Abstract: There is provided a method of controlling an industrial gas plant complex comprising a plurality of industrial gas plants powered by one or more renewable power sources, the method being executed by at least one hardware processor, the method comprising receiving time-dependent predicted power data for a pre-determined future time period from the one or more renewable power sources; receiving time-dependent predicted operational characteristic data for each industrial gas plant; utilizing the predicted power data and predicted characteristic data in an optimization model to generate a set of state variables for the plurality of industrial gas plants; utilizing the generated state variables to generate a set of control set points for the plurality of industrial gas plants; and sending the control set points to a control system to control the industrial gas plant complex by adjusting one or more control set points of the industrial gas plants.

Abstract: Systems and methods for monitoring environmental variables associated with temperature-sensitive products during shipping may include sensors attached to or inside a container being shipped and a remote processor receiving data provided by the sensors. For instance, temperature and orientation may be sensed over time and a status associated with a temperature-sensitive product may be determined based on the temperature and/or orientation and based on changes in the temperature and/or orientation over time. In this manner, potentially deleterious temperature and/or orientation excursions may be predicted and monitored.

Abstract: A method and system for selecting a design configuration of an industrial gas plant complex comprising one or more industrial gas plants and powered by one or more renewable power sources.

Abstract: There is provided a method of determining and utilizing predicted available power resources from one or more renewable power sources for one or more industrial gas plants comprising one or more storage resources. The method is executed by at least one hardware processor and comprises: obtaining historical time-dependent environmental data associated with the one or more renewable power sources; obtaining historical time-dependent operational characteristic data associated with the one or more renewable power sources; training a machine learning model based on the historical time-dependent environmental data and the historical time-dependent operational characteristic data; executing the trained machine learning model to predict available power resources for the one or more industrial gas plants for a pre-determined future time period; and controlling the one or more industrial gas plants in response to the predicted available power resources for the pre-determined future time period.

Abstract: There is provided a method of controlling an industrial gas plant complex comprising a plurality of industrial gas plants powered by one or more renewable power sources, the method being executed by at least one hardware processor, the method comprising receiving time-dependent predicted power data for a pre-determined future time period from the one or more renewable power sources; receiving time-dependent predicted operational characteristic data for each industrial gas plant; utilizing the predicted power data and predicted characteristic data in an optimization model to generate a set of state variables for the plurality of industrial gas plants; utilizing the generated state variables to generate a set of control set points for the plurality of industrial gas plants; and sending the control set points to a control system to control the industrial gas plant complex by adjusting one or more control set points of the industrial gas plants.

Abstract: There is provided a method of determining and utilizing predicted available power resources from one or more renewable power sources for one or more industrial gas plants comprising one or more storage resources. The method is executed by at least one hardware processor and comprises: obtaining historical time-dependent environmental data associated with the one or more renewable power sources; obtaining historical time-dependent operational characteristic data associated with the one or more renewable power sources; training a machine learning model based on the historical time-dependent environmental data and the historical time-dependent operational characteristic data; executing the trained machine learning model to predict available power resources for the one or more industrial gas plants for a pre-determined future time period; and controlling the one or more industrial gas plants in response to the predicted available power resources for the pre-determined future time period.

Abstract: A method of controlling an industrial gas production facility comprising: receiving time-dependent power data receiving time-dependent operational characteristic data; defining one or more power constraints for the operational parameters of the power network; defining one or more process constraints for the operational parameters of each industrial gas plant; generating, based on the power data, the operational characteristic data, the one or more power constraints and the one or more process constraints, control set point values for the one or more industrial gas plants to achieve a pre-determined production parameter for the industrial gas production facility; and sending the control set point values to a control system to control the one or more industrial gas plants by adjusting one or more control set points of the industrial gas plants to achieve the pre-determined production parameter for the industrial gas production facility.

Abstract: There is provided a method of determining and utilizing predicted available power resources from one or more renewable power sources for one or more industrial gas plants comprising one or more storage resources. The method is executed by at least one hardware processor and comprises: obtaining historical time-dependent environmental data associated with the one or more renewable power sources; obtaining historical time-dependent operational characteristic data associated with the one or more renewable power sources; training a machine learning model based on the historical time-dependent environmental data and the historical time-dependent operational characteristic data; executing the trained machine learning model to predict available power resources for the one or more industrial gas plants for a pre-determined future time period; and controlling the one or more industrial gas plants in response to the predicted available power resources for the pre-determined future time period.

