A method and device for structuring coal blending based on coal functional mechanism

Abstract

This application provides a structured coal blending method and apparatus based on the functional mechanism of coal, relating to the field of intelligent coal blending optimization. The method includes: functionally partitioning coal types using a coal functional zoning model to obtain a set of functional dimensions such as structural support functional dimensions and reaction-promoting functional dimensions; calculating the contribution intensity value of each coal type in different functional dimensions using a coal type functional contribution quantification model based on the response data of each coal type, obtaining a functional contribution matrix; determining the target functional structure corresponding to the target industrial process based on historical operating data and the functional contribution matrix; calculating the deviation evaluation value between the blended coal functional structure and the target functional structure based on the candidate coal type proportion vector and the functional contribution matrix; and searching for an optimized coal blending scheme corresponding to the target industrial process under proportion constraints based on the deviation evaluation value. This application solves the problem that existing coal blending systems cannot meet the actual process matching requirements of coal blending schemes in different industrial processes.

Description

Technical Field

[0001] This application relates to the field of intelligent coal blending optimization, and in particular to a structured coal blending method and apparatus based on the functional mechanism of coal. Background Technology

[0002] Currently, coal blending relies heavily on coal quality indicators and empirical rules. While data-driven optimization is being gradually introduced, the overall approach still relies mainly on indicator weighting or empirical correction, lacking a unified structural expression based on the mechanism of action, making it difficult to adapt to complex working conditions and coal source fluctuations.

[0003] Existing coal blending systems typically focus on optimizing coal quality indicators, treating each coal type as a combination of several parameters. Calculations are made using single or limited indicators such as ash content, volatile matter, and calorific value. This approach fails to reflect the differences in the roles of different coal types in actual industrial processes. For instance, when switching from boiler combustion to gasification furnace feedstock, or when gasification products are further used in downstream processes like coking, the existing coal blending schemes optimized based on combustion indicators still prioritize calorific value and volatile matter. This fails to reflect the requirements of coal types in terms of reactivity, ash fusion characteristics, and structural support during gasification or coking, leading to problems such as decreased gasification conversion rates, abnormal slag flow, or insufficient coke strength. Such scenario transitions represent reasonable process conversions within the same coal utilization chain. However, in combustion, gasification, or coking processes, different coal types often exhibit significant dominant or complementary roles in structural support, reactivity, and process regulation. These roles are difficult to characterize uniformly using a single indicator system, resulting in coal blending schemes that fail to meet the actual process matching requirements of different industrial processes.

[0004] Therefore, there is an urgent need for a structured coal blending method and device based on the functional mechanism of coal. Summary of the Invention

[0005] This application provides a structured coal blending method and apparatus based on the functional mechanism of coal, which solves the problem that existing coal blending systems cannot meet the actual process matching requirements of coal blending schemes in different industrial processes.

[0006] The first aspect of this application provides a structured coal blending method based on the functional mechanism of coal. The method includes: constructing a coal functional zoning model based on the operational requirements of the target industrial process, and dividing coal types into functional zones through the coal functional zoning model to obtain a set of functional dimensions; the functional dimensions include structural support functional dimensions, reaction promotion functional dimensions, and process regulation functional dimensions; calculating the contribution intensity value of each coal type in different functional dimensions through a coal type functional contribution quantification model based on the response data of each coal type to obtain a functional contribution matrix; determining the target functional structure corresponding to the target industrial process based on historical operating data and the functional contribution matrix; calculating the mixed coal functional structure vector based on the candidate coal type proportion vector and the functional contribution matrix, and calculating the deviation evaluation value between the mixed coal functional structure and the target functional structure; the candidate coal type proportion vector is used to characterize the proportion relationship of each coal type in the mixed coal; searching for a target proportion vector that makes the deviation evaluation value meet the preset deviation evaluation conditions under the proportion constraint conditions, and using the target proportion vector as the optimized coal blending scheme corresponding to the target industrial process.

[0007] Optionally, a coal functional zoning model is constructed based on the operational requirements of the target industrial process. Specifically, this includes: obtaining process type information corresponding to the target industrial process, including process information of combustion industrial process, gasification industrial process, and coking industrial process; determining the dominant role of coal in the target industrial process based on the process type information, and constructing a coal functional zoning model.

[0008] Optionally, based on the response data of each coal type, the contribution intensity value of each coal type in different functional dimensions is calculated through the coal type functional contribution quantification model to obtain the functional contribution matrix. Specifically, this includes: using the functional dimension division results as the structural basis and the measured data of the relevant response quantities of each coal type in each functional dimension as the target input, calculating the contribution intensity value of each coal type in different functional dimensions through functional mapping weights; and combining the contribution intensity values ​​in each functional dimension into vectors to obtain the functional contribution matrix.

[0009] Alternatively, the contribution intensity value can be calculated using the following formula: in, Indicates the first Type of coal in Contribution intensity values ​​in each functional dimension; Indicates the relationship with the first The number of responses associated with each functional dimension; Indicates the first The number of response quantities corresponding to each functional dimension; Indicates the first In the first functional dimension The functional mapping weights corresponding to each response quantity; Indicates the first Type of coal in relation to the first The first functional dimension related to the The response data value on the response quantity.

[0010] Optionally, the target functional structure corresponding to the target industrial process is determined based on historical operating condition data and in conjunction with the functional contribution matrix. Specifically, this includes: determining the production operating condition requirements corresponding to the target industrial process based on historical operating condition data; determining the target functional structure corresponding to the target industrial process based on the production operating condition requirements and in conjunction with the functional contribution matrix; the historical operating condition data includes the historical statistical values ​​of stable operating conditions, process design parameters, and numerical results of expert experience parameters.

