1705results 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 provides a boiler combustion state visualization method, which comprises the steps of embedding a plurality of thermocouple temperature sensors in gaps of refractory bricks on the inner wall of a boiler hearth, acquiring temperature data of the hearth at different depths in real time, and transmitting the temperature data to a data acquisition terminal through a wireless communication module; fitting the normalized temperature data matrix by adopting a three-dimensional thin-plate spline interpolation algorithm to generate a hearth three-dimensional real-time temperature distribution model, performing mesh generation on the three-dimensional temperature distribution model by combining hearth geometric structure parameters obtained from computer aided design software, and constructing a hearth temperature finite element model; the hearth temperature finite element model is imported into ANSYS finite element analysis software, hearth wall surface material attributes and combustion heat load boundary condition parameters are set, transient thermal analysis of a hearth temperature field is carried out, and a temperature estimation value of any position of the hearth changing along with time is obtained.

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 belongs to the field of pollutant control, and particularly discloses a method and system for reducing generation of nitric oxide in the ammonia coal combustion process. The method specifically comprises the steps that liquid ammonia is gasified and then subjected to a thermocatalytic reaction to generate hydrogen and nitrogen, and ammonia-doped mixed gas is obtained; and judging whether the ratio of the concentration of hydrogen to the concentration of ammonia in the ammonia-doped mixed gas is greater than or equal to a threshold value or not, if so, discharging the ammonia-doped mixed gas for blending combustion, and if not, carrying out thermal catalytic reaction on the ammonia-doped mixed gas again. According to the method, the ratio of the concentration of hydrogen to the concentration of ammonia in the ammonia-doped mixed gas serves as the judgment standard for whether blending combustion can be conducted or not, the calculation formula of the threshold value is provided by balancing the influence of H2 and NH3 on NOx generation in the combustion process, then flexible regulation and control can be conducted on the raw material side according to the actual blending combustion condition, and the blending combustion efficiency is improved. The method effectively reduces the generation of nitrogen oxides in the ammonia coal combustion process, does not need to regulate and control the combustion side, does not affect the combustion stability and the boiler thermal efficiency, and has the advantages of simple operation and low cost.

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 provides a combustion device CO2 emission concentration prediction method based on a CNN-LSTM model, and the method comprises the following steps: screening an input variable data set according to a correlation coefficient between each input variable and the CO2 emission concentration; performing data conversion processing; constructing a CNN-LSTM model as an initial prediction model, inputting the training set into the initial prediction model for training until the average absolute error loss is less than 5%, and storing the model as a training model; changing hyper-parameters of the initial prediction model, performing multiple times of training, and selecting a training model with the minimum root mean square error and the maximum decision coefficient as a final prediction model; and inputting the relevant characteristic variable parameters of the current time step into the final prediction model to obtain a CO2 emission concentration prediction value of the next time step. The CO2 emission concentration of the combustion device can be accurately predicted in real time.

The invention discloses an optimized combustion control method and system for a combustor, and the method comprises the steps: obtaining a real-time combustion state parameter, and evaluating the real-time combustion state parameter based on a plurality of combustion evaluation indexes to obtain a real-time combustion evaluation value; when the real-time combustion evaluation value is smaller than the standard combustion evaluation value, a preset control model is determined according to the combustion evaluation value difference in combination with the real-time operation working condition, and an initial control method of the combustor is obtained; performing control simulation based on an initial control method to obtain simulation change characteristics of each real-time combustion state parameter, and optimizing corresponding control parameters in the initial control method according to the simulation change characteristics to obtain an optimized control method; by continuously optimizing and adjusting the control parameters of the combustor, the control efficiency and control precision of the combustor are improved, and efficient and stable operation of the combustor is achieved.

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.

Device for monitoring and controlling the high-temperature corrosion of water-cooled walls of a wall-like cut-circle boiler, characterized in that it comprises the following: a creation module for a meshed model, which serves to obtain a structure of the water-cooled wall of the wall-like circular boiler and to create a meshed model based on the structure of the water-cooled wall of the wall-like circular boiler; a creation module for a correlation model, which serves to obtain real-time parameters of the water-cooled wall of the wall-like circular vessel and to create a correlation model corresponding to the real-time parameters in combination with the networked model; a creation module for a three-dimensional model, which serves to simulate with the CFD data and the corresponding model in order to obtain the three-dimensional model of the temperature field and the CO field in the combustion chamber; a detection module for opening angles, which serves to obtain the deflection angles of various primary air nozzles and secondary air nozzles at different air volumes within the temperature field in the combustion chamber and the flap openings at different air volumes within the CO field on the basis of the three-dimensional models of the temperature field and the CO field of the combustion chamber; an adjustment module for deflection angles, which serves to obtain the optimal deflection angles of the primary air nozzles and the secondary air nozzles for the real-time air volume and the current temperature field in the combustion chamber based on the temperature and the real-time air volume at the appearance of the flame wall brushing in the boiler; an adjustment module for damper openings, which serves to obtain an optimal damper opening for the real-time air volume and the current CO field based on the CO concentration and the real-time air volume in the boiler.

