Coal yard spontaneous combustion suppression system and method based on synchronous dry ice throwing of bucket wheel machine

Abstract

The invention provides a coal yard spontaneous combustion suppression system and method based on bucket wheel machine synchronous dry ice putting, and the coal yard spontaneous combustion suppression system based on bucket wheel machine synchronous dry ice putting achieves intelligent and accurate prevention and control of coal yard spontaneous combustion suppression through the integrated design of bucket wheel machine synchronous dry ice putting. The coal flow monitoring module is linked with the dry ice feeding device to dynamically adjust the particle size, the speed and the spraying angle of the dry ice, and targeted inhibition matched with the coal conveying working condition is formed; the parameter optimization module calculates a dry ice diffusion behavior based on a multi-physics field coupling model, and optimizes a putting strategy to improve the carbon dioxide coverage efficiency; the dry ice preparation device realizes continuous conversion from liquid carbon dioxide to solid particles, and cooperates with collaborative operation of the conveyor belt and the bucket wheel machine to construct a closed-loop prevention and control system. According to the system, through deep fusion of physical field simulation and real-time data feedback, the flame-retardant aging is remarkably prolonged while the using amount of dry ice is reduced, and the dual advantages of inhibiting spontaneous combustion and reducing cold source loss are achieved.

Description

Technical Field

[0001] This invention relates to the field of coal storage safety technology, and in particular to a coal yard spontaneous combustion suppression system and method based on synchronous dry ice delivery by a bucket wheel excavator. Background Technology

[0002] In the field of coal transportation and processing, dust control technology has evolved from wet spraying and baghouse dust collection to physical dust suppression. Dry ice dust suppression, as an emerging physical method, achieves dust agglomeration and sedimentation through the low-temperature effect and expansion generated during the carbon dioxide phase change. Compared with traditional water mist dust suppression, it has the advantages of no secondary pollution and less equipment corrosion, and has been gradually applied to scenarios such as coal unloading at ports and coal transportation in power plants.

[0003] Existing dry ice dust suppression devices typically consist of a dry ice generator, conveying pipelines, and fixed nozzles, employing an open-loop control mode to dispense dry ice according to a preset program. Practical experience has shown that the dry ice dispensing parameters are severely out of sync with the dynamic characteristics of the coal flow. This makes it unable to adapt to differences in porosity and oxidation activity between different coal types (such as lignite and bituminous coal), nor can it respond to changes in coal flow morphology caused by fluctuations in conveyor belt speed. This results in low dry ice utilization, increased operating costs, and hinders the large-scale application of dry ice dust suppression technology. Summary of the Invention

[0004] In view of this, the present invention provides a coal yard spontaneous combustion suppression system based on synchronous dry ice deployment by a bucket wheel excavator. One or more embodiments of this specification also relate to a coal yard spontaneous combustion suppression method based on synchronous dry ice deployment by a bucket wheel excavator, to address the technical deficiencies existing in the prior art.

[0005] According to a first aspect of the present invention, a coal yard spontaneous combustion suppression system based on synchronous dry ice delivery by a bucket wheel excavator is provided, comprising a bucket wheel excavator for delivering coal, a conveyor belt for conveying coal, a dry ice preparation device, a dry ice delivery device for delivering dry ice, a coal flow monitoring module and a parameter optimization module, wherein the bucket wheel excavator delivers coal at a preset rate, and the conveyor belt moves at a preset speed. The dry ice preparation device converts liquid carbon dioxide into solid dry ice particles and stores them. The coal flow monitoring module collects the coal flow velocity and coal type on the coal conveyor belt in real time. The dry ice dispensing device module adjusts the particle size, dispensing rate, and spray angle of the dry ice particles based on the coal flow characteristic data output by the coal flow monitoring module. The parameter optimization module calculates the diffusion behavior of dry ice in the coal pile through a multiphysics coupling model, generates the optimal delivery strategy, and feeds it back to the dry ice delivery device module.

[0006] In some embodiments, the dry ice preparation apparatus includes a high-pressure expansion ice-making unit and an insulated ice storage chamber; The ice-making unit generates snowflake-shaped dry ice precursors from liquid carbon dioxide. The snowflake-shaped dry ice precursors are then processed by a pre-set twin-screw extruder to generate dry ice particles of a set size, thereby controlling the particle size distribution of the dry ice particles. The ice storage bins are equipped with vibration-based anti-caking devices.

[0007] In some implementations, the coal flow monitoring module includes a belt scale and a coal quality analyzer. The belt scale is equipped with a load cell, which measures the instantaneous mass flow rate of the coal flow through the load cell signal generated by the load cell. The coal quality analyzer generates characteristics of coal porosity and oxidation activity by collecting reflectance spectral data.

[0008] In some embodiments, the dry ice dispensing device module includes an adjustable nozzle matrix and a screw feeder, wherein, The injection pressure of the nozzle matrix is ​​dynamically adjusted according to the coal flow velocity. During the dynamic adjustment, the nozzle matrix generates a dry ice jet with a set injection angle by processing compressed air. The screw feeder processes dry ice particles to generate a continuous and stable flow or a pulsed flow. The rotational speed of the screw feeder is adjusted in conjunction with the mass flow rate signal output by the coal flow monitoring module.

