Soil remediation assessment method and system for contaminated sites

Abstract

The present invention provides a contaminated site soil remediation assessment method and system, belonging to the field of contaminated site soil remediation assessment; the method comprises calculating the average carbon emissions and desorption efficiency of the current period; fusing the output of the temperature prediction submodel with the output of the phase change characteristic submodel to obtain a target temperature; calculating the temperature control efficiency index and the temperature carbon efficiency ratio, and calculating the phase change completion degree according to the output of the phase change characteristic submodel; comprehensively assessing the contaminated site soil remediation effect according to the carbon emission baseline, the average carbon emissions of the current period, the desorption efficiency, the phase change completion degree, the temperature control efficiency index and the temperature carbon efficiency ratio. The present invention can comprehensively assess the energy utilization efficiency and carbon emission efficiency in the contaminated site soil remediation process by monitoring key data such as carbon emissions, desorption efficiency and temperature control.

Description

Technical Field

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

[0002] To achieve safe and sustainable land resource utilization, extensive soil remediation activities are being conducted to reduce environmental and health risks at contaminated sites. However, large-scale remediation activities require the consumption of large quantities of chemicals, materials, and energy, resulting in significant environmental costs. Carbon emissions are a key indicator for assessing the environmental impact of remediation activities.

[0003] Ex-situ thermal desorption, a typical energy-intensive remediation technology, has attracted widespread attention for its carbon emissions. Natural gas heating during soil heating accounts for over 70% of carbon emissions. Currently, assessments of soil remediation effectiveness at contaminated sites primarily rely on metrics such as pollutant removal rate and remediation cost, lacking an assessment 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 evaluating soil remediation at contaminated sites, which comprehensively evaluates the energy utilization efficiency and carbon emission efficiency during the soil remediation process at contaminated sites by monitoring key data such as carbon emissions, desorption efficiency, and temperature control.

[0005] In order to achieve the above-mentioned objectives, the present invention provides a method for evaluating soil remediation at a contaminated site, comprising: collecting and preprocessing soil remediation-related data; calculating the average carbon emissions and desorption efficiency of the current period based on the preprocessed soil remediation-related data; constructing a temperature prediction submodel and a phase change characteristic submodel based on the preprocessed soil remediation-related data, and fusing the output of the temperature prediction submodel with the output of the phase change characteristic submodel to obtain a target temperature; calculating a temperature control efficiency index and a temperature carbon efficiency ratio based on the target temperature, and calculating a phase change completion ratio based on the output of the phase change characteristic submodel; and evaluating the soil remediation effect of the contaminated site based on a carbon emission baseline, the average carbon emissions of the current period, the desorption efficiency, the phase change completion ratio, the temperature control efficiency index, and the temperature carbon efficiency ratio, wherein the carbon emission baseline is constructed based on historical data and a power grid emission factor.

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

[0007] Optionally, 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 a temperature prediction sub-model architecture, using the training set to train the temperature prediction sub-model, using the validation set to tune the parameters of the trained temperature prediction sub-model, and using the test set to evaluate the temperature prediction sub-model after parameter tuning; determining the parameters of the phase change characteristic sub-model, and constructing the phase change characteristic sub-model.

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

[0009] Optionally, the output of the temperature prediction sub-model is fused with the output of the phase change characteristic sub-model to obtain the target temperature, including: calculating the phase change temperature corresponding to the pollutant concentration; using fuzzy control to dynamically output the current temperature adjustment weight; setting the thermal lag compensation amount; and weightedly fusing the output of the temperature prediction sub-model and the output of the phase change characteristic sub-model according to the current temperature adjustment weight and the thermal lag compensation amount to obtain the target temperature.

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

[0011] Optionally, the calculation of the phase change completion degree based on 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 based on 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.

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

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

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

[0015] The above technical solution focuses on the pollutant removal efficiency and carbon emissions in the soil remediation process by combining carbon emission baselines with monitoring data; with the help of key data such as desorption efficiency, phase change completion, temperature control efficiency index and temperature-carbon efficiency ratio, it accurately quantifies the pollutant removal effect and temperature control energy efficiency in the remediation process; through real-time calculation of the target temperature, it can dynamically reflect changes in temperature demand and promptly discover the potential impact of temperature deviation on the remediation effect, making the evaluation results more in line with reality; combined with the carbon emission deviation rate and the carbon-temperature synergy efficiency index, it comprehensively evaluates the synergistic optimization level of carbon emission reduction and temperature control, as well as the energy utilization efficiency in the carbon emission reduction process, thereby realizing the evaluation of the energy utilization efficiency and carbon emission efficiency of the soil remediation process of contaminated sites, providing a quantifiable decision support tool for green remediation.

[0016] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the following detailed description, they are used to explain the present invention but do not constitute a limitation of the present invention. In the accompanying drawings:

[0018] Figure 1 It is a flow chart for soil remediation assessment of contaminated sites.

[0019] Figure 2 It is the target temperature calculation flow chart. DETAILED DESCRIPTION

[0020] The following is combined with Figure 1 -Attached Figure 2 The specific implementation of the embodiment of the present invention is described in detail. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0021] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations. In the embodiments of this application, certain software, components, models, and other existing solutions in the industry may be mentioned. These should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but it does not mean that the applicant has or will necessarily use such solutions.

[0022] During the process of realizing the present invention, the inventors of the present application discovered that the evaluation of the contaminated soil remediation effect in the existing technology mainly relies on the pollutant removal rate, but ignores the energy consumption and carbon emissions during the remediation process, resulting in an incomplete evaluation, which will cause the problems of low contaminated soil remediation efficiency and high remediation energy consumption.

[0023] Example 1

[0024] Reference Figure 1-Figure 2 This is the first embodiment of the present invention, which provides a method for evaluating contaminated site soil remediation. This method is primarily used to dynamically evaluate the effectiveness of ex situ thermal desorption technology in remediating contaminated soil. In particular, it uses data such as temperature control efficiency, carbon emissions, and carbon-temperature synergy efficiency to assess the energy efficiency and carbon emissions during the contaminated soil remediation process. The evaluation results of this solution can serve to optimize the thermal desorption process, thereby achieving optimal contaminated soil remediation results. This solution includes:

[0025] S100: Collect soil remediation related data and perform preprocessing.

[0026] 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 steps:

[0027] 1. Excavation: Use excavators and other equipment to dig out the contaminated soil, and carry out foundation pit support and groundwater removal at the same time.

[0028] 2. Infrastructure construction: level and harden the site, build material loading and unloading sheds, lay high-density polyethylene film (HDPE film) and take other anti-seepage measures.

[0029] 3. Transportation: The excavated contaminated soil will be transported to the feed shed via closed transport vehicles.

[0030] 4. Pretreatment: Crush, screen and adjust the moisture content of the contaminated soil in a negative pressure shed to meet the feed requirements.

[0031] 5. Thermal desorption: The contaminated soil is transported to the rotary kiln through a feeder and a conveyor, heated to the set temperature by natural gas and maintained for a certain period of time, and then sent to the discharge bin to separate the pollutants from the soil in the rotary kiln.

[0032] 6. Tail gas treatment: The tail gas is discharged after meeting the standards through dust removal, purification, spray cooling, activated carbon adsorption and other processes.

[0033] 7. Wastewater treatment: Wastewater generated during the restoration process is collected and transported to the on-site water treatment system, and discharged into the municipal pipeline network after treatment meets the standards.

[0034] 8. Backfill: The soil after thermal desorption treatment will be transported to a temporary storage area after passing the test and then backfilled to the original site.

[0035] Specifically, temperature, pressure, concentration sensors and infrared thermal imagers are deployed according to process requirements, and energy consumption monitoring sensors (such as natural gas flow meters and electricity meters) are laid out. Three groups of temperature sensor arrays are arranged axially in the rotary kiln (kiln head / middle section / tail), and infrared thermal imagers are added at the preheater outlet and secondary air duct to generate a thermal distribution map of the material in the kiln; further, the sensor uploads data such as temperature (kiln head / middle section / tail), pressure, and pollutant concentration in the exhaust gas, and simultaneously records energy data such as natural gas consumption and electricity consumption. Through sliding window denoising and outlier detection (such as 3 The collected data is preprocessed by methods such as missing value interpolation to obtain real-time time series data streams, which include temperature, pollutant concentration in exhaust gas, feed rate, moisture content, gas flow rate, etc.

