551 results about "Coal blending" patented technology

The invention relates to a coking coal blending method based on intelligent optimization, and the method comprises the following steps: S1, collecting historical feed coal mass ratio data and corresponding coke quality data, carrying out data cleaning preprocessing and data standardization, and analyzing the correlation between each coke quality parameter and a feed coal quality parameter; s2, performing hyper-parameter optimization of a base algorithm in combination with a correlation analysis result and historical data, determining each layer of base algorithm composition of a multi-algorithm multi-layer superposition model, and obtaining a coke parameter prediction model set; and S3, according to the coke quality constraint, on the basis of the coke parameter prediction model set, establishing a multi-constraint single-target random optimization model, and searching a lowest cost ratio scheme. According to the method, manual coal blending is converted into intelligent coal blending, the model is trained according to historical data, the proportioning scheme is automatically generated through the model, the manual participation degree is reduced, the process is simplified, and the proportioning cost and the proportioning time are lower.

The intelligent coal blending system is composed of a basic data layer, a data platform layer and an intelligent application layer, and basic data of four external systems including a coal blending operation system, a raw coal tracking system, a coal yard management system and an inspection and test system are collected; data acquisition, data transmission, data preprocessing, data storage and model creation, model training, model verification, model use and API interface opening are carried out. The intelligent coal blending method comprises six steps of raw coal proportioning abnormity monitoring, raw coal quality fluctuation analysis, coke quality automatic early warning, coke quality fluctuation analysis, conflict coal type combination identification and coke quality process tracing. After the inspection and test indexes of the raw coal, the coal type mixing proportion and the target coke quality are given, coke quality prediction and coke quality change early warning are automatically carried out, and a new coal blending proportion or new coal type recommendation is given. The whole coal blending process can be managed and controlled, and the coke quality can be rapidly detected.

The invention relates to an operation optimization method and device for coal blending combustion of a coal-fired unit, and the method comprises the steps: obtaining at least one of parameters related to the operation state of the coal-fired unit, flue gas component parameters related to combustion, parameters related to a coal pulverizing system, historical coal quality parameter data and coal price; performing data cleaning on at least one item of data; establishing at least one of a safety index, an environmental protection index, an economic index and a combustibility index of the coal-fired unit, and establishing a case library of the coal-fired unit according to the target working condition; retrieving operation parameters of the load interval of the corresponding coal-fired unit in the case library; and optimizing the stock coal type blending scheme by using the at least one piece of historical coal blending quality information to generate a predicted blending scheme meeting a preset optimal condition. According to the embodiment, the requirement for the temporary peak in the operation process can be met with the highest load historical coal blending quality as the target, and the situation that a unit cannot respond to the high load requirement is avoided.

The invention relates to a fire coal blending method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a blending proportion of a plurality of coal types, blending the plurality of coal types based on the blending proportion to obtain a blended coal type, acquiring combustion condition monitoring data of a boiler under the condition that the blended coal type is combusted in the boiler, and determining the combustion condition of the boiler based on the combustion condition monitoring data of the boiler. Under the condition that it is judged that the combustion condition of the boiler does not meet the preset unit load requirement, based on the combustion condition monitoring data of the boiler, the blending proportion of the multiple coal types or the operation parameters of the boiler are adjusted, and the step of obtaining the combustion condition monitoring data of the boiler when the blended coal types are combusted in the boiler is executed again; and the combustion condition of the boiler meets the preset unit load requirement. By adopting the method, the fire coal blending accuracy can be improved.

The invention relates to a large silo coal blending analysis system, and belongs to the technical field of coking. The system comprises a silo coal resource access statistics module, a switching analysis module, a coal blending ratio difference analysis module, a coal blending and coke quality prediction module and an information display module. Firstly, the silo coal seam distribution condition is established, then the coal seam resource switching condition is analyzed and judged according to the discharging weight, the accumulated coal blending execution deviation rate is calculated, and the coal blending amount and the switching condition of the next period are estimated; the blended coal and coke quality prediction module predicts the quality and analyzes the change condition according to the actual coal blending ratio and the like; and the information display module stores and visually displays the result. The system has strong analysis and prediction capabilities, and can comprehensively and deeply analyze yesterday coal blending conditions, scientifically predict coke quality, intelligently plan today coal blending by means of accumulated coal blending execution deviation rates, simply, conveniently and quickly grasp dynamics and trends, guide coal blending and quality control, improve product quality, reduce cost and enhance competitiveness.

