A method of process monitoring of an industrial process involving a tangible material run in an industrial facility: providing an integrated facility digital twin implemented by a computer system including a processor and non-transitory memory running software stored in said memory which implements an aggregation algorithm that utilizes models including: (i) a plurality of inter-related static models for said industrial facility comprising an asset model that describes devices and systems in said industrial facility including sensors coupled to processing equipment and a flowsheet model (264, 264'), wherein the flowsheet model defines how material and utility flows traverse various processing equipment in the industrial process, and (ii) dynamic models of said industrial facility comprising calculation models to calculate at least one of a status of said processing equipment or said industrial process or to calculate an efficiency of said processing equipment or said industrial process, symptom and fault models to determine health of said processing equipment or health of said industrial process including cause and effect logic, dynamic simulation models to support simulation of said processing equipment or said industrial process to predict future performance or compare current performance against simulation, and machine learning models to detect changes in said processing equipment or performance of said industrial process based on data analysis, and said aggregation algorithm using outputs from said static models and said dynamic models, generating an aggregated view including performance alerts for at least one of said processing equipment and said industrial process based on said current performance of said industrial process. providing an integrated facility digital twin implemented by a computer system including a processor and non-transitory memory running software stored in said memory which implements an aggregation algorithm that utilizes models including: (i) a plurality of inter-related static models for said industrial facility comprising an asset model that describes devices and systems in said industrial facility including sensors coupled to processing equipment and a flowsheet model (264, 264'), wherein the flowsheet model defines how material and utility flows traverse various processing equipment in the industrial process, and (ii) dynamic models of said industrial facility comprising calculation models to calculate at least one of a status of said processing equipment or said industrial process or to calculate an efficiency of said processing equipment or said industrial process, symptom and fault models to determine health of said processing equipment or health of said industrial process including cause and effect logic, dynamic simulation models to support simulation of said processing equipment or said industrial process to predict future performance or compare current performance against simulation, and machine learning models to detect changes in said processing equipment or performance of said industrial process based on data analysis, and (i) a plurality of inter-related static models for said industrial facility comprising an asset model that describes devices and systems in said industrial facility including sensors coupled to processing equipment and a flowsheet model (264, 264'), wherein the flowsheet model defines how material and utility flows traverse various processing equipment in the industrial process, and (ii) dynamic models of said industrial facility comprising calculation models to calculate at least one of a status of said processing equipment or said industrial process or to calculate an efficiency of said processing equipment or said industrial process, symptom and fault models to determine health of said processing equipment or health of said industrial process including cause and effect logic, dynamic simulation models to support simulation of said processing equipment or said industrial process to predict future performance or compare current performance against simulation, and machine learning models to detect changes in said processing equipment or performance of said industrial process based on data analysis, and said aggregation algorithm using outputs from said static models and said dynamic models, generating an aggregated view including performance alerts for at least one of said processing equipment and said industrial process based on said current performance of said industrial process.