Computer-implemented methods and systems construct a calibrated operation-centric first-principles model suitable for online deployment to monitor, predict, and control real-time plant operations. The methods and systems identify a plant-wide first-principles model configured for offline use and select a modeled operating unit contained in the plant-wide model. The methods and systems convert the plant-wide model to an operation-centric first-principles model of the selected modeled operating unit. The methods and systems recalibrate the operation-centric model to function using real-time measurements collected by physical instruments of the operating unit at the plant. The recalibration may include reconciling flow and temperature, estimating feed compositions, and tuning liquid and vapor traffic flow in the model. The methods and systems deploy the operation-centric model to calculate KPIs (Key Performance Indicators) using real-time measurements. A processor employs the KPIs and automatically predicts and controls behavior of the physical operating unit at the plant.

Oscillatory instabilities are ubiquitous of systems, and these usually arise out of low amplitude aperiodic oscillations. These oscillatory instabilities generally affect the performance and the lifespan of systems in an adverse manner An apparatus and a method are disclosed here to estimate the rms value or the amplitude of limit cycle oscillations for control of the oscillatory instability.

A control device generates a second command value by compensating a first command value output at every control cycle according to a predetermined pattern with a correction amount output at every control cycle according to correction data, updates the correction data based on a deviation between the first command value and a feedback value from the control object, and determines an initial value of the correction data. The control device acquires a response characteristic indicating a relationship between an assigned command value and a feedback value shown in the control object in response to the command value, estimates a feedback value to be shown in the control object based on a value obtained by compensating the first command value with temporary correction data and the response characteristic, and updates the temporary correction data based on a deviation between the first command value and the estimated feedback value.

A multiple fluid model tool for multi-dimensional fluid modeling of a biological structure is presented. For example, a system includes a modeling component, a machine learning component, and a three-dimensional health assessment component. The modeling component generates a three-dimensional model of a biological structure based on multi-dimensional medical imaging data. The machine learning component predicts one or more characteristics of the biological structure based on input data and a machine learning process associated with the three-dimensional model. The three-dimensional health assessment component that provides a three-dimensional design environment associated with the three-dimensional model. The three-dimensional design environment renders physics modeling data of the biological structure based on the input data and the one or more characteristics of the biological structure on the three-dimensional model.

A state prediction control apparatus acquires first sensor information obtained by observing a state of an observation object at a first-clock-time by an observation sensor, reads a state series from a state series dictionary that defines a series of plural state changes accompanying a lapse of time corresponding to the kind of the observation object, obtains a route according to an objective matching the observation object and a target parameter for achieving the objective, predicts a state at a prediction-clock-time that is beyond the first-clock-time, compares second sensor information obtained at a second-clock-time after the first-clock-time and the prediction state, and outputs a control parameter defined based on the comparison result in order that a state at the prediction-clock-time comes close to the objective and the target parameter to a control unit that affects state change of the observation object.

The present disclosure is drawn to a low-cost commissioning method for the air-conditioning systems in existing large public buildings, that mainly aims at the commissioning of the air-conditioning system. The system comprises a system analysis sub-module, a load prediction sub-module, an optimization scheme sub-module, and a control strategy sub-module. The main method in the commissioning system is a low-cost commissioning method for the air-conditioning systems in existing large public buildings.

A method for controlling power consumption includes receiving a flexibility request from an electrical utility and discovering energy relevant devices in a building. The energy relevant devices include electrical loads and alternative energy sources. Power requirements are predicted from an electrical utility for the loads, the prediction takes into account available power from the alternative power sources. Power set points are determined for the energy relevant devices based on the prediction. The power set points meet the flexibility request. The energy relevant devices are directed to operate at the power set points. A building energy management system is also disclosed.

A controller reference trajectory design technique to enable high-performing automatic grade change performed by a model predictive control (MPC). Techniques include: (1) automatic determination of appropriate process output reference trajectory delays to enable optimum coordination of process input movements; (2) providing the entire planning process output reference trajectory ramp at the start of the grade change instead of just incrementally as the grade change progresses, again enabling movement of the process inputs to drive process outputs along the planned future path instead of just towards the current target; and (3) use of the process input forced ramping to allow linear ramping of process inputs with optimal coordination of other process input movements to keep all process outputs following the desired trajectories. The technical benefits are faster and higher performing grade changes. In addition, the use of this technology allows easier setup and maintenance of the automatic grade change package.

A method for reducing and/or managing energy-related stress in an electrical system includes processing electrical measurement data from or derived from energy-related signals captured by at least one intelligent electronic device (IED) in the electrical system to identify and track at least one energy-related transient in the electrical system. An impact of the at least one energy-related transient on equipment in the electrical system is quantified, and one or more transient-related alarms are generated in response to the impact of the at least one energy-related transient being near, within or above a predetermined range of the stress tolerance of the equipment. The transient-related alarms are prioritized based in part on at least one of the stress tolerance of the equipment, the stress associated with one or more transient events, and accumulated energy-related stress on the equipment. One or more actions are taken in the electrical system in response to the transient-related alarms to reduce energy-related stress on the equipment in the electrical system.

Provided is a highly reliable technology for optimizing an action for controlling an environment in a target space. An action optimization device for optimizing an action for controlling an environment: acquires environmental data related to a state of the environment; performs time/space interpolation on the acquired environmental data; trains an environment reproduction model, based on the time/space-interpolated environmental data, such that, when a state of an environment and an action for controlling the environment are input, a correct answer value for an environmental state after the action is output; trains an exploration model such that an action to be taken next is output when an environmental state output from the environment reproduction model is input; predicts a second environmental state corresponding to a first environmental state and a first action by using the trained environment reproduction model; explores for a second action to be taken for the second environmental state; and outputs a result of the exploration.