19 results about "Multivariable model" patented technology

The invention belongs to the technical field of temperature control, and discloses a temperature control method and device of a hot runner system for an inverted mold. The method comprises the following steps: defining a critical melt area in a physical sprue which directly influences the product quality in a sprue of a hot runner system as a virtual sprue; dynamically generating a melt viscosity target track of the virtual gate region according to the type of an injection molding material and an injection molding process stage; based on measurable injection molding process parameters and sprue area temperature, estimating a current melt viscosity value of a virtual sprue area in real time through a state observer; calculating to obtain an optimal power control sequence by adopting a multivariable model prediction controller; and driving a sprue area heater according to the optimal power control sequence, so that the actual change of the melt viscosity of the virtual sprue area tracks a melt viscosity target track. According to the invention, the defects such as salivation and wire drawing can be effectively eliminated, so that the sprue quality stability and the production efficiency are improved.

The invention discloses a membrane filtration sewage treatment system based on fine control, relates to the technical field of sewage treatment, and solves the technical problems of delayed response to water quality and water quantity fluctuation and extensive biochemical reaction control caused by water inlet load impact in an existing sewage treatment system. The system comprises an inflow water pretreatment module used for predicting inflow water quality in real time by using a water quality prediction model based on a neural network, and carrying out water quantity balance and water quality adjustment on inflow water according to a prediction result to obtain a first water body; the biochemical reaction module is used for cooperatively controlling external carbon source addition, nitrification liquid backflow and aeration intensity in the biological reaction process of the first water body by utilizing a multivariable model predictive control method to obtain a second water body after biochemical reaction; and the deep purification module is used for carrying out solid-liquid separation and deep purification on the second water body to obtain an effluent water body, and triggering cleaning and maintenance operation of the membrane based on a prediction result of the membrane pollution characteristics.

The invention discloses an ammonia spraying real-time feedback regulation method based on an SCR (Selective Catalytic Reduction) denitration system, which comprises the following steps: dividing a cross section partition on an outlet flue of the SCR denitration system, and synchronously collecting a flue gas sample on the cross section partition; dynamically adjusting the flow of a cooling medium of a gas cold air coil pipe through a self-adaptive fuzzy PID control algorithm, and cooling and dehumidifying each path of collected flue gas sample to obtain a dry flue gas sample; the concentration of each path of dry flue gas sample is analyzed in real time, and the optimal ammonia spraying valve opening strategy of each cross section partition is predicted by combining the ammonia spraying amount of each path, the flue gas flow of the SCR denitration system, the unit load and the ammonia escape concentration and adopting a multivariable model prediction control algorithm; and according to the predicted optimal ammonia injection valve opening strategy of each cross section partition, the ammonia injection valves of the corresponding cross section partitions are adjusted, and real-time feedback adjustment of ammonia injection is achieved.

The invention discloses a graded blasting construction method for a shallow-buried soft rock tunnel sensitive section. The method comprises the steps that measuring points are arranged in a tunnel chamber, a ground surface non-cavity section and a cavity section, and blasting vibration data are obtained; peak vibration velocity prediction models are established in a partitioned mode, an attenuation model is adopted in a chamber, a correction term is introduced into a non-cavity section of the earth surface to correct the attenuation model, and a multivariable model is established for the cavity section through dimensional analysis by integrating the horizontal distance, the elevation difference, the explosive quantity, the rock mass density and the tensile strength; determining a ground surface vibration velocity safety threshold, and quantifying a cavity vibration velocity amplification coefficient and an influence range thereof; based on the peak vibration velocity probability distribution and a preset safety level, establishing a safe dose prediction model and a safe range model; and dividing control areas according to the model, and implementing graded construction. According to the method, the vibration propagation law can be accurately described, the cavity amplification effect can be quantified, dynamic prediction of the blasting safety explosive quantity and range is achieved, and disturbance of blasting vibration to surrounding rock is effectively controlled through hierarchical management and control.

