59 results about "Process variable" patented technology

The invention relates to the field of metallurgical automation control, and discloses an intelligent metallurgical automation control method and system, and the method comprises the steps: collecting the real-time operation data of multiple working procedures of metallurgical production, carrying out the time alignment processing, and quantifying the correlation features between the working procedures, thereby obtaining an initial quantification feature set; dynamically simulating the feature set, acquiring intensity and direction parameters of process variable fluctuation conduction, and constructing a cross-process influence model; a global coordination instruction set is generated by combining downstream constraint feedback optimization distributed controller coordination parameters; updating a multi-process state vector, and if a local optimal risk exists, correcting a conduction path; and generating an optimal control scheme based on downstream real-time feedback iterative optimization, and distributing adjustment parameters to realize multi-process cooperative control. The process coupling relation can be accurately quantified, and the metallurgical production efficiency and the quality stability are improved.

The invention discloses a real-time quality monitoring method based on a production process parameter dynamic optimization model, relates to the technical field of intelligent quality monitoring, and solves the technical problems of empirical parameter adjustment and insufficient pertinence of exception handling. The method captures time sequence association of process parameters, process variables and quality indexes through a dynamic optimization model, quantifies uncertainty in combination with a probability density function, avoids limitation of a fixed threshold value, realizes early warning accuracy through deviation degree grading, reduces false alarm and missing alarm, screens core influence parameters based on model feature importance, and improves early warning accuracy. Quantitative adjustment suggestions are generated in combination with historical cases and inversion calculation, and empirical operation is replaced; multiple parameters are ranked and adjusted according to influence degrees, coupling interference is avoided, a single-point problem and a linkage problem are distinguished through parameter association chain analysis, a processing flow is formulated in a targeted mode, the single-point problem focuses on local repair and rapid recovery, the linkage problem focuses on cutting off a conduction chain and radically treating the source, and invalid intervention is reduced.

The invention discloses a gate valve opening degree control method and device, and relates to the technical field of industrial automation control, and the method comprises the following steps: obtaining valve structure data and actuator types, reading an initial value of a valve position sensor, building a valve position zero point, and loading an initial valve position-flow mapping model; an error is calculated according to the target process variable and the current measured value, and a valve position target value is generated through an outer ring controller; querying the valve position-flow mapping model according to the valve position target value, and performing deviation correction in combination with real-time flow measurement to obtain a corrected valve position target value; the friction state is predicted according to the valve position change rate and the actuator current signal, the friction compensation amount is generated, the corrected valve position target value is corrected, and a final valve position instruction is obtained; and the final valve position instruction is compared with the actual valve position, the driving amount is calculated through an inner ring controller, an actuator is driven to act, and the actual valve position is made to approach the final valve position instruction.

The invention provides an automatic temperature control method and system for an environment-friendly brick roasting kiln, and the method comprises the steps: constructing a thermotechnical process knowledge graph skeleton, associating a thermodynamic law with an actual process variable, dynamically correcting a causal structure through multi-batch production data, and forming a multi-version causal path set through combining anti-fact deduction, context causal branches and graph labels; based on a causal atlas and a reinforcement learning mechanism, an intelligent temperature control strategy network is designed, action-causal chain-effect collaborative interpretation is realized, control suggestions with physical feasibility and process context self-adaption are generated, and continuous optimization is performed through closed-loop feedback. And the dynamic collaborative optimization capability of temperature control intellectualization and product quality gain is enhanced.

The invention discloses an intelligent control method for equipment for chemical production, and relates to the technical field of intelligent control, and the method comprises the following steps: collecting process variable data, an automatic control instruction, an operator intervention behavior and equipment health state information; based on the collected process variable data, constructing a causal association relationship model between the operator intervention behavior and the equipment health state; identifying abnormal points by combining sliding time window prediction and a dynamic tolerance threshold value, and removing polluted data; constructing a multivariable causal diagram based on process variable deviation, and distinguishing control error paths caused by equipment degradation and intervention; deploying a multi-version control strategy model in the control system, and evaluating and screening an optimal strategy; an operator is guided to record the intervention purpose and mode on a human-computer interface; continuously evaluating the performance of the control strategy, and triggering a model retraining and replacing mechanism; the control logic is always close to the real state.

