37 results about "Process industry" patented technology

The invention discloses a multi-model fusion process quality prediction method based on ensemble learning, and relates to the technical field of process industry quality prediction. The method comprises three stages of data preprocessing, model construction and model fusion. In the data preprocessing stage, cleaning, normalization and feature screening are performed on multi-process production data; in the model construction stage, an overall prediction model and a segmented prediction model are constructed respectively, the overall prediction model adopts RF, LightGBM and KNN algorithms, and the segmented prediction model adopts an LSTM-KAN combined neural network; in the model fusion stage, prediction results of the two models are fused through an XGBoost ensemble learning algorithm, and advantage complementation is achieved. According to the method, the quality prediction problem caused by high process coupling degree and remarkable nonlinear relation in complex process manufacturing is effectively solved, the prediction precision and generalization performance are improved, and the method is suitable for a multi-process complex production scene.

The invention relates to the technical field of intelligent fault prediction, and provides a multi-dimensional time sequence anomaly detection method for the process industry, which aims at historical and real-time multi-source data of the process industry, performs adaptive feature extraction to obtain better features through a feature selection module fusing mRMR and a self-attention mechanism, and improves the detection accuracy. Multivariable time series data double-fusion prediction calculation is completed through a sequence prediction model, the incidence relation of data at different time points and the incidence relation of different features at the same time are mined, and after cross entropy loss and optimizer training optimization are carried out, abnormal state judgment is finally achieved in combination with a dynamic threshold value. The invention further discloses a system used for the method, the method and the system can mine mechanism information implied by equipment from historical multi-source data in the process industry, time and features are subjected to relevance fusion respectively, and therefore the method and the system can be more suitable for the unsteady state and strong coupling conditions of the process industry; and particularly, a remarkable effect is achieved in a slag grinding system.

The invention relates to the technical field of process industry process modeling and data-driven prediction, and discloses a process industry key index prediction method and device based on time-delay distribution learning. The method and the device are used for solving the problems of unreasonable historical information alignment and insufficient key index prediction precision and stability caused by difficulty in measurement of time-delay distribution attributes in process industry multivariable data. According to the method, a current window, a candidate lag historical window and a future target window are constructed by adopting a sliding window, time-lag weight distribution is learned on a candidate lag axis based on a state gating cross attention mechanism, time-lag distribution learning and a future window prediction task are jointly optimized through a time-lag modeling auto-encoder, and finally, time-lag prediction is performed on the candidate lag historical window and the future target window. And outputting a key index prediction value under the guidance of time-delay distribution. The method is suitable for online prediction of key indexes such as quality, yield and energy consumption of the process industry, and can be used for process monitoring and optimization control.

A method and system for attack and defense based on dynamic simulation in chemical engineering includes: constructing an adversarial training environment for chemical equipment and processes using dynamic simulation units; setting attack and defense training parameters through a task configuration unit and generating corresponding fault interference test cases from a fault interference mode library; the attacker selecting a fault interference test case, adjusting the fault interference parameters using an attack strategy, and injecting it into the adversarial training environment; the defender receiving alarm information from the fault interference test cases and performing handling operations on them; and an evaluation unit performing real-time evaluation and display. This invention, through the deep integration of dynamic simulation technology and attack and defense mechanisms, upgrades chemical skills training from static procedure drills to dynamic engineering practice, providing core technical support for the cultivation of highly skilled personnel in the process industry, and has significant economic and social value.

The present application belongs to the technical field of liquid overpressure energy recycling in process industry, and discloses a self-driven double-rotating disc type energy recycling device, which comprises a first cylinder and a second cylinder which are symmetrical and connected in the same structure, and the internal space of the first cylinder and the second cylinder is provided with a special-shaped shaft, a stator, a first rotating disc, a second rotating disc, a first sleeve and a second sleeve which are coaxially arranged; the first rotating disc and the first sleeve are located at one end of the stator, and the second rotating disc and the second sleeve are located at the other end of the stator; the first sleeve and the second sleeve are fixedly connected with the stator through bolts, and the first rotating disc and the second rotating disc are coaxially and staggeredly connected at both ends of the stator through the special-shaped shaft. Through the technical measures of circumferential inflow impact driving double-rotating disc coaxial rotation movement and pressure exchange of high and low pressure fluids in the stator channel, the present application realizes independent operation of rotary switching and pressure exchange, better dynamic balance performance and other effects, which is beneficial to improve the operation stability and efficiency of the device, and is easy to realize engineering amplification of the treatment capacity.

The invention relates to a working condition automatic clustering method, system and device for process industry time series data and a medium, and the method comprises the steps: carrying out the sliding window segmentation of preprocessed multivariable time series data, and obtaining subsequence data; based on behavior pattern information of the sub-sequence data, calling a corresponding multi-dimensional feature extraction strategy to perform feature extraction and fusion, and generating a global feature vector; performing clustering analysis on the global feature vector set on the basis of an optimal clustering number obtained by analyzing, evaluating and optimizing the contour coefficient and the in-cluster consistency to obtain a cluster label of data working condition classification; and mapping the cluster labels back to a time axis of the multivariable time series data, and carrying out visualization processing of typical working condition pattern recognition on the obtained working condition label sequence data to obtain an analysis result representing the multi-working condition characteristics of the flow industrial process. The method can effectively capture time sequence dynamic characteristics, has good anti-noise capability and high calculation efficiency, and is highly suitable for operation state identification and analysis in the process industry.

This invention relates to the fields of industrial control and intelligent manufacturing, and particularly to an adaptive method, system, device, and medium for control parameters in variable-distribution process industries. The method includes: extracting features from process variable data, combining their distribution offset with control performance indicators for collaborative judgment, and outputting a trigger signal; upon receiving the trigger signal or system startup, parsing historical operating data to construct a behavioral proxy model and a historical benchmark distribution, and determining initial parameters by optimizing the consistency measure between the predicted behavioral distribution under the parameters to be optimized and the historical benchmark distribution; shaping the process variable data and inputting it into a strategy model to perform component extraction and inference to adjust the control parameters and obtain the parameters to be activated; evaluating the parameters to be activated, and if they exceed limits, reverting the parameters to the initial parameters, and adjusting the strategy model based on system response data. This invention improves the performance stability and adaptability of process industry control systems under complex and variable operating conditions.

The application discloses a kind of process industry production control method and system based on waste heat recovery optimization, it is related to industrial production control field, the method comprises: according to the thermodynamic measurement data of each process stream in production system, under the minimum heat transfer temperature difference constraint, constructs multiple temperature intervals;According to the thermodynamic measurement data of each process stream, cascade calculation is carried out to multiple temperature intervals, to determine the upper limit of available waste heat;With the upper limit of available waste heat as the energy constraint of production system, with the maximum output flow of primary steam, secondary steam and heat medium water of production system as target, successively constructs and solves the optimization model of the output flow of primary steam, secondary steam and heat medium water, obtains the optimized output flow of primary steam, secondary steam and heat medium water, to control production system, to realize waste heat recovery of production system.By implementing the present application, the calculation efficiency of solving waste heat recovery and reuse in the production system can be improved.

The application discloses a multi-agent manufacturing process optimization method based on a multi-objective particle swarm algorithm, and comprises the following steps: for a manufacturing process of a process industry, a manufacturing process optimization model based on multi-agents is constructed, including a two-layer structure, wherein the upper layer structure is a total control agent and a pool agent used for storing algorithms and data information, and the lower layer structure is a raw material agent, a device agent, a management agent and a waste agent; each intelligent agent can interact with other agents based on a communication module of the intelligent agent; the agent is used as a particle in the multi-objective particle swarm algorithm, the agent population evolution ability is given, a corresponding data model is established for the multi-agent-based manufacturing process optimization model, the multi-objective particle swarm algorithm is used for solving, and an effective solution set is obtained. The cost after the optimization is lower than the actual cost, so the non-inferior solution set after the optimization can be used as a reference in actual production, so that the process operation manufacturing is improved in low consumption, high yield, high quality and cost reduction.

The application relates to the technical field of operation and maintenance management, and discloses an operation and maintenance management method and system for process industrial equipment, the method comprising the following steps: coupling multi-modal features of time sequence operation parameters and field state information of the process industrial equipment to obtain a fusion feature set of the process industrial equipment; quantifying fluctuation trends of the fusion feature set to obtain a degradation feature index of the process industrial equipment; performing evolution evaluation on the degradation feature index to obtain a health quantitative value of the process industrial equipment; constructing operation and maintenance decision boundary conditions of the process industrial equipment; performing collaborative mapping on the fusion feature set and the health quantitative value to obtain an optimized operation and maintenance strategy of the process industrial equipment; and associating and integrating the operation and maintenance decision boundary conditions and the optimized operation and maintenance strategy to obtain an operation and maintenance management report of the process industrial equipment; and the application can improve the operation and maintenance management efficiency of the process industrial equipment.

