A low-carbon leaching remediation decision method for contaminated soil

Abstract

The patent discloses a contaminated soil low-carbon leaching repair decision method, which comprises seven steps of process parameter analysis S1, test data collection S2, removal rate fitting and screening S3, environmental impact analysis S4, data standardization processing S5, evaluation model construction S6 and process parameter decision S7. The technical scheme of the patent realizes multi-objective collaborative decision by constructing a repair comprehensive benefit accounting model covering removal rate, carbon emission and energy consumption; the subjective weighting method is used to determine the weight factor of the evaluation index, which can meet the different pollution reduction, energy saving and carbon reduction decision needs of the construction party. The decision scheme also comprehensively uses the response surface experiment design and its data fitting prediction method, reduces the data collection workload, and can quickly provide the removal rate, energy consumption value and carbon emission prediction results, providing strong data support for the repair process parameter decision. The transformation and application of the patent will provide key support for realizing green low-carbon repair of contaminated soil.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of soil pollution remediation, and particularly relates to a contaminated soil low-carbon leaching remediation decision-making method. BACKGROUND

[0002] Green and low carbon is a new trend of global social and economic development. The Party and the state attach great importance to energy saving and emission reduction work, and have successively issued various energy saving and carbon emission reduction guidance documents and action plans. Soil pollution remediation activities consume a large amount of energy and resources, and it is of great significance for the sustainable development of the country to realize energy saving and carbon emission reduction in soil pollution remediation.

[0003] In recent years, scholars have carried out researches on the energy consumption and carbon emission characteristics of soil pollution remediation technology, and some theoretical achievements and method tools have been preliminarily formed. CN 202211318566.X discloses a carbon emission accounting method for ex-situ remediation technology of contaminated soil, proposes a carbon emission calculation formula in the construction process, and clearly defines the carbon emission accounting process. "Carbon emission calculation method and case analysis of risk control of contaminated site" and "Carbon emission calculation method and engineering case study of heavy metal contaminated soil stabilization technology" respectively study the process flow of in-situ risk control and ex-situ stabilization remediation of contaminated sites, propose specific carbon emission calculation methods, and complete method application verification based on engineering cases, and discuss the carbon emission reduction strategies of the technology. However, the existing researches mainly focus on the distribution law of energy and resource consumption in the remediation process and its carbon footprint, and the proposed energy saving and carbon emission reduction strategies focus on reducing environmental impact without considering the change of pollutant removal rate, so there is a risk of substandard remediation effect after the implementation of the strategies. At present, the comprehensive benefit evaluation method of soil remediation which can simultaneously investigate the pollutant removal rate, energy consumption and carbon emission is still lacking, which affects the decision of remediation technology.

[0004] Soil washing is one of the key technologies for soil remediation, which can remove heavy metals and / or organic pollutants from soil efficiently. The remediation effect is closely related to the process parameters of soil washing, which are mainly affected by the type and concentration of washing agent, pH, washing time, and solid-liquid ratio. In order to determine the optimal process parameters for target soil, the construction party often compares the removal efficiency of pollutants under different process conditions through a large number of tedious single-factor pilot tests, which provides the basis for the implementation plan of remediation project. The data collection work is heavy, the screening basis is single, and the decision-making procedure lacks relevant standards. In the fields of soil vapor extraction, risk management and control, and extraction-treatment, some decision-making methods based on removal effect monitoring and evaluation have been formed (such as patents CN 202010042282.7, CN 201210313796.7, and CN 202311771671.3). However, the decision-making method of soil washing process parameters still lacks systematic research, especially the lack of research on the comparison method of remediation process parameters based on comprehensive remediation benefits, and there is an urgent need to develop a multi-objective decision-making method for soil washing remediation process that can realize the synergistic promotion of energy saving, carbon reduction, and pollution reduction. SUMMARY

[0005] In view of the above problems, the present patent provides a low-carbon soil washing remediation decision-making method. The method takes reaching the standard, low emission, and low energy consumption as the decision-making target, builds a comprehensive benefit evaluation model, and clearly defines the operation requirements of data collection, fitting and screening, and process parameter decision-making, providing a method tool for sustainable remediation of contaminated soil.

[0006] The technical solution disclosed by the present patent is as follows:

[0007] A low-carbon soil washing remediation decision-making method, characterized in that the decision-making method comprises seven steps of process parameter analysis (S1), experimental data collection (S2), removal rate fitting and screening (S3), environmental impact analysis (S4), data standardization processing (S5), evaluation model construction (S6), and process parameter decision-making (S7).

