Abstract: Predetermined Key Performance Indicators (KPIs) of an industrial process may be predicted. A plurality of tags each identify a corresponding KPI of the industrial process and historical values for the KPIs that are identified by the plurality of tags. A KPI forecast model is trained for each of the KPIs that are identified by the plurality of tags, wherein each of the KPI forecast models is trained based at least in part on the received historical values for at least some of the KPIs that are identified by the plurality of tags. A forecasted KPI value is generated for each of the KPIs identified by the plurality of tags based at least in part on the corresponding KPI forecast model that corresponds to the respective KPI.

Abstract: A plurality of tags each identify a corresponding process parameter of an industrial process and historical values for the process parameter. A parameter forecast model is trained for each of the parameters that are identified by the plurality of tags, wherein each of the parameter forecast models is trained based at least in part on the received historical values for at least some of the parameters that are identified by the plurality of tags. A forecasted parameter value is generated for each of the parameters identified by the plurality of tags based at least in part on the corresponding parameter forecast model that corresponds to the respective parameter. A plurality of forecasted steady state periods and a plurality of forecasted transient state periods are predicted for each of one or more of the identified parameters.

Abstract: Predetermined Key Performance Indicators (KPIs) of an industrial process may be predicted. A plurality of tags each identify a corresponding KPI of the industrial process and historical values for the KPIs that are identified by the plurality of tags. A KPI forecast model is trained for each of the KPIs that are identified by the plurality of tags, wherein each of the KPI forecast models is trained based at least in part on the received historical values for at least some of the KPIs that are identified by the plurality of tags. A forecasted KPI value is generated for each of the KPIs identified by the plurality of tags based at least in part on the corresponding KPI forecast model that corresponds to the respective KPI.

Abstract: A method and the corresponding apparatus are carried out to interpret custom graphics to compile all of the equipment and relationships of the equipment, controls, the relative hierarchy of the graphics and other elements in a manufacturing plant and to then use all of that information to produce a standard operating procedure.

Abstract: A method and the corresponding apparatus are carried out to interpret custom graphics to compile all of the equipment and relationships of the equipment, controls, the relative hierarchy of the graphics and other elements in a manufacturing plant and to then use all of that information to produce a standard operating procedure.

Abstract: A method includes receiving a plurality of legacy graphics files associated with a control system for an industrial process, each legacy graphics file comprising a plurality of legacy graphics objects. The method also includes parsing the legacy graphics files to identify primitive graphic objects in each legacy graphics file. The method also includes determining relationships among the identified primitive graphic objects to determine a group of primitive graphic objects that form one component of the industrial process. The method also includes determining one or more patterns among the identified primitive graphic objects. The method also includes saving the one or more patterns in a pattern library.

Abstract: A method includes receiving a plurality of legacy graphics files associated with a control system for an industrial process, each legacy graphics file comprising a plurality of legacy graphics objects. The method also includes parsing the legacy graphics files to identify primitive graphic objects in each legacy graphics file. The method also includes determining relationships among the identified primitive graphic objects to determine a group of primitive graphic objects that form one component of the industrial process. The method also includes determining one or more patterns among the identified primitive graphic objects. The method also includes saving the one or more patterns in a pattern library.