Reference number : GC-2017-Q1-01672, amendment number : , agreement type : G, recipient legal name : Shah, Sirish (University of Alberta), recipient country : CA, recipient province : nan, recipient city : Alberta, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Objectives 
The fusion of information from disparate sources of data is the key step in devising strategies for the foundation of a smart analytics platform. In the context of application of analytics in the process industry, this grant proposal is to develop a theoretical framework and a tool for seamless integration of information from process and alarm databases complimented with process connectivity information. 
The discovery of information from such diverse and complex data sources can be subsequently used for process and performance monitoring including alarm rationalization, root cause diagnosis of process faults, HAZard and OPerability (HAZOP) analysis, safe and optimal process operation. Such multivariate process data analytics involves information extraction from routine process data, that is typically non-categorical (as in numerical process data from sensors), plus categorical (or non-numerical or qualitative and binary) data from Alarm and Event (A&amp;E) logs combined with process connectivity or topology information that can be inferred from the data through causality analysis or as obtained from piping and instrument diagrams of a process. The later refers to the capture of material flow streams in process units as well information flow-paths in the process due to control loops. 
Novelty 
Highly interconnected process plants are now common and the analysis of root causes of process abnormality including predictive risk analysis is non-trivial. The thrust of this proposal is to develop a theoretical framework for extracting information and knowledge from archived process data using statistical inference schemes and integrating and validating such models with alarm data and process connectivity information. The unique aspect of this proposal is the inclusion of data-based process connectivity information for process monitoring and thus represents a major paradigm shift in process data analytics. Such a methodology would serve as an enabling tool for predictive and pro-active process asset maintenance and safe and optimal process operation. 
 Expected significance 
There is currently an explosion of applications of analytics in diverse areas (e.g. engineering, medicine, etc). In the same vein the volume of data currently archived by the process industry is massive (BIG Data) and the key aim of this proposal is to find value in this data and use this on-line for safe and optimal process operation. 
The socio-economic significance of this proposal will be pro-active, as opposed to reactive, management combined with highly productive and energy efficient process operation of plants that dot the Canadian landscape including pulp&amp;paper, petro-chemical, food processing , power generation, mineral processing and oil and gas exploration. An equally important aspect of this project is the education and training of manpower with statistical data mining skills that are in high demand., program purpose french : Objectives 
The fusion of information from disparate sources of data is the key step in devising strategies for the foundation of a smart analytics platform. In the context of application of analytics in the process industry, this grant proposal is to develop a theoretical framework and a tool for seamless integration of information from process and alarm databases complimented with process connectivity information. 
The discovery of information from such diverse and complex data sources can be subsequently used for process and performance monitoring including alarm rationalization, root cause diagnosis of process faults, HAZard and OPerability (HAZOP) analysis, safe and optimal process operation. Such multivariate process data analytics involves information extraction from routine process data, that is typically non-categorical (as in numerical process data from sensors), plus categorical (or non-numerical or qualitative and binary) data from Alarm and Event (A&amp;E) logs combined with process connectivity or topology information that can be inferred from the data through causality analysis or as obtained from piping and instrument diagrams of a process. The later refers to the capture of material flow streams in process units as well information flow-paths in the process due to control loops. 
Novelty 
Highly interconnected process plants are now common and the analysis of root causes of process abnormality including predictive risk analysis is non-trivial. The thrust of this proposal is to develop a theoretical framework for extracting information and knowledge from archived process data using statistical inference schemes and integrating and validating such models with alarm data and process connectivity information. The unique aspect of this proposal is the inclusion of data-based process connectivity information for process monitoring and thus represents a major paradigm shift in process data analytics. Such a methodology would serve as an enabling tool for predictive and pro-active process asset maintenance and safe and optimal process operation. 
 Expected significance 
There is currently an explosion of applications of analytics in diverse areas (e.g. engineering, medicine, etc). In the same vein the volume of data currently archived by the process industry is massive (BIG Data) and the key aim of this proposal is to find value in this data and use this on-line for safe and optimal process operation. 
The socio-economic significance of this proposal will be pro-active, as opposed to reactive, management combined with highly productive and energy efficient process operation of plants that dot the Canadian landscape including pulp&amp;paper, petro-chemical, food processing , power generation, mineral processing and oil and gas exploration. An equally important aspect of this project is the education and training of manpower with statistical data mining skills that are in high demand., agreement title english : Process data analytics, agreement title french : Process data analytics, agreement number : RGPIN, agreement value : 235000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada