1353results about "Combustion regulation" patented technology

The invention relates to the field of power equipment control data processing, in particular to a boiler combustion optimization control method based on data driving, which comprises the following steps of: acquiring multi-source data such as temperature field distribution, air and smoke pressure, smoke components and coal quality characteristics, and eliminating noise interference by adopting sliding window mean filtering; generating a standardized feature matrix in combination with principal component analysis and a dynamic time warping algorithm; constructing a dynamic coupling model fusing a gradient boosting decision tree and a long short-term memory network, analyzing a nonlinear relationship between pulverized coal particle size distribution and a wind-coal ratio, and predicting combustion efficiency, pollutant concentration and temperature field uniformity; and model parameter self-correction and weight dynamic adjustment are triggered through actual combustion data feedback, and a closed-loop control link is formed. According to the method, accurate modeling of the multi-physical field coupling characteristic of the combustion system is achieved, the time sequence generalization ability under the dynamic working condition is improved, and the purposes of heat efficiency improvement and pollutant emission reduction are effectively balanced.

In an intelligent pollution and carbon reduction method based on combustion control and load distribution, a data processing layer, a multi-unit load distribution and operation optimization layer and a single-unit boiler multi-objective combustion optimization layer are used. The data processing layer, the multi-unit load distribution and operation optimization layer and the single-unit boiler multi-objective combustion optimization layer are embedded in a power plant information system in the form of modules. A load distribution and operation optimization method for a multi-source fuel blending combustion unit with economy as an objective is provided to overcome the operation optimization difficulty of the key production process of a multi-source fuel blending combustion cogeneration unit, such as sludge drying-steam distribution.

The invention relates to the technical field of mechanical stress and thermal deformation monitoring, in particular to a combustion fine adjustment method based on full-hearth expansion monitoring, which is characterized in that expansion sensor arrays are arranged on each layer of a main burner area of a boiler hearth, and vibration monitoring units are arranged in a layered manner from a main burner to an SOFA air area synchronously; normal deformation, three-dimensional expansion displacement data and vibration spectrum data of the four furnace walls are collected; generating a standardized monitoring data set through wavelet noise reduction and temperature compensation processing, fusing boiler operation parameters, and inputting the boiler operation parameters into the reinforcement learning model to generate a combustor adjustment instruction; water-cooled wall expansion rate change data is collected in real time to optimize model weight and update a control strategy, and a closed-loop feedback mechanism is formed. According to the method, through multi-source sensing data fusion and dynamic modeling, sub-meter analysis of thermal load distribution and quick response of the combustion state are achieved, the boiler operation efficiency is improved, and the service life of equipment is prolonged.

The invention relates to a pellet sintering fine control method for a belt type roasting machine, and belongs to the technical field of belt type roasting machines. In order to solve the problems that in the prior art, process parameters depend on experience setting, dynamic interference compensation lags behind, and multivariable coupling control precision is insufficient, controlled variable parameter ranges of a drum drying section, a pumping section, a preheating section, a roasting section, a soaking section and a cooling section are determined through a process mechanism model; and establishing a dynamic association relationship between the controlled variable and the fan frequency and the air valve opening degree by utilizing a transfer function matrix, correcting an operation variable set value in real time by combining production data, and predicting the influence of the green pellet conveyor speed, the material layer thickness and the moisture content on the process parameters based on a feed-forward compensation model. The method is used for improving the pellet sintering process control precision and reducing energy consumption and process fluctuation.

The invention discloses a boiler combustion strategy optimization method, system, equipment and medium, and relates to the technical field of combustion strategy optimization, and the method comprises the steps: employing a width-adaptive triangular convolutional neural network to establish a prediction model of boiler thermal efficiency, NOx emission and superheated steam temperature; constructing a boiler multi-target combustion optimization intelligent agent, and introducing a maximum entropy parameter to obtain all boiler combustion optimal strategies as a target, and obtaining any strategy of which the value is higher than a threshold value; and based on the combustion strategy, outputting a next state through the trained prediction model, obtaining a reward corresponding to the current state, and performing iterative optimization on the boiler multi-target combustion optimization agent according to the current state, the combustion strategy, the next state and the reward corresponding to the current state. And taking the combustion strategy until the optimal reward is obtained as the optimal boiler combustion strategy. On the premise of guaranteeing the stability of the hot steam temperature, the effects of reducing the emission concentration and guaranteeing the stability of the heat efficiency of the boiler are achieved.

