The invention relates to a method for generating data on the basis of data already available having individual annotated data points, the method comprising: training a first predictor on the basis of data already available; determining a prediction error of the first predictor for each data point; training a second predictor to determine an anticipated prediction error of the first predictor and an uncertainty; determining a data description which maximizes a combination of anticipated prediction error and uncertainty; and generating data on the basis of the previously determined data description.

Pulverizers are very critical equipment in overall functioning of a plant. They need to be controlled and monitored properly for the optimized operation of the pulverizers. A system and method for performance and health monitoring to optimize operation of a pulverizer is provided. The system comprises a digital twin that can mimic the performance of the pulverizer in real-time and assist the operators in decision making related to operation, maintenance and scheduling. The digital twin is configured to receives real-time sensor data from a plurality of data sources and provides real-time soft sensing of key health and performance parameters of the pulverizer. One more key aspect of the solution is the advisory system that alerts and recommends corrective actions in terms of parameters controlled through other equipment or changes in operation or design or changes in cleaning schedule.

Embodiments of modelling a fluid treatment system are provided herein. One embodiment comprises obtaining synthetic data for a fluid treatment system from a data store. The fluid treatment system comprises a membrane and the fluid treatment system is configured to receive a stream of fluid for treatment. The embodiment comprises training a machine learning pressure prediction model using the synthetic data to predict a pressure for the membrane of the fluid treatment system. The trained pressure prediction model is combinable with an operator training simulator (OTS) model to update the OTS model to improve accuracy of simulation pressure output from the OTS model.

Embodiments of modelling a fluid treatment system are provided herein. One embodiment comprises obtaining synthetic data for a fluid treatment system from a data store. The fluid treatment system comprises a membrane and the fluid treatment system is configured to receive a stream of fluid for treatment. The embodiment further comprises training a performance indicator model using the synthetic data to predict a performance indicator for the fluid treatment system. The performance indicator comprises a permeate sulfate performance indicator and the performance indicator model predicts a sulfate content value in a permeate stream.

A method of controlling a microgrid includes retrieving, by an energy management system, EMS, a forecast variable value for a forecast variable. The EMS determines an operating point value for a controllable asset that depends on the retrieved forecast variable value. The EMS determines an operating point shift value for the controllable asset, the operating point shift value representing a shift in operating point value in response to a variation in forecast variable value. The operating point value and the operating point shift value are provided to a power management system, PMS, of the microgrid.

A method and apparatus for controlling a process in a system comprising pre-processing of a raw material, processing the pre-processed raw material and acquisition of the result of the processing of the pre-processed raw material, comprising the steps of: capturing input and output variables of the pre-processing; capturing output variables of the processing of the pre-processed raw material; creating a first, second and third process model for at least two different time scales, which describes the effects of adapting the pre-processing of raw material, the effects of adapting the processing of the pre-processed raw material, the effects of adapting the pre-processing of raw material and adapting the processing of pre-processed raw material on the output variables of the processing of pre-processed raw material; wherein the process in the system is controlled using the prediction of the process model which currently provides the best predictions for the process in the system.

There is a need for more effective and efficient data modeling and/or data visualization solutions. This need can be addressed by, for example, solutions for performing data modeling and/or data visualization in an effective and efficient manner. In one example, solutions for generating a data model with dynamic relational awareness are disclosed. In another example, solutions for processing data retrieval queries using data models with dynamic relational awareness are disclosed. In yet another example, solutions for generating data visualizations using data models with dynamic relational awareness are disclosed. In a further example, solutions for integrating external data objects into data models with dynamic relational awareness are disclosed.

To keep plant performance constant in a control apparatus for controlling a plant including a plurality of units. The control apparatus (a PCM) controls an automobile including a plurality of units. The control apparatus includes a model controller that generates a target value of a characteristic to be achieved by each unit based on a model set for each unit, a unit specifier (a performance change determinator) that specifies a unit in which performance unique to the unit has changed among the units, and a target value corrector (an FF updater) that corrects the target value for the unit that has been specified by the unit specifier.

A thin-film deposition system deposits thin films on semiconductor wafers. The thin-film deposition system includes a machine learning based analysis model. The analysis model dynamically selects process conditions for a next deposition process by receiving static process conditions and target thin-film data. The analysis model identifies dynamic process conditions data that, together with the static process conditions data, result in predicted thin-film data that matches the target thin-film data. The deposition system then uses the static and dynamic process conditions data for the next thin-film deposition process.

The present disclosure provides systems and methods for inferring knowledge about manufacturing process metrics. In an aspect, the present disclosure provides a method for inferring knowledge about manufacturing process metrics. The method may comprise: (a) receiving one or more metrics associated with a manufacturing process; and (b) using a hierarchy of models to generate one or more inferences about the manufacturing process based on the one or more metrics, wherein the hierarchy of models comprises one or more individual models and one or more ensemble models configured to generate the one or more inferences based on a combination or an aggregation of outputs generated by the one or more individual models.