Method for measuring and calculating concentration of sulfate radical in mine water in goaf driven by release kinetics

Abstract

The invention provides a method for measuring and calculating the concentration of sulfate radicals in goaf mine water driven by release kinetics. According to the method, the concentration of the sulfate radicals in the goaf mine water is measured and calculated by adopting a release kinetics driven model. According to the method, the source and formation mechanism of the sulfate radicals in the mine water are fully considered, the concentration of the sulfate radicals in the mine water in the goaf is predicted on the basis of the mineral dissolution and release dynamics principle, the prediction method is developed and formed, and the method can be directly popularized and applied to a coal mine site. According to the predicted sulfate radical concentration, a suitable mine water sulfate radical treatment process can be accurately selected, operation parameters can be determined, the change trend of the sulfate radical concentration in the mine water can be mastered through measurement and calculation, corresponding measures are taken to cope with fluctuation, and efficient removal and comprehensive utilization of the mine water sulfate radicals are ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of mine management, relates to goaf mine water, and particularly relates to a method for measuring sulfate concentration in goaf mine water driven by release dynamics. Background Art

[0002] The mine water in the western mining area is mostly highly mineralized, with total dissolved solids (TDS) greater than 1000 mg / L, exceeding the Class III limit of the "Groundwater Quality Standard" and the "Drinking Water Standard".

[0003] The main ions in highly mineralized mine water in western China include sodium, chloride, calcium, and sulfate. Sulfate is a key indicator of concern in the Groundwater Quality Standard and the Drinking Water Standard, with a limit of 250 mg / L. Excessive sulfate levels in mine water can affect soil microbial activity, disrupt soil ecological balance, and hinder plant growth if directly discharged. Untreated use in production can corrode pipeline systems. Untreated direct consumption for drinking or daily life can burden the kidneys, and long-term accumulation can lead to chronic diseases such as kidney disease. Therefore, detecting and estimating sulfate concentrations based on the primary sources of sulfate in mine water is crucial for the treatment and utilization of highly mineralized sulfate-containing water.

[0004] Sulfate mainly comes from the dissolution and release of minerals such as gypsum and pyrite in coal-bearing rock strata. Coal mining disturbance accelerates water-rock interaction, leading to further increase in concentration. High mineralization and excessive sulfate concentration will directly affect the ecological environment and human health.

[0005] The dissolution and release of minerals such as gypsum and pyrite in coal-bearing rock formations form sulfate in coal-bearing groundwater. Under the influence of coal mining disturbance, groundwater containing sulfate rushes into the coal mining face and goaf, forming mine water containing sulfate. In the goaf, the roof rock breaks and collapses, and the groundwater and rock further interact with each other, causing the concentration of sulfate in the mine water to increase further. The dissolution and release of sulfate-containing minerals in the water-rock interaction process is driven by the release force, which directly determines the concentration of sulfate in the mine water.

[0006] However, the existing technology lacks a convenient and accurate method for predicting sulfate concentration, which makes it difficult to guide the optimization of mine water treatment processes and resource utilization. Summary of the Invention

[0007] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a method for measuring the sulfate concentration in mine water in the goaf driven by release dynamics, so as to solve the technical problem that the accuracy of the sulfate concentration prediction method in the existing technology needs to be further improved.

[0008] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0009] The invention discloses a method for calculating sulfate concentration in goaf mine water driven by release kinetics. The method adopts a release kinetics driven model to calculate sulfate concentration in goaf mine water.

[0010] The construction process of the release dynamics driving model is as follows:

[0011] Step S601, setting parameters:

[0012] The parameters include sulfate concentration C in water, water flow rate Q(t), initial mineral surface area S0, surface reaction rate constant k, sulfate saturation concentration C sat and the time delay factor t.

[0013] Step S602: Release dynamics driving model construction:

[0014] The expression of the release kinetics driving model is:

[0015]

[0016] Where:

[0017] C represents the sulfate concentration in water, unit: mg / L;

[0018] C sat Indicates the saturated concentration of sulfate in water, unit: mg / L;

[0019] ρ represents the mineral bulk density, unit: g / L;

[0020] t represents the time delay factor, unit: day;

[0021] k represents the surface reaction rate constant, unit is: L / (m 2 ·sky);

[0022] S0 represents the initial mineral surface area, unit: m 2 / g;

[0023] α represents the cumulative rate of fragmentation, unit: 1 / day;

[0024] Q represents the dilution rate constant, and its unit is 1 / day.

