Trajectory optimization is process of designing a trajectory of operating variables that optimizes measure of performance while satisfying a set of constraints, when the system moves from one state to another. It is very necessary to achieve optimization in real time. A system and method for real-time trajectory optimization has been provided. The trajectory optimization of a process can be performed in any dynamical automated system. The system is configured to optimize the trajectory in both online and offline mode. In the online mode, the system optimizes the trajectory of the process in real-time. The system has the ability to handle both machine learning and deep learning based time series models along with first principles based models represented by ordinary/partial differential equation or differential algebraic equation based dynamic models of the process to estimate process variables given the disturbance profile and the actuation profile of manipulated variables.

Methods and systems are disclosed for determining a plan to optimize key performance indicators (KPIs) of an industrial process. Such a plan is determined based on generating a query for information associated with the KPIs and based on receiving user-provided object information corresponding to the KPIs. The method includes receiving, at a user interface, one or more KPIs associated with an industrial process. The method includes generating, based on the one or more KPIs, at least one query for information associated with the KPI. The method includes receiving, at the user interface, a response to the at least one query. The method includes determining, by an artificial intelligence agent, a plan for optimizing the KPI.

An apparatus, methods and systems for data collection related to an oil and gas process and disclosed. A system may include a multi-sensor acquisition component including a plurality of inputs and a plurality of outputs, a sensor data storage profile circuit structured to determine a data storage profile including a data storage plan for the plurality of inputs, a sensor communication circuit structured to interpret a plurality of inputs, a sensor data storage implementation circuit structured to store at least a portion of the inputs in response to the data storage profile, a data analysis circuit structured to analyze the plurality of inputs and determine a data quality parameter, and a data response circuit structured to adjust at least one of the data storage profile and the data collection routine in response to the data quality parameter.

A prediction method implemented by a computer, the method includes: receiving a classification model from a server, the classification model being a model for classifying logs of an electronic device into two or more classes, the server being a computer configured to distribute the classification model; calculating, with respect to different time points, a prediction error by using a predicted value outputted by the classification model and an actual measured value observed at each of the different time points; performing sequential machine learning for the classification model to have the prediction error satisfy a certain condition; and when a cumulative sum with respect to the prediction error of the sequential machine learning is equal to or greater than a threshold, requesting the server apparatus to relearn the classification model.

A platform for facilitating development of intelligence in an Industrial Internet of Things (IIoT) system generally includes a plurality of distinct data-handling layers having an industrial monitoring systems layer that collects data from or about a plurality of industrial entities in an industrial environment; an industrial entity-oriented data storage systems layer that stores the data collected by the industrial monitoring systems layer; and an adaptive intelligent systems layer that facilitates the coordinated development and deployment of intelligent systems in the IIoT system; wherein the adaptive intelligent systems layer includes an adaptive edge compute management system that adaptively manages edge computation, storage, and processing in the IIoT system.

A system and method of simulating and optimizing industrial and other processes includes a computer that performs multivariate analysis of input variables and output variables to generate a data model of the operation of the process. For industrial applications, the input variables include process variables and the output variables include result variables from the operation of the industrial process. The data model determines contributions to changes in the output or result variables by the respective input or process variables and is provided to a predictive algorithm to identify parameter values for input or process variables expected to have a most significant impact on the output or result variables during performance of the process. The outputs of the predictive algorithm are parameter values that are provided as input or process variables to the industrial process for simulation or performance optimization or product recommendations/optimizations.

A system configured to determine candidate sets of values for enforceable parameters for a physical system, and for each candidate set of values, determine a performance measurement for the physical system and generate a data point having an input portion indicative of the candidate set of values and an output portion indicative of the determined performance measurement. The system is further arranged to augment each data point to include an additional dimension comprising a bias value; project each augmented data point onto a surface of a unit hypersphere of the first number of dimensions; determine, using the projected augmented data points, a set of parameter values for a sparse variational Gaussian process, GP, on said unit hypersphere; and determine, using the sparse variational GP with the determined set of parameter values, a further set of values for the set of enforceable parameters.

The disclosure relates to libraries used in conjunction with integrated ventilation and temperature controls for enclosed structures. Specifically, the disclosure is directed to libraries, systems and methods for minimizing pollution while simultaneously conserving energy and maintaining required levels of fresh air inside a multi-storied structure and its internal spaces, in an optimal manner, utilizing dynamic, user-defined threshold values and implementing strategies based on user defined goals.

An irrigation maintenance system for facilitating irrigation of a farming area includes a sensor disposed at a main disconnect of a utility, a processor, and a memory. The sensor is configured to generate a signal indicative of abnormal operation of at least one component of a plurality of components of an irrigation system for the farming area based on network power quality, the network power quality including a phase balance, an inrush current, a power factor, or combinations thereof. The memory includes instructions stored thereon, which when executed by the processor, cause the irrigation maintenance system to: determine abnormal operation of the at least one component of the irrigation system based on the signal; and predict, by a machine learning model, a maintenance requirement of the at least one component based on the determined abnormal operation.

A building system of a building operates to receive historical data values of the plurality of data points for a plurality of historical times. The building system operates to execute an artificial intelligence to generate a plurality of different operating scenarios for operating equipment of the building for the plurality of historical times and simulate a plurality of values of data points of the building for the plurality of historical times based on the plurality of different operating scenarios and the historical data values. The building system operates to cause a user device to display a graphic representation of the building and a selectable element of a historical time of the plurality of historical times.