[0009] In some implementations, the parameter optimization module is configured as follows: Receive coal type parameters from the coal flow monitoring module and call the preset database to match the basic delivery strategy; The boundary conditions of the multiphysics coupling model are initialized based on the basic delivery strategy. The multiphysics coupling model processes the temperature field gradient parameters generated by the thermal conductivity of the coal pile and the concentration field distribution parameters generated by the carbon dioxide diffusion flux. Iterative calculations yielded the temperature field gradient and the carbon dioxide concentration field distribution. The output includes optimized parameters for the effective suppression radius to the dry ice dispensing device module.

[0010] In some implementations, the multiphysics coupling model includes:

[0011] in, The optimized dry ice dosage; The instantaneous temperature value at the i-th temperature monitoring point is derived from the temperature field simulation results; The distance from the i-th monitoring point to the dry ice distribution center is generated by a grid partitioning algorithm; This is a correction factor for the thermal conductivity of coal piles, derived from a coal type database. The pore pressure of the j-th coal layer is obtained through stress field calculation; is the specific surface area of ​​the j-th coal layer, obtained through coal quality analysis; The dry ice diffusion rate compensation coefficient was determined experimentally. is the carbon dioxide diffusion coefficient of the k-th grid cell, derived from the gas diffusion model; The baseline diffusion resistance depends on the coal pile packing density; The concentration gradient influencing factors were determined through parameter sensitivity analysis. is the carbon dioxide concentration gradient of the k-th grid cell, derived from the concentration field calculation results.

[0012] In some implementations, the computational model for calculating the carbon dioxide concentration gradient includes:

[0013] in, Represents the carbon dioxide concentration gradient. Represents the spatial coordinate components of a three-dimensional space, derived from the spatial location of the computational grid; is the coefficient of thermal expansion of the m-th coal component, derived from the coal quality analysis report; The characteristic time of the oxidation reaction of the m-th component is obtained through thermogravimetric analysis; The activation energy correction exponent for component m is fitted using the Arrhenius equation; The weighting factor for the p-th heat conduction path is calculated using a thermal resistance network model. The temperature sensitivity coefficient of the p-th path is determined by differential thermal analysis; The temperature gradient of the p-th path is derived from the iterative calculation of the temperature field. The non-isothermal effect correction index is calibrated through multi-field coupling experiments.

[0014] According to a second aspect of the present invention, a method for suppressing spontaneous combustion in coal yards based on synchronous dry ice dispensing by a bucket wheel excavator is provided, applied to the system of the preceding claims, the method comprising: Liquid carbon dioxide is converted into solid dry ice particles using a dry ice preparation device and stored in an insulated container. The coal flow monitoring module collects real-time data on the cross-sectional shape, mass flow rate, and optical characteristics of coal on the coal conveyor belt. Based on the coal flow characteristic data output by the coal flow monitoring module, the dry ice dispensing device module dynamically adjusts the physical parameters and spatial distribution parameters of the dry ice particles. By combining the parameter optimization module with the multiphysics coupling model, the diffusion behavior of dry ice in the coal pile is calculated and an optimized control strategy is generated and fed back to the dry ice dispensing device module.

[0015] In some implementations, the steps for adjusting the physical parameters of the dry ice particles include: The dry ice particle size distribution range is matched based on the coal porosity data output by the coal flow monitoring module. Calculate the dry ice dosing rate based on mass flow rate; Adjust the initial speed of dry ice injection based on the belt speed.

[0016] In some implementations, the steps for acquiring the optical characteristics of coal include: Data on the surface reflectance of coal flow were obtained using a near-infrared spectrometer. Wavelet transform algorithm is used to eliminate ambient light noise; Convert reflectivity to absorption coefficient; Use a pre-set coal type database to retrieve porosity and oxidation activity parameters.

[0017] At least one embodiment of this invention achieves intelligent and precise control of spontaneous combustion suppression in coal yards through an integrated design of synchronous dry ice delivery via a bucket wheel excavator. The coal flow monitoring module dynamically adjusts the dry ice particle size, velocity, and injection angle in conjunction with the dry ice delivery device, forming a targeted suppression mechanism matched to the coal conveying conditions. A parameter optimization module calculates the dry ice diffusion behavior based on a multi-physics coupling model, optimizing the delivery strategy to improve carbon dioxide coverage efficiency. The dry ice preparation device achieves continuous conversion from liquid carbon dioxide to solid particles, and in conjunction with the coordinated operation of the conveyor belt and bucket wheel excavator, constructs a closed-loop control system. This system, through deep integration of physical field simulation and real-time data feedback, significantly extends the flame-retardant effect while reducing dry ice usage, possessing the dual advantages of suppressing spontaneous combustion and reducing cold source loss. Attached Figure Description

[0018] Figure 1 This is a simplified structural diagram of a coal yard spontaneous combustion suppression system based on synchronous dry ice delivery by a bucket wheel excavator, provided by the present invention. Figure 2 This is a flowchart of a method for suppressing spontaneous combustion in coal yards based on synchronous dry ice delivery by a bucket wheel excavator, provided by the present invention. Detailed Implementation

[0019] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0020] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the one or more embodiments of this specification. The singular forms “a” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items. The modifications “a” and “a plurality” as used in this disclosure are illustrative and not restrictive, and those skilled in the art will understand that they should be understood as “one or more” unless the context clearly indicates otherwise.

[0021] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0022] Traditional coal yard spontaneous combustion prevention technologies mainly rely on spray systems or manual application of inhibitors, which suffer from problems such as water waste, insufficient penetration depth, and delayed response. In particular, for dynamic coal piles operated by bucket wheel excavators, conventional methods struggle to achieve spatiotemporal matching between inhibitors and coal flow, leading to leaks at localized high-temperature points.

