissions due to material use and equipment energy consumption. Simultaneously, they also generate carbon reductions through green space carbon sequestration and runoff reduction. The sum of these two is the net carbon emission. A negative net carbon emission indicates that the facility's total emissions over its entire life cycle are less than zero, demonstrating its carbon reduction effect. The facility structure affects the amount of materials used, transportation, and construction work during construction, and also impacts runoff reduction and carbon sequestration. The accounting framework covers the life cycle stages and corresponding emission sources. In this invention, the multi-objective optimization calculation process only considers carbon emissions and carbon sink sources related to the facility's structural design. Detailed calculation formulas and methods are available in the instruction manual. The emission factor values ​​involved in the calculation process are shown in Table 3.

[0072] Table 3. Values ​​of various emission factor parameters in the carbon emission accounting model.

[0073] Step 2 involves writing Python code on a computer to complete the carbon emission accounting module.

[0074] Step 3: Establish the investment cost function

[0075] A cost function for bioretention facilities was established. The calculation method for the unit area construction investment cost of bioretention facilities can be determined using the "Design Guidelines for Sponge City Construction in Wuhan" and "Case Studies of Cost Indicators for Urban Public Facilities (Sponge City Construction Project)". This involves adding the material costs of the soil layer and filler layer to the unit area foundation construction cost. The independent variables are the thickness of the soil layer and filler layer, and the dependent variable is the construction investment cost. The unit area and unit thickness cost of the soil layer is taken as 0.141 yuan, and the unit area and unit thickness cost of the filler layer is taken as 0.206 yuan. The cost calculation function with specific data is CI=223+0.141×h1+0.206×h2 (7)

[0076] Step 3 involves writing Python code on a computer to complete the cost calculation module.

[0077] Step 4: Runoff Control Capacity Evaluation Function

[0078] An evaluation function for the runoff control capacity of bioretention facilities is established. Bioretention facilities have a certain reduction effect on the total runoff and peak flow of a rainfall event. The objective function is constructed as follows, where V in Q in These represent the total inflow and peak value during the inflow process; V out Q out These represent the total outflow and peak value of the outflow process, respectively. The outflow process of the bioretention facility was simulated using the HYDRUS-1D model, and these values ​​were calculated. v w q These are the weighting coefficients for total runoff reduction and peak flow reduction, respectively, with a weighting of 1:1 for each.

[0079] Step 4 involves writing Python code on a computer to complete the performance evaluation module.

[0080] Step 5: Establish the NSGA-II optimization framework

[0081] An NSGA-II optimization framework was established. The NSGA-II framework utilizes a genetic algorithm for optimization, employing three objective functions: runoff control capacity, net carbon emissions, and facility investment cost. The objective functions are shown below.

[0082] The main steps of the genetic algorithm include individual generation, crossover, mutation, and selection until a convergent Pareto front solution is obtained. Each individual is composed of numerical codes representing the soil layer thickness and the filler layer thickness, with both variables ranging from 100 to 600 mm. During the iterative selection process, the NSGA-II algorithm is used to select non-dominated solutions, forming the Pareto front. In the specific calculation process, the optimization algorithm parameters are set as shown in Table 4: population size is set to 20, crossover probability to 0.9, mutation probability to 0.1, and generation number to 50.

[0083] Table 4 Parameter values ​​for the NSGA-II optimization algorithm

[0084] Step 5 involves writing Python code on a computer or calling the Python third-party library deap to complete the NSGA-II optimization calculation module.

[0085] Step 6: Pareto Front Solution Set

[0086] As shown in Figure 5 (this figure illustrates the Pareto front solution set obtained by the NSGA-II algorithm, presenting the trade-offs between the three objectives (runoff control capability RCF, net carbon emissions CE, and investment cost CI) in a three-dimensional coordinate system), the Pareto front solution is obtained based on the convergence results of the NSGA-II optimization algorithm. The optimal runoff control performance, minimum carbon emissions, and minimum cost trade-offs under different preferences are then analyzed. Step 6 involves computer-based result analysis.

[0087] Specifically:

[0088] The partial solutions of the optimized Pareto front solution set are shown in Table 5. The soil layer thickness converges to two values: 117 mm and 328 mm. The filler layer thickness converges to two values: 225 mm and 491 mm. The RCF ranges from 50.99% to 150.05%, and the CE ranges from -48.33 kg CO2. 2 / m 2 to -19.12kg CO 2 / m 2 The CI ranges from RMB 285.84 to RMB 370.25. Analysis reveals that the frontier solution set provides three optimal design schemes: the best control performance scheme (328mm soil layer + 491mm filler layer); the lowest carbon emission scheme (117mm soil layer + 491mm filler layer); and the lowest cost scheme (117mm soil layer + 225mm filler layer). It should be noted that this solution is only the numerically optimal solution obtained through calculation. For real-world applications, adjustments can be made based on the optimized solution according to the actual situation.