[0011] Optionally, the proportioning constraints are constructed, specifically including: constructing proportioning constraints based on the coal blending requirements adapted to the target industrial process, the proportioning constraints including inventory constraints, process constraints and safety constraints.

[0012] Optionally, under the condition of ratio constraint, a target ratio vector that makes the deviation evaluation value meet the preset deviation evaluation conditions is searched. Specifically, this includes: adjusting the candidate coal type ratio vector while meeting the ratio constraint conditions, and taking the corresponding candidate coal type ratio vector as the target ratio vector when the deviation evaluation value reaches the minimum value.

[0013] A second aspect of this application provides a structured coal blending device based on the functional mechanism of coal. The device includes an acquisition module and a processing module, wherein... The acquisition module is used to construct a coal functional zoning model based on the operational requirements of the target industrial process, and to perform functional zoning of coal types through the coal functional zoning model to obtain a set of functional dimensions. The functional dimensions include structural support functional dimensions, reaction promotion functional dimensions, and process regulation functional dimensions. Based on the response data of each coal type, the contribution intensity value of each coal type in different functional dimensions is calculated through the coal type functional contribution quantification model to obtain the functional contribution matrix.

[0014] The processing module is used to determine the target functional structure corresponding to the target industrial process based on historical operating data and the functional contribution matrix; calculate the blended coal functional structure vector according to the candidate coal type ratio vector and the functional contribution matrix, and calculate the deviation evaluation value between the blended coal functional structure and the target functional structure; the candidate coal type ratio vector is used to characterize the ratio of each coal type in the blended coal; under the ratio constraint, the target ratio vector that makes the deviation evaluation value meet the preset deviation evaluation conditions is searched, and the target ratio vector is used as the optimized coal blending scheme corresponding to the target industrial process.

[0015] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described above.

[0016] A fourth aspect of this application provides a non-transitory computer-readable storage medium storing a computer program, the computer program being executed by a processor using any of the methods described above.

[0017] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. Using a coal functional zoning model, coal types are functionally zoned to obtain a set of functional dimensions, including structural support and reaction promotion dimensions. Based on the response data of each coal type, the contribution intensity value of each coal type in different functional dimensions is calculated using a coal type functional contribution quantification model, resulting in a functional contribution matrix. Based on historical operating data and the functional contribution matrix, the target functional structure corresponding to the target industrial process is determined. The deviation evaluation value between the blended coal functional structure and the target functional structure is calculated based on the candidate coal type proportion vector and the functional contribution matrix. Based on the deviation evaluation value, an optimized coal blending scheme corresponding to the target industrial process is searched under proportion constraints. This achieves a structured and quantitative expression of the functional role of coal types, improves the functional matching degree between the coal blending scheme and different industrial processes, and enhances the stability and adaptability of coal blending under coal source fluctuation conditions.

[0018] 2. Determine the production operating conditions requirements corresponding to the target industrial process based on historical operating condition data; determine the target functional structure corresponding to the target industrial process based on the production operating condition requirements and in combination with the functional contribution matrix; historical operating condition data includes historical statistical values ​​of stable operating conditions, process design parameters, and numerical results of expert experience parameters, thereby realizing the unified mapping of scattered operating experience and process parameters to the target contribution level on each functional dimension, so that the coal blending target is transformed from experience setting into a quantifiable functional structure expression, improving the accuracy and calculability of target setting.

[0019] 3. Based on the functional dimension division results, and with the measured data of the relevant response quantities of coal types in each functional dimension as the target input, the contribution intensity value of each coal type in different functional dimensions is calculated through functional mapping weights; the contribution intensity values ​​in each functional dimension are vector-combined to obtain the functional contribution matrix, thereby realizing the mapping expression of multi-source response quantities to a unified functional dimension, so that the functional roles of different coal types can be quantitatively characterized and compared under a unified structural space. Attached Figure Description

[0020] Figure 1This is a schematic flowchart of a structured coal blending method based on the functional mechanism of coal provided in an embodiment of this application; Figure 2 This is a schematic diagram of a structured coal blending device based on the functional mechanism of coal, provided in an embodiment of this application. Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0021] Explanation of reference numerals in the attached drawings: 21. Acquisition module; 22. Processing module; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 305. Memory. Detailed Implementation

[0022] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0023] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0024] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0025] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0026] Please refer to Figure 1 The diagram illustrates a flow chart of a structured coal blending method based on the functional mechanism of coal provided in this application embodiment. The flow chart mainly includes the following steps: S101 to S105.

[0027] Step S101: Construct a coal functional zoning model based on the operational requirements of the target industrial process, and perform functional zoning of coal types through the coal functional zoning model to obtain a set of functional dimensions.

[0028] Specifically, existing coal blending methods typically focus on the numerical optimization of coal quality indicators, treating each coal type as a combination of several indicator parameters. The proportions are calculated using single or a few indicators such as ash content, volatile matter, and calorific value. This approach fails to reflect the differences in the roles of different coal types in actual industrial processes. In particular, during combustion, gasification, or coking processes, different coal types often exhibit significant dominant or complementary roles in structural support, reactivity, and process regulation. These roles are difficult to characterize uniformly using a single indicator system, resulting in coal blending results that are sensitive to fluctuations in coal sources and lack stability. Furthermore, it is difficult to express and control coal blending objectives at the mechanistic level.

