1195 results about "Furnace temperature" 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 provides a thermal power plant boiler combustion visual monitoring and early warning method and system, a storage medium and electronic equipment. According to the method, obtained multi-measuring-point temperature data are input into a pre-constructed temperature field reconstruction model, the model comprehensively considers the geometric structure and combustion thermodynamic characteristic factors of a hearth, model parameters are dynamically adjusted through a particle swarm optimization algorithm, the temperature field reconstruction precision is continuously improved, and a three-dimensional real-time temperature distribution diagram of the hearth is generated; fusing and presenting the diagnosed combustion abnormal information and the visual image of the hearth temperature field, generating a visual in-furnace combustion state monitoring interface, and meanwhile, pushing the abnormal information to a mobile terminal to realize remote monitoring and early warning; the combustion state data in the long-period operation process of the boiler are mined and analyzed, typical combustion modes are extracted, a combustion mode library is continuously enriched and optimized, an online learning algorithm is used for adaptively updating a combustion abnormity diagnosis model, and the intelligent level of combustion state sensing and abnormity early warning is continuously improved.

The invention discloses a nodular cast iron pipe continuous annealing furnace temperature distribution optimization control system and method, and belongs to the technical field of temperature control, and the system comprises a temperature detection module, a data processing module, a control module and a fault diagnosis module; the temperature detection module is used for collecting temperature data in the nodular cast iron pipe annealing furnace in real time; the data processing module is used for preprocessing and analyzing the collected temperature data and constructing a temperature distribution model; the control module comprises a temperature optimization unit and an energy optimization unit; the temperature optimization unit is used for calculating the temperature adjustment amount according to the real-time temperature distribution condition in the furnace in the length direction, the radial direction and the circumferential direction, and formulating a partition heating and local compensation strategy; the energy optimization unit is used for dynamically adjusting the heating power according to the change of the production takt, and generating a dynamic heating strategy; the fault diagnosis module is used for monitoring the operation state of the nodular cast iron pipe annealing furnace in real time and conducting fault detection.

The invention relates to a data-driven smelting control optimization method, and belongs to the technical field of smelting control. Comprising the steps that working condition classification is carried out through historical operation data of a current smelting furnace by adopting clustering analysis, association rule mining is carried out in each cluster, and a parameter rule base is established; obtaining the working condition category of the current smelting furnace temperature through the parameter rule base according to the smelting furnace operation production data; according to the current furnace temperature characteristic data and the working condition category, the change of the furnace temperature is predicted in real time; according to the current working condition type and the predicted furnace temperature data, a control parameter combination with the optimal energy consumption is selected from the parameter data set; and monitoring the state of the smelting furnace in real time, predicting the furnace temperature, adjusting the control parameter combination of smelting operation, and performing early warning through the furnace temperature fluctuation degree. Furnace temperature prediction is carried out through working condition recognition, then control parameter combinations are screened according to prediction results and working condition categories, and therefore the smelting process is regulated and controlled to achieve energy-saving optimization.

The invention relates to the field of intelligent temperature control of industrial kilns, and discloses a graphitization furnace temperature uniformity optimization processing system which comprises a temperature monitoring module, a dynamic matrix control module, a material-control coupling module, a lattice feedback module and a multi-objective optimization module. The method comprises the following steps: constructing an equivalent temperature field through multi-source temperature data fusion; predicting a graphitization trend and generating a dynamic matrix control instruction in combination with a multi-physical field model; dynamically adjusting a thermal conductivity matching temperature gradient of the guard plate; online correcting a material constant based on lattice feedback; closed-loop control is formed, and the process stability and energy efficiency of the graphitization furnace are improved. Through multi-source fusion monitoring, multi-physical field coupling modeling, dynamic thermal conductivity regulation and control, lattice feedback online correction and reinforcement learning multi-objective optimization, temperature uniformity control and energy efficiency improvement of the graphitization furnace are realized, and material performance and process economy are guaranteed.

The invention provides a cement decomposing furnace temperature prediction control method based on a KAN enhanced neural network, and belongs to the technical field of cement calcination. A CNN-LSTM-KAN network is established as an f-CaO and Nox prediction model, and a state parameter optimization objective function is established in connection with actual production requirements; and solving an optimal solution and an optimal solution interval of the state parameters of the decomposition furnace through a multi-target particle swarm optimization algorithm. A KAN-Enhanced-Informer network is established to predict the temperature of the decomposition furnace, a target function is established by combining a state optimization result with the more accurate prediction result, the less coal consumption and the reasonable alternative fuel quantity as targets, the optimal solutions of the coal feeding quantity and the alternative fuel quantity are obtained, and the optimal solutions are applied to a control model to ensure that the temperature of the decomposition furnace reaches the optimal set value. The problems that in the actual cement production process, energy consumption is too high, the yield is not ideal, and much sewage is discharged are solved.

