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: 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: An alarm rationalization system receiving and responsive to industrial process information collected from a process control system for identifying one or more alarms and executing an artificial intelligence (AI) alarm engine. The AI alarm engine builds a process/domain model based on the received industrial process information and historized alarm information to evaluate the alarms in accordance with a predefined alarm philosophy. The AI alarm engine then generates a plurality of alarm definitions based on the model to optimize the alarms. The AI alarm engine automatically populates a Master Alarm Database (MADB) with the alarm definitions. The alarms are then rationalized based on the alarm definitions stored in the MADB.