The invention relates to the field of coking and coal blending, in particular to a coal coking and coal blending control method and system based on artificial intelligence. The method comprises the following steps: collecting a plurality of preprocessed coal coke block grey-scale maps, and constructing a grey-scale matching matrix of the coal coke block grey-scale maps; calculating the importance of each element in the gray level matching matrix, calculating a coke block loss value after each experiment according to the element value and the importance, and taking the ratio of the coke block loss values of two adjacent experiments as the coke block stability; inputting the coke block loss value and the coke block stability into a preset neural network to obtain a coal caking index; and constructing historical coal blending samples, clustering all the historical coal blending samples, and carrying out coking coal blending according to the blending proportion in the clustering result. By means of the technical scheme, the accuracy of coking coal blending results can be improved, and the quality and characteristics of final coke are guaranteed.

The invention belongs to the technical field of coal blending, and discloses a two-way real-time coal blending method based on momentum mixing and coal drop pipe optimization. The method comprises the following steps: S1, monitoring coal flow information of a first coal feeder and a second coal feeder in real time, and respectively calculating target momentum values P of the first coal feeder and the second coal feeder at a mixing point; s2, spectral analysis is conducted on the first coal feeder and the second coal feeder, a fluctuation feature library is established, real-time detection and active compensation are conducted on discharging fluctuation, and the compensation amount delta P is output; and S3, constructing a bidirectional regulation and control model based on a multivariable cooperative control architecture, and respectively inputting target momentum values P and compensation amounts of the two paths of coal flows as control targets. According to the invention, the inherent mechanical fluctuation of the coal feeder and the change of the material level of the coal bunker can be actively inhibited, the high precision of the final coal blending proportion and the mixing uniformity is ensured, meanwhile, efficient turbulent mixing can be realized by utilizing the kinetic energy of the coal flow, and active mixing equipment such as an external mechanical stirrer or a roller mixer is omitted.

A method and system for determining coal blending schemes for different coal types in a thermal power plant. The method comprises: acquiring coal type data of a coal yard and historical operation data of a coal treatment apparatus and a coal environmental protection system; on the basis of the coal type data of the coal yard and the historical operation data of the coal treatment apparatus and the coal environmental protection system, establishing constraint conditions for minimum calorific value, ignition stability, sulfur content, moisture content and ash content of blended coal fed into a boiler; on the basis of the constraint conditions, determining the blending and firing ratio for different coal types, so as to obtain a plurality of coal blending schemes; and by using the lowest comprehensive power supply cost as an objective, establishing an objective function, solving objective function values for the plurality of coal blending schemes, and, on the basis of the objective function values, obtaining a final coal blending scheme of the thermal power plant. The present invention determines various constraint conditions for blended coal on the basis of the current operation status of unit apparatuses, such that the coal blending and firing scheme better conforms to the actual operation conditions of a unit. In addition, since the coal blending scheme is obtained by performing big data analysis on various data, the method is simple and feasible, involves relatively low cost, and can increase the blending proportions of economical coal types, thereby lowering power generation cost, improving resource utilization efficiency, and realizing sustainable development of energy.

The invention belongs to the technical field of coal-fired boiler control, and particularly relates to a system and a method for dynamically regulating and controlling the structural design of a separated coal bunker and the blending combustion proportion of fire coal. According to the method, the correction coefficient is generated through the small-scale blending combustion experiment data, and the initial proportion calculation model is calibrated, so that the proportion design conforms to the theoretical constraint and adapts to the actual combustion characteristics of the coal type, and the theoretical and actual deviation is reduced; combustion efficiency and pollutant emission trend are predicted on line based on a fire coal combustion prediction model, dynamic adjustment of a fire coal blending combustion proportion is realized in combination with a progressive adjustment rule and an extreme working condition response rule, combustion parameter fluctuation is avoided, a verified alternative scheme is called, environmental protection and combustion stability are met, response time is shortened, and the method is suitable for large-scale popularization and application. The reliability in a fault state is improved; by automatically updating the fire coal combustion prediction model, prediction-adjustment-update closed-loop optimization is realized, the regulation and control error is gradually reduced along with the operation time, and the high regulation and control precision is kept for a long time.

