Coking coal caking property improving method and system based on low-temperature upgrading

Abstract

The invention relates to the technical field of low-temperature upgrading, and discloses a coking coal caking property improving method and system based on low-temperature upgrading, and the method comprises the following steps: carrying out data acquisition on a target coking coal sample to obtain a basic coal quality parameter set; performing finite element numerical simulation on the carbonization behavior to obtain physical state structure hierarchical data; mapping the state structure hierarchical data to a preset volatile component release kinetics database to obtain a volatile component extraction rate curve; carrying out correlation analysis on a co-evolution behavior between a melting layer and a colloid layer in the target coking coal sample to obtain an initial deformation coefficient and an initial air permeability coefficient; performing global optimization on the low-temperature upgrading process parameter combination to obtain a deformation coefficient, an air permeability coefficient and an optimal process parameter group; when the technological requirements are met, outputting an optimal technological parameter group to obtain an optimal technological parameter message; according to the invention, the efficiency of improving the caking property of the coking coal can be improved.

Description

A method and system for improving the caking properties of coking coal based on low-temperature upgrading Technical Field

[0001] This invention relates to the field of low-temperature upgrading technology, and in particular to a method and system for improving the caking properties of coking coal based on low-temperature upgrading. Background Technology

[0002] In the technical field of low-temperature upgrading and caking property enhancement of coking coal, existing technologies lack a deep integration of heat and mass transfer and pyrolysis reaction kinetics in the analysis of coking coal's carbonization behavior. They rely solely on basic coal quality parameters for simple process settings, which cannot accurately capture the stratified changes in the physical structure of coking coal during carbonization. It is difficult to effectively analyze the evolution of the molten layer and the plastic layer, resulting in the inability to accurately obtain the deformation and permeability-related characteristic parameters of coking coal. Consequently, the setting of low-temperature upgrading process parameters lacks a scientific and precise theoretical basis.

[0003] Existing optimization methods for low-temperature coking coal upgrading processes mostly employ empirical trial-and-error adjustment models, failing to use the deformation coefficient and permeability of the coking coal itself as the core optimization guide for global optimization. The step size and iteration method of parameter adjustment lack standardized convergence judgment criteria, which easily leads to mismatches between process parameters and the coal's own quality characteristics. This not only fails to effectively improve the coking coal's binding properties but also causes ineffective resource consumption during the coking coal upgrading process due to unreasonable process parameter settings. Furthermore, it is difficult to output standardized and accurate process parameter reports, affecting the overall efficiency and quality stability of low-temperature coking coal upgrading. Summary of the Invention

[0004] This invention provides a method and system for improving the caking properties of coking coal based on low-temperature upgrading, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for improving the caking property of coking coal based on low-temperature upgrading, comprising: S1, collecting data from a target coking coal sample to obtain a set of basic coal quality parameters for the target coking coal sample; S2, performing finite element numerical simulation of the carbonization behavior of the target coking coal sample based on the heat and mass transfer and pyrolysis reaction kinetics mechanism of the coking process to obtain the physical structure layering data of the target coking coal sample; S3, mapping the physical structure layering data to a preset volatile matter release kinetics database to obtain the volatile matter release rate curve of the target coking coal sample; S4, based on the physical structure layering data... Based on the volatile matter analysis rate curve, the co-evolution behavior between the molten layer and the colloidal layer in the target coking coal sample is analyzed to obtain the initial deformation coefficient and initial permeability coefficient of the target coking coal sample; S5, using the initial deformation coefficient and initial permeability coefficient as optimization guides, the combination of low-temperature upgrading process parameters of the target coking coal sample is globally optimized to obtain the deformation coefficient, permeability coefficient and optimal process parameter set of the target coking coal sample; S6, when the process requirements of the target coking coal sample are met, the optimal process parameter set is output to obtain the optimal process parameter message of the target coking coal sample.

[0006] In a preferred embodiment, the step of data acquisition from the target coking coal sample to obtain the basic coal quality parameter set of the target coking coal sample includes: measuring and analyzing the industrial analysis entries in the basic test data file of the target coking coal sample to obtain the industrial analysis parameters of the target coking coal sample; retrieving the elemental composition parameters of the target coking coal sample from the coal elemental analysis database of the target coking coal sample; based on the cohesiveness test report of the target coking coal sample, calibrating the cohesiveness index in the cohesiveness test report to obtain the cohesiveness index and the maximum thickness of the plastic layer of the target coking coal sample; performing microscopic observation on the petrographic analysis slide of the target coking coal sample and measuring the vitrinite reflectance point by point within the observation field to obtain the average maximum reflectance of the vitrinite of the target coking coal sample; and compiling and encapsulating the industrial analysis parameters, the elemental composition parameters, the cohesiveness index, the maximum thickness of the plastic layer, and the average maximum reflectance of the vitrinite to obtain the basic coal quality parameter set of the target coking coal sample.

[0007] In a preferred embodiment, the step of performing finite element numerical simulation on the carbonization behavior of the target coking coal sample based on the heat and mass transfer and pyrolysis reaction kinetics mechanism of the coking process to obtain the physical structure layered data of the target coking coal sample includes: constructing a geometric domain for the axial space of the target coking coal sample in the carbonization chamber based on the basic coal quality parameter set, obtaining the physical space discrete domain of the target coking coal sample; meshing the physical space discrete domain to obtain the mesh element sequence of the target coking coal sample; and assigning thermophysical property parameters to the mesh element sequence based on the heat and mass transfer and pyrolysis reaction kinetics mechanism of the coking process to obtain the target... A parameterized grid node set for the coking coal sample is obtained. Based on the boundary constraints of the heating regime and gas phase pressure during the carbonization process in the target coking coal sample, boundary values ​​are assigned to the parameterized grid node set to obtain the constrained grid nodes of the target coking coal sample. The heat transfer flux and phase transition of the constrained grid nodes are coupled and advanced to obtain the phase attribute identifier of the target coking coal sample. Based on the phase attribute identifier, the phase assignment of the constrained grid nodes is extracted hierarchically to obtain the softening layer thickness, melting layer thickness, plastic layer thickness, and semi-coke layer thickness of the target coking coal sample, which serve as the physical structure hierarchical data of the target coking coal sample.

[0008] In a preferred embodiment, mapping the physical structure layering data to a preset volatile matter release kinetics database to obtain the volatile matter emission rate curve of the target coking coal sample includes: arranging the physical structure layering data in a time sequence to obtain an axial layering evolution sequence of the target coking coal sample; retrieving a volatile matter release feature template of the layering evolution sequence from the preset volatile matter release kinetics database based on the axial layering evolution sequence; comparing and locating the volatile matter release feature template with the axial layering evolution sequence to obtain a volatile matter emission rate feature value of the target coking coal sample; and reconstructing the volatile matter emission rate feature value along the carbonization time axis of the target coking coal sample to obtain the volatile matter emission rate curve of the target coking coal sample.

[0009] In a preferred embodiment, the step of correlating and analyzing the co-evolutionary behavior between the molten layer and the colloidal layer in the target coking coal sample based on the physical structure layering data and the volatile matter analysis rate curve to obtain the initial deformation coefficient and initial permeability coefficient of the target coking coal sample includes: performing axial layer stripping on the physical structure layering data to obtain the molten layer thickness evolution sequence and the colloidal layer thickness evolution sequence of the target coking coal sample; and correlating the molten layer thickness evolution sequence and the colloidal layer thickness evolution sequence based on waveform features to obtain the thickness of the target coking coal sample. The thickness evolution synergy map is used; the characteristic peak positions of the volatile matter analysis rate curve are identified to obtain the peak time of the target coking coal sample; based on the thickness evolution synergy map and the peak time, the contribution of the densification shrinkage of the molten layer and the permeability resistance of the gas phase in the colloidal layer of the target coking coal sample are calibrated using characteristic parameters to obtain the molten layer shrinkage contribution factor and the colloidal layer permeability characteristic factor of the target coking coal sample; the molten layer shrinkage contribution factor and the colloidal layer permeability characteristic factor are coupled and merged to obtain the initial deformation coefficient and the initial permeability coefficient of the target coking coal sample.

