17260 results about "Flue gas" patented technology

The invention discloses a distributed real-time monitoring and early warning system for a temperature field of a smelting furnace, and relates to the technical field of industrial process intelligent monitoring. The problems of accumulated measurement errors and non-stationary hotspot escape reconstruction hysteresis caused by static emissivity setting in an existing system are solved. Collecting multiband radiation intensity and voltage signals through time domain alignment of the multispectral sensor array and the thermocouple array; iterating emissivity parameters in real time by adopting a dynamic ash body spectrum ratio algorithm in combination with flue gas absorption characteristics; fusing non-contact and contact temperature measurement data based on weighted Kalman filtering and complementary filtering; constructing a space-time variable covariance function to carry out non-stationary Kriging interpolation; dynamically optimizing the local grid resolution by combining an adaptive grid module; the processing flow is accelerated through the parallel computing module; early warning is triggered based on abnormal probability judgment and is fed back to emissivity correction and grid optimization; according to the invention, the monitoring precision and real-time performance of the temperature field are obviously improved, and the risks of false alarm, missing alarm and equipment melting loss are effectively inhibited.

The invention relates to the technical field of thermal power plant flue gas denitration, in particular to a thermal power plant APC advanced process control system and a denitration optimization method.The system comprises a data acquisition module for acquiring boiler combustion parameters, flue gas emission data and the operation state of a denitration system in real time; the multivariable predictive control module is used for optimizing the combustion efficiency and the NOx generation amount based on a dynamic matrix control algorithm; the denitration optimization decision module is used for dynamically adjusting the ammonia spraying amount through an ammonia escape feedback model; the intelligent coordination module integrates a unit load instruction and an environmental protection constraint condition and is used for cooperative control of combustion-denitration; according to the system, boiler combustion parameters, flue gas emission data and the operation state of the denitration system are obtained in real time through the data acquisition module, a dynamic matrix control algorithm of the multivariable prediction control module is combined, the NOx generation trend can be predicted in advance, the combustion efficiency can be optimized, and therefore the problem that adjustment is lagged when loads fluctuate in traditional PID control is effectively solved.

The invention relates to the technical field of anode carbon block roasting, and discloses an energy-saving control system of an anode carbon block roasting furnace, which comprises a data acquisition module, an energy-saving analysis module, a digital twin modeling module, a control execution module and a feedback optimization module, when the combustion state of the roasting furnace is monitored in real time, a multi-layer monitoring framework is constructed to dynamically track the change trend of temperature, flue gas oxygen content and pressure parameters, continuous comparison is performed based on a preset energy-saving reference interval, and when the parameters deviate from an optimization interval, a digital twinborn model is immediately triggered to intervene in analysis, so that the real-time monitoring of the combustion state of the roasting furnace is realized. Prejudgment identification of potential risks of combustion efficiency reduction and abnormal heat loss is achieved, hidden energy loss caused by imbalance of the air-fuel ratio is avoided, the non-planned shutdown frequency of equipment is reduced, and the continuous energy efficiency management and control stability of the production process is guaranteed.

An SCR flue gas denitration intelligent control method based on multivariable collaborative optimization specifically comprises the following steps: S1, collecting multi-key variable data of SCR flue gas denitration, processing the multi-key variable data, and storing the processed multi-key variable data into a historical database; s2, on the basis of the processed data, constructing a multivariable correlation model, and predicting operation states of the denitration system under different working conditions; s3, according to the actual operation condition of the system and the equipment performance, determining constraint conditions and optimization targets of SCR flue gas denitration; s4, performing multivariable collaborative optimization calculation in combination with the multivariable correlation model, the optimization target and the constraint condition, and searching an optimal control variable combination; and S5, intelligent control is implemented according to the optimal variable combination, and re-optimization is performed by monitoring feedback deviation in real time. The system can keep stable control precision in different operation scenes, and denitration efficiency fluctuation or parameter adjustment lag caused by sudden change of working conditions is avoided.