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 provides an in-furnace temperature intelligent management method and system for a thermal power plant boiler, and belongs to the field of industrial automation control, and the method comprises the steps: 1, installing a plurality of sensors of a preset type at in-furnace key parts of the thermal power plant boiler, analyzing the data of the sensors, and determining the combustion parameters of the thermal power plant boiler based on the analysis result; 2, the combustion efficiency of the thermal power plant boiler is determined based on the combustion parameters, and then a combustion efficiency curve is drawn; 3, analyzing the combustion efficiency curve, and determining a comprehensive efficiency deviation coefficient based on an analysis result; and 4, determining a combustion adjustment coefficient based on the comprehensive efficiency deviation coefficient, and determining an in-furnace temperature intelligent management strategy in combination with the initial in-furnace temperature. The boiler operation is optimized, and the combustion efficiency and the temperature control precision are improved.

The invention provides a low-load combustion optimization control system for a unit, and the system comprises a sensing network module which collects key parameters of the unit in a combustion process; the data preprocessing module is used for preprocessing the collected key parameters; the index analysis module is used for analyzing the energy balance deviation degree, chemical balance and combustion uniformity evaluation indexes of the current unit based on the preprocessed key parameters; the control strategy execution module is used for carrying out threshold value judgment on the result of each index and correspondingly executing preset energy balance, chemical balance and combustion uniformity strategies; the feedback regulation module is used for acquiring the executed feedback signal and performing new index analysis and cyclic regulation to form a closed-loop feedback mechanism; energy balance, chemical balance and combustion uniformity in the combustion process are analyzed and controlled, so that combustion stability and combustion efficiency are improved, pollutant emission is reduced, the service life of equipment is prolonged, and meanwhile, the adaptability of a unit to a power grid is enhanced.

System for determining the amount of sludge added to the boiler based on a coupled neural BP network, characterized in that it comprises the following: a creation module for the networked model of the combustion chamber, which serves to obtain a detailed structure of the denitrification system and the desulfurization system in the boiler, and to create a networked model of the combustion chamber based on the detailed structure of the denitrification system and the desulfurization system; a measurement module for the temperature field of the combustion chamber, which serves to create a relevant model for different coal qualities in the combustion chamber according to the networked model of the combustion chamber and in combination with the real-time data of the denitrification system and the desulfurization system, and to introduce CFD simulation values ​​into the relevant model and to simulate in order to obtain a temperature field of the combustion chamber; a denitrification efficiency acquisition module that serves to combine the detailed structure of the denitrification system with the temperature field of the combustion chamber and to obtain the denitrification efficiency and the NOx emission index within the framework of the 3D model; a measurement module for desulfurization efficiency, which serves to combine the detailed structure of the desulfurization system with the temperature field of the combustion chamber and to obtain the desulfurization efficiency and the SO2 emission index within the framework of the 3D model; a NOx dosing quantity module that serves to obtain the criterion for the minimum NOx emission value during boiler denitrification and the critical criterion for the NOx emission value with increasing sludge dosing quantity based on the denitrification efficiency and the NOx emission index within the framework of the 3D model, and to compare the different temperatures of the boiler chamber with the criterion of the minimum NOx emission value and the critical criterion of the NOx emission value with increasing sludge dosing quantity and to obtain the maximum regulated sludge dosing quantity corresponding to the NOx emission value; an SO2 dosing quantity module, which serves to obtain the criterion for the minimum SO2 emission value during the denitrification of the boiler and the critical criterion for the SO2 emission value with increasing sludge dosing quantity based on the desulfurization efficiency and the SO2 emission index within the framework of the 3D model, and to compare the different temperatures of the boiler chamber with the criterion of the minimum SO2 emission value and the critical criterion of the SO2 emission value with increasing sludge dosing quantity, and to obtain the maximum regulated sludge dosing quantity corresponding to the SO2 emission value; a model processing module that serves to train the maximum regulated sludge dosing quantity corresponding to the NOx emission value and the maximum regulated sludge dosing quantity corresponding to the SO2 emission value through a neural BP network and to obtain the maximum sludge dosing quantity of the boiler.

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.

A safety and control philosophy of a burner management system (100) for fired heating appliances, comprising: a flame detection unit configured to monitor the presence and stability of the flame in real time; an ignition control unit configured to control the ignition sequence of the burner based on flame detection signals; a fuel flow control unit configured to control the fuel flow to the burner; a real-time diagnostics and predictive analysis module that continuously monitors operating parameters such as flame stability, fuel flow rate, air-fuel ratio, and ignition timing, and generates fault detection signals in the event of abnormal conditions;an automatic response unit that adjusts the burner's operating parameters based on fault detection signals to optimize performance and safety, including dynamic regulation of fuel flow, air-fuel ratio, and ignition timing; a fail-safe protocol unit configured to shut down the burner or transfer it to a safe mode upon detection of critical faults or unsafe conditions.

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 discloses an online monitoring method and device for combustion oscillation of a combustion chamber and a storage medium, and the method comprises the steps: obtaining a time domain vibration signal and a pulsating pressure signal of the combustion chamber in the operation process of a gas turbine through a vibration acceleration sensor and a pulsating pressure sensor of the combustion chamber; performing fast Fourier transform on the time domain vibration signal and the pulsating pressure signal to obtain a frequency domain vibration signal and a frequency domain pulsating pressure signal; performing frequency segmentation based on the frequency domain vibration signal and the frequency domain pulsating pressure signal, and determining at least one target frequency band; determining a characteristic value of the target frequency band based on the frequency domain vibration signal and the frequency domain pulsating pressure signal of the target frequency band; and determining a combustion oscillation fault result of the combustion chamber based on the characteristic value of the at least one target frequency band. Therefore, the monitoring rate of combustion oscillation is improved, the monitoring range is wider, support is provided for accurate and reliable combustion mode adjustment and gas turbine protection of the gas turbine, and the service life of the combustion chamber is prolonged.