According to one embodiment, a method, computer system, and computer program product for dynamic seating arrangement is provided. The embodiment may include identifying a seating arrangement of a venue. The embodiment may also include identifying an obstruction created by one or more viewers seated in one or more seats within the venue. The embodiment may further include generating a model that minimizes or eliminates the identified obstruction. The embodiment may also include calculating a movement differential for each seat in the identified seating arrangement to replicate the generated model. The embodiment may further include moving each seat according to the calculated movement differential.

Methods and systems for reducing substrate particle scratching using machine learning are provided. A machine learning model is trained to predict process recipe settings for a substrate temperature control process to be performed for a current substrate at a manufacturing system. First training data and second training data are generated for the machine learning model. The first training data includes historical data associated with prior process recipe settings for a prior substrate temperature control process performed for a prior substrate at a prior process chamber. The second training data is associated with a historical scratch profile of one or more surfaces of the prior substrate after performance of the prior substrate temperature control process according to the prior process recipe settings. The first training data and the second training data are provided to train the machine learning model to predict which process recipe settings for the substrate temperature control process to be performed for the current substrate correspond to a target scratch profile for one or more surfaces of the current substrate.

Distributed analytics system used to control the operation of at least one monitored system; the system includes an architect subsystem and an edge subsystem, wherein the edge subsystem comprises at least one edge processing device associated with at least one monitored system. The architect subsystem deploys at least one analytic model to an edge processing device based on characteristics of a monitored system associated with the edge processing device, the analytic model to be used by the edge processing device to provide control signals to a monitored system; and, receives information related to the monitored system from the edge processing device, the information utilized by the architect subsystem to modify the analytic model deployed to the at least one edge processing device to improve system performance of the monitored system. An edge processing device receives an analytic model from the architect subsystem; provides control signals to the monitored system according to the analytic model; and, sends information related to the monitored system to the architect subsystem, the information to be used by the architect subsystem to modify the analytic model to improve system performance of the monitored system.

A multiple fluid model tool for training and/or refining of fluid models using disparate and/or aggregated machine data is presented. For example, a system includes a modeling component, a machine learning component, a three-dimensional design component and a data collection component. The modeling component generates a three-dimensional model of a mechanical device based on a library of stored data elements. The machine learning component predicts one or more characteristics of the mechanical device based on a machine learning process associated with the three-dimensional model. The three-dimensional design component provides a three-dimensional design environment associated with the three-dimensional model. The three-dimensional design environment renders physics modeling data of the mechanical device on the three-dimensional model based on the one or more characteristics of the mechanical device. The data collection component collects machine data via a communication network to update the three-dimensional model associated with the three-dimensional design environment.

A method for producing a well includes receiving production information associated with wells within a field; deriving a field specific model from the production information; receiving production information associated with the well; projecting production changes associated with installing artificial lift at the well at a projected date, the projecting using a production analysis engine applied to the field specific model, the projecting including determining a set of artificial lift parameters; and installing the artificial lift at the well in accordance with the artificial lift parameters.

[Problem] To enable a highly effective medium to be manufactured.

[Solution] a manufacturing method of a medium is provided with: a step of creating a prediction model at least based on values of parameters related to manufacturing of another media manufactured in the past and being different in at least any one among an object of culture, an index of the culture, and a manufacturing condition of the medium manufacturing; a step of creating a value of the parameter using the prediction model; and a step of manufacturing the medium using the created value of the parameter.

A control system includes one or more levels of control of power and energy. At one level, a first controller optimally divides power between two or more processes, to maximize instantaneous production, for a given amount of currently available power. In the case of EDR desalination, electric power is optimally divided between ion exchange membranes and pumps to maximize instantaneous production of desalinated water for a given amount of available electric power. Optionally, at another level, a second controller divides time-varying power between the processes fed by the first level controller and an energy storage unit, based on a prediction of future power availability and a function. In the EDR case, power generated by a photovoltaic array is divided between the EDR desalination process and a battery, based on a prediction of future PV power availability and a function, to ensure reliable water production in the future.

Systems and methods for training a surrogate model for predicting system states for a building management system based on generated data from a simulation model are disclosed herein. The simulation model is calibrated for a building of interest. The building of interest includes building equipment operable to control a variable state of the building. The simulated data of system states are generated using the calibrated simulation model. A surrogate model is trained based on the simulated data of system states from the calibrated simulation model. System state predictions are generated using the surrogate model. The surrogate model is re-trained based on updated operational data. An updated series of system state predictions is generated using the re-trained surrogate model.

This invention provides an energy storage device manager, a system comprising the energy storage device manager, computer-readable media configured for providing the energy storage device manager, and methods of using the energy storage device manager. The energy storage device manager can optionally control charge buses and/or load buses to modulate the state of charge of an energy storage device. The energy storage device manager can optionally be configured with a plurality of modes that target different states of charge. The plurality of modes can optionally comprise a maintain mode which targets a nominal (e.g. 50%) charge state and a high-charge mode that targets a state of charge greater than the maintain mode. The plurality of modes can optionally further include an in-use mode which targets a state of charge greater than the maintain mode, and turns on a load bus that is turned off in the preparation mode. The energy storage device manager can optionally be configured to determine a charge start time to execute the preparation mode. The energy storage device manager can optionally be configured to determine the charge start time based on forecast data (e.g. power prediction forecast determined based on weather forecast).

A system and method are provided for operating a power generating asset coupled to an electrical grid. Accordingly, a controller receives an environmental data set indicative of at least one environmental variable projected to affect the power generating asset over a plurality of potential modeling intervals. The controller then determines the variability of the environmental data set and a corresponding modeling-confidence level at each of the potential modeling intervals based on the variability. A modeling interval is thus selected corresponding to a desired modeling-confidence level. A computer-implemented model is employed to predict a future power profile for the power generating asset over the selected modeling interval. The future power profile is indicative of a power-delivery capacity of the power generating asset at each of a plurality of time intervals of the modeling interval. Based, at least in part, on the future power profile, the controller determines an obligated-power-production schedule for the power generating asset over the modeling interval. The obligated-power-production schedule corresponds to a power production agreement with the electrical grid. In accordance with the obligated-power-production schedule, the controller modifies at least one setpoint of the power generating asset to deliver electrical power to the electrical grid.