[0008] Among them, S1 determines the key process parameters of the contaminated soil leaching remediation process decision, collects auxiliary factor data, establishes the energy consumption evaluation formula of the remediation activity and the carbon emission evaluation formula of the remediation activity; S2 designs a response surface experiment based on the key process parameters proposed by S1, collects the pollutant removal rate data; S3 fits the pollutant removal rate data by the response surface method, and obtains the process parameter combination for reaching the standard decontamination; S4 calculates the energy consumption and carbon emission data corresponding to the process parameter combination, and completes the forward conversion of the energy consumption and carbon emission index data; S5 standardizes the removal rate, energy consumption and carbon emission data of S3 and S4 to realize dimensionless; S6 determines the weight coefficients of the removal rate, energy consumption and carbon emission, and constructs a comprehensive benefit evaluation model of remediation; S7 calculates the comprehensive benefit value of remediation based on the standardized data of S5 and the evaluation model of S6, selects the optimal process parameter combination from the process parameter combination of S3, and forms a low-carbon leaching remediation decision scheme.

[0009] The specific implementation content of the process parameter analysis (S1) step is: constructing a general model of contaminated soil leaching remediation process, determining key process parameters, collecting auxiliary factor data, and establishing energy consumption evaluation formula and carbon emission evaluation formula of remediation activity.

[0010] The system boundary of the energy consumption and carbon emission evaluation of the remediation activity includes but is not limited to the production, procurement and application of reagents, the input of water, and the energy input of leaching remediation equipment operation. The leaching remediation equipment includes but is not limited to slurry leaching equipment, vibration screening equipment, solid-liquid separation equipment and wastewater treatment equipment.

[0011] The key process parameters include three or more indicators of leaching reagent concentration (C f ), pH (pH e ), leaching time (T f ), liquid-solid ratio (R f ), and temperature (Tem f ).

[0012] The auxiliary factor data includes: the operating power (P f ) of the slurry leaching equipment; the initial pH (pH i ) of the leaching reagent solution; the input amount (W a ) of acid-base regulator per unit pH change in the leaching reagent; the heating input (P tem ) per unit temperature change in the slurry leaching equipment; the number of stages (K z ) and operating power (P z ) of the vibration screening equipment; the operating power (P s ) of the solid-liquid separation equipment; the operating time (T w ), operating power (P w ) and dosage (Cw ) ; the procurement distance of elution agents, acid-base regulators, and capture agents (L f , L a , L w ) ; the carbon emission factors of elution agents, acid-base regulators, water, capture agents, diesel, and electricity (F f , F a , F w , F b , F g , F e ) ; the unit kilometer fuel consumption of transport vehicles (N l ) and the single transport volume (K) ; the standard coal coefficients of diesel and electricity (E g , E e ).

[0013] The above key process parameters and auxiliary factor types can be adjusted according to the actual process.

[0014] The energy consumption evaluation formula of the repair activity is: energy consumption value E sum = energy consumption value of agent transportation + energy consumption value of elution repair equipment operation = N l × (L f + L a + L w ) × E g + (T f × (P f + P z × K z + P s ) + (Tem f - RT) × P tem × T f + T w × P w × R f × 1) × E e .

[0015] Wherein, the unit of E sum is kgce / t soil. RT is room temperature, the default value is 25℃, which can be adjusted according to requirements.

[0016] The carbon emission evaluation formula of the repair activity is: carbon emission C sum = carbon emission of agent resource input + carbon emission of agent transportation + carbon emission of elution repair equipment energy consumption = carbon emission of elution agent input + carbon emission of water input + carbon emission of acid-base regulator input + carbon emission of capture agent input + carbon emission of agent transportation + carbon emission of elution repair equipment energy consumption = C f × R f × 1 × F f + R f × 1 × Fw +|pH e -pH i |×W a ×F a +(T f ×(P f +P tem +P z ×K z +P s )+N l ×(L f ×((C f ×(R f ×1)) / K)+L a ×((|pH e -pH i |×W a ) / K)+L w ×((R f ×1)×(C w1 +C w2 +C w3 )) / K))×F g +(Tem f -RT)×P w ×T f +T w ×P w ×R f ×1)×F e +R f ×1×C w ×F b 。

[0017] wherein, C sum is in kg CO2eq / t soil; |pH e -pH i represents the absolute value of the difference in acidity of the leaching agent before and after adjustment; RT is room temperature, the default value is 25℃, which can be adjusted according to requirements.

[0018] The above repair activity energy consumption and carbon emission evaluation formula can be adjusted according to the type of key process parameters.

[0019] The specific implementation content of the test data collection (S2) step is: based on the principle of response surface method (RSM), designing the test gradient of key process parameters; taking the target repaired soil as a small test object, carrying out response surface experiment, collecting the pollutant removal rate data corresponding to the test combination of process parameters; according to the S1 repair activity energy consumption and carbon emission evaluation formula, calculating the energy consumption and carbon emission data corresponding to the test combination of process parameters.

[0020] The pollutant removal rate calculation formula is: pollutant removal rate R = (C i-C e ) / C i ×100%

[0021] wherein C i is the initial concentration of soil pollutants, C e is the concentration of soil pollutants after remediation.

[0022] The pollutant removal rate data can be the removal rate data of a single pollutant, or a weighted average of the removal rates of multiple pollutants. The pollutant is one or more of heavy metal pollutants or organic pollutants.

[0023] The design of the test gradient of the key process parameters adopts the Box-Behnken Designs (BBD) method in RSM, and is realized through professional response surface analysis software such as Design Expert. In the BBD test scheme, the number of center point processing is not less than 3, that is, the center point processing needs to carry out 3 times or more parallel tests with the same process parameter combination, and the data of each parallel test group is used separately; the non-center point processing all adopts 3 parallel tests, and the average value is taken. According to the requirements, the test gradient design can be adjusted to the Central Composite Designs (CCD) method to increase the amount of test data collection.

[0024] The investigation range of the above process parameter test gradient is provided by the construction party according to the previous engineering experience, or can be determined through single factor experiment.

[0025] (3) The specific implementation content of the removal rate fitting and screening (S3) step is: based on the pollutant removal rate data obtained in S2, the pollutant removal rate equation is obtained through response surface method fitting; taking the remediation target value as the critical point, 50 groups of process parameter candidate combinations for environmental impact analysis are fitted and screened according to the principle of large removal rate, small energy consumption value and small carbon emission. The number of process parameter candidate combinations can be adjusted according to requirements.

[0026] The pollutant removal rate equation is a multivariate quadratic equation based on process parameter combination.

[0027] The response surface method fitting process is realized through professional response surface analysis software such as Design Expert. The significance test result P1 value of the model fitting degree in the response surface method fitting result should be less than 0.05, and the significance test result P2 value of the loss of fitting should be greater than 0.05. When the P1 value and the P2 value do not meet the requirements, step 2 should be re-conducted, the test gradient design of the key process parameters is optimized, and the data is re-collected.

[0028] (4) The specific implementation of the environmental impact analysis (S4) step is: according to the process parameter candidate combination obtained in S3, the energy consumption and carbon emission data corresponding to each process parameter candidate combination are calculated by the energy consumption evaluation formula and the carbon emission evaluation formula of the repair activity in S1; the maximum method is used to perform positive processing on the repair energy consumption and carbon emission data, and the formula is converted into Item ki =Item max –Item ks , wherein Item ki is the normalized data of the energy consumption value or the carbon emission amount, Item max is the maximum value of the energy consumption value or the carbon emission amount in the 50 process parameter candidate combinations, and Item ks is the original data of the energy consumption value or the carbon emission amount.

[0029] (5) The specific implementation of the data standardization processing step (S5) is: the Z-score method is used to perform dimensionless standardization on the removal rate, energy consumption and carbon emission data of S3 and S4.

[0030] The data conversion formula of the Z-score method is: Item std =(Item i –Item ave ) / δ

[0031] , wherein Item std is the normalized data of the removal rate, the energy consumption value or the carbon emission amount, Item i is the original data of the removal rate or the positive processed data of the energy consumption value or the carbon emission amount, and Item ave and δ are respectively the average value and the standard deviation of the removal rate, the energy consumption value or the carbon emission amount in the 50 process parameter combinations.

[0032] (6) The specific implementation of the evaluation model construction step (S6) is: the evaluation weights of the pollutant removal rate, the energy consumption and the carbon emission are determined, and a repair comprehensive benefit evaluation model considering energy saving, pollution reduction and carbon reduction is constructed.

[0033] The specific formula of the repair comprehensive benefit evaluation model is: the repair comprehensive benefit value F sum =(K R ×R i +K E ×E i +K C ×C i )×100.

[0034] , wherein F sum is the repair comprehensive benefit value; K R , K E and K C are respectively the evaluation weights of the removal rate, the energy consumption and the carbon emission.respectively are the evaluation weight coefficients of removal rate, energy consumption and carbon emission; R i , E i , C i respectively are the removal rate, energy consumption value or carbon emission standardization value of the i th process parameter alternative combination.

[0035] The repair comprehensive benefit evaluation model adopts a subjective weighting method. The evaluation weight coefficients K R , K E , K C of the pollutant removal rate, energy consumption and carbon emission require the value range to be [0, 1], and the initial values are set to 1, 1 and 1. The specific weight coefficients can be adjusted according to the preferences of the decision maker.

[0036] (7) The specific implementation content of the process parameter decision (S7) step is: inputting the removal rate, energy consumption and carbon emission standardization data corresponding to the process parameter alternative combination in S5 into the S6 repair comprehensive benefit evaluation model, calculating the repair comprehensive benefit value, and selecting the process parameter alternative combination with the highest repair comprehensive benefit value as the final low-carbon leaching repair decision scheme.

[0037] The patent proposes a soil pollution low-carbon leaching repair decision method, which is suitable for soil pollution ex-situ leaching repair engineering and can assist the construction party in quickly screening and determining the optimal low-carbon repair process parameters. The soil pollution includes heavy metal pollution, organic pollution and heavy metal-organic complex pollution.

