985 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 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 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 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 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 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 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 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 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 discloses a heating furnace control method and system based on expert control and a storage medium, and the method comprises the following steps: constructing an expert knowledge employing a generative rule to represent combustion control knowledge, and the rule form is IFlt; a precondition gt; tHNt, THNt, THNt; performing conclusion operation gt; the preconditions comprise a furnace temperature error range, a furnace pressure state, a flue gas temperature and a reversing period parameter, and the conclusion operation comprises gas flow adjustment, air-fuel ratio correction, and setting of a flue valve opening degree or a burner reversing period; dynamic parameter prediction and optimization: calculating a predicted temperature value under the current working condition based on a furnace temperature prediction model; performing multi-target collaborative reasoning: searching a matching rule set in a heuristic manner, and combining a Hash algorithm to quickly position a control conclusion; the optimized air-fuel ratio, reversing period and furnace pressure parameters are issued to a PLC, and an execution mechanism is driven to adjust the frequency of a gas valve, the frequency of an air valve and the frequency of a smoke exhaust fan; and self-learning and knowledge base updating: dynamically correcting the rule weight according to the historical control effect.

The invention provides an industrial furnace temperature control optimization method and system based on model predictive control. The method comprises the following steps: defining a state space and an action space of a temperature control system; an MPC prediction model is established based on the state space and the action space, a future temperature trajectory is predicted, and a prediction error compensation mechanism and a self-adaptive prediction domain are introduced; constructing a deep reinforcement learning model which comprises a state value network and a strategy network and is used for learning an optimal temperature control strategy; based on the MPC prediction model and the deep reinforcement learning model, constructing a multi-objective optimization model for controlling performance, energy consumption and temperature uniformity objectives, and solving to obtain an optimal control sequence; and taking the optimal control sequence as a reference trajectory, and alternately updating parameters of the deep reinforcement learning model by adopting an Actor-Critic architecture. Through multi-level collaborative optimization, the adaptability, the control precision and the energy utilization efficiency of the industrial furnace temperature control system are improved.

The invention relates to the technical field of industrial furnace temperature control, and discloses a tunnel furnace fuzzy PID temperature control process with strong anti-interference adaptive ability, which comprises the following steps: temperature signal acquisition: acquiring actual temperature values of each area in real time through temperature sensors arranged in different heating areas of a tunnel furnace, and calculating temperature signals of the actual temperature values; the collected temperature signal is converted into a digital signal to be transmitted to the temperature controller; and interference signal identification and classification: calling a preset interference identification model by the temperature controller, and carrying out fluctuation characteristic analysis on the acquired temperature signal. By adopting a dual-redundancy temperature acquisition mode combining distributed optical fibers and K-type thermocouples and combining an interference identification model based on an LSTM neural network, accurate identification and classification of interferences such as feed quantity fluctuation, power supply fluctuation and airflow disturbance in a kiln are realized, and through dynamic correction of fuzzy PID (Proportion Integration Differentiation) parameters and an interference compensation mechanism, the disturbance of the kiln can be accurately identified and classified. And the anti-interference capability of the tunnel kiln temperature control system is obviously improved.

The automatic soot blowing furnace comprises a furnace body, a conveying device is arranged outside the furnace body, the conveying device is used for conveying a cupel into the furnace body, and a furnace door capable of being automatically opened and closed is arranged on the furnace body; and an endoscopic camera is arranged in the furnace body and is used for monitoring a soot blowing end point. The device can control the cupel to automatically enter and exit from the hearth, frequent door opening and closing are not needed, the temperature in the furnace is stable, energy consumption loss is reduced, harmful gas emission is reduced, and cost reduction and efficiency improvement are facilitated; image data are acquired by adopting a light supplementing lamp and an endoscopic camera, so that the method is more reliable and accurate; and the cupel subjected to soot blowing is taken out in time through the material taking module, the loss of metal silver is reduced, and the extraction content is increased. Due to feeding and discharging of the cupel, automatic operation is achieved, the working efficiency is improved by 50%, and the cupel can be in butt joint with full-automatic equipment.

The invention discloses a coal-fired unit hearth temperature soft measurement method based on data fusion. The method comprises the following steps: acquiring a hearth combustion image and operation data in a coal-fired unit distributed control system (DCS) at the same moment; preprocessing the combustion image; establishing a feature extraction network mixed with deconvolution and interpolation upsampling; adding the structural similarity index loss into a network training loss function; training the feature extraction network to obtain a feature extraction model; extracting an M * N two-dimensional feature map obtained by the last layer of encoder; dividing the two-dimensional feature map into S same areas, and adding feature values in the single areas to serve as representative feature values of the areas; dCS operation parameter characteristics are analyzed and selected, and Q characteristics with the highest correlation degree with the hearth temperature are selected; performing data fusion on the S combustion image features and the Q DCS operation parameters; and establishing a bidirectional long-short-term memory neural network prediction model to predict the hearth temperature. According to the invention, the precision and robustness of the hearth temperature soft measurement model can be improved.

