315 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 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 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 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 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 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.

According to the fire coal blending combustion control system and method provided by the invention, the fire coal blending combustion control system realizes intelligent and accurate control of the fire coal blending combustion process by constructing a cooperative control system of data acquisition, modeling analysis, decision control and man-machine interaction. The technical limitation of traditional manual coal blending is broken through, real-time accurate modeling of the coal yard form is achieved through the three-dimensional laser scanning and multi-source data fusion technology, and the coal diffusion prediction precision is remarkably improved by combining dynamic repose angle parameter correction; genetic algorithm optimization and a closed-loop compensation mechanism are adopted for synergistic effect, coal blending calorific value fluctuation is effectively controlled, and the risk that sulfur content exceeds the standard is greatly reduced; an intelligent operation and maintenance interface is constructed through a three-dimensional visualization and data tracing technology, and the abnormal working condition handling efficiency is remarkably improved. And obvious economic benefits and environmental benefits are realized while the stability of the calorific value of the fire coal is ensured.

The invention discloses a coal blending remote control system and method, and the system comprises a coal blending bunker module which is used for generating a coal blending plan scheme according to coal blending data; the metering monitoring module is used for collecting material related data in real time and transmitting the data to the data processing module; the data processing module is used for generating a coal blending control signal according to the coal blending plan scheme and the material related data; the control module is used for generating material cleaning, conveying and shutdown control signals according to the coal blending control signal; the sound wave material cleaning module is used for executing material cleaning action in combination with sound wave material cleaning parameter matching according to the material cleaning control signal; and the material conveying module is used for executing mixing and conveying operation of the coal blending materials according to the conveying and shutdown control signals. Coal blending can be remotely and accurately monitored, accumulated materials can be automatically processed, the labor intensity and the safety risk of operators are reduced, the working environment is improved, the coal blending plan execution accuracy and the system stability are improved, and it is guaranteed that coal blending operation is efficient and continuous.

This invention relates to the field of coal blending and coking technology, specifically to a method, equipment, and storage medium for establishing a dry-quenched coke quality prediction model. The method includes: acquiring a first coal blending scheme, and first reactivity data, second reactivity data, first post-reaction intensity data, and second post-reaction intensity data of the coal corresponding to the first coal blending scheme after coking; establishing a first coke quality prediction model based on first linear relationship values, second linear relationship values, the proportion of a single type of coal in the blend, and third reactivity data corresponding to a single type of coal; and determining a dry-quenched coke quality prediction model based on the first coke quality prediction model, third linear relationship values, and fourth linear relationship values. The dry-quenched coke quality prediction model established by this application has high accuracy, good generalization ability, and can quickly acquire dry-quenched coke quality data under different coal blending schemes and different coke oven production load conditions.

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.

The invention discloses a coking coal quality evaluation method and system based on a functional group structure, and belongs to the technical field of coal quality evaluation. The method comprises the following steps: drying and crushing a coal sample, and carrying out FT-IR spectrum analysis to obtain the relative content of aromatic and aliphatic functional groups; carrying out applicability judgment according to preset requirements on the lowest functional group content of different coal types; querying a specific scoring standard table for scoring the qualified coal sample according to the functional group content of the qualified coal sample; and finally, carrying out weighted calculation on a comprehensive score and dividing quality grades. The invention also provides a system for realizing the method. The system comprises a sample pretreatment module, a spectral analysis module, a data processing module and a scoring and rating module. According to the method, the quantification, objectification and rapidness of coking coal quality evaluation are realized from the chemical structure essence for determining the coking property, the experience dependence and hysteresis of a traditional method are overcome, and a scientific basis is provided for accurate grading and coal blending optimization of the coking coal.