The present application relates to the field of chemical process simulation, and particularly relates to a simultaneous equation simulation block solving method for process industry. The method comprises the following steps: extracting equations in an activated state and variables in an unfixed state from a chemical process model to form an original equation and variable set to be solved; converting the equation and variable set into a graph data structure reflecting system dependency according to a preset block strategy; using a strongly connected component identification algorithm to traverse the graph data structure to obtain a strongly connected component set, each strongly connected component in the strongly connected component set corresponding to a sub-equation block in the process equation; determining a solving order of the sub-equation blocks according to the dependency of the sub-equation blocks; solving each sub-equation block according to the solving order of the sub-equation blocks; and collecting the solving results of each sub-equation block to obtain a global solution of the complete chemical process model.

The application provides a prediction method for irregular time series data in a process industry, and belongs to the technical field of industrial time series data prediction, and comprises the following steps: data acquisition and preprocessing; constructing a graph representation of irregular time series data; constructing and training a space-time relationship graph convolutional neural network model; online prediction, and practical application, the space-time relationship graph convolutional neural network model comprises: a full connection graph conversion module, a space-time relationship graph convolutional layer and a hierarchical sandwich structure, the application constructs a full connection graph representation without pre-alignment for irregularly sampled multivariate time series data in a process industry environment, and high-precision prediction of an asynchronous and complex space-time dependence structure is realized based on a space-time relationship graph convolutional module, and the hierarchical sandwich strategy further guarantees the calculation efficiency and model performance on large-scale graph data, and is suitable for real-time prediction and fault early warning and other requirements of an industrial site containing a large number of asynchronous sensor measuring points.

A system and method of supply chain planning of process industry production include a processor and memory and are configured to model a supply chain planning problem for two or more products of a process industry, wherein a coproduct is produced for at least one of the products, group the two or more products into groups, receive a weight and a yield for each raw material that produces each of the products in at least one of the groups, cluster each of the raw materials using weight-yield clustering, generate BOM grouping, and assign one BOM grouping to each of the raw materials of a single cluster.

The present invention relates to a system (100) for dry sorption. The system comprises a gas inlet (130) through which exhaust gas from processing industry is flowing into the system (100), a velocity increasing device which is arranged downstream of the gas inlet (130), and a reaction chamber (140) is arranged downstream of the velocity increasing device. The exhaust gas is brought into contact with the sorbent from a sorbent distributor (150) in the reaction chamber (140), wherein the velocity increasing device is a booster (110) and comprises a plurality of resistances to the flow of gas for creation of a turbulent flow of exhaust gas at the outlet of the booster for enhanced sorption. Further, the present invention0relates to a method for cleaning exhaust gas from processing industry utilizing the system (100) for dry sorption.

The application discloses a disturbance detection method and device based on trend extraction, equipment and a control system, and belongs to the field of process industry control. After obtaining time series data, a segmented polynomial fitting method is used to extract the trend of the time series data to obtain a target segmentation result. Then, a trend index and a normalized mean absolute error of the time series data are calculated based on the target segmentation result. Finally, when the trend index is greater than or equal to a first threshold value and the normalized mean absolute error is less than a second threshold value, it is determined that a disturbance exists. When the trend index is greater than or equal to the first threshold value and the normalized mean absolute error is less than the second threshold value, it indicates that the time series data has a significant trend, that is, a certain disturbance exists. With the double criteria of the trend index and the normalized error, the small disturbance hidden in the trend can be effectively identified, the noise resistance is high, and the accuracy of disturbance detection is greatly improved.

The application discloses a kind of process industry fault intelligent diagnosis methods, it is related to process industry technical field, solve the problem that alarm information is not accurate in prior art, and cannot early on the working condition is pre-judged, the method includes: critical heat flux density data is input to thermocouple temperature variation model, determine data set;Data set is normalized;According to normalized data set, calculate the set of femto feature vectors, and the set of femto feature vectors is input to the SVM model trained, output the position relationship of femto feature vector and two-dimensional hyperplane, determine femto data point and non-femto data point;Whether femto data point is false femto data point is judged, if it is false femto data point, then femto data point is placed as non-femto data point;According to femto data point, determine critical working condition point, and output critical working condition point, realize that system is slightly changed, flexibly and conveniently judge critical information, it is easy to operate, reduce human resource cost.