The invention belongs to the technical field of cement production, and particularly discloses a cement decomposing furnace temperature field simulation method based on digital twinning, and the method comprises the steps: firstly, carrying out the preliminary selection of input parameters according to a cement clinker sintering technology, and then building a time sequence data set from a decomposing furnace; secondly, data preprocessing is carried out through mean filtering and homogenization, and a time sequence data set is divided into a training set, a verification set and a test set; establishing a deep learning model for training to obtain a digital twin virtual model layer, and establishing a decomposition furnace geometric model for training to obtain a prediction model; and carrying out anomaly detection and prediction simulation on the input decomposition furnace variables by using the two trained models. According to the method, the decomposition furnace digital twinborn model is established through the decomposition furnace mechanism model and the data driving model, so that the method has the advantages of prediction capability, operation optimization, decision support and the like, temperature distribution and dynamic change in the decomposition furnace can be reflected in real time, and abnormal conditions in operation can be found.

The invention discloses a furnace body temperature control system, which belongs to the technical field of industrial automation control, and comprises the steps of monitoring and collecting multi-dimensional data of a physical furnace body in real time, performing preprocessing, generating a state vector, setting a working condition judgment method, classifying various working conditions, outputting working condition labels and characteristic parameters, and establishing a health degree evaluation model; the state of the actuator is monitored in real time, and frequent maintenance and short service life caused by overuse of equipment are prevented; constructing a furnace body virtual model, setting a real-time prediction method, and simulating furnace body temperature distribution so as to predict a future temperature trend, judge whether to start a rehearsal mode, output an optimal scheme, and realize advanced intervention of prediction and decision making; a working condition identification and temperature prediction result is read, a decision engine method is set, and a dynamic control instruction is generated and executed; and comparing data of the physical furnace body with data of the digital twinborn body, setting a self-evolution method, and carrying out online calibration and correction on the virtual model.

The invention provides a smelting method for improving the stability of a blast furnace under the condition of high gas utilization rate, which comprises the following steps: step 1, using a bell-less furnace top, and preparing charging materials in a tank combination manner, the charging materials comprising coke and ore; step 2, annularly distributing the coke and the ore at a preset angle position in the blast furnace by adopting a simultaneous loading and distributing method; and step 3, keeping the gas utilization rate at 48-54.0%, carrying out blast furnace smelting, dynamically adjusting the oxygen enrichment amount and the coal injection amount of the blast furnace according to the furnace building condition, dynamically adjusting the fuel ratio or the air volume of the blast furnace according to the gas utilization rate decreasing condition, and dynamically adjusting the coal injection amount or the clean coke of the blast furnace according to the stockline decreasing condition. By accurately controlling the ore coke distribution gradient, forming a coal gas flow structure with heavy loads on the edge and an open center, the pressure difference in the furnace can be stabilized through linkage adjustment of the oxygen enrichment amount and the coal injection amount, the fuel ratio / air volume dynamically adjusts and compensates a heat notch, furnace temperature fluctuation is avoided, and the activity of the hearth is rapidly recovered according to the stockline abnormity, the coal injection or net coke supplement strategy.

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 relates to the field of control, in particular to a zone control method and system for a temperature field of a copper material heat treatment furnace. In the off-line stage, a strategy library containing various high-efficiency affine control laws is calculated in advance by solving a multi-parameter planning problem, the furnace temperature state space is divided into a plurality of explicit control subareas according to the coupling strength of all temperature areas in the furnace, and a set of alternative control laws are assigned for each subarea; during on-line operation, the partition to which the current furnace temperature belongs is determined according to the current furnace temperature, the partition with the minimum performance cost is quickly selected from the alternative control laws of the partition through short-time domain prediction to serve as the current optimal law, and when the predicted furnace temperature enters the boundary of the partition, the optimal control laws of the current partition and the adjacent partition are weighted and mixed; otherwise, directly adopting the current optimal law to generate a control instruction and applying the control instruction to the heater.

The invention discloses a method for preparing anisotropic samarium-iron-nitrogen magnetic powder, and belongs to the technical field of magnetic materials, the method comprises the following steps: preparing Sm and Fe according to design components, and smelting to obtain samarium-iron alloy molten steel; casting the samarium-iron alloy molten steel onto a cooling roller to obtain an iron-samarium alloy sheet; the iron-samarium alloy sheet is placed in a vacuum heat treatment furnace to be subjected to homogenization treatment, the alloy sheet obtained after homogenization treatment is cooled to 200-300 DEG C, in the vacuum environment, hydrogen is introduced into the furnace, the absolute pressure of the hydrogen is controlled to be 0.05-0.1 MPa, and hydrogenation treatment is conducted for 2 h; after hydrogenation is completed, the temperature is increased to 400 DEG C, nitrogen is introduced to 0.5 MPa, the temperature is increased to 530 DEG C, then the furnace temperature is reduced to the room temperature, and the anisotropic samarium-iron-nitrogen magnetic powder is obtained.The preparation method of the samarium-iron-nitrogen magnetic powder has the advantages of being simple in preparation process, low in energy consumption, high in production efficiency, small in magnetic powder particle size, high in magnetic performance and good in magnetocrystalline anisotropy.

The invention relates to the technical field of task scheduling, in particular to a task scheduling optimization system for a smelting process, which comprises a furnace temperature identification module, a hot load analysis module, a load screening module, a sorting regulation and control module and a task joint control module. According to the method, a stable operation section is screened based on thermocouple time sequence temperature difference fluctuation characteristics, task identification is ensured to be in tight fit with a real metallurgical process state, furnace profile thermal parameters and raw material attributes are introduced to dynamically estimate task thermal loads, and the heat adaptation relation between tasks and equipment is determined; safety load interval limitation is set in the screening task and furnace body combination process, the safety and matching precision of resource allocation are improved, meanwhile, cooling period limitation is embedded in task sorting to recognize delay tasks, scheduling continuity and task connection reasonability are enhanced, the capacity of a scheduling strategy for responding to actual operation constraints is improved, and the service life of the scheduling strategy is prolonged. The overall optimization of accurate task screening, reasonable load distribution and ordered scheduling sequence is realized.

The invention discloses an annealing furnace temperature parameter control method and system based on multi-source data, and relates to the technical field of annealing furnace temperature parameter control, and the method comprises the following steps: carrying out the data collection of the operation process of an annealing furnace, and generating an annealing process record; a temperature sensor is arranged in an annealing furnace to collect the temperature in the furnace in real time, the uniformity of a temperature field in the furnace is measured by calculating a temperature equilibrium coefficient, and time parameters used for compensating the heat conduction hysteresis effect are determined according to material characteristics and thickness parameters of a workpiece to be processed; based on the temperature equilibrium state monitored in real time and process requirements, the annealing process is subjected to control stage division; a comprehensive uniformity index is calculated by analyzing the temperature balance degree and the cooling rate uniformity at all moments in the annealing process, whether the control stage of the annealing furnace is abnormal or not is judged according to the condition that the fluctuation amplitude of the index at all moments does not exceed the preset multiple of the average value of historical uniformity indexes of workpieces of the same type, and temperature parameter control over the annealing furnace is achieved.

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 relates to the technical field of intelligent manufacturing, in particular to a nodular cast iron alloy component self-adaptive optimization method and system based on artificial intelligence, and the method comprises the following steps: obtaining furnace temperature and component parameters, generating a stable comparison legend, screening offset abnormal combinations to form an available set, extracting performance curve characteristics to construct a trend chart, and obtaining a nodular cast iron alloy component self-adaptive optimization result. And superposing the structural parameters and the element distribution positioning segregation area, and outputting feeding correction parameters to generate an optimization scheme. According to the method, normalization processing is carried out on furnace temperature, carbon-silicon ratio and other multi-factor offset values, a component offset map is constructed, trend feature extraction and sorting of response curves under multiple working conditions are combined, component combinations with stable performance are screened out, a mapping relation is established through the structural curvature, the wall thickness slope and the injection rate, and a spatial coincidence map is generated through coincidence analysis. And the feeding direction and the injection rate are inversely calculated, dynamic adaptation of components and the structure is achieved, and the structure uniformity and the performance consistency are improved.

