30 results about "Soft sensor" patented technology

A method to perform oil in produced water analysis allows measuring the large volume of oil in produced water reliably. In the method, a time-series and physics based machine learning model of a gas oil separation plant is generated, advisory actionable items for maintaining a crude oil quality within a pre-determined threshold are generated based on machine learning model coefficients and outputs of soft sensors, and then the advisory actionable items are presented to a user.

The invention relates to the technical field of production line control, and discloses a drinking water production line water quality real-time monitoring and intelligent adjusting system based on the Internet of Things. Registration equipment is unified with units, and NTP time synchronization is performed to eliminate deviation; the shift / CIP calibrates the zero range, and the EWMA monitors the drift weight drop; constructing state vectors containing IDs / batches, and performing auditing and warehousing; median / bilateral filtering denoising is carried out, and grid interpolation is unified; sliding window multi-scale aggregation is carried out, and self-adaptive sampling is carried out according to curvature and events; normalizing and interpolating a near-time training window; estimating indexes difficult to measure by a soft sensor, and depicting residual chlorine by a mechanism model; and carrying out uncertainty weighted fusion, and outputting estimation and intervals and auditing. According to the method, the whole process of real-time monitoring, quick response and traceable compliance is realized, the risk caused by chemical shortage or ratio error and environmental protection constraint is remarkably reduced, the capacity loss caused by cleaning and plan conflict is reduced, and the factory water quality stability and the treatment transparency are improved.

The invention provides an intelligent control method for natural gas continuous heat treatment equipment based on digital twinning, and relates to the technical field of heat treatment equipment control. The method comprises the steps that a digital twinning model of the natural gas continuous heat treatment equipment is established, and production monitoring data collected on line are utilized; the method comprises the following steps: predicting combustion process characteristics and unmeasurable states through a digital twin model, performing multi-layer strategy search by adopting a hierarchical control strategy according to a state prediction result, performing safety constraint simulation verification on the multi-layer strategy in the digital twin model, and predicting unmeasurable state parameters through a soft sensor. And introducing the quality index as a soft constraint condition into optimization control, and generating a control execution parameter meeting the constraint condition. The technical problems that in the prior art, natural gas continuous thermal treatment equipment is poor in control effect and unstable in workpiece quality are solved. The technical effects that the natural gas continuous heat treatment equipment is intelligently, accurately and reliably controlled, and the product quality is improved are achieved.

The invention discloses a semiconductor packaging process dynamic optimization system and method, and relates to the technical field of semiconductor packaging process optimization. A dynamic optimization system for a semiconductor packaging process comprises a data acquisition module, a fingerprint generation module, an index evaluation module, an optimization model construction module, a parameter solving module, a parameter bias module, a feedback detection module and a test analysis module. According to the method, a soft sensor model taking batch fingerprints as a core is introduced into a semiconductor packaging process, and dynamic process data and equipment state information are combined, so that high-precision prediction of a key process quality index and an energy consumption index is realized; through fusion of an extreme value theoretical model and a tail risk quantile evaluation method, deviation correction is carried out on a preliminary prediction result of the soft sensor, and robustness and safety of process quality prediction are improved.

The invention relates to the technical field of fluid machinery intelligent control, and discloses a centrifugal circulating pump non-differential pressure sensor variable pressure control method based on edge intelligence. Water pump rotating speed and power signals are collected, subjected to normalization processing and then input to a lightweight recurrent neural network (RNN) model deployed at an edge device end; the RNN model serves as a soft sensor, and the flow and the lift value of the most unfavorable point of the pipe network are deduced in real time; and the system dynamically calculates the target lift based on a pipe network characteristic curve according to the reasoned flow, the target lift serves as a set value and forms a closed-loop control loop with the actual lift fed back by the RNN, the rotating speed adjustment amount is output to the frequency converter through a PID algorithm, and finally accurate variable pressure control without the physical differential pressure sensor is achieved. According to the method, the dependence on a tail end physical sensor is eliminated, the cost and the complexity are reduced, meanwhile, the real-time control and the data security of ultra-low delay are realized by utilizing edge calculation, and the response speed, the control precision and the energy-saving effect of the system are effectively improved.

