Abstract: Certain manufacturing processes, such as crude oil refining, operate continuously, wherein a facility produces one product as the feed stock for that product changes and/or the output product transitions from one product to another. Inputs to the process include physical items (e.g., feed stock) as well as control inputs (e.g., temperature, pressure, etc.), and often a change in one affects one or more others. As a result, a facility may take time to reach a steady state. An artificial intelligence is provided to model the facility and process and generate a second command input to reduce the time required for the facility to reduce the lag time until the facility has returned to a steady state.

Abstract: Methods, apparatus, and processor-readable storage media for automatically migrating process capabilities using AI techniques are provided herein.

Abstract: Methods, apparatus, and processor-readable storage media for automatically identifying and correcting erroneous process actions using artificial intelligence techniques are provided herein. An example computer-implemented method includes discovering, during execution of a given process, one or more process action variants by processing data related to the given process using at least a first set of artificial intelligence techniques; categorizing at least a portion of the discovered process action variants into one or more groups based at least in part on historical process-related data and at least one density-based clustering algorithm; determining at least one resolution action in response to at least a portion of the one or more discovered process action variants by processing data pertaining to the one or more groups using at least a second set of artificial intelligence techniques; and performing the at least one determined resolution action.

Abstract: Methods, apparatus, and processor-readable storage media for automatically identifying and correcting erroneous process actions using artificial intelligence techniques are provided herein. An example computer-implemented method includes discovering, during execution of a given process, one or more process action variants by processing data related to the given process using at least a first set of artificial intelligence techniques; categorizing at least a portion of the discovered process action variants into one or more groups based at least in part on historical process-related data and at least one density-based clustering algorithm; determining at least one resolution action in response to at least a portion of the one or more discovered process action variants by processing data pertaining to the one or more groups using at least a second set of artificial intelligence techniques; and performing the at least one determined resolution action.

Abstract: In some examples, a server may use machine learning to determine, based on service requests associated with multiple computing devices, that a component included in the multiple computing devices is predicted to fail at a particular date. The server may use the machine learning to determine, based on the particular date and an expiration date of a warranty associated with the computing devices, that a customer may initiate a service request on a predicted service date. The machine learning may determine recommended solutions including purchasing an extended warranty, purchasing extended services (e.g., on-site service), purchasing a depot clinic service, or trading in the multiple computing devices for newer computing devices. In response to receiving a purchase order to purchase at least one of the recommended solutions, the server may initiate at least one of the recommended solutions.