[0038] Compared with the prior art, the technical solution proposed by the patent has the following advantages and benefits:

[0039] (2) The patent proposes a three-target collaborative decision method, which is significantly different from the conventional repair process parameter development method based on pollutant removal rate, can simultaneously investigate the influence of process parameter changes on pollutant removal, energy consumption and carbon emission, and meets the current green and low-carbon development needs. According to the needs, the benefit evaluation index of the repair comprehensive benefit evaluation model in the decision method can be expanded to other environmental, social and economic influence indexes, forming a multi-dimensional evaluation and decision tool for repair process parameters, and providing strong support for the development of green and sustainable repair technology.

[0040] (3) The technical solution uses a response surface experiment method to collect data, reduces the data collection workload, and shortens the decision-making cycle of repair process parameters. Further, the response surface method is used to complete the fitting of the pollutant removal rate of different process parameter combinations, and to quickly provide process parameter combinations closer to the optimal solution. Compared with traditional numerical simulation methods, the response surface fitting method proposed in the technical solution is convenient to operate and easy to implement; compared with emerging machine learning or deep learning methods, the fitting method requires less basic data and can provide a clear and explicit removal rate fitting equation, which is convenient for subsequent calculations.

[0041] (4) The weight coefficient determination of the repair comprehensive benefit evaluation model in the technical solution uses a subjective weighting method, which can adjust the weight values of the removal rate, energy consumption, and carbon emissions according to the needs of the owners and the preferences of the decision-makers, and is suitable for different decision-making scenarios. This method can also be used to compare the comprehensive benefits of different process parameter combinations, providing intuitive benefit value references for decision-makers to choose repair strategies, which helps to achieve efficient, low-carbon, and low-consumption leaching repair of contaminated soil. After adjusting the process parameter indicators, this method can also be used in the decision-making process of other soil and groundwater pollution repair technologies such as solidification / stabilization and chemical oxidation, and has a wide range of applications and good prospects for promotion. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 Flowchart of a contaminated soil low-carbon leaching repair decision-making method;

[0043] Figure 2 Correlation of each step in the decision-making method;

[0044] Figure 3 Process generalization model for contaminated soil leaching repair in Example 1;

[0045] Figure 4 Response surface fitting results of pollutant removal rate based on process parameters in Example 1. DETAILED DESCRIPTION

[0046] The present application will be further described in detail below in conjunction with specific embodiments to better demonstrate the advantages of the present application.

[0047] Example 1: Decision-making of ex-situ leaching repair process parameters for lead-contaminated soil

[0048] A lead-contaminated site in Fujian was selected as the research object in the example, and the target contaminant in the soil of the site was heavy metal lead (Pb) with an average content of 15267 mg / kg. Based on the technical solution proposed in the present application, the soil pollution leaching repair process parameter decision-making was carried out. The flowchart of the decision-making method is shown in Figure 1 The correlation of each step is shown inFigure 2 The results are shown in Table 1.

[0049] (1) Process parameter analysis (S1) step:

[0050] Collect the basic information of the target site, and master the repair technology, repair equipment, process route and input product information to be used by the construction party. The research data are summarized as follows: the site selects ex-situ leaching repair process, uses a domestic leaching repair equipment as the repair equipment, and EDTA as the leaching reagent. The contaminated soil is excavated and sent into the leaching repair equipment, treated by slurry making and leaching, one or more stages of vibration screening, and solid-liquid separation process, and the screened soil sample is collected for testing and analysis to judge the repair effect; the contaminated liquid after screening is treated by adding capture agents PAC, PAM and Na2S, and discharged after reaching the standard.

[0051] According to the above data, a general model of the contaminated soil leaching repair process is constructed. The contaminated soil leaching repair process is divided into four stages: slurry making and leaching, vibration screening, solid-liquid separation, and wastewater treatment; the input equipment includes slurry making and leaching equipment (including reagent pump and slurry making equipment), vibration screening equipment, solid-liquid separation equipment, and wastewater treatment equipment; the input resources are leaching reagent, water, acid and alkali adjusting agent, and capture agent; and the input energy is diesel and electricity.

[0052] According to the decision needs of the construction party, the leaching reagent concentration (C f ), leaching time (T f ), and liquid-solid ratio (R f ) are determined as the three key process parameters for investigation in this embodiment.

[0053] Collect and arrange the equipment and reagent input parameters of each process link related to the leaching reagent concentration, leaching time, and liquid-solid ratio. The parameter information summary results are as follows: the operating power of the reagent pump and slurry making equipment in the slurry making and leaching equipment is P f1 = 6.6 kW and P f2 = 25 kW, respectively; the number of stages of the vibration screening equipment is 1, and the operating power P z = 71 kW; the operating power of the solid-liquid separation equipment is P s = 93.5 kW; the operating time of the wastewater treatment equipment is T w = 0.03 h / m 3 , the operating power is P w = 28.2 kW, and the dosages of the capture agents PAC, PAM and Na2S are C w1 = 1.90 kg / m 3 soil, C w2 = 0.19 kg, and C w3 = 0.19 kg; the procurement distance of the leaching reagent is L f = 50 km, and the procurement distance of the capture agent is L w= 50 km; carbon emission factors (F f = 4.3859 kg CO2eq / kg, F w = 0.21 kg CO2eq / m 3 , F b1 = 1.6879 kg CO2eq / kg, F b2 = 3.2546 kg CO2eq / kg, F b3 = 2.9396 kg CO2eq / kg, F g = 3.7659 kg CO2eq / kg, F e = 0.5703 kg CO2eq / kWh); unit km fuel consumption (N l = 35 L / 100 km) of the transport vehicle, single transport volume (K = 60 t); standard coal coefficients (E g = 1.4571 kgce / kg, E e = 0.1229 kgce / kWh) of diesel and electricity. Since the present embodiment does not involve the investigation of pH and temperature parameters, the pH i and pH e data of the elution agent solution are not collected, and the values of the acid-base regulator input W a per unit pH change in the elution agent, the heating power P tem per unit temperature change in the pulp elution and washing equipment, the procurement distance L a of the acid agent, and the carbon emission factor F a of the acid-base regulator are all assigned as 0.