The invention relates to the technical field of intelligent control of industrial heating equipment, and provides an intelligent temperature control method and system for a melting holding furnace, and the method comprises the steps: dividing a temperature control system into a temperature monitoring module and a temperature adjusting module, and building a real-time data interaction region in a central controller; the temperature monitoring module performs multi-sensor signal fusion through a data acquisition unit, performs dynamic parameter analysis through an adjustment execution unit, and writes control parameters into an interaction area; the temperature adjusting module monitors parameters of the interaction area, matches a preset algorithm to execute heating / cooling adjustment and feeds back a state; the temperature monitoring module updates the strategy according to the feedback. The response speed and the control precision of temperature control of the melting holding furnace can be improved, and the reliability and the maintainability of the system are enhanced.

The invention relates to the technical field of temperature control, and discloses an MPC-based hearth temperature adaptive control system and method.A model building module builds a dynamic prediction model based on thermodynamic characteristics and dynamic characteristics; the data output module obtains historical operation data and outputs a future temperature change prediction table, and the factor determination module analyzes the historical operation data and determines a temperature influence indirect factor and a temperature influence direct factor; the data calculation module calculates a temperature influence factor degree value; an updating optimization module sets an adjustment coefficient based on the temperature influence factor degree value, updates and optimizes the future temperature change prediction table, and obtains a target future temperature change prediction table; the temperature control module carries out furnace temperature self-adaptive control according to the target future temperature change prediction table, temperature control parameters can be automatically adjusted according to changes of actual working conditions, the self-adaptive capacity is improved, high-precision and high-stability control over the furnace temperature is achieved, and the product quality and the production efficiency are improved.

The invention provides an insulator sintering furnace temperature control system and method. The method comprises the steps that a preset temperature control scheme is generated; monitoring the real-time heating data of each area in the sintering furnace, and determining the real-time temperature rise data of the insulator in each area based on the real-time heating data; analyzing a first abnormal temperature value under a first area temperature time sequence in the real-time heating data to obtain first predicted sintering quality; performing simulation based on the first abnormal temperature value so as to generate simulation data of the first abnormal temperature value; and analyzing and judging the simulation data to obtain a first judgment result of the first area at the first moment. When the temperature of a certain area in the sintering furnace is abnormal, the abnormal temperature can be taken as a temperature regulation response in time, the technical effects of realizing high-precision temperature control and ensuring excellent sintering yield of the insulator are achieved, meanwhile, when the temperature of a single sintering area is regulated, the temperature influence on the area adjacent to the regulation area is reduced, and the stability of the sintering temperature is improved. And the temperature adjusting precision of the sintering area of the sintering furnace is ensured.

The present invention relates to the technical field of heat treatment methods for special metal materials, and provides a heat treatment method for ultrahigh-strength and high-toughness steel. The heat treatment method comprises a quenching treatment, a subzero treatment and a tempering treatment. By accurately controlling the treatment temperature, the treatment time, the furnace temperature uniformity, the charging amount, etc. during each stage of the heat treatment, the formation of a structure having good strength and toughness is synergistically promoted, such that ultrahigh-strength and high-toughness steel having well matched strength and toughness can be obtained, thereby stably realizing the technical indexes of a room-temperature tensile strength of greater than or equal to 1930 MPa, a room-temperature yield strength of greater than or equal to 1620 MPa and a fracture toughness of greater than or equal to 110 MPa·m1 / 2. In addition, the variation coefficient of the mechanical properties of the prepared ultrahigh-strength and high-toughness steel reaches an ultrahigh standard of the variation coefficient of tensile strength being less than or equal to 0.18 and the variation coefficient of fracture toughness being less than or equal to 2.0, such that the product has mechanical properties that are up to standard with small fluctuations and has strong stability, which can satisfy the requirements for production.

The invention discloses a pre-oxidation furnace temperature real-time control system and method based on data processing, and relates to the field of pre-oxidation furnace temperature control, and the pre-oxidation furnace temperature real-time control system based on data processing comprises a data feature acquisition module, a data analysis module, an optimal temperature prediction module and a temperature real-time control module. According to the method, the analysis precision of temperature, transmission speed and tension data is improved, a more reliable temperature change trend and a more reliable stability analysis result are obtained, and a random forest regression model is trained by collecting historical data including a transmission speed stability analysis result, a tension stability analysis result and a corresponding temperature adjustment value and utilizing the data, so that the accuracy of the temperature change trend is improved. The complex interaction between the features is better captured, so that the prediction precision is improved; the temperature set value is dynamically adjusted according to the temperature change trend and quantitative transmission speed and tension stability analysis results, and transmission speed stability and tension stability changes can be responded.