The invention discloses an automatic control method and system for fumaric acid production. The method comprises the following steps: collecting process variables in a fermentation process; constructing a soft measurement model fusing physical modeling and a residual neural network to obtain an acid production rate prediction value and prediction confidence; calculating a feed-forward control quantity, and adjusting the weight according to the prediction confidence to obtain a feed-forward control output; constructing a state vector, and establishing a Markov decision process; training a neural network control strategy by adopting a near-end strategy optimization algorithm, and outputting a proportionality coefficient, an integral coefficient and a differential coefficient; calculating feedback control quantity, fusing the feedback control quantity with feed-forward control output, and applying change rate and amplitude limitation to obtain final control output; and switching to a feedback priority control mode under a specific condition, and regularly updating a soft measurement model and a control strategy parameter. According to the invention, high-precision stable control of pH in the fumaric acid fermentation process is realized, the production efficiency and the process robustness are improved, and the method is suitable for automatic control of the large-scale fermentation process.

The invention relates to a method for training a soft sensor for determining a target measured value of a fuel cell system (100), the fuel cell system (100) comprising a first sensor (10) for detecting a first measured value, a second sensor (20) for detecting a second measured value, and a control device (50), the control device (50) is designed to determine a target measured value from at least the first measured value and the second measured value on the basis of a model stored on the control device (50). Furthermore, the fuel cell system (100) comprises a target value sensor (40) for determining a target measurement value. The method comprises, as a step, operating (320) at least one system unit (110) of the fuel cell system (100) that affects a target value in at least one first operating point. Furthermore, the method comprises, as a step, adjusting (340) at least one model parameter of the model stored on the control device (50) in such a way that a deviation between the target value determined by the soft sensor and the target value determined by the target value sensor (40) at the first operating point of the system unit (110) is at least reduced.

The invention belongs to the technical field of biological process monitoring and optimization, and discloses a fermentation-hydrolysis process parameter automatic control system and method, and the method comprises the steps: collecting online process parameter time sequence data for pre-selected key process parameters; taking the online process parameter time sequence data as real-time input, and utilizing a pre-trained soft measurement model to obtain a key metabolite estimation concentration value sequence; calculating to obtain a key metabolite estimation generation rate value sequence based on the key metabolite estimation concentration value sequence; forming a real-time two-dimensional state vector based on the current estimated concentration value of the key metabolite and the current estimated generation rate value of the key metabolite; comparing and evaluating the real-time two-dimensional state vector with a predefined decision logic; when the decision logic judges that a preset triggering condition is met, a decision signal for starting hydrolysis is obtained; and the optimal opportunity for starting hydrolysis is accurately captured.

Proposed is a multi-channel soft-sensor-based interface device and an operating method therefor. The glove-type multi-channel soft-sensor-based interface device can recognize the degree of flexibility of finger joints. The device may include a sensing module which includes a plurality of strain sensors and which collects strain sensing signals of the plurality of strain sensors. The device may also include a switching unit configured to select any one of the strain sensing signals of the plurality of strain sensors. The device may further include an amplification module configured to amplify the strain sensing signal selected by the switching unit. The device may further include an analog-to-digital (A / D) converter configured to convert the amplified sensing signal into a digital signal.

The application discloses a kind of microbial manufacturing process bacteria concentration perception method and system based on experience transmission, method includes: according to the historical data of microbial manufacturing process, construct bacteria concentration prediction general model, obtains its model parameters based on source system historical data training and establishes target system model structure;By introducing latent variable quantification model difference between source system and target system, a unified experience transmission probability framework is constructed;Based on the framework, the iterative algorithm combining expectation and gradient descent is used to estimate the target system parameters until convergence;The converged parameters are substituted into the general model to construct an online soft sensor, and process auxiliary variable data is collected in real time and the bacteria concentration perception value is output.The application effectively suppresses negative transfer by explicitly modeling the differences between systems, and in the industrial scenario where data is scarce and distribution is inconsistent, it realizes robust, accurate and real-time perception of bacteria concentration.

