Reference number : GC-2017-Q3-00595, amendment number : , agreement type : G, recipient legal name : Yacout, Soumaya (École Polytechnique de Montréal), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : The production of Chlor-Alkli by using electrolysis of aqueous solutions of sodium chloride (or brine) is one of_x000D_
the largest industrial scale electro-synthesis worldwide. Plants with more than 1000 individual reactors, in_x000D_
which 0.2 mm thin membranes separate chlorine and hydrogen, are common. The Chlor-Alkali manufacturing_x000D_
process must be fully controlled in order to avoid any wrong operation which can cause explosions, highly_x000D_
toxic gas releases, irreversible damages of very expensive cell components and dramatic maintenance costs and_x000D_
production loss._x000D_
At R2, which is a leading Canadian enterprise in this field, expert systems are combined to well-known_x000D_
machine learning techniques in order to monitor and to detect any abnormal performance and operating_x000D_
conditions. This combined system faces some scientific challenges due to the nature of the acquired data. Thus,_x000D_
the objective of this project is to address these challenges and to propose some solutions that are based on the_x000D_
latest advancement in the field of machine learning._x000D_
In this research, we seek to study these challenges and to propose solutions, in order to construct a warning and_x000D_
decision support system by using machine-learning techniques. This system will detect and predict any_x000D_
abnormal operating conditions, and will advise the operator of the best possible action that must be taken., program purpose french : The production of Chlor-Alkli by using electrolysis of aqueous solutions of sodium chloride (or brine) is one of_x000D_
the largest industrial scale electro-synthesis worldwide. Plants with more than 1000 individual reactors, in_x000D_
which 0.2 mm thin membranes separate chlorine and hydrogen, are common. The Chlor-Alkali manufacturing_x000D_
process must be fully controlled in order to avoid any wrong operation which can cause explosions, highly_x000D_
toxic gas releases, irreversible damages of very expensive cell components and dramatic maintenance costs and_x000D_
production loss._x000D_
At R2, which is a leading Canadian enterprise in this field, expert systems are combined to well-known_x000D_
machine learning techniques in order to monitor and to detect any abnormal performance and operating_x000D_
conditions. This combined system faces some scientific challenges due to the nature of the acquired data. Thus,_x000D_
the objective of this project is to address these challenges and to propose some solutions that are based on the_x000D_
latest advancement in the field of machine learning._x000D_
In this research, we seek to study these challenges and to propose solutions, in order to construct a warning and_x000D_
decision support system by using machine-learning techniques. This system will detect and predict any_x000D_
abnormal operating conditions, and will advise the operator of the best possible action that must be taken., agreement title english : Automatic fault and performance loss detection of industrial chlorine electrolysis reactors at R2, agreement title french : Automatic fault and performance loss detection of industrial chlorine electrolysis reactors at R2, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-11-08, 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 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., 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