Simulation method for enrichment process of rare metal gallium in well field coal

Abstract

The invention provides a method for simulating the enrichment process of rare metal gallium in well field coal, and relates to the technical field of simulation methods, and the method comprises the following steps: constructing a geologic model and initializing parameters of a coal sample; performing pyrolysis process dynamics simulation; simulating a gas-solid phase gallium migration path; carrying out selective enrichment modeling on gallium in ash; optimizing a solvent extraction and separation process; and carrying out whole-process coupling and economical efficiency verification. According to the method, through geological exploration and molecular dynamics multidisciplinary models, full-process digital mapping from the coal seam occurrence form to the high-purity gallium product is achieved, the prediction error is low, meanwhile, based on the Monte Carlo risk model, the influence of variables such as grade fluctuation and energy consumption cost is quantified, an operation window is dynamically optimized, the cost of per ton of gallium is reduced, and the prediction efficiency is improved. Through waste heat power generation and reagent circulation, the emission of gallium per ton is reduced, the engineering problem of coal series gallium extraction is systematically solved, it is ensured that solvent extraction separation factors can meet the high-purity requirement, and the productivity loss can be reduced.

Description

Technical Field

[0001] This invention relates to the field of enrichment simulation technology, specifically a method for simulating the enrichment process of gallium, a rare metal, in coal mines. Background Technology

[0002] The enrichment mechanism of gallium in coal-based materials mainly relies on selective migration processes driven by combustion kinetics. When coal is burned at high temperatures of 1000-1300℃, gallium's melting point is significantly lower than that of other metals in the coal, causing its elemental form or oxides to preferentially gasify and enter the exhaust gas treatment system with the flue gas. In the slow cooling zone of the flue, gaseous gallium is adsorbed onto the surface of fly ash particles through heterogeneous nucleation. Fine particles with a diameter <10μm exhibit a stronger gallium enrichment capacity due to their superior specific surface area. The gallium content in this type of fly ash often reaches 0.5-1.89%, which is 20-60 times higher than that in conventional bauxite.

[0003] However, existing extraction technologies face multiple bottlenecks. Dynamic leaching experiments show that even under optimized conditions (6 mol / L HCl, 90℃), the gallium leaching rate is only 68-72%, and the subsequent electrolysis / displacement process results in a total conversion rate of less than 1% due to side reactions.

[0004] In the field of coal-based gallium extraction technology, the following problems also exist: gallium is in a highly dispersed state with ultra-low grade in coal. This is due to the extremely sparse and uneven distribution of gallium elements in the coal matrix, which makes it impossible for traditional physical separation methods (such as gravity separation or flotation) to effectively enrich the target element, and the recovery rate is generally less than 40%.

[0005] Furthermore, despite the achievements in laboratory research, these technologies are difficult to scale up for industrial application, mainly due to uneven flow field distribution within the reactor during scale-up and frequent clogging issues during solid-liquid separation. These factors not only reduce production continuity but also cause capacity losses of up to 20%-30%, severely restricting economic efficiency. Existing technologies cannot effectively solve these problems in the laboratory setting. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the present invention aims to provide a method for simulating the enrichment process of gallium, a rare metal, in coal mines, thereby solving the problems mentioned in the background. This invention uses computer modeling techniques, combined with multidisciplinary models of geological exploration and molecular dynamics, to accurately reproduce the volatilization, condensation, and adsorption behavior of gallium on the surface of fly ash particles during high-temperature combustion of coal. This allows for in-depth analysis of the migration mechanism and enrichment patterns of gallium, identification of key control parameters, and realization of a full-process digital mapping from coal seam occurrence morphology to high-purity gallium products, with low prediction error.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solution: a method for simulating the enrichment process of rare metal gallium in coal mines, comprising the following steps: S1. Geological model construction and coal sample parameter initialization: Based on the geological exploration data of the target mine field, a three-dimensional geological structure model is established, the laboratory analysis results of the coal sample are imported, and the occurrence form and background content of gallium are initialized. S2. Pyrolysis process kinetic simulation: Construct a coal pyrolysis reactor model, set the heating program and inert atmosphere, describe the coal macromolecule cracking process through the Flynn-Wall-Ozawa kinetic equation, and output key parameters including volatile matter yield, semi-coke pore structure change and gas phase gallium migration ratio. S3. Simulation of gas-solid gallium migration path: Computational fluid dynamics is used to simulate the flow of pyrolysis products in the reactor, focusing on tracking the diffusion path of gallium-containing gas phase components, and monitoring the gallium content in fly ash in real time to correct model parameters. S4. Modeling of selective gallium enrichment in pyrolysis ash: Hydrometallurgical enrichment pathway simulation was performed on pyrolysis ash. The leaching system was constructed by using hydrochloric acid as a medium to simulate gallium dissolution kinetics at 80℃ and introducing a competitive ion suppression strategy to ensure that the gallium leaching rate is >85% while the aluminum dissolution rate is <15%. S5. Solvent extraction and separation process optimization: A countercurrent extraction tower model was built, 7-dodecenyl-8-hydroxyquinoline (Kelex 100) was selected as the extractant and kerosene as the diluent; and the 7-dodecenyl-8-hydroxyquinoline extractant was recycled. S6. Full-process coupling and economic verification: The integrated module constructs a system dynamic model, introduces a mass balance equation, and uses Monte Carlo simulation to quantify the impact of geological parameter fluctuations on economics, ultimately outputting the feasibility range of the enrichment process.

