193 results about "Coal blending" patented technology

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 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 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 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 relates to a thermal power plant coal blending combustion optimization method based on multi-source data and an intelligent algorithm, and the method comprises the steps: obtaining the multi-source data from a fuel management and control platform, a coal yard digital system, a unit operation monitoring system, and an environmental protection monitoring system in real time, establishing a dynamic database including coal quality data, real-time inventory, unit operation constraints and environmental protection constraints; based on the dynamic database, taking the minimization of the total fire coal cost in the future T hours as a target function, and taking stable operation of a boiler, an environmental protection emission index and equipment processing capacity as constraint conditions, establishing a corresponding fire coal blending optimization model for each load period, and forming a multi-period optimization model; solving the multi-period optimization model by adopting an improved genetic algorithm to generate an optimal blending scheme sequence; through online monitoring, according to the optimal blending scheme, adopting a preset control strategy to realize closed-loop optimization of the combustion process; according to the invention, the comprehensive optimization of the fire coal cost, the combustion efficiency and the environment-friendly emission is realized.

This invention relates to the field of coking coal blending control technology, and discloses a method for controlling the coking stabilization of bituminous coal and coking coal blends, comprising the following steps: obtaining the thermoplastic parameters, petrographic parameters, and moisture parameters of bituminous coal and coking coal in the current control batch; establishing a dual-coal plasticity synergistic stabilization window based on the thermoplastic parameters and petrographic parameters, and determining the target dry basis blending ratio within the dual-coal plasticity synergistic stabilization window; converting the target dry basis blending ratio into the actual wet basis feed rate based on the moisture parameter; obtaining the actual particle size distribution after crushing, and adjusting the crushing parameters and feed compensation correction when there is a particle size deviation; blending according to the corrected actual wet basis feed rate, and obtaining the thermoplastic verification parameters of the blended coal; when the blended coal deviates from the target verification range, identifying the cause of deviation as moisture deviation, particle size deviation, or coal quality deviation according to a preset order, and performing corresponding corrections.

The invention provides a thermal power plant boiler dynamic coal blending management system and method based on a cloud platform, and the system comprises a data collection layer which is used for obtaining multi-source data; the cloud platform processing layer comprises a constraint generation module and an optimization decision module, the constraint generation module is used for constructing an optimization constraint condition according to the multi-source data, the optimization decision module is used for determining an optimal coal blending scheme according to the optimization constraint condition and a preset optimization target, and the optimal coal blending scheme at least comprises the blending combustion proportion of each coal type in the total coal feeding amount; and the execution control layer is used for generating a control instruction of the coal feeder according to the optimal coal blending scheme. According to the method, the multi-source data and the intelligent algorithm are integrated through the cloud platform, the constraint condition of actual operation is considered, the blending combustion proportion of each coal type in the total coal feeding amount is determined, real-time dynamic optimization of the coal blending process of the thermal power plant is achieved, the preset optimization target is met, the safety, reliability and response rate of system operation are greatly improved, and the coal blending efficiency of the thermal power plant is improved. Different operation requirements of a thermal power plant are met.

The invention discloses a coking device for lignite blending, and relates to the technical field of coal coking, the coking device comprises a coking furnace, the top of the coking furnace is fixedly provided with a box body, the two sides of the box body are both provided with storage boxes, the bottoms of the storage boxes are fixedly provided with hollow cylinders, and the sides, away from each other, of the two hollow cylinders are both fixedly provided with first servo motors; output shafts of the first servo motors penetrate through one side of the hollow cylinder and are fixedly provided with first augers; the two independent first servo motors are used for driving the augers at the bottom of the storage box respectively, the conveying amount of lignite and other coal types can be accurately controlled by adjusting the rotating speed of the motors, the deviation of the coal blending proportion is greatly reduced, it is ensured that the mixed coal meets the preset formula requirement all the time, and the coal blending efficiency is improved. Coke quality fluctuation caused by proportion imbalance is reduced from the source, stable performance of subsequent coking products is guaranteed, and the coal crushing and mixing effect is improved.