[0054] The energy consumption evaluation formula of the repair activity is constructed, and the specific formula is as follows:

[0055] Energy consumption value E sum = energy consumption value of agent transportation + energy consumption value of elution and repair equipment operation = unit km fuel consumption of the transport vehicle × (procurement distance of the elution agent + procurement distance of the acid-base regulator + procurement distance of the capture agent) × (agent input amount / single transport volume) × standard coal coefficient of diesel + ((elution time × (pulp elution and washing equipment operation power + heating power + vibration screen equipment operation power × series number + solid-liquid separation equipment operation power) + temperature change × heating power × elution time + wastewater treatment equipment operation time × operation power × liquid-solid ratio × unit soil repair volume) × standard coal coefficient of electricity = N l × (L f × ((C f × (R f×1)) / K)+L a ×((|pH e -pH i |×W a ) / K)+L w ×((R f ×1)×(C w1 +C w2 +C w3 )) / K))×E g

[0056] +(T f ×(P f +P z ×K z +P s )+(Tem f -RT)×P tem ×T f +T w ×P w ×R f ×1))×E e

[0057] = -0.96 + 10.23 × C f +0.47×R f +0.0007×T f

[0058] Among them, E sum The unit is kgce / t soil, which is the amount of standard coal required to remediate 1 ton of soil; C f T represents the concentration of the EDTA eluent, in mol / L. f R is the rinsing time, in minutes. f The liquid-to-solid ratio is calculated as (leachate volume: soil volume).

[0059] A formula for assessing carbon emissions from remediation activities is constructed, and the specific formula is as follows:

[0060] Carbon emissions C sum= carbon emission of elution agent input + carbon emission of water resource input + carbon emission of acid-base adjusting agent input + carbon emission of capturing agent input + carbon emission of agent transportation + carbon emission of energy consumption of elution remediation equipment = elution agent concentration × (liquid-solid ratio × unit soil remediation amount) × carbon emission factor of elution agent + (liquid-solid ratio × unit soil remediation amount) × carbon emission factor of water + elution liquid pH change × unit pH change input amount of acid-base adjusting agent × carbon emission factor of acid-base adjusting agent + (liquid-solid ratio × unit soil remediation amount) × capturing agent concentration × carbon emission factor of capturing agent + unit kilometer oil consumption of transportation vehicle × (elution agent transportation distance + acid-base adjusting agent transportation distance + capturing agent transportation distance) × (agent input amount / single transportation amount) × carbon emission factor of diesel oil + ((elution time × (slurry-making elution equipment running power + heating power + vibration screening equipment running power × number of stages + solid-liquid separation equipment running power) + temperature change × heating power × elution time + waste water treatment equipment running time × running power × liquid-solid ratio × unit soil remediation amount) × carbon emission factor of electricity

[0061] = C f × (R f × 1) × F f + (R f × 1) × F w + |pH e -pH i | × W a × F a + (R f × 1) × (C w1 × F b1 +C w2 × F b2 +C w3 × F b3 ) + N l × (L f × ((C f × (R f × 1)) / K) + L a × ((|pH e -pH i | × W a ) / K) + L w × ((R f × 1) × (C w1 +C w2 +C w3 )) / K)) × F g +

[0062] (T f × (P f +P z × K z +P s ) + (Temf -RT) x P tem x T f + T w x P w x (R f x 1)) x F e

[0063] = -1171 + 7839 x C f + 197 x R f + 0.003 x T f

[0064] wherein C sum is in kg CO2eq / t soil, i.e. the amount of carbon dioxide equivalent emitted by 1 ton of soil remediated.

[0065] (2) Test data collection (S2) step:

[0066] EDTA concentration, leaching time, liquid-solid ratio were selected as the investigation parameters, and the response surface experiment treatment groups were designed by Box-Behnken Designs (BBD) method of Design Expert software, a total of 17 groups (see Table 1). Among them, the number of central point treatment was 5, i.e. the central point treatment needed to carry out 5 parallel experiments of the same process parameter combination, and the data results were used separately; the non-central point treatment all used 3 parallel experiments, and the average value was taken.

