Grant: Alliance Grants
Carleton University
$68,333.00
Quantitative, In-Line Spectroscopic Monitoring of Lubricating Oils in Diesel Engines
FY 2022-23
Summary
Our industrial partner, Gastops Ltd, is a Canadian world leader in condition monitoring of rotary machinery, notably wind turbines, gas turbines for power generation, and aerospace engines. In collaboration with Carleton University, the company is developing technologies for real-time condition monitoring using on-line sensors capable of predicting the remaining useful life of a lubricating oil. This is based on a real-time analysis of the light absorbed and emitted by the oil, which can tell us about the concentration of key additives and their degradation by-products. When these additives are depleted, the oil will lose its desirable properties. The current prototype the company uses is only capable of approximately detecting one type of additive (a family of antioxidants typified by a molecule called PAN). In this work, we will work with them to extend this to a second important type (another family of antioxidants typified by a molecule called BHT). This will require different analytical techniques and modeling of the oil degradation process. Our combined theoretical, computational and experimental approach will lead to the development of high-fidelity, predictive combination of sensors and models for this purpose. The impacts will be reduced waste oil from unnecessary lubricant changes, longer lives for rotary machinery, and more efficient energy utilization in Canadian industry.
Program
Alliance Grants
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
576376-2022
Name nom
Miller, Ronald Earle RE
Department département
Mechanical & Aerospace Engineering
Organization id
23
Institution établissement
Carleton University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2022
Award amount
68333
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
703
Area of application group en
Transportation systems and services
Area of application group fr
Services et systèmes de transport
Area of application en
Aerospace
Area of application fr
Aérospatial
Research subject code
3707
Research subject group en
Organic chemistry
Research subject group fr
Chimie organique
Research subject en
Kinetics, reactive intermediaries and mechanisms of reactions
Research subject fr
Cinétique, intermédiaires réactifs et mécanismes des réactions
Installment
inst:1
Partie
1
Nb partie
1
Application title
Quantitative, In-Line Spectroscopic Monitoring of Lubricating Oils in Diesel Engines
Application summary
Our industrial partner, Gastops Ltd, is a Canadian world leader in condition monitoring of rotary machinery, notably wind turbines, gas turbines for power generation, and aerospace engines. In collaboration with Carleton University, the company is developing technologies for real-time condition monitoring using on-line sensors capable of predicting the remaining useful life of a lubricating oil. This is based on a real-time analysis of the light absorbed and emitted by the oil, which can tell us about the concentration of key additives and their degradation by-products. When these additives are depleted, the oil will lose its desirable properties. The current prototype the company uses is only capable of approximately detecting one type of additive (a family of antioxidants typified by a molecule called PAN). In this work, we will work with them to extend this to a second important type (another family of antioxidants typified by a molecule called BHT). This will require different analytical techniques and modeling of the oil degradation process. Our combined theoretical, computational and experimental approach will lead to the development of high-fidelity, predictive combination of sensors and models for this purpose. The impacts will be reduced waste oil from unnecessary lubricant changes, longer lives for rotary machinery, and more efficient energy utilization in Canadian industry.