The present invention relates to the technical field of waste heat recovery equipment, and specifically discloses a waste heat recovery device for flue gas in a thermal power plant. It includes a box body with an upper smoke chamber, a heat exchange chamber and a lower smoke chamber inside. A number of smoke pipes connecting the upper smoke chamber and the lower smoke chamber are fixed in the heat exchange chamber; above the smoke pipes, there is a piston cylinder fixed by the box body; the upper end of the piston cylinder is connected to a high-pressure gas supply pipe, and a piston is arranged inside. The piston is connected with a scraping head through a push rod; side holes are provided on the push rod; when the high-pressure gas drives the piston to move downward, the scraping head moves along the inner wall of the smoke pipe to scrape off the ash slag. The waste heat recovery device for flue gas in this thermal power plant has a cleaning function and a better cleaning effect, high heat exchange efficiency and good waste heat recovery effect. The waste heat recovery device for flue gas in this thermal power plant can be automatically cleaned without disassembling components, is fast and efficient, and can be operated online. The ash slag scraped off during the cleaning process can be centrally recovered, avoiding re-mixing into the flue gas again, reducing the workload in the flue gas dust treatment link, and saving the subsequent environmental protection treatment cost.

The invention relates to the technical field of industrial flue gas purification, and discloses a flue gas denitration pollution reduction and carbon reduction method and system based on model prediction control. The method comprises the following steps: collecting flue gas data through a sensor to obtain a flue gas distribution state; the state is processed through a preset model, and a nitrogen oxide concentration predicted value is determined; judging a regulation and control demand based on the predicted value and generating an adjustment coefficient sequence; calculating an opening value of an ammonia spraying distributor by adopting a particle swarm optimization algorithm, and determining ammonia spraying amount distribution; a control instruction is sent according to ammonia spraying amount distribution, and a temperature uniformity index is evaluated based on temperature feedback data; adjusting optimization algorithm parameters according to the indexes, and generating an optimized ammonia spraying strategy; updating the predicted value and determining the stable range of the denitration efficiency; verifying the ammonia escape concentration in the stable range to obtain a system performance index; and forming a continuous regulation and control sequence according to cyclic feedback of the indexes. According to the invention, accurate dynamic regulation and control of ammonia spraying amount are realized, denitration efficiency and system stability are effectively improved, and the risk of ammonia escape is significantly reduced.

The invention discloses an operation balance optimization method for a waste incineration thermal power plant. Efficient and environment-friendly waste treatment and green energy recovery are achieved through regulation and control of core parameters. Comprising an intelligent control system, a carbon cyclic utilization technology and a flue gas purification technology, an optimization model taking comprehensive net income maximization and carbon emission intensity minimization as targets is constructed, a garbage heat value, incinerator temperature, steam parameters, power generation efficiency, station service power consumption rate and the like are selected as core parameters, an operation balance optimization target function is constructed, and the comprehensive net income maximization and the carbon emission intensity minimization are realized. And through an intelligent control system, key parameters such as the incinerator temperature, the excess air coefficient and the flue gas treatment temperature are monitored and adjusted in real time, it is ensured that garbage is fully combusted in the incinerator, harmful gas emission is reduced, and the power generation efficiency is improved. Compared with the prior art, efficient operation of the waste incineration thermal power plant and maximum utilization of green energy are achieved, and remarkable economic and environmental benefits are achieved.

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 present application provides a molten salt heat storage and release unit and a deep peak regulation system for a boiler unit, the molten salt heat storage and release unit comprising a molten salt storage assembly, a heat exchange energy storage assembly, and a heat exchange energy release assembly, wherein the molten salt storage assembly comprises a high-temperature molten salt storage tank and a low-temperature molten salt storage tank; the heat exchange energy storage assembly is connected to the high-temperature molten salt storage tank and the low-temperature molten salt storage tank, and comprises a flue gas heat exchange device and a steam heat exchange device, and molten salt in the low-temperature molten salt storage tank sequentially passes through the flue gas heat exchange device and the steam heat exchange device and then flows into the high-temperature molten salt storage tank; and the heat exchange energy release assembly is connected to the high-temperature molten salt storage tank and the low-temperature molten salt storage tank, and comprises a pulverized coal heating device and a feedwater heating device, and molten salt in the high-temperature molten salt storage tank releases energy through the pulverized coal heating device and / or the feedwater heating device and then flows into the low-temperature molten salt storage tank. The present application provides a molten salt heat storage and release unit and a deep peak regulation system for a boiler unit with flexible peak regulation and high energy efficiency.