[0025] The present invention also has the following technical features:

[0026] The method specifically comprises the following steps:

[0027] Step S1, build a test device:

[0028] To simulate the hydrogeological conditions of the goaf, a water-rock reaction device was built, equipped with a pumping system that can adjust the water flow rate, and typical coal-bearing rock samples were selected and crushed into different particle sizes to form a test device. The test device is used to simulate the degree of rock collapse.

[0029] Step S2, setting test parameters:

[0030] Set multiple groups of water flow rate, temperature and rock fragmentation.

[0031] Step S3, data collection and recording:

[0032] Set the sampling frequency and test indicators, record the sampled data and the set indicators in a standardized spreadsheet, and attach a test condition label.

[0033] Step S4, quality control:

[0034] Standard samples were used for instrument calibration and verification, blank control groups were set up to eliminate background interference, abnormal data points were marked and retested to ensure that the error was ≤5%.

[0035] Step S5, data preprocessing:

[0036] The data were cleaned and Z-score normalization was used for multi-dimensional parameters.

[0037] Step S6: Release dynamics driving model construction.

[0038] Step S7, release dynamics driving model verification and optimization:

[0039] Step S701, verification:

[0040] The release kinetics driving model constructed in step S6 was verified using the root mean square error, mean relative error and Nash efficiency coefficient as verification indicators.

[0041] Step S702, optimization:

[0042] Regularization is performed on the release dynamics driving model verified in step S701 to obtain an optimized release dynamics driving model.

[0043] Step S8, sulfate concentration calculation:

[0044] The release kinetics driving model optimized in step S7 is used to calculate the change in sulfate concentration.

[0045] Compared with the prior art, the present invention has the following technical effects:

[0046] (I) The method of the present invention fully considers the source and formation mechanism of sulfate in mine water, predicts the concentration of sulfate in goaf mine water based on the principle of mineral dissolution and release kinetics, and develops a prediction method that can be directly promoted and applied on-site in coal mines.

[0047] (II) Based on the predicted sulfate concentration, the present invention can accurately select an appropriate mine water sulfate treatment process and determine the operating parameters. Moreover, through measurement, the changing trend of the sulfate concentration in the mine water can be grasped, and corresponding measures can be taken to cope with the fluctuation, thereby ensuring the efficient removal and comprehensive utilization of the sulfate in the mine water.

[0048] The specific contents of the present invention are further described in detail below with reference to the embodiments. DETAILED DESCRIPTION

[0049] It should be noted that, unless otherwise specified, all devices, components, equipment and methods in the present invention are components, equipment and methods known in the prior art.

[0050] In accordance with the above technical solution, specific embodiments of the present invention are given below. It should be noted that the present invention is not limited to the following specific embodiments, and all equivalent changes made on the basis of the technical solution of this application fall within the protection scope of the present invention.

[0051] Example:

[0052] This embodiment provides a method for measuring sulfate concentration in goaf mine water driven by release kinetics, characterized in that the method comprises the following steps:

[0053] Step S1, build a test device:

[0054] To simulate the hydrogeological conditions of the goaf, a water-rock reaction device was built, equipped with a pumping system that can adjust the water flow rate, and typical coal-bearing rock samples were selected and crushed into different particle sizes to form a test device. The test device is used to simulate the degree of rock collapse.

[0055] In this embodiment, the water-rock reaction device adopts a water-rock reaction device known in the art. The specific core components and materials of the water-rock reaction device are shown in Table 1.

[0056] Table 1 Specific core components and materials of the water-rock reaction device

[0057]

[0058]

[0059] In this embodiment, the pumping system adopts a pumping system known in the art.

[0060] In this embodiment, the typical coal-bearing rock samples are typical coal-bearing rock samples known in the art.

[0061] Step S2, setting test parameters:

[0062] Multiple sets of water flow rate (Q(t) gradient: 0.5-5 L / min), temperature (10-30°C) and rock fragmentation (particle size 0.5-5 cm) were set.

[0063] In this embodiment, each group of experiments was repeated three times to ensure data reliability. The specific settings are shown in Table 2 below.

[0064] Table 2 Test parameter settings

[0065]

[0066] Step S3, data collection and recording:

[0067] Set the sampling frequency and test indicators, record the sampled data and the set indicators in a standardized spreadsheet, and attach a test condition label.