The invention relates to the technical field of boiler combustion control, in particular to a boiler combustion multi-target collaborative optimization control method and system. The method comprises the following steps: firstly, acquiring coal quality parameters in real time through a laser-induced breakdown spectrum analyzer, carrying out qualitative and quantitative analysis on spectral data by utilizing a machine learning algorithm, constructing a boiler combustion digital twinborn model fusing a combustion mechanism model and a data driving model, and predicting combustion characteristics, NOx generation trends and coking risks under different blending schemes; and then, dynamically generating an optimization instruction combination according to a prediction result of the digital twinborn model, establishing a safety interlocking mechanism while executing the optimization instruction, and automatically switching to a manual control mode when detecting that the working condition fluctuates severely or the model prediction deviation exceeds a preset threshold value. According to the method, the problems of coal quality information lag and untimely combustion adjustment caused by traditional manual sampling are effectively solved, and the economical efficiency, the safety and the environmental protection property of boiler operation are improved.

The invention discloses a machine learning multi-objective optimization control method based on coal quality characteristics and combustion state feedback, and belongs to the technical field of industrial process automatic control. According to the method, collaborative optimization control over the combustion efficiency, NOx emission and combustion stability is achieved under the multi-working-condition operation condition of the combustor. According to the method, coal quality characteristic parameters and combustion state data are collected, working condition clustering is carried out after data fusion, and a historical combustion operation data set coupling coal quality characteristics and combustion states is constructed; a combustion state evaluation model and a combustion control model are established by adopting machine learning, on the basis of identifying working conditions in real time and evaluating combustion performance, a control parameter adjustment amount is generated in combination with trend extrapolation and a multi-objective cost function, and a combustion state feedback rolling correction model and a control strategy are utilized to realize combustion control. The self-adaptive closed-loop collaborative optimization control of the combustion process under multiple coal types and multiple working conditions is realized, the boiler efficiency is improved, the NOx emission is reduced, and the operation robustness is enhanced.

The invention discloses a gasification slag mixed biomass fuel blending combustion control method and system, and relates to the field of energy and environmental engineering. The method comprises the steps of collecting sensor data for data fusion, and preprocessing original data; according to the preprocessed data, an environment combustion synergy index is constructed, and the raw materials are classified; screening an optimal mixing ratio by utilizing dynamic game scores; temperature and flame stability parameters are collected in real time to construct a closed-loop feedback system, and intelligent adjustment of the combustion process is achieved; and a dynamic emission risk index is constructed, and closed-loop optimization control of pollutant emission is realized in combination with variable regression and a dynamic adjustment algorithm. Advanced technologies such as sensor data fusion, environment combustion synergy index, dynamic game scoring and feedback control are combined, on the premise that the combustion efficiency and stability are ensured, the blending combustion strategy of the gasified slag mixed biomass fuel can be optimized, and pollutant emission is effectively reduced.

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.

A system for compensating for a relative motion between a probe and an apparatus. The system includes a probe assembly, a sensor, and a computer. The probe assembly includes an actuator pack positioned exterior to the apparatus and a probe insertable into the apparatus. The probe includes a first end coupled to the actuator pack, a second end, and an elongated body extending therebetween. The sensor senses data indicative of a relative motion between the apparatus and the probe assembly. The computer is operably coupled with the sensor and the probe assembly to move the probe of the probe assembly in response to the sensor sensing the data indicative of the relative motion between the apparatus and the probe assembly.