The present invention provides a refractory furnace temperature control method, the method comprising: obtaining a BP neural network constructed with refractory furnace data as input and PID configuration parameters as output; using a differential evolution algorithm to solve the hidden layer weights, initial bias values ​​and hyperparameters of the BP neural network; wherein, in the solution process, the scaling ratio of the mutation process in the differential evolution algorithm changes linearly with the number of iterations in a negative correlation; assigning the hidden layer weights, initial bias values ​​and hyperparameters obtained by the solution to the BP neural network, and training the BP neural network; inputting the real-time collected refractory furnace data into the trained BP neural network to obtain real-time PID configuration parameters; inputting the desired temperature into the PID controller, and configuring the real-time PID configuration parameters to the PID controller to control the temperature of the refractory furnace. The present invention can quickly and accurately complete the initialization of the BP neural network and control the PID controller in real time.

The invention discloses a textile process protection method based on a furnace temperature curve model, and the method comprises the steps: installing a plurality of temperature sensors on a heat treatment furnace, and collecting the real-time temperature data of the heat treatment furnace through the temperature sensors; by taking the real-time temperature data as input, constructing a furnace temperature curve model comprising a heating stage, a maintaining stage and a cooling stage, adjusting and optimizing the model, and outputting a temperature change curve of the heat treatment furnace; and the temperature change curve is compared with the real-time temperature data, and the heat treatment furnace is adjusted through the central controller based on the comparison result. The temperature change in the spinning process can be monitored in real time, the temperature fluctuation can be predicted and adjusted through the furnace temperature curve model, it is ensured that the temperature is always kept within the ideal range, and damage to the spinning material due to the temperature change is avoided.

The invention relates to the technical field of temperature control, and discloses a furnace temperature adaptive control system and method based on MPC. A model building module builds a dynamic prediction model based on thermodynamic characteristics and kinetic characteristics; a data output module obtains historical operation data and outputs a future temperature change prediction table; a factor determination module analyzes the historical operation data and determines the temperature influence indirect factors and the temperature influence direct factors; a data calculation module calculates the degree value of the temperature influence factor; an update optimization module sets an adjustment coefficient based on the degree value of the temperature influence factor, updates and optimizes the future temperature change prediction table, and obtains a target future temperature change prediction table; a temperature control module performs furnace temperature adaptive control according to the target future temperature change prediction table, and can automatically adjust temperature control parameters according to changes in actual working conditions, thereby improving the adaptive ability, realizing high-precision and high-stability control of the furnace temperature, and improving product quality and production efficiency.

The invention discloses a method for inhibiting temperature fluctuation of an oriented silicon steel BAF furnace, which comprises the following steps: adding 0.005-0.01 wt% of Nb, and forming a nanoscale Nb (C, N) precipitate; a three-dimensional heat conduction model is established in a mode of linkage of infrared thermal imaging and electromagnetic induction heating in combination with a finite element method, and real-time monitoring and accurate control of a temperature field in the furnace are achieved. On the basis of a fuzzy PID hybrid control strategy, the optimal heating power and scanning path are predicted in combination with a convolutional neural network, and closed-loop control of local temperature compensation is achieved; a random forest model with the temperature gradient delta T, the Als content and the Nb precipitated phase density as input parameters is constructed, technological parameters are iteratively optimized in combination with a genetic algorithm, and dynamic self-adaptive adjustment of an annealing temperature curve is achieved. According to the method, uniform growth of Gaussian grains in the secondary recrystallization process is achieved, and the effects of reducing annealing temperature fluctuation, improving secondary recrystallization uniformity and optimizing grain orientation distribution are achieved.

The invention belongs to the technical field of quartz sand, and discloses a roasting method for controlling the pore defect of quartz sand and increasing the stacking density, which comprises the following steps: 1) drying silicon dioxide gel prepared by a sol-gel method to obtain silicon dioxide powder; (2) the silicon dioxide particles are placed in a roasting furnace, the furnace temperature is preheated to 400-700 DEG C after vacuumizing is conducted, then the furnace is maintained in a vacuum environment for a period of time, then oxygen-containing atmosphere is filled into the furnace and maintained for a period of time, the vacuum environment and the oxygen-containing atmosphere environment are alternately conducted, and sufficient decarburization is achieved; and (3) vacuumizing again, raising the furnace temperature to 1100-1300 DEG C, maintaining for a period of time in a vacuum environment, filling inert gas into the furnace, maintaining for a period of time, alternately vacuumizing and filling the inert gas, and sintering the quartz sand. According to the method, preheating is performed for sufficient decarburization, then high-temperature sintering is performed, the finally obtained quartz sand does not have obvious air holes, and the bulk density is larger than or equal to 1.3 g / cm < 3 >.