[0068] The specific process of step S3 is as follows: a parameter water quality probe is installed at the water outlet of the reaction vessel. The parameter water quality probe monitors pH, dissolved oxygen (DO), and electrical conductivity (EC) in real time. The data acquisition frequency is once per minute. The flow rate Q(t) is recorded in real time by the flow meter, and the data is synchronized to the PLC controller. The temperature T is collected by a PT100 temperature sensor, and the constant temperature water bath is automatically adjusted. The signals of the parameter water quality probe, flow meter, and temperature sensor are transmitted to the PLC controller via the RS485 / Modbus protocol. The PLC controller uploads the data in real time to the host computer installed with LabVIEW or Python data acquisition software via Ethernet.

[0069] In step S3 of this embodiment, data on changes in sulfate concentration during the interaction between fine sandstone and water over a period of time are collected and recorded. Specific experimental data are shown in Table 3.

[0070] Table 3 Variation of sulfate concentration during the interaction between fine sandstone and water measured in the application example

[0071]

[0072]

[0073] Based on the test data shown in Table 3, the sulfate concentration in fine sandstone was calculated.

[0074] Step S4, quality control:

[0075] Standard samples were used for instrument calibration and verification, blank control groups were set up to eliminate background interference, abnormal data points were marked and retested to ensure that the error was ≤5%.

[0076] Specifically, in this embodiment, the instrument calibration and verification are as follows: calibration with a standard sulfate solution is used to verify the linear response of the ion chromatograph; the flow rate is measured using a standard graduated cylinder, the corresponding relationship between the pump speed and the flow rate is adjusted, and the flow meter is calibrated; the data of the temperature sensor is compared with the laboratory standard thermometer, and recalibration is performed when the deviation exceeds ±0.5°C.

[0077] Specifically, in this embodiment, real-time monitoring and abnormality detection are performed: pH, dissolved oxygen, and conductivity parameters are monitored, and alarm thresholds of online sensors are set; flow fluctuations are recorded every 30 minutes, with a tolerance of ±5%. If the limit is exceeded, the test is suspended and the pumping system is checked to ensure flow stability.

[0078] In this embodiment, the differential method is used in step S4 to calibrate the parameters in Table 4.

[0079] Table 4 Parameter calibration using the differential method

[0080]

[0081] Based on the test data shown in Table 3, numerical integration and error analysis are carried out. Specifically:

[0082] Preferably, take the data of the 4th day as an example:

[0083] Rate equation:

[0084] dC / dt=0.45*3.2*(1+0.03*4)*(235-C)-0.2*C;

[0085] dC / dt=1.44*1.12*(235-C)-0.2C=1.61*(235-C)-0.2*C.

[0086] Numerical integration: Cmodel(4)≈119.8mg / L.

[0087] The relative error is: Error = (119.8-118.04) / 118.04*100%≈1.5%.

[0088] Preferably, take the data on the 14th day as an example:

[0089] Rate equation:

[0090] dC / dt=0.45*3.2*(1+0.03*4)*(235-C)-0.2*C;

[0091] dC / dt=1.44*1.42*(235-C)-0.2C=2.04*(235-C)-0.2*C.

[0092] Numerical integration: Cmodel(14)≈176.3 mg / L.

[0093] The relative error is: Error = (176.3-178.26) / 178.26*100%≈1.1%.

[0094] Step S5, data preprocessing:

[0095] The data were cleaned and Z-score normalization was used for multi-dimensional parameters (such as Q(t), temperature, and fragmentation).

[0096] Step S6, release dynamics driving model construction:

[0097] The construction process of the release dynamics driving model is as follows:

[0098] Step S601, setting parameters:

[0099] The parameters include sulfate concentration C in water, water flow rate Q(t), initial mineral surface area S0, surface reaction rate constant k, sulfate saturation concentration C sat and the time delay factor t.