The invention relates to the field of thermal power generation, and discloses a thermal power boiler combustion optimization method based on multi-parameter feedback control, which comprises the following steps: acquiring key parameters such as oxygen concentration, temperature, pulverized coal concentration, air flow, pulverized coal flow and boiler load in boiler operation; establishing a distributed parameter model for describing dynamic changes of oxygen concentration, temperature and pulverized coal concentration in the hearth along with time and space; key characteristic variables representing the combustion state are extracted through multi-scale analysis; dynamically optimizing multiple parameters of different areas in the hearth based on a distributed feedback controller; a global optimization objective function is constructed, and the ratio of the air flow to the pulverized coal flow is dynamically adjusted; and the optimized control instruction is sent to a boiler system, and combustion parameters are adjusted in real time through an execution mechanism. According to the method, through distributed modeling, multi-scale analysis and feedback control, the combustion efficiency is remarkably improved, pollutant emission is effectively reduced, and the method can adapt to coal quality fluctuation and load change.

The invention relates to the technical field of industrial intelligent control, in particular to a combustion system fault correlation analysis and diagnosis method and system based on a knowledge graph. The method comprises the following steps: constructing a mechanism topological graph of a combustion system, obtaining real-time load data of the combustion system, calculating dynamic conduction lag time of a parent node influencing a child node in the mechanism topological graph based on a fluid mechanics load correction mechanism in combination with a flow resistance correction factor and reference transmission time, performing time sequence alignment based on dynamic conduction delay time, calculating causal association strength of a father node pointing to a child node and performing confidence attenuation processing, performing reverse search on a mechanism topological graph when an abnormal trigger point is monitored, calculating accumulated abnormal energy of each father node based on the causal association strength, and determining the abnormal trigger point according to the accumulated abnormal energy. And determining the father node with the highest accumulated abnormal energy as a fault root cause. According to the scheme of the invention, accurate time sequence alignment under working condition fluctuation can be realized, diagnosis failure can be prevented, and fault root causes can be accurately positioned.

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 discloses a thermal power plant boiler combustion optimization method and system, and the method comprises the steps: obtaining historical combustion data and historical control data of a thermal power plant boiler, and carrying out the analysis to determine control parameters affecting the combustion; extracting features of the control parameters, and constructing a combustion data prediction model based on the features and a preset neural network model for prediction to obtain combustion prediction data; analyzing the combustion prediction data, determining combustion data characteristics, and determining a combustion state coefficient of the thermal power plant boiler based on the combustion data characteristics; an expected combustion state coefficient preset in advance by the thermal power plant boiler is obtained, and the difference value between the combustion state coefficient and the expected combustion state coefficient is determined; and determining a parameter control optimization coefficient according to the difference value to optimize the current control parameter. According to the invention, refined and intelligent monitoring and optimal control of the combustion process can be realized, so that the combustion efficiency of the thermal power plant boiler is improved, the cost is reduced, the emission is reduced, and the stability and the safety of boiler operation are improved.

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 relates to the technical field of boiler combustion control, in particular to a boiler combustion automatic detection control system based on a big data model. The system comprises a data acquisition module used for acquiring various parameter data in a boiler combustion process; the data processing module is used for preprocessing and fusing the collected data; the big data model module constructs a model based on the processed data, learns a combustion process rule and predicts the combustion process rule; the intelligent control module is used for generating a control instruction according to a model prediction result and performing optimization control on boiler combustion equipment; and the monitoring and alarming module is used for monitoring the combustion state and the equipment operation condition in real time, and giving an alarm and taking emergency measures when abnormity occurs. The system overcomes the limitation of traditional boiler combustion control means, realizes comprehensive and accurate monitoring, intelligent and efficient control and advanced fault prediction and treatment of the combustion process, effectively improves the combustion efficiency, reduces the energy consumption and pollutant emission, enhances the operation safety and reliability of the boiler, and is suitable for various industrial boiler application scenes.