The invention discloses an automatic combustion control method and system for a slag pulverizer hot blast stove. The method comprises the following steps: acquiring operation parameters of a hot-blast stove and a slag pulverizer; selecting one of a furnace temperature control mode and a flow control mode for execution according to the production stage; the total target gas flow is calculated in the selected mode, wherein the flow control mode is subjected to prospective adjustment according to a linkage rule base of mill operation parameters; the air-fuel ratio is corrected based on double feedback of coal gas components and flue gas residual oxygen; and finally, distributing the target flow to a plurality of burners and executing combustion. Through dual-mode switching and intelligent linkage with the mill, conversion from reaction type control to predictive type control is achieved, thermal lag is effectively overcome, combustion efficiency is ensured through dynamic air-fuel ratio correction, and the energy utilization rate, operation stability and automation level of the system are remarkably improved.

The invention relates to the technical field of control systems, and discloses a multi-zone cooperative control method and system for a temperature field of a carbon nanotube CVD growth furnace, and the method comprises the steps: obtaining the real-time temperatures of a plurality of temperature zones, and calculating the temperature deviation of each temperature zone; the actual temperature gradient is calculated, if the change rate of the actual temperature gradient exceeds a preset threshold value, the gradient maintaining deviation is calculated, and otherwise, the gradient maintaining deviation is zero; calculating a gain weight; calculating predictive thermal disturbance compensation amount of each temperature zone; calculating a first control item; the predictive thermal disturbance compensation amount forms a second control item; the gradient feed-forward correction value forms a third control item, and the gradient feed-forward correction value is proportional to the gradient maintenance deviation of the temperature zone; and taking the sum of the first control item, the second control item and the third control item as the power control quantity of the temperature zone. Unification and coordination of local temperature control and overall temperature are realized, a stable thermal environment is provided for growth of the carbon nanotubes, and consistency and uniformity of growth of the carbon nanotubes are improved.

A temperature field test system for a thermocouple calibration furnace (1) having a temperature uniformity block (8). In the system, two ends of the thermocouple calibration furnace (1) are sealed by means of positioning blocks (9); a standard thermocouple (4) is placed in a quartz tube (2), a reference junction of the standard thermocouple (4) is connected to one end of a wire (3) and is then placed in a glass test tube (5), an opening of the glass test tube (5) is sealed, the glass test tube (5) is placed in a reference junction thermostat (6), and the other end of the wire (3) is connected to an electrical measurement device (7); when empty-furnace temperature field test parameters of the thermocouple calibration furnace (1) are to be acquired, the quartz tube (2) axially passes through the thermocouple calibration furnace (1) and the positioning blocks (9); and when temperature field test parameters of the thermocouple calibration furnace (1) having the temperature uniformity block (8) are to be acquired, the temperature uniformity block (8) is arranged in the thermocouple calibration furnace (1), and an end portion of the quartz tube (2) is inserted into the temperature uniformity block (8). Thus, the placement position of the temperature uniformity block (8) in the thermocouple calibration furnace (1) can be determined, and whether the temperature field of the thermocouple calibration furnace (1) having the temperature uniformity block (8) meets the technical specification requirements is determined. Further disclosed is a temperature field test method for a thermocouple calibration furnace (1) having a temperature uniformity block (8).

The invention belongs to the technical field of steel production, and particularly relates to a new energy steel grade normalizing pickling process and pickling equipment, and the new energy steel grade normalizing pickling process comprises shot blasting machine parameter dynamic regulation and control, pickling process intelligent optimization, normalizing furnace temperature field reconstruction and rolling parameter collaborative matching. The product quality stability is improved through multi-parameter cooperative control, and real-time accurate regulation and control of key parameters such as the steel shot throwing amount and the acid liquor activation index are achieved through linkage of a shot blasting strength dynamic adjustment model and an acid pickling closed-loop compensation mechanism. And in combination with normalizing furnace temperature field gradient reconstruction and oxygen content three-level linkage control, surface oxide scale residues are effectively eliminated, the steel grain size level is improved, and the product yield strength fluctuation range is reduced.

The invention discloses an in-furnace temperature real-time prediction and control method and device for an ultra-fast high-temperature sintering furnace, and the method comprises the steps: collecting in-furnace technological parameters and temperature data in real time, and carrying out the preprocessing, and obtaining a training data set; introducing a multi-task learning framework and an attention mechanism into the MLP model, and constructing an improved MLP model; training the improved MLP model by adopting the training data set to obtain a trained improved MLP model; the preprocessed real-time data are input into an improved MLP model, the temperature in the furnace is predicted in real time through the improved MLP model, and the predicted temperature is obtained; and the closed-loop control module monitors the temperature in the furnace in real time according to the predicted temperature. The in-furnace temperature real-time prediction and control method for the ultra-fast high-temperature sintering furnace can solve the problems that a traditional temperature prediction method is low in precision, low in response speed and lack of closed-loop control under complex process conditions.