The application discloses a numerical calculation method and system for fuel blending turbulent combustion differential diffusion, and belongs to the field of turbulent combustion numerical simulation. The method comprises the following steps: a small flame table is established based on one-dimensional counterflow flame equation; through a mapping algorithm, thermodynamic state is parameterized as a function of mixing fraction and reaction process variable; a differential diffusion term of multi-fuel component is derived; control equations of a differential diffusion two-equation small flame model suitable for multi-fuel component under a large eddy simulation framework are established and solved; the model is discretized and coupled with a pressure-velocity field; in the process, simulation results of a small flame model without considering the differential diffusion effect are used as a comparison by the same method to highlight the influence of the differential diffusion effect. The application extends the differential diffusion two-equation model to multi-fuel blending conditions, takes a hydrogen-ammonia blended combustion two-fuel turbulent diffusion flame as an example, compares the mass fraction and temperature distribution of main components through a priori and posteriori method, and quantitatively analyzes the influence of the differential diffusion effect.

The application relates to the technical field of intelligent temperature control, and discloses a pressure roller heating temperature control method and system of a PE packaging line and a storage medium, the method comprising the following steps: taking process variables and environmental variables in high-dimensional time sequence data as nodes and taking dynamic causal relationships as edges to construct a dynamic causal structure diagram of the packaging line; taking the pressure roller temperature as a target node, screening a causal influence relationship to obtain a local network, tracing a key path to obtain a precursor variable; mapping a dynamic behavior mode of the precursor variable to generate a pressure roller temperature feedforward compensation strategy; analyzing the feedforward compensation strategy and a real-time state vector of the precursor variable to obtain a predictive compensation amount; based on the predictive compensation amount, a pressure roller temperature control instruction is cooperatively decided, and a comprehensive control instruction is generated; according to the execution effect of the comprehensive control instruction and topological consistency, the strategy applicability is judged, and a reconstruction trigger signal is obtained; and the application can improve the pressure roller temperature control operation efficiency of the PE packaging line.

An optimized simulation method for the internal high-pressure forming process of preformed aluminum alloy truck axle housing tubes includes the following steps: Step 1: Determine the basic simulation parameters and performance evaluation indicators, and clarify the initial dimensions, material properties, and key process variables of the tube blank; Step 2: Construct an ABAQUS simulation model and a sequential simulation process, establish a finite element model including geometry, material, mesh, and contact, and set up a multi-analysis step process that can sequentially simulate diameter reduction and internal high-pressure forming; Step 3: Perform bidirectional multi-slider diameter reduction simulation and parameter screening; Step 4: Perform preliminary preforming simulation and parameter optimization; Step 5: Perform a second internal high-pressure forming simulation, and based on the preliminary forming results, collaboratively optimize various parameters to obtain better second forming process parameters and final product parameters; Step 6: Compare and verify the simulation results and output the process parameter set. This invention reduces the number of experiments, saves investment, and produces qualified products more efficiently.

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 present application relates to the technical field of industrial process control, in particular to an intelligent control method for multi-process collaborative management of injection stretch blow molding machines, comprising: in the mold cooling main process for medical consumable production, when the monitored main process variable reaches the preset threshold value, based on the material property model associated with the rheological melt index, the operation instruction of the preparatory process executor is adaptively generated, and the preparatory process parallel to the main process is started; using the model prediction double-loop control loop, through real-time control of the inner loop, according to the dynamic prediction of the synchronization deviation of the two process procedures, the preparatory process executor is continuously adjusted to realize synchronization, and at the same time, through online learning of the outer loop, according to historical error feedback, the prediction model inside the loop is continuously self-corrected. Through the establishment of a model prediction double-loop self-correcting control loop, the present application realizes predictive collaborative control of multiple asynchronous executed process procedures.

A process system according to an embodiment may include a plurality of process equipments configured to process a wafer based on a plurality of process variables, a structural parameter estimation circuit configured to generate, based on a first plurality of actually measured correlated parameter values which are provided from the plurality of process equipments and corresponding to a first target structural parameter of the wafer, a first estimated target structural parameter value for the first target structural parameter, and a process equipment control device configured to control at least one process variable among the plurality of process variables based on the first estimated target structural parameter value.

