Method and system for recycling industrial tail water from wet-process stone coal vanadium extraction

Abstract

The invention relates to the technical field of wastewater treatment, and discloses a method and a system for recycling industrial tail water from wet-process stone coal vanadium extraction. The method comprises the following steps: carrying out multi-stage grid filtration and carbide slag synergistic tempering on the industrial tail water; collecting a floc image of the pretreated tail water, analyzing to obtain three-dimensional characteristics of the floc, analyzing to obtain a predicted settling velocity in combination with the three-dimensional characteristics of the floc, obtaining a regulated stirring velocity in combination with a preset dynamic control strategy, stirring the tail water in the intelligent settling tank, and re-collecting real-time three-dimensional characteristics of the floc. Intelligent agent adding is carried out, and precipitation tank supernatant and vanadium-containing floc are obtained through separation; carrying out filter pressing on the vanadium-containing flocs, analyzing real-time filter pressing data, and recovering filtrate and filter residues meeting preset output conditions; performing recovery treatment on the filtrate and the filter residues through a predetermined recovery strategy; the bottleneck problems of slow floc sedimentation, difficult pollutant removal, ammonia nitrogen pollution, solid waste treatment and the like in the traditional process are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of wastewater treatment, and in particular to a method and system for recycling industrial tail water from wet-process stone coal vanadium extraction. Background Art

[0002] The wet stone coal vanadium extraction process produces highly acidic (pH < 2), high salinity, and highly toxic tail water during production, which contains heavy metals (Fe 2+ / Fe 3+ etc.), metal-like poisons (As 3+ / A 5+ 、F - ) and radioactive elements (U, Th), the traditional sodium roasting process is also accompanied by Cl2 and other waste gas pollution; tail water treatment faces multiple bottlenecks, the conventional lime neutralization method generates loose flocs, slow sedimentation, high energy consumption of filter press, As 3+ With F - Because the complexed state is difficult to remove, and the ammonium salts added during the vanadium precipitation process cause the tailwater ammonia nitrogen concentration to exceed 500mg / L, treatment costs have skyrocketed. The precipitation reaction, a core step in tailwater purification, suffers from a "black box" problem: the strong acid and high-solid environment during the reaction process causes sensor failure, making it impossible to monitor floc dynamics in real time. Manual control of parameters such as dosage and stirring intensity relies on experience, resulting in large fluctuations in floc size and settling rate. Furthermore, the quantitative relationship between variables such as pH and ionic strength and floc dynamics is unclear, making it difficult to establish a predictive model. Existing technologies, such as carbide slag co-precipitation, can reduce costs but still cannot solve problems such as uncontrollable floc settling. Membrane separation is limited by rapid membrane fouling, and ion exchange methods aggravate ammonia nitrogen pollution, which overall restricts the efficiency and stability of tailwater treatment.

[0003] In order to solve the above problems, the present invention proposes a method and system for recycling industrial tail water of wet-process stone coal vanadium extraction. Summary of the Invention

[0004] The purpose of the present invention is to propose a method and system for recycling industrial tail water from wet stone coal vanadium extraction to solve the problems raised in the background technology:

[0005] There is no real-time monitoring method for the formation, growth and sedimentation of flocs in the neutralization tank. Manual sampling and microscopic observation are relied upon, which cannot capture transient changes.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for recycling industrial tail water from wet stone coal vanadium extraction comprises the following steps:

[0008] The industrial tail water is subjected to multi-stage grid filtration and coordinated conditioning of carbide slag to obtain pretreated tail water;

[0009] The pre-treated tail water is put into the intelligent sedimentation tank, and the floc images of the pre-treated tail water are collected and analyzed to obtain the three-dimensional characteristics of the flocs. The predicted settling velocity is obtained by combining the three-dimensional characteristics of the flocs, and the stirring speed is controlled by combining the preset dynamic control strategy.

[0010] The tail water in the intelligent sedimentation tank is stirred according to the controlled stirring speed, and the real-time three-dimensional characteristics of the flocs are recollected. Then, the reagent is intelligently added to separate the sedimentation tank supernatant and the vanadium-containing flocs.