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 furnace tube intelligent diagnosis, in particular to a big data AI-based furnace tube leakage intelligent diagnosis method, which comprises the following steps: collecting boiler operation data in real time through a multi-source sensor, including temperature, pressure, vibration, sound wave and flue gas component data; performing time sequence alignment, abnormal value elimination and standardized preprocessing on the collected original data; inputting the preprocessed data into a pre-trained deep neural network model, wherein the deep neural network model is obtained through comparison training of historical normal data and leakage accident data; generating a real-time diagnosis result and an early warning level based on the leakage probability value and the feature contribution degree analysis output by the model; through multi-source sensing, AI deep analysis, dynamic early warning and predictive maintenance closed loop, early, accurate and automatic diagnosis of boiler tube leakage is realized, and the safety and operation and maintenance efficiency of industrial equipment are remarkably improved.

The invention discloses an optimal control method for a straw pyrolysis carbonization process. The method comprises: performing multipoint temperature real-time acquisition and data processing on a carbonization zone of a carbonization furnace, obtaining temperature information and calculating a deviation from a preset ideal temperature curve (S01); dynamically adjusting an operating parameter of the combustion zone using a control algorithm based on the temperature deviation (S02); performing on-line monitoring on the flue gas components (S03); and regulating and controlling the return air flow and the secondary air supply amount of the combustion area in a linkage manner according to the flue gas monitoring result so as to optimize combustion and energy recovery (S04). Through accurate and intelligent process control, efficient, environment-friendly and high-valued utilization of the straw carbonization process is realized.

The invention relates to the technical field of data processing, and discloses a flue gas treatment intelligent regulation and control method and system based on artificial intelligence and a medium. The method comprises the following steps: collecting parameter data of a flue gas system through a sensor network and processing to obtain preprocessed data; using unsupervised clustering to identify working condition features and categories; constructing an emission concentration error active switching control model according to the working condition information; different working condition control parameters are optimized by adopting a differential evolution algorithm; optimized parameters are input into a controller, and modes are dynamically switched according to pollutant errors. The control strategy can be automatically switched according to the working condition characteristics of the flue gas treatment system, the complex and changeable industrial production environment can be effectively dealt with, and the system operation cost is optimized while stable and up-to-standard emission of pollutants is guaranteed.

The invention discloses a water heater and a control method thereof, and relates to the technical field of water heaters. The condensing heat exchanger has a water collecting cavity; the atomization module is arranged in the water collecting cavity; the water path switching assembly is arranged among the water inlet main pipe, the main heat exchanger and the condensing heat exchanger, has a first working mode and a second working mode, and is used for sequentially communicating the water inlet main pipe, the condensing heat exchanger and the main heat exchanger to form a first flow path in the first working mode; in the second working mode, the water inlet header pipe communicates with the main heat exchanger to form a second flow path, and the water inlet end and / or the water outlet end of the condensation heat exchanger are / is disconnected from the second flow path; according to the condensation gas water heater, condensed water is discharged along with flue gas after being atomized, meanwhile, the water inlet path is switched through the water path switching assembly, and the problems that in the efficient flue gas waste heat recovery process of an existing condensation gas water heater, the condensed water generated is inconvenient to discharge, a pipeline is prone to being corroded, or frequent manual intervention is needed can be solved.