The invention discloses an intelligent coal blending method and system for dual-engine staged solution in a power coal scene. The method comprises the following steps: performing structured processing on coal source data and establishing a hundred-ton integer batch variable; the method comprises the following steps of: constructing a hard constraint integer programming (ILP) model, and sequentially executing three stages of solving of cost minimization, heat value deviation minimization under the fixed cost and coal source quantity minimization under the fixed cost and deviation; when any stage is not feasible, automatically switching to a soft constraint target planning (GP) model, performing soft constraint on the coal quality index through a deviation variable, and sequentially performing deviation minimization, cost minimization and coal source quantity minimization; and calculating a combustion stability index (CSI) on the basis of volatile component fluctuation, fixed carbon, ash content change and sulfur content change, and carrying out stability verification on a final coal blending scheme. The system comprises a data management module, a first engine module, a second engine module, an automatic rollback module, a combustion stability index module and a result output module.

The invention discloses an AI intelligent coal blending method based on deep learning, relates to the technical field of intelligent coal blending, and solves the technical problems of low efficiency in feature association recognition and screening and poor model architecture scene adaptability. The Pearson coefficient is used for linear features, the mutual information method is used for hierarchical redundancy elimination for nonlinear features, the limitation of traditional single statistic feature screening is avoided, hidden association is generated and mined through composite features, feature dimensions are more simplified, association with coal blending targets is more direct, the model training efficiency and prediction precision are effectively improved, and the method is suitable for large-scale popularization and application. Models are customized for different scenes, compared with an existing method that a single model adapts to a full scene, precise coverage of lightweight-dynamic-complex scenes is achieved, and the applicability and optimization effect of coal blending schemes under different scenes are greatly improved.

The invention relates to the field of cooperative control of atmospheric pollutant and carbon dioxide emission of a power system, and discloses a power system pollution and carbon reduction method based on source-network interaction, which relates to an analysis method and specifically comprises four steps. The method comprises the following steps: 1, establishing an atmospheric pollutant and CO2 emission model; 2, space-time diffusion characteristics of atmospheric pollutants are described through a Gaussian plume diffusion model, and the influence of the space-time diffusion characteristics on a typical load area is analyzed; 3, establishing a power plant coal blending and power grid dispatching combined optimization model containing pollution reduction and carbon reduction constraints; and 4, establishing a collaborative optimization framework and dividing responsibilities of a source network. Compared with a power coal blending and power dispatching technology adopted by a current power plant and a power grid, the method has the advantages that on the basis of daily load prediction, collaborative interaction and joint response between the power grid and the power plant are enhanced through refined decision making, and emission of atmospheric pollutants and CO2 in power generation and transmission links is reduced to the maximum extent.

The invention provides a thermal power generating unit operation control method and device, electronic equipment and a readable storage medium, and belongs to the technical field of thermal power generation control. The method comprises the steps of obtaining a current operation load rate of a thermal power generating unit; based on the current operation load rate, according to a preset relation table or a preset relation curve, the high-alkali coal blending proportion is determined, and the preset relation table or the preset relation curve is used for representing the corresponding relation between different operation load rates and the high-alkali coal blending proportion; and on the basis of the high-alkali coal blending proportion, an in-furnace raw coal switching system of the thermal power generating unit is controlled to adjust the input amount of high-alkali coal powder and low-alkali coal powder input into the boiler. According to the operation load rate of the thermal power generating unit, the blending proportion of the high-alkali coal in the pulverized coal needed for boiler combustion is determined, normal work of the boiler can be guaranteed, the slag bonding amount can be reduced, the use amount of the high-alkali coal can be increased, and the power generation cost is effectively reduced.

The invention discloses a time-sharing coal blending combustion method, device and equipment for a coal-fired power plant and a storage medium, and relates to the technical field of coal blending control, and the method comprises the following steps: establishing a dynamic database containing coal reserves and coal quality parameters of the coal-fired power plant, and updating coal characteristic data in real time; dividing the whole day into a plurality of time periods according to a power grid dispatching instruction and historical load data, and setting a reference load range for each time period; and by taking the fire coal characteristic data and the reference load range as constraint conditions and taking the maximum fire coal profit as a target function, generating a fire coal blending combustion scheme of the coal mill in each time period. According to the method, coal quality parameters and inventory data are integrated in real time by constructing a dynamic database, time periods are intelligently divided and a load reference is set in combination with a power grid dispatching instruction, and a coal blending combustion scheme of each time period is generated by using a mathematical optimization algorithm; the problems of poor coal quality fluctuation adaptation, lagging load adjustment, high fuel cost, difficulty in emission control and the like of a traditional artificial experience method are effectively solved.

