Polluted site soil remediation evaluation method and system

Abstract

The invention provides a contaminated site soil remediation evaluation method and system, and belongs to the field of contaminated site soil remediation evaluation. The method comprises the following steps: calculating the average carbon emission and desorption efficiency at the current time period; fusing the output of the temperature prediction sub-model and the output of the phase change feature sub-model to obtain a target temperature; calculating a temperature control efficiency index and a temperature carbon efficiency ratio, and calculating a phase change completion degree according to the output of the phase change characteristic sub-model; according to a carbon emission base line, the average carbon emission in the current time period, the desorption efficiency, the phase change completion degree, the temperature control efficiency index and the temperature-carbon efficiency ratio, the soil remediation effect of the contaminated site is comprehensively evaluated. The energy utilization efficiency and the carbon emission efficiency in the contaminated site soil remediation process are comprehensively evaluated.

Description

Technical Field

[0001] The present invention relates to the field of soil remediation assessment for contaminated sites, and particularly to a method and system for soil remediation assessment of contaminated sites. Background Art

[0002] To achieve the safe and sustainable use of land resources, a large number of soil remediation activities are carried out to reduce the environmental and health risks of contaminated sites. However, large-scale remediation activities consume a large amount of chemicals, materials and energy, which brings huge environmental costs. Among them, carbon emissions are one of the important indicators for evaluating the environmental impact of remediation activities.

[0003] As a typical energy-intensive remediation technology, the carbon emissions of ex-situ thermal desorption have received wide attention. The carbon emissions caused by natural gas during the soil heating process account for more than 70%. At present, the evaluation of the soil remediation effect of contaminated sites mainly depends on indicators such as pollutant removal rate and remediation cost, lacking the evaluation of carbon emissions during the remediation process. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for soil remediation assessment of contaminated sites, which comprehensively evaluate the energy utilization efficiency and carbon emission efficiency during the soil remediation process of contaminated sites by monitoring key data such as carbon emissions, desorption efficiency and temperature control.

[0005] To achieve the above purpose, the present invention provides a method for soil remediation assessment of contaminated sites, including: collecting soil remediation-related data and performing preprocessing; calculating the average carbon emissions and desorption efficiency in the current period based on the preprocessed soil remediation-related data; constructing a temperature prediction sub-model and a phase change characteristic sub-model based on the preprocessed soil remediation-related data, and fusing the output of the temperature prediction sub-model with the output of the phase change characteristic sub-model to obtain the target temperature; calculating the temperature control efficiency index and the temperature carbon efficiency ratio according to the target temperature, and calculating the phase change completion degree according to the output of the phase change characteristic sub-model; evaluating the soil remediation effect of the contaminated site according to the carbon emission baseline, the average carbon emissions in the current period, the desorption efficiency, the phase change completion degree, the temperature control efficiency index and the temperature carbon efficiency ratio, wherein the carbon emission baseline is constructed based on historical data and grid emission factors.

[0006] Optionally, the calculating the average carbon emissions and desorption efficiency in the current period based on the preprocessed soil remediation-related data includes: calculating the real-time carbon emissions based on the preprocessed soil remediation-related data according to the IPCC emission factor method; setting the period length and calculating the arithmetic mean of the real-time carbon emissions; calculating the average carbon emissions in the current period based on the arithmetic mean of the real-time carbon emissions and the period length.

[0007] Optionally, constructing a temperature prediction sub-model and a phase change feature sub-model based on the pre-processed soil remediation-related data includes: dividing the pre-processed soil remediation-related data into a training set, a validation set, and a test set; building a temperature prediction sub-model architecture, training the temperature prediction sub-model using the training set, tuning the parameters of the trained temperature prediction sub-model using the validation set, and evaluating the temperature prediction sub-model with tuned parameters using the test set; determining the parameters of the phase change feature sub-model and constructing the phase change feature sub-model.

[0008] Optionally, the temperature prediction sub-model adopts a three-layer long short-term memory network structure.

[0009] Optionally, fusing the output of the temperature prediction sub-model and the output of the phase change feature sub-model to obtain the target temperature includes: calculating the phase change temperature corresponding to the pollutant concentration; dynamically outputting the current temperature adjustment weight using fuzzy control; setting the thermal hysteresis compensation amount; and weighted-fusing the output of the temperature prediction sub-model and the output of the phase change feature sub-model according to the current temperature adjustment weight and the thermal hysteresis compensation amount to obtain the target temperature.

[0010] Optionally, calculating the temperature control efficiency index and the temperature carbon efficiency ratio according to the target temperature includes: obtaining the measured temperature at the current moment and the target temperature at the current moment; calculating the absolute error between the measured temperature at the current moment and the target temperature at the current moment; normalizing the absolute error and calculating the temperature control efficiency index; and calculating the temperature carbon efficiency ratio according to the target temperature at the current moment and the carbon emissions at the current moment.