[0036] Preferably, by arranging a temperature sensor array and combining it with an infrared thermal imager to generate a material thermal distribution map, a three-dimensional monitoring of the heat conduction process can be achieved, thereby avoiding the error of a single sensor. The standard outlier detection and missing value interpolation eliminate sensor noise and abnormal fluctuations, ensure the data reliability of subsequent carbon emission calculations, desorption efficiency evaluation and temperature model prediction, and avoid distortion of evaluation conclusions due to data deviation.

[0037] S200: Calculate the average carbon emissions and desorption efficiency for the current period based on the pretreated soil remediation related data.

[0038] S210: Calculate the average carbon emissions for the current period.

[0039] Specifically, the real-time carbon emissions are calculated according to the IPCC emission factor method; 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 emissions of the current time period are calculated.

[0040] Furthermore, according to the IPCC (Intergovernmental Panel on Climate Change) emission factor method, real-time carbon emissions are calculated in real time. The calculation expression of real-time carbon emissions is as follows:

[0041]

[0042] in, is the carbon emission at time t, is the activity data at time t, indicating the input 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 derived from the relevant data of soil remediation after pretreatment; is the emission factor, which represents 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).

[0043] 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).

[0044] Preferably, the IPCC emission factor method is used to dynamically adjust the calculation based on the real-time carbon intensity of the power grid, avoiding the ±15% error caused by traditional static factors and reflecting the actual carbon emissions.

[0045] S220: Calculate the desorption efficiency.

[0046] Specifically, the calculation expression of desorption efficiency is as follows:

[0047]

[0048] in, represents the cumulative desorption efficiency at time t, represents the initial soil pollutant concentration detected by the laboratory, express The concentration of pollutants in the exhaust gas at the moment, express Exhaust flow rate at any moment, Indicates the total amount of contaminated soil treated, in kg.

[0049] 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). A lower value (<70%) indicates that the desorption temperature is insufficient, the residence time is insufficient, or the pollutants are not completely released.

[0050] Desorption efficiency provides real-time feedback on process effectiveness. When the temperature is less than 70%, the temperature / residence time adjustment is triggered to avoid ineffective energy consumption.

[0051] when When it is less than 70%, it indicates that the temperature is insufficient or the residence time is insufficient, which provides a basis for parameter adjustment (such as increasing the heating temperature or extending the treatment time). It is an important parameter for evaluating the desorption effect of pollutants.

[0052] S230: Based on the preprocessed soil remediation related data, a temperature prediction sub-model and a phase change characteristic sub-model are constructed, and the output of the temperature prediction sub-model is integrated with the output of the phase change characteristic sub-model to obtain the target temperature.

[0053] Specifically, the preprocessed soil remediation-related data are divided into training set, validation set and test set; a temperature prediction sub-model architecture is built, the training set is used to train the temperature prediction sub-model, the validation set is used to tune the parameters of the trained temperature prediction sub-model, and the test set is used to evaluate the temperature prediction sub-model after parameter tuning; the parameters of the phase change characteristic sub-model are determined, and the phase change characteristic sub-model is constructed.

[0054] It should be noted that the temperature prediction submodel is used to predict the material temperature within a thermal desorption unit, such as a rotary kiln. Specifically, it includes the temperature distribution at the head, middle, and tail of the rotary kiln. This temperature prediction submodel dynamically predicts the target temperature within the thermal desorption unit over a period of time, such as the next 10 minutes, to guide real-time adjustments to the heating control system (e.g., gas valve opening, electric heating power, etc.). Parameter tuning primarily focuses on the hyperparameters and training strategy of the temperature prediction submodel, including model structure parameters and training strategy parameters.