This invention provides a method for evaluating the cost-effectiveness of semi-coke based on the effective calorific value of pulverized coal, relating to the field of iron and steel metallurgy. First, a formula for calculating the effective calorific value of pulverized coal in a blast furnace is established. By calculating the heat released from incomplete combustion of carbon and deducting the heat absorbed by moisture decomposition, hydrocarbon cracking, ash slag formation, and desulfurization, an effective calorific value more consistent with the actual operating conditions of the blast furnace is obtained. Subsequently, this effective calorific value is divided by the injection cost to construct a cost-effectiveness index. By calculating and comparing the cost-effectiveness index of the target fuel combination after semi-coke partially replaces anthracite with the benchmark combination cost-effectiveness index, the economic viability of semi-coke substitution can be accurately determined, and the critical price or price range for cost-effective semi-coke can be directly calculated. This invention overcomes the shortcomings of traditional evaluation methods that are detached from the actual thermal environment of the blast furnace, providing a scientific and quantitative decision-making basis for fuel procurement and coal blending optimization.

This disclosure proposes an evaluation method and system for auxiliary coal blending, relating to the field of coal blending technology. The evaluation method for auxiliary coal blending includes the following steps: collecting basic blending data, which includes at least coal blending information, combustion parameters, environmental parameters, and equipment status data; establishing a multi-dimensional analysis model based on the basic blending data, and analyzing the basic blending data through the multi-dimensional analysis model to obtain analysis results; generating visualization charts based on the analysis results at a preset cycle; establishing an A / B comparison analysis mechanism; inputting the blending scheme verified by the A / B comparison analysis, along with the applicable conditions of the blending scheme, into a dynamic knowledge base; and establishing a review mechanism to analyze long-term data trends and continuously optimize the multi-dimensional analysis model and the dynamic knowledge base. This disclosure achieves closed-loop management of the entire process of blending schemes, from data collection, multi-dimensional evaluation, visualization, comparison verification to knowledge accumulation and continuous optimization.

The invention relates to a coal blending optimization method based on coked coal inert capacity, which can improve the coke quality prediction accuracy, improve the inert component utilization efficiency, reduce the coke coal blending cost and optimize the coal blending method by taking inert capacity as a third factor by establishing an industrial experience-based inert capacity evaluation system. The coal blending technology is promoted to be converted from'empirical 'to'precise', and iron and steel enterprises can deal with fluctuation of cohesive coal resources. According to the method, on the basis of long-term accumulation and analysis of industrial data, differential calculation parameters are provided according to caking contribution values of caking coal (fat coal, coking coal, 1 / 3 coking coal and gas coal) with different characteristics in a coal blending scheme, the total inert capacity Q of the caking coal in coal blending is controlled to be within the range of 19.0-20.0, accurate improvement is achieved, the coke quality is predicted, and the coal blending efficiency is improved. And the waste of cohesiveness is avoided.

This invention relates to the field of dynamic load section stress monitoring and feedforward adaptive intelligent correction technology for coal conveyor belts in thermal power plants. The invention provides an automatic monitoring and correction device for coal conveyor belts, comprising: a control host that extracts speckle decorrelation index and maps the transverse segregation gradient matrix through a data acquisition and detection module; a rheological state analysis module that introduces a fractional-order calculus dynamics model to analyze the creep stress field and outputs a deformation potential energy time series; a spatial feature prediction module that uses the Koopman operator to elevate the nonlinear system to a linear functional space, identifies outbreak nodes by tracking eigenvalue drift trajectories, and issues early warning commands; and an acoustic field adhesion breaking correction module that drives piezoelectric ceramic elements to construct a local acoustic radiation standing wave field, breaking van der Waals forces to release transverse shear potential energy. This invention solves the risk of belt misalignment and tearing caused by material segregation and adhesion under multi-coal blending conditions.