[0008] Furthermore, in step S1, based on the geological exploration data of the target well field, a high-precision three-dimensional geological model is constructed in GMS software using the Kriging interpolation algorithm and three-dimensional geostatistics method. The model integrates geophysical logging data with X-ray fluorescence spectroscopy (XRF) analysis results from borehole cores.

[0009] Furthermore, the geophysical logging data includes gamma rays and resistivity curves; the occurrence format includes: The clay minerals are in an adsorbed state, accounting for 60%-70%, and are bonded to the interlayer hydroxyl groups of kaolinite. Organic matter bound state, accounting for 20%-30%, exists in the form of carboxylic acid or phenolic hydroxyl groups; Sulfide inclusions, accounting for 5%-10%, pyrite microcrystal inclusions.

[0010] Furthermore, in step S2, based on the constructed three-dimensional geological model, the CoalDECOAL reaction module is invoked, combined with the thermal-fluid-solid coupling module, to simulate the gallium release behavior during the coal seam pyrolysis process.

[0011] Furthermore, the parameters of the programmed temperature control curve are: room temperature is increased to the target temperature of 600-900℃ at a rate of 20℃ / min, held at that temperature for 30 minutes, and inertia is maintained. The flow rate is 1.5 L / min.

[0012] Furthermore, the simulation of the gas-solid gallium migration path in step S3 includes the following: constructing a three-dimensional pyrolysis reactor CFD model; solving the gas-solid two-phase flow based on the gas phase composition and fly ash particle composition output in step S2; simulating the gas flow field using a turbulence model; analyzing gallium-containing gas phase migration using the Maxwell-Stefan multi-component diffusion equation; monitoring the gallium content in fly ash using an online laser-induced breakdown spectroscopy system; scanning the fly ash flow at the pyrolysis furnace outlet using a laser probe; exciting the fly ash particles to generate plasma using the laser focusing point; acquiring characteristic spectral line intensities using a fiber optic spectrometer (resolution 0.1 nm, wavelength range 200–500 nm); converting the spectral line intensity ratio into instantaneous gallium content values ​​using a pre-calibrated curve based on the internal standard method; collecting 300 sets of data every 10 seconds; removing outliers and using the moving average as the feedback signal input to the model; automatically adjusting the gas-solid mass transfer coefficient or thermophoretic capture efficiency parameter when the deviation between the measured and simulated values ​​exceeds a set threshold; until the model error is compressed to below 3.5% after calibration; and simultaneously generating an error distribution heatmap to guide probe site optimization.

[0013] Furthermore, the boundary conditions and verification process include: inlet / outlet settings, wall adsorption, and model calibration; the probe site optimization process is as follows: High-deviation areas were identified by error distribution heatmaps, and particle trajectory concentration cloud maps simulated by Fluent were used to optimize the probe's spatial coordinates using a multi-objective genetic algorithm. With the dual objectives of maximizing signal capture rate and minimizing dust interference, the optimal installation site was calculated, and it was verified whether the laser penetration path avoided the high-temperature turbulence zone. Finally, an adjustable universal bracket was deployed for precise three-dimensional positioning. The universal bracket had an accuracy of ±2 mm, and a backflushing nitrogen curtain was installed simultaneously. The nitrogen curtain was a 0.5 MPa pulsed structure to suppress contamination of the probe mirror surface.

[0014] Furthermore, based on the gallium-rich fly ash output in step S3, a multiphase leaching reaction system is constructed, and the selective extraction of gallium in hydrochloric acid medium is simulated using a thermodynamic-kinetic coupling algorithm.