The present invention relates to the technical field of tar residue separation, and in particular to an online crushing, slag collection and separation system and process for large tar residue based on a pre-separator, wherein the online crushing, slag collection and separation system for large tar residue based on a pre-separator includes a pre-separator and a pre-separation pipe A and a pre-separation pipe B connected to the outlet end of the pre-separator, a crushing device respectively connected to the outlet ends of the pre-separation pipe A and the pre-separation pipe B, a centrifugal pump and a pretreatment device connected to the outlet end of the centrifugal pump, the crushing device is used to crush tar residue and graphite blocks, and the pretreatment device is used to precipitate and layer the crushed tar, tar residue, graphite blocks and ammonia water mixture; the present invention realizes the crushing, slag collection and drying of large tar residue and graphite blocks through a two-stage crushing treatment method composed of a crushing device and a drying unit, significantly reduces the risk of blockage in subsequent processes, meets the requirements of the coal blending process for material particle size, and improves the availability of tar residue.

The invention discloses a multispectral combined coal multi-index on-line rapid detection system and a multispectral combined coal multi-index on-line rapid detection method. The multispectral combined coal multi-index on-line rapid detection system comprises a coal sample conveying device, a spectrum fusion analysis device and a control device. The coal sample conveying device is used for conveying coal samples; the spectrum fusion analysis device is used for detecting and analyzing the coal sample and comprises an X-ray spectrum detection unit, a near-infrared detection unit and a laser-induced breakdown spectroscopy unit, the X-ray spectrum detection unit is used for obtaining an X-ray fluorescence spectrum of the coal sample, and the near-infrared detection unit is used for obtaining a near-infrared spectrum of the coal sample; the laser-induced breakdown spectroscopy unit is used for acquiring a plasma spectrum of the coal sample; and the spectrum fusion analysis device is electrically connected with the control device. According to the rapid detection system provided by the embodiment of the invention, online real-time prediction of the caking index, the calorific value, the ash content, the total moisture content, the total sulfur content, the volatile component and the ash fusion point of the coal is realized, and real-time data support is provided for coking coal blending process optimization and metallurgical coke quality control.

The invention relates to the technical field of coal deep processing equipment, and particularly discloses a coal preparation device which comprises coal receiving equipment, pre-crushing equipment, a coal distributing mechanism, a coal blending bin, coal blending crushing equipment and a coal tower which are sequentially arranged along a production process, and materials are conveyed among the equipment through belt conveyors; the coal receiving equipment comprises a coal receiving pit and a coal feeder arranged below the coal receiving pit; the coal distributing mechanism comprises a first coal distributing mechanism which is arranged in front of the pre-crushing equipment and is used for conveying coal types which do not need to be pre-crushed to the coal blending bin; the second coal distributing mechanism is arranged behind the pre-crushing equipment and is used for conveying the pre-crushed coal types to the coal blending bin; the plurality of coal blending bunkers are used for respectively storing different coal types, and electronic automatic batching scales are arranged below the coal blending bunkers; and iron removers are arranged between the coal receiving equipment and the pre-crushing equipment as well as between the coal blending bunker and the coal blending crushing equipment. The invention aims to solve the problems that coal with high hardness cannot be finely crushed and coal easy to crush is excessively crushed due to a process with coal blending equipment.

The invention provides a subcritical boiler platen reheater clamping pipe leakage prevention method, device and equipment. The subcritical boiler platen reheater clamping pipe leakage prevention method comprises the steps that dissimilar steel welding connection of a clamping pipe and a pulling plate is relieved; assembling a pipe ring on the outer wall of the clamping pipe to form non-welding type mechanical isolation, and welding and fixing a pulling plate on the outer wall of the pipe ring to complete reconstruction of a stress conduction path; after reconstruction is completed, a two-factor monitoring mechanism for spheroidizing degradation of the clamping pipe and temperature fluctuation of the screen reheater is established; monitoring data are collected based on a two-factor monitoring mechanism, and combustion parameter pre-adjustment and pipeline preventive replacement are dynamically triggered; according to the method, the leakage correlation event is periodically analyzed, the coal blending combustion and maintenance strategy is reversely optimized, the problem that leakage is likely to be caused in a traditional welding mode is solved by removing welding and completing stress conduction path reconstruction, faults can be warned in advance through two-factor monitoring and periodic analysis, and the possibility of leakage is reduced.