The invention discloses a boiler combustion optimization method and system based on multi-parameter adjustment, and the method comprises the steps: obtaining and extracting combustion data related to the combustion efficiency of a boiler in the historical operation data of the boiler, and extracting the characteristics of the combustion data, constructing a boiler combustion prediction model based on the features and a preset neural network model to perform prediction to obtain combustion prediction data; analyzing the combustion prediction data to determine key combustion parameters influencing the combustion state of the boiler, and determining data characteristics of the key combustion parameters; evaluating the boiler combustion state based on the data characteristics to obtain a boiler state evaluation value, and determining the weight of each control parameter based on the boiler state evaluation value; and determining an adjustment coefficient corresponding to each control parameter based on the weight so as to optimize and adjust each control parameter. The advanced data analysis technology is combined with real-time monitoring data and model prediction, multiple control parameters in the boiler combustion process are intelligently adjusted and optimized, the boiler combustion efficiency is improved, energy consumption is reduced, emission is reduced, and the stability of the boiler is improved.

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 relates to the technical field of smart home, and discloses an automatic range hood starting device and method based on a sensing function, and the device comprises a sensor module which is used for collecting kitchen environment data and comprises an infrared sensor, a lampblack concentration sensor, a sound sensor and a temperature and humidity sensor; the data processing module comprises a microcontroller and is used for receiving and fusing the real-time data of the sensor module, calculating the weight of the data of each sensor through a mutual information algorithm and generating a comprehensive cooking state index; and the control execution module is connected with the range hood fan and the fan blade adjusting mechanism, and dynamically adjusts the rotating speed of the fan, the fan blade angle and the operation mode according to the comprehensive cooking state index and the pollution diffusion model. According to the technical scheme of determining the weight by adopting the multi-sensor data fusion and mutual information algorithm, the effect of accurately judging the cooking state is achieved by calculating the association degree of the data of each sensor and the cooking state and allocating the weight.

The invention discloses a self-adaptive optimization control system and method for the combustion process of a biomass generator set. The self-adaptive optimization control system comprises a multi-dimensional sensing array module, an edge calculation module, a self-adaptive decision server, an execution mechanism cluster and a closed-loop verification terminal. The multi-dimensional sensing array module comprises a temperature field monitoring unit, a flue gas component analysis unit and a fuel characteristic detection unit; a data fusion processor is arranged in the edge calculation module; the self-adaptive decision-making server carries a dual-module decision-making engine; the executing mechanism cluster comprises a frequency conversion fan, a precise feeding machine and an adjustable fire grate. Through multi-source sensing data fusion and a self-adaptive decision-making mechanism, real-time response to biomass component fluctuation and load change is achieved, and the combustion efficiency is improved; a deep learning prediction model is combined, the air-coal ratio and the hearth temperature set value are adjusted in advance, and CO emission is reduced; the manual intervention frequency is reduced through an optimization mechanism, and the equipment maintenance period is prolonged.

The invention relates to the technical field of energy conservation and environmental protection, in particular to an industrial boiler combustion oscillation modal suppression system and a method thereof, and constructs a system which is highly intelligent, high in adaptability and remarkable in suppression effect by fusing innovative technologies such as multi-dimensional perception, intelligent identification, active suppression and adaptive optimization. The data acquisition module accurately captures hearth temperature, flame video and sound pressure signals; the signal processing module deeply analyzes and extracts oscillation modes of a temperature field and a flame area, and analyzes a sound pressure signal frequency spectrum through short-time Fourier transform; the mode identification module accurately identifies the combustion oscillation mode and determines the characteristic frequency and amplitude of the combustion oscillation mode; the suppression control module generates an infrasonic wave intervention signal to form an anti-phase sound field so as to effectively suppress oscillation; the fuel adjusting module adjusts the opening time sequence of the gas nozzle according to the instruction, and fuel supply is optimized. According to the system, the operation stability and heat efficiency of the boiler are remarkably improved, NOx emission is greatly reduced, and powerful technical support is provided for clean and efficient operation of the industrial boiler.