[0011] Optionally, calculating the phase change completion degree according to the output of the phase change feature sub-model includes: obtaining the output of the phase change feature sub-model at the current moment, obtaining the measured temperature at the current moment and the initial phase change temperature, and calculating the phase change completion degree according to the output of the phase change feature sub-model at the current moment, the measured temperature at the current moment, and the initial phase change temperature.

[0012] Optionally, evaluating the soil remediation effect of the polluted site according to the carbon emission baseline, the average carbon emissions in the current period, the desorption efficiency, the phase change completion degree, the temperature control efficiency index, and the temperature carbon efficiency ratio includes: calculating the carbon emission deviation rate according to the carbon emission baseline and the average carbon emissions in the current period, and evaluating the thermal desorption carbon-temperature synergy efficiency according to the carbon emission deviation rate and the temperature carbon efficiency ratio.

[0013] Optionally, the evaluation of the soil remediation effect of the contaminated site based on the carbon emission baseline, the average carbon emission in the current period, the desorption efficiency, the phase change completion degree, the temperature control efficiency index, and the temperature-carbon efficiency ratio further includes: evaluating the pollutant desorption effect based on the desorption efficiency, the phase change completion degree, and the temperature control efficiency index.

[0014] On the other hand, the present invention provides a soil remediation evaluation system for a contaminated site, which is used to implement the soil remediation evaluation method for a contaminated site. The system includes a control module, and the control module includes a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement the soil remediation evaluation method for a contaminated site.

[0015] Through the above technical solutions, by combining the carbon emission baseline with the monitored data, the pollutant removal efficiency and carbon emission situation during the soil remediation process are concerned; with the help of key data such as desorption efficiency, phase change completion degree, temperature control efficiency index, and temperature-carbon efficiency ratio, the pollutant removal effect and temperature control energy efficiency during the remediation process are accurately quantified; by calculating the target temperature in real time, the change of temperature demand can be dynamically reflected, and the potential impact of temperature deviation on the remediation effect can be discovered in time, making the evaluation result more in line with the actual situation; by combining the carbon emission deviation rate and the carbon-temperature coordination efficiency index, the coordinated optimization level of carbon emission reduction and temperature control, as well as the energy utilization efficiency during the carbon emission reduction process, are comprehensively evaluated, so as to realize the evaluation of the energy utilization efficiency and carbon emission efficiency of the soil remediation process of the contaminated site, and provide a quantifiable decision-making support tool for green remediation.

[0016] Other features and advantages of the present invention will be described in detail in the subsequent specific implementation section. Description of the Drawings

[0017] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the following specific implementation, but do not constitute a limitation to the present invention. In the drawings: Figure 1 is the flowchart of the soil remediation evaluation for a contaminated site.

[0018] Figure 2 is the flowchart of the target temperature calculation. Specific Implementation

[0019] [[ID=[]]]The following will be described in detail the specific implementation of the embodiments of the present invention in conjunction with the attached Figure 1 - attached Figure 2 Drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiments of the present invention, and is not used to limit the embodiments of the present invention.

[0020] It should be noted that the acquisition, transmission, storage, use, processing, etc. of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations. In the embodiments of this application, some existing solutions in the industry such as certain software, components, models, etc. may be mentioned. They should be regarded as exemplary. The purpose is only to illustrate the feasibility in the implementation of the technical solution of this application, but it does not mean that the applicant has already or necessarily used this solution.

[0021] In the process of implementing the present invention, the inventors of this application found that the evaluation of the remediation effect of contaminated soil in the prior art mainly relies on the pollutant removal rate, but ignores the energy consumption and carbon emissions during the remediation process, resulting in incomplete evaluation and problems such as low remediation efficiency and high remediation energy consumption of contaminated soil.

[0022] Embodiment 1 Refer to Figures 1 - 2 , which is the first embodiment of the present invention. This embodiment provides a method for evaluating the remediation of contaminated site soil. This method is mainly used to dynamically evaluate the remediation effect of soil contaminated by the ex-situ thermal desorption technology, especially to evaluate the energy utilization efficiency and carbon emission efficiency during the remediation of contaminated soil through data such as temperature control efficiency, carbon emissions, and carbon-temperature synergy efficiency. The evaluation results of this solution can serve the optimization of the thermal desorption process, thereby achieving the effect of optimizing the remediation of contaminated soil. This solution includes: S100: Collect data related to soil remediation and perform preprocessing.

[0023] It should be noted that thermal desorption refers to the process of separating volatile or semi-volatile pollutants from the contaminated medium (soil) by heating the soil to a certain temperature. Ex-situ thermal desorption (ESTD) usually includes the following links: 1. Excavation: Use equipment such as excavators to dig out the contaminated soil, and at the same time carry out foundation pit support and groundwater drainage.