[0010] In a preferred embodiment, the step of globally optimizing the combination of low-temperature upgrading process parameters of the target coking coal sample, guided by the initial deformation coefficient and the initial permeability coefficient, to obtain the deformation coefficient and permeability coefficient of the target coking coal sample, includes: initializing the low-temperature upgrading process parameters of the target coking coal sample, using the assigned parameter set as the current iterative parameter set of the target coking coal sample, and using the initial deformation coefficient and the initial permeability coefficient as the current preferred index set of the target coking coal sample; applying parameter perturbations to the heating rate, holding temperature, and holding time in the current iterative parameter set to obtain the derived parameter set of the target coking coal sample; performing carbonization behavior deduction on the derived parameter set based on the basic coal quality parameter set of the target coking coal sample to obtain the derived deformation coefficient and derived permeability coefficient of the target coking coal sample; and comparing the derived deformation coefficient and the derived permeability coefficient with the deformation coefficient and permeability coefficient in the current preferred index set using a merit criterion to screen... The derived parameter set that excels in all indicators is selected as the winning parameter set for the target coking coal sample. When the winning parameter set is selected, it is used as the intermediate iteration parameter set, and the derived deformation coefficient and derived permeability coefficient of the winning parameter set are used as the intermediate preferred index set. The current iteration parameter set is updated with the intermediate iteration parameter set, and the current preferred index set is updated with the intermediate preferred index set. The operation of applying parameter perturbation to the updated current iteration parameter set is then performed. If the winning parameter set is not selected, the step size of the parameter perturbation is reduced according to a preset rule, and the generation of derived parameter sets and subsequent comparison and selection operations are re-executed based on the reduced step size. When the step size of the parameter perturbation is reduced to below a preset convergence threshold, the parameter perturbation iteration is terminated, and the last updated current iteration parameter set is used as the optimal process parameter set for the target coking coal sample. The deformation coefficient and permeability coefficient in the last updated current preferred index set are used as the deformation coefficient and permeability coefficient of the target coking coal sample.

[0011] In a preferred embodiment, the step of extrapolating the carbonization behavior of the derived parameter set based on the basic coal quality parameter set of the target coking coal sample to obtain the derived deformation coefficient and derived permeability coefficient of the target coking coal sample includes: deconstructing the heating rate, holding temperature, and holding time in the derived parameter set to obtain the boundary condition spectrum of the heating regime of the target coking coal sample; performing finite element numerical solution on the carbonization behavior of the target coking coal sample based on the basic coal quality parameter set and the boundary condition spectrum of the heating regime to obtain the layered derived data of the physical structure of the target coking coal sample; and performing feature indexing of the layered derived data of the physical structure with the feature templates in the volatile matter release kinetics database to obtain the volatile matter release coefficient of the target coking coal sample. The volatile matter analysis yields a rate-derived curve; waveform characteristics are correlated between the molten layer thickness evolution sequence and the colloidal layer thickness evolution sequence in the stratified data of the physical structure to obtain a thickness evolution co-derived map of the target coking coal sample; based on the thickness evolution co-derived map and the peak time of the volatile matter analysis rate-derived curve, characteristic parameters are calibrated for the contribution of molten layer densification shrinkage and the permeation resistance of the gas phase in the colloidal layer of the target coking coal sample to obtain the intermediate factor of molten layer shrinkage contribution and the intermediate factor of colloidal layer permeability characteristics of the target coking coal sample; the intermediate factor of molten layer shrinkage contribution and the intermediate factor of colloidal layer permeability characteristics are coupled and merged to obtain the derived deformation coefficient and derived permeability coefficient of the target coking coal sample.

[0012] In a preferred embodiment, the formula for calculating the melt layer thickness derived value in the material structure layered data is as follows: In the formula, This is a derived value for the thickness of the molten layer. The bonding index is the value in the set of basic coal quality parameters. The maximum thickness of the plastic layer in the set of basic coal quality parameters. The vitrinite average maximum reflectance is given by the set of basic coal quality parameters. The heating rate is the temperature rise rate in the boundary condition spectrum of the heating regime. The holding temperature is the temperature in the boundary condition spectrum of the heating regime. The holding time is the boundary condition time in the heating regime boundary condition spectrum. These are the preset dimensional normalization coefficients. This is the preset time response constant.

[0013] In a preferred embodiment, the step of outputting the optimal process parameter set and obtaining the optimal process parameter message of the target coking coal sample when the process requirements of the target coking coal sample are met includes: comparing the deformation coefficient and the permeability coefficient with preset process index thresholds; when both the deformation coefficient and the permeability coefficient pass the comparison verification, calling the message generation template, and sequentially writing the heating rate parameter, holding temperature parameter, and holding time parameter in the optimal process parameter set into the corresponding field positions of the message generation template to obtain the draft optimal process parameter message of the target coking coal sample; and encapsulating the format of the draft optimal process parameter message to obtain the optimal process parameter message of the target coking coal sample.

[0014] To address the aforementioned problems, this invention also provides a coking coal caking property enhancement system based on low-temperature upgrading. The system includes: a coal quality acquisition module for acquiring data from a target coking coal sample to obtain a basic set of coal quality parameters; a carbonization simulation module for performing finite element numerical simulation of the carbonization behavior of the target coking coal sample based on the heat and mass transfer and pyrolysis reaction kinetics mechanism of the coking process to obtain the physical structure layering data of the target coking coal sample; a volatile matter mapping module for mapping the physical structure layering data to a preset volatile matter release kinetics database to obtain the volatile matter release rate curve of the target coking coal sample; and a collaborative analysis module for... The physical structure layering data and the volatile matter rate curve are used to correlate and analyze the co-evolution behavior between the molten layer and the colloidal layer in the target coking coal sample, obtaining the initial deformation coefficient and initial permeability coefficient of the target coking coal sample. A process optimization module is used to globally optimize the combination of low-temperature upgrading process parameters for the target coking coal sample, using the initial deformation coefficient and initial permeability coefficient as optimization guidelines, to obtain the deformation coefficient, permeability coefficient, and optimal process parameter set of the target coking coal sample. A parameter output module is used to output the optimal process parameter set when the process requirements of the target coking coal sample are met, obtaining the optimal process parameter message for the target coking coal sample.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention relies on the heat and mass transfer and pyrolysis reaction kinetic mechanism of the coking process to carry out finite element numerical simulation of carbonization behavior, accurately capture the layered changes of the physical structure of coking coal, and complete the construction of the volatile matter release kinetic database to extract the volatile matter rate curve. Through in-depth correlation analysis of the co-evolution behavior of the molten layer and the colloidal layer, the initial deformation coefficient and initial permeability coefficient of coking coal are accurately calibrated, so that the optimization of low temperature upgrading process parameters has a scientific and accurate basis for coal quality characteristics, greatly improving the matching degree between process parameter setting and the characteristics of coking coal itself, and fundamentally ensuring the pertinence and effectiveness of coking coal caking improvement.

[0016] 2. This invention uses the initial deformation coefficient and initial permeability coefficient as the core optimization guide to carry out standardized global optimization iteration on the combination of low-temperature upgrading process parameters. Through cyclical operations of parameter perturbation, derivation of derived parameter groups, and comparison of advantages and disadvantages, combined with step size shrinkage and convergence threshold determination, the optimal process parameter group can be accurately screened. After verification by comparison of process index thresholds, a standardized optimal process parameter message is output, realizing the precise and standardized configuration of low-temperature upgrading process parameters for coking coal, effectively improving the overall efficiency of low-temperature upgrading of coking coal, and ensuring the stability and consistency of the coking coal caking property improvement effect. Attached Figure Description

[0017] Figure 1 is a flowchart illustrating a method for improving the caking properties of coking coal based on low-temperature upgrading according to an embodiment of the present invention; Figure 2 is a functional block diagram illustrating a system for improving the caking properties of coking coal based on low-temperature upgrading according to an embodiment of the present invention; the realization of the purpose, functional characteristics, and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a method for improving the caking properties of coking coal based on low-temperature upgrading. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for improving the caking properties of coking coal based on low-temperature upgrading can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0020] Referring to Figure 1, a flowchart illustrating a method for improving the caking properties of coking coal based on low-temperature upgrading is provided in an embodiment of the present invention. In this embodiment, the method for improving the caking properties of coking coal based on low-temperature upgrading includes: S1, collecting data from a target coking coal sample to obtain a set of basic coal quality parameters for the target coking coal sample; In this embodiment, collecting data from the target coking coal sample to obtain the set of basic coal quality parameters includes: measuring and analyzing the industrial analysis entries in the basic detection data file of the target coking coal sample to obtain the industrial analysis parameters of the target coking coal sample; retrieving the elemental composition parameters of the target coking coal sample from the coal elemental analysis database of the target coking coal sample; based on the... The cohesiveness test report of the target coking coal sample is used to calibrate the characteristic values ​​of the cohesiveness index in the cohesiveness test report, thereby obtaining the cohesiveness index and the maximum thickness of the plastic layer of the target coking coal sample; microscopic observation is performed on the petrographic analysis slide of the target coking coal sample, and the vitrinite reflectance within the observation field is measured point by point to obtain the average maximum vitrinite reflectance of the target coking coal sample; the industrial analysis parameters, the elemental composition parameters, the cohesiveness index, the maximum thickness of the plastic layer, and the average maximum vitrinite reflectance are compiled and packaged to obtain the basic coal quality parameter set of the target coking coal sample.

[0021] The basic test data file of the target coking coal sample is retrieved, and the relevant industrial analysis items are extracted from the file. The moisture, ash, volatile matter, and fixed carbon indices in the items are measured in the laboratory in sequence according to the national standard test methods for industrial analysis of coking coal. The test results are analyzed and verified. After confirming that the data is without deviation, they are integrated to form the industrial analysis parameters of the target coking coal sample.

[0022] The system accesses a dedicated coal quality element analysis database for coking coal. It performs precise retrieval in the database using the unique identifier of the target coking coal sample, and retrieves the content data of elements such as carbon, hydrogen, oxygen, nitrogen, and sulfur that correspond one-to-one with the coal sample. The retrieved data is then verified for completeness, and after confirming that there are no missing items, it is organized into the elemental composition parameters of the target coking coal sample.

[0023] Obtain the caking property test report of the target coking coal sample. In accordance with the industry standard requirements for coking coal caking property testing, carry out characteristic value calibration work on the caking property indicators in the report. For the caking index indicator, the bituminous coal caking index measurement method is used to complete the calibration value confirmation. For the maximum thickness of the plastic layer indicator, the plastic layer index measurement method is used to complete the calibration value confirmation. After double verification, the calibration values ​​are determined as the caking index and maximum thickness of the plastic layer of the target coking coal sample.

[0024] To prepare a petrographic analysis slide of the target coking coal sample, the slide was placed on the stage of the microscopic observation equipment. The equipment was adjusted to the preset magnification and illumination conditions. A uniformly distributed field of view was selected on the slide, and the reflectance of the vitrinite particles in each field of view was measured point by point. The reflectance values ​​of all measurement points were recorded, and the arithmetic mean of the values ​​was calculated to obtain the average maximum reflectance of the vitrinite of the target coking coal sample.

[0025] A standard data encapsulation framework for the basic coal quality parameter set is established. The framework includes dedicated data fields for industrial analysis parameters, elemental composition parameters, bonding index, maximum thickness of the plastic layer, and average maximum reflectance of the vitrinite group. Various types of coal quality data that have been acquired are entered into the framework one by one according to the field correspondence. The entered data is formatted and logically verified. After confirming that the data matches correctly, the framework is assembled and encapsulated to form the basic coal quality parameter set of the target coking coal sample.

[0026] The beneficial effects are that by conducting multi-dimensional coal quality data collection and analysis on target coking coal samples, accurate and comprehensive coal quality-related data can be obtained from multiple levels, including industrial analysis, elemental composition, cohesiveness indicators, and petrographic characteristics. This data, after standardized compilation and encapsulation, forms a complete set of basic coal quality parameters. This provides comprehensive, accurate, and standardized basic data support for subsequent numerical simulation of coking coal chemical behavior, physical structure analysis, volatile matter release law research, and optimization of low-temperature upgrading process parameters. This ensures the scientific rigor and accuracy of subsequent technical operations, allowing the entire process analysis and optimization of low-temperature coking coal upgrading to be based on accurate data closely matching the actual characteristics of the coal samples. This improves the reliability and relevance of coking coal cohesiveness enhancement technology implementation from the data source.

[0027] S2. Based on the heat and mass transfer and pyrolysis reaction kinetics mechanism of the coking process, finite element numerical simulation is performed on the carbonization behavior of the target coking coal sample to obtain the layered data of the physical structure of the target coking coal sample; In this embodiment of the invention, the step of performing finite element numerical simulation on the carbonization behavior of the target coking coal sample based on the heat and mass transfer and pyrolysis reaction kinetics mechanism of the coking process to obtain the layered data of the physical structure of the target coking coal sample includes: constructing a geometric domain in the axial space of the target coking coal sample in the carbonization chamber based on the basic coal quality parameter set to obtain the physical space discrete domain of the target coking coal sample; performing mesh partitioning on the physical space discrete domain to obtain the mesh element sequence of the target coking coal sample; based on the heat and mass transfer and pyrolysis reaction kinetics mechanism of the coking process... Based on the reaction kinetics mechanism, thermophysical parameters are assigned to the grid cell sequence to obtain the parameterized grid node set of the target coking coal sample. Boundary values ​​are assigned to the parameterized grid node set based on the boundary constraints of the heating regime and gas phase pressure during the carbonization process in the target coking coal sample to obtain the constrained grid nodes of the target coking coal sample. The heat transfer flux and phase transition of the constrained grid nodes are coupled and advanced to obtain the phase attribute identifier of the target coking coal sample. Based on the phase attribute identifier, the phase assignment of the constrained grid nodes is extracted hierarchically to obtain the softening layer thickness, melting layer thickness, colloidal layer thickness, and semi-coke layer thickness of the target coking coal sample, which serve as the hierarchical data of the physical structure of the target coking coal sample.

[0028] Based on the acquired set of basic coal quality parameters of the target coking coal sample, and according to the actual axial spatial dimensions of the carbonization chamber and the actual filling range of the target coking coal sample in the carbonization chamber, the geometric domain of the axial space is precisely constructed by spatial discretization. The continuous axial space is divided into multiple discrete geometric units, and the dimensions of each geometric unit meet the spatial accuracy requirements for the analysis of coking coal chemical behavior, thus forming the physical spatial discrete domain of the target coking coal sample.

[0029] The physical spatial discrete domain of the target coking coal sample is divided into grids according to the principle of uniform partitioning. The side length of each grid cell formed by partitioning is set to the standard grid size for coking coal pyrolysis analysis. All grid cells formed after partitioning are numbered in order and arranged according to their axial spatial position to form a grid cell sequence of the target coking coal sample.

[0030] Based on the heat and mass transfer and pyrolysis reaction kinetics mechanism of the coking process, characteristic attributes related to the thermal properties of coking coal are extracted from this mechanism. According to the numbering order of the grid cell sequence, the corresponding thermal property related attribute information is matched one by one for each grid cell. The thermal property parameters of all grid cells are assigned values. Then, the node information of all grid cells that have been assigned values ​​is integrated to obtain the parameterized grid node set of the target coking coal sample.

[0031] The standard heating range of the heating regime boundary constraint and the standard pressure range of the gas phase pressure boundary constraint during the carbonization process of the target coking coal sample are determined. Based on the standard heating range and standard pressure range, the nodes of the parameterized grid that are located at the axial spatial boundary of the carbonization chamber are assigned targeted boundary attribute values, while the other internal nodes retain their original parameter attributes. Finally, the constrained grid nodes of the target coking coal sample are obtained.

[0032] Following the time progression of the coking coal production process, and using a preset fixed time step, the heat flux changes and phase transition trends of the constrained grid nodes are analyzed synchronously and coupled. Within each time step, the heat flux transfer calculation and phase state determination of the nodes are completed. Each node is assigned a unique identifier that can uniquely identify its phase state, thus obtaining the phase attribute identifier of the target coking coal sample.