The invention provides a post-evaluation method, device and equipment for industrial heterogeneous agents and a storage medium, unification of a heterogeneous agent evaluation framework is realized through abstract feature vectors and scene configuration, and the problem of evaluation islands is solved; the method comprises the following steps: acquiring periodic original data, processing the periodic original data according to a mapping rule to obtain a periodic feature vector, inputting the periodic feature vector into a pre-trained multivariable analysis model with inherent interpretability, and receiving a synchronously output comprehensive efficiency score, so that the purpose of outputting the comprehensive efficiency score through a data-driven model is realized; subjective experience judgment is completely replaced, so that evaluation results are consistent, fair and traceable; the contribution degree of each operation factor is analyzed through a multivariable model, the spanning from surface statistics to deep root cause analysis is realized, and the problems of good or bad effects and what good or bad can be accurately answered.

This invention discloses a multi-parameter coupling-based deep peak-shaving full-condition optimization control method for coal-fired power units. Addressing the problems of existing technologies, this invention constructs a multi-parameter coupling index system, calculates the comprehensive coupling strength between each parameter and the load, and selects core parameters. Dynamic clustering is used to finely divide the full-condition operation from 20% to 100% rated load into multiple operating zones. An LSTM-GRU dual-channel deep learning prediction model is established for each operating zone. A three-layer hierarchical control architecture is designed, comprising load optimization scheduling, boiler-turbine coordination control, and combustion optimization execution. The boiler-turbine coordination layer uses multivariate model predictive control and online self-tuning of multi-objective weights through fuzzy inference. The combustion execution layer uses the NSGA-III multi-objective evolutionary algorithm to optimize air distribution. A constraint-adaptive tuning mechanism based on safety margin is established to form a closed-loop iterative optimization across all operating conditions. This invention can significantly improve the load response speed, main parameter stability, and combustion economy of the unit during wide-load operation.

This invention discloses an online assessment system, method, equipment, and medium for visual pollution and transparency of emission plumes based on image recognition. The system includes: an image acquisition module for continuously capturing images of the plume at the chimney outlet; an image preprocessing module for receiving pollutant concentration data from a continuous emission monitoring system; an intelligent assessment module for extracting visual features of the plume from the preprocessed images and outputting plume opacity level and morphodynamic characteristics; and a correlation analysis module for receiving the plume opacity level, morphodynamic characteristics, and pollutant concentration data, performing correlation analysis by calculating correlation coefficients, and determining whether emissions are abnormal based on a multivariate model. This invention achieves real-time online monitoring of emission plumes by continuously capturing images of the plume at the chimney outlet through the image acquisition module, overcoming the limitations of manual visual inspection in terms of time and space.

The invention discloses a spodumene clinker processing collaborative optimization control method and a spodumene clinker processing collaborative optimization control system based on multipoint real-time quality feedback, which relate to the technical field of intelligent control, and execute closed-loop optimization control based on a process control system in combination with key quality data and temperature distribution data, operating parameters of a vertical mill system are dynamically adjusted, and the discharging granularity is stabilized; performing double-closed-loop optimization control on the acidification device, and cooperatively performing sulfuric acid dosage pre-control and acidification rate verification adjustment on the basis of free acid content FA, residual acid content RA data and temperature distribution data; the key quality data comprises vertical mill discharge granularity, free acid content FA and residual acid content RA; and when the double-closed-loop optimization control of the acidification device cannot reach the target, a cooperation request is sent to vertical mill multivariable model prediction control, and the target control range of the discharging granularity is adjusted. The problems of unstable quality, poor continuity, high consumption and low automation level in spodumene clinker processing are solved.