[0024] 2. Infrastructure construction: Carry out site leveling and hardening, build feed and discharge sheds, and lay anti-seepage measures such as high-density polyethylene membranes (HDPE membranes).

[0025] 3. Transportation: Transport the excavated contaminated soil to the feed shed through closed transportation vehicles.

[0026] 4. Pretreatment: Crush, screen and adjust the moisture content of the contaminated soil in a negative pressure shed to meet the feeding requirements.

[0027] 5. Thermal desorption: The contaminated soil is transported to the rotary kiln through the feeder and conveyor, heated to the set temperature with natural gas and maintained for a certain time, and then sent to the discharge bin to achieve the separation of pollutants from the soil in the rotary kiln.

[0028] 6. Tail gas treatment: The tail gas is discharged up to standard after processes such as dust removal, purification, spray cooling, and activated carbon adsorption.

[0029] 7. Wastewater treatment: The wastewater generated during the repair process is collected and transported to the on-site water treatment system, and is discharged into the municipal pipe network after reaching the standard.

[0030] 8. Backfilling: The soil after thermal desorption treatment is transported to the temporary storage area after passing the inspection, and is backfilled to the original site.

[0031] Specifically, temperature, pressure, concentration sensors and infrared thermal imagers are deployed according to the process requirements, and energy consumption monitoring sensors (such as natural gas flow meters, electricity meters) are arranged. Three groups of temperature sensor arrays (kiln head / middle section / tail) are axially arranged in the rotary kiln, and infrared thermal imagers are added at the outlet of the preheater and the secondary air duct to generate a thermal distribution map of the materials in the kiln; further, the sensors upload data such as temperature (kiln head / middle section / tail), pressure, and pollutant concentration in the tail gas, and synchronously record energy data such as natural gas consumption and electricity consumption. The collected data is preprocessed by means of sliding window denoising, outlier detection (such as 3 criteria), missing value interpolation, etc. to obtain a real-time time series data stream, which includes temperature, pollutant concentration in the tail gas, feeding rate, moisture content, gas flow rate, etc.

[0032] Preferably, by arranging a temperature sensor array and combining with an infrared thermal imager to generate a thermal distribution map of the materials, three-dimensional monitoring of the heat conduction process is realized; the error of a single sensor is avoided. Through sliding window denoising, 3 criteria outlier detection and missing value interpolation, sensor noise and abnormal fluctuations are eliminated to ensure the data reliability for subsequent carbon emission calculation, desorption efficiency evaluation and temperature model prediction, and to avoid the distortion of evaluation conclusions caused by data deviation.

[0033] S200: Calculate the average carbon emission and desorption efficiency of the current period based on the preprocessed data related to soil remediation.

[0034] S210: Calculate the average carbon emission of the current period.

[0035] Specifically, according to the IPCC emission factor method, the real-time carbon emission is calculated; the time period length is set, and the arithmetic mean of the real-time carbon emissions is calculated; based on the arithmetic mean of the real-time carbon emissions and the time period length, the average carbon emission of the current period is calculated. [[ID=2,7]]

[0036] Further, according to the IPCC (Intergovernmental Panel on Climate Change) emission factor method, the real-time carbon emission is calculated in real time, and the calculation expression of the real-time carbon emission is as follows:

[0037] Among them, is the carbon emission at time t, is the activity data at time t, representing the input amount of the i-th energy or material at time t (such as the instantaneous flow rate of natural gas, the instantaneous power of electricity), which is obtained from the preprocessed soil remediation-related data; is the emission factor, representing the carbon emission coefficient of the i-th energy or material (for example, the emission factor of natural gas is 2.05 kgCO2 / m³, and the electricity emission factor is dynamically adjusted according to the regional power grid).

[0038] Furthermore, the energy consumption in the thermal desorption stage is monitored in real time, the time period length is set, and the arithmetic mean of the real-time carbon emissions is calculated, such as calculating the average carbon emissions in the current time period (such as 10 minutes).

[0039] Preferably, the IPCC emission factor method is adopted, and the calculation is dynamically adjusted according to the real-time carbon intensity of the power grid, avoiding the ±15% error caused by the traditional static factor and being able to reflect the true carbon emissions.

[0040] S220: Calculate the desorption efficiency.

[0041] Specifically, the calculation expression of the desorption efficiency is as follows:

[0042] Among them, represents the cumulative desorption efficiency at time t, represents the initial soil pollutant concentration detected in the laboratory, represents the pollutant concentration in the tail gas at time represents the tail gas flow rate at time represents the total treatment amount of contaminated soil, with the unit of kg.

[0043] The closer it is to 1 (i.e., 100%), the more complete the pollutant desorption is (the outlet concentration is much lower than the inlet concentration). Lower < 70% indicates insufficient desorption temperature, insufficient residence time, or incomplete release of pollutants.