[0011] Perform filter press on vanadium-containing flocs, analyze real-time filter press data, and recover filtrate and filter residue that meet preset output conditions;

[0012] The filtrate and filter residue are recycled through a predetermined recycling strategy.

[0013] Preferably, the method for obtaining pretreated tail water comprises:

[0014] The industrial tail water is filtered through a grid with preset stepped gaps to obtain filtered tail water;

[0015] The filtered tail water and carbide slag are then reacted in a preset volume ratio under preset reaction conditions to obtain pretreated tail water.

[0016] Preferably, the method for obtaining the three-dimensional characteristics of flocs comprises:

[0017] The floc image is used as the input of the floc segmentation model to obtain the three classification results of flocs, water, and impurities. The floc volume is measured and the equivalent diameter is calculated based on the floc volume.

[0018] The minimum number of cubes covering flocs at scale r is counted, and the corresponding logarithm is calculated. The logarithm of scale r is calculated, and the limit of the ratio of the logarithm of the minimum number of cubes covering flocs at scale r to the logarithm of scale r is calculated to obtain the fractal dimension.

[0019] The porosity was calculated by the ratio of the floc solid volume to the total floc volume;

[0020] The equivalent diameter, fractal dimension and porosity are combined to obtain three-dimensional features.

[0021] Preferably, the floc segmentation model is a deep learning segmentation network, comprising downsampling, upsampling and output layers; wherein the first layer of the downsampling layer extracts the basic features of the floc image through 3D convolution; the second layer compresses the features through maximum pooling and then extracts deep features through 3D convolution; the upsampling layer maps the deep features back to the original scale through transposed 3D convolution and fuses them with the basic features of the downsampling layer; the output layer outputs the three classification results of flocs, water and impurities through 3D convolution and softmax activation function.

[0022] Preferably, the method for obtaining the predicted settling velocity comprises:

[0023] Based on the equivalent diameter and fractal dimension of flocs and combined with empirical constants, a settling velocity prediction model was established, and the predicted settling velocity was obtained based on the settling velocity prediction model.

[0024] Preferably, the method for obtaining the controlled stirring speed comprises:

[0025] When the first low threshold value of the sedimentation velocity is predicted, the stirring speed is adjusted to the first stirring threshold value;

[0026] When the second highest threshold of the sedimentation velocity is predicted, the stirring speed is adjusted to the second stirring threshold;

[0027] When the predicted sedimentation velocity is between the first low threshold and the second high threshold, the current stirring velocity is maintained, and the adjusted stirring velocity is used as the controlled stirring velocity.

[0028] Preferably, the method for obtaining the sedimentation tank supernatant and vanadium-containing flocs comprises:

[0029] When the real-time fractal dimension is lower than the dimension threshold, the flocculant dosage is increased to the first dosage value, and the preset ultrasound of the preset duration is triggered synchronously;

[0030] When the real-time equivalent diameter is greater than the preset diameter threshold, reducing the coagulant dosage of the second dosage value;

[0031] The supernatant of the sedimentation tank and the vanadium-containing flocs were separated and obtained.

[0032] Preferably, the method for recovering the filtrate and filter residue that meet the preset output conditions includes:

[0033] Detect the turbidity of the filtrate. If the turbidity of the filtrate is higher than the turbidity threshold, extend the backlog time by t seconds.

[0034] The moisture content of the filter cake is detected. If the moisture content of the filter cake is higher than the moisture content threshold, hot air drying is performed until the moisture content of the filter cake is no higher than the moisture content threshold.