[0055] Furthermore, the temperature prediction sub-model uses a three-layer LSTM network (Long Short-Term Memory Network) as its core structure to predict theoretical temperature requirements. The input layer receives real-time time series data streams, which are divided into a training set of 70%, a validation set of 20%, and a test set of 10%. The loss function is mean square 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.

[0056] Optimally, in temperature prediction for soil remediation, 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 lag effects (such as heat conduction delay) of 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 latency of 8ms, can filter sensor noise (±2°C fluctuations) while meeting the real-time control requirements of embedded devices. It avoids the high computational load of Transformer (2.1 million parameters) and the long-term dependence defects of CNN (Convolutional Neural Network), becoming the preferred solution with both accuracy, efficiency, and engineering adaptability. Specific data are shown in Table 1.

[0057] Table 1 Temperature prediction sub-model performance comparison

[0058]

[0059] Furthermore, after determining the parameters of the phase change characteristic sub-model, the phase change characteristic sub-model is constructed. The expression of the phase change characteristic sub-model is as follows:

[0060]

[0061] in, represents the phase transition temperature corresponding to the pollutant concentration in the soil, represents the initial phase transition temperature, represents the complete phase transition temperature, Indicates the concentration of pollutants remaining in the soil. is the lower limit of concentration normalization, is the upper limit of concentration normalization, is the phase transition index, which is used to control the nonlinearity of the concentration-temperature curve. >1 indicates accelerated phase transition, <1 indicates that the phase transition is decelerated.

[0062] Furthermore, under ideal conditions, the concentration of pollutants remaining in the soil = the amount of pollutant residue in the soil ÷ the total amount of polluted soil treated;

[0063] Residual amount of soil pollutants = total amount of initial soil pollutants - cumulative release of exhaust pollutants;

[0064] Total initial soil pollutant amount = initial soil pollutant concentration × total mass of contaminated soil;

[0065] The calculation formula for the residual pollutant concentration in the soil is as follows:

[0066]

[0067] in, Indicates the concentration of pollutants remaining in the soil. represents the initial soil pollutant concentration detected by the laboratory, express The concentration of pollutants in the exhaust gas at the moment, express Exhaust flow rate at any moment, represents the total amount of contaminated soil treated, and t here represents the target time point.

[0068] It should be noted that the phase transition in this scheme refers to the transformation of pollutants from an adsorbed state (solid phase) to a free state (gas phase). By heating the soil, pollutants overcome their adsorption forces (physical adsorption or chemical bonding) to soil particles and are released from the soil matrix into the gas phase. The temperature required to trigger effective desorption at different pollutant concentrations is the phase transition temperature. The initial phase transition temperature is determined through experimental measurements, theoretical calculations, and historical remediation project data. The complete phase transition temperature is determined by experimental data demonstrating complete desorption of pollutants or actual temperature records of complete desorption achieved during historical remediation. The upper and lower limits of concentration normalization are set based on the actual measured pollutant concentration range. The lower limit is the safety threshold for the remediation target or the lowest effective concentration in historical data, while the upper limit is the maximum initial concentration at the contaminated site or the upper limit of the technical treatment allowed. The phase transition index is determined by fitting historical data (concentration-temperature curves) through nonlinear regression. The phase transition characteristic submodel quantifies the thermodynamic process of pollutant desorption from soil and dynamically adjusts the target temperature by calculating the phase transition temperature and phase transition completion, ensuring efficient pollutant desorption with minimal energy consumption.

[0069] Furthermore, the phase change temperature corresponding to the pollutant concentration is calculated; the current temperature control weight is dynamically output using fuzzy control; the thermal hysteresis compensation amount is set; and based on the current temperature control weight and the thermal hysteresis compensation amount, the output of the temperature prediction sub-model and the output of the phase change characteristic sub-model are weighted and fused to obtain the target temperature.