The invention discloses an anti-condensation real-time intelligent regulation and control system and method in a spectrum monitoring process, and relates to the technical field of process industrial process analysis, the system comprises a sample detection module, a temperature control dehumidification module, a high-pressure gas source module, a multi-channel purging module and a PLC control module, and the PLC control module is in signal connection with the other four modules. The sample detection module detects a to-be-detected sample in the flow cell by using an incident spectral signal in a preset wavelength range, and feeds back a transmitted spectral signal; the temperature control dehumidification module maintains the temperature and humidity of the detection area in a preset range; the high-pressure air source module generates dry compressed air with different flow rates and flow speeds, and the multi-channel purging module purges light spots, prone to dew formation, on the two sides of the flow cell. According to the system, through cooperation of multiple modules, the condensation risk can be recognized in real time and precisely regulated, condensation of the outer wall of the flow cell is effectively restrained, optical signal distortion is avoided, the accuracy and stability of a spectrum monitoring result are improved, and the requirements for real-time performance and reliability of online detection in the process industry are met.

The invention discloses a method and system for converting a heat exchange network flow chart into a multi-stage superstructure, and belongs to the technical field of petrochemical engineering in the process industry. According to the method, the initial cold material flow level and the initial hot material flow level of each heat exchanger are determined, so that the process of converting the heat exchange network flow chart into the multi-level superstructure is strictly based on the actual process logistics logic; then, the heat exchange network flow chart is divided into an area above a pinch point and an area below the pinch point according to the pinch point which is a key boundary in thermodynamics, and then the initial cold material flow level and the initial hot material flow level of the heat exchanger in each area are updated; a cold material flow final stage and a hot material flow final stage which are used for solving the multi-stage superstructure can be accurately and effectively constructed; by calculating the superstructure parameters and the superstructure total series, it is ensured that the generated multi-stage superstructure has the minimum series, redundant variables and constraints of the multi-stage superstructure are reduced to the maximum extent, and the conversion efficiency and the solving efficiency of the multi-stage superstructure are improved.

The invention relates to soft measurement modeling in the process industry, and discloses a data filling end-to-end soft measurement modeling method fusing an attention mechanism, which comprises the following steps of: firstly, constructing complex encoder layer-by-layer learning for better capturing dynamic nature in process data; specifically, an attention mechanism is embedded to capture global dependence on long-distance time and feature dimensions, data mapping vectors are expressed, and a long short-term memory network LSTM focuses on short-distance time dependence on the expression to enhance local information expression; the vector representation is efficiently restored to an original feature space through linear decoding; secondly, the soft measurement module can be flexibly replaced according to specific requirements, and finally, in order to make a filling result more accord with downstream soft measurement modeling requirements and keep filling and prediction target consistency, missing filling and soft measurement are directly integrated into an end-to-end framework for joint optimization, and the performance of the model in the industrial process is jointly improved.

The invention relates to the technical field of industrial automatic polishing and environment-friendly dust removal, in particular to a polishing and dust removal control system for machining an industrial fan heater. The system comprises a digital twin modeling module, a feedforward prediction control module, a dynamic air curtain compensation module and a self-adaptive iteration module. The system generates a flow field risk distribution diagram based on workpiece three-dimensional model simulation, and identifies an easy-to-leak area; the method is characterized in that according to risk distribution and real-time motion data, before a polishing power head reaches a discontinuous structure, a vector air knife is driven in advance to construct a virtual air wall; meanwhile, dynamic feedback adjustment is carried out by combining real-time negative pressure and concentration data; according to the invention, the conversion from passive lagging plugging to active advanced flow field intervention is realized, and the problem of dust escape in the processing of complex structures such as heat dissipation holes is effectively solved.