The invention relates to the technical field of dynamic regulation, in particular to a CFD (computational fluid dynamics) simulation-based flue gas flow field dynamic regulation system, which comprises a multi-dimensional data collaborative acquisition module, an anti-ash deposition sensor array is deployed at a key position of a flue, and a time-space fusion input data set is generated; the dual-mechanism CFD dynamic coupling module operates the main CFD prediction model and the simplified fluid network model in parallel, compares output differences through an evidence theory fusion algorithm, corrects boundary conditions and reconstructs network parameter characteristics when the confidence coefficient is lower than a threshold value, and updates a dynamic parameter knowledge base; the multi-target self-adaptive regulation and control module is used for solving a Pareto optimal strategy meeting the safety constraint by taking the baffle opening degree and the pump frequency as variables based on the updating model, driving an actuator and feeding back flow field response data; and the system forms a closed-loop adaptive mechanism, so that the CFD model continuously tracks the degradation state of the equipment, and the effectiveness of the regulation and control strategy is maintained.

The invention relates to the technical field of thermal equipment operation state dynamic test and compensation control, in particular to an AI-based boiler air pre-heater temperature uniformity regulation and control system, which comprises a collaborative closed-loop control network of a data acquisition module, an AI prediction module, a heat storage system control module, a temperature compensation control module and a thermal stress analysis module. The data acquisition module acquires flue gas temperature, coal quality parameters and load instructions in real time, the flue gas temperature, the coal quality parameters and the load instructions are transmitted to the AI prediction module through an industrial bus, XGBoost, LightGBM and a deep neural network model are used for carrying out spatial-temporal feature extraction on the evolution trend of a temperature field, and the heat storage system control module dynamically switches heat storage and release modes according to the load state. The heat distribution proportion among the multiple units is adjusted by combining the three-dimensional stress field distribution data of the thermal stress analysis module; the bottleneck of lack of thermal inertia regulation of an existing system is broken through, dynamic balance of a temperature field under the condition that thermal stress of a heat transfer element is controllable is achieved, and the heat exchange efficiency and operation stability of the air pre-heater are improved.

The invention discloses a pyrolysis recovery system for cooperative treatment of waste wind power blades by a power station boiler, and belongs to the technical field of solid waste recovery. The system comprises a power station boiler, a pyrolyzing furnace, a mixed flue, a nitrogen air flue and a controller. Oxygen is accurately controlled by mixing flue gas at different temperature levels and mixing nitrogen, and a safe and stable heat source is provided for the pyrolyzing furnace; pyrolysis gas returns to a boiler hearth through an outlet flue to be burnt out, and recyclable fibers and residues are conveyed out through a chain grate stoker; and the controller is linked with each sensor to execute oxygen control, temperature regulation, fiber quality protection and safety interlocking operation. The problems that in pyrolysis of the waste wind power blade, heat source adaptability is poor, oxygen concentration is out of control, fiber quality is poor, the risk is high, and pyrolysis gas is difficult to dispose are solved, and gradient utilization of energy, intrinsically safe operation, high-quality and high-purity recovery of fibers and collaborative treatment of pollutants are achieved.

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 relates to the technical field of boiler control, and discloses a gas-fired boiler starting control method and system. The method comprises the following steps: acquiring initial operation parameters such as hearth pressure, water inlet temperature, flue gas oxygen content and fuel valve opening of the gas-fired boiler, establishing a thermodynamic state model in a starting stage according to the initial operation parameters, and dividing dynamic response thresholds of different load intervals; and calculating a theoretical fuel supply rate of the combustor based on the model, generating and dynamically updating a fuel supply correction coefficient in combination with a threshold value, synchronously marking key nodes according to abnormal distribution of hearth temperature, and adjusting a spatial air distribution strategy of the combustor. And after the correction coefficient is subjected to multi-stage buffer smoothing, a fuel control instruction is output to an executing mechanism. And switching to steady state control after the boiler reaches the preset load. The method adapts to the characteristics of a power plant unit, so that the starting process is more stable, the damage to a heated surface and pollutant emission are reduced, and rapid grid connection is assisted.