[0035] The industrial tail water recycling system for wet-process stone coal vanadium extraction implements the industrial tail water recycling method for wet-process stone coal vanadium extraction, comprising:

[0036] Pretreatment module: multi-stage grid filtration and carbide slag coordinated conditioning of industrial tail water to obtain pretreated tail water;

[0037] Intelligent analysis module: The pre-treated tail water is put into the intelligent sedimentation tank, and the floc images of the pre-treated tail water are collected and analyzed to obtain the three-dimensional characteristics of the flocs. The predicted settling velocity is obtained by combining the three-dimensional characteristics of the flocs, and the stirring speed is controlled by combining the preset dynamic control strategy;

[0038] Intelligent processing module: Stirs the tail water in the intelligent sedimentation tank according to the controlled stirring speed, recollects the real-time three-dimensional characteristics of the flocs, and then intelligently adds reagents to separate the sedimentation tank supernatant and vanadium-containing flocs;

[0039] Filter pressing and separation module: Filter the vanadium-containing flocs, analyze the real-time filter pressing data, and recover the filtrate and filter residue that meet the preset output conditions;

[0040] Recovery module: Recycle the filtrate and residue through a predetermined recovery strategy.

[0041] Compared with the prior art, the present invention provides a method and system for recycling industrial tail water from wet-process stone coal vanadium extraction, which has the following beneficial effects:

[0042] The present invention uses multi-stage grid filtration and carbide slag synergistic conditioning pretreatment, combined with industrial CT real-time monitoring, deep learning analysis of floc three-dimensional characteristics, and dynamic regulation of stirring and reagent addition. Intelligent precipitation technology breaks through the "black box" dilemma of precipitation reaction, realizes precise control of floc formation and sedimentation, improves the removal rate of multiple pollutants such as arsenic and fluorine, and increases the vanadium recovery rate; through intelligent filter pressing and directional recovery strategies, the vanadium-rich filtrate can be treated to obtain a high-purity vanadium product, and the filter residue is mixed with cement clinker and calcined to achieve building material utilization, converting hazardous waste into resources and reducing hazardous waste disposal. Cost; The deep purification process combines photocatalytic oxidation with blockchain traceability to ensure that the tail water reuse rate is improved and the effluent water quality is stable and meets the standards. At the same time, through intelligent reagent addition and energy cascade utilization, the reagent consumption and energy consumption are reduced compared with traditional processes, forming an overall closed-loop system of "pretreatment-intelligent reaction-resource recovery-circulation and reuse". It not only solves the bottleneck problems of slow floc sedimentation, difficult pollutant removal, ammonia nitrogen pollution and solid waste disposal in traditional processes, but also significantly improves the stability and economy of tail water treatment, providing an efficient solution for the green transformation of the wet vanadium extraction industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a flow chart of the method mentioned in Example 1 of the present invention;

[0044] Figure 2 This is a schematic diagram of the floc segmentation model framework mentioned in Example 1 of the present invention;

[0045] Figure 3 This is the system block diagram mentioned in Example 2 of the present invention. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0047] The present invention uses multi-stage grid filtration and carbide slag synergistic conditioning pretreatment, combined with industrial CT real-time monitoring, deep learning analysis of floc three-dimensional characteristics, and dynamic regulation of stirring and reagent addition. Intelligent precipitation technology breaks through the "black box" dilemma of precipitation reaction, realizes precise control of floc formation and sedimentation, improves the removal rate of multiple pollutants such as arsenic and fluorine, and increases the vanadium recovery rate; through intelligent filter pressing and directional recovery strategies, the vanadium-rich filtrate can be treated to obtain a high-purity vanadium product, and the filter residue is mixed with cement clinker and calcined to achieve building material utilization, converting hazardous waste into resources and reducing hazardous waste disposal. The deep purification process combines photocatalytic oxidation with blockchain traceability to ensure increased tailwater reuse and consistent effluent quality. Furthermore, through intelligent reagent dosing and cascaded energy utilization, this reduces reagent and energy consumption compared to traditional processes. This creates a closed-loop system of "pretreatment-intelligent reaction-resource recovery-recycling." This system addresses bottlenecks in traditional processes, such as slow floc settling, difficult pollutant removal, ammonia nitrogen pollution, and solid waste disposal. It also significantly improves the stability and cost-effectiveness of tailwater treatment, providing a highly effective solution for the green transformation of the hydrometallurgical vanadium extraction industry. This includes the following:

[0048] Example 1:

[0049] See also Figure 1-2 The method for recycling industrial tail water from wet-process stone coal vanadium extraction of the present invention comprises the following steps:

[0050] The industrial tail water is subjected to multi-stage grid filtration and coordinated conditioning of carbide slag to obtain pretreated tail water;

[0051] Methods for obtaining pretreated tail water include:

[0052] Filter industrial tailwater through a grid with stepped gaps, the gaps can be set to different levels, such as 2mm, 1mm and 0.5mm;

[0053] Industrial tail water and carbide slag are reacted at a volume ratio of 10:1 at a pH of 2.8-3.2 and a stirring speed of 70-90 rpm for 10-20 minutes to obtain pretreated tail water.