The utility model discloses a small stockyard for automatic material piling and taking, which belongs to the technical field of material piling and taking and dust suppression equipment of small stockyards, and comprises a gripping device, a warehouse-out device and a warehouse-in device, the warehouse-out device is arranged below the gripping device, the warehouse-out device is arranged on the ground, the gripping device is arranged above the inside of the stockyard, the warehouse-in device is arranged on one side in the stockyard, and the warehouse-in device is arranged on the other side in the stockyard. And a micro-fog dust suppression device is mounted on the lower surface of the stock yard roof. According to the small stockyard capable of automatically stacking and taking the materials, the telescopic belt conveyor is used for the discharging trolley, the discharging range can be further expanded, the transferring workload is reduced, and the storage capacity of the stockyard is increased; the number of the coal blending machines is flexibly set, the coal type proportion is measured through weighing, and coal blending is convenient and accurate; equipment such as the micro-fog dust suppression spray head on the upper portion can be directly maintained and overhauled through the travelling crane platform, other climbing equipment does not need to be independently used, convenience and rapidness are achieved, an unattended control system and a central control room are adopted for operation, and unmanned operation of a stock yard can be achieved.

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 belongs to the technical field of coking coal blending, and particularly relates to coke, a coal blending system for smelting the coke and a method for improving the granularity of the coke. The coal blending system for smelting coke comprises M coal, F coal and high-stability pore structure lean coking coal. The M coal, the F coal and the high-stability pore structure lean coking coal are blended in a coal blending system according to a certain proportion, the coke particle size can be controlled and improved, the average coke particle size can reach 59-63 mm, the proportion of the coke with the particle size being 40-60 mm is larger than or equal to 55%, high-quality metallurgical coke with more scientific and reasonable particle size distribution is provided for large blast furnaces with the size being 4000 m < 3 > or above, and the economic benefit is high. Basic conditions are created for stable and efficient operation of the blast furnace.

The utility model relates to the technical field of raw coal storage, in particular to a wall-adhesion-free coal blending anti-blocking partition bin which comprises a bin body, an anti-blocking mechanism and a partition plate, the lower end of the bin body is in a funnel shape, the partition plate is connected with the bin body in a sliding mode and located in the bin body, and the anti-blocking mechanism comprises a rotating block, a connecting frame, four rotating rollers and a driving assembly. The rotating block is rotationally connected with the partition plate, the cross section of the rotating block located below the partition plate is in a T shape, the connecting frame is fixedly connected with the rotating block, the connecting frame is in a cross shape, the four rotating rollers are fixedly connected with the connecting frame, the driving assembly is arranged below the connecting frame, and two openings are formed in the lower end of the partition plate; through the arrangement of the structure, the problems that in the prior art, a raw coal bunker separately stores different coal briquettes through a partition plate, and during coal blending, the coal briquettes fall down and are prone to blocking the lower end of the raw coal bunker are solved.

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 discloses a coal blending combustion strategy making method and a related device. The method comprises the steps of obtaining unit related data of a target generator set; the unit related data comprises unit operation data, coal quality test data, market price data and carbon measurement data; the carbon measurement data comprises carbon emission data of different coal combustion; determining a coal blending combustion proportion according to the unit operation data, the coal quality test data and the market price data; determining the unit carbon emission according to the unit operation data, the coal quality test data and the carbon measurement data; determining a multi-target cost optimal model according to the coal blending combustion ratio and the unit carbon emission; solving the multi-target cost optimal model to obtain a target coal blending combustion strategy; the coal blending cost and the carbon emission cost of the target coal blending combustion strategy are optimal. Therefore, the target coal blending combustion strategy with the optimal coal blending cost and carbon emission cost can be obtained by constructing the multi-target cost optimal model and solving the multi-target cost optimal model.

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.

The invention belongs to the technical field of hydrogenation modification of low-rank coal, and particularly relates to a method for coal blending coking through non-catalytic hydrogenation modification tackifying of low-rank coal. In order to solve the problems that the low-rank coal mixing hydrogenation process is complex, a catalyst is difficult to recycle and the like, the method comprises the following steps: weighing low-rank coal and a hydrogen-donor solvent according to different mass proportions, carrying out hydrogenation reaction according to different reaction temperatures, reaction pressures and reaction time of an autoclave, firstly, fully stirring and uniformly mixing the materials in the autoclave, and then carrying out heating reaction; and after the autoclave is cooled, directly carrying out suction filtration separation on the reaction mixture to obtain a solid product which is the modified coal with a certain caking index.