2731 results about "Coal fired" patented technology

The invention relates to a boiler combustion optimization control system of a thermal power plant, relates to the technical field of thermal power generation control, and aims to solve the problems of inaccurate multi-source data perception, slow control response and insufficient multi-target collaborative optimization capability under fire coal quality fluctuation and load change. The system comprises a data perception and fusion module, a working condition self-adaptive identification module, a multi-target dynamic optimization decision module and a distributed execution control module, and through multi-source data acquisition and feature extraction, depth time sequence mode identification, multi-target optimization model solving and distributed coordination control, the multi-target dynamic optimization decision module and the distributed execution control module are subjected to multi-source data fusion. Dynamic balance of boiler heat efficiency improvement and nitrogen oxide emission reduction is achieved, and adaptability and control precision of the system under complex working conditions are enhanced.

The invention discloses a low-carbon flexible coal-fired unit power control system and method, and relates to the technical field of power systems, and the system comprises a cascade waste heat recovery module, a boiler deep regulation and control module, a thermal coordination module, an environmental protection and emission reduction module, an intelligent operation scheduling module and an energy efficiency monitoring module. The gradient waste heat recovery module carries out gradient recovery on flue gas heat energy according to temperature zone grades; the boiler depth regulation and control module is used for regulating combustion air supply and heat absorption paths to realize low-load stable combustion; the thermal coordination module dynamically distributes thermal loads between the multiple heat sources and the thermal units; the environmental protection and emission reduction module jointly controls desulfurization, denitration and wastewater resourceful treatment; the intelligent operation scheduling module generates a load regulation and control instruction and coordinates multi-module response; the energy efficiency monitoring module collects and analyzes energy consumption and emission indexes and assists strategy optimization. The energy efficiency utilization and emission control capability of the unit under the deep peak regulation condition is improved, and flexible, efficient and low-carbon operation is achieved.

The invention discloses a coal-fired unit SCR denitration system ammonia injection prediction control method based on data driving, which is used for realizing better SCR denitration system ammonia injection control based on a data driving model in combination with an implicit generalized prediction control algorithm. The SCR inlet NOx concentration and the flue gas temperature serve as feedforward of an ammonia spraying amount instruction, the initial control instruction increment of the ammonia spraying amount is calculated through the SCR outlet NOx concentration, the SCR outlet NOx concentration set value and the ammonia spraying amount instruction at the previous moment through an implicit generalized predictive control algorithm, and the final ammonia spraying amount instruction is obtained through summation and amplitude limiting of the last three instructions. Wherein the feedforward instruction of the NOx concentration of the SCR inlet is obtained by a BP neural network prediction model, and the feedforward instruction of the SCR inlet is obtained by a long-short term memory network prediction model fused with an attention mechanism. According to the method, the problem that the concentration of NOx at an SCR outlet fluctuates severely in the frequent load changing process can be effectively solved, meanwhile, transient overshoot of NOx in tail flue gas of a coal-fired unit is restrained, and the SCR denitration control effect is improved.

The invention relates to the technical field of boiler equipment fault prediction, in particular to a boiler four-tube wall tube wear prediction method based on a big data algorithm, comprising the following steps: constructing a three-dimensional digital model of boiler four tubes, and spatially binding design parameters, historical operation and maintenance records and real-time monitoring data to form a multi-source associated database; constructing a pipe wall temperature prediction model by using a heat conduction mechanism model and a neural network algorithm, and outputting wall temperature distribution data of a full heating surface; inputting the data into a wear, creep and corrosion model, and calculating the residual life and generating a life map in combination with a historical pipe explosion case; generating a three-dimensional risk thermodynamic distribution diagram by adopting a leakage risk probability algorithm and a coupling risk coefficient model; and superposing the thermodynamic diagram into the three-dimensional digital model, and displaying the risk level based on color gradient mapping, so that the method can be widely applied to online state monitoring and intelligent early warning of the four tubes of the coal-fired power plant boiler.

