Reference number : GC-2017-Q1-01535, amendment number : , agreement type : G, recipient legal name : Huang, Biao (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 : Everyone in a process plant, from plant managers to engineers to technicians, relies on a massive amount of data, which plays a significant role in daily analysis and decision making. Common process control practice is to develop models based on data with the aid of process knowledge. But as modern process data has increased in dimensionality, diversity and complexity, traditional analytical tools have been unable to keep up with this onslaught of complex data. High dimensionality of data and irregularities during data collection pose many challenges in data-based modeling, thereby casting serious doubt on the validity of traditional modeling techniques. As a result, the process control research community is under ever increasing pressure to deliver analytic tools to cope with the challenges of the modern day practices of the process industries. 
 
Responding to this pressure and motivated by the real-life challenges faced by process industries, the shorter term objective of this proposal is to provide a solution to fundamental problems encountered in process identification in the presence of high dimensionality and irregularities in modern datasets. This research program will develop new process modeling techniques by which this enormous amount of data can be fruitfully utilized, to achieve safe and intelligent process operations. In the long term, the objective is to develop an integrated framework for identification and control of process systems by employing complex process data. Modeling and controller design are inseparable. The entangling of modeling and control design problems in the presence of complex data poses a significant challenge and is a relatively untouched field. This research program will contribute to the establishment of a new data-based control design theory and methodology. 
 
Our methodology deals with two critical problems simultaneously: data dimensionality and data irregularities. First, we establish a new dynamic feature analysis methodology, and then we make the methodology robust in the presence of data irregularities. Our solutions will be applicable to a wide range of industries that employ or will employ automation systems. Our research program will train young people who are highly qualified in data analytics and data-based modeling. They will be the next generation of technical leaders who will integrate these technologies into process plants to boost the competitiveness of Canadian industry and spearhead the drive to sell solutions worldwide., program purpose french : Everyone in a process plant, from plant managers to engineers to technicians, relies on a massive amount of data, which plays a significant role in daily analysis and decision making. Common process control practice is to develop models based on data with the aid of process knowledge. But as modern process data has increased in dimensionality, diversity and complexity, traditional analytical tools have been unable to keep up with this onslaught of complex data. High dimensionality of data and irregularities during data collection pose many challenges in data-based modeling, thereby casting serious doubt on the validity of traditional modeling techniques. As a result, the process control research community is under ever increasing pressure to deliver analytic tools to cope with the challenges of the modern day practices of the process industries. 
 
Responding to this pressure and motivated by the real-life challenges faced by process industries, the shorter term objective of this proposal is to provide a solution to fundamental problems encountered in process identification in the presence of high dimensionality and irregularities in modern datasets. This research program will develop new process modeling techniques by which this enormous amount of data can be fruitfully utilized, to achieve safe and intelligent process operations. In the long term, the objective is to develop an integrated framework for identification and control of process systems by employing complex process data. Modeling and controller design are inseparable. The entangling of modeling and control design problems in the presence of complex data poses a significant challenge and is a relatively untouched field. This research program will contribute to the establishment of a new data-based control design theory and methodology. 
 
Our methodology deals with two critical problems simultaneously: data dimensionality and data irregularities. First, we establish a new dynamic feature analysis methodology, and then we make the methodology robust in the presence of data irregularities. Our solutions will be applicable to a wide range of industries that employ or will employ automation systems. Our research program will train young people who are highly qualified in data analytics and data-based modeling. They will be the next generation of technical leaders who will integrate these technologies into process plants to boost the competitiveness of Canadian industry and spearhead the drive to sell solutions worldwide., agreement title english : Robust Process Identification with Dynamic Feature Analysis, agreement title french : Robust Process Identification with Dynamic Feature Analysis, agreement number : RGPIN, agreement value : 290000.0, 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