[0029] To address the aforementioned issues, this paper proposes transforming coal types from a set of indicators into functional contribution units and introducing a structured expression based on functional mechanisms. This approach decomposes the role of coal in the target industrial process into multiple functional dimensions and characterizes the functions of different coal types under a unified functional coordinate system, thereby providing a mechanistic basis for subsequent coal blending calculations.

[0030] To address the operational needs of target industrial processes, this study identifies the primary mechanisms by which coal functions within these processes. Based on these mechanisms, a coal functional zoning model is established, classifying coal types according to their functional performance under different mechanisms. This results in a set of functional dimensions, allowing each coal type to be represented within a unified functional dimension space. These functional dimensions describe the different directions of coal's action in the target industrial processes, transforming the evaluation method, which was originally centered on coal quality indicators, into a structured expression centered on functional effects. These functional dimensions include structural support functional dimensions, reaction-promoting functional dimensions, and process regulation functional dimensions.

[0031] In one possible implementation, step S101 further includes: obtaining process type information corresponding to the target industrial process, including process information of combustion industrial process, gasification industrial process, and coking industrial process; determining the dominant role of coal in the target industrial process based on the process type information, and constructing a coal functional zoning model.

[0032] Specifically, step S101 includes not only constructing a coal functional zoning model based on the operational requirements of the target industrial process, but also first acquiring the process type information corresponding to the target industrial process, then identifying the dominant role of coal in the industrial process based on the process type information, thereby completing the construction of the coal functional zoning model, and using the coal functional zoning model to functionally zon the coal types, obtaining a set of functional dimensions. The reason for adopting the above processing method is that existing coal blending schemes usually focus on optimizing the proportion of single coal quality indicators such as ash content, volatile matter, calorific value, sulfur content, or caking index. Although this method can reflect some of the physicochemical properties of coal types, it is difficult to reveal the differences in the actual roles of coal types in specific industrial processes. Especially in combustion, gasification, and coking processes, different coal types play different core roles. Some coal types are more inclined to maintain the stability of the bed or coke skeleton, some are more inclined to promote ignition and increase reaction rate, and some are more inclined to regulate ash melting behavior, release characteristics, or process fluctuations. When relying solely on a single coal quality indicator for evaluation, it is difficult to uniformly express the functional complementarity, dominant roles, and buffering capacity against fluctuations in operating conditions among different coal types. This results in coal blending results being highly sensitive to changes in coal sources and making it difficult to provide a structured description of the blending objectives at the mechanistic level. Therefore, by first identifying the process type of the target industrial process, then determining the dominant role of coal under that process type, and establishing a functional zoning model accordingly, the originally fragmented expression of coal quality indicators can be transformed into a functional expression oriented towards industrial mechanisms. This provides a unified foundation for subsequent quantification of coal type functional contributions, determination of target functional structures, and matching of blended coal functional structures.

[0033] The target industrial process can be a combustion process, a gasification process, or a coking process. The process information for the combustion process characterizes the technological operating background and performance focus of coal in the combustion scenario. This information may include the type of combustion device, furnace temperature range, excess air coefficient, combustion residence time, ash and slag discharge method, ignition stability requirements, burnout efficiency requirements, and pollutant emission control requirements. The dominant role of coal in the combustion process is typically manifested as a reaction-promoting effect and a process-regulating effect. The former characterizes the impact of coal on ignition performance, combustion reaction rate, and burnout behavior, while the latter characterizes the impact of coal on ash fusion characteristics, slagging tendency, sulfur release regulation, and combustion stability. When the combustion scenario places higher demands on rapid ignition, complete burnout, and stable flame maintenance, the reaction-promoting function dimension can be prioritized; when the combustion scenario focuses more on ash and slag behavior, coking risk, or pollutant release regulation, the role of the process-regulating function dimension in the model can be enhanced.

[0034] Process information in the gasification industry is used to characterize the process operation background and performance focus of coal in gasification scenarios. This information may include gasifier type, gasifying agent type, gasification temperature range, pressure conditions, bed conditions, carbon conversion efficiency requirements, effective gas generation requirements, and ash discharge requirements. The dominant role of coal in the gasification process is typically a synergistic effect of reaction promotion and process regulation. Reaction promotion is mainly reflected in the activity, reaction rate, and conversion capacity of coal in the gasification reaction, while process regulation is mainly reflected in the influence of coal on ash melting characteristics, slag flow behavior, gasifier operational stability, and side reaction control. In some fixed-bed or fluidized-bed gasification scenarios, coal may also play a structural support role in bed maintenance and support; therefore, this role can be described within the structural support function dimension according to specific process requirements.

[0035] Process information in the coking industry is used to characterize the technological operating background and performance focus of coal in the coking scenario. This includes coking oven type, charging density, heating regime, final carbonization temperature, insulation conditions, coke strength requirements, coke reactivity requirements, and post-reaction strength requirements. The dominant role of coal in the coking process is usually manifested primarily as structural support, mainly characterizing its contribution to coke skeleton formation, skeleton integrity maintenance, and coke mechanical stability during carbonization. Simultaneously, some coal types also play a reaction-promoting and process-regulating role in coking, such as regulating the pyrolysis reaction process, volatile matter emission behavior, and ash or sulfur migration behavior. For the coking process, if the goal is to obtain coke with high mechanical strength and a stable skeleton structure, the structural support function dimension is usually the dominant dimension; if coke reactivity, thermal regime adaptability, or by-product release control also need to be considered, the reaction-promoting and process-regulating functions also need to be included in the model.