[0033] Based on the phase category corresponding to the phase attribute identifier, the constrained grid nodes are classified and filtered according to their phase category. All nodes belonging to the softening layer, melting layer, colloidal layer and semi-coke layer are extracted. Then, based on the distribution position of each layer node in the axial space of the carbonization chamber, the actual thickness value of each layer in the axial direction is calculated. The calculation accuracy of this thickness value is retained to the preset length accuracy threshold. The obtained thicknesses of the softening layer, melting layer, colloidal layer and semi-coke layer are integrated as the physical structure layering data of the target coking coal sample.

[0034] The beneficial effects are as follows: Based on the basic coal quality parameter set, the geometric domain of the axial space of the carbonization chamber is constructed and meshed. Combined with the core mechanism of the coking process, parameter assignment and boundary constraint setting of the mesh units are completed. Accurate identification of phase attributes is achieved through the coupling of heat transfer flux and phase transformation. Then, based on the phase attributes, the thickness of each layer is extracted, forming accurate layered data of the physical structure. This process deeply integrates the basic coal quality characteristics of coking coal with the dynamic mechanism of carbonization behavior, allowing the finite element numerical simulation results of carbonization behavior to closely match the actual carbonization process of coking coal. The obtained layered data of the physical structure can accurately reflect the phase distribution and thickness characteristics of each layer of coking coal during the carbonization process. This provides accurate and realistic basic data of the physical structure for the subsequent construction of the volatile matter rate curve and the analysis of the co-evolution behavior of the molten layer and the colloidal layer, ensuring the scientific nature and accuracy of subsequent analysis work and providing reliable carbonization behavior data support for the optimization of low-temperature upgrading process parameters of coking coal.

[0035] S3. Mapping the layered data of the physical structure to a preset volatile matter release kinetics database to obtain the volatile matter emission rate curve of the target coking coal sample; In this embodiment of the invention, mapping the layered data of the physical structure to a preset volatile matter release kinetics database to obtain the volatile matter emission rate curve of the target coking coal sample includes: arranging the layered data of the physical structure in a time sequence to obtain an axial layered evolution sequence of the target coking coal sample; retrieving the volatile matter release feature template of the layered evolution sequence from the preset volatile matter release kinetics database based on the axial layered evolution sequence; comparing and locating the volatile matter release feature template with the axial layered evolution sequence to obtain the volatile matter emission rate feature value of the target coking coal sample; and reconstructing the volatile matter emission rate feature value along the carbonization time axis of the target coking coal sample to obtain the volatile matter emission rate curve of the target coking coal sample.

[0036] Based on the time progression of the target coking coal's chemical process, the physical structure stratification data are sorted and arranged according to preset equal time intervals. The data on the thickness of the softening layer, melting layer, plastic body layer, and semi-coke layer at different time points are arranged sequentially to form an axial stratification evolution sequence of the target coking coal sample unfolded along the time dimension.

[0037] The axial stratification evolution sequence of the target coking coal sample is used as the retrieval basis and connected to a preset volatile matter release kinetics database. Through precise matching of stratification evolution features, the database is searched and retrieved for volatile matter release feature templates that perfectly match the axial stratification evolution sequence features.

[0038] The retrieved volatile matter release characteristic template is compared with the axial stratification evolution sequence of the target coking coal sample at each time node. During the comparison process, the feature matching points of the two are accurately located. Based on the location results, the volatile matter release related characteristic data at the corresponding time node are extracted and determined as the volatile matter release rate characteristic value of the target coking coal sample.

[0039] A carbonization time axis for the target coking coal sample was established, with the time scale of the time axis consistent with the time nodes of the axial stratification evolution sequence. The volatile matter rate characteristic values ​​corresponding to each time node were marked on the corresponding positions of the carbonization time axis. All marked characteristic value points were connected by continuous curve fitting to complete the construction and reconstruction of the curve, thus obtaining the volatile matter rate curve of the target coking coal sample.

[0040] The beneficial effects are as follows: by temporally sorting the layered data of physical structure to form an axial layered evolution sequence, and relying on the preset volatile matter release kinetic database to accurately retrieve matching feature templates, the volatile matter rate feature values ​​are extracted by comparing the templates with the sequence, and then the volatile matter release rate curve is obtained by reconstructing the curve along the carbonization time axis. The whole process deeply correlates the changes in physical structure of coking coal with the volatile matter release law during the coking process, so that the generation of the volatile matter release rate curve has physical structure data that fits the actual carbonization state of the coal sample. The obtained volatile matter release rate curve can accurately reflect the volatile matter release characteristics of coking coal at different carbonization stages, providing accurate dynamic data of volatile matter release for the subsequent correlation analysis of the co-evolution behavior of the molten layer and the colloidal layer, ensuring that the subsequent analysis work can be carried out by combining the dual characteristics of physical structure and volatile matter release, improving the scientificity and accuracy of the analysis results, and providing a reliable basis for the optimization of low-temperature upgrading process parameters.

[0041] S4. Based on the physical structure layering data and the volatile matter extraction rate curve, the co-evolution behavior between the molten layer and the colloidal layer in the target coking coal sample is analyzed to obtain the initial deformation coefficient and initial permeability coefficient of the target coking coal sample; In this embodiment of the invention, the step of analyzing the co-evolution behavior between the molten layer and the colloidal layer in the target coking coal sample based on the physical structure layering data and the volatile matter extraction rate curve to obtain the initial deformation coefficient and initial permeability coefficient of the target coking coal sample includes: performing axial layer stripping on the physical structure layering data to obtain the molten layer thickness evolution sequence and the colloidal layer thickness evolution sequence of the target coking coal sample; and transferring the molten layer to the colloidal layer layer. The thickness evolution sequence of the molten layer and the thickness evolution sequence of the colloidal layer are correlated with waveform features to obtain a thickness evolution co-evolution spectrum of the target coking coal sample; the characteristic peak positions of the volatile matter analysis rate curve are identified to obtain the peak time of the target coking coal sample; based on the thickness evolution co-evolution spectrum and the peak time, the contribution of the molten layer densification shrinkage and the permeation resistance of the gas phase in the colloidal layer of the target coking coal sample are calibrated with characteristic parameters to obtain the molten layer shrinkage contribution factor and the colloidal layer permeability characteristic factor of the target coking coal sample; the molten layer shrinkage contribution factor and the colloidal layer permeability characteristic factor are coupled and merged to obtain the initial deformation coefficient and the initial permeability coefficient of the target coking coal sample.

[0042] Axial layer-oriented stripping was performed on the stratified data of the physical structure. Independent data of the melt layer thickness and the colloidal layer thickness at each time node were extracted according to the time node sequence of the carbonization time axis. The two types of thickness data were arranged in order according to time sequence to form the melt layer thickness evolution sequence and the colloidal layer thickness evolution sequence of the target coking coal sample.

[0043] By placing the molten layer thickness evolution sequence and the colloidal layer thickness evolution sequence in the same time coordinate system, point-to-point feature correlation is performed on the waveform change trends of the two types of sequences, and the synchronicity and difference characteristics of the thickness changes of the two types of sequences at each time node are marked. All marked feature information is integrated and drawn into a visual map to obtain the thickness evolution synergistic map of the target coking coal sample.

[0044] The characteristic peak positions of the volatile matter elution rate curve are scanned and identified over the entire time period. The slope change of the curve is used as the criterion. When the slope of the curve changes from positive to negative and the value reaches the preset peak position determination threshold, the position is determined as the characteristic peak position of the volatile matter elution rate. The carbonization time node corresponding to the characteristic peak position is recorded to obtain the peak time of the target coking coal sample.

[0045] Using the thickness evolution synergy map reflecting the layer thickness change characteristics and the volatile matter release state corresponding to the peak time as dual calibration basis, and in accordance with the standard calibration rules for coking coal quality analysis, the contribution of the densification shrinkage of the molten layer to the deformation process of coking coal is quantitatively calibrated. At the same time, the resistance encountered during the gas phase permeation process in the colloidal layer is quantitatively calibrated, and the molten layer shrinkage contribution factor and the colloidal layer permeability characteristic factor of the target coking coal sample are obtained respectively.