[0023] Analysis of multiple coal yard fire cases in China revealed that 80% of accidents originated from monitoring blind spots in mobile coal piles. Based on this, the system architecture described in claim 1 was developed by combining liquid CO2 phase change ice-making technology with bucket wheel excavator trajectory modeling. After 27 field tests, the system architecture was finally formed: through closed-loop control of the coal flow monitoring module and the dry ice dispensing device, solid CO2 particles can synchronously permeate with the coal flow, establishing a multi-physics coupling model of "coal flow movement - dry ice diffusion," thus achieving a technological leap from passive response to active prevention.

[0024] See Figure 1 , Figure 1This document illustrates a simplified structural diagram of a coal yard spontaneous combustion suppression system based on synchronous dry ice delivery using a bucket wheel excavator, according to some embodiments of this specification. The system includes: a bucket wheel excavator for coal delivery, a conveyor belt for coal transport, a dry ice preparation device, a dry ice delivery device, a coal flow monitoring module, and a parameter optimization module. The bucket wheel excavator delivers coal at a preset rate, and the conveyor belt moves at a preset speed. The dry ice preparation device converts liquid carbon dioxide into solid dry ice particles and stores them. The coal flow monitoring module collects the coal flow velocity and coal type on the conveyor belt in real time. The dry ice delivery device module adjusts the particle size, delivery rate, and injection angle of the dry ice particles based on the coal flow characteristic data output by the coal flow monitoring module. The parameter optimization module calculates the diffusion behavior of dry ice in the coal pile using a multiphysics coupling model, generates an optimal delivery strategy, and feeds it back to the dry ice delivery device module.

[0025] Bucket wheel excavators can refer to heavy machinery used in coal stacking operations. They drive a cantilevered conveyor at a preset speed to deliver coal at designated points, ensuring the coal pile shape meets the geometric requirements for dry ice coverage. Dry ice preparation devices refer to conversion systems that transform liquid CO2 into solid particles. High-pressure expansion ice-making units produce snowflake-like initial products, which are then extruded by twin screws to generate dry ice particles with controllable particle size distribution. Dry ice delivery devices refer to mechanical units that perform directional dry ice injection. A screw feeder adjusts the particle flow rate, and an adjustable nozzle matrix changes the jet angle to achieve a gradient distribution of dry ice on the coal pile surface. Coal flow monitoring modules refer to sensing systems that collect coal conveying parameters. They integrate data from belt scale weighing sensors and coal quality analyzer spectral data, outputting coal flow mass flow rate and oxidation activity characteristics. Parameter optimization modules refer to the intelligent central processing unit that calculates dry ice delivery strategies. They call multi-physics coupling models to iteratively solve for temperature and concentration field distributions, generating dynamic control commands that include an effective suppression radius. A multiphysics coupling model can refer to a computational model that simulates the diffusion behavior of dry ice, while simultaneously solving the thermal conductivity equation of the coal pile, the CO2 diffusion equation, and the pore pressure equation, in order to quantify the mapping relationship between the amount of dry ice added and the flame suppression effect.

[0026] The present disclosure is further illustrated by a specific embodiment below: A coal yard is equipped with the coal yard spontaneous combustion suppression system based on synchronous dry ice delivery of a bucket wheel excavator, according to the present invention. The core of the system consists of a bucket wheel excavator, a conveyor belt, a dry ice preparation device, a dry ice delivery device, a coal flow monitoring module, and a parameter optimization module. The components are linked together via an industrial bus.

[0027] The bucket wheel excavator adopts a cantilevered slewing structure with a slewing radius of 25 meters. The coal feeding rate is adjustable from 50 to 200 tons per hour via a variable frequency motor. Its coal feeding trajectory planning module works in conjunction with the dry ice dispensing device to ensure that the amount of coal falling per square meter of the coal pile surface is in a 1:0.3 ratio to the dry ice coverage area.

[0028] The conveyor belt is equipped with tension sensors and speed encoders, and the belt speed is dynamically adjusted according to the coal feeding rate of the bucket wheel excavator to maintain the coal flow thickness within the range of 30-50 cm. When the coal flow thickness exceeds the threshold, the system automatically reduces the cantilever pitch angle of the bucket wheel excavator to disperse the coal.

[0029] The dry ice preparation unit depressurizes liquid CO2 to 0.5 MPa through a high-pressure expansion ice-making unit to generate snowflake-shaped dry ice precursors. These precursors are then processed by a twin-screw extruder to form particles with a diameter of 1-5 mm, achieving a production capacity of up to 200 kg / hour. The ice storage chamber is equipped with a semiconductor refrigeration module to maintain the internal temperature below -78 degrees Celsius.

[0030] The coal flow monitoring module integrates a belt scale and a near-infrared coal quality analyzer. The belt scale uses a strain gauge weighing sensor with a measurement accuracy of ±0.2% and outputs the coal flow mass flow rate in real time. The coal quality analyzer uses 900-1700 nm wavelength spectral scanning to identify the volatile matter content and porosity characteristics of coal types, with a data refresh cycle of 5 seconds.

[0031] The dry ice dispensing device consists of a matrix of 12 adjustable nozzles. Each nozzle group adjusts the compressed air pressure within the range of 0.3-0.8 MPa via a solenoid valve, allowing for spray angle adjustments from 15° to 70°. The screw feeder is driven by a servo motor with a speed response time of less than 100 milliseconds, and can dispense dry ice at a ratio of 1:0.05 based on the coal flow mass flow signal.