The invention relates to a combustion oscillation fault rapid detection method and device, and the method comprises the steps: obtaining a key signal of a combustion chamber in a combustion process, and determining a plurality of combustion states; the key signal comprises time sequence data; performing symbol segmentation on the time sequence data based on a maximum entropy segmentation method to obtain a symbol time sequence; obtaining a reference state according to the symbol time sequence and the plurality of combustion states; determining a current combustion state in the plurality of combustion states according to the reference state; when the current combustion state is a combustion oscillation mode, an early warning mechanism is started; according to the method, the time sequence data and the combustion state of the combustion chamber in the combustion process through the maximum entropy segmentation method can be processed, and the current combustion state is determined through the combustion state and the reference state, so that the combustion oscillation is monitored in real time through the current combustion state, complex sensor arrangement and algorithm processing are not needed, and the reliability is high. The cost is reduced, the calculation amount is reduced, and the early warning efficiency is improved.

The invention discloses an ammonia-doped combustion optimization method and device for a coal-fired power plant, a medium and equipment. According to the method, by obtaining the coal type parameter, the air volume parameter and the initial ammonia mixing proportion of the target coal-fired power plant and combining the preset ammonia-doped combustion simulation model, the emission of the nitric oxide at the hearth outlet is accurately predicted. The model is constructed based on a hearth subarea reactor network, covers a preheating area, a main combustion area and a burnout area, and can accurately simulate the combustion process of each area and the generation of nitrogen oxides through nitrogen conversion path training under a deep air classification working condition. And according to the predicted value, the air volume parameter and the ammonia mixing proportion are dynamically adjusted, the combustion process is optimized, nitrogen oxide emission is effectively reduced, the combustion efficiency is improved, the coal-fired power plant is assisted to achieve low-carbon and environment-friendly operation, and the problem that the ammonia-doped combustion process cannot be accurately and efficiently optimized in the prior art is effectively solved.

The embodiment of the invention provides a flue gas circulation hydrogen-doped combustion peak regulation and combustion stabilization system. The flue gas circulation hydrogen-doped combustion peak regulation and combustion stabilization system comprises a boiler, a water electrolysis device, a flue gas-hydrogen mixer and a flue gas-hydrogen combustor. The boiler is provided with a burner, and the flue gas-hydrogen burner is arranged on the lowermost layer of the boiler; the water electrolysis device is powered by the power supply device, an anode outlet of the water electrolysis device is communicated with a combustion improver inlet of the combustor, and a cathode outlet of the water electrolysis device is communicated with a hydrogen inlet of the flue gas-hydrogen mixer; a flue gas outlet of the boiler is respectively communicated with an inlet of the chimney and a flue gas inlet of the flue gas-hydrogen mixer; and a mixing outlet of the flue gas-hydrogen mixer is communicated with an inlet of the flue gas-hydrogen burner. According to the system, through hydrogen-doped combustion and oxygen-enriched combustion, boiler combustion is stable and safe during deep peak regulation of a unit, coal consumption is reduced, and cost is saved.

The utility model relates to a double-chamber furnace burner gas energy-saving device which comprises a gas inlet pipeline, the gas inlet pipeline is provided with an air flow sensor and an air flow control valve, and a gas pipe is provided with a gas flow sensor and a gas flow control valve. The output end of the air flow sensor and the output end of the gas flow sensor are electrically connected with the input end of the controller, and the output end of the controller is connected with the air flow control valve and the gas flow control valve. The controller is provided with a master controller, the master controller is provided with a double-cross control module, and the master controller controls the air flow control valve and the gas flow control valve through the double-cross control module and the controller. The controller and the master controller are arranged, the controller transmits detection parameters of the air flow sensor and the gas flow sensor to the master controller, the master controller achieves double-cross control through the double-cross control module, and the controller adjusts the valve opening degree of the air flow control valve and the valve opening degree of the gas flow control valve through the double-cross control. And thus, energy-saving control is realized.