[0044] The desorption efficiency provides real-time feedback on the process effectiveness. When < 70%, it triggers the temperature / residence time adjustment to avoid ineffective energy consumption.

[0045] When < 70%, it indicates insufficient temperature or insufficient residence time, providing a basis for parameter adjustment (such as increasing the heating temperature or extending the treatment time), and is an important parameter for evaluating the pollutant desorption effect.

[0046] S230: Based on the preprocessed data related to soil remediation, construct a temperature prediction sub-model and a phase change feature sub-model, and fuse the output of the temperature prediction sub-model with the output of the phase change feature sub-model to obtain the target temperature.

[0047] Specifically, divide the preprocessed data related to soil remediation into a training set, a validation set, and a test set; build the architecture of the temperature prediction sub-model, use the training set to train the temperature prediction sub-model, use the validation set to tune the parameters of the trained temperature prediction sub-model, and use the test set to evaluate the temperature prediction sub-model with tuned parameters; determine the parameters of the phase change feature sub-model and construct the phase change feature sub-model.

[0048] It should be noted that the temperature prediction sub-model is used to predict the material temperature in a thermal desorption device, such as a rotary kiln, specifically including the temperature distribution at the head, middle section, and tail of the rotary kiln. This temperature prediction sub-model can dynamically predict the target temperature value that needs to be reached in the thermal desorption device in the next period of time, such as the next 10 minutes, to guide the real-time adjustment of the heating control system (such as the opening of the gas valve, the electric heating power, etc.). Parameter tuning mainly targets the hyperparameters and training strategies of the temperature prediction sub-model, including model structure parameters and training strategy parameters, etc.

[0049] Furthermore, the temperature prediction sub-model adopts a three-layer LSTM network (Long Short-Term Memory Network) as the core structure to predict the theoretical temperature demand. The input layer receives real-time time-series data streams, and the real-time time-series data streams are divided into 70% for the training set, 20% for the validation set, and 10% for the test set. The loss function is the mean squared error, the optimizer is Adam (Adaptive Moment Estimation), the initial learning rate is set to 0.001, and the output layer uses a fully connected layer with a linear activation function to directly output the temperature prediction value.

[0050] Preferably, in the temperature prediction of soil remediation, using a three-layer LSTM network (Long Short-Term Memory Network) can effectively balance model performance and efficiency. Its gating mechanism (forget gate, input gate) can accurately capture the lag effect (such as heat conduction delay) up to 50 time steps, and its validation set MSE (0.15) is significantly better than that of GRU (Gated Recurrent Unit) (0.18) and 1D-CNN (One-Dimensional Convolutional Neural Network) (0.25). At the same time, the three-layer structure, with a moderate number of parameters (285,000) and a low inference delay of 8 ms, can filter sensor noise (±2°C fluctuation) while meeting the real-time control requirements of embedded devices, avoiding the high computational load of Transformer (2.1 million parameters) and the long-term dependence defect of CNN (Convolutional Neural Network), and becoming an optimal solution with both accuracy, efficiency and engineering adaptability. The specific data is shown in Table 1.

[0051] Table 1 Comparison of the performance of the temperature prediction sub-model

[0052] Furthermore, after determining the parameters of the phase change characteristic sub-model, a phase change characteristic sub-model is constructed. The expression of the phase change characteristic sub-model is as follows:

[0053] where, represents the phase change temperature corresponding to the pollutant concentration in the soil, represents the initial phase change temperature, represents the complete phase change temperature, represents the remaining pollutant concentration in the soil, is the lower limit of concentration normalization, is the upper limit of concentration normalization, is the phase change index, which is used to control the nonlinear degree of the concentration-temperature curve, > 1 indicates an accelerated phase change, < 1 indicates a decelerated phase change.

[0054] Furthermore, under ideal conditions, the remaining pollutant concentration in the soil = the amount of residual soil pollutants ÷ the total amount of polluted soil treated; The amount of residual soil pollutants = the total initial amount of soil pollutants - the cumulative release amount of tail gas pollutants; The total initial amount of soil pollutants = the initial soil pollutant concentration × the total mass of polluted soil; The calculation formula for the concentration of pollutants remaining in the soil is as follows:

[0055] Wherein, represents the concentration of pollutants remaining in the soil, represents the initial soil pollutant concentration detected in the laboratory, represents the concentration of pollutants in the tail gas at time represents the tail gas flow rate at time represents the total treatment amount of contaminated soil, where t represents the target time point.