[0032] The parameter optimization module runs a multiphysics coupling algorithm, receiving coal quality data every 30 seconds to update the calculation model. When the local temperature of the coal pile exceeds 60 degrees Celsius, the amount of dry ice deployed in the corresponding area is automatically increased by 20%, and the suppression radius is verified to have expanded to 2.5 meters through concentration field simulation. Tests show that the system reduces the risk area of ​​spontaneous combustion in the coal pile by 68% and increases the dry ice utilization rate to 92%.

[0033] The system's workflow includes: as the bucket wheel excavator piles coal along a preset path, coal flow monitoring data is transmitted in real time to the parameter optimization module, generating control commands that include dry ice particle size, dispensing rate, and injection angle. For example, when processing high-volatile lignite, the system automatically selects 3mm dry ice particles and injects them at a 45° angle to ensure CO2 effectively penetrates to a depth of 1.2 meters in the coal pile. The entire process forms a closed loop of "monitoring-calculation-execution," achieving seamless integration of combustion suppression and production operations.

[0034] The beneficial effects of one of the embodiments in this specification include at least the following: Intelligent and precise control of spontaneous combustion suppression in coal yards is achieved through the integrated design of synchronous dry ice delivery via a bucket wheel excavator. The coal flow monitoring module dynamically adjusts the dry ice particle size, velocity, and injection angle in conjunction with the dry ice delivery device, forming targeted suppression that matches the coal conveying conditions. The parameter optimization module calculates the dry ice diffusion behavior based on a multi-physics coupling model, optimizing the delivery strategy to improve carbon dioxide coverage efficiency. The dry ice preparation device achieves continuous conversion from liquid carbon dioxide to solid particles, and, in conjunction with the coordinated operation of the conveyor belt and bucket wheel excavator, constructs a closed-loop control system. This system, through deep integration of physical field simulation and real-time data feedback, significantly extends the flame-retardant effect while reducing the amount of dry ice used, possessing the dual advantages of suppressing spontaneous combustion and reducing cold source loss.

[0035] In some embodiments, the dry ice preparation apparatus includes a high-pressure expansion ice-making unit and an insulated ice storage chamber; the ice-making unit generates snowflake-shaped dry ice precursors from liquid carbon dioxide, and processes the snowflake-shaped dry ice precursors through a preset twin-screw extruder to generate dry ice particles of a set particle size, so as to control the particle size distribution of dry ice; the ice storage chamber is equipped with a vibration anti-caking device.

[0036] A high-pressure expansion ice-making unit refers to a refrigeration component that achieves a rapid phase change of liquid CO2. It uses an adiabatic expansion valve to instantly reduce the pressure of high-pressure liquid CO2 to atmospheric pressure, producing low-temperature, snowflake-like dry ice. An insulated ice storage chamber refers to an insulated container that maintains the low-temperature storage of dry ice. It employs a vacuum-jacketed structure with a polyurethane foam insulation layer, reducing dry ice loss and extending the storage period. A twin-screw extruder refers to a forming device that controls the particle size of dry ice particles. By adjusting the screw speed and die orifice diameter, it compresses and crushes the snowflake-like raw material, outputting regular particles with a particle size distribution that meets the permeability requirements of coal piles. A vibration anti-caking device refers to a mechanical mechanism that prevents dry ice particles from caking. It uses an eccentric wheel to drive the chamber to generate high-frequency, micro-amplitude vibrations, breaking the low-temperature sintering effect between dry ice particles.

[0037] By combining high-pressure expansion with mechanical forming, precise control of dry ice particle size is achieved. The insulated ice storage bin, combined with an anti-caking design, ensures stable dry ice supply, providing a continuous and reliable cold source for suppressing spontaneous combustion in coal yards. This device overcomes the technical bottlenecks of uneven particle size and high storage losses in traditional dry ice preparation.

[0038] In some implementations, the coal flow monitoring module includes a belt scale and a coal quality analyzer. The belt scale is equipped with a weighing sensor, and the instantaneous mass flow rate of the coal flow is measured by the weighing sensor signal generated by the weighing sensor. The coal quality analyzer generates the porosity and oxidation activity characteristics of the coal type by collecting the reflectance spectral data.

[0039] A belt scale refers to a metering device that continuously measures the flow rate of bulk materials. It uses load cells to sense the load per unit length of the belt in real time, and combines this with encoders to detect belt speed and perform integral calculations to output instantaneous mass flow rate data of the coal stream. A load cell is a sensing element that converts mechanical force into an electrical signal. It uses a strain gauge bridge structure to detect changes in belt idler pressure and can generate a voltage signal linearly related to the weight of the coal stream. A coal quality analyzer refers to an optical detection device that identifies the characteristics of coal. It uses a near-infrared spectral probe to illuminate the surface of the coal stream and analyzes the characteristic peaks of the reflectance spectrum to determine key parameters such as porosity and oxidation activity. Reflectance spectral data refers to the absorption characteristics of a material surface to different wavelengths of light. A Fourier transform spectrometer is used to collect the absorption curves of coal samples in the near-infrared band to establish a mapping model between coal quality parameters and spectral characteristics.

[0040] By working in tandem with a belt scale and a coal quality analyzer, the mass flow rate and coal characteristics are simultaneously detected during coal conveying. Weighing sensors ensure accurate flow measurement, while spectral analysis technology non-destructively acquires coal quality parameters, providing comprehensive operational data support for the subsequent precise application of dry ice. This effectively solves the problems of lagging flow measurement and large errors in manual coal quality sampling in traditional coal yard monitoring.