[0100] Step S602: Release dynamics driving model construction:

[0101] Based on the mineral dissolution kinetics and water dilution effect, the influence of dynamic surface area is introduced, and the expression of the release kinetic driving model is obtained as follows:

[0102]

[0103] Where:

[0104] C represents the sulfate concentration in water, unit: mg / L;

[0105] C sat Indicates the saturated concentration of sulfate in water, unit: mg / L;

[0106] ρ represents the mineral bulk density, unit: g / L;

[0107] t represents the time delay factor, the unit is: 1 / day;

[0108] k represents the surface reaction rate constant, unit is: L / (m 2 ·sky);

[0109] S0 represents the initial mineral surface area, unit: m 2 / g;

[0110] α represents the cumulative rate of fragmentation, with the unit of 1 / day; it represents the linear growth of surface area over time;

[0111] Q represents the dilution rate constant, in units of 1 / day; in this embodiment, Q is calculated from the water flow rate Q(t) = 2 L / min and the water volume V = 10 L (Q = Q(t) / V = 0.2 min -1 =0.2 / (60×24) days -1 ).

[0112] Step S7, release dynamics driving model verification and optimization:

[0113] Step S701, verification:

[0114] The release kinetics driving model constructed in step S6 was verified using the root mean square error, mean relative error and Nash efficiency coefficient as verification indicators.

[0115] In this embodiment, the root mean square error is:

[0116]

[0117] Where:

[0118] RMSE stands for root mean square error;

[0119] represents the average weight;

[0120] C model (t i ) represents the sulfate concentration predicted by the model;

[0121] C exp (t i ) represents the sulfate concentration measured experimentally;

[0122] (C model (t i )-C exp (t i )) 2 represents the square of single point error;

[0123] N represents the total number of data points.

[0124] In this embodiment, the average relative error is:

[0125]

[0126] In this embodiment, the Nash efficiency coefficient is:

[0127]

[0128] Where:

[0129] NSE represents the Nash efficiency coefficient;

[0130] ∑(C exp -C model ) 2 represents the total squared error of the model prediction;

[0131] represents the total variance of the observations;

[0132] C model , which is C model (t i ), represents the sulfate concentration predicted by the model;

[0133] C exp , which is C exp (t i ), represents the sulfate concentration measured experimentally;

[0134] It represents the average value of sulfate concentration measured in the experiment.

[0135] More specifically, in this embodiment, the root mean square error (RMSE) is 4.2 mg / L, the average relative error is ≈3.8%, and the Nash efficiency coefficient (NSE) is 0.98. From these calculation results, it can be seen that the release kinetics driven model can provide a high-precision tool for the dynamic prediction of sulfate concentration.

[0136] Step S702, optimization:

[0137] Regularization is performed on the release dynamics driving model verified in step S701 to obtain an optimized release dynamics driving model.

[0138] In this embodiment, the specific method of regularization is: adding L2 regularization to prevent overfitting; L2 regularization is:

[0139] Where:

[0140] min means minimization operation;

[0141] The error is expressed as That is, the least squares phase error;

[0142] represents the L2 regularization term;

[0143] λ represents the regularization coefficient;

[0144] θ i represents the i-th parameter in the model;

[0145] Represents the square of the parameter.

[0146] Step S8, sulfate concentration calculation:

[0147] The release kinetics driving model optimized in step S7 is used to calculate the change in sulfate concentration.

[0148] In step S8, the specific measurement process is: real-time collection of water flow rate Q(t), water temperature T and rock fragmentation P, and data preprocessing according to step S5, and the sulfate concentration C in the water at the future time point is obtained after calculation by the release dynamics driven model.

[0149] Furthermore, in this embodiment, the release kinetics-driven sulfate concentration calculation method in goaf mine water is implemented using a release kinetics-driven sulfate concentration calculation system in goaf mine water, which includes a data input module, a model calculation module and a result output module.

[0150] The data input module is compatible with real-time sensor data through the API interface, pre-processes the data, and supports Excel file import.

[0151] The model calculation module is embedded in the Python backend, calls the pre-trained model parameter library, dynamically calculates the sulfate concentration, and supports exponential / Sigmoid model switching.

[0152] The result output module generates concentration-time curves, Q(t) impact heat maps, and automatically exports PDF reports containing predicted values, confidence intervals, and treatment recommendations.

[0153] The locally deployed hardware parts of the data input module, model calculation module and result output module include mining servers and edge computing devices, and the locally deployed software parts include Docker containerized deployment; the cloud deployment of the data input module, model calculation module and result output module realizes centralized management of multi-mine data through the AWS platform.

[0154] Furthermore, in this embodiment, the system also includes arranging water flow rate and temperature sensors in the test device, which are connected to the edge computing device via a data transmission link.