[0015] Furthermore, it also includes optimization processes for solvent extraction and separation: In the leachate, a three-stage countercurrent extraction system was constructed using 7-dodecenyl-8-hydroxyquinoline as the extractant. By precisely controlling the pH of the aqueous phase to 2.5–3.0, high selective separation of gallium was achieved. The concentration of the organic phase, the flow ratio, and the mixing time were optimized. Ascorbic acid was added to inhibit iron ions in response to interference from iron and silicon. A three-step regeneration and recycling system is established to recover and utilize the 7-dodecenyl-8-hydroxyquinoline, including the following: Organic phase purification after back-extraction: Gallium-loaded organic phase is purified... + After back-extraction, gallium is transferred to the aqueous phase, and the residual organic phase enters the regeneration tank. It is washed with 5% NaOH solution (volume ratio 1:3) at 45°C with stirring for 20 minutes to neutralize the residual acidity (pH rises to 7.0±0.5) and decompose degradation products (such as quinolinone impurities). After washing, the organic phase is separated from the aqueous phase entrainment by a coalescing plate (entrainment amount <50 ppm). Activated adsorption for impurity removal: Activated alumina particles (0.5 g / L) are injected into the regenerated organic phase, and the mixture is circulated and filtered at 50°C for 30 minutes for selective adsorption. and Metallic impurities are removed by passing colloidal suspended matter through a 0.1 μm precision filter, the permeability of which is >99%. Vacuum dehydration and regeneration: The treated organic phase enters a vacuum flash evaporator with parameters of -0.08 MPa and 80°C. Trace amounts of moisture are removed and reduced to less than 0.05%, while volatile kerosene components are recovered, allowing the regenerated organic phase to be returned to the extraction system for reuse.

[0016] Furthermore, the mass balance equation is: ; in: n The total number of raw material input streams, with a value range of ≥3; Let be the mass of gallium in the i-th input stream, with a value ranging from . ; The value represents the total mass of the final gallium product, and its range is determined by the enrichment efficiency. k This represents the number of gallium loss paths, with a value ranging from 2 to 4. Let the gallium mass be the value of the j-th loss path. Meanwhile, the model in step S4 receives real-time data from factory sensors, automatically optimizes parameters every 24 hours, synchronizes the physical equipment status every 5 seconds, and couples with the simulation model in steps S1–S5. The LSTM time-series prediction algorithm is used to compare the actual operating data with the simulated values ​​and automatically correct the deviation of the dynamic parameters.

[0017] The beneficial effects of this invention are: 1. The method for simulating the enrichment process of gallium, a rare metal in coal, in this coalfield uses computer modeling techniques combined with multidisciplinary models of geological exploration and molecular dynamics to accurately reproduce the volatilization, condensation, and adsorption behavior of gallium on the surface of fly ash particles during high-temperature combustion of coal. This allows for in-depth analysis of the migration mechanism and enrichment law of gallium, identification of key control parameters, and realization of a full-process digital mapping from coal seam occurrence morphology to high-purity gallium products, with low prediction error.

[0018] 2. The simulation method for the enrichment process of rare metal gallium in coal mines solves the problems of difficult impurity separation and insufficient conversion rate in existing technologies, and predicts the optimal operating conditions to improve the recovery rate. On the other hand, it can significantly reduce industrialization risks, reduce experimental costs and time, and promote economic feasibility and environmental sustainability.

[0019] 3. The simulation method for the enrichment process of rare metal gallium in coal in this mining area is based on the Monte Carlo risk model to quantify the impact of variables such as grade fluctuations and energy consumption costs, dynamically optimize the operating window, reduce the cost per ton of gallium, and achieve higher enrichment per ton of gallium through waste heat power generation and reagent recycling. Emissions are reduced, and the engineering challenges of coal-based gallium extraction are systematically addressed. Attached Figure Description

[0020] Figure 1 This is a flowchart of a method for simulating the enrichment process of gallium, a rare metal, in coal mines according to the present invention. Figure 2 This is a schematic diagram illustrating the principle of simulating the enrichment process of gallium, a rare metal, in coal mines according to the present invention. Detailed Implementation

[0021] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0022] Please see Figures 1 to 2 The present invention provides the following technical solution: a method for simulating the enrichment process of gallium, a rare metal, in coal mines. This method uses computer modeling techniques, combined with geological exploration and molecular dynamics multidisciplinary models, to accurately reproduce the volatilization, condensation, and adsorption behavior of gallium on the surface of fly ash particles during high-temperature combustion of coal. This allows for in-depth analysis of the migration mechanism and enrichment law of gallium, identification of key control parameters, and realization of a full-process digital mapping from coal seam occurrence morphology to high-purity gallium products, with a low final prediction error.

[0023] In this embodiment, based on a gallium enrichment project in coal from a certain basin, the following specific simulation steps are provided, wherein the coal quality characteristics in this gallium enrichment project are as follows: Ash content 25.8%, volatile matter 32.5%, vitrinite reflectance Ro=0.86% Gallium occurrence forms: clay-adsorbed state (68%), organically bound state (27%), sulfide-encapsulated state (5%) Background grade: 38.7 ppm (XRF verified value ±2.1 ppm).