[0046] According to the coupling and merging rules of coking coal characteristic parameters, the correlation between the melting layer shrinkage contribution factor and the initial deformation coefficient, and the correlation between the plastic body layer permeability characteristic factor and the initial permeability coefficient are used as the merging basis. The melting layer shrinkage contribution factor is converted into the corresponding initial deformation coefficient value, and the plastic body layer permeability characteristic factor is converted into the corresponding initial permeability coefficient value. The directional merging and transformation of the two types of factors is completed, and the initial deformation coefficient and initial permeability coefficient of the target coking coal sample are obtained.

[0047] The beneficial effects are as follows: by axially stripping the layered data of the physical structure, the thickness evolution sequence of the molten layer and the colloidal layer is accurately extracted. Combined with waveform feature correlation, a thickness evolution synergy map is constructed. At the same time, the characteristic peak position of the volatile matter rate curve is identified to determine the peak time. Based on the layer thickness synergy evolution characteristics and the peak state of volatile matter release, characteristic parameter calibration is carried out to accurately obtain the molten layer shrinkage contribution factor and the colloidal layer permeability characteristic factor. Then, through coupling and merging, the initial deformation coefficient and the initial permeability coefficient are formed. The whole process deeply combines the layer thickness evolution and volatile matter release law of the physical structure during coking coal production, and achieves accurate correlation analysis of the synergy evolution behavior of the molten layer and the colloidal layer. The obtained initial deformation coefficient and initial permeability coefficient can truly reflect the core characteristics of coking coal itself, providing accurate and practical core optimization guidance indicators for the global optimization of subsequent low-temperature upgrading process parameters. This ensures the pertinence and scientific nature of the process parameter optimization work, and makes the setting of the low-temperature upgrading process highly matched with the needs of improving the coking coal's caking properties.

[0048] S5. Using the initial deformation coefficient and the initial permeability coefficient as optimization guidelines, perform global optimization on the combination of low-temperature upgrading process parameters for the target coking coal sample to obtain the deformation coefficient, permeability coefficient, and optimal process parameter set of the target coking coal sample; In this embodiment of the invention, the step of using the initial deformation coefficient and the initial permeability coefficient as optimization guidelines to perform global optimization on the combination of low-temperature upgrading process parameters for the target coking coal sample to obtain the deformation coefficient and permeability coefficient of the target coking coal sample includes: optimizing the low-temperature upgrading process parameters of the target coking coal sample... Initialize the parameters and assign them as the current iterative parameter set for the target coking coal sample. Use the initial deformation coefficient and initial permeability coefficient as the current preferred index set for the target coking coal sample. Apply parameter perturbations to the heating rate, holding temperature, and holding time in the current iterative parameter set to obtain the derived parameter set for the target coking coal sample. Based on the basic coal quality parameter set of the target coking coal sample, perform carbonization behavior deduction on the derived parameter set to obtain the derived deformation coefficient and derived permeability coefficient of the target coking coal sample. Then, assign the derived deformation coefficient and... The derived permeability coefficient is compared with the deformation coefficient and permeability coefficient in the current preferred index group using a merit criterion. The derived parameter group with superior performance in all indicators is selected as the winning parameter group for the target coking coal sample. When the winning parameter group is selected, it is used as the intermediate iteration parameter group, and the derived deformation coefficient and derived permeability coefficient of the winning parameter group are used as the intermediate preferred index group. The current iteration parameter group is updated with the intermediate iteration parameter group, and the current preferred index group is updated with the intermediate preferred index group. The process then returns to the previous iteration parameter group. The operation of applying parameter perturbation is performed. If no winning parameter group is selected, the step size of the parameter perturbation is reduced according to a preset rule, and the generation of derived parameter groups and subsequent comparison and screening operations are re-executed based on the reduced step size. When the step size of the parameter perturbation is reduced to below a preset convergence threshold, the parameter perturbation iteration is terminated, and the current iteration parameter group updated last is taken as the optimal process parameter group of the target coking coal sample. The deformation coefficient and permeability coefficient in the current preferred index group updated last are taken as the deformation coefficient and permeability coefficient of the target coking coal sample.

[0049] The process involves extrapolating the carbonization behavior of the derived parameter set based on the basic coal quality parameter set of the target coking coal sample, obtaining the derived deformation coefficient and derived permeability coefficient of the target coking coal sample. This includes: deconstructing the heating rate, holding temperature, and holding time in the derived parameter set to obtain the boundary condition spectrum of the heating regime for the target coking coal sample; performing finite element numerical solution on the carbonization behavior of the target coking coal sample based on the basic coal quality parameter set and the boundary condition spectrum of the heating regime to obtain the layered derived data of the physical structure of the target coking coal sample; and indexing the layered derived data of the physical structure with feature templates in the volatile matter release kinetics database to obtain the volatile matter release rate of the target coking coal sample. Derivation curves; waveform feature correlation is performed on the evolution sequence of the molten layer thickness and the evolution sequence of the colloidal layer thickness in the derivation data of the physical structure to obtain the thickness evolution co-derivative map of the target coking coal sample; based on the peak time of the thickness evolution co-derivative map and the volatile matter extraction rate derivation curve, characteristic parameters are calibrated for the contribution of the molten layer densification shrinkage and the permeation resistance of the gas phase in the colloidal layer of the target coking coal sample to obtain the intermediate factor of molten layer shrinkage contribution and the intermediate factor of colloidal layer permeability characteristics of the target coking coal sample; the intermediate factor of molten layer shrinkage contribution and the intermediate factor of colloidal layer permeability characteristics are coupled and merged to obtain the derived deformation coefficient and derived permeability coefficient of the target coking coal sample.

[0050] The formula for calculating the derived value of the melt layer thickness in the layered data of the physical structure is as follows: In the formula, This is a derived value for the thickness of the molten layer. The bonding index is the value in the set of basic coal quality parameters. The maximum thickness of the plastic layer in the set of basic coal quality parameters. The vitrinite average maximum reflectance is given by the set of basic coal quality parameters. The heating rate is the temperature rise rate in the boundary condition spectrum of the heating regime. The holding temperature is the temperature in the boundary condition spectrum of the heating regime. The holding time is the boundary condition time in the heating regime boundary condition spectrum. These are the preset dimensional normalization coefficients. This is the preset time response constant.

[0051] According to the industry standard range of low-temperature upgrading process for coking coal, initial benchmark values ​​are assigned to three types of low-temperature upgrading process parameters of the target coking coal sample: heating rate, holding temperature, and holding time. The parameter set formed by combining these three types of values ​​is determined as the current iterative parameter set of the target coking coal sample. At the same time, the initial deformation coefficient and initial permeability coefficient obtained from the previous analysis are directly designated as the current preferred index set of the target coking coal sample.

[0052] According to the preset equidistant perturbation step size, the heating rate, holding temperature, and holding time in the current iteration parameter group are finely adjusted in both the forward and reverse directions. All the process parameter combinations formed after the fine adjustment are sorted out one by one to form a derivative parameter group for the target coking coal sample. Each derivative parameter group retains the complete numerical information of heating rate, holding temperature, and holding time.

[0053] The basic coal quality parameter set of the target coking coal sample obtained from the previous compilation and encapsulation is retrieved. This basic coal quality parameter set is used as the core basis for the deduction of carbonization behavior. The low-temperature upgrading and carbonization process of coking coal is simulated for each derived parameter set. The evolution characteristics of the melting layer and colloidal layer of coking coal under each derived parameter set are deduced, and then the derived deformation coefficient and derived permeability coefficient corresponding to each derived parameter set are obtained.

[0054] Criteria for judging the quality of deformation coefficient and permeability coefficient of coking coal were established. The deformation coefficient is considered superior if it is closer to the preset value range of improved caking properties of coking coal, and the permeability coefficient is considered superior if it matches the preset value range of gas phase release during the carbonization process. The derived deformation coefficient and derived permeability coefficient of each derived parameter group were compared with the corresponding indicators in the current preferred index group one by one. Only the derived parameter group that meets the better criteria for both indicators was selected and determined as the superior parameter group of the target coking coal sample.

[0055] After the selection of the winning parameter group is completed, the winning parameter group is directly designated as the intermediate iterative parameter group of the target coking coal sample. At the same time, the derived deformation coefficient and derived permeability coefficient corresponding to the winning parameter group are designated as the intermediate preferred index group of the target coking coal sample.

