An energy control system (ECS) for controlling an energy storage system (ESS's) that includes energy storage devices(s) or an energy storage combination (ESDC's) including≥1 of the energy storage devices and ≥1 of the energy storage combinations. A power conversion system is coupled to an output of the ESDC, and a transformer is coupled to an output of the power conversion system. The ECS includes an ECS server and an ESS adapter configured for providing an interface between ECS server and the ESS. The ECS server is configured for reading status data from the ESS and submitting schedules including selected charging and discharging times to the ESS, monitoring or displaying a variance between an expected performance of the ESS based on the schedules and an actual ESS performance, and responsive to the variance being determined to be above a predetermined threshold, sending an update of the schedules to the ESS.

A digital product network system includes a set of digital products each having a product memory, a product network interface, and a product processor programmed with product instructions. The digital product network system includes a product network control tower having a control tower memory, a control tower network interface, and a control tower processor programmed with control tower instructions. The digital product network system includes a digital twin system defined at least in part by at least one of the product instructions or the control tower instructions to encode a set of digital twins representing the set of digital products

The present disclosure describes a method of controlling a manufacturing system using multivariate time series, the method comprising: recording data from one or more devices in the manufacturing system; storing the recorded data in a data storage as a plurality of time series, wherein each time series has a first recorded value corresponding to a first time and a final recorded value corresponding to an end of the time series; interpolating, within a first time window, missing values in the plurality of time series using a Bayesian model, wherein the missing values fall between the first and end time of the respective time series; storing the interpolated values as prediction data in a prediction storage, wherein the interpolated values include the uncertainty of each interpolated value; loading the recorded data that fall within a second time window from the data storage; loading prediction data from the prediction storage that fall within the second time window and for which no recorded data are available; optimizing the parameters of the Bayesian model using the loaded recorded data and the prediction data; predicting, using the Bayesian model, values for each of the time series for which loaded recorded and prediction data are not available; storing the predicted values as prediction data in the prediction storage, wherein the prediction values include the uncertainty of each prediction value; and adjusting one or more of the devices that generate the recorded data based on the prediction data within the second time window.

A digital product network system includes a set of digital products each having a product processor, a product memory, and a product network interface. The digital product network system includes a product network control tower having a control tower processor, a control tower memory, and a control tower network interface. The product processor and the control tower processor collectively include non-transitory instructions that program the digital product network system to generate product level data at the product processor, transmit the product level data from the product network interface, receive the product level data at the control tower network interface, encode the product level data as a product level data structure configured to convey parameters indicated by the product level data across the set of digital products, and write the product level data structure to at least one of the product memory and the control tower memory.

Embodiments are directed towards controlling uncontrolled release of ammonia from an engine of a vehicle. An estimated status of the engine is determined prior to an event, such as an estimated load on the engine prior to the vehicle going up a hill. A predictive model of uncontrolled ammonia release is generated for the estimated status. At least one engine-related countermeasure is selected based on the predictive model. If the predictive model of uncontrolled ammonia release with the selected countermeasures satisfies a threshold condition, then the selected engine-related countermeasure is employed.

Aspects of the invention include implemented method includes selecting an optimization algorithm for the control system of a processing plant based on whether the control system is guided by a linear-based predictive model or a non-linear-based predictive model, in which a gradient is available. Calculating set-point variables using the optimization algorithm. Predicting an output based on the calculated set-point variables. Comparing an actual output at the processing plant to the predicted output. Suspending a physical process at the processing plant in response to the actual output being a threshold value apart from the predicted output.

A system and method are presented for optimizing choices of control parameters. A method includes collecting demand sequences each associated with a resource managed by a technology platform; processing a demand sequence for a resource to calculate an optimized control parameter (CP) value set to manage an automated process within the technology platform, wherein calculating includes: processing the demand sequence with an advanced bootstrap process to generate a collection of bootstrapped demand sequences; processing the bootstrapped demand sequences with a performance prediction process that models the automated process to predict a performance metric for an initially selected CP value set; identifying a neighborhood of CP value sets that includes neighbors and the initially selected CP value set; predicting the performance metric for each neighbor with the performance prediction process; and identifying from the neighborhood of CP value sets the optimized CP value set that provides a best performance metric.

To control a hybrid dynamical system, a predictive feedback controller formulates a mixed-integer nonlinear programming (MINLP) problem including nonlinear functions of continuous optimization variables representing the continuous elements of the operation of the hybrid dynamical system and/or one or multiple linear functions of integer optimization variables representing the discrete elements of the operation of the hybrid dynamical system. The MINLP problem is formulated into a separable format ensuring that the discrete elements of the operation are present only in the linear functions of the MINLP problem. The MINLP problem is solved over multiple iterations using a partial convexification of a portion of a space of the solution including a current solution guess. The partial convexification produces a convex approximation of the nonlinear functions of the MINLP without approximating the linear functions of the MINLP to produce a partially convexified MINLP.

A method for prioritizing predictive model data streams includes receiving, by a device, a plurality of predictive model data streams. Each predictive model data stream includes a set of model parameters for a corresponding predictive model. Each predictive model is trained to predict future data values of a data source. The method includes prioritizing, by the device, each of the plurality of predictive model data streams. The method includes selecting at least one of the predictive model data streams based on a corresponding priority. The method includes parameterizing, by the device, a predictive model using the set of model parameters included in the selected at least one predictive model data stream. The method includes predicting, by the device, the future data values of the data source using the parameterized predictive model.

Computer-implemented methods for fault diagnosis in an industrial environment generally includes processing the plurality of sensor data values to determine a recognized pattern therefrom; retrieving at least one industrial-environment digital twin corresponding to the industrial environment, the at least one industrial-environment digital twin comprising a plurality of component digital twins, with each of the plurality of component digital twins corresponding to one of the plurality of components in the industrial environment, and wherein the at least one industrial-environment digital twin and the plurality of component digital twins are visual digital twins that are configured to be rendered in a visual manner; and rendering the at least one industrial-environment digital twin and the at least one respective component digital twin corresponding to the particular component in the client application in response to the received request and based on the operational condition of the particular component.