A coal blending method and a use thereof, relating to the technical field of thermal power engineering, and solving the problems of low-temperature oxidation, spontaneous combustion and pollutant formation in stored coal. The specific method is as follows: selecting coal M and coal N, and blending same at a specific ratio, wherein the coal M has an oxygen uptake weight gain ΔM>1% and Vdaf>37%, the coal N has an oxygen uptake weight gain ΔM≤1% and Vdaf≤37%, and the weight ratio of the coal M to the coal N is (10-90): (10-90); the oxygen uptake weight gain ΔM=M2-M1, ΔM is the difference between the peak and valley values during the oxygen uptake weight gain stage in a thermogravimetric test, M1 is the valley value of a thermogravimetric curve, M2 is the peak value of the thermogravimetric curve, and Vdaf is the dry ash‑free volatile matter of coal.

The present application belongs to the technical field of coking production feed, and particularly relates to a coal blending system and a coal blending method in a coking production process. The system comprises a first coal blending system and a second coal blending system, and the method comprises the following contents: (1) when the number of furnaces is greater than or equal to 160 furnaces per day, two sets of systems are used to blend coal simultaneously, and 1# and 2# coal humidifying buffer bins are supplied respectively; (2) when the number of furnaces is less than 160 furnaces per day, a single system is used to blend coal, and the single system is used to blend coal in time, and 1# and 2# coal humidifying buffer bins are supplied in turn; (3) when the coal in the coal tank is low in inventory, is empty or the equipment is limited in any system, the coal is switched to be complementary in different regions. The system and the method use two sets of systems in parallel under high yield conditions, maximize the coal carrying efficiency; under low yield conditions, the single system is used to blend coal in time, energy is saved and consumption is reduced, and the production rhythm is flexibly adapted; when the coal in the coal tank is low in inventory or empty in any system, the coal is used complementarily across regions, the partition barriers are broken, and the coal tank is scheduled globally.

The invention relates to the technical field of coal distribution, in particular to an adjacent raw coal bunker interconnection and intercommunication coal distribution system and a use method. A coal blending system with interconnected and intercommunicated adjacent raw coal bins comprises a first raw coal bin containing first fire coal; the second raw coal bunker and the first raw coal bunker are arranged in a spaced mode, and second fire coal is contained in the second raw coal bunker; a first buffer coal bucket; the second buffer coal hopper and the first buffer coal hopper are arranged at an interval; and the conveyor is respectively communicated with the first raw coal bunker, the second raw coal bunker, the first buffer coal hopper and the second buffer coal hopper. The invention provides an adjacent raw coal bunker interconnection and intercommunication coal blending system and a use method, and aims to solve the problems that most raw coal bunkers are in an independent coal supply mode, no interconnection and intercommunication structure exists between adjacent coal bunkers, and the coal quality cannot be quickly allocated.

The present application relates to the technical field of high-alkali coal blending combustion, and particularly relates to a method, system and equipment for determining combustion parameters of high-alkali coal blending combustion in different furnaces, wherein the method comprises: collecting structure data of a current furnace type; preprocessing and encoding the structure data to obtain furnace type characteristic quantities of the current furnace type; inputting the furnace type characteristic quantities of the current furnace type into a preset combustion parameter prediction neural network model, and obtaining target combustion parameters of high-alkali coal blending combustion in the current furnace type based on preset constraint conditions, wherein the preset combustion parameter prediction neural network model is trained based on a mapping relationship between furnace type characteristic data and fuel characteristic data. Thus, the problem that operating personnel have difficulty in understanding the optimal combustion parameters of high-alkali coal blending combustion under various furnace types and fuel conditions in the application of high-alkali coal blending combustion is solved, the problem of ash deposition and slagging caused by high-alkali coal combustion can be effectively alleviated by reasonably determining the combustion parameters, and reliable technical support is provided for clean and efficient utilization of high-alkali coal.