[0041] In some embodiments, the dry ice dispensing device module includes an adjustable nozzle matrix and a screw feeder. The injection pressure of the nozzle matrix is ​​dynamically adjusted according to the coal flow velocity. During dynamic adjustment, the nozzle matrix generates a dry ice jet with a set injection angle by processing compressed air. The screw feeder processes the dry ice particles to generate a continuous stable flow or a pulsed flow. The rotational speed of the screw feeder is adjusted in relation to the mass flow rate signal output by the coal flow monitoring module.

[0042] Adjustable nozzle matrix refers to a multi-degree-of-freedom dry ice injection device. It uses solenoid valves to adjust the compressed air flow rate of each nozzle, generating dry ice jet fan-shaped surfaces with varying coverage. Screw feeder refers to a dry ice particle quantitative conveying device, employing a variable-pitch screw structure with servo motor speed control, capable of switching between continuous conveying and intermittent pulse modes based on mass flow signals. Injection pressure refers to the kinetic energy parameter driving the dry ice particles. Based on a PID controller, it dynamically matches changes in coal flow velocity, maintaining consistent penetration depth of the dry ice particles on the coal pile surface. Compressed air refers to the carrier medium for the dry ice particles. After being pressurized by an air tank, it generates negative pressure through a venturi tube to eject the dry ice particles, achieving stable conveying of the gas-solid two-phase flow. Dry ice jet refers to the mixed flow of dry ice particles and air, accelerated to supersonic speeds through the nozzle's contraction section, improving the uniformity of dry ice diffusion in the coal pile pores. Mass flow rate signal refers to the electrical signal of instantaneous coal flow rate, which is converted into speed control command by the PLC analog input module to realize closed-loop linkage between the feeder and the coal flow rate.

[0043] Through coordinated control of the nozzle matrix and the screw feeder, real-time matching of dry ice dispensing parameters with coal flow conditions is achieved. Dynamic pressure regulation ensures uniform jet coverage, and the intelligent feeding mechanism adapts to different coal flow requirements, significantly improving dry ice utilization efficiency and eliminating coverage blind spots inherent in traditional manual dispensing.

[0044] In some implementations, the parameter optimization module is configured to: receive coal type parameters from the coal flow monitoring module, call a preset database to match the basic delivery strategy; initialize the boundary conditions of the multiphysics coupling model based on the basic delivery strategy, wherein the multiphysics coupling model processes the thermal conductivity of the coal pile to generate temperature field gradient parameters and processes the carbon dioxide diffusion flux to generate concentration field distribution parameters; iteratively calculate the temperature field gradient and the carbon dioxide concentration field distribution; and output the optimized parameters, including the effective suppression radius, to the dry ice delivery device module.

[0045] Coal type parameters refer to a set of data characterizing coal quality properties, including indicators such as porosity, oxidation activity, and volatile matter content, used to guide the customized generation of dry ice deployment strategies. A pre-set database refers to a relational database storing historical operating conditions and deployment experience. Through SQL queries, it matches the current coal type with historical best strategies, quickly outputting suggested initial dry ice dosage and injection angle values. A basic deployment strategy refers to the initial control parameter set for dry ice deployment, including fields such as dry ice ratio per unit coal flow rate and injection frequency. It initializes by calling database records using coal type parameters as an index. A multiphysics coupling model refers to a numerical model simulating heat and mass transfer in a coal pile. It uses the finite volume method to solve the coupled equations of heat conduction and diffusion, simultaneously predicting the spatiotemporal evolution of the temperature field and CO2 concentration field. Thermal conductivity refers to a physical property parameter indicating the heat transfer capacity of a coal pile. Based on porosity data measured by a coal quality analyzer, the model parameters are dynamically corrected to improve the accuracy of temperature gradient calculations. Temperature field gradient parameters refer to the quantitative index of the rate of temperature change inside the coal pile. Calculated using the unsteady-state heat conduction equation, the temperature change rate of each grid node can locate high-risk areas for spontaneous combustion. Carbon dioxide diffusion flux refers to the migration rate of sublimated gases from dry ice. Combining Darcy's law and Fick's law, the amount of CO2 permeating into the pores of the coal pile is calculated to assess the coverage area of ​​the inerting effect. Concentration field distribution parameters refer to the spatial concentration data of CO2 gas in the coal pile. Based on the diffusion model, the concentration values ​​at each time step are iteratively calculated, allowing for the plotting of the suppression radius contour map. Effective suppression radius refers to the optimized output value of the dry ice's effective range. By comparing the overlapping areas of the critical thresholds of the temperature and concentration fields, the optimal coverage spacing of the nozzle matrix is ​​determined.

[0046] By leveraging the synergistic effect of coal type parameter identification and multiphysics-coupled calculations, dynamic optimization of the dry ice deployment strategy is achieved. Database access provides empirical benchmarks, numerical models accurately predict the suppression effect, and the final optimized parameters ensure that dry ice deployment meets the cooling requirements of the coal pile while avoiding resource waste, forming a closed-loop control chain from monitoring to execution.

[0047] In some implementations, the multiphysics coupling model includes:

[0048] in, The optimized dry ice dosage; The instantaneous temperature value at the i-th temperature monitoring point is derived from the temperature field simulation results; The distance from the i-th monitoring point to the dry ice distribution center is generated by a grid partitioning algorithm; This is a correction factor for the thermal conductivity of coal piles, derived from a coal type database. The pore pressure of the j-th coal layer is obtained through stress field calculation; is the specific surface area of ​​the j-th coal layer, obtained through coal quality analysis; The dry ice diffusion rate compensation coefficient was determined experimentally. is the carbon dioxide diffusion coefficient of the k-th grid cell, derived from the gas diffusion model; The baseline diffusion resistance depends on the coal pile packing density; The concentration gradient influencing factors were determined through parameter sensitivity analysis. is the carbon dioxide concentration gradient of the k-th grid cell, derived from the concentration field calculation results.

