Canada Spends Logo

Explorez notre analyse du budget 2026

Grant: Alliance Grants

McMaster University

$400,000.00

Next Generation Data Driven Modeling and Control Design and Applications

EF 2024-25

Résumé
With newer sensing and data transfer capabilities, the availability of process data has increased manifold. Thus real time accoustic/thermal/Ultra Violet vis- spectroscopic measurements of process variables in processes such as bioprocessing to oil and gas production can be obtained. Simultaneously, there has been a resurgence in data modeling capabilities and tools such as neural networks in particular, and machine learning in general, but the majority of the applications have been in image recognition and classification (or even in generative language processing tools such as ChatGPT). The McMaster Advanced Control Consortium is uniquely positioned in that there are researchers with expertise in data driven modeling and control and they work in close collaboration with industrial partners looking to turn the wide availability of data into meaningful information for the purposes of operating their processes better and more safely. This proposal will therefore develop the next generation of data driven modeling, plant operations monitoring and fault detection, control, and optimization tools with applicability to several industrial sectors including energy systems, metal refining, bio-pharmaceutical production, and polymers processing. Since the tools will be data driven, practitioners will be able to readily adapt them to utilize data from their processes and implement control and optimization solutions that will not just significantly enhance process operation but make the process operation more environmentally friendly and sustainable. The impact to Canada is manifold- first, it will widely impact Canada's goal of moving towards a sustainable economy and manufacturing and do it through highly engaged industrial partners. Second, due to the broad applicability of the proposed methods, it will benefit widely the industrial sectors that are either already core to (such as metal refining), or expected to be, a central part of Canada's manufacturing sector (such as biopharmaceuticals). Last, but not the least, it will create HQP in areas needed by Canada.
Programme
Alliance Grants
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
590260-2023
Name nom
Mhaskar, Prashant P
Department département
Chemical Engineering
Organization id
27
Institution établissement
McMaster University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2024
Competition year année de concours
2023
Award amount
400000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
500
Area of application group en
Manufacturing processes and products
Area of application group fr
Procédés et produits industriels
Area of application en
Manufacturing processes and products
Area of application fr
Procédés et produits industriels
Research subject code
1800
Research subject group en
Chemical engineering
Research subject group fr
Génie chimique
Research subject en
Chemical engineering
Research subject fr
Génie chimique
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
Next Generation Data Driven Modeling and Control Design and Applications
Keywords
data driven models; hybrid models; neural networks; physics informed neural networks; process control
Application summary
With newer sensing and data transfer capabilities, the availability of process data has increased manifold. Thus real time accoustic/thermal/Ultra Violet vis- spectroscopic measurements of process variables in processes such as bioprocessing to oil and gas production can be obtained. Simultaneously, there has been a resurgence in data modeling capabilities and tools such as neural networks in particular, and machine learning in general, but the majority of the applications have been in image recognition and classification (or even in generative language processing tools such as ChatGPT). The McMaster Advanced Control Consortium is uniquely positioned in that there are researchers with expertise in data driven modeling and control and they work in close collaboration with industrial partners looking to turn the wide availability of data into meaningful information for the purposes of operating their processes better and more safely. This proposal will therefore develop the next generation of data driven modeling, plant operations monitoring and fault detection, control, and optimization tools with applicability to several industrial sectors including energy systems, metal refining, bio-pharmaceutical production, and polymers processing. Since the tools will be data driven, practitioners will be able to readily adapt them to utilize data from their processes and implement control and optimization solutions that will not just significantly enhance process operation but make the process operation more environmentally friendly and sustainable. The impact to Canada is manifold- first, it will widely impact Canada's goal of moving towards a sustainable economy and manufacturing and do it through highly engaged industrial partners. Second, due to the broad applicability of the proposed methods, it will benefit widely the industrial sectors that are either already core to (such as metal refining), or expected to be, a central part of Canada's manufacturing sector (such as biopharmaceuticals). Last, but not the least, it will create HQP in areas needed by Canada.