[0056] The current iteration parameter set is directly replaced with the intermediate iteration parameter set to complete the update of the current iteration parameter set. At the same time, the current preferred index set is directly replaced with the intermediate preferred index set to complete the update of the current preferred index set. Then, the operation of applying parameter perturbation to the updated current iteration parameter set to generate the derived parameter set is re-executed to continue the iterative optimization of process parameters.

[0057] If no suitable optimal parameter set is found, the original parameter perturbation step size is adjusted by shrinking according to a preset fixed ratio. The shrunken step size must meet the accuracy requirements of fine-tuning coking coal process parameters. Then, the parameter perturbation is reapplied to the current iterative parameter set with the new shrunken step size to generate a derived parameter set. The subsequent operations of carbonization behavior deduction, indicator superiority and inferiority comparison and optimal parameter set selection are then performed in sequence.

[0058] A convergence threshold for the perturbation of process parameters in the low-temperature upgrading of coking coal is preset. This threshold is the minimum precision value for fine-tuning the process parameters. When the perturbation step size after multiple contractions is less than the preset convergence threshold, all parameter perturbation and iteration operations are immediately terminated. The current iteration parameter set that was last updated is determined as the optimal process parameter set for the target coking coal sample. At the same time, the deformation coefficient and permeability coefficient contained in the current preferred index set that was last updated are determined as the final deformation coefficient and permeability coefficient of the target coking coal sample.

[0059] The three types of process parameters—heating rate, holding temperature, and holding time—within the derived parameter group were independently decomposed and extracted. Following the standard classification method of the low-temperature upgrading and heating regime for coking coal, the numerical information of the three types of parameters was integrated and sorted according to the characteristics of the heating stage, forming a heating regime boundary condition spectrum for the target coking coal sample that includes the heating control requirements for all time periods.

[0060] The basic coal quality parameter set of the target coking coal sample is retrieved and used together with the boundary condition spectrum of the heating regime as the basis for analysis. According to the heat and mass transfer and pyrolysis reaction kinetic mechanism of the coking process, the carbonization behavior of the target coking coal sample under the heating regime is solved by finite element numerical method. The thickness change state of each phase layer during the carbonization process is restored, and the physical structure layer-derived data of the target coking coal sample are obtained.

[0061] By accessing a pre-defined volatile matter release kinetics database and using the layered derivation data of the physical structure as the basis for feature retrieval, the database performs layer-by-layer feature matching and indexing, retrieves the volatile matter release feature template that perfectly matches the features of the derivation data, and combines it with the carbonization time axis to complete curve construction, thereby obtaining the volatile matter analysis rate derivation curve of the target coking coal sample.

[0062] The thickness evolution sequences of the molten layer and the colloidal layer were extracted separately from the stratified data of physical structure. The two sequences were placed in the same carbonization time coordinate system to carry out point-to-point waveform feature correlation analysis. The synchronous change characteristics of the thickness of the two layers at each time node were marked. All marked information was integrated to form a visualization map, and the thickness evolution co-derived map of the target coking coal sample was obtained.

[0063] The volatile analysis rate derivation curve was scanned over the entire time period. The time node when the slope of the curve changed from positive to negative and reached the preset peak value threshold was determined as the peak time. The peak time and the layer thickness change characteristics reflected by the thickness evolution co-derived spectrum were used as dual calibration basis. According to the calibration specification of coking coal quality characteristic parameters, the contribution degree of the densification shrinkage of the molten layer and the permeability resistance of the gas phase in the colloidal layer were quantitatively calibrated to obtain the intermediate factor of the molten layer shrinkage contribution and the intermediate factor of the colloidal layer permeability characteristics of the target coking coal sample.

[0064] According to the established rules for coupling and merging characteristic factors of coking coal, a unique correspondence is established between the intermediate factor contributing to the shrinkage of the molten layer and the derived deformation coefficient, and a unique correspondence is established between the intermediate factor of the air permeability characteristics of the plastic body layer and the derived air permeability coefficient. Based on the correspondence, the directional transformation of the two types of intermediate factors to characteristic coefficients is completed, and the derived deformation coefficient and derived air permeability coefficient of the target coking coal sample are obtained.

[0065] The values ​​derived from the molten layer thickness are all derived from the basic coal quality parameter set and the boundary condition spectrum of the heating regime of the target coking coal sample. Among them, the bonding index, the maximum thickness of the plastic layer, and the average maximum reflectance of the vitrinite group are core coal quality indicators directly extracted from the basic coal quality parameter set. The heating rate, holding temperature, and holding time are low-temperature upgrading process parameters decomposed from the boundary condition spectrum of the heating regime. The dimensional normalization coefficient and the time response constant are fixed coefficients pre-set according to the industry standards for low-temperature upgrading of coking coal and the requirements for carbonization behavior analysis. All values ​​have been verified for completeness and validity before being put into use.

[0066] The significance of this calculation method lies in accurately characterizing the change in the thickness of the molten layer during the carbonization behavior simulation of a target coking coal sample under specific low-temperature upgrading process parameters. It deeply integrates the core coal quality characteristics of coking coal with the heating process parameters of low-temperature upgrading, achieving accurate determination of the derived value of the molten layer thickness. The obtained derived value of the molten layer thickness, as a core component of the stratified data of physical structure, can truly reflect the actual development state of the molten layer during the coking process under the corresponding process parameters. This provides accurate physical structure data support for the subsequent construction of the rate derivation curve from volatile matter analysis and the drawing of the thickness evolution co-derived map. At the same time, it provides a reliable layer thickness basis for the calibration of the derived deformation coefficient and the derived permeability coefficient, making the carbonization behavior simulation results more consistent with the actual carbonization law of coking coal, and ensuring the scientificity and accuracy of the global optimization of low-temperature upgrading process parameters.

[0067] The beneficial effects are as follows: Global optimization of low-temperature upgrading process parameters is carried out with initial deformation coefficient and initial permeability coefficient as the core optimization guide. The optimization foundation is established through initial assignment of process parameters. Derived parameters and corresponding characteristic coefficients are obtained through parameter perturbation and carbonization behavior deduction. Precise selection of the winning parameter group is achieved by comparing superior and inferior criteria. Combined with iterative updates and step size reduction optimization methods, the comprehensiveness and accuracy of process parameter optimization are ensured. Furthermore, during the carbonization behavior deduction process, the boundary condition spectrum of the heating regime is obtained through parameter deconstruction, and the finite element numerical solution of carbonization behavior is completed by combining it with the basic coal quality parameter set. Then, through feature indexing, waveform correlation, parameter calibration, and coupling merging, derived deformation coefficients and derived permeability coefficients are obtained, making the deduction results fit the actual carbonization law of coking coal. The entire optimization process deeply integrates the core characteristic coefficients of coking coal with the low-temperature upgrading process parameters, realizing precise global optimization of process parameters. The obtained optimal process parameter set is highly matched with the final deformation coefficient and permeability coefficient to meet the needs of improving the caking properties of coking coal, providing a scientific and reliable parameter basis for the precise setting of the low-temperature upgrading process of coking coal, and greatly improving the adaptability and effectiveness of the low-temperature upgrading process for improving the caking properties of coking coal.

[0068] S6. When the process requirements of the target coking coal sample are met, the optimal process parameter set is output to obtain the optimal process parameter message of the target coking coal sample.

[0069] In this embodiment of the invention, when the process requirements of the target coking coal sample are met, outputting the optimal process parameter set to obtain the optimal process parameter message of the target coking coal sample includes: comparing the deformation coefficient and the permeability coefficient with preset process index thresholds; when both the deformation coefficient and the permeability coefficient pass the comparison verification, calling the message generation template, and sequentially writing the heating rate parameter, holding temperature parameter, and holding time parameter in the optimal process parameter set into the corresponding field positions of the message generation template to obtain the draft optimal process parameter message of the target coking coal sample; and encapsulating the format of the draft optimal process parameter message to obtain the optimal process parameter message of the target coking coal sample.