[0056] It should be noted that the phase change in this solution refers to the transformation of pollutants from the adsorbed state (solid phase) to the free state (gas phase). By heating the soil, the pollutants overcome the adsorption force with soil particles (physical adsorption or chemical bonding) and are released from the soil matrix into the gas phase. At different pollutant concentrations, the temperature required to trigger effective desorption is the phase change temperature. The initial phase change temperature is determined through experimental measurement, theoretical calculation, and data from historical remediation projects. The complete phase change temperature is determined by experimental data of complete pollutant desorption or actual temperature records at the time of complete desorption in historical remediation. The upper / lower limits of concentration normalization are set according to the range of pollutant concentrations actually detected. The lower limit of concentration normalization is the safety threshold of the remediation target or the lowest effective concentration in historical data, and the upper limit of concentration normalization is the maximum initial concentration of the contaminated site or the upper limit allowed by technology. The phase change index is determined by non-linear regression fitting of historical data (concentration-temperature curve). The phase change characteristic sub-model is used to quantify the thermodynamic process of pollutant desorption from the soil and can dynamically adjust the target temperature by calculating the phase change temperature and the degree of phase change completion in real time to ensure efficient pollutant desorption with the lowest energy consumption.

[0057] Furthermore, calculate the phase change temperature corresponding to the pollutant concentration; dynamically output the current temperature adjustment weight using fuzzy control; set the thermal hysteresis compensation amount; according to the current temperature adjustment weight and the thermal hysteresis compensation amount, weighted fusion of the output of the temperature prediction sub-model and the output of the phase change characteristic sub-model to obtain the target temperature.

[0058] Specifically, fuse the output of the temperature prediction sub-model and the output of the phase change characteristic sub-model to obtain the target temperature. The calculation expression of the target temperature is as follows:

[0059] Wherein, is the target temperature at time t, used to guide the temperature control of the thermal desorption process, is the current temperature adjustment weight, is the temperature value predicted by the temperature prediction sub-model, Represents the phase change temperature corresponding to the pollutant concentration in the soil, which is the thermal hysteresis compensation amount used to compensate for the inherent hysteresis of the device.

[0060] It should be noted that the thermal hysteresis compensation amount is jointly determined by the step response test and the device heat transfer model.

[0061] Furthermore, the coupling weight in the output fusion formula is adjusted using fuzzy control.

[0062]

[0063]

[0064]

[0065]

[0066] Among them, is the current temperature adjustment weight, is the weight adjustment amount, is the temperature adjustment weight at the next moment, represents the fuzzy controller function, is the temperature control error, is the target temperature at time t, is the real-time measured temperature, represents the concentration change rate, represents the residual pollutant concentration in the soil, is the truncation function, where t represents the current moment.

[0067] Preferably, when the measured temperature is lower than the target temperature and the desorption efficiency decreases, it indicates that the pollutants are not effectively desorbed and the system response is insufficient. The fuzzy controller will reduce (reducing the dependence on the prediction model and relying more on the empirical phase change temperature); when the temperature is too high and the concentration decreases slowly, it indicates that the pollutants are basically exhausted and the system is overheated. The fuzzy controller will increase (increasing the trust in the prediction model and reducing the phase change temperature weight).

[0068] Preferably, by dynamically adjusting , the dependence on the LSTM prediction model and the phase change physical model can be automatically adjusted; by dynamically modifying the weight according to the fuzzy rules, the failure of a single model can be avoided; when the grid emission factor changes dynamically, the weight adjustment can also reduce the error rate of the evaluation index (such as the temperature-carbon efficiency ratio).

[0069] Preferably, calculating the target temperature is the core link for evaluating the soil remediation effect of contaminated sites. Its dynamic calculation is beneficial to enhancing the accuracy and authenticity of multi-dimensional indicators in the final evaluation (such as desorption effect, energy utilization rate). By integrating the outputs of the temperature prediction sub-model and the physical model to obtain the target temperature, the evaluation indicators can be made closer to the actual process requirements. Combining the regional energy structure and the phase change characteristic sub-model to dynamically adjust the target temperature can improve the technical universality. The calculation logic of the target temperature (such as fuzzy rules, phase change model parameters) provides a clear basis for the evaluation results and can support decision-making optimization. Through the intelligent calculation of the target temperature, the final evaluation is upgraded from the traditional single-result determination to a multi-dimensional dynamic optimization system including energy efficiency, environmental protection, and economy, which can provide scientific and quantifiable support for the soil remediation of contaminated sites.

[0070] S231: Calculate the temperature control efficiency index and the temperature carbon efficiency ratio according to the target temperature, and calculate the phase change completion degree according to the output of the phase change characteristic sub-model.

[0071] Furthermore, obtain the measured temperature at the current moment and the target temperature at the current moment; calculate the absolute error between the measured temperature at the current moment and the target temperature at the current moment; normalize the absolute error to calculate the temperature control efficiency index; calculate the temperature carbon efficiency ratio according to the target temperature at the current moment and the carbon emission at the current moment.