The invention discloses a coal-fired power plant operation parameter adaptive optimization method and system. The method comprises the steps of operation data fusion acquisition, dynamic load response modeling, fire coal load coordination optimization, emission efficiency adaptive balance and operation parameter adaptive optimization. The invention relates to the technical field of intelligent adjustment of parameters of a coal-fired power plant, and aims at realizing future load minute-level rolling prediction by constructing a standardized multi-source data structure and adopting a long-short-term memory network, and realizing dynamic generation of combustion parameters by introducing adaptive dual-stage collaborative scheduling and an improved multi-objective optimization algorithm. On the basis, the dynamic causal regulation and control diagram is used for carrying out rolling weighted balance on the emission efficiency, the energy efficiency, the responsiveness and the emission compliance are comprehensively considered, the power generation efficiency is improved, the pollutant emission is reduced, and the adaptability and the stability of the system are enhanced.

The invention discloses a method for preventing main steam pressure fluctuation on the basis of controlling the coal combustion amount. The method comprises the following steps: acquiring running state data of the coal-fired boiler in real time; based on the operation state data and a preset boiler dynamic prediction model, predicting a steam pressure sequence of the boiler in a future preset time range, and judging whether to trigger a control action or not in combination with an event triggering strategy obtained by learning of a reinforcement learning agent; if it is judged that the control action is triggered, the reinforcement learning agent outputs an adjustment instruction of the total coal feeding amount according to the current operation state data; and controlling the total coal feeding amount of the boiler according to the adjusting instruction. According to the method, the timing and the force of control intervention can be intelligently determined according to the real-time state and future trend prediction of the boiler, and the accuracy and the foresight of main steam pressure control are improved.

The invention relates to a power plant temperature control system safety protection device and system. The power plant temperature control system safety protection device comprises a redundant temperature acquisition module, a temperature control safety processing module, an actuator control protection module, an intelligent strategy recovery module and a communication and alarm module. Through multi-point temperature data acquisition, abnormal trend intelligent identification and local linkage control, the device realizes quick response and protection control of temperature control abnormity of a key area of a boiler thermodynamic system. The system supports data interconnection with a DCS platform and an alarm analysis platform, has local independent operation and remote centralized management capabilities, and is suitable for temperature control safety protection of high heat load areas such as boilers of coal-fired power plants.

The invention discloses a cementing material for a solid waste-based road of a coal-fired power plant and a preparation method of the cementing material. The cementing material mainly comprises a cementing component, an alkali-activated component and a reinforcing component, the gelling component is a mixture of coal-fired power plant fly ash and furnace slag; the alkali-activated component is a mixture of alkali residues, red mud, blast furnace slag and coal-fired power plant desulfurized gypsum; the reinforcing component is rice hull ash or peanut hull ash. The mass ratio of the gelling component to the alkali-activated component is (4 / 1)-(10 / 1); and the addition amount of the reinforcing component is 1-3% of that of the main body material. The coal-fired power plant solid waste fly ash, the furnace slag and the desulfurized gypsum are used as main bodies, a small amount of other industrial solid waste is added, and the cementing material belongs to a full-solid-waste low-carbon cementing material and can be applied to a road base, a subbase or a base layer. The invention has the characteristics of low carbon emission, high solid waste consumption, simple production process and strong pavement performance, and in addition, the invention has a good stabilization effect on the problem that part of solid waste heavy metals and sulfate ions in a coal-fired power plant exceed the standard.

The invention relates to the technical field of fire coal control, and discloses a thermal power plant fly ash floating black online detection control method and system, and the method comprises the steps: 1, employing microwave, infrared and laser sensors for cooperative work, detecting carbon dielectric characteristics through microwave dust interference resistance, identifying thermal radiation difference through infrared, and analyzing particle distribution through laser; 2, on the basis of the detection requirements in the step 1, triple protection of air curtain barrier, ultrasonic cleaning and hydrophobic coating is designed, dust condensation is blocked, and it is ensured that the light transmittance of the sensor is larger than 90%; the invention provides a multi-mode sensing fusion-anti-interference current stabilization-closed loop optimization full-link solution for the industrial problem of signal distortion caused by optical window dew formation and dust deposition, airflow disturbance and environmental interference in fly ash floating black online detection. The light transmittance of the optical window is greater than 93% by combining triple protection of air curtain barrier, ultrasonic cavitation stripping and nano hydrophobic coating.