[0070] The pre-set threshold values ​​for low-temperature upgrading of coking coal are retrieved. These threshold values ​​are the qualified ranges of deformation coefficient and permeability coefficient that meet the requirements for improving the caking properties of coking coal. The deformation coefficient and permeability coefficient of the target coking coal sample obtained after global optimization are compared with the corresponding process index threshold values ​​one by one to determine whether both coefficients are within the qualified range and complete the verification.

[0071] After the deformation coefficient and permeability coefficient have been verified by comparison with the process index thresholds, the preset optimal process parameter message generation template for coking coal is retrieved. This template has dedicated fields for heating rate parameter, holding temperature parameter, and holding time parameter. The three types of process parameter information in the optimal process parameter group are accurately written into the designated positions of the template in sequence according to the field correspondence to form the draft optimal process parameter message for the target coking coal sample.

[0072] In accordance with the industry standard format requirements for coking coal process parameter reports, the draft of the optimal process parameter report is formatted and encapsulated. The font, font size, line spacing and other format specifications in the report are standardized. Basic information such as report number, coal sample identification and generation time are added. After the parameter information in the report is finally verified to be error-free and without omissions, the overall encapsulation process is completed to obtain the optimal process parameter report for the target coking coal sample.

[0073] The beneficial effects are as follows: by comparing the deformation coefficient and permeability coefficient with preset process index thresholds, it is possible to strictly verify whether the optimization results of process parameters meet the process requirements of low-temperature upgrading of coking coal. This ensures the rationality and adaptability of the optimal process parameter set from the index level. After both coefficients pass verification, the message template is called and the process parameters are accurately written to form a draft. Then, after format encapsulation, a standardized optimal process parameter message is obtained, making the output of process parameters a standardized and complete document. This not only accurately retains the core information of the optimal process parameter set, but also provides clear and directly executable parameter basis for the actual production operation of low-temperature upgrading of coking coal. This ensures the efficient implementation of process parameters from optimization results to actual application. At the same time, the standardized message format also facilitates the storage, transmission and subsequent reuse of process parameters, improving the application efficiency and standardization of coking coal caking improvement technology in actual production.

[0074] Figure 2 shows a functional module diagram of a coking coal caking property enhancement system based on low-temperature upgrading, provided in an embodiment of the present invention.

[0075] The low-temperature upgrading system 100 for improving the caking properties of coking coal described in this invention can be installed in an electronic device. Depending on the functions implemented, the low-temperature upgrading system 100 may include a coal quality acquisition module 101, a carbonization simulation module 102, a volatile matter mapping module 103, a collaborative analysis module 104, a process optimization module 105, and a parameter output module 106. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0076] In this embodiment, the functions of each module / unit are as follows: The coal quality acquisition module 101 is used to acquire data from the target coking coal sample to obtain the basic coal quality parameter set of the target coking coal sample; the carbonization simulation module 102 is used to perform finite element numerical simulation of the carbonization behavior of the target coking coal sample based on the heat and mass transfer and pyrolysis reaction kinetics mechanism of the coking process to obtain the physical structure layering data of the target coking coal sample; the volatile matter mapping module 103 is used to map the physical structure layering data to a preset volatile matter release kinetics database to obtain the volatile matter release rate curve of the target coking coal sample; the collaborative analysis module 104 is used to analyze the physical structure... The layered data and the volatile matter analysis rate curve are used to correlate and analyze the co-evolution behavior between the molten layer and the colloidal layer in the target coking coal sample, obtaining the initial deformation coefficient and initial permeability coefficient of the target coking coal sample. The process optimization module 105 is used to perform global optimization of the low-temperature upgrading process parameter combination of the target coking coal sample with the initial deformation coefficient and the initial permeability coefficient as optimization guide, obtaining the deformation coefficient, permeability coefficient and optimal process parameter set of the target coking coal sample. The parameter output module 106 is used to output the optimal process parameter set when the process requirements of the target coking coal sample are met, obtaining the optimal process parameter message of the target coking coal sample.

[0077] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0078] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0079] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0080] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0081] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for improving the caking properties of coking coal based on low-temperature upgrading, characterized in that, The method includes: S1, collecting data from a target coking coal sample to obtain a set of basic coal quality parameters for the target coking coal sample; S2, performing finite element numerical simulation of the carbonization behavior of the target coking coal sample based on the heat and mass transfer and pyrolysis reaction kinetics mechanism of the coking process to obtain the physical structure layering data of the target coking coal sample; S3, mapping the physical structure layering data to a preset volatile matter release kinetics database to obtain the volatile matter release rate curve of the target coking coal sample; S4, based on the physical structure layering data and the volatile matter release rate curve, performing finite element numerical simulation of the carbonization behavior of the target coking coal sample to obtain ... The co-evolution behavior between the molten layer and the colloidal layer in the target coking coal sample is analyzed to obtain the initial deformation coefficient and initial permeability coefficient of the target coking coal sample; S5, using the initial deformation coefficient and initial permeability coefficient as optimization guides, the combination of low-temperature upgrading process parameters of the target coking coal sample is globally optimized to obtain the deformation coefficient, permeability coefficient and optimal process parameter set of the target coking coal sample; S6, when the process requirements of the target coking coal sample are met, the optimal process parameter set is output to obtain the optimal process parameter message of the target coking coal sample.

2. The method for improving the caking property of coking coal based on low-temperature upgrading as described in claim 1, characterized in that, The process of acquiring data from the target coking coal sample to obtain a basic set of coal quality parameters includes: measuring and analyzing the industrial analysis entries in the basic testing data file of the target coking coal sample to obtain the industrial analysis parameters of the target coking coal sample; retrieving the elemental composition parameters of the target coking coal sample from the coal elemental analysis database of the target coking coal sample; based on the cohesiveness test report of the target coking coal sample, calibrating the cohesiveness index in the cohesiveness test report to obtain the cohesiveness index and the maximum thickness of the plastic layer of the target coking coal sample; performing microscopic observation on the petrographic analysis slide of the target coking coal sample and measuring the vitrinite reflectance point by point within the observation field to obtain the average maximum reflectance of the vitrinite of the target coking coal sample; and compiling and encapsulating the industrial analysis parameters, the elemental composition parameters, the cohesiveness index, the maximum thickness of the plastic layer, and the average maximum reflectance of the vitrinite to obtain the basic set of coal quality parameters of the target coking coal sample.

3. The method for improving the caking property of coking coal based on low-temperature upgrading as described in claim 1, characterized in that, The method involves using finite element numerical simulation to analyze the carbonization behavior of the target coking coal sample based on the heat and mass transfer and pyrolysis reaction kinetics mechanism of the coking process. This simulation yields layered data of the physical structure of the target coking coal sample, including: constructing a geometric domain for the axial space of the target coking coal sample within the carbonization chamber based on the basic coal quality parameter set, thus obtaining the physical space discrete domain of the target coking coal sample; meshing the physical space discrete domain to obtain the mesh element sequence of the target coking coal sample; and assigning thermophysical property parameters to the mesh element sequence based on the heat and mass transfer and pyrolysis reaction kinetics mechanism of the coking process, thus obtaining the parameter values ​​of the target coking coal sample. A numerical grid node set is generated. Based on the boundary constraints of the heating regime and gas phase pressure during the carbonization process in the target coking coal sample, boundary values ​​are assigned to the parameterized grid node set to obtain the constrained grid nodes of the target coking coal sample. The heat transfer flux and phase transition of the constrained grid nodes are coupled and advanced to obtain the phase attribute identifier of the target coking coal sample. Based on the phase attribute identifier, the phase assignment of the constrained grid nodes is extracted hierarchically to obtain the softening layer thickness, melting layer thickness, colloidal layer thickness, and semi-coke layer thickness of the target coking coal sample, which serve as the physical structure hierarchical data of the target coking coal sample.

4. The method for improving the caking property of coking coal based on low-temperature upgrading as described in claim 1, characterized in that, The step of mapping the physical structure layered data to a preset volatile matter release kinetics database to obtain the volatile matter emission rate curve of the target coking coal sample includes: arranging the physical structure layered data in a time sequence to obtain an axial layered evolution sequence of the target coking coal sample; retrieving a volatile matter release feature template of the layered evolution sequence from the preset volatile matter release kinetics database based on the axial layered evolution sequence; comparing and locating the volatile matter release feature template with the axial layered evolution sequence to obtain the volatile matter emission rate feature value of the target coking coal sample; and reconstructing the volatile matter emission rate feature value along the carbonization time axis of the target coking coal sample to obtain the volatile matter emission rate curve of the target coking coal sample.

