1889 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 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 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 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 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 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 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 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.

The invention relates to the crossing field of mechanical engineering and intelligent prediction technologies, particularly discloses a coal mill efficiency and service life prediction method and device, and aims to solve the problem that a traditional model is difficult to deal with nonlinear coupling prediction of equipment performance degradation under variable load and coal quality fluctuation. The method comprises the following steps: receiving a multi-source sensing data stream and constructing a structured feature matrix with aligned time sequences; efficiency degradation implicit features are extracted through a nonlinear dynamic encoder, and the interaction influence of grinding roller abrasion, lining plate fatigue and bearing degradation is quantified in combination with a multi-failure-mode coupling analysis module; and cooperatively predicting a network output efficiency attenuation curve and residual life probability distribution through a bidirectional attention mechanism. According to the method, through fusion of multi-source time sequence characteristics and multi-failure coupling modeling, limitation of a static threshold value and linear extrapolation is broken through, prediction precision and timeliness are remarkably improved, intelligent maintenance decision support is provided for a coal-fired power plant, non-planned shutdown risks are reduced, and operation economy and system reliability are optimized.

The invention discloses an intelligent deep peak regulation system for a coal-fired power generation unit, and particularly relates to the technical field of power generation control, and the system comprises a multi-source data collection module which carries out the real-time parallel collection of all-dimensional operation parameters through a sensing network and unit decentralized control system interface disposed at a key node, and builds a multi-modal time sequence data flow; according to the method, a foresight control strategy is formed by constructing a fusion prediction model, sensing the running state of a unit in real time and predicting the change trend of key parameters, the defect of response lag of a traditional system is effectively overcome, and the purposes of accurate load tracking and advanced regulation and control under the working condition of deep peak regulation are achieved; a global optimization decision-making mechanism is established by adopting a deep reinforcement learning algorithm, optimal control parameters are calculated in real time through the intelligent peak regulation module, a boiler, a steam turbine and an environmental protection system are dynamically coordinated, the problem of insufficient stability caused by subsystem independent control is solved, and the optimal balance of safety, stability and economic benefits is achieved.

The invention provides an operation regulation and control method for a pulverizing unit matched with a coal-fired boiler during thermal power peak shaving, and belongs to the technical field of deep peak shaving boilers of coal-fired power plants. The operation regulation and control method comprises the following steps: determining a first typical particle size distribution interval when the coal-fired boiler is in a normal state and a second typical particle size distribution interval when the coal-fired boiler is in a peak regulation state, and establishing a mathematical intelligent model between the coal-fired particle size and the thermal power generation system performance; the number and working parameters of flour mills are configured; and when the thermal power generation system is in a normal state or a peak regulation state, according to the calculated fire coal granularity requirement, in combination with the real-time monitoring data and the optimization result of the mathematical intelligent model, determining and starting a corresponding number of flour mills, and dynamically adjusting the working parameters of the flour mills. The number of the flour mills is increased on the basis of one original flour mill, and the running states of the flour mills are finely regulated and controlled, so that the flour mills can efficiently run under different working conditions, and the requirements of a thermal power generation system are met.

The invention provides a deep peak regulation method for a coal-fired unit, relates to the technical field of thermal power generation, and can solve the problem of low-load flame shift, stabilize the combustion core and inhibit the fluctuation of NOx emission concentration by dynamically regulating and controlling a combustion field in a boiler. The energy storage system and the boiler are configured for cooperative operation, the energy storage system is used for rapidly transferring / releasing heat energy, the limitation of the heat storage rate of the boiler is broken through, the two-way peak regulation capacity and the regulation speed of the coal-fired unit in the load reducing stage and the load increasing stage can be remarkably improved, and therefore the boiler efficiency is improved under the low load, and the station service power consumption rate is reduced. By constructing an intelligent control system in communication connection with the DCS, automatic control can be realized.

The invention discloses a carbon transaction-oriented biomass gas coupling coal-fired power generation full-life-cycle carbon emission accounting method. The method comprises the steps of determining accounting boundaries of collection and transportation, storage pretreatment, gas production and coupling power generation of covering raw materials; activity data are collected, space-time mapping of upstream batch data and unit side continuous operation data is established, and data quality grading and uncertainty measurement are carried out; when actually measured synthesis gas components or lower calorific values are missing, a soft measurement model is constructed based on operation data, batch characteristic feedforward coupling is introduced, and an estimated value and uncertainty are output; calculating an intermediate quantity based on a material-energy-carbon conservation mechanism model, performing dynamic correction by adopting a residual correction model containing physical consistency constraint, and adjusting a physical constraint weight according to uncertainty; and an emission list and an MRV data packet are generated in combination with the dynamic emission parameters, and are used for monitoring, reporting, checking and transaction settlement, so that the precision and the traceability are improved, and the physical consistency is ensured.