The invention relates to the technical field of spraying automation control, in particular to a plastic part spraying control method and system. The method comprises the steps that after it is detected that the temperature of a curing oven reaches a preset target temperature, a feeding event is responded; physical parameters related to the feeding event are obtained, the current actually-measured temperature of the curing oven is obtained, and a first power value used for conducting feedforward compensation on thermal disturbance caused by the feeding event is calculated based on the physical parameters; based on the first power value, the current actually measured temperature and a preset target temperature, total compensation power for the curing oven is determined; and generating and outputting a heating control instruction corresponding to the total compensation power. The power of the feeding stage is compensated through feed-forward compensation and real-time regulation and control, passive adjustment lag after temperature is suddenly reduced is avoided, and the furnace temperature is kept stable during feeding.

The invention relates to a sintering furnace temperature prediction method and device based on an LSTM model and a medium. The method comprises the steps that sintering furnace multi-source sensor data are collected and preprocessed to generate time sequence data, binary features are generated through continuous stable temperature interval detection, and the binary features are fused with the time sequence data to form a feature set; the sensor combination is dynamically screened according to a real-time temperature threshold, and a feature set is converted into a sequence sample by adopting a sliding window. And carrying out standardization processing on the input features, and normalizing the target temperature value into a supervision label. And constructing an LSTM network model, and training by using the sequence sample and the supervision label. And finally, performing temperature prediction on real-time data through the trained model, and outputting a normalized prediction result. According to the method, a dynamic feature switching mechanism and an LSTM network are combined, high-precision real-time prediction and control of the sintering furnace temperature field are achieved through adaptive sensor optimization and time sequence feature fusion, and the temperature regulation response speed and prediction accuracy are effectively improved.

The invention relates to the technical field of industrial electric furnace control, and discloses a graphitization furnace power transmission power curve optimization method, which comprises the following steps: S1, data acquisition and preprocessing: acquiring historical operation data of a graphitization furnace, and carrying out cleaning and standardization processing on the data; the historical operation data comprises a power transmission power curve, in-furnace temperature and pressure data, material initial parameters and finished product quality indexes; s2, key factor analysis: determining key factors influencing the quality of a finished product from historical operation data through correlation analysis and principal component analysis methods; s3, constructing an optimization model; s4, performing model training and verification; s5, generating an optimization curve; and S6, applying and iterating. Compared with the prior art, the method has the advantages that the optimal transmission power curve is automatically generated through data driving and a machine learning model, so that the problems of unstable product quality, high energy consumption and process adjustment lagging caused by dependence on artificial experience are solved.

The invention relates to the technical field of impurity removal and purification, in particular to a high-temperature impurity removal and purification method for a graphite material. Obtaining the furnace temperature and the output power at each moment in each monitoring period in each high-temperature purification process and the temperature and the voltage of each induction coil at each moment; calculating a first evaluation value, a power fluctuation degree, a second evaluation value and an evaluation coefficient of each monitoring period; determining the bridging confidence coefficient of each monitoring period in each high-temperature purification process; and the bridging phenomenon occurring in each monitoring period in the high-temperature purification process is evaluated, and the bridging phenomenon existing in the high-temperature purification furnace is eliminated, so that the graphite material is subjected to high-temperature purification. According to the method, the bridging phenomenon in the high-temperature purification furnace can be accurately evaluated, and stable operation of a high-temperature purification process and high-quality purification of a graphite material are ensured.

The invention relates to the technical field of calcining furnace temperature control, in particular to an automatic temperature control method and system for a pot-type calcining furnace, and the method comprises the steps: obtaining the pressure data of each layer of flame path in the pot-type calcining furnace and the temperature data of each charging bucket in each layer of flame path in real time; dividing the whole data acquisition time into a plurality of time periods; acquiring a comprehensive influence coefficient of each layer of flame path in each time period according to the difference degree between the temperature data of different charging buckets in each layer of flame path in each time period and the correlation degree between the pressure data of each layer of flame path in each time period and the adjacent time period and the temperature deviation state; and according to the similarity degree between the temperature distribution of each charging bucket in each time period and the average level of the comprehensive influence coefficients of all layers of flame paths in each time period, obtaining a temperature anomaly factor of each time period, and further obtaining a PID parameter of the next time period. According to the method, the weight of the rise time in the fitness function is adaptively adjusted according to the temperature anomaly feature, and the accuracy of temperature control is improved.