[0155] Furthermore, in this embodiment, the system detects sulfate concentration in real time through an ion chromatograph, and after data preprocessing, generates a sulfate concentration trend report by combining the constructed release kinetics driving model with historical data.

[0156] The present invention is based on the principle of mineral dissolution and release dynamics. By simulating the water-rock interaction environment in the goaf, the release dynamics driving model is constructed in combination with water-rock test data to dynamically predict the spatiotemporal evolution of sulfate concentration. The model parameters are calibrated through experimental data fitting and optimization algorithms, and embedded in the prediction system to achieve real-time monitoring and trend analysis. This method accurately quantifies the impact of release dynamics on concentration, with an error rate of less than 10%. It provides a scientific basis for mine water treatment process parameters, significantly reduces operating costs, and at the same time reduces the damage to the ecology caused by excessive sulfate emissions, promotes the resource utilization of mine water, and takes into account both environmental safety and economic benefits.

[0157] The present invention takes the mineral dissolution and release dynamics as the core, and realizes the dynamic prediction and active regulation of the sulfate concentration of mine water in the goaf through multi-source data fusion and intelligent algorithm driving; based on the water-rock interaction mechanism, the system combines high-precision sensor networks and laboratory simulation test data to construct a hybrid model framework: an exponential decay model is used to capture the rapid dissolution stage in the early stage, and then switches to a Sigmoid model to simulate the nonlinear equilibrium process; the model parameters are dynamically calibrated through Bayesian optimization and random forest residual correction, and embedded in edge computing devices to achieve low-latency response; after the system is deployed, the water treatment unit can be linked to adaptively adjust the operating parameters according to the prediction results, so that the sulfate concentration control error is ≤8%, and the processing energy consumption is reduced by more than 25%. It has accurate prediction, real-time regulation and ecological benefits, and provides an intelligent solution for the treatment of highly mineralized mine water.

Claims

1. A method for measuring sulfate concentration in goaf mine water driven by release dynamics, characterized in that: This method uses a release kinetics driven model to calculate the sulfate concentration in goaf mine water; The construction process of the release dynamics driving model is as follows: Step S601, setting parameters: The parameters include sulfate concentration C in water, water flow rate Q(t), initial mineral surface area S0, surface reaction rate constant k, sulfate saturation concentration C sat and the time delay factor t; Step S602: Release dynamics driving model construction: The expression of the release kinetics driving model is: Where: C represents the sulfate concentration in water, unit: mg / L; C sat Indicates the saturated concentration of sulfate in water, unit: mg / L; ρ represents the mineral bulk density, unit: g / L; t represents the time delay factor, unit: day; k represents the surface reaction rate constant, unit is: L / (m 2 ·sky); S0 represents the initial mineral surface area, unit: m 2 / g; α represents the cumulative rate of fragmentation, unit: 1 / day; Q represents the dilution rate constant, and its unit is 1 / day.

2. The method for calculating sulfate concentration in goaf mine water driven by release kinetics according to claim 1, comprising the following steps: Step S1, build a test device: Simulating the hydrogeological conditions of the goaf, a water-rock reaction device was built, equipped with a pumping system with adjustable water flow rate, and typical coal-bearing rock samples were selected and crushed to different particle sizes to form a test device used to simulate the degree of rock collapse; Its characteristics are: Step S2, setting test parameters: Set multiple groups of water flow rate, temperature and rock fragmentation; Step S3, data collection and recording: Set the sampling frequency and test indicators, record the sampled data and the set indicators in a standardized spreadsheet, and attach test condition labels; Step S4, quality control: Use standard samples for instrument calibration and verification, set up blank control groups, eliminate background interference, mark abnormal data points and re-measure to ensure that the error is ≤5%; Step S5, data preprocessing: The data were cleaned and Z-score normalization was used for multi-dimensional parameters; Step S6, release dynamics driving model construction; Step S7, release dynamics driving model verification and optimization: Step S701, verification: The release kinetics driving model constructed in step S6 was verified using the root mean square error, mean relative error, and Nash efficiency coefficient as verification indicators; Step S702, optimization: Regularizing the release dynamics driving model verified in step S701 to obtain an optimized release dynamics driving model; Step S8, sulfate concentration calculation: The release kinetics driving model optimized in step S7 is used to calculate the change in sulfate concentration.

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