The invention relates to the technical field of thermal power plant operation optimization, and discloses a thermal power plant coal blending combustion proportion optimization decision method based on a feasible region model, comprising the following steps: step 1, constructing a basic model of a coal blending combustion proportion feasible region; step 2, determining a first type of feasible region boundary function based on a coal blending combustion constraint condition, and determining a second type of feasible region boundary function based on a coal blending total proportion constraint condition; the coal blending combustion constraint condition comprises coal blending calorific value, coal blending volatile component, coal blending sulfur content, moisture, ash content and single coal proportion; 3, determining a coal blending combustion proportion feasible region under the condition of multiple coal types by adopting a vertex search-based space projection algorithm; and 4, setting a target function, and outputting an optimal blending combustion ratio based on the coal blending combustion ratio feasible region. According to the method, rapid calculation of the optimal blending combustion proportion and rapid adjustment of the blending combustion proportion facing sudden change of working conditions can be achieved, and thermal power plant operation personnel are accurately assisted in performing blending combustion proportion optimization decision making in real time.

The invention relates to a detailed classification method for coking coal, which comprises the following steps of: dividing the coking coal into gas coal, 1 / 3 coking coal, gas-fat coal, fat coal, coking coal, lean coal, meager lean coal and meager coal according to the standard of China Coal Classification; detailed classification is carried out according to coal quality parameter indexes including volatile components Vdaf, a caking index G value, a gelatinous layer maximum thickness Y and a vitrinite reflectivity histogram distribution state; wherein the gas coal is divided into gas coal 1 and gas coal 2; dividing the fat coal into fat coal 1, fat coal 2 and fat coal 3; dividing 1 / 3 coking coal into 1 / 3 coking coal 1 and 1 / 3 coking coal 2; the coking coal is divided into coking coal 1, coking coal 2 and coking coal 3; dividing the lean coal into lean coal 1 and lean coal 2; the gas-fat coal and the meager lean coal are not classified in detail. And through detailed classification of the coking coal, the interchangeability of the coking coal from different sources during coal blending is improved, and the coking quality and stability of the blended coal are ensured.

The invention relates to the technical field of tar residue separation, in particular to a large-block tar residue online crushing and residue taking separation system and process based on a preseparator, and the large-block tar residue online crushing and residue taking separation system based on the preseparator is provided with a pre-separator. The pre-separation pipe A and the pre-separation pipe B are connected with the outlet end of the pre-separator, the crushing device and the centrifugal pump are connected to the outlet end of the pre-separation pipe A and the outlet end of the pre-separation pipe B respectively, the pretreatment device is connected with the outlet end of the centrifugal pump, and the crushing device is used for crushing tar residues and graphite blocks. The pretreatment device is used for precipitating and layering the crushed tar, tar residue, graphite block and ammonia water mixture; through a two-stage crushing treatment mode formed by the crushing device and the drying unit, crushing, slag taking and drying of large tar slag and graphite blocks are achieved, the blocking risk in the follow-up process is remarkably reduced, the requirement of the coal blending process for the material particle size is met, and the usability of the tar slag is improved.

The invention provides an intelligent coal blending combustion method and system, and relates to the technical field of thermal power generation, and the method comprises the steps: collecting combustion parameter information of different coal types, related indexes of each combustor of a boiler corresponding to the combustion parameter information, the consumption amount of each type of fire coal per month, and the minimum load requirement of the boiler; establishing a coal blending combustion DMD model based on the combustion parameter information, the related indexes, the fuel coal consumption amount and the minimum boiler load requirement, wherein the coal blending combustion DMD model is used for minimizing the fuel cost and meeting the boiler load requirement; solving the coal blending combustion DMD model by adopting an Excel planning solving tool, determining the usage amount of each combustor for different coal types, and generating an optimal coal blending scheme; according to the method, the coal blending combustion DMD model based on the combustion parameter information, the related indexes, the fire coal consumption amount and the boiler minimum load requirement is established, and an Excel planning solving tool is adopted for solving, so that the fuel cost can be minimized, and meanwhile, the boiler load requirement is met, and pollutant emission is controlled.