[0024] The parameters for the pyrolysis kinetics simulation are shown below: Table 1 Pyrolysis kinetics simulation parameter Setting value Output Final temperature 780℃ Gas-phase gallium release rate: 81.3% Constant temperature time 25 min Gallium enrichment concentration in fly ash: 396 ppm heating rate 18℃ / min The porosity of the semi-coke is 48.7%. The parameters for gas-solid migration simulation are as follows: Boundary conditions: Inlet flow velocity: 1.6 m / s (N2 carrier gas) Fly ash injection: 0.28 kg / s (particle size Rosin-Rammler distribution, d 50 =30 μm). Based on the above characteristics, the following specific steps will be taken to explain this scheme: S1. Geological Model Construction and Coal Sample Parameter Initialization: Based on the geological exploration data of the target mining area, a three-dimensional geological structure model is established. Laboratory analysis results of the coal samples are imported to initialize the occurrence forms and background content of gallium. Based on the geological exploration data of the target well field, a high-precision three-dimensional geological model was constructed in GMS software using the Kriging interpolation algorithm and three-dimensional geostatistics method. The model integrates geophysical logging data and X-ray fluorescence spectroscopy (XRF) analysis results from borehole cores to clarify the spatial distribution pattern of coal seams and interbedded gangue layers. The background content of gallium in coal samples was determined by laboratory inductively coupled plasma mass spectrometry, and its occurrence morphology was analyzed by combining synchrotron radiation μ-XANES technology.

[0025] The geophysical logging data includes gamma rays and resistivity curves; the occurrence format includes: The clay minerals are in an adsorbed state, accounting for 60%-70%, and are bonded to the interlayer hydroxyl groups of kaolinite. Organic matter bound state, accounting for 20%-30%, exists in the form of carboxylic acid or phenolic hydroxyl groups; Sulfide inclusions, accounting for 5%-10%, pyrite microcrystal inclusions; Simultaneously input coal and petrographic composition data (vitrinite reflectance Ro=0.5%-1.2%, inertinite proportion 15%-35%) and ash composition ( With a ratio > 2.5, the heterogeneity of the coal seam was quantified by Monte Carlo stochastic simulation (porosity standard deviation ± 0.05, permeability coefficient of variation 0.3-0.5), and finally a digital geological model containing 12-15 layered units was generated, providing high-resolution input parameters for subsequent thermochemical-fluid coupled simulation; S2. Pyrolysis Process Kinetic Simulation: A coal pyrolysis reactor model is constructed, and the heating program and inert atmosphere are set. The Flynn-Wall-Ozawa kinetic equation is used to describe the coal macromolecule cracking process. Key parameters are output, including volatile matter yield, changes in semi-coke pore structure, and the proportion of gaseous gallium migration. Based on the constructed three-dimensional geological model, the CoalDECOAL reaction module is called, combined with the thermo-fluid-solid coupling module, to simulate the gallium release behavior during coal seam pyrolysis. A programmed temperature control curve is set, and an inert atmosphere is established. Under a specific atmosphere, the macromolecular cracking reaction of coal was analyzed using the Flynn-Wall-Ozawa kinetic equation.

[0026] The parameters of the programmed temperature control curve are: room temperature is increased to the target temperature of 600-900℃ at a rate of 20℃ / min, held at that temperature for 30 minutes, and inertia is maintained. The flow rate is 1.5 L / min; key output parameters include: half-foam pore evolution, gas phase gallium mobility, and fly ash adsorption gallium ratio, and the model is calibrated using in-situ infrared spectroscopy data; S3. Simulation of gas-solid phase gallium migration path: Computational fluid dynamics is used to simulate the flow of pyrolysis products in the reactor, focusing on tracking the diffusion path of gallium-containing gas phase components, and monitoring the gallium content in fly ash in real time to correct model parameters. The Maxwell-Stefan equation is combined to calculate the mass transfer efficiency of multiple components and obtain the secondary adsorption mechanism of gallium by fly ash particles.

[0027] The simulation of the gas-solid phase gallium migration path includes the following: constructing a three-dimensional pyrolysis reactor CFD model; solving the gas-solid two-phase flow based on the gas phase composition and fly ash particle composition output in step S2; simulating the gas flow field using a turbulence model; and analyzing gallium-containing gas phase migration using the Maxwell-Stefan multi-component diffusion equation. The gallium content in fly ash is monitored using an online laser-induced breakdown spectroscopy system. A laser probe scans the fly ash flow at the pyrolysis furnace outlet, and the laser focus point excites the fly ash particles to generate plasma. Characteristic spectral line intensities are collected using a fiber optic spectrometer (resolution 0.1 nm, wavelength range 200–500 nm). The spectral line intensity ratio is converted into an instantaneous gallium content value using a pre-calibrated curve based on the internal standard method. 300 sets of data are collected every 10 seconds. After removing outliers, the moving average is used as the feedback signal input to the model. When the deviation between the measured and simulated values ​​exceeds a set threshold, the gas-solid mass transfer coefficient or thermophoretic capture efficiency parameter is automatically adjusted. This process continues until the model error is compressed to below 3.5% after calibration, and a heat map of the error distribution is generated simultaneously to guide probe site optimization.