The invention relates to the technical field of operation and maintenance management, and discloses an operation and maintenance management method and system for process industrial equipment, and the method comprises the steps: carrying out the multi-modal feature coupling of the time sequence operation parameters and field state information of the process industrial equipment, and obtaining a fusion feature set of the process industrial equipment; performing fluctuation trend quantification on the fusion feature set to obtain a degradation feature index of the process industrial equipment; performing evolution evaluation on the degradation characteristic indexes to obtain a health quantized value of the process industrial equipment; constructing operation and maintenance decision boundary conditions of the process industrial equipment; performing collaborative mapping on the fused feature set and the health quantized value to obtain an optimized operation and maintenance strategy of the process industrial equipment; performing association integration on the operation and maintenance decision boundary conditions and the optimized operation and maintenance strategy to obtain an operation and maintenance management report of the process industrial equipment; according to the invention, the operation and maintenance management efficiency of the process industrial equipment can be improved.

The invention relates to a process industrial equipment maintenance intelligent decision-making method based on deep learning, and the method comprises the following steps: 1), building an equipment operation and maintenance database which is used for collecting operation fault data of an industrial equipment end and operation data of a whole life cycle from normal operation to failure, carrying out the preprocessing, and carrying out the data annotation processing through combining with expert experience, generating and storing mapping relations between the operation data and the fault, the health state, the operation trend and the residual life; 2) constructing an equipment fault diagnosis model, an equipment operation state evaluation model, an equipment operation trend analysis model and an equipment remaining service life prediction model based on a deep learning algorithm, and training the models based on equipment operation and maintenance data; 3) dividing the difficulty degree of the equipment fault based on expert experience, and dividing the importance degree of the equipment in combination with the comprehensive influence of economy, society, environment and the like caused by the equipment failure; and 4) formulating equipment operation and maintenance decision logic, and screening and formulating an equipment maintenance sequence. The method can be used for formulating a predictive maintenance strategy of the process industrial equipment, provides guarantee for timely repair of equipment faults and continuous and reliable production, and can be widely applied to the technical field of process industrial equipment management.

The application discloses a kind of key factor identification method and its computer equipment influencing process industry load participation demand response, it is related to electric power system dispatching and control field, first, the coupling relationship of production link is described using state task network, to maximize the total system profit as optimization goal, build general industrial process-oriented energy optimization scheduling model.Second, facing the interaction scene of power grid and process industry production, the common influencing factors faced in production process are extracted.Based on Morris screening method, the above-mentioned large number of influencing factors are preliminarily and quickly screened, and finally, based on Sobol' global sensitivity analysis method, the global sensitivity coefficient of the influencing factors screened to the three core indexes of economy, productivity and energy efficiency is quantified, so as to identify the key factors influencing the process industry load participation demand response.

The present application relates to the technical field of industrial automation polishing and environmental protection dust removal, in particular to a polishing and dust removal control system for processing industrial warm air machines; comprising digital twin modeling, feedforward predictive control, dynamic air curtain compensation and adaptive iteration module; the system generates a flow field risk distribution map based on the simulation of the three-dimensional model of the workpiece, and identifies the easy leakage area; the core is to drive the vector air knife to build a virtual air wall in advance before the polishing power head reaches the discontinuous structure according to the risk distribution and real-time motion data; at the same time, real-time negative pressure and concentration data are combined for dynamic feedback adjustment; the present application realizes the transformation from passive lag plugging to active early flow field intervention, effectively solving the dust escape problem in the processing of complex structures such as heat dissipation holes.

This invention belongs to the field of liquid residual pressure energy recovery and utilization technology in process industries. It discloses a novel fluid switcher and, based on this, develops a novel seawater desalination energy recovery tower and a typical parallel array configuration, aiming to solve the problems of complex structure and low reliability of the original valve-controlled device. This invention vertically arranges the pressure exchange cylinder, significantly reducing the footprint and making capacity expansion more flexible and convenient. The internal cavity design of the fluid switcher at the brine end and the check valve assembly at the seawater end is more compact and smooth, and the design of the inlet and outlet pipe interfaces for each fluid stream is more rational. The flow pattern and uniformity of the fluid flowing directly into or out of the main pipe are better, resulting in less flow resistance loss and further improving the energy recovery efficiency and reliability of the device.

The invention discloses a water pressure type vapor-liquid balance adjusting device and method, and belongs to the technical field of process industrial pressure control. Comprising a high-pressure-resistant outer cylinder, the high-pressure-resistant outer cylinder is of a vertical open cylindrical structure, a water inlet is formed in the top of the high-pressure-resistant outer cylinder, a water outlet is formed in the bottom of the high-pressure-resistant outer cylinder, a pressure sensor is installed on the side wall of the high-pressure-resistant outer cylinder, and a flexible container is arranged in the high-pressure-resistant outer cylinder; a mechanical compressor is abandoned, the system energy consumption can be reduced by 30%-40%, meanwhile, noise and vibration pollution caused by the compressor are eliminated, the system structure is simple, the number of moving parts is small, the fault rate and the maintenance cost can be reduced, the maintenance cost is reduced, and the service life of the system is prolonged. The pressure can be intuitively and continuously adjusted by controlling the water level, the response speed is high, and the pressure change requirements of different process stages are met.