A plant process interface method according to one embodiment of the present invention comprises the steps of: loading a process design drawing; assigning attribute data to an entity shown in the process design drawing; loading a widget for displaying digital twin data via the attribute data; and displaying the process design drawing, the entity, and the widget on one canvas. The digital twin data may include sensor data, soft sensor data calculated from the sensors, or external environment data, and the widget may be a modal window expressing the digital twin data as text, an image, a table, a graph, or a 3D model.

This invention discloses a soil volumetric water content (VWC) measurement system based on LoRa-RSSI and a drone. The system comprises several underground LoRa nodes for collecting environmental monitoring data; a host computer embedded in the soil; and an aerial node carried by a small drone. Combining IoUT technology, this invention designs an innovative system for soil VWC measurement based on LoRa received signal strength and a drone. The system utilizes the changes in LoRa-RSSI between the soil's internal transmitter and the drone's aerial receiver during the drone's angular rotation. Combined with a Long Short-Term Memory (LSTM) network, it collects differential LoRa-RSSI values ​​and uses a deep learning (DL) algorithm to calculate soil VWC, achieving relatively accurate soil VWC data. This invention eliminates the need for depth measurement of VWC data, utilizes soft sensors to measure soil VWC, and offers low cost, high efficiency, and small measurement error, providing a novel approach for soil VWC measurement design.

Methods and systems for controlling vertical glass distribution are provided. A traversing pyrometer periodically measures a parison actual temperature after the parisons exit a blank mold. The thermal camera takes a thermal image of each glass container after the glass container exits the blow mold. A vertical glass signature extraction module extracts a vertical glass distribution signature. A parison temperature estimator determines a parison estimated temperature for each vertical glass distribution signature obtained based on the vertical glass distribution signature, a most recently measured parison actual temperature and a parison stretch time. A parison temperature summer compares the parison estimated temperature to a parison set point temperature to determine a parison temperature error. A parison temperature control controls a blank mold contact time based on the parison temperature error.

The application discloses a NOx redundancy detection method based on soft and hard sensors in an SCR denitration system, and the method comprises the following steps: constructing a NOx detection model by using a NOx analyzer and a plurality of soft sensors, selecting a most credible data from input data according to a preset switching decision, and calculating the NOx concentration at the outlet of the SCR denitration system; the switching decision in the NOx detection model comprises the following steps: calculating the quality indexes of the NOx analyzer and all the soft sensors; analyzing the process constraint condition and the mechanical constraint condition of the SCR denitration system, and eliminating uncredible input data; for each remaining input data, the NOx concentration at the outlet of the SCR denitration system is calculated, and a tolerance model is analyzed; and finally, the NOx concentration at the outlet of the SCR denitration system is output, the fault detection of the NOx analyzer is realized, and the redundant switching of the soft sensor is realized without relying on additional hardware.

A method for providing a widget interface according to an embodiment of the present invention comprises the steps of: loading a process design drawing; assigning attribute data to an entity shown in the process design drawing; loading a widget displaying digital twin data by means of the attribute data; and displaying the process design drawing, the entity, and the widget on one canvas. The digital twin data may include sensor data, soft sensor data calculated from a sensor, or external environment data, and the widget may be a modal window representing the digital twin data as text, an image, a table, a graph, or a 3D model.

The invention provides a D-pantothenic acid fermentation multi-target dynamic optimization method based on biological-digital twinning, which comprises the following steps: constructing a biological soft sensor through multiple machine learning model algorithms, and combining the constructed biological soft sensor with a neural network. Meanwhile, fermentation process conditions are optimized by applying a particle swarm algorithm and a genetic algorithm, so that the whole fermentation process simulation monitoring under data driving is realized, the problems of insufficient accuracy and poor robustness when a traditional prediction technology is used for processing complex fermentation data are effectively solved, and the predictability and controllability of the fermentation process are improved.