[0089] Table 5 Pareto Front Solution Set for Optimal Design of Bioretention Facilities

[0090] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A method for optimizing the structural design of bioretention facilities considering carbon emissions, characterized in that... Includes the following steps: A multi-objective evaluation system for bioretention facilities is constructed. This system is used to comprehensively evaluate the facility's runoff control capacity, carbon emissions during construction and operation, and construction investment costs. The indicators of the multi-objective evaluation system for bioretention facilities include runoff volume control rate, peak flow reduction rate, total carbon emissions, and construction investment costs. A HYDRUS-1D model was constructed, and initial parameters were set according to the local sponge facility planning and design documents. The initial parameters included soil layer thickness and filler layer thickness. The HYDRUS-1D model was used to simulate the runoff regulation capacity of the bioretention facility under different rainfall scenarios. A carbon emission accounting model is constructed to calculate the carbon emissions generated during the construction and operation of facilities. The carbon emission accounting model covers multiple stages, including material production, material transportation, construction and operation and maintenance. Establish an investment cost calculation model to calculate the construction cost of facilities under different structural design parameters; The NSGA-II multi-objective optimization algorithm was used to optimize the structural design parameters of bioretention facilities. The objective functions included maximizing runoff regulation capacity, minimizing carbon emissions, and minimizing investment costs. The Pareto front method was used to obtain the optimal solution set during the optimization process. The Pareto front solution set is obtained, and multi-objective trade-offs and preference classifications are performed on each design scheme in the solution set to obtain the optimal structural design scheme suitable for different needs.

2. The method for optimizing the structural design of a bioretention facility considering carbon emissions according to claim 1, characterized in that: The runoff control capacity includes the total runoff control rate and the peak flow reduction rate. The runoff control capacity is calculated by simulating the facility performance under rainfall scenarios using the HYDRUS-1D model. The rainfall scenarios include design rainfall under different return periods.

3. The method for optimizing the structural design of a bioretention facility considering carbon emissions according to claim 1, characterized in that: The life cycle stage of the carbon emission accounting model includes four stages: material production, material transportation, construction and operation and maintenance. The life cycle stage also considers the carbon sink effect achieved through plant photosynthesis.

4. The method for optimizing the structural design of a bioretention facility considering carbon emissions according to claim 3, characterized in that: (1) Material production stage: The formula for calculating carbon emissions generated during the raw material production process is as follows: In the formula, InCE material The carbon emissions generated during the material production process represent the amount of material; mi represents the i-th type of building material; M mi EF represents the amount of the i-th type of building material used; mi The carbon emission factor represents the carbon emissions produced by the i-th type of building material.

5. The method for optimizing the structural design of a bioretention facility considering carbon emissions according to claim 3, characterized in that: (2) Material Transportation Stage: The carbon emissions generated during material transportation are related to the transportation volume, transportation distance, and energy type of the transportation vehicle. The calculation formula is as follows: In the formula, InCE transport D represents the carbon emissions during the transportation of building materials; mi EF represents the transport distance of the i-th type of building material; tr-mi The carbon emission factor represents the transportation vehicle for the i-th type of building material.

6. The method for optimizing the structural design of a bioretention facility considering carbon emissions according to claim 1, characterized in that: (3) Construction phase: During the construction process, carbon emissions are generated due to the energy consumption of construction equipment. The calculation formula is as follows: In the formula, InCE construction Represents the carbon emissions during the construction process; T i,j,k V represents the number of machine shifts used per unit of work for the i-th type of project, the j-th type of construction equipment, and the k-th type of energy; i R represents the quantity of work for the i-th type of project; j EF represents the energy consumption per unit shift of the j-th type of construction equipment; k The carbon emission factor represents the k-th energy type.

7. The method for optimizing the structural design of a bioretention facility considering carbon emissions according to claim 1, characterized in that: (4) Operation and Maintenance Phase: Indirect carbon emissions generated by equipment energy consumption during the operation or maintenance of the facility are calculated using the following formula: In the formula, InCE operation InCE maintenance These represent carbon emissions generated by equipment energy consumption during operation and maintenance, respectively; E i,k EF represents the energy consumption of the i-th type of equipment and the k-th type of energy; k The carbon emission factor represents the k-th energy type.

8. The method for optimizing the structural design of a bioretention facility considering carbon emissions according to claim 7, characterized in that: During the operation and maintenance phase, the sponge city infrastructure reduces the amount of water entering the stormwater pipes through runoff reduction, thereby reducing the operating energy consumption of downstream stormwater pumping stations. The calculation formula is as follows: Q control =q×Area×α×T (7); In the formula, CR raincontrol This represents the carbon emission reduction achieved through runoff reduction during operation; ρ represents the density of water; g represents the acceleration due to gravity; h represents the average head of the downstream pumping station; Q control Represents the reduction in stormwater runoff; η represents the operating efficiency of the downstream pumping station; EF E Carbon emission factor representing the region's electricity consumption; q representing the region's average annual rainfall; Area representing the catchment area; α representing the annual runoff control rate; T represents the number of years of operation.

9. The method for optimizing the structural design of a bioretention facility considering carbon emissions according to claim 7, characterized in that: During the operation and maintenance phase, photosynthesis by green plants can serve as a carbon sink. Different types of vegetation are suitable for planting in soils of varying depths and filler materials. The main types of green plants include herbaceous plants, shrubs, and trees. The calculation formula is as follows: In the formula, CR green Area represents the carbon sink generated through green space sequestration; i represents the i-th facility capable of carbon sequestration; i The area of ​​facility i; EF * i T represents the carbon sequestration rate of facility i; i This represents the number of years that facility i has been in operation.

10. The method for optimizing the structural design of a bioretention facility considering carbon emissions according to claim 1, characterized in that: Structural design parameters include soil layer thickness and filler layer thickness.

11. The method for optimizing the structural design of a bioretention facility considering carbon emissions according to claim 1, characterized in that: The objective function is optimized using the NSGA-II optimization algorithm. The optimization process includes three steps: random population generation, crossover and mutation, and non-dominated sorting. During the optimization process, the weights of each objective are adjusted to balance them. Finally, the optimal solution set is obtained through non-dominated sorting.

12. A system for optimizing the structural design of bioretention facilities considering carbon emissions, characterized in that: The method for optimizing the structural design of a bioretention facility that takes carbon emissions into account, as described in any one of claims 1-11, was adopted.

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