[0036] After identifying the process type information corresponding to the target industrial process, it is necessary to further determine the dominant role of coal in the target industrial process. The dominant role refers to the functional direction of coal's main influence on the target process outcome in a specific industrial process. It is not simply a correspondence to a single coal quality index, but rather an abstract expression of the functional role undertaken by coal based on its actual mechanism of action after participating in the industrial process. The determination of the dominant role can be based on a comprehensive identification of process objectives, historical stable operating conditions, design parameter requirements, and expert experience. For example, in the coking scenario, if the process objective mainly points to the stability of the coke skeleton and the quality of coke, then the structural support role is identified as the dominant role; in the combustion scenario, if the process objective mainly points to rapid ignition and high burnout rate, then the reaction promoting role is identified as the dominant role; in the gasification scenario, if the process objective mainly points to high carbon conversion rate and stable gasification state, then the reaction promoting role and process regulating role can be identified as the dominant roles. Based on the above dominant role identification results, a coal functional zoning model adapted to the target industrial process is established, so that all subsequent coal types are characterized within a unified functional role framework.

[0037] In the model construction process, the range of coal types participating in coal blending is first defined. Assuming there are n selectable coal types in the system, the expression for the set of coal types is:

[0038] in, Represents the overall set of available coal types; Indicates the first Types of coal; This is a coal type index used to distinguish between different coal types; This represents the total number of coal types participating in the coal blending decision. The coal type set is used to define the scope of subsequent functional zoning and functional contribution quantification, ensuring that each coal type can obtain a corresponding functional positioning within a unified model. The selectable coal types here can originate from inventory coal types, coal sources to be selected, or candidate coal samples that meet the process access criteria. By defining the coal type set first, it is ensured that subsequent functional zoning is not a description of an abstract coal concept, but rather a structured division based on the specific coal types actually participating in the coal blending decision.

[0039] After defining the set of coal types, a set of functional dimensions is established around the dominant role of coal in the target industrial process. Based on the dominant role of coal in industrial processes such as combustion, gasification, or coking, coal functions are divided into... Each functional dimension is expressed as follows:

[0040] in, Represents the overall set of functional dimensions; Indicates the first One functional dimension; This is a functional dimension index used to distinguish different functional directions; This represents the total number of functional dimensions. The set of functional dimensions essentially constitutes a coordinate system for coal functional evaluation, used to describe the functional role of coal types in different directions of action, rather than relying solely on a single coal quality indicator as the evaluation basis. By constructing this set of functional dimensions, the complex mechanisms of coal action can be mapped onto several analyzable, quantifiable, and comparable functional dimensions, providing a structural foundation for the subsequent formation of a coal type functional contribution matrix.

[0041] In one exemplary configuration, the functional dimensions include structural support, reaction promotion, and process regulation, and can be further expanded to include a stability buffer dimension based on specific industrial scenarios. The structural support dimension characterizes the contribution of coal to structural stability, skeleton maintenance, or bed bearing capacity during industrial processes. For example, in coking scenarios, this dimension can describe the impact of coal on the stability of the coke skeleton; in certain bed reaction or bed process scenarios, this dimension can also describe the coal's support capacity for bed stability. The reaction promotion dimension characterizes the coal's ability to promote ignition performance, reactivity, reaction rate, and conversion efficiency. In combustion scenarios, this dimension corresponds to the coal's ignition and burnout behavior; in gasification scenarios, this dimension corresponds to the coal's gasification reactivity and conversion efficiency. The process regulation dimension characterizes the coal's ability to regulate ash melting behavior, sulfur release behavior, slag flow state, coking tendency, process fluctuation suppression, and process stability. The stability buffering function dimension characterizes the ability of coal to buffer system operational stability when faced with fluctuations in coal supply, operating conditions, or external disturbances. This dimension can be added or removed as an optional extension dimension according to process requirements. Therefore, the number and meaning of functional dimensions are not fixed but are constructed around their actual dominant role in the target industrial process, thus the functional zoning model has significant scenario adaptability.

[0042] Step S102: Based on the response data of each coal type, calculate the contribution intensity value of each coal type in different functional dimensions through the coal type functional contribution quantification model to obtain the functional contribution matrix.

[0043] In one possible implementation, step S102 further includes: using the functional dimension division results as the structural basis and the measured data of the relevant response quantities of coal types in each functional dimension as the target input, calculating the contribution intensity value of each coal type in different functional dimensions through functional mapping weights; and combining the contribution intensity values ​​in each functional dimension into vectors to obtain a functional contribution matrix.

[0044] Specifically, in existing technologies, the role of coal types is typically characterized by single indicators such as ash content, volatile matter, calorific value, or caking index. While these indicators can reflect some of the physicochemical properties of coal, they lack a unified structural relationship, making it difficult to map multiple response behaviors to the same evaluation space. Consequently, they cannot accurately reflect the comprehensive intensity of the coal type's role in different functional directions. Therefore, by constructing a functional contribution quantification model, the performance of coal types on multiple response quantities is uniformly mapped to the contribution intensity in the functional dimension. This allows different coal types to be compared and combined under a unified functional coordinate system, thus providing a calculable basis for subsequent calculations of the functional structure of blended coals.

[0045] In the functional contribution quantification model, the functional dimension division result obtained in step S101 is first used as the structural basis. The expression for the functional dimension set is:

[0046] in, Represents a set of functional dimensions; Indicates the first One functional dimension; Indexed by functional dimensions; This represents the total number of functional dimensions. This set is used to determine the computational space for quantifying functional contributions; that is, each coal type needs to be included in the above... The corresponding contribution intensity value is calculated for each functional dimension.