The invention provides an online near-infrared monitoring and closed-loop quality control system for an industrial hemp primary processing flow, and the system comprises a sensing monitoring module which is used for constructing an NIR detection subsystem; the data processing module is used for collecting and processing online spectrum and synchronization process parameters to obtain a robust feature set; the model building module is used for building a multi-task stoichiometric model family, training a soft measurer and calculating quality health scores to obtain a quality health score engine; the closed-loop control module is used for carrying out equipment hardware direct adjustment, comprehensive cost minimization and equipment parameter feed-forward adjustment driven by incoming material spectrum and environment humidity change; and the operation and maintenance and compliance module is used for packaging the online operation subsystem and carrying out early warning, self-adaptive calibration and compliance recording on the industrial hemp primary processing flow. According to the method, full-chain end-to-end closed loop from target to execution is achieved, compliance, quality consistency and energy efficiency are remarkably improved, and meanwhile operation risks and comprehensive cost are reduced.

The invention belongs to the technical field of automatic control, and particularly relates to a reaction kettle control method and system for perfume production, and the method comprises the steps: selecting a production batch with the optimal fragrance quality as a gold batch, collecting the spectral data, a reference temperature curve and the real concentration of key components, building a soft sensor model through a chemometrics algorithm, and calculating the real concentration of the key components; and determining the component concentration evolution path of the gold batch as a component target trajectory. In the production process, calculating a real-time component vector according to the real-time spectral data by using a soft sensor model, and comparing the real-time component vector with a target component vector to obtain a quality deviation degree; the outer loop calculates the temperature correction amount according to the quality deviation degree, the temperature correction amount is combined with the reference temperature curve to generate a self-adaptive temperature set value to serve as the temperature set value of the inner loop, and dynamic adjustment of the temperature of the reaction kettle is achieved. Through internal and external double-loop cascade control, disturbance compensation is realized, and the quality consistency between batches of perfume products is improved.

Systems and a method are disclosed. The method includes collecting observed input data and observed output data from a data-generating system; determining reduced order observed output data; simulating reduced order simulated output data; performing a quality check; splitting the observed input data into a first observed input data group and a second observed input data group; training a first machine learning model; generating inferred input data with the first machine learning model; training a second machine learning model with the observed input data, the inferred input data, and the observed output data; and generating inferred output data with the second machine learning model. The method further includes adding noise to the inferred input data and the inferred output data to create synthetic input data and synthetic output data; and designing soft sensors based on the synthetic input data and the synthetic output data for deployment in an operational plant.

The invention relates to a method for providing a model (101) of a soft sensor for measuring mechanical damage of an electric rotating machine (103), in which the model (101) is contained in the soft sensor, receiving acceleration sensor data (106) measured at the electric rotating machine (103) and magnetic field sensor data (107) corresponding to the acceleration sensor data (106) via the first interface (104), determining an operating point (111) of the electric rotating machine (103) from the magnetic field sensor data (107) by means of the computing device (105), acceleration sensor data (106) corresponding to the operating point (103) is determined and assigned to the operating point (111), and from the acceleration sensor data (106) corresponding to the operating point (111), a severity of a damage, i.e. A damage severity (114), of a mechanical component of the electric rotating machine (103) is determined, forming a model (101) on the basis of the magnetic field sensor data (107), the acceleration sensor data (106), the operating point (111) and the damage severity (114), the model receiving the magnetic field sensor data and the acceleration sensor data (106, 107) as input variables and outputting the operating point (111) and the damage severity (114) as output variables, providing the model (101) via the second interface (120).

To provide enhanced search capabilities in a process control system, a knowledge repository is generated that includes both contextual data and time series data. The contextual data organizes process plant-related data according to semantic relations between the process plant-related data and the process plant entities. When a user submits a process plant search query related to process plant entities within a process plant, search results are obtained by identifying a data set from the knowledge repository. The contextual data categorizes process parameters so that users can search for a particular process parameter category. Users can tag previous searches to execute them once again at a later time. Users can also execute queries for predicted or future states of process plant entities, batch queries regarding batch processes, soft sensor analytics and monitoring applications, parameter lifecycle applications, perturbation applications, step testing applications, or batch provisioning and scheduling applications using the knowledge repository.