[0070] Specifically, the output of the temperature prediction sub-model is fused with the output of the phase change characteristic sub-model to obtain the target temperature. The calculation expression of the target temperature is as follows:

[0071]

[0072] in, is the target temperature at time t, used to guide the temperature control of the thermal desorption process, is the current temperature control weight, The temperature value predicted by the temperature prediction sub-model, represents the phase transition temperature corresponding to the pollutant concentration in the soil, It is the thermal hysteresis compensation amount, which is used to compensate for the inherent hysteresis of the device.

[0073] It should be noted that the thermal hysteresis compensation Determined by a combination of step response testing and equipment heat transfer modeling.

[0074] Furthermore, the coupling weight in the output fusion formula Adopt fuzzy control adjustment.

[0075]

[0076]

[0077]

[0078]

[0079] in, is the current temperature control weight, is the weight adjustment amount, Adjust the temperature weight for the next moment, represents the fuzzy controller function, is the temperature control error, is the target temperature at time t, To measure temperature in real time, represents the concentration change rate, Indicates the concentration of pollutants remaining in the soil. is a truncated function, where t represents the current moment.

[0080] Preferably, when the measured temperature is lower than the target temperature and the desorption efficiency decreases, it means that the pollutants are not effectively desorbed and the system response is insufficient, and the fuzzy controller will reduce the (Reduce reliance on prediction models and rely more on empirical phase transition temperature); when the temperature is high and the concentration decreases slowly, it means that the pollutants have basically escaped and the system is overheated, and the fuzzy controller will increase (Increase trust in the prediction model and reduce the weight of phase transition temperature).

[0081] Preferably, by dynamically adjusting , which can automatically adjust the degree of dependence on the LSTM prediction model and the phase change physical model; dynamically correct the weights through fuzzy rules to avoid the failure of a single model; when the grid emission factor changes dynamically, the weight adjustment can also reduce the error rate of evaluation indicators (such as temperature-carbon efficiency ratio).

[0082] Calculating the target temperature is a key step in evaluating the effectiveness of soil remediation at contaminated sites. Its dynamic calculation enhances the accuracy and authenticity of the final evaluation's multi-dimensional metrics (such as desorption efficiency and energy efficiency). By integrating the outputs of the temperature prediction sub-model with the physical model to obtain the target temperature, the evaluation metrics are more closely aligned with actual process requirements. Dynamic adjustment of the target temperature, combined with the regional energy structure and phase change characteristic sub-models, enhances the technology's universality. The target temperature calculation logic (e.g., fuzzy rules and phase change model parameters) provides a clear basis for evaluation results and supports decision optimization. Through intelligent calculation of the target temperature, the final evaluation evolves from a traditional single-outcome assessment to a multi-dimensional dynamic optimization system encompassing energy efficiency, environmental protection, and economic performance, providing scientific and quantifiable support for soil remediation at contaminated sites.

[0083] S231: Calculate the temperature control efficiency index and the temperature-to-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.

[0084] Furthermore, the actual temperature at the current moment and the target temperature at the current moment are obtained; the absolute error between the actual temperature at the current moment and the target temperature at the current moment is calculated; the absolute error is normalized and the temperature control efficiency index is calculated; and the temperature carbon efficiency ratio is calculated based on the target temperature at the current moment and the carbon emissions at the current moment.

[0085] Specifically, the temperature control efficiency index is calculated. The calculation expression of the temperature control efficiency index is as follows:

[0086]

[0087] in, represents the temperature control energy efficiency index, is the measured temperature at time t, is the target temperature at time t.

[0088] Preferably, the normalized absolute error is used to directly reflect the responsiveness of the temperature control system. The closer the index is to 1 (e.g., ≥0.95), the smaller the deviation between the actual temperature and the target temperature, and the higher the process stability.

[0089] Furthermore, the temperature carbon efficiency ratio (TCER) is calculated. The calculation expression of the temperature carbon efficiency ratio is as follows:

[0090]

[0091] in, is the temperature carbon efficiency ratio at time t, is the target temperature at time t, is the carbon emission at time t.

[0092] It should be noted that The higher it is, the higher the target temperature can be supported per unit of carbon emissions, and the better the energy efficiency. The lower the value, the higher the carbon emissions but insufficient temperature increase, which is inefficient.