The invention provides a combustion optimization method and system for a coal-fired boiler, electronic equipment and a storage medium. The combustion optimization method for the coal-fired boiler comprises the steps that historical operation data and operation condition data of the boiler are obtained; determining the combustion efficiency of the boiler according to the historical operation data of the boiler; working condition division is carried out according to the boiler combustion efficiency and the operation working condition data, and information of all working condition intervals is obtained; constructing a combustion efficiency prediction model driven by time series data according to the information of each working condition interval; the combustion efficiency prediction model is embedded into a deep reinforcement learning method framework for training, and an optimization model is generated; and inputting real-time operation data of the boiler into the optimization model to generate optimization decision data.

The invention relates to the technical field of environmental monitoring, and discloses an uncertainty evaluation method based on establishment of a carbon emission monitoring method and an accounting method, and the method comprises the following steps: establishing an uncertainty evaluation model of a coal-fired unit carbon emission monitoring method to quantify the uncertainty of the carbon dioxide emission of the monitoring method; establishing a coal-fired unit carbon emission accounting method uncertainty evaluation model to quantify the uncertainty of the carbon dioxide emission of the accounting method; based on results obtained by the monitoring method uncertainty evaluation model and the accounting method uncertainty evaluation model, carbon emission data quality is analyzed, and improvement measures are put forward. By constructing an uncertainty evaluation model of a monitoring method and an accounting method, various error sources in the whole process of flue gas sampling, analysis, fuel metering, coal quality analysis and the like are comprehensively considered, and a rigorous mathematical method is adopted for synthesis. The quantitative evaluation enables the accuracy of the carbon emission data to have a clear measurement standard.

The invention belongs to the technical field of coal-fired power plant combustion control, and discloses a boiler combustion control method based on blending combustion proportion dynamic optimization and a related device. The boiler combustion control method based on blending combustion proportion dynamic optimization comprises the steps that combustion efficiency parameters, safety parameters and flue gas environmental protection parameters of a boiler are monitored and collected in real time, and the combustion efficiency parameters, the safety parameters and the flue gas environmental protection parameters are preprocessed; inputting the pretreated combustion efficiency parameters, safety parameters and flue gas environmental protection parameters into the trained RF-LSTM double-layer model to obtain an optimal blending combustion ratio, setting an optimal blending combustion ratio threshold according to the optimal blending combustion ratio, and dynamically regulating and controlling the blending combustion ratio according to the optimal blending combustion ratio threshold and a preset wall temperature threshold through a double-screw feeder; the sludge blending combustion proportion can be dynamically adjusted, the reliability and flexibility of the system are guaranteed, and proportional blending combustion is more stable.

The invention relates to the technical field of boiler combustion optimization, in particular to a group combustion optimization method under flexible peak regulation of a coal-fired boiler, which comprises the following steps of: acquiring characteristic historical operation data of the coal-fired boiler, dividing operation conditions, calculating key operation parameters of the coal-fired boiler under different working conditions, and constructing a coal-fired boiler combustion model; coal loading information and load information of a future operation plan of the coal-fired boiler are read, and a planned operation condition group of the coal-fired boiler is constructed in combination with key operation variable action time; key operation parameter analysis is carried out on the planned operation condition group of the coal-fired boiler, group optimization is carried out based on a combustion model, a key operation variable adjustment scheme is given, and operation of the coal-fired boiler is guided. According to the method, the operation condition of the coal-fired boiler under the flexible peak regulation can be analyzed and judged, group combustion optimization of the coal-fired boiler is comprehensively guided in combination with the key operation variable action time, and the high efficiency and the environmental protection property of the coal-fired boiler under the flexible peak regulation can be greatly improved.