[0028] This embodiment also provides the following boundary conditions and verification process: Inlet / outlet settings: gas inlet velocity 1.8 m / s, fly ash injection rate 0.25 kg / s; Wall adsorption: The reactor wall is coated with a SiC coating, and the simulated gallium deposition loss rate is <3%; Model calibration: The gallium content in fly ash was monitored in real time by online laser-induced breakdown spectroscopy, and the surface Ga-O-Si bonding was verified by X-ray photoelectron spectroscopy. This embodiment also provides the following specific process for optimizing the probe site: High-deviation areas were identified by error distribution heatmaps, and particle trajectory concentration cloud maps simulated by Fluent were combined with multi-objective genetic algorithms to optimize the probe's spatial coordinates. With the dual objectives of maximizing signal capture rate and minimizing dust interference, the optimal installation site was calculated, and it was verified whether the laser penetration path avoided the high-temperature turbulence zone. Finally, an adjustable universal bracket was deployed for three-dimensional precise positioning. The universal bracket had an accuracy of ±2 mm, and a backflushing nitrogen curtain was installed simultaneously. The nitrogen curtain was a 0.5 MPa pulsed structure to suppress contamination of the probe mirror surface. S4. Modeling of selective enrichment of gallium in ash: The enrichment path of pyrolysis ash is simulated by hydrometallurgical process. The leaching system is constructed by using hydrochloric acid as a medium to simulate the gallium dissolution kinetics at a temperature of 80℃. A competitive ion suppression strategy is introduced to make the gallium leaching rate >85% and the aluminum dissolution rate <15%.

[0029] Based on the gallium-rich fly ash output in step S3, a multiphase leaching reaction system was constructed. The selective extraction of gallium under hydrochloric acid medium was simulated by a thermodynamic-kinetic coupling algorithm. The model calibration was performed by monitoring the intensity change of the characteristic peak of GaCl-4 through in-situ ATR-FTIR spectroscopy, and the gallium residue in the residue was verified by scanning electron microscopy-energy dispersive spectroscopy. S5. Solvent Extraction Separation Process Optimization: A countercurrent extraction tower model was constructed, using 7-dodecenyl-8-hydroxyquinoline (Kelex 100) as the extractant and kerosene as the diluent. The 7-dodecenyl-8-hydroxyquinoline extractant was recycled. Mass transfer efficiency of the multi-stage extraction was simulated based on the partition coefficient, and the pH of the aqueous phase was controlled between 2.5 and 3.0 to maximize efficiency. Selectivity was achieved by constructing a three-stage countercurrent extraction system using 7-dodecenyl-8-hydroxyquinoline as the extractant. High selective separation of gallium was achieved through precise control of the aqueous phase pH (2.5–3.0). The organic phase concentration, flow ratio, and mixing time were optimized. To address interference from iron and silicon, ascorbic acid was added to inhibit iron ion formation. Pre-filtration eliminated the risk of silica emulsification. The back-extraction stage used… or A pure gallium solution with a concentration >15 g / L was obtained, and the organic phase was regenerated by alkaline washing; A three-step regeneration and recycling system is established to recover and utilize the 7-dodecenyl-8-hydroxyquinoline, including the following: Organic phase purification after back-extraction: Gallium-loaded organic phase is purified... + After back-extraction, gallium is transferred to the aqueous phase, and the residual organic phase enters the regeneration tank. It is washed with 5% NaOH solution (volume ratio 1:3) at 45°C with stirring for 20 minutes to neutralize the residual acidity (pH rises to 7.0±0.5) and decompose degradation products (such as quinolinone impurities). After washing, the organic phase is separated from the aqueous phase entrainment by a coalescing plate (entrainment amount <50 ppm). Activated adsorption for impurity removal: Activated alumina particles (0.5 g / L) are injected into the regenerated organic phase, and the mixture is circulated and filtered at 50°C for 30 minutes for selective adsorption. and Metallic impurities are removed by passing colloidal suspended matter through a 0.1 μm precision filter, the permeability of which is >99%. Vacuum dehydration and regeneration: The treated organic phase enters a vacuum flash evaporator with parameters of -0.08 MPa and 80°C. Trace amounts of moisture are removed and reduced to less than 0.05%, while volatile kerosene components are recovered, allowing the regenerated organic phase to be returned to the extraction system for reuse. S6. Full-process coupling and economic verification: The integrated module constructs a system dynamic model, introduces a mass balance equation, and uses Monte Carlo simulation to quantify the impact of geological parameter fluctuations on economics, ultimately outputting the feasibility range of the enrichment process.