[0093] Furthermore, 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, is obtained, the actual measured temperature at the current moment and the initial phase change temperature are obtained, and the phase change completion degree is calculated based on the output of the phase change characteristic sub-model at the current moment, the actual measured temperature at the current moment and the initial phase change temperature.

[0094] Specifically, the calculation expression of phase change completion is as follows:

[0095]

[0096] in, Indicates the phase transition completion, represents the phase transition temperature corresponding to the pollutant concentration in the soil, represents the initial phase transition temperature, is the measured temperature at time t.

[0097] It should be noted that the calculation formula for phase change completion is only used for < When ≥ When the temperature reaches the target or exceeds the target, the phase change completion degree is 100%, and the completion degree will not be improved even if the temperature continues to rise.

[0098] Preferably, the phase change completion can reveal the actual progress of pollutant desorption. When the desorption efficiency is high but the phase change completion is low, it can expose the contradiction between the concentration monitoring or the phase change model parameters. The phase change completion and desorption efficiency reflect the pollutant desorption effect from two dimensions: the thermodynamic model and the actual monitoring data. When there is a large deviation between the two, such as the actual monitoring data shows that the desorption is sufficient, but the thermodynamic model shows that the desorption is not as expected, it may be that the pollutant aerosol escapes and is not detected by the exhaust gas detection-related sensors, or the phase change characteristic sub-model parameters are inaccurate and do not match the actual thermodynamic characteristics of the pollutants. At this time, the exhaust gas detection part is checked first. If the exhaust gas detection part is correct, the online fast calorimetry system (Fast-Scanning Calorimeter, FSC) is used to obtain the phase change enthalpy data in real time when the rotary kiln is in operation, and the phase change characteristic sub-model parameters are dynamically updated through the adaptive algorithm. When the thermodynamic model determines that desorption is sufficient but the actual monitoring data shows insufficient desorption, it may be due to fluctuations in soil moisture content leading to a decrease in the effective heat transfer coefficient, uneven distribution of the temperature field in the rotary kiln resulting in cold areas, etc. In this case, first check the temperature field distribution data of the infrared thermal imager and optimize the rotary kiln speed and inclination angle; if the temperature field is uniform, the phase change index needs to be corrected. .

[0099] S300: Evaluate the soil remediation effect of contaminated sites based on the carbon emission baseline, average carbon emissions in the current period, desorption efficiency, phase change completion, temperature control efficiency index and temperature-carbon efficiency ratio.

[0100] S310: The carbon emissions baseline is constructed based on historical data and grid emission factors.

[0101] Furthermore, the carbon emission deviation rate is calculated based on the carbon emission baseline and the average carbon emissions in the current period, and the thermal desorption carbon-temperature synergy efficiency is evaluated based on the carbon emission deviation rate and the temperature-carbon efficiency ratio.

[0102] It should be noted that the thermal desorption carbon-temperature synergy efficiency is a comprehensive evaluation indicator used to measure the level of synergistic optimization between temperature control accuracy and low-carbon remediation. Its core lies in quantifying the effective thermal desorption temperature control capability supported by unit carbon emissions, and evaluating whether the remediation technology can achieve efficient desorption of pollutants at the lowest carbon cost, so as to evaluate the green level of soil remediation technology for contaminated sites.

[0103] Specifically, the calculation expression of carbon emission deviation rate is as follows:

[0104]

[0105] in, Indicates the carbon emission deviation rate of the current period t, which indicates the degree of deviation of the average carbon emissions of the current period from the benchmark value. It represents the average carbon emissions in the current period t, which is calculated based on the arithmetic mean of the real-time carbon emissions after preprocessing. Indicates the carbon emission baseline value, which is the carbon emission amount used as a comparison benchmark.

[0106] It should be noted that when When <0, it means that under the same restoration effect, the current carbon emissions are lower than the carbon emission baseline, that is, the carbon emissions per unit soil restoration amount are reduced. When it is >0, it means that under the same restoration effect, the current carbon emissions are higher than the carbon emission baseline, that is, the carbon emissions per unit soil restoration amount increase.