[0047] Furthermore, the core input to the functional contribution quantification model is the raw response data of the coal type. For any coal type... Its response quantities related to each functional dimension can be expressed as: , Indicates the first Type of coal in relation to the first The first functional dimension related to the Response data values ​​on each response quantity; Coal type index; Indexed by functional dimensions; This is an index for response quantities. Response quantities can be physical quantities or statistics related to the function, such as ignition time, reaction rate index, ash melting point, coke strength index, sulfur release characteristic parameters, etc. The specific selection depends on the physical meaning of the corresponding functional dimension.

[0048] After obtaining the various response quantities, a function mapping weight is introduced to uniformly map the different response quantities. The function mapping weight is represented as follows: , Indicates the first In the first functional dimension The weighting coefficient corresponding to each response quantity is used to reflect the importance of that response quantity in the corresponding functional dimension. This weight can be determined through historical data regression analysis, expert experience assignment, or multi-index optimization methods, thereby realizing the mapping of multiple response quantities to the contribution value of a single function.

[0049] Based on the above response data and functional mapping weights, the contribution intensity of coal type in each functional dimension is calculated, and the expression is as follows: in, Indicates the first Type of coal in Contribution intensity values ​​in each functional dimension; Indicates the relationship with the first The number of responses associated with each functional dimension, that is, the total number of responses participating in the mapping calculation under that functional dimension; For response volume index; Indicates the first In the first functional dimension The weight of each response quantity; Indicates the first The actual observed values ​​of each coal type in its corresponding response quantity. Through the above weighted summation process, multiple response quantities are uniformly mapped to contribution intensity values ​​on a single functional dimension, thereby realizing the aggregation of multiple indicators into a functional expression.

[0050] The aforementioned contribution intensity value is not directly derived from a single coal quality indicator, but is obtained through comprehensive processing of multiple response behaviors. The acquisition methods can include the following: measuring the incremental value of the corresponding functional response of the coal type under industrial test conditions and comparing the system performance changes with and without the addition of the coal type; obtaining the value based on historical production data by statistically analyzing and normalizing the system performance changes after the coal type's participation in operation; or mapping multiple coal quality indicators or process response indicators to a unified functional score using a multi-indicator comprehensive mapping method. In any of these methods, weighted mapping is used to convert multiple response quantities into a single functional contribution value.

[0051] After obtaining the contribution intensity values ​​of each coal type in each functional dimension, for any coal type Its functional contribution vector is expressed as: in, Indicates the first Functional contribution vector of each type of coal; This indicates that the type of coal was in the [number]th [year]. Contribution intensity in each functional dimension; This vector represents the number of functional dimensions. It is used to describe the comprehensive role of a single coal type across multiple functional dimensions, transforming its expression from a single indicator to a multi-dimensional functional expression.

[0052] Furthermore, by combining the functional contribution vectors of all coal types, a functional contribution matrix is ​​obtained, the expression of which is: in, Represents the functional contribution matrix; Indicates the quantity of coal type; Indicates the number of functional dimensions; elements in the matrix Indicates the first Type of coal in The matrix represents the contribution intensity values ​​across each functional dimension. Each row corresponds to a functional contribution vector for a specific coal type, and each column corresponds to the distribution of a functional dimension across different coal types. By constructing this matrix, the relationships between multiple coal types and multiple functional dimensions can be uniformly expressed, providing a basic data structure for subsequent calculations of the functional structure of blended coal and deviation evaluation.

[0053] Step S103: Based on historical operating data and combined with the functional contribution matrix, determine the target functional structure corresponding to the target industrial process.

[0054] Specifically, based on historical operating data, the functional requirements characteristics of the target industrial process under stable operating conditions are extracted, and the functional contribution matrix is ​​combined to map and quantify each functional dimension, determine the target contribution level corresponding to each functional dimension, and thus form the target functional structure.

[0055] In one possible implementation, step S103 further includes: determining the production operating condition requirements corresponding to the target industrial process based on historical operating condition data; determining the target functional structure corresponding to the target industrial process based on the production operating condition requirements and in conjunction with the functional contribution matrix; the historical operating condition data includes the historical statistical values ​​of stable operating conditions, process design parameters, and numerical results of expert experience parameters.

[0056] Specifically, the production operating conditions required for the target industrial process are determined based on historical operating condition data. This historical operating condition data includes historical statistical values ​​of stable operating conditions, process design parameters, and numerical results of expert experience parameters. Historical statistical values ​​of stable operating conditions represent the statistical results of key operating indicators recorded during long-term stable production processes. Examples include burnout rate, furnace temperature distribution, and ash state during combustion; carbon conversion rate, gas composition ratio, and slag flow state during gasification; and coke strength and reactivity indices during coking. These statistical values ​​reflect the operating characteristics of the system under ideal or stable conditions. Process design parameters represent the target operating parameters determined during the unit design or process setting stage, such as design temperature range, pressure range, residence time, and target product indicators. These parameters constrain the operating boundaries of the industrial process. Numerical results of expert experience parameters represent the results after converting long-term production experience or process knowledge into calculable parameters, such as the empirical evaluation coefficient for the role of a certain type of coal under specific operating conditions or the empirical weight for the importance of functional dimensions. By comprehensively processing the above three types of data, the production conditions requirements of the target industrial process under the current conditions are obtained. These production conditions requirements essentially reflect the comprehensive demand characteristics of the system in various functional dimensions.

[0057] After obtaining the production requirements, the functional dimensions are mapped using the functional contribution matrix to determine the target functional structure corresponding to the target industrial process. The target functional structure is represented in vector form, and its expression is:

[0058] in, Represents the target functional structure vector; Indicates the first Functional Dimensions The target contribution level to be achieved; Indexed by functional dimensions; This represents the total number of functional dimensions. Each component of this vector corresponds to a target value in a functional dimension, describing the intensity of demand for that industrial process in that functional direction.