[0030] The mass balance equation is: ; in: n The total number of raw material input streams, with a value range of ≥3; Let be the mass of gallium in the i-th input stream, with a value ranging from . ; The value represents the total mass of the final gallium product, and its range is determined by the enrichment efficiency. k This represents the number of gallium loss paths, with a value ranging from 2 to 4. Let the gallium mass be the value of the j-th loss path. Meanwhile, the model in step S4 receives real-time data from factory sensors, automatically optimizes parameters every 24 hours, synchronizes the physical equipment status every 5 seconds, and couples with the simulation model in steps S1–S5. The LSTM time-series prediction algorithm is used to compare the actual operating data with the simulated values ​​and automatically correct the deviation of the dynamic parameters.

[0031] The above scheme enabled the simulation to guide a gallium extraction line processing 100,000 tons of coal annually, achieving the target production capacity and verifying the model's industrial applicability under complex geological conditions. Furthermore, the scheme, based on a Monte Carlo risk model, quantifies the impact of variables such as grade fluctuations and energy costs, dynamically optimizing the operating window to reduce the cost per ton of gallium. Through waste heat power generation and reagent recycling, the cost per ton of gallium can be further reduced. It reduces emissions, systematically solves the engineering challenges of coal-based gallium extraction, addresses the difficulties in impurity separation and insufficient conversion rates in existing technologies, and predicts optimal operating conditions to improve recovery rates. On the other hand, it can significantly reduce industrialization risks, reduce experimental costs and time, and promote economic feasibility and environmental sustainability.

[0032] This embodiment also provides an operation procedure for online LIBS monitoring of gallium content in fly ash based on the above-set simulation parameters, specifically including the following: A LIBS system was deployed at the outlet of the pyrolysis furnace in a 50 t / d pilot-scale production line. This system employed a YAG laser with a wavelength of 1064 nm and a pulse energy of 120 mJ, positioned downwards at a 45° angle 1.2 m from the furnace body. It scanned the fly ash gas flow with a 10 Hz high-frequency pulse at a temperature of 780℃ and a dust density of 180 g / m³. The laser was focused onto the fly ash particles (particle size...). Plasma was generated at a depth of 30 μm. Characteristic spectral lines of Ga I₂ (287.42 nm) and Al I₂ (308.22 nm, internal standard) were acquired using a fiber optic spectrometer, with 300 sets of spectral data acquired every 10 seconds. Based on a pre-calibrated curve (concentration 0–600 ppm, fitting equation I_Ga / I_Al=0.017C_Ga+0.11, R²=0.996), the peak intensity ratio was converted to gallium content in real time. After ±3σ outlier removal and moving average filtering, the instantaneous concentration (e.g., monitored value 396 ppm) was output. When the deviation from the Fluent model prediction value is >8% (e.g., simulated value 360 ​​ppm), a dynamic calibration protocol is automatically triggered.

[0033] This embodiment also provides a specific operational procedure for the recycling of 7-dodecenyl-8-hydroxyquinoline extractant: In a three-stage countercurrent extraction system (RDC tower Φ1.2×9.6 m), gallium-loaded organic phase (containing (complex) via 2M +0.5M After back-extraction (flow rate 1.8 m³ / h, mixing time 120 s), gallium is transferred to the aqueous phase (back-extraction rate 99.7%), and the remaining organic phase enters the regeneration section.

[0034] Step 1: Alkaline washing to remove degradation products The organic phase was pumped into a stirred regeneration tank (made of Hastelloy C276), and a 5% NaOH solution (45±2℃) was added at a volume ratio of organic phase to alkali solution of 3:1. The tank was stirred at 200 rpm for 20 minutes. During this stage, NaOH hydrolyzes quinoline ketone degradation products (generating water-soluble sodium salts) and simultaneously destroys residual [AlQ3] complexes. Precipitation The organic phase was then allowed to stand for 15 minutes to separate into layers (phase separation efficiency >99.9%). After alkali washing, the organic phase was separated from the entrained aqueous phase by a polypropylene fiber coalescer (pore size 10 μm), and the entrainment amount was reduced to <45 ppm.

[0035] Step 2: Deep adsorption and impurity removal The alkaline-washed organic phase was fed into an activated alumina fluidized bed (Φ0.6×2 m, filled with 0.5 g / L δ-Al₂O₃ particles) and circulated at 50 °C for 30 minutes. Selective adsorption of hydroxyl groups on the alumina surface was observed. , Divalent metals were adsorbed (adsorption rate >96%), and silica gel was captured by hydrogen bonding (removal rate 88%). The effluent was then filtered through a 0.1 μm PTFE membrane filter (cross-flow filtration, pressure difference <0.15 MPa) to remove suspended particles, and the filtrate transmittance was >95% (detected at 660 nm wavelength).