[0067] Table 1 Treatment group design of response surface experiment

[0068] Serial number EDTA concentration mol / L Elution time min Liquid-solid ratio L / kg 1 0.15 240 8 2 0.10 150 8 3 0.15 60 8 4 0.10 60 6 5 0.15 60 4 6 0.20 60 6 7 0.15 150 6 8 0.20 150 8 9 0.15 150 6 10 0.15 150 6 11 0.10 240 6 12 0.15 150 6 13 0.20 240 6 14 0.15 150 6 15 0.10 150 4 16 0.20 150 4 17 0.15 240 4

[0069] The batch leaching experiment was carried out with the soil sample of the site as the pilot test material. Different concentrations of EDTA solution were prepared and mixed with the contaminated soil sample for oscillation leaching; the setting gradient of EDTA concentration, liquid-solid ratio and leaching time is shown in Table 1. The solid-liquid separation was carried out, the liquid sample was taken for Pb concentration determination, and the pollutant Pb removal rate was calculated. The calculation formula of the pollutant Pb removal rate is: pollutant removal rate R = (C i -C e ) / C i x 100%. C i is the initial Pb concentration of the soil, C e is the Pb concentration of the soil after remediation. At the same time, the energy consumption and carbon emission data corresponding to each treatment group were calculated according to the energy consumption evaluation formula and the carbon emission evaluation formula of the remediation activity provided in S1. The test data are shown in Table 2.

[0070] Table 2 Data of response surface experiment

[0071]

[0072]

[0073] (3) Removal rate fitting screening (S3) step:

[0074] Using the pollutant removal rate data obtained in S2, the pollutant removal rate equation based on the combination of process parameters is obtained by fitting with Design Expert software.

[0075] The fitting results show that the pollutant removal rate equation is a multivariate quadratic equation based on the combination of process parameters, and the specific formula is

[0076] Removal rate R = 72.57 - 204.65 x C f + 0.19 x T f - 3.68 R f - 0.31 x C f x T f - 16.16 x C f x R f - 0.013 x T f x R f + 1460 x C f 2 - 0.000037 x T f 2 + 0.70 x R f 2

[0077] The significance test result of model fitting degree P1 = 0.0002 < 0.05, P2 = 0.5019 > 0.05, and the significance test result meets the requirements, which can enter the next step of process parameter candidate combination fitting screening.

[0078] According to the site remediation requirements, taking 80% removal rate as the remediation target, using the principle of removal rate tending to be large, energy consumption and carbon emission tending to be small, 50 groups of process parameter candidate combinations are fitted and screened by Design Expert software as the data source of S4. The fitting data are shown in Table 3.

[0079] (4) Environmental impact analysis (S4) step:

[0080] Using the process parameter candidate combinations obtained in S3, the remediation activity energy consumption evaluation formula and the remediation activity carbon emission evaluation formula in S1 are substituted, and the energy consumption and carbon emission data corresponding to 50 groups of process parameter candidate combinations are calculated by Excel software. Subsequently, because the energy consumption and carbon emission requirements of the construction party are opposite to the removal rate, i.e. hoping that the removal rate tends to be large, the energy consumption value and the carbon emission amount tend to be small, so the maximum value method is selected to perform positive processing on the remediation energy consumption and carbon emission data, and the conversion formula is

[0081] Item ki=Item max –Item ks . Wherein, Item ki is the normalized data of carbon emission, Item max is the maximum value of carbon emission in 50 groups of process parameter alternative combinations, Item ks is the original data of carbon emission.

[0082] The processed data is shown in Table 3.

[0083] Table 3 Process parameter alternative combinations and their values

[0084]

[0085]

[0086] (5) Data standardization processing step (S5):

[0087] The Z-score method was used to non-dimensionalize the removal rate, energy consumption and carbon emission data of S3 and S4. The data conversion formula of the Z-score method is: Item std = (Item i - Item ave ) / δ. Wherein, Item std is the normalized data of removal rate, energy consumption value or carbon emission, Item i is the original data of removal rate or the processed data of energy consumption value or carbon emission, Item ave and δ are the average value and standard deviation of the removal rate, energy consumption value or carbon emission value in 50 groups of process parameter alternative combinations.

[0088] Based on this method, after substituting the data, the conversion formula of the removal rate in this experiment is R std = (R i - R ave ) / δ R = (R i - 81.53%) / 0.02; the conversion formula of the carbon emission data is C std = (C i - C ave ) / δ C = (C i - 897) / 213; and the conversion formula of the energy consumption data is E std = (E i - E ave ) / δ E = (E i - 3.25) / 0.27.

[0089] The data standardization values are shown in Table 2.

[0090] (6) The evaluation model construction step (S6) is as follows:

[0091] An evaluation weight of the pollutant removal rate, energy consumption and carbon emission is determined, and a comprehensive remediation benefit evaluation model considering energy saving, pollution reduction and carbon reduction is constructed. The specific formula of the comprehensive remediation benefit evaluation model is as follows:

[0092] The comprehensive remediation benefit value F sum = (K R × R i + K E × E i + K C × C i ) × 100

[0093] Wherein, F sum is the comprehensive remediation benefit value; K R , K E and K C are the evaluation weight coefficients of the removal rate, energy consumption and carbon emission respectively; R i , E i and C i are the normalized values of the removal rate, energy consumption and carbon emission of the i th process parameter combination.