[0059] In the specific construction process The parameters are not directly given by a single parameter, but are determined comprehensively through the mapping relationship between historical operating data and the functional contribution matrix. For historical statistical values ​​of stable operating conditions, the target ranges for each functional dimension can be deduced by analyzing the performance of the functional contribution matrix corresponding to each coal type combination during the stable operating phase. For process design parameters, design indicators can be converted into constraints on each functional dimension. For example, the burnout rate requirement can be mapped to the target level of the reaction promotion function dimension, and the coke strength requirement can be mapped to the target level of the structural support function dimension. For the numerical results of expert experience parameters, corresponding experience targets can be assigned to each functional dimension.

[0060] Step S104: Calculate the blended coal functional structure vector based on the candidate coal type ratio vector and functional contribution matrix, and calculate the deviation evaluation value between the blended coal functional structure and the target functional structure.

[0061] Specifically, the calculation relationship of the functional structure of blended coal is established based on the candidate coal type ratio vector and the functional contribution matrix, and the deviation evaluation result is formed by comparing it with the target functional structure, thereby realizing the unified quantitative evaluation of different ratio schemes.

[0062] The candidate coal type proportion vector is used to characterize the proportion of each coal type in the blended coal, and its expression is: in, Indicates the first The proportioning coefficient of a particular type of coal in a blended coal; Coal type index; This indicates the quantity of each type of coal involved in the blending. The blending ratio coefficient satisfies the non-negativity and normalization constraints, and its expression is:

[0063] The above constraints are used to ensure that the proportion of each coal type is non-negative and that the sum of the proportions of all coal types is 1, so that the proportion vector can truly reflect the composition of the blended coal.

[0064] After obtaining the candidate coal type proportion vectors, the functional structure of the blended coal is calculated in conjunction with the functional contribution matrix. The comprehensive contribution vector of the blended coal in each functional dimension is expressed as follows:

[0065] in, Represents the functional structure vector of the mixed coal; Indicates the first Functional contribution vectors for each coal type. This expression obtains the comprehensive contribution level of the blended coal in each functional dimension by weighting and superimposing the functional contribution vectors of each coal type according to their proportion coefficients.

[0066] Expanding further to the functional dimensions, its expression is: in, Indicates the mixed coal in the first The overall contribution value across all functional dimensions; Indicates the first Type of coal in Contribution intensity in each functional dimension; Indexed by functional dimensions; This represents the number of functional dimensions. The expansion reflects that the contribution of each functional dimension stems from the proportional sum of the contributions of all coal types in that dimension.

[0067] After obtaining the functional structure vector of the mixed coal, it is compared with the target functional structure vector to construct the functional deviation vector, the expression of which is: in, Represents the functional deviation vector; Represents the target functional structure vector; Indicates the first The deviation is the difference between the actual contribution of the blended coal and the target requirement in each functional dimension. This deviation vector is used to characterize the degree of matching between the current blending scheme and each functional dimension.

[0068] Based on this, we introduce the importance weights of functional dimensions, perform weighted processing on the deviations of each dimension, and construct a functional structure deviation evaluation function, the expression of which is: in, Indicated in the proportion vector The overall deviation evaluation value is as follows; Indicates the first The weight coefficients of each functional dimension are used to reflect the importance of that functional dimension in the target industrial process; Indicates the first The deviations across each functional dimension are calculated. By squaring the deviations and then weighting and summing them, the deviations across different functional dimensions can be cumulatively evaluated on a unified scale, while avoiding the cancellation of positive and negative deviations.

[0069] Step S105: Under the constraint of proportioning, search for the target proportioning vector that makes the deviation evaluation value meet the preset deviation evaluation conditions, and use the target proportioning vector as the optimized coal blending scheme corresponding to the target industrial process.

[0070] Specifically, under the premise of meeting the ratio constraints, the ratio vector of candidate coal types is iteratively adjusted and searched. Based on the deviation evaluation value, each ratio scheme is compared and screened to determine the target ratio vector that meets the preset deviation evaluation conditions, and output as the optimized coal blending scheme.

[0071] In one possible implementation, step S105 further includes: constructing blending constraints based on the coal blending requirements adapted to the target industrial process, the blending constraints including inventory constraints, process constraints and safety constraints; adjusting the candidate coal blending vector while satisfying the blending constraints, and taking the corresponding candidate coal blending vector as the target blending vector when the deviation evaluation value reaches the minimum value.

[0072] Specifically, blending constraints are constructed based on coal blending requirements adapted to the target industrial process. Coal blending requirements originate from the comprehensive requirements of coal type usage, process adaptability, and operational safety in actual production operations, and are transformed into constraints on the blending ratio vector. Blending constraints include inventory constraints, process constraints, and safety constraints. Inventory constraints limit the available proportion range of each coal type, process constraints limit the applicable proportion range of each coal type under specific process conditions, and safety constraints prevent operational risks or abnormal operating conditions caused by improper blending.

[0073] After establishing the constraints, the candidate coal type ratio vector is adjusted to optimize the deviation evaluation value while satisfying the constraints. The corresponding optimization objective expression is:

[0074] in, Indicated in the proportion vector The functional structure deviation evaluation value is obtained by minimizing the function to make the functional structure of the mixed coal as close as possible to the target functional structure.

