Reference number : 172-2023-2024-Q4-1015386, amendment number : , agreement type : G, recipient legal name : The Royal Institution for the Advancement of Learning/McGill University, recipient country : CA, recipient province : QC, recipient city : Montreal|Montréal, recipient postal code : H3A 2M7, program name english : Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives, program name french : Programme de collaboration en science, en technologie et en innovation - Initiatives de collaboration en R-D, program purpose english : Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas., program purpose french : Collaborer à des programmes multipartites de recherche-développement dans le but de profiter de la synergie entre la recherche transformatrice à haut risque et à haut rendement et le potentiel de découvertes scientifiques et de percées technologiques révolutionnaires dans des secteurs d’intervention prioritaires., agreement title english : Leveraging digital twin for process eco-efficiency and optimization of pea protein fractionation, agreement title french : Utilisation du jumeau numérique pour favoriser l’écoefficacité des procédés et l’optimisation du fractionnement des protéines de pois, agreement number : 1015386, agreement value : 249920.0, agreement start date : 2024-03-22, agreement end date : 2026-03-31, description english : Thermal pre-treatment, specifically Radio Frequency (RF) treatment of pulses prior to protein extraction has been proven to improve fractionation efficiency and functionality of protein concentrates. Towards fully adopting these methods at industrial scale, it is necessary to establish the optimum process configuration and conditions that maximizes the overall technoeco-environmental performance. While the experimental optimization approach provides a reflective model for optimization, just a few scenarios can be carried out. Therefore, this project seeks to develop a digital twin model to reflect pilot-scale pea and faba bean flour and protein fractionation, thus providing a means for control, optimization, and productprocess performance prediction. This project will support the CSTIP goals in terms of enabling the innovation of new technologies. The dataset generated will support the design and improvement of plant protein fractionation technologies. Moreover, this will strengthen collaborations among different stakeholders in pulse protein production towards enhancing the overall Canadian bioeconomy., description french : Utilisation du jumeau numérique pour favoriser l’écoefficacité des procédés et l’optimisation du fractionnement des protéines de pois, expected results english : In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems., expected results french : À court terme, les résultats prévus seront des collaborations renforcées entre l'industrie, le milieu universitaire et le gouvernement pour soutenir l'excellence en recherche. À moyen terme, les résultats attendus seront le développement de nouvelles technologies potentiellement perturbatrices avec des collaborateurs. À long terme, trouver des solutions collaboratives aux défis des politiques publiques et créer des systèmes d'innovation plus solides., additional information english : nan, additional information french : nan, owner organization : nrc-cnrc, owner organization title : National Research Council Canada | Conseil national de recherches Canada