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 utility model relates to a treatment system for purifying high-salinity wastewater, which is suitable for treating high-salinity wastewater, particularly desulfurization wastewater of a coal-fired power plant and coal chemical wastewater. By combining efficient flocculation, ozone catalysis and membrane distillation technologies, a self-made chitosan-based flocculant is added in a flocculation stage, and suspended solids and heavy metal ions in wastewater are removed by utilizing good adsorption performance and flocculation capacity of the self-made chitosan-based flocculant; in the ozone catalysis stage, a self-made ternary composite catalyst is added, ozone decomposition is promoted, and organic pollutants are further removed; in the membrane distillation stage, a self-made antifouling and moisture-resistant composite membrane is used for efficient desalination and concentration, and finally efficient purification and recycling of the high-salinity wastewater are achieved. The process can significantly improve the wastewater treatment effect, reduce the concentration of salt and harmful substances in the wastewater, also reduce the operation cost, and is suitable for large-scale industrial wastewater treatment.

The invention discloses a coal-fired unit low-carbon operation output optimization method and system suitable for low-carbon transformation, and the method comprises the steps: selecting a to-be-studied biomass blending combustion coal-fired coupling carbon capture generator set under coal-electricity low-carbon transformation, and obtaining the related data of a transformed coal-fired unit; a mechanism-data hybrid driven modeling method is adopted, and a mechanism model of the biomass blending combustion coal coupling carbon capture unit obtained after coal-electricity low-carbon transformation is constructed; constructing a low-carbon economic objective function of the biomass blending combustion coal coupled carbon capture generator set after coal-electricity low-carbon transformation, and constructing a calculation model of related low-carbon economic parameters in the objective function; constructing constraint conditions of a biomass blending combustion coal coupling carbon capture generator set after coal-electricity low-carbon transformation, and constraining input and output variables of the mechanism model at the same time; and an optimal low-carbon economic operation output scheme under the scene uncertainty is analyzed and considered, and an optimization result of the operation output of the biomass blending combustion coal coupled carbon capture generator set after coal power low-carbon transformation under the scene uncertainty is obtained through a mode of minimizing an objective function. According to the method, guidance can be provided for the operation output of the biomass blending combustion coal coupling carbon capture power station in each time period in the day.

The invention belongs to the technical field of coal combustion, and particularly relates to an environment-friendly rare earth catalyst as well as a preparation method and application thereof in promoting coal combustion. The catalyst is prepared from composite rare earth oxide, calcium oxide, potassium carbonate, ferrous sulfate, kaolin, diatomite and a multi-component composite assistant component in a specific proportion through the steps of slurry ultrasonic activation, rare earth coating, assistant step-by-step doping, microwave drying, protective atmosphere roasting and the like. The catalyst is unique in structure and good in dispersity, the coal combustion efficiency can be remarkably improved, the burnout temperature and the carbon content of fly ash are reduced, emission of flue gas pollutants such as CO, NOx and SO2 is effectively reduced, and the catalyst is suitable for the fields of energy conservation, carbon reduction and environment-friendly treatment of coal-fired power plants and industrial boilers.

The invention belongs to the field of artificial intelligence, particularly relates to a coal-fired power plant denitration catalyst full-life-cycle intelligent management system, and aims to solve the problems of information splitting, state evaluation lagging, inaccurate life prediction, subjective regeneration decision and the like. The system comprises catalyst identity identification, multi-source data fusion acquisition, operation state real-time evaluation, life attenuation dynamic prediction, regeneration decision intelligent optimization, disposal path closed-loop management and a full-period digital twin platform. The whole process tracking from delivery to disposal, health quantitative evaluation, residual life accurate prediction, regeneration opportunity and process intelligent decision and compliance disposal closed loop are realized, and the operation economy and environmental protection reliability of the denitration system are improved.