[0036] Step 3: Vacuum dehydration and component restoration The purified organic phase enters a vacuum flash evaporator (operating conditions: -0.08 MPa, 80℃), where moisture evaporates from 1.5% to <0.03% (dew point -42℃). Volatile kerosene components are collected by a condenser (5℃ refrigerant) (recovery rate 98.5%). The regenerated organic phase output from the bottom of the column is monitored by an online ultraviolet spectrometer (λ=385 nm) for Kelex 100 characteristic peak intensity (attenuation rate <1.2%), and the total metal residue is verified by ICP-MS (Σ<0.8 ppm). The qualified organic phase is replenished with fresh sulfonated kerosene (compensating for 0.7% v / v volatility loss) and returned to the extraction system for reuse.

[0037] Step 4: Closed-loop control of regeneration quality Each batch of regenerated organic phase must pass three validations: complexation capacity test, emulsification tendency assessment, and lifetime acceleration test.

[0038] Through the above experimental process, the final result is that this recycling process transforms the Kelex 100 from a consumable into a recyclable asset, achieving gallium enrichment costs below the international competitive threshold. The regeneration technology has been embedded in a digital twin model (AVEVA PISystem) to optimize the NaOH injection amount in real time (±0.1% accuracy).

[0039] The basic principles, main features and advantages of the present invention are shown and described above. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention.

[0040] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for simulating the enrichment process of gallium, a rare metal, in coal mines, characterized in that: Includes the following steps: S1. Geological model construction and coal sample parameter initialization: Based on the geological exploration data of the target mine field, a three-dimensional geological structure model is established, the laboratory analysis results of the coal sample are imported, and the occurrence form and background content of gallium are initialized. S2. Pyrolysis process kinetic simulation: Construct a coal pyrolysis reactor model, set the heating program and inert atmosphere, describe the coal macromolecule cracking process through the Flynn-Wall-Ozawa kinetic equation, and output key parameters including volatile matter yield, semi-coke pore structure change and gas phase gallium migration ratio. S3. Simulation of gas-solid gallium migration path: Computational fluid dynamics is used to simulate the flow of pyrolysis products in the reactor, focusing on tracking the diffusion path of gallium-containing gas phase components, and monitoring the gallium content in fly ash in real time to correct model parameters. S4. Modeling of selective gallium enrichment in pyrolysis ash: Hydrometallurgical enrichment pathway simulation was performed on pyrolysis ash. The leaching system was constructed by using hydrochloric acid as a medium to simulate gallium dissolution kinetics at 80℃ and introducing a competitive ion suppression strategy to ensure that the gallium leaching rate is >85% while the aluminum dissolution rate is <15%. S5. Solvent extraction and separation process optimization: A countercurrent extraction tower model was built, 7-dodecenyl-8-hydroxyquinoline was selected as the extractant and kerosene as the diluent; and the 7-dodecenyl-8-hydroxyquinoline extractant was recycled. S6. Full-process coupling and economic verification: The integrated module constructs a system dynamic model, introduces a mass balance equation, and uses Monte Carlo simulation to quantify the impact of geological parameter fluctuations on economics, ultimately outputting the feasibility range of the enrichment process.

2. The method for simulating the enrichment process of rare metal gallium in coalfields according to claim 1, characterized in that: In step S1, based on the geological exploration data of the target well field, a high-precision three-dimensional geological model is constructed in GMS software using the Kriging interpolation algorithm and three-dimensional geostatistics method. The model integrates geophysical logging data and X-ray fluorescence spectroscopy (XRF) analysis results from borehole cores.

3. The method for simulating the enrichment process of rare metal gallium in coalfields according to claim 2, characterized in that: The geophysical logging data includes gamma rays and resistivity curves; the occurrence format includes: The clay minerals are in an adsorbed state, accounting for 60%-70%, and are bonded to the interlayer hydroxyl groups of kaolinite. Organic matter bound state, accounting for 20%-30%, exists in the form of carboxylic acid or phenolic hydroxyl groups; Sulfide inclusions, accounting for 5%-10%, pyrite microcrystal inclusions.

4. The method for simulating the enrichment process of rare metal gallium in coalfields according to claim 2, characterized in that: In step S2, based on the constructed three-dimensional geological model, the CoalDECOAL reaction module is invoked, combined with the thermal-fluid-solid coupling module, to simulate the gallium release behavior during the coal seam pyrolysis process.

5. The method for simulating the enrichment process of rare metal gallium in coalfields according to claim 4, characterized in that: The parameters of the programmed temperature control curve are: room temperature is increased to the target temperature of 600-900℃ at a rate of 20℃ / min, held at that temperature for 30 minutes, and inertia is maintained. The flow rate is 1.5 L / min.