[0075] Meanwhile, the allocation vector must satisfy the following constraints: in, Indicates the first The proportioning coefficient of different coal types; and They represent the first Minimum and maximum proportions of each type of coal; Coal type index; This represents the total number of coal types. The minimum and maximum blending ratios are determined by inventory conditions, process compatibility requirements, and safety constraints, ensuring that the resulting blending scheme remains within an executable range during the optimization process.

[0076] While satisfying the above constraints, the candidate coal type proportion vectors are searched and adjusted, and when the deviation evaluation value reaches the minimum value, the corresponding candidate coal type proportion vector is determined as the target proportion vector. This process can be implemented through traversal search, heuristic optimization, or numerical optimization methods. Its essence lies in finding the proportion combination that minimizes the functional structure deviation within the constraint space.

[0077] The final coal blending scheme is expressed as follows: in, Represents the target proportion vector; Indicates the first The proportion of each type of coal under the optimal blending ratio. Simultaneously, output the coal blending functional structure corresponding to the target blending ratio vector. and deviation evaluation results By simultaneously outputting the above results, the degree of functional matching of the coal blending scheme can be given at the same time as obtaining the coal blending scheme, thereby providing an interpretable basis for production decisions and verifying the rationality of the blending scheme in terms of functional structure.

[0078] Please refer to Figure 2 This illustration shows a schematic diagram of a structured coal blending device based on the functional mechanism of coal, according to an embodiment of this application. The device includes an acquisition module 21 and a processing module 22. The acquisition module 21 is used to construct a coal functional zoning model based on the operational requirements of the target industrial process, and to perform functional zoning of coal types through the coal functional zoning model to obtain a set of functional dimensions. The functional dimensions include structural support functional dimensions, reaction promotion functional dimensions, and process regulation functional dimensions. Based on the response data of each coal type, the contribution intensity value of each coal type in different functional dimensions is calculated through the coal type functional contribution quantification model to obtain the functional contribution matrix.

[0079] Processing module 22 is used to determine the target functional structure corresponding to the target industrial process based on historical operating data and the functional contribution matrix; calculate the mixed coal functional structure vector according to the candidate coal type ratio vector and the functional contribution matrix, and calculate the deviation evaluation value between the mixed coal functional structure and the target functional structure; the candidate coal type ratio vector is used to characterize the ratio relationship of each coal type in the mixed coal; under the ratio constraint, search for the target ratio vector that makes the deviation evaluation value meet the preset deviation evaluation conditions, and use the target ratio vector as the optimized coal blending scheme corresponding to the target industrial process.

[0080] In one possible implementation, the acquisition module 21 is used to construct a coal functional zoning model based on the operational requirements of the target industrial process. Specifically, it includes: acquiring process type information corresponding to the target industrial process, including combustion industrial process information, gasification industrial process information, and coking industrial process information; determining the dominant role of coal in the target industrial process based on the process type information, and constructing a coal functional zoning model.

[0081] In one possible implementation, the acquisition module 21 is used to calculate the contribution intensity value of each coal type in different functional dimensions based on the response data of each coal type through the coal type functional contribution quantification model, and obtain a functional contribution matrix. Specifically, it includes: using the functional dimension division results as the structural basis and the measured data of the relevant response quantities of each coal type in each functional dimension as the target input, calculating the contribution intensity value of each coal type in different functional dimensions through functional mapping weights; and combining the contribution intensity values ​​in each functional dimension into vectors to obtain the functional contribution matrix.

[0082] In one possible implementation, the acquisition module 21 is used to calculate the contribution intensity value using the following formula: in, Indicates the first Type of coal in Contribution intensity values ​​in each functional dimension; Indicates the relationship with the first The number of responses associated with each functional dimension; Indicates the first The number of response quantities corresponding to each functional dimension; Indicates the first In the first functional dimension The functional mapping weights corresponding to each response quantity; Indicates the first Type of coal in relation to the first The first functional dimension related to the The response data value on the response quantity.

[0083] In one possible implementation, the processing module 22 is used to determine the target functional structure corresponding to the target industrial process based on historical operating condition data and in combination with the functional contribution matrix. Specifically, this includes: determining the production operating condition requirements corresponding to the target industrial process based on historical operating condition data; determining the target functional structure corresponding to the target industrial process based on the production operating condition requirements and in combination with the functional contribution matrix; the historical operating condition data includes the historical statistical values ​​of stable operating conditions, process design parameters, and numerical results of expert experience parameters.

[0084] In one possible implementation, the processing module 22 is used to construct proportioning constraints, specifically including: constructing proportioning constraints based on coal blending requirements adapted to the target industrial process, the proportioning constraints including inventory constraints, process constraints and safety constraints.

[0085] In one possible implementation, the processing module 22 is used to search for a target proportion vector that makes the deviation evaluation value meet the preset deviation evaluation conditions under the proportion constraint conditions. Specifically, it includes: adjusting the candidate coal type proportion vector while meeting the proportion constraint conditions, and taking the corresponding candidate coal type proportion vector as the target proportion vector when the deviation evaluation value reaches the minimum value.

[0086] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0087] This application also provides an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: at least one processor 301, at least one communication bus 302, a user interface 303, at least one network interface 304, and a memory 305.

[0088] The communication bus 302 is used to enable communication between these components.

[0089] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0090] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0091] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0092] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a structured coal blending application based on the functional mechanism of coal.

[0093] exist Figure 3In the illustrated electronic device, the user interface 303 is primarily used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call the structured coal blending application stored in the memory 305 based on the coal functional mechanism. When executed by one or more processors 301, the electronic device performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0094] This application also provides a non-transitory computer-readable storage medium storing instructions. When executed by one or more processors, these instructions cause an electronic device to perform one or more of the methods described in the above embodiments.