The invention provides an industrial soft measurement probability diffusion modeling optimization method and system for limited data, and relates to the technical field of soft sensors, and the method comprises the steps: firstly generating condition information based on original training data, and constructing a time condition denoising diffusion probability model; generating a virtual input sequence by utilizing the model, and screening out a high-quality virtual sequence by adopting a dual-index filtering strategy fusing diversity and uncertainty; a corresponding virtual output sequence is obtained through a pre-training soft sensor model, and an enhanced training data set is constructed; and finally training to obtain a soft sensor model capable of accurately predicting a quality variable. The problem of data scarcity is effectively solved by generating a virtual track keeping time correlation, and the data enhancement quality is improved by means of intelligent screening.

To provide a capacitive-type soft sensor capable of suppressing damage in the vicinity of a terminal part even if a substrate sheet is made of silicone elastomer, and a capacitive-type sensor module using the same.SOLUTION: In a capacitive-type soft sensor 1, in which a detection electrode 20 for detecting a change in capacitance is provided on a flexible substrate sheet 10, a sheet composed of a silicone elastomer is used as the substrate sheet 10, a terminal part 16 is formed at the tip of a protruding part 14 protruding from a body part 12 on which the detection electrode 20 is disposed in the substrate sheet 10, and a stretch inhibiting layer 30 is provided on the side of the protruding portion 14 on which wiring 22 is formed via an adhesive layer 40 composed of a UV curable adhesive.SELECTED DRAWING: Figure 2

This invention discloses a real-time monitoring system and method for the loss on ignition (LOI) of hazardous waste in a rotary kiln incineration, relating to the field of hazardous waste rotary kiln incineration treatment and process control technology. The real-time monitoring system for LOI of hazardous waste in a rotary kiln incineration includes: a data acquisition module, a soft sensor model construction module, an online calibration module, and a DCS integration and display module. Through these modules, the system achieves second-level prediction, dynamic display, and early warning of LOI exceeding limits. The DCS integration and display module sets two-level alarm thresholds to achieve graded early warning for high alarms and very high alarms. Simultaneously, all predicted values ​​are stored in a historical database, supporting post-event traceability and model optimization. The system avoids the traditional "overburning" strategy, and in practical applications, it can save approximately 4.1% of natural gas consumption and significantly reduce thermal stress damage to refractory materials. It extends the service life of the rotary kiln and solves the problems of lagging, high energy consumption, and low control accuracy of traditional detection methods.

The present application relates to the technical field of production line control, and discloses a drinking water production line water quality real-time monitoring and intelligent adjustment system based on the Internet of Things, which registers equipment and units uniformly, eliminates deviation by NTP time correction, calibrates zero point range by shift / CIP, monitors drift by EWMA and reduces weight, constructs a state vector containing ID / lot and audits into a warehouse, removes noise by median / bilateral filtering, unifies raster interpolation, adaptively samples according to curvature and events by sliding window multi-scale aggregation, normalizes and interpolates a near-time training window, estimates difficult-to-measure indexes by a soft sensor, depicts residual chlorine by a mechanism model, and outputs estimation and interval and audits by uncertainty weighted fusion. The present application realizes whole-process management of'real-time monitoring-quick response-traceable compliance', significantly reduces risks caused by chemical shortages or proportioning errors and environmental protection constraints, reduces production capacity loss caused by cleaning and plan conflicts, and improves water quality stability and management transparency.