The invention relates to the technical field of industrial process quality control, in particular to a process industry oriented quality prediction method with time-varying random fluctuation dynamics. A traditional quality prediction method has the following technical bottlenecks: a deterministic model or a static fluctuation rate hypothesis is difficult to accurately describe time-varying random dynamics caused by raw material characteristic fluctuation and frequent production load adjustment; meanwhile, a traditional data processing method based on interpolation or deletion easily introduces estimation deviation or causes information loss, and the random missing phenomenon in process data cannot be effectively handled. The invention provides a novel quality prediction method aiming at the characteristics of frequent load change, multi-working-condition operation and the like in the process industry and the problems of multi-scale data and high mixing performance. Firstly, a probabilistic fluctuation state modeling method is put forward, a state equation of a PCIR model is constructed, time-varying mean regression characteristics of a hidden fluctuation state are described through a special probability structure, and an interpretable modeling framework is provided for system non-stationarity. Secondly, for the problem of data missing, a missing interval dynamic weight mechanism is designed, a weight vector is fused into an observation equation of a state space model, adaptive processing of random missing is achieved by dynamically adjusting the contribution degree of data of different missing durations to the model, and the limitation of a traditional method is overcome. Finally, according to the multi-working-condition operation characteristics of the process industry, a quality prediction framework suitable for the process is provided, and automatic screening of key state characteristics can be achieved.

The application provides a kind of adaptive deep learning predictive control method and device suitable for process industry.It includes: according to the historical control information of process industry, based on deep learning technology, the full-condition dynamic model of target control variable is constructed, based on the adaptive segmented algorithm of threshold, the change gain curve of full-condition dynamic model is segmented, and the segmented linear gain model is obtained;Determine the segmented linear variable constraint of segmented linear gain model, and determine the optimization goal of target control variable based on segmented linear variable constraint;Based on segmented linear gain model, the step response sequence of target control variable at the current working point is obtained, and the future control execution sequence of target control variable is predicted according to step response sequence and optimization goal.Solve the problem that the mapping relationship between control variable and controlled variable is usually constructed based on linear model in existing predictive control method, which cannot accurately reflect the nonlinear characteristics of control variable, and further leads to the problem of low accuracy of predictive control.

The invention relates to a process industry key quality variable prediction method based on causal space-time diagram convolution. The method comprises the following steps: constructing a first causal relationship between process variables in an industrial production process of a target process; based on the causal time convolution and the attention mechanism, obtaining a second causal relationship between the process variables; then constructing a space-time diagram convolution model, and training the space-time diagram convolution model by using the training set and the test set based on a second causal relationship between process variables to obtain a key quality variable prediction model of the target process industry; and finally, inputting real-time data of the target process industrial production process into the prediction model, and outputting a prediction result of the target process industrial key quality variables. Compared with the prior art, the method has the advantages of improving the accuracy, robustness, interpretability and the like of overall prediction.

This invention discloses a data-driven model predictive control method for superheated steam temperature based on knowledge distillation. The method first constructs a superheated steam temperature prediction model as the teacher model using a Physical Information Guided Neural Network (PINN). The model input includes control and disturbance variables, and it integrates the system's physical mechanisms and data characteristics. Subsequently, a multilayer perceptron (MLP) network is trained using knowledge distillation to construct a student model, which serves as the final predictive model for control. This student model takes the main control variables and historical outputs as inputs, learns the physical laws and disturbance response characteristics inherent in the teacher model, and achieves efficient fitting of the system behavior. Based on this, a model predictive controller (MPC) for superheated steam temperature regulation is designed using the student model, and the effectiveness and engineering feasibility of the proposed method are verified using actual operating data. Compared with traditional methods, this invention has advantages such as lightweight structure, high prediction accuracy, and strong physical consistency, and is suitable for modeling and intelligent control tasks of superheated steam temperature systems in thermal power units and other complex equipment in process industries.