The invention belongs to the technical field of industrial flue gas purification, and discloses an ammonia desulfurization optimization control system based on a machine learning algorithm, which comprises a feature extraction module, a working condition clustering module, a dynamic optimization module and a self-adaptive feedback module, the feature extraction module is used for collecting multi-dimensional operation parameters in a coal burning process, performing data dimension reduction through a PCA algorithm, and extracting key working condition features; the working condition clustering module identifies different operation working condition modes; the dynamic optimization module can construct a multi-modal optimization neural network based on different operation condition modes, and generates optimal control parameters in real time. Through PCA dimension reduction processing of the feature extraction module and in combination with a working condition clustering algorithm, automatic mode recognition and strategy switching under complex working conditions are achieved, fluctuation of desulfurization efficiency is reduced, and meanwhile the problems that the ammonia water adding amount depends on experience setting, raw material waste and secondary pollution are likely to be caused, and the operation and maintenance cost is large are solved.

The invention discloses a carbon emission evaluation method and system for coal-fired biomass direct-mixing coupling power generation, and relates to the related field of carbon emission evaluation, and the method comprises the steps: determining the type and characteristics of a biomass raw material, and setting blending combustion conditions; establishing a carbon emission parameter calculation model; establishing a coal-fired biomass direct-mixing coupling carbon emission full-life-cycle evaluation model by combining carbon emission in stages of growth, collection, transportation, pretreatment and ash treatment of biomass raw materials; evaluating the influence of the retention duration of the biomass raw material on the combustion carbon emission to obtain a retention duration influence factor, and correcting the coal-fired biomass direct-mixing coupling carbon emission full-life-cycle evaluation model according to the retention duration influence factor; and performing carbon emission evaluation by adopting the corrected coal biomass direct mixing coupling carbon emission full life cycle evaluation model. The technical problem that existing carbon emission evaluation is insufficient in accuracy and comprehensiveness is solved, and the technical effect of improving the accuracy and comprehensiveness of carbon emission evaluation is achieved.

The invention discloses an intelligent optimization operation method for a biomass gas coupling coal-fired boiler, and belongs to the technical field of boiler operation control. Historical operation parameters of a gasification furnace and the coal-fired boiler are collected, a multi-dimensional coordinate system is constructed, historical operation data are mapped into coordinate points, a high-efficiency area boundary, a stable area boundary and a risk area boundary are divided, and a dynamic operation map is formed; meanwhile, the historical operation data are cut into track fragments, and the state evolution process and operation instructions of the coal-fired boiler are recorded; the method comprises the following steps: acquiring gasifier temperature, gas components and boiler temperature data in real time, determining the position of a current state in a dynamic operation map, and predicting a future evolution path of the coal-fired boiler by fitting a motion track and matching a plurality of historical tracks; and when the evolution path reaches the boundary of the risk area, triggering early warning, screening a historical successful track by taking an efficient area as a target, extracting related operation instructions to form a candidate intervention strategy set, selecting the operation instructions according to real-time working conditions, converting the operation instructions into specific adjustment instructions, and sending the specific adjustment instructions to corresponding control equipment.

The invention discloses an omnidirectional tension iterative compensation type array electronic belt scale measuring device and method. The device comprises a signal acquisition module, a signal processing module, a dynamic iterative compensation and fusion module, a redundancy check module and an integral operation module. Multi-source signal synchronous acquisition is realized through an array type weighing unit, a three-axis tension detection unit and a speed detection unit, dynamic gain amplification, anti-aliasing filtering and AD conversion are carried out, then a three-axis tension signal is adopted to carry out dynamic iteration compensation on a weighing signal, belt deformation compensation, transverse deviation correction and frequency domain vibration suppression are executed, and the weighing precision is improved. And environmental interference is eliminated in combination with redundant cross check. And finally, the real-time coal feeding amount is output through integral operation. According to the method, the limitation of traditional static compensation is broken through, errors caused by tension fluctuation, mechanical vibration and transverse deviation are effectively restrained through the multi-dimensional dynamic fusion compensation technology, the anti-interference capacity is high, and high-reliability metering support is provided for energy efficiency management of the coal-fired unit.