The utility model discloses a thermal power plant cross coal blending device with an anti-blocking function and a coal blending system, and relates to the technical field of coal blending, the coal blending device comprises a plurality of coal feeders, the plurality of coal feeders comprise a first coal feeder, a second coal feeder, a third coal feeder and a fourth coal feeder; the first coal feeder is arranged between a first coal bunker in the plurality of coal bunkers and a first coal mill in the plurality of coal mills and is used for conveying coal in the first coal bunker to the first coal mill; the second coal feeder is arranged between a second coal bunker in the plurality of coal bunkers and a second coal mill in the plurality of coal mills and is used for conveying coal in the second coal bunker to the second coal mill; the third coal feeder is arranged between the first coal bunker and the second coal mill and is used for conveying coal in the first coal bunker to the second coal mill; and the fourth coal feeder is arranged between the second coal bunker and the first coal mill and is used for conveying the coal in the second coal bunker to the first coal mill. According to the utility model, the coal blending accuracy can be improved.

The application discloses a kind of multi-objective collaborative optimization method and device of coal blending of thermal power plant, belong to energy management and intelligent control technical field.Integrating the coal quality of thermal power plant, operation, emission and scheduling data, construct unified feature library, and based on this, set the initial weight of economic, safety, environmental protection target, establish multi-objective optimization model;Adopt parallel computing architecture to solve model, generate multiple candidate blending scheme;Subsequently, combined with the artificial adjustment result of operator and actual combustion feedback data, carry out secondary prediction and comparative analysis;Finally, through continuous learning adjustment behavior and operation effect, drive model automatically update weight and optimization algorithm combination, form closed loop self-learning mechanism, to realize multi-objective collaborative optimization and adaptive decision of thermal power plant.The application improves the intelligence, economy and environmental protection of system operation.

The application discloses a kind of accurate coal blending car loading system and method of coal preparation plant, the system includes warehouse belt, online coal quality detector a, coal bunker, 3D radar volume monitor, coal feeder, belt scale a, loading belt, online coal quality detector b and belt scale b;Online coal quality detector a is installed on warehouse belt, coal bunker is connected downstream of warehouse belt, 3D radar volume monitor is installed in coal bunker, the outlet of coal bunker is connected with coal feeder, the downstream of coal feeder is connected with belt scale a, the downstream of belt scale a is connected with loading belt, the downstream of loading belt is connected with loading train, online coal quality detector b and belt scale b are installed on loading belt.The method compares the coal quality detection result of loading coal sample and loading requirement, combines the coal quantity of loading and the coal quantity demand required for loading, adjusts the opening of coal feeder under coal bunker to adjust the blending ratio of different coal bunkers, and finally ensures that the coal quality of the whole loading train meets the standard.

The invention discloses a dry quenching metallurgical coke fat-free coal and prime coking coal tamping coking method, which comprises the following steps: S01, selecting Ningdong gas coal in Ningxia and 1 / 3 coking coal in Wuhai from four specific producing areas, and strictly controlling the volatile components, caking index and other quality indexes of each coal; s02, mixing the four types of coal according to the weight ratio of 80%, 5%, 8% and 7%, not adding fat coal and prime coking coal, and controlling the volatile component of the mixed coal to be 33.5-34.5% and the caking index to be greater than or equal to 60; and S03, adopting a tamping coking process (coking for 26-27 hours) and dry quenching to obtain dry quenching metallurgical coke (M40 is greater than or equal to 86.0% and the like) reaching the standard. Selection of specific coal types and index control are core prerequisites, coal blending cost can be reduced, high-quality coal resources are saved, and blast furnace efficiency is improved. According to some embodiments, indexes such as specific ash content and sulfur content of various coal types, blended coal and coke are supplemented; a coal blending scheme can be verified by using a coke quality prediction model in advance, and the coal blending scheme is verified by a 40kg load test coke oven before industrial production, so that the production stability and the coke quality are guaranteed.