[0072] Specifically, calculate the temperature control efficiency index. The calculation expression of the temperature control efficiency index is as follows:

[0073] Where, represents the temperature control energy efficiency index, is the measured temperature at time t, is the target temperature at time t.

[0074] Preferably, the response ability of the temperature control system is directly reflected by normalizing the absolute error. The closer the index is to 1 (such as ≥0.95), the smaller the deviation between the actual temperature and the target temperature, and the higher the process stability.

[0075] Furthermore, calculate the temperature carbon efficiency ratio (TCER, Temperature Carbon Efficiency Ratio). The calculation expression of the temperature carbon efficiency ratio is as follows:

[0076] Where, is the temperature carbon efficiency ratio at time t, is the target temperature at time t, is the carbon emission at time t.

[0077] It should be noted that The higher it is, the higher the target temperature that can be supported per unit carbon emission, and the better the energy efficiency. The lower it is, the greater the carbon emission consumption but the insufficient temperature increase, indicating low efficiency.

[0078] Furthermore, obtain the output of the phase change characteristic sub-model at the current moment, that is, the phase change temperature corresponding to the current pollutant concentration, obtain the measured temperature and the initial phase change temperature at the current moment, and calculate the phase change completion degree according to the output of the phase change characteristic sub-model at the current moment, the measured temperature at the current moment and the initial phase change temperature.

[0079] Specifically, the calculation expression of the phase change completion degree is as follows:

[0080] Among them, represents the phase change completion degree, represents the phase change temperature corresponding to the pollutant concentration in the soil, represents the initial phase change temperature, is the measured temperature at time t.

[0081] It should be noted that the calculation formula of the phase change completion degree is only used for < When, ≥ When, the temperature has reached the standard or exceeded the temperature, and the phase change completion degree is 100%. Even if the temperature continues to rise, the completion degree will not increase.

[0082] Preferably, the phase change completion degree can reveal the actual progress of pollutant desorption. When the desorption efficiency is high but the phase change completion degree is low, it can expose contradictions between concentration monitoring or phase change model parameters. The phase change completion degree and desorption efficiency respectively reflect the pollutant desorption effect from two dimensions of the thermodynamic model and actual monitoring data. When there is a large deviation between the two, such as the actual monitoring data shows sufficient desorption, but the thermodynamic model shows that the desorption does not meet the expectation, it may be that the pollutant aerosol escapes and is not detected by the sensors related to tail gas detection, or the parameters of the phase change characteristic sub-model are inaccurate and do not conform to the actual thermodynamic characteristics of the pollutant; at this time, the tail gas detection part is preferably verified first. If the tail gas detection part is correct, then use an online fast scanning calorimeter (Fast-Scanning Calorimeter, FSC) to obtain the phase change enthalpy data in real time under the operating state of the rotary kiln, and dynamically update the parameters of the phase change characteristic sub-model through an adaptive algorithm. When the thermodynamic model determines that the desorption is sufficient while the actual monitoring data shows insufficient desorption, it may be caused by fluctuations in soil moisture content resulting in a decrease in the effective heat transfer coefficient, uneven temperature field distribution in the rotary kiln generating cold zones, etc. At this time, first check the temperature field distribution data of the infrared thermal imager and optimize the rotational speed and inclination angle of the rotary kiln; if the temperature field is uniform, then the phase change index needs to be corrected .

[0083] S300: Evaluate the remediation effect of contaminated site soil based on the carbon emission baseline, the average carbon emission in the current period, the desorption efficiency, the phase change completion degree, the temperature control efficiency index, and the temperature-carbon efficiency ratio.

[0084] S310: The carbon emission baseline is constructed based on historical data and grid emission factors.

[0085] Furthermore, calculate the carbon emission deviation rate based on the carbon emission baseline and the average carbon emission in the current period, and evaluate the carbon-temperature synergy efficiency of thermal desorption according to the carbon emission deviation rate and the temperature-carbon efficiency ratio.

[0086] It should be noted that the carbon-temperature synergy efficiency of thermal desorption is a comprehensive evaluation index used to measure the synergy optimization level between temperature control accuracy and low-carbon remediation. Its core lies in evaluating whether the remediation technology can achieve efficient pollutant desorption at the lowest carbon cost by quantifying the effective thermal desorption temperature control ability supported by unit carbon emission, so as to evaluate the green level of the contaminated site soil remediation technology.

[0087] Specifically, the calculation expression of the carbon emission deviation rate is as follows:

[0088] Among them, represents the carbon emission deviation rate in the current period t, indicating the degree of deviation of the average carbon emission in the current period from the reference value, represents the average carbon emission in the current period t, which is calculated based on the arithmetic mean of the preprocessed real-time carbon emissions, represents the carbon emission baseline value, the carbon emission as the comparison reference.