The invention relates to the technical field of peak regulation of coal-fired power units, and discloses a self-adaptive regulation method and system for full-load peak regulation of a coal-fired power unit. The method comprises the steps of collecting unit operation parameters such as boiler temperature, turbine rotating speed and generator voltage, and power grid peak regulation instructions such as real-time load demand and predicted load fluctuation; analyzing a peak regulation state based on the collected data, and dividing the peak regulation state into three types of stable operation, transition regulation and limit peak regulation; then calculating an adjustment characteristic quantity according to the current state and the operation parameters, wherein the characteristic quantity reflects the matching relationship between the unit performance and the peak regulation requirement; generating a plurality of groups of adjustment schemes including combustion optimization, steam adjustment and power control based on the characteristic quantity and the peak regulation instruction; and selecting and executing an optimal scheme by evaluating indexes such as efficiency loss, stability influence and maintenance cost. The method can dynamically respond to the load change of the power grid, accurately adapt to the peak regulation requirement in the full load range, and enable the peak regulation of the unit to be more flexible and harmonious.

The invention provides a coal-fired unit pulverized coal distribution adjustment control method based on a boiler wall temperature distribution field. According to the method, automatic adjustment of pulverized coal distribution of the coal-fired unit under the full-load working condition can be achieved, the uniformity of combustion intensity distribution is effectively improved, and the safety, the environmental protection property and the economical efficiency of unit operation are enhanced.

The invention belongs to the technical field of power plant thermodynamic and control, and particularly relates to a fractional order model of an ultra-supercritical coal-fired unit coordination control system and an identification method. The fractional order model comprises a coal pulverizing system, a boiler steam-water system and a steam turbine power generation system; the modeling method comprises the following steps: S1, acquiring and storing data of an ultra-supercritical coal-fired unit coordinated control system DCS; s2, constructing a fractional order model of the coordination control system; and S3, fitting a to-be-solved function through acquired data, and identifying dynamic parameters of the fractional order model offline by adopting a segmented time-sharing adaptive particle swarm method. According to the method, fractional order modeling is carried out on the coordinated control system of the coal-fired unit, the dynamic parameters of the system are identified by adopting a segmented time-sharing adaptive particle swarm identification method, the optimal model parameters are obtained through iterative calculation, the precision of the unit coordinated control system model under the wide load is greatly improved, and the system performance is improved. And implementation of a control strategy based on a system model is facilitated.

The invention discloses a coal-fired power plant belt conveyor driving gear box bearing fault diagnosis method, which relates to the field of fault diagnosis and evaluation, and is used for extracting gear box bearing fault features based on an order tracking analysis method of tachometer-free instantaneous rotation frequency estimation. The method specifically comprises the following steps: S1, obtaining an instantaneous rotation frequency ridge line through the time frequency distribution of a vibration signal, carrying out the rotation frequency estimation of a rotating shaft, and improving the precision of the instantaneous rotation frequency ridge line through an improved multi-source ridge line fusion algorithm; and S2, extracting bearing fault feature order information by using a calculation order tracking COT algorithm, and adaptively processing a bearing fault diagnosis problem of the driving gearbox of the belt conveyor. According to the bearing fault diagnosis method for the rubber belt conveyor driving gearbox of the coal-fired power plant, the bearing inner ring fault is successfully diagnosed, and the feasibility of a bearing fault diagnosis strategy for the rubber belt conveyor driving gearbox is verified.

The invention relates to the technical field of power system dispatching and control, in particular to a fire coal-fused salt energy storage dynamic characteristic decoupling method based on multi-time scale control, which comprises the following steps: constructing a multi-physical field coupling model of a coal-fired unit and a fused salt energy storage system; dividing a minute-level adjustment scale and a second-level response scale based on the coupling model; aiming at the minute-level regulation scale, designing a power distribution strategy based on model prediction control, matching a load trend in a power grid, and reserving a power regulation margin for second-level response; aiming at a second-level response scale, designing a compensation strategy based on sliding mode control, and correcting power deviation to track a real-time instruction of a power grid; and associating the minute-level strategy with the second-level strategy through a dynamic weight coefficient to form a closed loop to cooperatively complete dynamic characteristic decoupling. The problem of power supply and demand mismatch of the coal-fired unit and the fused salt energy storage system in coupling operation can be solved, the response speed, the adjustment precision and the operation stability of the system are improved, and technical support is provided for efficient operation of a novel power system.

The invention relates to the technical field of industrial measurement, and provides an industrial process soft measurement method and device, a storage medium and computer equipment, and the method comprises the steps: obtaining a historical process data set of a coal-fired boiler, training a dynamic measurement and prediction model based on the historical process data set, and obtaining a trained dynamic measurement and prediction model; acquiring real-time process data of the coal-fired boiler and a working condition weight of a previous moment corresponding to the real-time process data; and inputting the real-time process data and the working condition weight of the previous moment corresponding to the real-time process data into the trained dynamic measurement and prediction model to obtain a real-time nitrogen oxide concentration estimated value. According to the embodiment of the invention, the working condition weight of the previous moment is introduced as model input, so that the dynamic measurement prediction model can effectively capture the dynamic association and evolution rule of the working condition state in the time sequence, and the accuracy and reliability of the real-time soft measurement estimation value of the nitrogen oxide concentration of the coal-fired boiler are effectively improved.