[0094] According to the preference of the decision maker, three weight coefficients are set, and the comprehensive remediation benefit evaluation model is constructed:

[0095] a. Stress pollution removal: set the removal rate weight to 1, the energy consumption weight to 0 and the carbon emission weight to 0, and finally obtain the comprehensive remediation benefit evaluation model in this scenario as F sum = R i × 100;

[0096] b. Bias remediation: set the removal rate weight to 1, the energy consumption weight to 0.2 and the carbon emission weight to 0.2, and finally obtain the comprehensive remediation benefit evaluation model in this scenario as F sum = (R i + 0.2 × E i + 0.2 × C i ) × 100;

[0097] c. Synergistic energy saving, carbon reduction and pollution reduction: set the removal rate weight to 1, the energy consumption weight to 1 and the carbon emission weight to 1, and finally obtain the comprehensive remediation benefit evaluation model in this scenario as F sum = (R i + E i + C i ) × 100.

[0098] (7) The process parameter decision step (S7) is as follows:

[0099] The removal rate, energy consumption and carbon emission standardized data in S5 are input into the S6 remediation comprehensive benefit evaluation model to calculate the remediation comprehensive benefit value corresponding to each process parameter combination; the combination with the highest remediation comprehensive benefit value is selected as the final decision scheme.

[0100] The evaluation score distribution and final process parameter decision scheme of the site remediation case under 3 preference scenarios are:

[0101] (1) emphasizing decontamination (removal rate weight is 1, energy consumption weight is 0, and carbon emission weight is 0)

[0102] Table 4 benefit value evaluation score of process parameter combination under the decontamination emphasis scenario

[0103] The benefit value evaluation scores of 50 groups of process parameter combinations are shown in Table 4. According to the above calculation results, the best process parameter combination under the decision requirement scenario is finally selected as K44 combination, the EDTA concentration is 0.18 mol / L, the liquid-solid ratio is 4.34 L / kg, and the leaching time is 240 min.

[0104] (2) emphasizing remediation (removal rate weight is 1, energy consumption weight is 0.2, and carbon emission weight is 0.2)

[0105] Table 5 benefit value evaluation score of process parameter combination under the remediation emphasis scenario

[0106] The benefit value evaluation scores of 50 groups of process parameter combinations are shown in Table 5. According to the above calculation results, the best process parameter combination under the decision requirement scenario is finally selected as K28 combination, the EDTA concentration is 0.17 mol / L, the liquid-solid ratio is 4.01 L / kg, and the leaching time is 240 min.

[0107] Synergistic energy saving, carbon reduction and pollution reduction (removal rate weight is 1, energy consumption weight is 1, and carbon emission weight is 1)

[0108] Table 6 benefit value evaluation score of process parameter combination under the synergistic energy saving, carbon reduction and pollution reduction scenario

[0109] The benefit value evaluation scores of 50 groups of process parameter combinations are shown in Table 6. According to the above calculation results, the best process parameter combination under the decision requirement scenario is finally selected as K2 combination, the EDTA concentration is 0.10 mol / L, the liquid-solid ratio is 4.05 L / kg, and the leaching time is 235 min.