[0089] It should be noted that when <0, it means that under the same remediation effect, the current carbon emission is lower than the carbon emission baseline, that is, the carbon emission per unit soil remediation amount decreases. When >0, it means that under the same remediation effect, the current carbon emission is higher than the carbon emission baseline, that is, the carbon emission per unit soil remediation amount increases.

[0090] Furthermore, calculate the comprehensive energy efficiency index and evaluate the carbon-temperature synergy efficiency of thermal desorption through the comprehensive energy efficiency index.

[0091] Specifically, the calculation expression of the comprehensive energy efficiency index is as follows:

[0092] Among them, represents the comprehensive energy efficiency index, and are both weight coefficients, represents the carbon emission deviation rate in the current period t. is the temperature-carbon efficiency ratio at time t.

[0093] It should be noted that the larger the value of

[0094] Preferably, by calculating the comprehensive energy efficiency index, the carbon emission and energy efficiency indicators are integrated, avoiding the limitations of single indicators. Through a clear mathematical formula, a reproducible evaluation standard is formed, avoiding evaluation differences caused by relying on manual experience. It can preferentially reflect the efficient utilization of clean energy by adapting different grid emission factors and adjusting weight coefficients. In areas with a high proportion of hydropower, can be adjusted upward, allowing an appropriate increase in energy consumption to improve the repair effect (such as extending the high-temperature section). At this time, even if the energy consumption is slightly higher, due to the low carbon intensity of the grid, the total carbon emission is still controllable; in areas with a high proportion of thermal power, can be adjusted upward, strictly controlling the carbon emissions of fossil energy. Under a high-carbon grid, it is necessary to preferentially reduce the absolute carbon emissions, even sacrificing some temperature efficiency (such as shortening the residence time in the high-temperature section). This makes the evaluation of the repair effect of the same technology in different regions more scientific and improves the flexibility of the evaluation. It should be noted that when adjusting the weights, must always be ensured. Comparing the traditional evaluation method with the evaluation of this scheme, Table 2 is obtained.

[0095] Table 2 Comparison table of experimental data on the synergistic optimization of carbon footprint and energy efficiency in thermal desorption repair

[0096] Furthermore, by comparing the key indicators of the traditional thermal desorption repair technology and the scheme of the present invention in three dimensions: carbon emission efficiency, energy utilization efficiency, and synergistic optimization ability, it is shown that in terms of carbon emission efficiency, compared with the traditional evaluation method, the present invention reduces the carbon emission calculation error rate from ±20% to ±5%, and the carbon intensity of unit pollutants is reduced by 46.4%; in terms of energy utilization efficiency, the gas utilization rate is increased by 26.2%, and the temperature-carbon efficiency ratio (TCER) is increased by 85.7%; in terms of synergistic optimization ability, the carbon-temperature synergistic efficiency (CEEI) index of the present invention reaches 0.78 (the A-level standard ≥0.6); all three dimensions have been improved.

[0097] Furthermore, the desorption effect of pollutants is evaluated according to the desorption efficiency, the degree of phase change completion, and the temperature control efficiency index.

[0098] Specifically, a hierarchical evaluation is carried out according to the desorption efficiency, the degree of phase change completion, and the temperature control efficiency index, as shown in Table 3.

[0099] Table 3 Hierarchical evaluation table of pollutant desorption effect

[0100] Preferably, grading and evaluation based on the desorption efficiency, the degree of phase change completion, and the temperature control efficiency index can avoid misjudgment caused by traditional single indicators, improve the accuracy of evaluation, and the evaluation here is a dynamic evaluation, and the evaluation results can support rapid decision-making.

[0101] The present invention also provides a contaminated site soil remediation evaluation system for implementing the contaminated site soil remediation evaluation method. The system includes a control module. The control module includes a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement the contaminated site soil remediation evaluation method.

[0102] An embodiment of the present invention provides a storage medium with a program stored thereon. When the program is executed by a processor, it implements the contaminated site soil remediation evaluation method.

[0103] An embodiment of the present invention provides a processor. The processor is used to run a program. When the program runs, it executes the contaminated site soil remediation evaluation method.

[0104] An embodiment of the present invention provides a device. The device includes a processor, a memory, and a program stored on the memory and executable on the processor. When the processor executes the program, it implements the contaminated site soil remediation evaluation method. The device herein may be a server, a PC, a PAD, a mobile phone, etc. [[ID=1,5]]

[0105] The present application also provides a computer program product. When executed on a data processing device, it is adapted to execute a program for initializing the steps of the contaminated site soil remediation evaluation method.

[0106] Those skilled in the art should understand that the embodiments of the present application may provide a method, a system, or a computer program product. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and combinations of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0108] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0109] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0110] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0111] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. The memory is an example of computer-readable media.