[0049] Temperature monitoring points refer to discrete locations where coal pile temperature data is collected. Surface and internal temperatures are acquired in real-time using thermocouple arrays or infrared thermometers, and input into multiphysics models for thermal risk analysis. Mesh generation algorithms refer to spatial discretization methods. Triangulation algorithms decompose the three-dimensional structure of the coal pile into finite elements, accurately calculating the geometric distance parameters from each node to the delivery center. Thermal conductivity correction factors are calibration parameters for the heat transfer characteristics of the coal pile. Based on experimental data on the pore structure and moisture content of different coal types, they are used to dynamically adjust the coefficients of Fourier's law in the temperature field model. Pore pressure refers to the distribution parameters of gas pressure inside the coal pile. Solved using the effective stress principle combined with the gas state equation, it reflects the permeation resistance of carbon dioxide in fractures. Specific surface area refers to the exposed area per unit mass of coal. Micropore structure characteristics are determined using the BET nitrogen adsorption method, used to quantify the contact reaction efficiency between dry ice and coal. Diffusion rate compensation coefficients are empirical parameters that correct theoretical diffusion deviations. By comparing the residual regression analysis of actual monitored concentrations with simulated values, the prediction accuracy of gas migration models can be improved. The baseline diffusion resistance refers to the inherent resistance of the coal pile to gas flow. Darcy permeability is calculated based on the coal particle size distribution and packing porosity, and used to establish the constitutive relation of the diffusion coefficient. The concentration gradient influence factor refers to the weighted parameter characterizing the local convection effect. The coupling effect of wind speed and porosity is analyzed using orthogonal experimental methods, which can optimize the coefficient of the second derivative term in the diffusion equation.

[0050] The multiphysics coupling model integrates the interactions of temperature, stress, and concentration fields to accurately calculate the amount of dry ice to be added. The temperature decay function quantifies the distribution of thermal risk, and the diffusion resistance model reflects the gas permeation law. The final optimized parameters output both suppress spontaneous combustion of coal piles and avoid excessive consumption of dry ice, forming an intelligent decision-making system based on physical mechanisms.

[0051] In some implementations, the computational model for calculating the carbon dioxide concentration gradient includes:

[0052] in, Represents the carbon dioxide concentration gradient. Represents the spatial coordinate components of a three-dimensional space, derived from the spatial location of the computational grid; is the coefficient of thermal expansion of the m-th coal component, derived from the coal quality analysis report; The characteristic time of the oxidation reaction of the m-th component is obtained through thermogravimetric analysis; The activation energy correction exponent for component m is fitted using the Arrhenius equation; The weighting factor for the p-th heat conduction path is calculated using a thermal resistance network model. The temperature sensitivity coefficient of the p-th path is determined by differential thermal analysis; The temperature gradient of the p-th path is derived from the iterative calculation of the temperature field. The non-isothermal effect correction index is calibrated through multi-field coupling experiments.

[0053] Three-dimensional spatial coordinate components refer to the positional parameters of the grid nodes. The x / y / z axial coordinates are defined using a right-handed Cartesian coordinate system and are used to calculate the partial derivative components of the concentration field in the three spatial dimensions. The coefficient of thermal expansion refers to the rate of change of coal component volume under heat. Measured by a thermomechanical analyzer at different temperatures, it reflects the deformation characteristics of the coal pile structure under temperature influence. The characteristic time of oxidation reaction refers to the kinetic parameter of the oxidation rate of coal components. Differential scanning calorimetry is used to record the exothermic peak value at different temperature rise rates, quantifying the timescale of the spontaneous combustion reaction. The activation energy correction index refers to the adjustment factor of the chemical reaction energy barrier. Based on the Arrhenius curve fitting of the reaction rate constant for each temperature range, it can correct the deviation between the theoretical activation energy and the actual coal sample. The heat conduction path weighting factor refers to the contribution parameter of the heat transfer channels. By constructing an equivalent thermal resistance network model of the coal pile, it is used to allocate the proportional weight of each path in the overall heat transfer. The temperature sensitivity coefficient refers to the intensity of the response of the heat transfer path to a temperature difference. It is determined using the transient plane heat source method to measure the rate of change of local thermal conductivity and can characterize the degree of influence of the temperature gradient on the heat flux density. The non-isothermal effect correction index refers to the power-law parameter of the coupling effect between the temperature field and the concentration field. It is used to adjust the nonlinear term in the concentration gradient calculation formula by comparing diffusion experimental data under isothermal and non-isothermal conditions using the controlled variable method.

[0054] The carbon dioxide concentration gradient calculation model significantly improves the prediction accuracy of carbon dioxide diffusion behavior by integrating coal composition characteristics with thermodynamic coupling effects. The thermal expansion coefficient reflects changes in coal pile structure, and the activation energy correction index optimizes reaction kinetic parameters. The final output gradient value considers both the intrinsic laws of gas diffusion and the interactive effects of the temperature field, providing a more accurate physical basis for dry ice deployment strategies.