[0107] Furthermore, the comprehensive energy efficiency index is calculated and the thermal desorption carbon temperature synergy efficiency is evaluated by the comprehensive energy efficiency index.

[0108] Specifically, the calculation expression of the comprehensive energy efficiency index is as follows:

[0109]

[0110] in, represents the comprehensive energy efficiency index, and are weight coefficients, represents the carbon emission deviation rate in the current period t, is the temperature-carbon efficiency ratio at time t.

[0111] It should be noted that The larger the value, the higher the thermal desorption carbon temperature synergy efficiency.

[0112] Preferably, by calculating the comprehensive energy efficiency index, the carbon emission and energy efficiency indicators are integrated, avoiding the limitations of a single indicator. Through a clear mathematical formula, a reproducible evaluation standard is formed to avoid the evaluation differences caused by reliance on manual experience. By adapting to different grid emission factors and adjusting the weight coefficient, the efficient use of clean energy can be prioritized. In areas with a high proportion of hydropower, the , it is allowed to increase energy consumption appropriately to improve the repair effect (such as extending the high temperature period). At this time, even if the energy consumption is slightly higher, the total carbon emissions can still be controlled due to the low carbon intensity of the power grid. In areas with a high proportion of thermal power, it can be adjusted higher. , strictly control fossil energy carbon emissions, and under high-carbon power grids, it is necessary to give priority to reducing absolute carbon emissions, even if it means sacrificing some temperature efficiency (such as shortening the residence time in the high-temperature section). This makes the evaluation of the restoration 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, it is always necessary to ensure that Comparing the traditional evaluation method with the evaluation of this solution, we get Table 2.

[0113] Table 2 Comparison of experimental data on carbon footprint and energy efficiency synergistic optimization of thermal desorption remediation

[0114]

[0115] Furthermore, by comparing the key indicators of the three dimensions of carbon emission efficiency, energy utilization efficiency and collaborative optimization capability between the traditional thermal desorption remediation technology and the solution of the present invention, it was demonstrated that the carbon emission efficiency of the present invention is better than that of the traditional evaluation method, reducing the carbon emission calculation error rate from ±20% to ±5%, and reducing the carbon intensity per unit pollutant 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 collaborative optimization capability, the carbon-temperature collaborative efficiency (CEEI) index of the present invention reaches 0.78 (A-level standard ≥0.6); all three dimensions have been improved.

[0116] Furthermore, the pollutant desorption effect was evaluated based on the desorption efficiency, phase change completion and temperature control efficiency index.

[0117] Specifically, a graded evaluation was conducted based on the desorption efficiency, phase change completion, and temperature control efficiency index, as shown in Table 3.

[0118] Table 3, Pollutant Desorption Effect Grading Evaluation Table

[0119] Preferably, a graded evaluation based on desorption efficiency, phase change completion and temperature control efficiency index can avoid misjudgment caused by traditional single indicators and improve the accuracy of the evaluation. The evaluation here is a dynamic evaluation, and the evaluation results can support quick decision-making.

[0120] The present invention also provides a contaminated site soil remediation assessment system for implementing a contaminated site soil remediation assessment method. The system includes a control module, which includes a memory, a processor, and a computer program stored in the memory and runnable on the processor. The processor executes the computer program to implement the contaminated site soil remediation assessment method.

[0121] An embodiment of the present invention provides a storage medium having a program stored thereon, which implements the contaminated site soil remediation assessment method when executed by a processor.

[0122] An embodiment of the present invention provides a processor, which is used to run a program, wherein the contaminated site soil remediation assessment method is executed when the program is run.

[0123] An embodiment of the present invention provides a device comprising a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, a method for assessing soil remediation at a contaminated site is implemented. The device herein may be a server, a PC, a PAD, a mobile phone, or the like.

[0124] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing the program steps of the initialization contaminated site soil remediation assessment method.

[0125] Those skilled in the art will appreciate that the embodiments of the present application may provide methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, 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 magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0126] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes 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 a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0127] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0128] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0129] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0130] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0131] 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.

[0132] 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.