[0110] In the above technical solution of the present application, the above is only a preferred embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structural transformation made under the technical concept of the present application, using the content of the present application specification and drawings, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for decision making for low carbon soil washing remediation of contaminated soil, characterized in that, The method comprises the following steps: Process parameter analysis (S1): construct a general model of contaminated soil leaching remediation process, determine key process parameters, collect auxiliary factor data, establish energy consumption evaluation formula and carbon emission evaluation formula of remediation activities; the key process parameters include leaching agent concentration (C f ), pH (pH e ), leaching time (T f ), liquid-solid ratio (R f ) and temperature (Tem f ); auxiliary factors include operating power of slurry leaching equipment (P f ), initial pH of leaching agent solution (pH i ), input amount of acid-base regulator per unit pH change in leaching agent (W a ), heating input per unit temperature change in slurry leaching equipment (P tem ), number of stages (K z ) and operating power (P z ) of vibrating screening equipment, operating power (P s ) of solid-liquid separation equipment, equipment operation time (T w ), operating power (P w ) and dosage of capturing agent (C w ) of wastewater treatment, procurement distance (L f ) of leaching agent, procurement distance (L a ) of acid-base regulator and procurement distance (L w ) of capturing agent, carbon emission factors (F f , F a , F w , F b , F g , F e ) of leaching agent, acid-base regulator, water, capturing agent, diesel and electricity, unit kilometer fuel consumption (N l ) and single transport volume (K) of transport vehicles, standard coal coefficients (E g , E e ) of diesel and electricity; the energy consumption evaluation formula of remediation activities is: Energy consumption value E sum =N l ×(L f ×((C f ×(R f ×1)) / K)+L a ×((|pH e -pH i |×W a ) / K)+L w ×((R f ×1)×(C w1 +C w2 +C w3 )) / K))×E g +(T f ×(P f +P z ×K z +P s )+(Tem f -RT)×P tem ×T f +T w ×P w ×R f ×1))×E e wherein: E sum in units of kg ce / t soil, RT is room temperature; The repair activity carbon emission evaluation formula is: Carbon emission C sum = C f x R f x 1 x F f + R f x 1 x F w + |pH e -pH i | x W a x F a +(T f x (P f +P tem +P z x K z +P s )+N l x (L f x ((C f x (R f x 1)) / K)+L a x ((|pH e -pH i | x W a ) / K)+L w x ((R f x 1) x (C w1 +C w2 +C w3 )) / K)) x F g +(Tem f -RT) x P w x T f +T w x P w x R f x 1) x F e +R f x 1 x C w x F b wherein: C sum in kg CO2eq / t soil, RT is room temperature; Test data collection (S2): design test gradient of key process parameters, collect pollutant removal rate data through response surface experiment (RSM); According to the repair activity energy consumption evaluation formula and the repair activity carbon emission evaluation formula in S1, calculate the energy consumption and carbon emission data corresponding to each process parameter combination; Removal rate fitting and screening (S3): use the pollutant removal rate data obtained in S2, and obtain the pollutant removal rate equation based on the process parameter combination through response surface method fitting; Take the repair target value as the critical point, adopt the principle of increasing removal rate, decreasing energy consumption value and carbon emission, and fit to obtain 50 groups of process parameter alternative combinations for environmental impact analysis; Environmental impact analysis (S4): based on the process parameter alternative combinations obtained in S3, calculate the energy consumption and carbon emission data corresponding to each process parameter alternative combination through the repair activity energy consumption and carbon emission evaluation formula in S1; The maximum value method is used for positive processing of the repair energy consumption and carbon emission data, and the formula of the positive processing is Item ki = Item max - Item ks , wherein Item ki is the normalized data of the energy consumption value or the carbon emission, Item max is the maximum value of the energy consumption value or the carbon emission in the 50 groups of process parameter alternative combinations, and Item ks is the original data of the energy consumption value or the carbon emission. Data standardization (S5): Z-score method was used to standardize the removal rate, energy consumption and carbon emission data of S3 and S4; the data conversion formula of the Z-score method is Item std =(Item i -Item ave ) / δ, wherein Item std is the data after standardization of the removal rate, energy consumption value or carbon emission, Item i is the original data of the removal rate, energy consumption value or carbon emission, and Item ave and δ are respectively the average value and standard deviation of the removal rate, energy consumption or carbon emission value in the 50 groups of process parameter alternative combinations; The evaluation model is constructed (S6): the evaluation weights of the pollutant removal rate, the energy consumption and the carbon emission are determined, and a comprehensive benefit evaluation model of the repair is constructed by taking into account the energy saving, pollution reduction and carbon reduction requirements; the formula of the comprehensive benefit evaluation model of the repair is: F sum = (K R ×R i +K E ×E i +K C ×C i ) × 100, wherein F sum K is the repair comprehensive benefit value R K E K C R, E, and C are the evaluation weight coefficients of removal rate, energy consumption, and carbon emission, respectively i E i C i Ri, Ei, and Ci are the normalized values of removal rate, energy consumption, and carbon emission of the i th process parameter alternative combination, respectively Process parameter decision (S7): input the removal rate, energy consumption value or carbon emission standardized data of the process parameter alternative combinations in S5 into the repair comprehensive benefit evaluation model in S6, and calculate the repair comprehensive benefit value; Select the process parameter alternative combination with the highest repair comprehensive benefit value as the final decision scheme.

2. The method according to claim 1, wherein, The test gradient design of the response surface experiment in step S2 is performed by using a Box-Behnken Design method, and the number of central point treatments is not less than 3; and the calculation formula of the pollutant removal rate is: Pollutant removal rate R=(C i -C e ) / C i x 100%, wherein C i is the initial concentration of the soil pollutant, and C e is the concentration of the soil pollutant after remediation.

3. The method according to claim 1, wherein, The pollutant removal rate equation in S3 is a multivariate quadratic equation based on the process parameter combination; the significance test result P1 of the model fitting degree in the response surface method fitting result should be less than 0.05, and the significance test result P2 of the loss of fitting should be greater than 0.

05.

4. The method according to claim 1, wherein, The comprehensive benefit evaluation model for the repair adopts a subjective weighting method, and the evaluation weight coefficients K R 、K E 、K C The numerical interval is [0, 1], and the initial values are 1, 1 and 1 respectively.

5. The method of claim 1, wherein, The decision method is used for determining the process parameters of soil pollution ex-situ leaching repair; the soil pollution includes soil heavy metal pollution, organic pollution and heavy metal-organic complex pollution.

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