A method for performing a process according to the invention, in particular by means of at least one robot (10), comprises the following steps: performing (S10) a run of the process with a process control, in particular a process control of the robot; detecting (S10) values of a first process variable (x) for the performance; and detecting (S10) an evaluation (E) of the performed process run; an evaluation learning step comprising the following multiple repetitions: changing (S20), by means of an optimizer (4), at least one parameter of the process control, in particular a regulator (31), to a changed process control based on values of a quality criterion of the performed process run; performing (S20) a run of the process with the changed process control; detecting (S20) values of the first process variable for the performance; and detecting an evaluation (E) of the performed process run; wherein a first quality factor model (51) of the process is machine learned based on the detected evaluations and values of the first process variable, the first quality factor model determining a quality factor (E’) of the process based on the first process variable (x); and an process control optimization step comprising the following multiple repetitions: changing (S40), by means of an optimizer, the process control to a changed process control based on values of a quality criterion of the performed process run; performing (S40) a run of the process with the changed process control; and detecting (S40) values of the first process variable for the performance, wherein values of the quality criterion for at least one performed process run with the changed process control are determined based on the quality factor (E’) determined by the machine learned first quality factor model from the detected values of the first process variable for the process run.

The invention discloses a temperature peak positioning method and system for a continuous reforming regenerator, and solves the problems of poor combustion temperature prediction precision and insufficient stability of the continuous reforming regenerator in the prior art, and the method comprises the steps: collecting process variables from a DCS system, building a three-dimensional model of the continuous reforming regenerator, and carrying out the prediction of the combustion temperature of the continuous reforming regenerator; obtaining temperature field and temperature peak position coordinates under each different working condition; establishing a mechanism model based on mass-energy balance and reaction kinetics in the regeneration process, adopting an XGBoost algorithm as data driving compensation, constructing a hybrid model of mechanism constraint and XGBoost data driving compensation, and performing training; and based on the trained hybrid model, batch processing reasoning is adopted, and current temperature peak coordinates and prediction credibility are output. A hybrid model with physical interpretability and high precision is constructed, prediction errors under extreme working conditions are reduced, and model stability is guaranteed.

The application discloses a strip steel hot rolling process multi-performance index abnormality monitoring method and system, and belongs to the technical field of industrial process control and fault diagnosis, and the method comprises the following steps: collecting process variable data and performance index data in a historical strip steel hot rolling process and performing standardization pretreatment; dividing the strip steel hot rolling process into multiple different process subblocks; for each process subblock, performing space-time feature extraction on the corresponding process variable data to obtain a space-time feature representation; constructing a local statistic quantity of each performance index; fusing the local statistic quantities to obtain a global statistic quantity and a global control limit; and based on this, realizing multi-performance index abnormality monitoring and alarm. The application effectively solves the problems that cross-process time lag dependence is difficult to capture, space-time feature extraction is not comprehensive, and single performance index monitoring leads to abnormality missed reports in the strip steel hot rolling process, significantly improves the fault detection rate and reduces the false alarm rate.

The invention discloses a numerical calculation method and system for turbulent combustion difference diffusion of mixed multiple fuels, and belongs to the field of turbulent combustion numerical simulation. Comprising the following steps: establishing a small flame table based on a one-dimensional hedging flame equation, parameterizing a thermodynamic state space into a function of a mixed fraction and a reaction process variable through a mapping algorithm, deducing a difference diffusion term of a multi-fuel component, and establishing and solving a difference diffusion double-equation small flame model control equation suitable for the multi-fuel component under a large vortex simulation framework; discretizing the processing model and coupling a pressure-velocity field; in the process, the same method is used for comparing a small flame model simulation result which does not consider the difference diffusion effect so as to highlight the influence of the difference diffusion effect. According to the method, a difference diffusion two-equation model is expanded to a multi-fuel mixing working condition, dual-fuel turbulence diffusion flame of hydrogen-ammonia mixing combustion is taken as an example, the mass fraction and temperature distribution of main components are compared through a prior method and a posterior method, and the influence of the difference diffusion effect is quantitatively analyzed.

The invention relates to an Installation assembly (1) for determining and / or monitoring a process variable, for example the temperature (T) of a medium (M), for installation at a container (10), with a failure detection unit (3). A welded metal seal (2) is provided at an upper end (32) of the failure detection unit (3) with a safety chamber (30).