The invention discloses an AI-driven thermal power plant combustion stability-economic benefit double-target intelligent coal blending method, and relates to the technical field of thermal power plant coal blending optimizing.The method comprises the steps that a coal-wrapped type combustion fingerprint feature database is constructed; inputting the combustion fingerprint feature data of the candidate coal blending scheme into a boiler combustion risk prediction model based on a weighted support vector machine to obtain a corresponding combustion risk probability value; the coal blending proportion is used as an optimization variable, a dual-objective optimization function is constructed, a penalty function is adopted to perform relaxation processing on a preset constraint condition, and the dual-objective optimization function comprises an economical efficiency function taking fuel purchase cost minimization as a target and a safety function taking combustion risk probability value minimization as a target; carrying out Pareto optimization on the economy function and the safety function based on a multi-objective evolutionary algorithm, and outputting a Pareto optimal coal blending scheme set; and executing the coal blending scheme. Therefore, collaborative optimization of boiler combustion safety and fuel purchase economy is realized.

The application discloses a mixed coal blending combustion method and system for relieving ash deposition and slagging of a boiler heating surface, and belongs to the technical field of mixed coal blending combustion, which focuses on the ash content, Na2O and K2O content data in the coal quality data of the coal type, constructs a calculation model of the maximum alkali metal oxide equivalent value of the combustion boiler and the actual alkali metal oxide equivalent value of the coal type, and designs the constraint condition of the mixed coal blending combustion method and system in the ash deposition and slagging of the coal-fired boiler, so that the acquisition and adjustment of the mixed coal blending ratio are accurately realized. The mixed coal blending combustion method and system are convenient and operable, can accurately realize the mixed coal blending combustion in the coal-fired boiler, improve the accuracy of the mixed coal blending combustion control, ensure the reliability of the fuel combustion in the coal-fired boiler, improve the ash deposition and slagging degree of the boiler heating surface, reduce the shutdown and maintenance frequency of the coal-fired boiler, ensure the reliability and stability of the coal-fired boiler operation, and reduce the operation and economic cost of the enterprise.

The invention discloses a preparation method of slag-reducing and reducing coke for a blast furnace, the coke and a use method of the coke. The preparation method comprises the following steps: blending the following coal in percentage by mass: 15-30% of gas coal, 13-30% of 1 / 3 coking coal, 8-12% of fat coal, 15-30% of coking coal and 6-15% of meager lean coal. The coking coal with low content of SiO2 and Al2O3 and high content of CaO, MgO and Fe2O3 is optimized, and the coal blending structure is optimized, so that the coke obtained by coking has low content of SiO2 and Al2O3 and high content of CaO, MgO and Fe2O3. A certain proportion of coke is used in blast furnace production, the content of SiO2 and Al2O3 in furnace charge is reduced, and then the yield of furnace slag is reduced; the method improves the content of CaO, MgO and Fe2O3 in the furnace burden, can reduce the addition amount of the slag solvent, can reduce the melting point of the slag, and can enhance the fluidity of the slag, thereby promoting better separation of the slag and the molten iron, reducing the slag inclusion amount of the molten iron, and improving the quality of the molten iron. Meanwhile, the coke has high reducing performance, and the temperature of a blast furnace heat reserve area is reduced. In addition, the air permeability of the blast furnace is effectively improved.