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 provides a flue gas waste heat recovery variable load working condition self-adaptive control method and system. The method comprises the steps that the boiler load change rate is determined; identifying the characteristics of the current boiler operation condition; adjusting or selecting a system response time constant for calculating the pre-adjustment amount; calculating the pre-adjustment amount of a flow control element of the flue gas waste heat recovery system; control parameters of a feedback control loop of the flue gas waste heat recovery system are adjusted; and the pre-adjustment amount and the adjusted feedback control loop control parameters are fused, a cooperative control instruction is generated, and the opening degree of a flow control element of the flue gas waste heat recovery system is adjusted. The method aims at solving the problem that real-time and dynamic collaboration is difficult to carry out between conflict targets in an existing control method, flue gas waste heat is recycled and effectively utilized to the maximum extent, safe and reliable operation of the system is guaranteed, and the operation cost is reduced.

The invention relates to the field of flue gas monitoring, and particularly discloses an automatic monitoring system and method for flue gas emission of a stationary pollution source, and the system employs an artificial intelligence technology based on the deep learning field to carry out the feature extraction and coding of a multispectral image of air at an input end of a sampling pipe and a multispectral image of air at an output end of the sampling pipe. And a classification result of whether the sampling tube has an abnormal condition is obtained. Thus, by intelligently judging whether the sampling pipe is loosened or not and other abnormal conditions, the difficulty of manually checking the sampling pipe is reduced, and the situation that CEMS data is false due to the abnormal sampling pipe can be effectively avoided.

The invention relates to the technical field of solid waste treatment, and discloses a waste incineration process modeling and optimizing method based on digital twinning, which comprises the following steps: S1, constructing a physical model of a waste incineration process, the physical model comprises geometric structures and physical parameters of equipment such as a garbage incinerator, a waste heat boiler, a flue gas purification system and the like, and a connection relationship among the equipment; s2, collecting real-time data in the waste incineration process, including waste components, feeding amount, combustion temperature, flue gas flow, pollutant emission concentration and the like, and transmitting the data to the digital twin platform by using a data acquisition system; by constructing a highly accurate physical model and combining with a real-time data acquisition system, the operation state of the waste incineration process can be reflected in real time on a digital twin platform. The digital twin model is optimized by using an intelligent optimization algorithm, and optimal operation parameters and control strategies can be automatically searched.

The invention relates to a boiler flue gas temperature dynamic compensation method based on CFD-reinforcement learning, and belongs to the technical field of boiler operation control. The method comprises the steps that boiler operation parameters and flue gas temperature data are collected in real time, a multi-scale coupling three-dimensional CFD model is constructed, a macroscopic gas-solid two-phase flow-combustion reaction-radiation heat transfer coupling algorithm and a microcosmic pulverized coal particle combustion dynamics algorithm are fused, and hearth and flue temperature distribution is simulated and output; building a CFD-reinforcement learning fusion agent based on a CFD model, taking similar working condition agent parameters as initial values, combining historical and virtual data staged training, and reinforcing the weight of a key temperature measurement point through a focusing regulation and control strategy; an echo state network-predictive control collaborative algorithm is adopted, real-time data are input to obtain an initial adjustment amount, and after security constraint optimization, a compensation execution mechanism is controlled to act. The flue gas temperature is accurately and dynamically compensated, the adaptability to complex working conditions is improved, and equipment safety and operation efficiency are both considered.

The invention discloses a thermal power generating unit, flywheel and compressed air hybrid energy storage system and an operation method, and relates to the technical field of flexibility improvement of novel energy storage and thermal power systems. The system comprises a flywheel energy storage subsystem, a compressed air energy storage subsystem, a thermal power collaborative heat exchange system, a flywheel-CAES switching coupling module and an intelligent control system. The flywheel energy storage subsystem is responsible for millisecond-level frequency modulation response, the compressed air energy storage subsystem provides minute-level stable output, and the flywheel energy storage subsystem and the compressed air energy storage subsystem cooperatively work through an intelligent control system to realize a composite regulation mode of'fast response-high capacity '; a compressed air side is coupled with a thermal power plant turbine steam extraction system, a boiler flue gas system and a condensed water system to improve the energy release efficiency; the flywheel side interacts with a power grid in real time through a bidirectional converter, and rapid frequency support is achieved. The system has the quick response capability and the long-time energy supporting capability, the frequency modulation and peak regulation performance of the thermal power generating unit is remarkably improved, and the system has high engineering popularization value and application prospects.