This invention relates to the field of industrial control technology, and provides a multivariate intelligent control method and device for urban solid waste incineration processes. The method includes: acquiring the current state of urban solid waste incineration processes... m j Process variables at time and before m i Key process parameters at each moment; the preceding m j Process variables at time and before m i The key process parameters at a given time are input into a key process parameter prediction model to obtain the predicted values ​​of the key process parameters at the current time. The predicted values ​​of the key process parameters at the current time and the set values ​​of the key process parameters at the current time are input into an objective function to solve the objective function and obtain the change in the manipulated variable when the objective function is minimized. Based on the change in the manipulated variable, the manipulated variable is adjusted to control the key process parameters. This invention achieves stable, accurate, and rapid coordinated control of key process parameters such as furnace temperature and main steam flow.

The invention discloses an industrial process variable optimization control method and system, and relates to the technical field of industrial control, and the method comprises the steps: collecting multi-source industrial process data, carrying out the preprocessing of the multi-source industrial process data, obtaining the preprocessing data, building a causal graph based on the preprocessing data through employing a course learning enhanced NOTEARS causal learning algorithm, and generating a disturbance decoupler; constructing a decoupling dynamic prediction model based on a disturbance decoupler, and constructing an MPC objective function containing an information entropy item by using a prediction mean value and variance of the decoupling dynamic prediction model; and in each control period, the controllability under the current working condition is evaluated in real time, a control variable is reconstructed, and the reconstructed control variable is substituted into the MPC target function for solving. Through GMM clustering and course learning enhanced NOTEARS causal discovery, the causal reliability and anti-interference accuracy of industrial process variable optimization control are improved in combination with a disturbance decoupling technology based on pseudo-inverse.

This invention relates to a fault detection method and system for industrial equipment, belonging to the field of industrial process monitoring technology. The method includes: acquiring process variables and product-related quality indicators from historical data of industrial equipment during product production; calculating the mutual information between the process variables and quality indicators, and dividing the process variables into subspaces related to and unrelated to the quality indicators based on the mutual information; performing deep orthogonal decomposition modeling on the subspace related to the quality indicators to extract relevant calculation factors; performing deep residual modeling on the subspace unrelated to the quality indicators to extract irrelevant calculation factors; acquiring real-time industrial equipment data to be detected; calculating first, second, and third comprehensive monitoring indicators based on the real-time industrial equipment data and the relevant and irrelevant calculation factors; and combining the comprehensive monitoring indicators to determine whether the industrial equipment has a fault. This invention can effectively detect whether industrial equipment has quality-related faults.

This invention discloses a diesel yield prediction method and system based on a time-delay-aware Transformer model, belonging to the field of industrial big data processing technology. It determines the effective time-delay window for each dynamic process variable through time-delay analysis constrained by physical mechanisms. Then, it embeds context vectors into static process variables and encodes dynamic process variables to generate dynamic feature sequences. A dynamic modulation gate generated from the context vectors is used to adaptively weight and modulate the dynamic feature sequences. Simultaneously, a global causal mask matrix is ​​constructed based on the effective time-delay windows of each variable and embedded with Transformer attention calculations, forcing the current query to access only historical moments within the effective time-delay window. The output is a temporal implicit representation conforming to the physical time-delay causality law, thereby predicting diesel yield. This method embeds the process mechanism knowledge of the refining process into a structured prior form within a deep learning architecture, achieving high-precision, high-robustness, and high-interpretability online rolling prediction of diesel yield.

The invention discloses an automatic feature selection ore grinding overflow granularity soft measurement method based on a time-delay causal model, and belongs to the technical field of automatic measurement of an ore grinding system. The method comprises the following steps: collecting ore grinding process data and forming a historical sample data set; using a method based on a time-delay causal model to find an optimal delay time point influencing the overflow granularity for each process variable, and selecting features having a strong time-delay causal relationship with the ore grinding overflow granularity to form a causal feature set; and by taking the causal feature set as an input variable and the ore grinding overflow granularity as an output, constructing and training a CH-SBiLSTM soft measurement model, and selecting a model with the lowest mini-batch RMSE as a final model. According to the method, deep fusion of feature selection and model training is realized, dynamic characteristics of the industrial process can be accurately captured, resistance to abnormal values of different degrees is achieved, the accuracy, robustness and practicability of ore grinding overflow particle size soft measurement are remarkably improved, and certain data support is provided for subsequent optimization of the ore grinding control process.

Methods, systems, and devices for event-based control in a process plant include storing in a field device a default update period for a process variable, a deadband for the process variable, and a setpoint tolerance for the process variable. The method also includes receiving, from a controller implementing a control strategy or from a second field device that receives the setpoint from the controller, a setpoint corresponding to the process variable. The method includes periodically determining a current value of the process variable, and transmitting to the controller the value of the process variable if any one of the following conditions is met: an amount of time elapsed since a most recent transmitted value was transmitted exceeds the default update period value; a difference between the current value and a most recent value exceeds the deadband value; a difference between the current value and the setpoint exceeds the setpoint tolerance.