[0112] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0113] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0114] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for evaluating the remediation of contaminated site soil, characterized in that, Including: Collecting soil remediation - related data and performing pre - processing; Calculating the average carbon emission and desorption efficiency for the current period based on the pre - processed soil remediation - related data; Based on the pre - processed soil remediation - related data, constructing a temperature prediction sub - model and a phase - change characteristic sub - model, and fusing the output of the temperature prediction sub - model with the output of the phase - change characteristic sub - model to obtain the target temperature; Calculating the temperature control effectiveness index and temperature - carbon efficiency ratio according to the target temperature, and calculating the phase - change completion degree according to the output of the phase - change characteristic sub - model; Evaluating the soil remediation effect of the polluted site based on the carbon emission baseline, the average carbon emission for the current period, the desorption efficiency, the phase - change completion degree, the temperature control effectiveness index, and the temperature - carbon efficiency ratio; Among them, the carbon emission baseline is constructed based on historical data and grid emission factors.

2. The soil remediation assessment method for contaminated sites according to claim 1, wherein The calculating the average carbon emission and desorption efficiency for the current period based on the pre - processed soil remediation - related data includes: Calculating the real - time carbon emission based on the pre - processed soil remediation - related data according to the IPCC emission factor method; Setting the time - period length and calculating the arithmetic mean of the real - time carbon emissions; Calculating the average carbon emission for the current period based on the arithmetic mean of the real - time carbon emissions and the time - period length.

3. The soil remediation assessment method for contaminated sites according to claim 1, wherein The constructing a temperature prediction sub - model and a phase - change characteristic sub - model based on the pre - processed soil remediation - related data includes: Dividing the pre - processed soil remediation - related data into a training set, a validation set, and a test set; Building the architecture of the temperature prediction sub - model, training the temperature prediction sub - model using the training set, tuning the parameters of the trained temperature prediction sub - model using the validation set, and evaluating the temperature prediction sub - model with tuned parameters using the test set; Determining the parameters of the phase - change characteristic sub - model and constructing the phase - change characteristic sub - model.

4. The soil remediation assessment method for contaminated sites according to claim 3, wherein The temperature prediction sub - model adopts a three - layer long short - term memory network structure.

5. The soil remediation assessment method for contaminated sites according to claim 1, wherein Fusing the output of the temperature prediction sub - model with the output of the phase - change characteristic sub - model to obtain the target temperature includes: Calculating the phase - change temperature corresponding to the pollutant concentration; Using fuzzy control to dynamically output the current temperature - adjustment weight; Setting the thermal hysteresis compensation amount; According to the current temperature - adjustment weight and the thermal hysteresis compensation amount, weighted - fusing the output of the temperature prediction sub - model with the output of the phase - change characteristic sub - model to obtain the target temperature.

6. The soil remediation assessment method for contaminated sites according to claim 1, wherein The calculating the temperature control effectiveness index and temperature - carbon efficiency ratio according to the target temperature includes: Obtaining the measured temperature at the current moment and the target temperature at the current moment; Calculating the absolute error between the measured temperature at the current moment and the target temperature at the current moment; Normalizing the absolute error and calculating the temperature control effectiveness index; Calculating the temperature - carbon efficiency ratio according to the target temperature at the current moment and the carbon emission at the current moment.

7. The soil remediation assessment method for contaminated sites according to claim 1, wherein The calculating the phase - change completion degree according to the output of the phase - change characteristic sub - model includes: Obtaining the output of the phase - change characteristic sub - model at the current moment, obtaining the measured temperature at the current moment and the initial phase - change temperature, and calculating the phase - change completion degree according to the output of the phase - change characteristic sub - model at the current moment, the measured temperature at the current moment, and the initial phase - change temperature.

8. The soil remediation assessment method for contaminated sites according to claim 1, characterized in that, Evaluating the remediation effect of contaminated site soil according to the carbon emission baseline, the average carbon emission in the current period, the desorption efficiency, the phase change completion degree, the temperature control efficiency index, and the temperature-carbon efficiency ratio includes: calculating the carbon emission deviation rate according to the carbon emission baseline and the average carbon emission in the current period, and evaluating the thermal desorption carbon-temperature synergy efficiency according to the carbon emission deviation rate and the temperature-carbon efficiency ratio.

9. The soil remediation assessment method for contaminated sites according to claim 8, wherein Evaluating the remediation effect of contaminated site soil according to the carbon emission baseline, the average carbon emission in the current period, the desorption efficiency, the phase change completion degree, the temperature control efficiency index, and the temperature-carbon efficiency ratio further includes: evaluating the pollutant desorption effect according to the desorption efficiency, the phase change completion degree, and the temperature control efficiency index.

10. A contaminated site soil remediation assessment system, characterized in that, The system includes a control module, the control module includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the contaminated site soil remediation evaluation method according to any one of claims 1-9.

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