The invention discloses a power plant fire coal cost management and control and dispatching system which comprises a data acquisition module, a management and control dispatching module and a data display window. The data acquisition module is used for acquiring power plant real-time load data, power plant fire coal consumption speed, historical fire coal price, inventory period, international energy price index and policy document; and the management and control dispatching module is used for inputting related data integration through a mathematical model, analyzing the market trend and price fluctuation, and predicting and controlling the fire coal cost. Limitations of a traditional fire coal management system can be broken through, complex decision-making elements are quantified through mathematical modeling, dynamic balance of cost and risks is achieved, the response speed and accuracy of a fire coal cost prediction model to market changes can be effectively improved through the method of combining a dynamic threshold value and online learning, and the market development prospect is wide. The enterprise can realize dynamic optimization and risk controllability of cost prediction in uncertainty of coal price fluctuation, transportation delay and the like.

The invention discloses a flue gas compensation type pulverized coal wide-load stable combustion system based on staged combustion and intelligent regulation and control and a control method, and belongs to the technical field of coal-fired power generation. The system is composed of a combustor, an intelligent control module and a flue gas backflow module, the combustor adopts a double-ring fuel channel design, an inner ring injects high-concentration pulverized coal and is provided with an adjustable swirler, and pulverized coal-flue gas mixed airflow is introduced into an outer ring; the intelligent control module predicts a load through an LSTM neural network, and realizes accurate matching of air and coal in combination with PID adjustment; and the flue gas backflow module adopts an oxygen concentration adjustable technology to maintain the combustion intensity. Under the working condition of 30%-40% low load, the system achieves the stable combustion effect that the flame center temperature is stabilized at 920 + / -20 DEG C by means of optimizing the proportion of inner ring pulverized coal to 75%-85%, increasing the primary air temperature to 145-170 DEG C, compressing the proportion of secondary air to 20%-30%, strengthening a swirling flow field to 45 degrees and the like in cooperation with flue gas backflow with the oxygen concentration of 23%-25%, and has the advantages of efficient combustion and low emission.

The invention discloses an auxiliary decision-making method and system for regional coal market purchase, and belongs to the technical field of coal market purchase decision-making, and the method comprises the steps: building model input parameters through collecting the data of a fire coal demand plan of a power plant, the maximum supply of a supplier, a coal quality index, a purchase price and a transportation cost; constructing a mixed integer linear programming model taking the minimization of the total purchase cost as a target function, and setting a demand satisfaction constraint, a supply capability constraint and a coal quality standard reaching constraint; solving the mixed integer linear programming model by adopting a two-stage approximation algorithm; and converting the obtained approximate optimal solution into a visual purchasing scheme chart and report, and supporting a user to dynamically adjust the purchasing scheme through an interactive interface to generate a final purchasing decision. According to the invention, on the basis of comprehensively considering the coal quality, the purchase cost, the transportation mode and the supplier capability, the coal purchase plan is optimized, the cost minimization is realized, and the overall quality of coal purchase is improved.

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 belongs to the technical field of equipment state monitoring, and particularly relates to a boiler wall temperature anomaly detection and early warning method based on approximate entropy calculation, which comprises the following steps: dividing a large coal-fired power plant boiler wall temperature time sequence into continuously overlapped sliding windows; calculating three statistical parameters in each sliding window; the three statistical parameters comprise standard deviation, range and approximate entropy; forming a three-dimensional time sequence matrix formed by n groups of parameters based on the three statistical parameters; and performing anomaly detection and early warning on the three-dimensional time sequence matrix by using an isolated forest anomaly detection algorithm. Abnormalities are accurately recognized from different dimensions, and high-dimensional data are efficiently dealt with. The method has the real-time performance and the dynamic performance, online calculation and real-time response can be achieved, the model is flexibly updated along with new data inrush, and the anomaly detection requirement of the dynamic data environment is met.