The invention belongs to the technical field of combustion equipment, and particularly relates to an ammonia-doped coal co-combustion direct-current combustor and application thereof in a corner tangential coal-fired boiler, and the ammonia-doped coal co-combustion direct-current combustor comprises a pulverized coal concentrator, an ammonia gas sleeve and a peripheral air sleeve; a pulverized coal concentration and dilution separation assembly is arranged in the pulverized coal concentrator in the airflow flowing direction and used for separating pulverized coal airflow entering the pulverized coal concentrator and outputting concentrated pulverized coal airflow and diluted pulverized coal airflow. The ammonia gas sleeve is arranged outside the pulverized coal concentrator in a sleeving manner, a plurality of ammonia injection holes are formed in the gas outlet end of the ammonia gas sleeve on one side of the pulverized coal concentrator for outputting thick pulverized coal airflow, and the ammonia injection holes are arranged towards the center line of the pulverized coal concentrator; the ammonia gas sleeve is sleeved with the peripheral air sleeve, and the gas outlet end of the peripheral air sleeve is arranged back to the center line of the pulverized coal concentrator. Compared with the prior art, the problems that nitrogen oxide emission is greatly increased and emission exceeds the standard easily due to ammonia and coal blending combustion in a direct-current combustor are solved. According to the scheme, the nitrogen oxide emission amount of the ammonia coal blending combustion power station boiler is greatly reduced.

This invention discloses a coking blend containing US gas-rich coal, belonging to the field of metallurgical coke preparation technology. The blend consists of the following individual coking coals in the following mass percentages: US gas-rich coal 2%–8%, coking coal 40%–45%, coking coal 16%–21%, 1 / 3 coking coal 20%–25%, and lean coal 7%–14%. The blend has a caking index (G) of 77–83, a maximum plastic layer thickness (Y) of 14 mm–19 mm, and a volatile matter (Vdaf) of 27%–30%. The US gas-rich coal has a volatile matter (Vdaf) of 40%–43%, a caking index (G) greater than 95, a plastic layer thickness (Y) greater than 20, and a maximum vitrinite reflectance of 0.75–0.85. This invention utilizes a specific proportion of US gas-rich coal to replace part of the high-priced prime coking coal and coking coal. Without the need for tamping or briquetting processes, the coke cake is produced through high-temperature dry distillation in a 7.1-meter top-loading coke oven, with the center temperature controlled at 1000±50℃. The resulting coke exhibits stable and compliant cold and hot strength indicators, which can reduce coal blending costs and diversify coal blending resources, thus ensuring supply chain security.

The invention discloses a coal blending structure optimization method and device, electronic equipment and a computer storage medium. The method comprises the following steps: acquiring an initial coal blending structure; the initial coal blending structure comprises mixing proportion information among the initial coals; and based on the initial coal blending structure, constructing an optimization target by minimizing the coal blending cost, and performing optimization search by taking the coal blending structure as an unknown quantity to obtain a target coal blending structure. Compared with a mode of manually determining the coal blending structure, the determination efficiency of the coal blending structure is improved; besides, the optimization search is carried out on the basis of the initial coal blending structure and by constructing the optimization target with the minimum coal blending cost, so that the coal blending cost corresponding to the finally obtained target coal blending structure can be relatively low. In conclusion, the coal blending structure optimization method provided by the invention can improve the determination efficiency of the coal blending structure and reduce the economic cost of coal blending.

The invention discloses a multi-coal-source coal blending data processing method based on a WSMOTE algorithm, and belongs to the technical field of coal processing data processing, and the method comprises the steps: data collection and classification: collecting multi-dimensional data of a raw coal floating and sinking test, a coal blending test and the like, carrying out the preprocessing, dividing the data into a missing data set and a complete data set, carrying out the interpolation optimization of a missing value, and carrying out the calculation of the complete data set. And carrying out adaptive processing according to data distribution types, or carrying out weighted combination normalization according to feature importance, analyzing the number and distribution characteristics of minority class samples, dynamically adjusting the neighbor sample size and the new sample synthesis amount, and completing data enhancement and standardization. According to the method, standardized parameters are dynamically adjusted to adapt to data distribution changes, abnormal values are accurately processed, the combined interpolation model is subjected to weight optimization, errors are reduced, and data integrity is improved. WSMOTE parameters are adaptively combined with data distribution to synthesize samples, data are balanced, and the over-fitting risk is reduced. In practical application, the quality of coal blending model training data can be improved, and optimization of a multi-coal-source coal blending scheme is assisted.