Abstract: A virtual plant operator system for use in an industrial plant. A data aggregator monitors operating data within the industrial plant and sends the operating data as a current state of the industrial plant to an operator assistant. The operator assistant includes a digital twin of the industrial plant and an artificial intelligence engine. The digital twin of the industrial plant receives the current state and simulates plant operations based on the current state. The artificial intelligence engine has at least one machine-learned model. The machine-learned model processes the simulated plant operations based on the current state to determine a recommendation output. The recommendation output includes stabilizing actions to the plant operations in the industrial plant and/or a predicted degree of shutdown responsive to each of the stabilizing actions.

Abstract: A machine-learned method and system for use in operating an industrial plant. A digital twin of the industrial plant is configured to simulate plant operations based on operating variables from a data store. A machine-learned model comprises a stabilizing agent and a disrupting agent. The stabilizing agent modifies the operating variables within the digital twin to perform a stabilizing action for limiting a degree of shutdown and the disrupting agent modifies the operating variables within the digital twin to perform a disruptive action for increasing the degree of shutdown. A composite action reward is configured to reward the machine-learned model for reducing the degree of shutdown from an initial state of the digital twin to a post-action-state of the digital twin.

Abstract: In a method of automating engineering design a knowledge base (KB) is queried for a template to map with new control loop (CL) data of a new CL that was identified in new digitized design data for a new engineering project, the query including the new CL data. The KB is trained to map past CL data of past CLs identified in past digitized design data from past engineering projects to respective templates based on past instantiation of the respective templates with the past CLs by the past engineering projects. The method further includes receiving a selected template in response to the query, wherein the selected template is selected based on its mapping with past CL data that matches the new CL data, and providing configuration data, including an instantiation of the selected template with the new CL data, for implementation of the new CL in an engineering system.

Abstract: A method of automating engineering design is provided. The method includes receiving a training set including pairings of control loop data for respective control loops identified in digitized design data and templates that were instantiated using the control loop data of the respective control loops and training, using machine learning, a knowledge base, based on the training set. The knowledge base, once trained, is configured to be queried with digitized new control loop data, predict a template to pair with the digitized new control loop data, and the predicted template, and the predicted template is configured to be instantiated with the new control loop data for implementation of a control loop in an engineering system.

Abstract: Automated hardware device engineering and design by representing hardware engineering requirements via a wiring typical schema. An engineering workbench application executing on a computing device utilizes a wiring typical schema representing generic and project-specific requirements to automate hardware device engineering and design tasks in real time.

Abstract: Automated hardware device engineering and design by representing hardware engineering requirements via a wiring typical schema. An engineering workbench application executing on a computing device utilizes a wiring typical schema representing generic and project-specific requirements to automate hardware device engineering and design tasks in real time.