The invention relates to a NOx emission concentration prediction system for a circulating fluidized bed boiler. The system comprises a data acquisition and dynamic feature construction module of a distributed sensor network, which realizes multi-dimensional characterization of a combustion state through space-time coupling analysis; according to the hybrid prediction model, a double-layer architecture is adopted to decouple a static combustion rule and a dynamic delay effect, and the prediction precision is improved by utilizing stable output of a base layer and self-adaptive compensation of a correction layer; the closed-loop control module is used for forming a synergistic effect of feedforward and feedback based on an intelligent decision-making mechanism of a working condition mapping model so as to avoid control overshoot; and the health guarantee module constructs a triple protection system, so that the data reliability is ensured, and redundancy protection can be realized under a fault working condition. According to the method, high-precision prediction and real-time control of NOx emission of the coal-fired unit in the full working condition range can be achieved, and the stability and the environmental protection performance of boiler operation are remarkably improved.

The invention discloses a coal and biomass blending combustion double-layer optimization operation method considering dynamic carbon absorption and economy cooperation. The method comprises the steps that coal and biomass fuel data and operation parameters are obtained; based on the fuel data and the operation parameters, a double-layer optimization model is constructed, the double-layer optimization model comprises an upper layer model and a lower layer model, the upper layer model is used for minimizing net carbon emission, and the lower layer model is used for minimizing the total operation cost of the system; based on the fuel data and the operation parameters, obtaining constraint conditions of the double-layer optimization model; according to the double-layer optimization model and the constraint conditions, a dynamic carbon absorption-economical efficiency collaborative model is constructed, the dynamic carbon absorption-economical efficiency collaborative model is used for achieving dynamic balance between carbon emission minimization and the total operation cost, and a scheduling optimization scheme of coal and biomass blending combustion is obtained. The problem that only mixed combustion of biomass fuel and coal with a fixed blending combustion ratio can be adopted when a coal-fired unit carries out blending combustion on biomass for power generation at present can be solved.

The invention discloses a fused salt heat storage and Carnot cell combined coal-fired unit peak-load regulation operation method, device and equipment, a medium and a product, and relates to the field of unit peak-load regulation operation. Acquiring information data; based on a power regulation demand prediction model, performing peak regulation demand prediction according to the information data to obtain a prediction result; the power regulation demand prediction model is obtained by adopting a multi-objective optimization model and performing time sequence analysis on a long-short-term memory network model; determining an energy scheduling strategy based on the prediction result; the energy scheduling strategy comprises a heat storage power coordination instruction and a heat release power coordination instruction; and according to the energy scheduling strategy, operation adjustment is conducted on the coal-fired unit, the fused salt heat storage subsystem and the Carnot battery subsystem based on the control system. The invention aims to improve the peak regulation performance of the coal-fired unit.

The invention relates to the technical field of automatic control, and discloses an intelligent ash deposition prevention self-cleaning system for a flue of a low-temperature economizer of a coal-fired power plant. According to the system, data such as temperature, humidity and flue gas components in a flue are acquired in real time through the data acquisition module, the dust deposition condition and cleaning requirements of the flue are accurately evaluated, the omission phenomenon caused by misjudgment in the manual cleaning process is avoided, the data processing and analysis module improves the data processing precision, and the data processing and analysis efficiency is improved. The system can also monitor the running state of the flue in real time, so that a cleaning instruction is automatically sent out when necessary, the timeliness and effectiveness of cleaning work are ensured, the cleaning execution module can efficiently remove accumulated dust while reducing the temperature of the flue through a mode of combining high-pressure airflow, a vibration device and a water spraying system, the time needed by cleaning is remarkably shortened, and the cleaning efficiency is improved. And the monitoring feedback module evaluates the cleaning effect, it is ensured that each time of cleaning can reach the preset standard, and the intelligent level and safety of flue cleaning are further improved.