The invention relates to a device (100) for determining and / or monitoring at least one process variable, comprising a housing region which is not in contact with a medium and has an electrically insulating shaped part (5). According to the invention, the shaped part (5) has an elongated, in particular at least partially cylindrical guide (52) in which the tubular element (4) containing the sensor element connecting lines (2a, 2b, 2c) is arranged, in particular pressed therein.

The invention relates to a system for modelling the dependency of process output variables on process variables of a process in an automation system, comprising: a server platform (SP) with a computer unit (CE) and a control unit (SE); first measuring devices (M1) for detecting measured values of the process variables (W1); second measuring devices (M2) for detecting measured values of the process output variables (W2); a closed communication network (KN) for connecting the measuring devices (M1, M2) and server platform (SP); wherein the first measuring devices (M1) detect measured values of the process variables (W1) and transmit them as input values (I) to the server platform (SP) by means of the closed communication network (KN); wherein the second measuring devices (M2) detect measured values of the process output variables (W2) and transmit them as output values (O) to the server platform (SP) by means of the closed communication network (KN); wherein the server platform (SP) stores transmitted input values (I) and output values (O) at least partially as time series (ZR); wherein the server platform (SP) trains an algorithm (ML) with stored time series (ZR) by means of computer unit (CE); wherein the server platform (SP) calculates values (WO) for the process output variables by means of the trained algorithm (ML) on the basis of the transmitted input values (I).

The embodiment of the application discloses a kind of soft measurement method and device based on supervised multiple latent variable structure, which method comprises: the process data obtained in the process of thermal power generation production scheduling is input into the quality extraction model established in advance;Extract the quality related information of fuel by the quality extraction model;The quality related information includes process variable and quality variable;Wherein, the quality extraction model is a mathematical prediction model of supervised multiple latent variable structure, and the multiple latent variable includes: dynamic latent variable and static latent variable, the dynamic latent variable is used to extract the cross-correlation of the process variable and the quality variable, and the static latent variable is used to preserve the cross-correlation of the process variable and the quality variable.By the embodiment scheme, the real-time measurement of quality variable is realized.

The invention provides a fault-tolerant control method for a crude oil distillation process with knowledge migration and a robust safety boundary, which comprises the following steps of: firstly, introducing a knowledge migration mechanism, and accelerating rapid and stable updating of parameters of a disturbance compensation observer by using a steady-state observer weight obtained based on historical data training as priori knowledge; therefore, the deviation of the prediction model is timely corrected. Meanwhile, a robust safety boundary is dynamically constructed according to a real-time estimation error of the observer and is used for performing dynamic safety bundle on key process variables, so that a safety optimal control instruction is solved and obtained under the constraint of the dynamic safety boundary. The method can effectively guarantee safe and stable operation of the crude oil distillation process under fault working conditions such as model mismatch.

The present disclosure relates to a method of training a machine learning agent for controlling an industrial process in an industrial plant. The method comprises, to the agent, inputting simulated values of process variables, from a simulation of the industrial process using a model of the industrial process, and example values of disturbance variables. An adjustment is inputted to the simulation, whereby the simulated PV values depend on said adjustment. The agent, in response to the simulated and example values, outputs values of manipulated variables. The MV values are used in the simulation, the simulation updating the simulated PV values. A cost of the simulated industrial process is estimated when using the MV values. As a function of the estimated cost, a reward is fed to the agent.

The invention relates to the technical field of industrial supervision control and data acquisition systems, and particularly discloses an esterification reaction multi-section temperature control process supervision control and data acquisition system. The system comprises a technological procedure analysis module, a multi-mode actuator driving module, a global data synchronous acquisition module, a process state depth supervision module and a self-adaptive strategy arbitration module. By integrating multi-model adaptive control, deep process supervision and dynamic decision feedback, accurate and smooth control and self-optimization of the multi-section temperature control process of the esterification reaction are realized, and the consistency of product quality and the robustness of the production process are improved. According to the system, a global data synchronous acquisition module and a process state depth supervision module are constructed, so that high-frequency synchronous acquisition and depth correlation analysis of process variables and quality parameters are realized.