[0095] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0096] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0097] The units described as separate components may or may not be physically separate. The components shown as units 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0098] Furthermore, the functional units in the various embodiments of this application 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 as a software functional unit.

[0099] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0100] The above description is merely an exemplary embodiment disclosed in this application and should not be construed as limiting the scope of this application. Any equivalent changes and modifications made in accordance with the teachings of this application shall still fall within the scope of this application.

[0101] This application is intended to cover any variations, uses, or adaptations disclosed herein that follow the general principles disclosed herein and include common knowledge or customary technical means in the art that are not described in this application.

Claims

1. A method for structuring coal blending based on the functional mechanism of coal, characterized in that, The method includes: A coal functional zoning model is constructed based on the operational requirements of the target industrial process, and coal types are functionally zoned using the coal functional zoning model to obtain a set of functional dimensions; the functional dimensions include structural support functional dimensions, reaction promotion functional dimensions, and process regulation functional dimensions. Based on the response data of each coal type, the contribution intensity value of each coal type in different functional dimensions is calculated through the coal type functional contribution quantification model to obtain the functional contribution matrix; Based on historical operating data and the aforementioned functional contribution matrix, the target functional structure corresponding to the target industrial process is determined. The mixed coal functional structure vector is calculated based on the candidate coal type proportion vector and the functional contribution matrix, and the deviation evaluation value between the mixed coal functional structure and the target functional structure is calculated; the candidate coal type proportion vector is used to characterize the proportion relationship of each coal type in the mixed coal. Under the constraint of proportioning, a target proportioning vector is searched that makes the deviation evaluation value meet the preset deviation evaluation conditions, and the target proportioning vector is used as the optimized coal blending scheme corresponding to the target industrial process.

2. The method of claim 1, wherein, The construction of the coal functional zoning model based on the operational requirements of the target industrial process specifically includes: Obtain the process type information corresponding to the target industrial process, including combustion industrial process information, gasification industrial process information, and coking industrial process information. Based on the process type information, the dominant role of coal in the target industrial process is determined, and the coal functional zoning model is constructed.

3. The method of claim 1, wherein, The step involves calculating the contribution intensity value of each coal type in different functional dimensions based on the response data of each coal type using a coal type functional contribution quantification model, thereby obtaining a functional contribution matrix. Specifically, this includes: Based on the functional dimension division results, and with the measured data of the relevant response quantities of coal types in each functional dimension as the target input, the contribution intensity value of each coal type in different functional dimensions is calculated through functional mapping weights. The contribution intensity values ​​in each of the functional dimensions are combined into a vector to obtain the functional contribution matrix.

4. The method of claim 3, wherein, The contribution intensity value is calculated using the following formula: in, Indicates the first Type of coal in The contribution intensity value in each functional dimension; Indicates the relationship with the first The number of response quantities associated with each of the aforementioned functional dimensions; Indicates the first The number of response quantities corresponding to each of the aforementioned functional dimensions; Indicates the first In the first functional dimension The functional mapping weights corresponding to each response quantity; Indicates the first Type of coal in relation to the first The first functional dimension related to the The response data value on the response quantity.

5. The method according to claim 1, characterized in that, The determination of the target functional structure corresponding to the target industrial process based on historical operating data and the functional contribution matrix specifically includes: Based on the historical operating data, determine the production operating requirements corresponding to the target industrial process; Based on the production conditions required and in conjunction with the functional contribution matrix, the target functional structure corresponding to the target industrial process is determined; the historical operating data includes historical statistical values ​​of stable operating conditions, process design parameters, and numerical results of expert experience parameters.

6. The method according to claim 1, characterized in that, The specific steps for constructing the ratio constraints include: The blending constraints are constructed based on the coal blending requirements adapted to the target industrial process. The blending constraints include inventory constraints, process constraints, and safety constraints.

7. The method according to claim 1, characterized in that, The step of searching for a target proportion vector that makes the deviation evaluation value satisfy a preset deviation evaluation condition under the proportion constraint specifically includes: While satisfying the ratio constraints, the ratio vector of the candidate coal type is adjusted, and when the deviation evaluation value reaches the minimum value, the corresponding ratio vector of the candidate coal type is taken as the target ratio vector.

8. A structured coal blending device based on the functional mechanism of coal, characterized in that, The device includes an acquisition module and a processing module, wherein, The acquisition module is used to construct a coal functional zoning model based on the operational requirements of the target industrial process, and to perform functional zoning of coal types through the coal functional zoning model to obtain a set of functional dimensions. The functional dimensions include structural support functional dimensions, reaction promotion functional dimensions, and process regulation functional dimensions. Based on the response data of each coal type, the contribution intensity value of each coal type in different functional dimensions is calculated through the coal type functional contribution quantification model to obtain a functional contribution matrix. The processing module is used to determine the target functional structure corresponding to the target industrial process based on historical operating data and the functional contribution matrix; calculate the blended coal functional structure vector according to the candidate coal type ratio vector and the functional contribution matrix, and calculate the deviation evaluation value between the blended coal functional structure and the target functional structure; the candidate coal type ratio vector is used to characterize the ratio of each coal type in the blended coal; under the ratio constraint, search for the target ratio vector that makes the deviation evaluation value meet the preset deviation evaluation conditions, and use the target ratio vector as the optimized coal blending scheme corresponding to the target industrial process.

9. An electronic device, characterized in that, The device includes a processor, a communication bus, a user interface, a network interface, and a memory. The memory is used to store instructions. The user interface and the network interface are both used to communicate with other devices. The communication bus is used to enable communication between the components within the electronic device. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 7.

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