The invention discloses a multi-mode heat supply energy consumption evaluation method and system, and relates to the technical field of heat supply energy consumption evaluation and dynamic thermal scheduling control, and the method comprises the steps: arranging a multi-source sensor in a high-temperature flue gas path to collect thermal parameters, and building a time sequence mapping model between a unit load and flue gas thermal characteristics; and according to the thermal power data output by the time sequence mapping model and the heat exchange characteristics of all the heat supply modes, a heat energy availability map of the target time period is generated, and heat demand adaptation data is constructed. And selecting a matching path according to energy consumption and switching constraints based on the heat energy availability map and the heat demand adaptive data, and executing mode dynamic switching control. According to the method, a complete technical closed loop from data perception to state evaluation to action execution is formed. In the multi-mode heat supply system, system fusion of three-layer logic of heat load prediction, map expression and dynamic scheduling is realized, and the intelligence, self-adaption and energy efficiency optimization capabilities of the heat supply process are remarkably 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 relates to the technical field of coal gas molten salt heat storage, and discloses a steel mill coal gas molten salt heat storage system and a heat storage operation method and device.According to the coal gas molten salt heat storage system, a coal gas combustion unit adjusts heat power to output flue gas; the high-temperature heat exchange unit and the combustion unit are integrated, and fused salt is heated to the target temperature; the flue gas waste heat exchange unit uses waste heat flue gas flowing out of the high-temperature heat exchange unit as a heat source to heat low-temperature fused salt to medium temperature; the molten salt flow mixing unit realizes high-temperature molten salt flow division backflow and closed-loop circulation; the closed-loop control system dynamically adjusts the backflow proportion and the combustion power based on real-time data to ensure stable and efficient operation of the system, the problems that a blast furnace gas heat source is unstable, temperature rise of molten salt is limited and an existing system lacks dynamic control are solved, safe operation of the system is guaranteed, the service life of equipment is prolonged, and the energy utilization efficiency is further improved. And stable fused salt temperature output and energy efficiency optimization are realized.

The invention relates to the technical field of thermal power plant environmental protection, in particular to a real-time early warning and operation evaluation system of a thermal power wet desulphurization device, which comprises a real-time early warning subsystem and an operation evaluation subsystem, and the intelligent data collector performs preliminary screening and storage. The intelligent diagnosis module analyzes parameters, judges possible fault types and positions, the prediction model adopts a deep neural network, inputs a diagnosis result and outputs a real-time slurry pH value, density and density difference at a slurry outlet of an absorption tower, the intelligent evaluation module evaluates a final alarm level, and the decision scheme module generates a processing suggestion. The intelligent control execution module converts the data into a control signal, the field alarm module gives out a corresponding alarm, the intelligent query module displays a decision recommendation level on a webpage, the establishment of a prediction model comprises data sampling, model establishment, verification application and update optimization, and the prediction precision is remarkably improved.

The invention discloses a condensation-free efficient heat exchange control method and system for a full-premixing water heater, and the method comprises the steps: collecting the multi-source information, such as air inlet temperature, humidity, gas flow, flue gas oxygen concentration and wall temperature, in real time, calculating a predicted dew point based on a dynamic dew point prediction model, and setting a dynamic safety boundary; the condensation risk is judged by comparing the wall temperature with the safety boundary, and active intervention is carried out in a staged cooperation mode that fuel gas and air volume are continuously adjusted firstly and then a partial combustion zone sectioning valve is rapidly turned off; the system comprises a main controller, a mesh-shaped infrared radiation burner, a sectional valve assembly, a gas proportional valve, a frequency conversion fan and related sensors. According to the invention, the system runs close to the condensation boundary through intelligent control, and the advantage of high operation heat efficiency is realized on the premise of ensuring high reliability.