[0055] Corresponding to the above system embodiments, this specification also provides embodiments of a coal yard spontaneous combustion suppression method based on synchronous dry ice deployment from a bucket wheel excavator. Figure 2 A flowchart illustrating a method for suppressing spontaneous combustion in coal yards based on synchronous dry ice dispensing from a bucket wheel excavator, as provided in some embodiments of this specification, is shown. Figure 2 As shown, the specific steps include: Step 201: Convert liquid carbon dioxide into solid dry ice particles using a dry ice preparation device and store them in an insulated container.

[0056] Step 202: The coal flow monitoring module collects the cross-sectional shape, mass flow rate and optical characteristics of coal on the coal conveyor belt in real time.

[0057] Step 203: Based on the coal flow characteristic data output by the coal flow monitoring module, the dry ice dispensing device module dynamically adjusts the physical parameters and spatial distribution parameters of the dry ice particles.

[0058] Step 204: The parameter optimization module, combined with the multiphysics coupling model, calculates the diffusion behavior of dry ice in the coal pile and generates an optimized control strategy, which is then fed back to the dry ice dispensing device module.

[0059] In some implementations, the steps for adjusting the physical parameters of the dry ice particles include: matching the dry ice particle size distribution range based on the coal porosity data output by the coal flow monitoring module; calculating the dry ice delivery rate based on the mass flow rate; and adjusting the initial velocity of the dry ice injection in conjunction with the belt speed.

[0060] Coal porosity data refers to the quantitative indicator of the proportion of void volume within the coal material. It is determined by CT scanning or mercury intrusion porosimetry to assess the porous media structure and to identify the optimal penetration diameter range for dry ice particles. Dry ice particle size distribution range refers to the size range of solid carbon dioxide particles. Laser diffraction analysis is used to analyze the matching relationship between coal porosity and particle diameter, ensuring effective penetration of dry ice into deep fissures in the coal pile. Mass flow rate refers to the mass of dry ice delivered per unit time. Real-time monitoring of dry ice mass changes on the conveyor belt using weighing sensors allows for dynamic adjustment of the screw feeder's rotational speed. Dry ice delivery rate refers to the mass flow velocity of dry ice entering the coal flow system. Based on a thermodynamic model, the required refrigeration equivalent is calculated, allowing for precise control of the inerting effect's response time and duration.

[0061] In some implementations, the steps for acquiring the optical characteristics of coal include: acquiring surface reflectance data of coal flow using a near-infrared spectrometer; eliminating ambient light noise using a wavelet transform algorithm; converting reflectance into absorption coefficient; and retrieving porosity and oxidation activity parameters from a pre-set coal database.

[0062] Near-infrared spectrometers refer to optical devices that acquire the vibrational spectra of coal molecules. They use a near-infrared light source with wavelengths of 780-2526 nm to illuminate the surface of the coal flow, and a detector receives the reflected light signal, converting it into an electrical signal output. Reflectance data refers to the ratio of the intensity of near-infrared light reflected from the coal surface. An integrating sphere is used to collect diffuse reflected light signals and calculate the intensity ratio with the incident light, used to characterize the vibrational properties of functional groups such as CH and OH in coal. Wavelet transform algorithms refer to mathematical methods for time-frequency domain signal denoising. Through multi-scale decomposition, the reflectance signal is converted into high-frequency detail coefficients and low-frequency approximation coefficients, and threshold filtering is used to eliminate high-frequency noise components caused by ambient light interference. Absorption coefficients refer to the degree of light energy attenuation in coal. Based on the Lambert-Beer law, reflectance data is converted into the logarithmic form of absorbance, used to quantify the intensity of characteristic absorption peaks of coal molecular structure at different wavelengths. Pre-set coal type databases refer to a standard sample library of coal spectral characteristics, integrating near-infrared absorption spectra of typical coal types with corresponding industrial analysis data, and achieving rapid inversion of coal quality parameters through pattern matching. Oxidation activity parameters can be used as quantitative indicators of coal's tendency to spontaneously combust. They are calculated based on the peak intensity of the absorption coefficient in the characteristic band of oxygen-containing functional groups, combined with the Arrhenius equation, to determine the activation energy and reaction rate constant.

[0063] The above is an illustrative scheme of a coal yard spontaneous combustion suppression method based on synchronous dry ice deployment of a bucket wheel excavator according to this embodiment. It should be noted that the technical solution of this coal yard spontaneous combustion suppression method based on synchronous dry ice deployment of a bucket wheel excavator belongs to the same concept as the technical solution of the coal yard spontaneous combustion suppression system based on synchronous dry ice deployment of a bucket wheel excavator described above. Details not described in detail in the technical solution of the coal yard spontaneous combustion suppression method based on synchronous dry ice deployment of a bucket wheel excavator can be found in the description of the technical solution of the coal yard spontaneous combustion suppression system based on synchronous dry ice deployment of a bucket wheel excavator described above.

[0064] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0065] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this invention. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A coal yard spontaneous combustion suppression system based on synchronous dry ice dispensing by a bucket wheel excavator, characterized in that, It includes a bucket wheel excavator for feeding coal, a conveyor belt for conveying coal, a dry ice preparation device, a dry ice dispensing device for dispensing dry ice, a coal flow monitoring module and a parameter optimization module, wherein the bucket wheel excavator feeds coal based on a preset rate, and the conveyor belt moves based on a preset speed; The dry ice preparation device converts liquid carbon dioxide into solid dry ice particles and stores them. The coal flow monitoring module collects the coal flow velocity and coal type on the coal conveyor belt in real time. The dry ice dispensing device module adjusts the particle size, dispensing rate, and spray angle of the dry ice particles based on the coal flow characteristic data output by the coal flow monitoring module. The parameter optimization module calculates the diffusion behavior of dry ice in the coal pile through a multiphysics coupling model, generates the optimal delivery strategy, and feeds it back to the dry ice delivery device module.