The invention belongs to the field of heat treatment of high-temperature alloy parts, and particularly relates to a heat treatment method. An existing conventional destressing heat treatment process of charging at the furnace temperature below 400 DEG C, heating to the temperature of 540-560 DEG C at any speed / heat preservation for 2-4 h / air cooling is adjusted into a gradient heat treatment process of charging at the room temperature of a heating furnace, heating to the first gradient of 600-620 DEG C along with the furnace at the speed of (50 + / -10) DEG C / h and heat preservation for 2-4 h, furnace cooling to the second gradient of 500-550 DEG C at the speed of (50 + / -10) DEG C / h and heat preservation for 2-4 h. According to the method, after the aging-state GH4169 alloy part is subjected to heat treatment, the residual stress level of the aging-state GH4169 alloy part is reduced by more than 50% compared with that of a conventional stress relief annealing process, so that the deformation tendency and degree of the part in the subsequent machining and using process are further reduced, and the manufacturing qualification rate of the part is increased.

The invention belongs to the technical field of heat treatment, and provides a bearing ring heat treatment quality evaluation method and system, according to relevant parameters of bearing ring heat treatment and a preset in-furnace temperature prediction model, an in-furnace temperature prediction value is obtained, and according to the in-furnace temperature prediction value, bearing ring heat treatment quality evaluation is carried out. Wherein the in-furnace temperature prediction model is a three-item mathematical model, and each monomial comprises a denominator item and a numerator item; the denominator item in each monomial is a squared value of a difference between the current station of the material frame and a preset constant; a molecular item in each monomial comprises a product of a corresponding fitting coefficient, a corresponding temperature zone actual temperature expression and an accumulated furnace entering time expression; by means of the mathematical model, the real in-furnace temperature of each frame of bearing ring can be predicted, and the method is more accurate and visual compared with pure checking of real-time parameters of heat treatment equipment.

The present invention discloses a method for optimizing a silicon carbide vacuum sintering furnace based on computational heat transfer simulation, comprising the following steps: establishing a heat radiation and heat conduction model of the vacuum sintering furnace during the heating process according to the parameters of the vacuum sintering furnace body; performing computational heat transfer simulation to obtain a temperature rise curve of the vacuum sintering furnace during the entire heating process; optimizing the original furnace type by calculating and comparing the temperature rise curves of workpieces under different heating rates, different graphite heating tube diameters, and different graphite heating tube-to-effective heating zone spacings, the temperature difference between the vertex workpiece and the center workpiece, and the temperature difference between the surface and the core of the same workpiece. The present invention provides a reference for the performance optimization of the vacuum sintering furnace by studying the effects of different heating rates, different graphite heating tube diameters, and different graphite heating tube-to-effective heating zone spacings on the heating efficiency and furnace temperature uniformity of the vacuum sintering furnace, meets the process production requirements of the silicon carbide vacuum sintering furnace, and improves the heating efficiency and furnace temperature uniformity of the heating system.

The invention relates to the technical field of temperature prediction, and discloses a steel furnace temperature prediction method and system, computer equipment and a storage medium, and the method comprises the steps: obtaining the historical operation data of a steel furnace, and carrying out the data processing of the historical operation data, and obtaining a model data set; the model data set is used for training a temperature prediction model, a trained temperature prediction model is obtained, the temperature prediction model comprises a composite embedding module, an encoder module and a decoder module, and the composite embedding module comprises a composite convolution layer, a category embedding layer, an attention layer and a splicing layer; and acquiring real-time operation data of the steelmaking furnace, and inputting the real-time operation data into the trained temperature prediction model to obtain a temperature prediction result of the steelmaking furnace. According to the method, the precision of a model prediction result is improved by comprehensively processing the data of the multi-dimensional sensor, so that the temperature control precision in the steelmaking process is improved, the energy consumption is reduced through a temperature feedback mechanism, and the steel quality is further improved.