The invention relates to a large silo coke quality prediction method, and belongs to the technical field of coking quality monitoring. According to the method, the specific calculation process of the coal blending execution deviation rate, the accumulated coal blending execution deviation rate and the estimated actual coal blending ratio of a single silo is displayed, the silo bottom coal distribution and the corresponding mass and weight are determined according to silo coal seam tracking, and the coal quality is comprehensively estimated. And performing blended coal quality prediction and coke quality prediction by combining the pre-estimated coal blending amount, the pre-estimated coal blending ratio and the pre-judged quality of the silo. According to the method, the concepts of the coal blending execution deviation rate and the accumulated coal blending execution deviation rate are introduced, the silo coal seam tracking technology is combined, the discharging amount of each silo in a certain time period is predicted, and then the predicted coal blending ratio is calculated. By predicting the coal blending ratio and the corresponding blanking quality of the silo, the accurate prediction of the blended coal quality and the coke quality is realized.

The invention provides a thermal power plant coal fuel intelligent co-combustion optimization system, and relates to the field of coal fuel co-combustion, and the system comprises a coal fuel detection module which is used for detecting coal quality characteristic parameters of coal fuel; the coal fuel storage bin group module comprises a plurality of independent coal fuel storage bins, and each coal fuel storage bin is provided with a weighing sensor and a discharging flow adjusting valve; the intelligent coal blending module is used for generating an optimal coal blending scheme through a multi-objective optimization algorithm on the basis of detection data of the coal fuel detection module and in combination with a boiler combustion characteristic database; the coal blending control module is used for adjusting discharging flow adjusting valves of all the coal fuel storage bins according to the optimal coal blending scheme, and accurate coal blending is achieved; and the correction module is used for collecting coal fuel combustion state data and feeding back the data to the intelligent coal blending module for scheme dynamic correction. A multi-objective optimization algorithm is adopted to replace artificial experience decision, combustion efficiency, pollutant discharge and equipment operation and maintenance cost are optimized synchronously, and a coal blending scheme is optimized dynamically and iteratively.

The invention is suitable for the technical field of coal, and provides a high alkali metal coal slag adhering method, which comprises the following steps: step 1, analyzing the occurrence form and content of alkali metal: firstly, analyzing the occurrence form and content of alkali metal in coal, including four forms of water-soluble alkali metal, ammonium acetate solubility, diluted hydrochloric acid solubility and insoluble alkali metal, according to the invention, the slag adhering problem of the high alkali metal coal is reduced, the combustion efficiency and environmental protection property of the coal are improved, the combustion and gasification efficiency of the coal is improved, the environmental pollution is reduced, through reasonable coal blending, the utilization efficiency of coal resources is improved, the production cost is reduced, the low alkali coal can be effectively utilized to improve the combustion characteristics of the high alkali coal, and the slag-bonding and contamination problems of a boiler are reduced; meanwhile, safe and economical operation of the boiler is guaranteed, the components of the coal ash can be effectively regulated and controlled, the utilization value of the coal ash is improved, the environmental influence is reduced, cyclic utilization of resources is achieved, and the slag adhering phenomenon of the high alkali metal coal in the combustion and gasification process can be more accurately predicted and controlled, so that effective prevention and solution measures are taken.

The invention relates to an intelligent coking coal feeding prediction method and system, and belongs to the technical field of intelligent coking management. An expert guidance model is applied to deeply analyze feed coal characteristics according to coal quality detection data, a feed coal analysis result is determined, and constraint conditions are determined according to feed coal, coke quality indexes and cost indexes; preliminarily generating a coal blending scheme by applying a mathematical calculation model, adopting a mixed integer rule and a genetic algorithm, taking coke quality index and cost minimization as targets and combining constraint conditions; the coal blending scheme is adjusted based on multiple interactions of the mathematical calculation model and the expert guidance model, and the expert guidance model determines scheme optimization suggestions according to the coal blending scheme; applying a market prediction model based on an LSTM neural network and time sequence causal analysis to predict the price trend of the feed coal, and generating a procurement scheme suggestion corresponding to the coal blending scheme according to the price trend and inventory information of the feed coal; and generating a coal feeding evaluation report. The method has the effect of accurately optimizing the coal blending scheme.