The present application belongs to the field of automatic control technology, and particularly relates to a reaction kettle control method and system for perfume production, which method comprises: selecting a production batch with the optimal aroma quality as a golden batch, collecting its spectral data, reference temperature curve and real concentration of key components, establishing a soft sensor model by using a chemometrics algorithm, and determining the component concentration evolution path of the golden batch as a component target trajectory. In the production process, the soft sensor model is used to calculate a real-time component vector according to real-time spectral data, and a quality deviation degree is obtained by comparing the real-time component vector with the target component vector; an outer loop calculates a temperature correction amount according to the quality deviation degree, and generates an adaptive temperature set value in combination with the reference temperature curve, which is used as the temperature set value of an inner loop, so as to realize dynamic adjustment of the temperature of the reaction kettle. The present application realizes disturbance compensation and improves the quality consistency among batches of perfume products through inner-outer loop cascade control.

A method to perform oil in produced water analysis allows measuring the large volume of oil in produced water reliably. In the method, a time-series and physics based machine learning model of a gas oil separation plant is generated, advisory actionable items for maintaining a crude oil quality within a pre-determined threshold are generated based on machine learning model coefficients and outputs of soft sensors, and then the advisory actionable items are presented to a user.

This invention relates to a method for controlling a chemical process, wherein the chemical process is one or more of the preparation of methanol, hydrogen sulfide, methanethiol, hydrogen cyanide, acrolein, 3-methylthiopropional, 5-(2-methylthioethyl)-hydantoin, methionine, methionine salts, and methionine derivatives, comprising the steps of: a) providing a training set TS1, wherein the training set TS1 includes process values ​​PV1 and PV2 associated with each other, and / or laboratory values ​​LV1 and PV2 associated with each other; b) training a processing unit based on the training set TS1 of step a) to identify one or more measured process variables and at least one... The correlation model between process variables is used to develop a calibration function CF1 for the calibrated soft sensor based on the identified correlation model. c) Predict at least one operating parameter for the chemical process as an approximation of LV1 and / or PV1. d) Calculate the deviation as the difference between the predicted operating parameter and the corresponding laboratory value LV1 and / or treatment value PV1 of the training set TS1 in step a). e) If the deviation calculated in step d) exceeds a threshold, proceed to step f); otherwise, proceed to step g). f) Recalibrate the soft sensor in step b). g) Write the predicted at least one operating parameter into the DCS. h) Repeat steps c) to g).

The sewage treatment device comprises a full-automatic control system, a carbon source slow release mechanism, a sludge return system, a sensing detection system, and an adjusting tank, a reaction tank and a sedimentation tank which are connected in sequence, the full-automatic control system comprises a data acquisition module, a soft sensor prediction module and a control module group; the soft sensor prediction module is used for outputting BOD, TN and TP predicted values based on the sewage flow and the sewage parameters acquired by the data acquisition module; according to the region-adjustable accurate switching method, anaerobic / aerobic partitions are dynamically adjusted according to the C / N ratio. According to the invention, COD, TN predicted values, sludge activity and temperature are incorporated into carbon nitrogen ratio calculation, on-demand supply and slow release of a carbon source are realized in combination with a porous ceramic slow release device, the problems of low precision, sudden increase of local concentration and index monitoring lagging of traditional carbon source feeding are solved, and the treatment efficiency, stability and cost effectiveness are improved.

The invention belongs to the crossing field of biomedical engineering, robots and acoustic metamaterials, and discloses a small robot sensor for wireless viscosity and temperature monitoring, a magnetic control robot for wireless viscosity monitoring comprises a base, a support plate, an embedded ultrasonic soft sensor, a connecting plate and a vibrating blade; the viscosity of the environment where the robot is located can be wirelessly monitored in real time by ultrasonically detecting the deviation of the natural resonant frequency of the embedded ultrasonic soft sensor. According to the invention, the EUSS sensor is coupled with the magnetic driving module, or the EUSS sensor is coupled with the thermal response metal, so that the obtained micro-robot can realize wireless viscosity or temperature monitoring, an external rotating magnetic field is utilized to drive the vibration plate to generate periodic motion, a damping effect of fluid viscosity on the vibration plate is converted into strain of the vibration plate, and the strain is transmitted to the EUSS sensor; and the viscosity change is read through the reflection frequency shift curve of the EUSS sensor to the ultrasonic wave before and after deformation.