The invention discloses an intelligent control system for an acetic acid production device. The intelligent control system comprises an advanced control server, a switch, an OPC server, a DCS engineer station, a DCS operation station, a DCS control station and a plurality of PID control loops. The temperature transmitters, the pressure transmitters, the flow transmitters and the remote transmission liquid level meters transmit field real-time monitoring data to the DCS control station. The DCS operation station reads field real-time monitoring data and automatically adjusts the remote control valve; the OPC server reads field real-time monitoring data and transmits the field real-time monitoring data to the advanced control server through the switch, the advanced control server monitors key variables and operation data, PID parameter setting multivariable model prediction control, expert strategy control and data analysis optimization are carried out, an obtained control operation result is returned to the DCS control station along an original path, and the DCS control station is connected with the switch. And the remote control valve is automatically adjusted. According to the invention, stable and optimal control of product quality is realized, the stability of device operation is improved, manual operation is reduced, energy is saved, consumption is reduced, and the purpose of improving economic benefits is achieved.

The invention discloses a wind generating set model prediction control method based on a Hammerstein structure, and the method achieves the linear modeling of a system by introducing the Hammerstein structure to approximate the nonlinear static characteristics and linear dynamic behaviors of a wind generating set. On the basis, a multivariable model predictive control framework is established by adjusting the torque and the pitch angle of the generator at the same time. In order to solve the problem of multi-objective optimization between power tracking and fatigue load suppression, a quadratic cost function including a power error term, a control change rate term and a load suppression term is designed, so that smooth power output and structure service life prolonging of the wind turbine unit are realized.

This invention discloses a comprehensive energy collaborative control and optimization system that solves the problem of energy scheduling incoordination in existing energy systems. It includes: a multivariable model predictive control module, based on an LPV model, which predicts and extrapolates the short-term process state, dynamically decouples the correlation characteristics between multiple variables through rolling optimization, obtains control variables through solution, and estimates unmeasurable variables through soft-sensor modeling; and a PID performance evaluation and parameter optimization module, which performs performance benchmark evaluation and fault diagnosis of the PID control loop based on big data analysis. When a decrease in the control performance or an abnormal state of the PID control loop is detected, it automatically triggers a rank-constrained identification and tuning method to adaptively correct the controller parameters. This system achieves dynamic perception, intelligent tuning, and multivariable predictive collaborative control of the energy system, maintaining stable operation of the device under complex conditions, improving energy utilization efficiency, and reducing the intensity of manual intervention.

The invention discloses a flood forecasting method based on an LSTM neural network, and relates to the technical field of hydrological forecasting. Comprising the following steps: collecting multi-source time sequence hydrological historical data of a target drainage basin, and carrying out space-time alignment to form historical multivariable space-time data blocks; constructing a plurality of future rainfall scene sequences, and respectively splicing the future rainfall scene sequences with the historical multivariable spatio-temporal data blocks to form a probabilistic input sample set; constructing a multivariable LSTM model fused with a space-time attention mechanism; constructing a mixed loss function containing data fitting loss and physical constraint loss; training and verifying the multivariable LSTM model fused with the space-time attention mechanism; and deploying the trained multivariable LSTM model fused with the space-time attention mechanism, and carrying out probabilistic flood forecasting. By introducing the space-time attention mechanism, the multi-source data fusion architecture and the mixed loss function, the flood forecasting precision, physical rationality and interpretability are remarkably improved.

A method and apparatus for testing a multivariable model for generating a value of an analyte concentration using a spectroscopic technique, the method comprising: a) providing a multivariable model for generating an output value of an analyte concentration based on spectral measurements; b) inputting a spectrum of an analyte to be measured with known concentration into the model; c) inputting a spectrum of the interferent, and d) processing the received spectrum of the analyte to be measured and of the interferent and generating an output value of the analyte to be measured using the model.Also described is a method for generating such a multivariate analysis model comprising spectral adulteration of the input spectrum of the model.

