Reference number : GC-2017-Q4-00509, amendment number : , agreement type : G, recipient legal name : Duchesne, Carl (Université Laval), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Collaborative Research and Development Grants - Project, program name french : Subventions de recherche et développement coopérative - projet, program purpose english : The aim of this research program is to improve the decision making of primary aluminum smelters and anode manufacturers to mitigate the impact of the declining quality and increasing variability of the anode raw materials (coke and pitch) on aluminum reduction cell performance. To achieve this, new process analytical sensors will be developed and tested for rapid and non-destructive assessment of green and baked anode quality. These will be used to design an automatic control strategy to minimize the anode electrical resistivity, and to develop a baked anode inspection scheme to detect the presence of defects within their internal structure (large pores and cracks) and diagnose them. This part of the research program is expected to contribute to producing higher quality anodes and more consistently, and to enable timely feedback corrective actions to be implemented on the anode manufacturing process when abnormal situations occur. In the second part of the program, modelling tools combining the information provided by the analytical sensors and the routinely collected data from the anode manufacturing and aluminum reduction processes will also be proposed in order to better understand how the anodes populating a cell affects its performance and how individual anodes are consumed. These new learnings will subsequently be utilized to establish multivariate specifications regions for baked anode electrical resistivity and inspection results in order to sort defect green anodes before baking, and defect baked anodes before being set in the cells. The models could also support decision making on which cells would be the best candidates to receive the next set of fresh anodes and in which position in the cell to minimize the risk of deteriorating its performance. Expected benefits from improved decision making using the proposed sensors and modelling tools includes costs savings, waste reduction, lower carbon consumption, and higher energy efficiency, in addition to help maintaining the competitiveness and leadership position of the Canadian aluminum industry Worldwide._x000D_, program purpose french : The aim of this research program is to improve the decision making of primary aluminum smelters and anode manufacturers to mitigate the impact of the declining quality and increasing variability of the anode raw materials (coke and pitch) on aluminum reduction cell performance. To achieve this, new process analytical sensors will be developed and tested for rapid and non-destructive assessment of green and baked anode quality. These will be used to design an automatic control strategy to minimize the anode electrical resistivity, and to develop a baked anode inspection scheme to detect the presence of defects within their internal structure (large pores and cracks) and diagnose them. This part of the research program is expected to contribute to producing higher quality anodes and more consistently, and to enable timely feedback corrective actions to be implemented on the anode manufacturing process when abnormal situations occur. In the second part of the program, modelling tools combining the information provided by the analytical sensors and the routinely collected data from the anode manufacturing and aluminum reduction processes will also be proposed in order to better understand how the anodes populating a cell affects its performance and how individual anodes are consumed. These new learnings will subsequently be utilized to establish multivariate specifications regions for baked anode electrical resistivity and inspection results in order to sort defect green anodes before baking, and defect baked anodes before being set in the cells. The models could also support decision making on which cells would be the best candidates to receive the next set of fresh anodes and in which position in the cell to minimize the risk of deteriorating its performance. Expected benefits from improved decision making using the proposed sensors and modelling tools includes costs savings, waste reduction, lower carbon consumption, and higher energy efficiency, in addition to help maintaining the competitiveness and leadership position of the Canadian aluminum industry Worldwide._x000D_, agreement title english : Quality control of baked carbon anodes and assessment of their performance in aluminium reduction cells, agreement title french : Quality control of baked carbon anodes and assessment of their performance in aluminium reduction cells, agreement number : CRDPJ, agreement value : 123770.0, agreement start date : 2018-01-10, 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 2020-2021)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2020-2021)., 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