The invention belongs to the field of artificial intelligence and energy power system cross control, and particularly relates to a coal-fired unit fused salt combined frequency modulation control method and device based on deep reinforcement learning. According to the method, a deep reinforcement learning agent model is constructed, and boiler fuel quantity, steam turbine valve position, fused salt heat charging and discharging power and pump starting and stopping signals are dynamically output based on state space vectors; and tracking precision, thermal stress, emission and coal consumption indexes are fused through a multi-target reward function, and strategy autonomous optimization is realized by adopting a double-Q network, experience playback and target network delay updating mechanism. After deployment, the boiler, the steam turbine and the molten salt unit can be coordinated on line, power space-time decoupling and closed-loop cooperation are achieved, on the premise that main steam parameter stability and equipment safety are guaranteed, the frequency modulation response speed and precision are remarkably improved, the thermal fatigue risk is reduced, and the unit economy and the power grid adaptability are enhanced. And a flexible adaptive adjustment capability is provided for a high-proportion new energy power system.

A panoramic scanning coal fire detection and disaster early-warning, prevention and control apparatus for a mining area, the apparatus comprising a detection apparatus, a laser scanning apparatus, a satellite remote-sensing monitoring apparatus, a data processing and analysis system, and an early-warning, prevention and control apparatus. High-precision thermistor temperature sensors (1) are arranged inside, on the surface and in surrounding areas of coal piles, such that subtle changes in the temperature of the coal piles can be accurately captured, and early-stage potential coal fire hazards can be effectively detected; gas sensors (2) installed at vents of a mining area can simultaneously measure the concentrations of gases such as carbon monoxide, carbon dioxide, methane and sulfur dioxide, so as to promptly identify anomalies in gas composition that are caused by coal fire combustion; an infrared thermal imager (3), which is located at a high position to acquire a panoramic view of the mining area, can clearly display high-temperature regions; and cameras (4) provide high-definition, wide-angle and low-illumination visual monitoring, and various sensors comprehensively collect data from different dimensions, thereby greatly improving the accuracy and comprehensiveness of monitoring.

The invention discloses a deep peak regulation and frequency modulation heat energy storage system and method coupled with a coal-fired unit, and belongs to the technical field of coal-fired unit flexible transformation and power grid regulation. Aiming at the problems that a single fused salt / solid heat storage occupies a large area, the initial investment is high, a steam generation system is complicated, and a conventional coal-fired unit is difficult to meet the requirements that the minimum power generation output of a new generation coal-electricity pilot demonstration unit is less than or equal to 20% Pe, and deep peak regulation is realized to give consideration to primary frequency modulation performance through self or auxiliary regulation as much as possible in the specification of power generation and transformation energy (2025) 363. The system comprises a heat energy storage unit and a coal-fired unit, the heat energy storage unit comprises a demineralized water supply water tank, an electrode steam boiler, a spherical tank and a heat storage type steam temperature increasing device, and the structure of spherical tank flash evaporation steam supply and heat storage temperature increasing sensible heat supplementing is adopted to be matched with steam supplementing of a steam turbine. The combined minimum power generation output of the two can be reduced to 10% Pe, the peak increase output is at least 5.6% Pe, primary frequency modulation is assisted by increasing and decreasing the power consumption of the electrode steam boiler, and new energy consumption is assisted.

The invention relates to the field of tool management, and provides a coal-fired power plant tool intelligent management method and system. The method comprises the following steps: carrying out digital identity construction on a purchasing tool to obtain a tool digital identity file comprising a unique identification code of the tool; constructing a tool circulation record based on the tool digital identity file, and obtaining a block chain traceability record; monitoring an inventory tool state through the block chain traceability record to obtain a tool behavior baseline model; based on the tool behavior baseline model, attenuation prediction and maintenance scheduling are carried out on an inventory tool, and a tool health attenuation trend prediction result and an optimal maintenance scheduling scheme are obtained; and performing decision analysis based on the optimal maintenance scheduling scheme to obtain a tool management decision instruction set. According to the method and the system, non-tampering recording of tool full-life-cycle data, accurate health state prediction and scientific decision support are realized, and the management efficiency and the data credibility are improved.