[0054] The pre-treated tail water is put into the intelligent sedimentation tank, and the floc images of the pre-treated tail water are collected and analyzed to obtain the three-dimensional characteristics of the flocs. The predicted settling velocity is obtained by combining the three-dimensional characteristics of the flocs, and the stirring speed is controlled by combining the preset dynamic control strategy.

[0055] Methods for obtaining three-dimensional characteristics of flocs include:

[0056] The floc image is used as the input of the floc segmentation model to obtain the three classification results of flocs, water, and impurities. The floc volume is measured and the equivalent diameter is calculated based on the floc volume.

[0057] The minimum number of cubes covering flocs at scale r is counted, and the corresponding logarithm is calculated. The logarithm of scale r is calculated, and the limit of the ratio of the logarithm of the minimum number of cubes covering flocs at scale r to the logarithm of scale r is calculated to obtain the fractal dimension.

[0058] The porosity was calculated by the ratio of the floc solid volume to the total floc volume;

[0059] The equivalent diameter, fractal dimension and porosity are combined to obtain three-dimensional features.

[0060] Reference Figure 2 The floc segmentation model is a deep learning segmentation network consisting of downsampling, upsampling, and output layers. The first downsampling layer extracts basic features from the floc image through 3D convolution (input channel 1, output channel 64, convolution kernel 3×3×3, padding = 1). The second layer compresses the features through max pooling (stride 2) and then extracts deep features through 3D convolution (input 64, output 128, convolution kernel 3×3×3). The upsampling layer projects the deep features back to the original scale through transposed 3D convolution (input 128, output 64, convolution kernel 2×2×2, stride 2) and fuses them with the basic features from the downsampling layer. The output layer uses 3D convolution (input 64, output 3, convolution kernel 1×1×1) and a softmax activation function to output the three-class classification results (3D spatial distribution) of flocs, water, and impurities.

[0061] Methods for obtaining predicted settling velocities include:

[0062] Based on the equivalent diameter and fractal dimension of flocs and combined with empirical constants, a settling velocity prediction model was established, and the predicted settling velocity was calculated based on the settling velocity prediction model; among them, the empirical constants were obtained by fitting historical data.

[0063] Methods for obtaining controlled stirring speed include:

[0064] When the first low threshold value of the sedimentation velocity is predicted, the stirring speed is adjusted to the first stirring threshold value;

[0065] When the second highest threshold of the sedimentation velocity is predicted, the stirring speed is adjusted to the second stirring threshold;

[0066] When the predicted sedimentation velocity is between the first low threshold and the second high threshold (including the first low threshold and the second high threshold), the current stirring speed is maintained, and the adjusted stirring speed is used as the controlled stirring speed.

[0067] The tail water in the intelligent sedimentation tank is stirred according to the controlled stirring speed, and the real-time three-dimensional characteristics of the flocs are recollected. Then, the reagent is intelligently added to separate the sedimentation tank supernatant and the vanadium-containing flocs.

[0068] The method for obtaining the supernatant liquid and vanadium-containing flocs from the sedimentation tank comprises:

[0069] When the real-time fractal dimension is lower than the dimension threshold, the dosage of the flocculant (such as PAM solution) of the first dosage value is increased, and the preset ultrasound of the preset duration is synchronously triggered;

[0070] When the real-time equivalent diameter is greater than the preset diameter threshold, the dosage of the coagulant (such as FeCl3) of the second dosage value is reduced;

[0071] The supernatant of the sedimentation tank and the vanadium-containing flocs were separated and obtained.

