System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g., substantially concurrently, by identifying process inputs and outputs (I/O), collecting data for process I/O, determining constraints on model behavior from prior knowledge, formulating an optimization problem, executing an optimization algorithm to determine model parameters subject to the determined constraints, and verifying the compliance of the model with the constraints.

System and methods for event prediction during drilling operations are provided. Regression data associated with coefficients of a predictive model are retrieved for a downhole event during a drilling operation along a planned path of a wellbore. The regression data includes a record of changes in historical coefficient values associated with prior occurrences of the event. As the wellbore is drilled over different stages of the operation, a value of an operating variable is estimated based on values of the coefficients and real-time data acquired during each stage. A percentage change in coefficient values adjusted between successive stages of the operation is tracked. An occurrence of the downhole event is estimated, based on a correlation between the percentage change tracked for at least one coefficient and a corresponding change in the historical coefficient values. The path of the wellbore is adjusted, based on the estimated occurrence of the event.

The present invention discloses a prediction control method and system for component contents in a rare earth extraction process. The prediction control method includes: establishing an Elman neural network model of a rare earth extraction process; obtaining a predicted output value of the rare earth extraction process through the Elman neural network model of the rare earth extraction process; calculating an optimal set value through steady-state optimization; dynamically predicting an extractant flow increment and a detergent flow increment based on the predicted output value and the optimal set value; and controlling component contents in the rare earth extraction process according to the extractant flow increment and the detergent flow increment. According to the present invention, an optimal setting problem of a set point is solved through steady-state optimization calculation, and then an optimal control effect is achieved in combination with a dynamic prediction control method, thereby achieving optimal setting control over the component contents in the rare earth extraction process, and ensuring the product quality of the rare earth extraction process.

A method for predicting lane error based on an identifier of a lane segment is disclosed. The method includes receiving a predicted location in a lane segment of a plurality lane segments for a first vehicle traveling on the lane segment. The method includes receiving a determined location of the first vehicle in the lane segment for the first vehicle. The method includes determining a difference between the predicted location in the lane segment received for the first vehicle and the determined location of the first vehicle in the lane segment. The method includes providing the determined difference between the predicted location in the lane segment received for the first vehicle and the determined location of the first vehicle in the lane segment and an identifier of the lane segment as first training data to a lane error estimation model.

An environmental control system for a building including heating, ventilation, or air conditioning (HVAC) equipment that operates to affect a zone of the building and a controller including a processing circuit. The processing circuit is configured to estimate a thermal resistance between air of the zone and of an external space using values of a temperature of the zone air, a temperature of the external space air, and a heat transfer rate of the HVAC equipment, each value corresponding to a different time step within a time period. The processing circuit is configured to use the thermal resistance, time step specific values of the temperatures, and time step specific values of the heat transfer rate to estimate corresponding values of a heat disturbance. The processing circuit is configured to operate the HVAC equipment using a model-based control technique based on the heat disturbance values.

The invention relates to a method for generating a control system for a target system, wherein: operational data is received; a first neural model component is trained with the received operational data for generating a prediction on a state of the target system based on the received operational data; a second neural model component is trained with the operational data for generating a regularizer for use in inverting the first neural model component; and the control system is generated by inverting the first neural model component by optimization and arranging to apply the regularizer generated with the second neural model component in the optimization. The invention relates also to a system and a computer program product.

A predictive analytics apparatus, engine, system and method capable of providing real time analytics in a manufacturing system. The apparatus, engine, system and method may include a data input capable of receiving raw data output from at least one machine operable to effect the manufacturing system embodiments, and a processor associated with a computing memory and suitable for executing code from the computing memory. The code may comprise an adaptor capable of pushing the received raw data to one or more databases to processed data; an extractor capable of extracting the processed data from the one or more databases; predictive analytics capable of receiving the extracted processed data and applying thereto at least one predictive model comprised of target data for the at least one machine, and capable of providing feedback to the at least one machine to modify performance of the at least one machine based on the application of the at least one predictive model; and a visualizer capable of providing at least a visualization of the feedback and of the performance.

A predictive controller for a building energy system includes one or more processing circuits configured to obtain a constraint that defines a total electric load to be served by the building energy system at each time step of a time period as a summation of multiple source-specific energy components. The source-specific energy components include a first energy component indicating a first amount of energy to obtain from a first energy source during the time step and a second energy component indicating a second amount of energy to obtain from a second energy source during the time step. The one or more processing circuits are configured to perform a predictive control process subject to the constraint to determine values of the source-specific energy components at each time step of the time period and operate equipment of the building energy system using the values of the source-specific energy components.

A heating, ventilation, or air conditioning (HVAC) system for a building includes HVAC equipment configured to provide heating or cooling to one or more building spaces and one or more controllers. The one or more controllers include one or more processing circuits configured to generate energy targets for the one or more building spaces using a thermal capacitance of the one or more building spaces to which the heating or cooling is provided by the HVAC equipment, generate setpoints for the HVAC equipment using the energy targets for the one or more building spaces to which the heating or cooling is provided by the HVAC equipment, and operate the HVAC equipment using the setpoints to provide the heating or cooling to the one or more building spaces.

“Just in time” operational techniques allow equipment of swimming pools or spas to achieve identified water temperatures at specified times. A user may supply information such as a desired water temperature (i.e. a temperature set point) and a time at which the water is desired to be at the desired temperature. After receiving the user-supplied information, software may account as well for certain environmental conditions to devise a suitable schedule for controlling heating of the water of the swimming pool or spa. Adjustments may be made to the schedule based on then-current water temperatures or other changed conditions.