The invention provides a circulating fluidized bed boiler collaborative optimization method and system based on model predictive control, boiler process data is obtained and a dynamic model is identified, the process data is obtained through a step test or historical data, and the dynamic model quantifies the relationship between variables; an embedded programming language is integrated into a multivariable model predictive control system, the influence of disturbance on variables is predicted according to a dynamic model, and a control strategy is adjusted; predictive control is conducted on the oxygen content of the boiler through a control system, and the optimization range is maintained through dynamic adjustment according to coal quality parameters; a dynamic model is applied to the boiler hearth pressure difference and temperature, and fan parameters are adjusted to stabilize the pressure difference and temperature; a denitration and desulfurization module is integrated, and additives are adjusted through sensor data and a dynamic model to control the emission concentration. The problems that traditional manual operation is lagged, fluctuation is large, energy consumption is high, emission is unstable and the like are solved, and the circulating fluidized bed boiler is promoted to move towards intelligent, refined and green operation.

The application provides a method and system for complex system component analysis under multiple scenes, the method comprising: optimizing linear model coefficients of multivariate analytical chemistry signals involving multiple scenes according to an objective function and corresponding parameter-free constraint conditions, to obtain a model for each analysis scene; and predicting the content of the target composition under multiple scenes according to a new target analysis model. The system executes the method. The application establishes a linear model for multivariate analytical chemistry data collected by an analytical instrument under each scene and system composition, simultaneously establishes a corresponding linear model for each terminal by combining the loss function and the constraint condition mentioned, and realizes component analysis of a new measurement system under different detection scenes based on the optimized target analysis model of each terminal, so that the accuracy of the prediction result is improved compared with a traditional multivariate model.

A receiver test and measurement system includes a signal generator that provides a stressed signal. The stressed signal must be calibrated according to several parameter specifications. Some parameters, including a set of two or more equalization parameters, can be calibrated on a test and measurement instrument that captures the stressed signal and performs the calibration process. A multivariable model is created that establishes the relationship between the parameters set on the signal generator and the measurements of those parameters on a test and measurement instrument, such as an oscilloscope. The multivariable model calculates coefficients that allow calculation of the settings that need to be set on the signal generator for any combination of two or more equalization parameters. This allows receiver testing to be performed in a calibrated manner for any combination of required equalization parameters.

The application discloses a new energy battery coating post-drying multivariable model predictive control system APC The control system performs real-time optimization control and rolling update according to the calling period. Specifically, the control system performs prediction and optimization calculation based on a model according to the set values of the return air valve opening, the exhaust fan frequency and the external circulating fan frequency of the last calling period, the NMP concentration measurement value at the current time, the oven pressure measurement value, the target value of the NMP concentration and the measurable external disturbance change data, and outputs the optimal sequence of the set values of the return air valve opening, the exhaust fan frequency and the external circulating fan frequency of the next period. Finally, the output values of the first time of all input variables in the optimal sequence are written back to the related actuators of the drying system, and the closed-loop control is completed. The application realizes real-time online optimization of the post-coating drying process based on the predicted dynamic optimization control under the premise of guaranteeing the stability of the system and the non-exceeding of the toxic solvent NMP concentration, and realizes energy consumption reduction while guaranteeing the collaborative and stable operation of the multiple ovens.

The application discloses a kind of multivariate LSTM method and equipment for predicting carbon isotope abundance, comprising: including: collecting carbon isotope data in air and meteorological factor and calculating relative index, then preprocessing collected data;The collected data is filled with missing value, abnormal value correction and normalization processing, and a multivariate time series dataset is constructed;Multivariate LSTM modeling is carried out based on the improved attention mechanism combined with carbon isotope fractionation constraint;The constructed model is trained and optimized in combination with the physical constraint term of carbon source mixing;Carbon isotope abundance prediction and error correction are carried out by the optimized model.The application realizes the physical consistency modeling of carbon isotope sequence by introducing the multivariate LSTM model of fractionation calculation mechanism and Keeling auxiliary task, and improves the precision and interpretability of carbon isotope abundance prediction.