[0072] Perform filter pressing on vanadium-containing flocs, analyze real-time filter pressing data, and recover filtrate that meets preset output conditions (e.g., vanadium content greater than the filtrate output threshold) and filter residue (e.g., vanadium content less than the filter residue output threshold);

[0073] Methods for recovering filtrate and filter residue that meet preset output conditions include:

[0074] Detect the turbidity of the filtrate. If the turbidity of the filtrate is higher than the turbidity threshold, extend the backlog time by t seconds.

[0075] The moisture content of the filter cake is detected. If the moisture content of the filter cake is higher than the moisture content threshold, hot air drying is performed until the moisture content of the filter cake is no higher than the moisture content threshold.

[0076] The filtrate and filter residue are recycled through a predetermined recycling strategy.

[0077] Predetermined recycling strategies include but are not limited to:

[0078] Filtrate recovery: The filtrate is filtered through a selective electrodialysis membrane to obtain vanadium raw materials; the filtrate is catalytically oxidized through a TiO2@MoS2 nanotube array reactor to obtain purified water;

[0079] Residue recovery: Mix the residue with cement clinker to produce building materials;

[0080] It can be adjusted according to the actual needs of the enterprise.

[0081] Example 2:

[0082] See also Figure 3 The industrial tail water recycling system for wet-process stone coal vanadium extraction of the present invention comprises:

[0083] Pretreatment module: multi-stage grid filtration and carbide slag coordinated conditioning of industrial tail water to obtain pretreated tail water;

[0084] Intelligent analysis module: The pre-treated tail water is put into the intelligent sedimentation tank, and the floc images of the pre-treated tail water are collected and analyzed to obtain the three-dimensional characteristics of the flocs. The predicted settling velocity is obtained by combining the three-dimensional characteristics of the flocs, and the stirring speed is controlled by combining the preset dynamic control strategy;

[0085] Intelligent processing module: Stirs the tail water in the intelligent sedimentation tank according to the controlled stirring speed, recollects the real-time three-dimensional characteristics of the flocs, and then intelligently adds reagents to separate the sedimentation tank supernatant and vanadium-containing flocs;

[0086] Filter pressing and separation module: Filter the vanadium-containing flocs, analyze the real-time filter pressing data, and recover the filtrate and filter residue that meet the preset output conditions;

[0087] Recovery module: Recycle the filtrate and residue through a predetermined recovery strategy.

[0088] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for recycling industrial tail water from wet stone coal vanadium extraction, characterized in that: The steps include: The industrial tail water is subjected to multi-stage grid filtration and coordinated conditioning of carbide slag to obtain pretreated tail water; The pre-treated tail water is put into the intelligent sedimentation tank, and the floc images of the pre-treated tail water are collected and analyzed to obtain the three-dimensional characteristics of the flocs. The predicted settling velocity is obtained by combining the three-dimensional characteristics of the flocs, and the stirring speed is controlled by combining the preset dynamic control strategy. The tail water in the intelligent sedimentation tank is stirred according to the controlled stirring speed, and the real-time three-dimensional characteristics of the flocs are recollected. Then, the reagent is intelligently added to separate the sedimentation tank supernatant and the vanadium-containing flocs. Perform filter press on vanadium-containing flocs, analyze real-time filter press data, and recover filtrate and filter residue that meet preset output conditions; The filtrate and filter residue are recycled through a predetermined recycling strategy.

2. The method for recycling industrial tail water from wet stone coal vanadium extraction according to claim 1, characterized in that: Methods for obtaining pretreated tail water include: The industrial tail water is filtered through a grid with preset stepped gaps to obtain filtered tail water; The filtered tail water and carbide slag are then reacted in a preset volume ratio under preset reaction conditions to obtain pretreated tail water.

3. The method for recycling industrial tail water from wet stone coal vanadium extraction according to claim 1, characterized in that: Methods for obtaining three-dimensional characteristics of flocs include: The floc image is used as the input of the floc segmentation model to obtain the three classification results of flocs, water, and impurities. The floc volume is measured and the equivalent diameter is calculated based on the floc volume. The minimum number of cubes covering flocs at scale r is counted, and the corresponding logarithm is calculated. The logarithm of scale r is calculated, and the limit of the ratio of the logarithm of the minimum number of cubes covering flocs at scale r to the logarithm of scale r is calculated to obtain the fractal dimension. The porosity was calculated by the ratio of the floc solid volume to the total floc volume; The equivalent diameter, fractal dimension and porosity are combined to obtain three-dimensional features.