6. The method for simulating the enrichment process of rare metal gallium in coalfields according to claim 4, characterized in that, Step S3, the simulation of the gas-solid gallium migration path, includes the following: constructing a three-dimensional pyrolysis reactor CFD model; solving the gas-solid two-phase flow based on the gas phase composition and fly ash particle composition output in step S2; simulating the gas flow field using a turbulence model; analyzing gallium-containing gas phase migration using the Maxwell-Stefan multi-component diffusion equation; monitoring the gallium content in fly ash using an online laser-induced breakdown spectroscopy system; scanning the fly ash flow at the pyrolysis furnace outlet using a laser probe; exciting the fly ash particles to generate plasma using the laser focusing point; and collecting the intensity of characteristic spectral lines using a fiber optic spectrometer. The calibration based on the internal standard method uses a pre-calibrated curve to convert the spectral intensity ratio into the instantaneous value of gallium content; 300 sets of data are collected every 10 seconds, and after removing outliers, the moving average is used as the feedback signal input to the model. When the deviation between the measured value and the simulated value is greater than the set threshold, the gas-solid mass transfer coefficient or thermophoretic capture efficiency parameter is automatically adjusted; until the model error is compressed to less than 3.5% after calibration, an error distribution heat map is generated simultaneously to guide the optimization of probe sites.

7. The method for simulating the enrichment process of rare metal gallium in coalfields according to claim 6, characterized in that, The boundary conditions and verification process include: inlet / outlet settings, wall adsorption, and model calibration; the probe site optimization process is as follows: High-deviation areas were identified by error distribution heatmaps, and particle trajectory concentration cloud maps simulated by Fluent were used to optimize the probe's spatial coordinates using a multi-objective genetic algorithm. With the dual objectives of maximizing signal capture rate and minimizing dust interference, the optimal installation site was calculated, and it was verified whether the laser penetration path avoided the high-temperature turbulence zone. Finally, an adjustable universal bracket was deployed for precise three-dimensional positioning. The universal bracket had an accuracy of ±2mm, and a backflushing nitrogen curtain was installed simultaneously. The nitrogen curtain was a 0.5MPa pulsed structure to suppress contamination of the probe mirror surface.

8. The method for simulating the enrichment process of rare metal gallium in coalfields according to claim 6, characterized in that: Based on the gallium-rich fly ash output in step S3, a multiphase leaching reaction system was constructed, and the selective extraction of gallium under hydrochloric acid medium was simulated using a thermodynamic-kinetic coupling algorithm.

9. The method for simulating the enrichment process of rare metal gallium in coalfields according to claim 1, characterized in that, It also includes optimization processes for solvent extraction and separation: In the leachate, a three-stage countercurrent extraction system was constructed using 7-dodecenyl-8-hydroxyquinoline as the extractant. By precisely controlling the pH of the aqueous phase to 2.5–3.0, high selective separation of gallium was achieved. The concentration of the organic phase, the flow ratio, and the mixing time were optimized. Ascorbic acid was added to inhibit iron ions in response to interference from iron and silicon. A three-step regeneration and recycling system is established to recover and utilize the 7-dodecenyl-8-hydroxyquinoline, including the following: Organic phase purification after back-extraction: Gallium-loaded organic phase is purified... + After back-extraction, gallium is transferred to the aqueous phase, and the residual organic phase enters the regeneration tank. It is washed with 5% NaOH solution at 45°C for 20 minutes to neutralize the residual acidity and decompose the degradation products. After washing, the organic phase is separated from the aqueous phase by a coalescing plate. Activated adsorption for impurity removal: Activated alumina particles are injected into the regenerated organic phase, and the mixture is circulated and filtered at 50°C for 30 minutes for selective adsorption. and Metallic impurities are removed by passing colloidal suspended matter through a 0.1μm precision filter, the permeability of which is >99%. Vacuum dehydration and regeneration: The treated organic phase enters a vacuum flash evaporator with parameters of -0.08 MPa and 80°C. Trace amounts of moisture are removed and reduced to less than 0.05%, while volatile kerosene components are recovered, allowing the regenerated organic phase to be returned to the extraction system for reuse.

10. The method for simulating the enrichment process of rare metal gallium in coalfields according to claim 1, characterized in that: The mass balance equation is: ; in: n The total number of raw material input streams, with a value range of ≥3; Let be the mass of gallium in the i-th input stream, with a value ranging from . ; The value represents the total mass of the final gallium product, and its range is determined by the enrichment efficiency. k This represents the number of gallium loss paths, with a value ranging from 2 to 4. Let the gallium mass be the value of the j-th loss path. Meanwhile, the model in step S4 receives real-time data from factory sensors, automatically optimizes parameters every 24 hours, synchronizes the physical equipment status every 5 seconds, and couples with the simulation model in steps S1–S5. The LSTM time-series prediction algorithm is used to compare the actual operating data with the simulated values ​​and automatically correct the deviation of the dynamic parameters.

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