The invention provides a boiler thermal expansion dynamic monitoring device and an intelligent compensation system, and relates to the technical field of boiler safety monitoring, the boiler thermal expansion dynamic monitoring device comprises a data acquisition module for acquiring monitoring key data of a target boiler in real time by using a set data acquisition device; the intelligent compensation module is used for carrying out abnormal judgment on thermal expansion of the boiler based on the monitoring key data of the target boiler and carrying out compensation control on the target boiler based on an abnormal judgment result; and the monitoring and alarming module is used for visualizing the expansion related data of the target boiler and providing an expansion abnormity alarming function. Abnormality judgment is carried out on thermal expansion of the boiler according to monitoring key data of the boiler acquired by using set data acquisition equipment, and compensation control is carried out on the boiler based on a judgment result; the expansion related data of the target boiler is visualized, the expansion abnormity alarm function is provided, the thermal expansion deformation condition of the boiler can be accurately monitored, dynamic compensation of the expansion amount is achieved, and therefore the boiler operation safety can be improved.

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 provides a water-cooled wall safety monitoring method and a combustion adjustment control method based on distributed optical fiber sensing, and the method specifically comprises the following steps: arranging a Bragg grating on a water-cooled wall, and obtaining the Bragg wavelength offset; predicting a temperature value and a strain value at the current moment according to historical temperature and strain data by using a state equation of stress and temperature; the Bragg wavelength prediction offset is obtained by combining the temperature value and the strain value at the current moment with the sensor error; obtaining a wavelength change residual error according to the Bragg wavelength offset and the Bragg wavelength prediction offset, and correcting the temperature value and the strain value at the current moment by using the wavelength change residual error to obtain a real temperature value and a real strain value at the current moment; a real strain value at the current moment is converted into a stress value, a water-cooled wall tearing factor is established by utilizing the stress value, the number of thermal cycles and the fatigue life of a water-cooled wall material, water-cooled wall safety monitoring and combustion adjustment control are realized by utilizing the water-cooled wall tearing factor, and the operation safety of the boiler is improved.

The invention relates to the technical field of coal-fired boiler control, in particular to a spectrum detection and intelligent diagnosis collaborative coal-fired boiler combustion optimization control system which comprises a spectrum detection module, and combustion flame spectrum information is collected at preset intervals by means of a fiber optic spectrometer. Collected information is transmitted to a data processing unit, and feature parameters related to the content of key substances are obtained through median filtering, wavelet transform preprocessing and PCA and PLS feature extraction. And the intelligent diagnosis module evaluates the combustion condition in the furnace based on a neural network or a support vector machine model and performs early warning in case of abnormality. And the optimization control module adjusts the air volume of the fan and the rotating speed parameter of the coal feeder according to the diagnosis result to optimize combustion. The display module is controlled to display the data through a graphical interface, and manual operation is supported. According to the system, real-time monitoring, accurate diagnosis and intelligent control are achieved, the combustion efficiency is improved, and pollutant emission is reduced.

The invention discloses a binary low-carbon fuel combustion control system, and relates to the field of combustion control. The method comprises the following steps: a central control unit which is in communication connection with a pressure sensor, a temperature sensor, a gas flowmeter and a waste gas detection system, monitors a fuel mixing ratio, a combustion state and waste gas components in real time according to pressure and temperature data, and outputs a control instruction for regulating and controlling the temperature, the pressure and the gas flow by utilizing a machine learning algorithm; and the double cross amplitude limiting control system is in communication connection with the central control unit and is used for dynamically adjusting the fuel mixing ratio in the premixing chamber and the air-fuel ratio in the combustion chamber according to a preset target. Through real-time monitoring, real-time data monitoring and proportion adjusting technologies, the combustion condition can be more accurately controlled, the combustion efficiency is improved, and meanwhile emission of harmful gas is reduced. Meanwhile, combustion parameters are adjusted in combination with a machine learning algorithm, accurate combustion control under different loads and environment conditions can be adapted, and intelligent adjustment is achieved.