Abstract: There is provided a method of controlling an industrial gas plant complex comprising a plurality of industrial gas plants powered by one or more renewable power sources, the method being executed by at least one hardware processor, the method comprising receiving time-dependent predicted power data for a pre-determined future time period from the one or more renewable power sources; receiving time-dependent predicted operational characteristic data for each industrial gas plant; utilizing the predicted power data and predicted characteristic data in an optimization model to generate a set of state variables for the plurality of industrial gas plants; utilizing the generated state variables to generate a set of control set points for the plurality of industrial gas plants; and sending the control set points to a control system to control the industrial gas plant complex by adjusting one or more control set points of the industrial gas plants.

Abstract: A method of controlling an industrial gas production facility comprising: receiving time-dependent power data receiving time-dependent operational characteristic data; defining one or more power constraints for the operational parameters of the power network; defining one or more process constraints for the operational parameters of each industrial gas plant; generating, based on the power data, the operational characteristic data, the one or more power constraints and the one or more process constraints, control set point values for the one or more industrial gas plants to achieve a pre-determined production parameter for the industrial gas production facility; and sending the control set point values to a control system to control the one or more industrial gas plants by adjusting one or more control set points of the industrial gas plants to achieve the pre-determined production parameter for the industrial gas production facility.

Abstract: There is provided a method of monitoring operational characteristics of an industrial gas plant complex comprising a plurality of industrial gas plants. The method being executed by at least one hardware processor and comprising: assigning a machine learning model to each of the industrial gas plants forming the industrial gas plant complex; training the respective machine learning model for each industrial gas plant based on received historical time-dependent operational characteristic data for the respective industrial gas plant; executing the trained machine learning model for each industrial gas plant to predict operational characteristics for each respective industrial gas plant for a pre-determined future time period; and comparing predicted operational characteristic data for each respective industrial gas plant for a pre-determined future time period with measured operational characteristic data for the corresponding time period to identify deviations in industrial gas plant performance.

Abstract: There is provided a method of determining and utilizing predicted available power resources from one or more renewable power sources for one or more industrial gas plants comprising one or more storage resources. The method is executed by at least one hardware processor and comprises: obtaining historical time-dependent environmental data associated with the one or more renewable power sources; obtaining historical time-dependent operational characteristic data associated with the one or more renewable power sources; training a machine learning model based on the historical time-dependent environmental data and the historical time-dependent operational characteristic data; executing the trained machine learning model to predict available power resources for the one or more industrial gas plants for a pre-determined future time period; and controlling the one or more industrial gas plants in response to the predicted available power resources for the pre-determined future time period.

Abstract: There is provided a method of controlling an industrial gas plant complex comprising a plurality of industrial gas plants powered by one or more renewable power sources, the method being executed by at least one hardware processor, the method comprising receiving time-dependent predicted power data for a pre-determined future time period from the one or more renewable power sources; receiving time-dependent predicted operational characteristic data for each industrial gas plant; utilizing the predicted power data and predicted characteristic data in an optimization model to generate a set of state variables for the plurality of industrial gas plants; utilizing the generated state variables to generate a set of control set points for the plurality of industrial gas plants; and sending the control set points to a control system to control the industrial gas plant complex by adjusting one or more control set points of the industrial gas plants.

Abstract: Apparatus and methods are disclosed that allow for the monitoring and analysis of production process data for a multi-step asynchronous cyclic production process (e.g. pressure swing adsorption) in a steady state plant (such as a steam methane reforming plant). Data collected from cooperating sensors is processed applying a moving window discrete Fourier transform (DFT). The transformed data can be further analyzed in the broader steady-state plant environment to accurately detect any process anomalies and avoid false alarms.

Abstract: Apparatus and methods are disclosed that allow for the monitoring and analysis of production process data for a multi-step asynchronous cyclic production process (e.g. pressure swing adsorption) in a steady state plant (such as a steam methane reforming plant). Data collected from cooperating sensors is processed applying a moving window discrete Fourier transform (DFT). The transformed data can be further analyzed in the broader steady-state plant environment to accurately detect any process anomalies and avoid false alarms.

Abstract: A system and method of for determining multiple uniformity metrics of a semiconductor wafer manufacturing process includes collecting a quantity across each one of a group of semiconductor wafers. The collected quantity data is scaled and a principal component analysis (PCA) is performed on the collected, scaled quantity data to produce a first set of metrics for the first group of semiconductor wafers. The first set of metrics including a first loads matrix and a first scores matrix.

Abstract: A system and method of for determining multiple uniformity metrics of a semiconductor wafer manufacturing process includes collecting a quantity across each one of a group of semiconductor wafers. The collected quantity data is scaled and a principal component analysis (PCA) is performed on the collected, scaled quantity data to produce a first set of metrics for the first group of semiconductor wafers. The first set of metrics including a first loads matrix and a first scores matrix.