4. The method for recycling industrial tail water from wet stone coal vanadium extraction according to claim 3, characterized in that: The floc segmentation model is a deep learning segmentation network, comprising downsampling, upsampling, and output layers. The first layer of the downsampling layer extracts the basic features of the floc image through 3D convolution. The second layer compresses the features through maximum pooling and then extracts deep features through 3D convolution. The upsampling layer maps the deep features back to the original scale through transposed 3D convolution and fuses them with the basic features of the downsampling layer. The output layer outputs the three-classification results of flocs, water, and impurities through 3D convolution and softmax activation function.

5. The method for recycling industrial tail water from wet stone coal vanadium extraction according to claim 3, characterized in that: Methods for obtaining predicted settling velocities include: Based on the equivalent diameter and fractal dimension of flocs and combined with empirical constants, a settling velocity prediction model was established, and the predicted settling velocity was obtained based on the settling velocity prediction model.

6. The method for recycling industrial tail water from wet stone coal vanadium extraction according to claim 1, characterized in that: Methods for obtaining controlled stirring speed include: When the first low threshold value of the sedimentation velocity is predicted, the stirring speed is adjusted to the first stirring threshold value; When the second highest threshold of the sedimentation velocity is predicted, the stirring speed is adjusted to the second stirring threshold; When the predicted sedimentation velocity is between the first low threshold and the second high threshold, the current stirring velocity is maintained, and the adjusted stirring velocity is used as the controlled stirring velocity.

7. The method for recycling industrial tail water from wet stone coal vanadium extraction according to claim 3, characterized in that: The method for obtaining the supernatant liquid and vanadium-containing flocs from the sedimentation tank comprises: When the real-time fractal dimension is lower than the dimension threshold, the flocculant dosage is increased to the first dosage value, and the preset ultrasound of the preset duration is triggered synchronously; When the real-time equivalent diameter is greater than the preset diameter threshold, reducing the coagulant dosage of the second dosage value; The supernatant of the sedimentation tank and the vanadium-containing flocs were separated and obtained.

8. The method for recycling industrial tail water from wet stone coal vanadium extraction according to claim 1, characterized in that: Methods for recovering filtrate and filter residue that meet preset output conditions include: Detect the turbidity of the filtrate. If the turbidity of the filtrate is higher than the turbidity threshold, extend the backlog time by t seconds. The moisture content of the filter cake is detected. If the moisture content of the filter cake is higher than the moisture content threshold, hot air drying is performed until the moisture content of the filter cake is no higher than the moisture content threshold.

9. An industrial tail water recycling system for wet-process stone coal vanadium extraction, implementing the industrial tail water recycling method for wet-process stone coal vanadium extraction according to any one of claims 1 to 8, characterized in that: include: Pretreatment module: multi-stage grid filtration and carbide slag coordinated conditioning of industrial tail water to obtain pretreated tail water; Intelligent analysis module: The pre-treated tail water is put into the intelligent sedimentation tank, and the floc images of the pre-treated tail water are collected and analyzed to obtain the three-dimensional characteristics of the flocs. The predicted settling velocity is obtained by combining the three-dimensional characteristics of the flocs, and the stirring speed is controlled by combining the preset dynamic control strategy; Intelligent processing module: Stirs the tail water in the intelligent sedimentation tank according to the controlled stirring speed, recollects the real-time three-dimensional characteristics of the flocs, and then intelligently adds reagents to separate the sedimentation tank supernatant and vanadium-containing flocs; Filter pressing and separation module: Filter the vanadium-containing flocs, analyze the real-time filter pressing data, and recover the filtrate and filter residue that meet the preset output conditions; Recovery module: Recycle the filtrate and residue through a predetermined recovery strategy.

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