The invention relates to the technical field of coal-fired power plant environmental protection, in particular to a coal-fired power plant intelligent environmental protection island control system based on multi-source data fusion. Comprising a multi-source data acquisition module, a data transmission module, a data preprocessing module, a multi-source data fusion module, an operation state analysis module, an intelligent control decision module, a control instruction execution module and a man-machine interaction module. According to the coal-fired power plant intelligent environmental protection island control system based on multi-source data fusion, equipment operation, environmental monitoring, power grid, fuel and meteorological data are widely acquired through the multi-source data acquisition module, the limitation of a single data source of a traditional system is broken through, a distributed Kalman filtering algorithm is combined with a neural network algorithm for data fusion, and the data fusion efficiency is improved. According to the method, the incidence relation between data can be deeply mined, temperature and pressure parameters of desulfurization equipment and flue gas sulfur dioxide concentration data are subjected to fusion analysis, the reason of desulfurization efficiency reduction can be accurately positioned, and compared with traditional single data source judgment, the fault diagnosis accuracy is improved.

The invention discloses a coal-fired boiler intelligent optimization method, system, equipment and medium based on multi-source perception, and belongs to the technical field of thermal power generation, and the method comprises the steps: carrying out the collection of boiler coal data of a coal-fired boiler, and carrying out the feedback compensation; collecting hearth data to reconstruct a combustion field, formulating an optimization control strategy, and constructing an optimization target to perform tail flue multi-component cooperative control; and carrying out dynamic adjustment based on an implementation result of the control strategy, and carrying out real-time monitoring to carry out coking early warning. According to the method, full-closed-loop intelligent control of coal quality perception, combustion optimization and coking prevention and control is formed, and by means of edge calculation layering processing, the boiler efficiency is improved, the power supply coal consumption is reduced and the cost is saved while it is ensured that emission reaches the standard.

The invention discloses a carbon quantity prediction method and device for coal-fired unit carbon emission, and a medium, and relates to the technical field of energy data intelligent analysis, and the method comprises the steps: calculating a carbon emission intensity prediction value and a confidence interval boundary value based on a real-time coal quality fusion operation parameter set and a device health index, and generating a dynamic carbon emission prediction package; comparing the dynamic carbon emission prediction packet with CEMS real-time monitoring data, calculating a prediction error rate, extracting a multi-dimensional error feature according to the prediction error rate, and generating a multi-dimensional error feature vector; and based on the multi-dimensional error feature vector, dynamically adjusting an equipment health index correlation factor and a coal quality confidence weight parameter, generating a dynamic correction instruction set, and generating a carbon emission prediction report in combination with a dynamic carbon emission prediction packet. According to the method, dynamic noise reduction, enhancement and feature quantization of the coal flow multispectral image are realized, the high-fidelity coal quality feature vector is directly generated, the problem of coal quality data lag is solved, and the coal quality sudden change response capability is improved.

The invention provides a coal-fired boiler digital twin modeling and intelligent control method, system, equipment and medium, and belongs to the technical field of thermal equipment intelligent control. The method comprises the steps that coal element analysis data, boiler operation parameters and environment parameters of a coal-fired boiler are collected in real time and preprocessed; calculating an external heat source sum and a heat loss sum, and outputting boiler heat efficiency; a twinborn model is constructed, model parameters are calibrated through data collected in real time and calculated boiler thermal efficiency, and boiler indexes are dynamically simulated and output; taking the collected data and the analog output boiler indexes as input characteristics, predicting boiler operation parameters through an LSTM model, generating an optimization control instruction by adopting a multi-objective optimization algorithm, inputting twin model simulation, outputting prediction parameters, and executing the control instruction when the prediction parameters meet preset constraints; and updating LSTM model training data according to the operation result. Intelligent monitoring optimization of the boiler is achieved, and efficiency, safety and environmental protection performance are improved.