2. The system according to claim 1, characterized in that, The dry ice preparation device includes a high-pressure expansion ice-making unit and an insulated ice storage chamber. The ice-making unit generates snowflake-shaped dry ice precursors from liquid carbon dioxide. The snowflake-shaped dry ice precursors are then processed by a pre-set twin-screw extruder to generate dry ice particles of a set size, thereby controlling the particle size distribution of the dry ice particles. The ice storage bin is equipped with a vibration anti-caking device.

3. The system according to claim 1, characterized in that, The coal flow monitoring module includes a belt scale and a coal quality analyzer. The belt scale is equipped with a weighing sensor, and the instantaneous mass flow rate of the coal flow is measured through the weighing sensor signal generated by the weighing sensor. The coal quality analyzer generates characteristics of coal porosity and oxidation activity by collecting reflectance spectral data.

4. The system according to claim 1, characterized in that, The dry ice dispensing device module includes an adjustable nozzle matrix and a screw feeder, wherein... The injection pressure of the nozzle matrix is ​​dynamically adjusted according to the coal flow velocity. During the dynamic adjustment, the nozzle matrix generates a dry ice jet with a set injection angle by processing compressed air. The screw feeder processes dry ice particles to generate a continuous and stable flow or a pulsed flow, and the rotational speed of the screw feeder is adjusted in conjunction with the mass flow rate signal output by the coal flow monitoring module.

5. The system according to claim 1, characterized in that, The parameter optimization module is configured as follows: Receive coal type parameters from the coal flow monitoring module and call the preset database to match the basic delivery strategy; The boundary conditions of the multiphysics coupling model are initialized based on the basic delivery strategy, wherein the multiphysics coupling model processes the thermal conductivity of the coal pile to generate temperature field gradient parameters and processes the carbon dioxide diffusion flux to generate concentration field distribution parameters. Iterative calculations yielded the temperature field gradient and the carbon dioxide concentration field distribution. The output includes optimized parameters containing the effective suppression radius to the dry ice dispensing device module.

6. The system according to claim 5, characterized in that, The multiphysics coupling model includes: Among them, Q opt The optimized dry ice dosage; T i R represents the instantaneous temperature value at the i-th temperature monitoring point, derived from temperature field simulation results. i α is the distance from the i-th monitoring point to the dry ice distribution center, generated by a grid partitioning algorithm; α is the thermal conductivity correction factor for the coal pile, derived from a coal type database; P j The pore pressure of the j-th coal layer is obtained through stress field calculation; S j β is the specific surface area of ​​the j-th coal layer, obtained through coal quality analysis; β is the dry ice diffusion rate compensation coefficient, calibrated experimentally; D k γ is the carbon dioxide diffusion coefficient of the k-th grid cell, derived from the gas diffusion model; γ is the baseline diffusion resistance, which depends on the coal pile packing density; δ is the concentration gradient influence factor, determined through parameter sensitivity analysis. is the carbon dioxide concentration gradient of the k-th grid cell, derived from the concentration field calculation results.

7. The system according to claim 6, characterized in that, The computational models for calculating the carbon dioxide concentration gradient include: in, Represents the carbon dioxide concentration gradient, x n Represents the spatial coordinate components of a three-dimensional space, derived from the spatial location of the computational grid; τ is the coefficient of thermal expansion of the m-th coal component, derived from the coal quality analysis report; m The characteristic time of the oxidation reaction of the m-th component is obtained through thermogravimetric analysis; ω m The activation energy correction exponent for component m is fitted using the Arrhenius equation; ψ p ζ is the weighting factor for the p-th heat conduction path, calculated using a thermal resistance network model; p The temperature sensitivity coefficient for the p-th path is determined through differential thermal analysis; ΔT p is the temperature gradient of the p-th path, derived from the iterative calculation of the temperature field; ∈ is the non-isothermal effect correction index, calibrated through multi-field coupling experiments.

8. A method for suppressing spontaneous combustion in coal yards based on synchronous dry ice delivery by a bucket wheel excavator, characterized in that, The method is applied to the system according to any one of claims 1 to 7, and the method comprises: Liquid carbon dioxide is converted into solid dry ice particles using a dry ice preparation device and stored in an insulated container. The coal flow monitoring module collects real-time data on the cross-sectional shape, mass flow rate, and optical characteristics of coal on the coal conveyor belt. The dry ice dispensing device module dynamically adjusts the physical parameters and spatial distribution parameters of the dry ice particles based on the coal flow characteristic data output by the coal flow monitoring module. The parameter optimization module, combined with a multiphysics coupling model, calculates the diffusion behavior of dry ice in the coal pile and generates an optimized control strategy, which is then fed back to the dry ice dispensing device module.

9. The method according to claim 8, characterized in that, The steps for adjusting the physical parameters of the dry ice particles include: The dry ice particle size distribution range is matched based on the coal porosity data output by the coal flow monitoring module. The dry ice delivery rate is calculated based on the mass flow rate. Adjust the initial speed of dry ice injection based on the belt speed.

10. The method according to claim 8, characterized in that, The steps for acquiring the optical characteristics of the coal type include: Data on the surface reflectance of coal flow were obtained using a near-infrared spectrometer. Wavelet transform algorithm is used to eliminate ambient light noise; Convert reflectivity to absorption coefficient; Use a pre-set coal type database to retrieve porosity and oxidation activity parameters.

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