Abstract: A method of monitoring and controlling an industrial process includes capturing images of a non-sensible process condition of the industrial process with an image capture device, receiving the images of the non-sensible process condition with a computer system, and converting each image into a corresponding numeric value with a machine learning model stored in and operable by the computer system. The corresponding numeric value is then received and compared to a predetermined numeric value for the non-sensible process condition with a closed-loop controller, and an actuator operates with the closed-loop controller to adjust operation of the industrial process when the corresponding numeric value fails to match the predetermined numeric value or is outside of a defined threshold near the predetermined numeric value.

Abstract: A virtual operation assistant for use in a processing/manufacturing facility (e.g., processing/manufacturing facility) may be an autonomous system that interfaces with a user. The virtual operation assistant may collect real-time and/or historical data about processes and/or hardware in a processing/manufacturing facility, analyze the data, provide reporting and/or recommendations based on the data, execute commands relating to the operational parameters of the processes and/or hardware, and any combination thereof.

Abstract: A method for operating a processing/manufacturing facility (e.g., processing/manufacturing facility) may comprise: collecting operational conditions in a processing/manufacturing facility from one or more automated systems and/or sensors; communicating the operational conditions to one or more of a plurality of agents that comprise an adaptive analytical model, wherein the plurality of agents comprises an agent selected from the group consisting of: a software sensor agent, a base control stability agent, an alarm agent, a transient process agent, an advanced control optimization agent, a computer vision agent, a display agent, an integrator/orchestrator agent, and any combination thereof; and deriving data, instructions for changing operational parameters of a process of the processing/manufacturing facility, and/or recommendations for changing operational parameters of the process of the processing/manufacturing facility using the adaptive analytical models of the agents.

Abstract: A method of monitoring and controlling an industrial process includes capturing images of a non-sensible process condition of the industrial process with an image capture device, receiving the images of the non-sensible process condition with a computer system, and converting each image into a corresponding numeric value with a machine learning model stored in and operable by the computer system. The corresponding numeric value is then received and compared to a predetermined numeric value for the non-sensible process condition with a closed-loop controller, and an actuator operates with the closed-loop controller to adjust operation of the industrial process when the corresponding numeric value fails to match the predetermined numeric value or is outside of a defined threshold near the predetermined numeric value.

Abstract: The present invention is a method for synchronizing multiple layers of constrained optimization with both layers having some common variables in a to processing plant. The layers of optimization can include Planning, Scheduling, Real-Time Optimization and Model Predictive Control.

Abstract: The present invention is a method for synchronizing multiple layers of constrained optimization with both layers having some common variables in a to processing plant. The layers of optimization can include Planning, Scheduling, Real-Time Optimization and Model Predictive Control.

Abstract: Methods and systems for substantially optimizing plant operations within a manufacturing environment. The method can include separating the manufacturing environment into two or more individual modules, wherein each individual module contains a fundamental principles-based model, and wherein the totality of the individual modules represents the entire manufacturing environment. Each individual module can be independently parameterized upon said module reaching steady state, wherein inter-module data flow can be provided to at least one of the individual modules during parameterization, and wherein an output of the parameterization comprises an individual, calibrated steady-state model of each individual module. A reduced order model can be derived from each parameterized module, and the reduced order models can be assembled to provide a facility reduced order model. The facility reduced order model can then be solved to provide improved or new operating conditions or operating condition targets.

Abstract: Methods and systems for substantially optimizing plant operations within a manufacturing environment. The method can include separating the manufacturing environment into two or more individual modules, wherein each individual module contains a fundamental principles-based model, and wherein the totality of the individual modules represents the entire manufacturing environment. Each individual module can be independently parameterized upon said module reaching steady state, wherein inter-module data flow can be provided to at least one of the individual modules during parameterization, and wherein an output of the parameterization comprises an individual, calibrated steady-state model of each individual module. A reduced order model can be derived from each parameterized module, and the reduced order models can be assembled to provide a facility reduced order model. The facility reduced order model can then be solved to provide improved or new operating conditions or operating condition targets.