[0133] 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 assessing soil remediation at a contaminated site, characterized in that: include: Collect soil remediation related data and perform pre-processing; Calculate the average carbon emissions and desorption efficiency of the current period based on the soil remediation related data after pretreatment; Based on the pre-processed soil remediation related data, the temperature prediction sub-model and the phase change characteristic sub-model are constructed. The expression of the phase change characteristic sub-model is as follows: in, represents the phase transition temperature corresponding to the pollutant concentration in the soil, represents the initial phase transition temperature, represents the complete phase transition temperature, Indicates the concentration of pollutants remaining in the soil. is the lower limit of concentration normalization, is the upper limit of concentration normalization, is the phase transition index, which is used to control the nonlinearity of the concentration-temperature curve. >1 indicates accelerated phase transition, <1 indicates phase transition deceleration; fusing the output of the temperature prediction sub-model with the output of the phase change characteristic sub-model to obtain a target temperature; According to the target temperature, the temperature control efficiency index and the temperature-carbon efficiency ratio are calculated. According to the output of the phase change characteristic sub-model, the phase change completion degree is calculated. The calculation expression of the phase change completion degree is as follows: in, Indicates the phase transition completion, represents the phase transition temperature corresponding to the pollutant concentration in the soil, represents the initial phase transition temperature, is the measured temperature at time t; Evaluate 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; The carbon emission baseline is constructed based on historical data and power grid emission factors.

2. The contaminated site soil remediation assessment method according to claim 1, characterized in that: The calculation of the average carbon emissions and desorption efficiency for the current period based on the pre-treated soil remediation related data includes: Based on the pre-treated soil remediation data, the real-time carbon emissions are calculated according to the IPCC emission factor method; Set the time period and calculate the arithmetic mean of real-time carbon emissions; The average carbon emissions for the current period are calculated based on the arithmetic mean of the real-time carbon emissions and the length of the period.

3. The contaminated site soil remediation assessment method according to claim 1, characterized in that: The temperature prediction sub-model and phase change characteristic sub-model are constructed based on the pre-processed soil remediation related data, including: Divide the preprocessed soil remediation related data into training set, validation set and test set; Building a temperature prediction sub-model architecture, training the temperature prediction sub-model using a training set, optimizing parameters of the trained temperature prediction sub-model using a validation set, and evaluating the optimized temperature prediction sub-model using a test set; The parameters of the phase change characteristic sub-model are determined, and the phase change characteristic sub-model is constructed.

4. The contaminated site soil remediation assessment method according to claim 3, characterized in that: The temperature prediction sub-model adopts a three-layer long short-term memory network structure.

5. The contaminated site soil remediation assessment method according to claim 1, characterized in that: The output of the temperature prediction sub-model is integrated with the output of the phase change characteristic sub-model to obtain a target temperature, including: Calculate the phase transition temperature corresponding to the pollutant concentration; Use fuzzy control to dynamically output the current temperature control weight; Set the thermal hysteresis compensation amount; According to the current temperature adjustment weight and the thermal hysteresis compensation amount, the output of the temperature prediction sub-model and the output of the phase change characteristic sub-model are weightedly fused to obtain the target temperature.

6. The contaminated site soil remediation assessment method according to claim 1, characterized in that: Calculating the temperature control efficiency index and the temperature-to-carbon efficiency ratio according to the target temperature includes: Get the current measured temperature and the current target temperature; Calculating the absolute error between the measured temperature at the current moment and the target temperature at the current moment; Normalizing the absolute error to calculate the temperature control efficiency index; The temperature-carbon efficiency ratio is calculated according to the target temperature at the current moment and the carbon emissions at the current moment.

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

9. The contaminated site soil remediation assessment method according to claim 8, characterized in that: The method of evaluating the soil remediation effect of a contaminated site based on the carbon emission baseline, the average carbon emissions in the current period, the desorption efficiency, the phase change completion, the temperature control efficiency index and the temperature-carbon efficiency ratio also includes: evaluating the pollutant desorption effect based on the desorption efficiency, the phase change completion and the temperature control efficiency index.

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

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