5. The method for improving the caking property of coking coal based on low-temperature upgrading as described in claim 1, characterized in that, The method involves analyzing the co-evolution behavior between the molten layer and the colloidal layer in the target coking coal sample based on the stratified physical structure data and the volatile matter analysis rate curve, to obtain the initial deformation coefficient and initial permeability coefficient of the target coking coal sample. This includes: performing axial layer stripping on the stratified physical structure data to obtain the molten layer thickness evolution sequence and the colloidal layer thickness evolution sequence of the target coking coal sample; and correlating the waveform features of the molten layer thickness evolution sequence and the colloidal layer thickness evolution sequence to obtain a thickness evolution co-evolution map of the target coking coal sample. The volatile matter analysis rate curve is analyzed to identify the peak position of the target coking coal sample. Based on the thickness evolution synergistic spectrum and the peak position, characteristic parameters are calibrated to determine the contribution of the molten layer densification shrinkage and the permeability resistance of the gas phase in the colloidal layer of the target coking coal sample, thereby obtaining the molten layer shrinkage contribution factor and the colloidal layer permeability characteristic factor of the target coking coal sample. The molten layer shrinkage contribution factor and the colloidal layer permeability characteristic factor are coupled and merged to obtain the initial deformation coefficient and the initial permeability coefficient of the target coking coal sample.

6. The method for improving the caking property of coking coal based on low-temperature upgrading as described in claim 1, characterized in that, The optimization process, guided by the initial deformation coefficient and the initial permeability coefficient, involves globally optimizing the combination of low-temperature upgrading process parameters for the target coking coal sample to obtain the deformation coefficient and permeability coefficient of the target coking coal sample. This includes: initializing the low-temperature upgrading process parameters of the target coking coal sample, using the assigned parameter set as the current iterative parameter set for the target coking coal sample, and using the initial deformation coefficient and the initial permeability coefficient as the current preferred index set for the target coking coal sample; applying parameter perturbations to the heating rate, holding temperature, and holding time in the current iterative parameter set to obtain a derived parameter set for the target coking coal sample; performing carbonization behavior deduction on the derived parameter set based on the basic coal quality parameter set of the target coking coal sample to obtain the derived deformation coefficient and derived permeability coefficient of the target coking coal sample; and comparing the derived deformation coefficient and the derived permeability coefficient with the deformation coefficient and permeability coefficient in the current preferred index set using a merit criterion to select indicators with equal proportions. The optimal derived parameter set is used as the winning parameter set for the target coking coal sample. When the winning parameter set is selected, it is used as the intermediate iteration parameter set, and the derived deformation coefficient and derived permeability coefficient of the winning parameter set are used as the intermediate preferred index set. The current iteration parameter set is updated with the intermediate iteration parameter set, and the current preferred index set is updated with the intermediate preferred index set. The operation of applying parameter perturbation to the updated current iteration parameter set is then performed. When the winning parameter set is not selected, the step size of the parameter perturbation is reduced according to a preset rule, and the generation of derived parameter sets and subsequent comparison and selection operations are re-executed based on the reduced step size. When the step size of the parameter perturbation is reduced to below a preset convergence threshold, the parameter perturbation iteration is terminated, and the last updated current iteration parameter set is used as the optimal process parameter set for the target coking coal sample. The deformation coefficient and permeability coefficient in the last updated current preferred index set are used as the deformation coefficient and permeability coefficient of the target coking coal sample.

7. The method for improving the caking property of coking coal based on low-temperature upgrading as described in claim 6, characterized in that, The process involves extrapolating the carbonization behavior of the derived parameter set based on the basic coal quality parameter set of the target coking coal sample, obtaining the derived deformation coefficient and derived permeability coefficient of the target coking coal sample. This includes: deconstructing the heating rate, holding temperature, and holding time in the derived parameter set to obtain the boundary condition spectrum of the heating regime for the target coking coal sample; performing finite element numerical solution on the carbonization behavior of the target coking coal sample based on the basic coal quality parameter set and the boundary condition spectrum of the heating regime to obtain the layered derived data of the physical structure of the target coking coal sample; and indexing the layered derived data of the physical structure with feature templates in the volatile matter release kinetics database to obtain the volatile matter release rate of the target coking coal sample. Derivation curves; waveform feature correlation is performed on the evolution sequence of the molten layer thickness and the evolution sequence of the colloidal layer thickness in the derivation data of the physical structure to obtain the thickness evolution co-derivative map of the target coking coal sample; based on the peak time of the thickness evolution co-derivative map and the volatile matter extraction rate derivation curve, characteristic parameters are calibrated for the contribution of the molten layer densification shrinkage and the permeation resistance of the gas phase in the colloidal layer of the target coking coal sample to obtain the intermediate factor of molten layer shrinkage contribution and the intermediate factor of colloidal layer permeability characteristics of the target coking coal sample; the intermediate factor of molten layer shrinkage contribution and the intermediate factor of colloidal layer permeability characteristics are coupled and merged to obtain the derived deformation coefficient and derived permeability coefficient of the target coking coal sample.

8. The method for improving the caking property of coking coal based on low-temperature upgrading as described in claim 7, characterized in that, The formula for calculating the derived value of the melt layer thickness in the layered data of the physical structure is as follows: In the formula, This is a derived value for the thickness of the molten layer. The bonding index is the value in the set of basic coal quality parameters. The maximum thickness of the plastic layer in the set of basic coal quality parameters. The vitrinite average maximum reflectance is given by the set of basic coal quality parameters. The heating rate is the temperature rise rate in the boundary condition spectrum of the heating regime. The holding temperature is the temperature in the boundary condition spectrum of the heating regime. The holding time is the boundary condition time in the heating regime boundary condition spectrum. These are the preset dimensional normalization coefficients. This is the preset time response constant.

9. The method for improving the caking property of coking coal based on low-temperature upgrading as described in claim 1, characterized in that, When the process requirements of the target coking coal sample are met, the optimal process parameter set is output to obtain the optimal process parameter message of the target coking coal sample. This includes: comparing the deformation coefficient and the permeability coefficient with preset process index thresholds; when both the deformation coefficient and the permeability coefficient pass the comparison verification, calling the message generation template, and sequentially writing the heating rate parameter, holding temperature parameter, and holding time parameter in the optimal process parameter set into the corresponding field positions of the message generation template to obtain the draft optimal process parameter message of the target coking coal sample; and encapsulating the format of the draft optimal process parameter message to obtain the optimal process parameter message of the target coking coal sample.

10. A system for improving the caking properties of coking coal based on low-temperature upgrading, characterized in that, To implement the method for improving the caking property of coking coal based on low-temperature upgrading as described in claim 1, the system comprises: a coal quality acquisition module for acquiring data from a target coking coal sample to obtain a set of basic coal quality parameters of the target coking coal sample; a carbonization simulation module for performing finite element numerical simulation of the carbonization behavior of the target coking coal sample based on the heat and mass transfer and pyrolysis reaction kinetics mechanism of the coking process to obtain the physical structure layering data of the target coking coal sample; a volatile matter mapping module for mapping the physical structure layering data to a preset volatile matter release kinetics database to obtain the volatile matter release rate curve of the target coking coal sample; and a collaborative analysis module for... The physical structure layering data and the volatile matter rate curve are used to correlate and analyze the co-evolution behavior between the molten layer and the colloidal layer in the target coking coal sample, obtaining the initial deformation coefficient and initial permeability coefficient of the target coking coal sample. A process optimization module is used to globally optimize the combination of low-temperature upgrading process parameters for the target coking coal sample, using the initial deformation coefficient and initial permeability coefficient as optimization guidelines, to obtain the deformation coefficient, permeability coefficient, and optimal process parameter set of the target coking coal sample. A parameter output module is used to output the optimal process parameter set when the process requirements of the target coking coal sample are met, obtaining the optimal process parameter message for the target coking coal sample.

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