The invention discloses a coal-fired power plant carbon emission intelligent control method and system based on machine learning, and relates to the technical field of coal-fired power plant carbon emission, and the method comprises the four steps: building a three-level sensing network to collect carbon flow data, importing various parameters to construct a carbon flow digital twinborn body, building a real-time mapping link, and obtaining twinborn body boiler data; establishing a solid waste related mapping model, and calculating and outputting a coal blending combustion proportion through an optimal solution; predicting the carbon release amount within 5 minutes in combination with boiler data and a blending combustion ratio, constructing a carbon capture agent model, and judging whether elastic adjustment is triggered or not through an improved Sarsa algorithm game; and analyzing a quota consumption rule based on prediction data, predicting a carbon price, optimizing multiple targets and analyzing a Pareto optimal strategy. According to the method, full-chain collaborative management and control are realized, the carbon emission control precision and intelligent level are improved, environmental protection standard reaching, quota management and cost control are balanced, and low-carbon and efficient operation of a power plant is assisted.

The invention discloses a coal lithofacies detection device and method, and relates to the technical field of maceral detection, the coal lithofacies detection method comprises the steps of coal rock X-ray plate preparation and microscopic observation, in the coal rock X-ray plate preparation step, crushing, screening, mixing, shaping and surface treatment are performed on a coal sample to form a coal rock X-ray plate; in the microscopic observation step, microscopic components and mineral substances in the coal rock polished section are observed and analyzed to judge the lithofacies type of the coal. According to the coal lithofacies detection device and method provided by the invention, the metamorphism degree, the combustion characteristic, the chemical reactivity, the processability and the like of coal are evaluated by analyzing the maceral components, the mineral components and the contents of the maceral components and the mineral components of the coal; in the coking process, coal blending is carried out based on the lithofacies of the coal, so that the proportions of different coal types can be more accurately blended, the optimal coal blending scheme is selected, combustion is more economical and effective, the calorific value of the coal and the yield of coke are increased, and the ash content and the sulfur content of the coke are reduced.

The invention discloses a dynamic optimization system for multi-coal blending combustion. The dynamic optimization system comprises an execution layer, a sensing layer and a decision-making layer, the execution layer executes the specific operation of mixed combustion, receives the control instruction generated by the decision-making layer, and feeds back the mixed combustion operation data executed according to the control instruction to the sensing layer; the sensing layer acquires data in real time through a combustion field sensing network, an emission monitoring module and a coal quality online detection array, and uploads the data to the decision-making layer through real-time data flow; the dynamic coal blending model of the decision-making layer generates an optimal coal blending proportion based on real-time coal quality data and operation feedback; and a multi-objective optimization engine of the decision-making layer can output and adjust a coal blending and combustion optimization strategy. Through the synergistic effect of the dynamic coal blending model, the multi-objective optimization engine and the self-adaptive compensation system, the intelligent level of coal blending combustion is remarkably improved; based on real-time data of a sensing layer and a DRL intelligent agent, coal blending dynamic real-time optimization adjustment is achieved, and coal quality fluctuation and working condition changes are adapted.

The invention relates to the technical field of production dynamic optimization, in particular to a coal mine production and sales decision-making method and system based on intelligent flexible production. Constructing a three-dimensional geologic model of the coal mine, and obtaining a production task generated based on a customer order; generating a production scheduling plan of the coal mine working face according to the three-dimensional geologic model and the production task; obtaining multi-source dynamic data in coal mine working face mining; constructing a coal mine anomaly recognition model to recognize the multi-source dynamic data and the three-dimensional geologic model to obtain a geologic anomaly type and a spatial position; quantifying the multi-dimensional influence of geological anomaly types and spatial positions on a production scheduling plan in combination with the operation state of coal mining equipment and mining historical data of a coal mine working face; and constructing a multi-objective optimization model, and planning the production quota, resource allocation and coal blending schemes of the coal mine working face by taking cost minimization and risk minimization as optimization objectives under the constraint of a production task to generate a dynamic balance production scheme.