Grant: Collaborative Research and Development Grants
University of New Brunswick
$52,600.00
A novel approach to potato processing using intelligent data analytics and advanced control
EF 2015-16
Résumé
In the manufacturer of food in the food industry, there are rigid operational standards and processing
methodologies that have to be adhered to. In many instances, these operations and processing methods demand
that they are well controlled within tight tolerances. The processing of the raw potatoes to a wide variety of
products is just one example. McCain Foods in Florenceville, NB, Canada since 1957 as a company has been
committed itself to be continuously improving and applying new technologies to its production of nearly
one-third of the French fried potatoes in the world. This project focuses on conducting research in order to
enhance the potato peeling process, which is key to overall efficiency of the production of potato fries.
Currently, the peeling process involves using steam at high flows entering a rotating vessel that contains the
raw material for a defined period of time. The optimal time for steaming is not known at this point as well as
the vessel's rotating speed. Other parameters influence the peeling process such as the potato dimensionality
and type. As the steaming process is key to the peeling of the raw potato, the company is keen on improving
this initial process for minimizing energy consumption and maximizing peeling efficiency. Over steaming or
under steaming can result in poor peeling, translating to millions of dollars annually in lost revenue. The
research would involve several researchers conducting sequential and concurrent tasks that include the
following: (a) formulating from first principles an analytical model of the overall potato peeling mechanism,
(b) conducting various experiments to formulate nonlinear time series models, (c) integrating high resolution
thermal imaging cameras that provide thermal signatures of the raw material during steaming and after, (c)
using the thermal data to enhance these time series models in realtime, and (d) developing an intelligent
controller that uses this information (thermal and many others) to make optimal actions to the several actuators
(valves, heaters, motors etc.). McCain Foods will be providing $50,000 in cash as well as $54,400 inkind
support on this project. The project has a timeline of 2 years.
Programme
Collaborative Research and Development Grants
Bénéficiaire
Emplacement
New Brunswick, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
453295-2013
Name nom
Dubay, Rickey
Department département
Mechanical Engineering
Organization id
75
Institution établissement
University of New Brunswick
Province en
New Brunswick
Province fr
Nouveau-Brunswick
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2015
Competition year année de concours
2013
Award amount
52600
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
Research partnerships programs
Group fr
Programmes de partenariat
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
2109
Research subject group en
Mechanical engineering
Research subject group fr
Génie mécanique
Research subject en
Mechanical systems and instrumentation
Research subject fr
Instrumentation et systèmes mécaniques
Partie
2
Nb partie
2
Application title
A novel approach to potato processing using intelligent data analytics and advanced control
Keyword
NSERC
Application summary
In the manufacturer of food in the food industry, there are rigid operational standards and processing
methodologies that have to be adhered to. In many instances, these operations and processing methods demand
that they are well controlled within tight tolerances. The processing of the raw potatoes to a wide variety of
products is just one example. McCain Foods in Florenceville, NB, Canada since 1957 as a company has been
committed itself to be continuously improving and applying new technologies to its production of nearly
one-third of the French fried potatoes in the world. This project focuses on conducting research in order to
enhance the potato peeling process, which is key to overall efficiency of the production of potato fries.
Currently, the peeling process involves using steam at high flows entering a rotating vessel that contains the
raw material for a defined period of time. The optimal time for steaming is not known at this point as well as
the vessel's rotating speed. Other parameters influence the peeling process such as the potato dimensionality
and type. As the steaming process is key to the peeling of the raw potato, the company is keen on improving
this initial process for minimizing energy consumption and maximizing peeling efficiency. Over steaming or
under steaming can result in poor peeling, translating to millions of dollars annually in lost revenue. The
research would involve several researchers conducting sequential and concurrent tasks that include the
following: (a) formulating from first principles an analytical model of the overall potato peeling mechanism,
(b) conducting various experiments to formulate nonlinear time series models, (c) integrating high resolution
thermal imaging cameras that provide thermal signatures of the raw material during steaming and after, (c)
using the thermal data to enhance these time series models in realtime, and (d) developing an intelligent
controller that uses this information (thermal and many others) to make optimal actions to the several actuators
(valves, heaters, motors etc.). McCain Foods will be providing $50,000 in cash as well as $54,400 inkind
support on this project. The project has a timeline of 2 years.