Canada Spends Logo

Explore our Budget 2026 analysis

Grant: Discovery Grants Program - Individual

Memorial University of Newfoundland

$28,000.00

Probabilistic predictive warning system for process operations

FY 2023-24

Summary
This program aims to develop a probabilistic predictive approach to the design and management of early warning systems used in process industries. The special features and novelty of the proposed approach is in the use of probabilistic models for alarm annunciation and the use of risk in every stage of warning design, namely, allocation, annunciation, prioritization and management. In addition, predictions will be used for issuing warnings. Industries significantly benefit from using predictions for control. On the other hand, considerable resources are used for risk assessment. However, current alarm systems are not risk-based; use of predictions is also restricted. Research efforts are required to make plant alarm systems an effective tool in preventing accidents. We plan to develop a comprehensive early warning system based on quantified risk and prediction of events from plant data. The current plant alarm systems use a deterministic setting where abnormality is described without any reference to its likelihood. Our objective is to develop tools to describe unwanted events in terms of likelihood and perform diagnosis by considering a set of possible combinations of failures that explain the observations. Methodologies will be developed to quantify uncertainties with plant models which arise from assumptions on model structures and noises in process data. Uncertainties associated with risk models will also be developed. Correlations and dependencies among events as well as their consequences will be quantified and taken into account in the design of warning system. We also want to propose an alarm design philosophy to complement the existing standards in incorporating risk. A prototype alarm system will be developed that will be integrate-able with the existing plant alarm systems. The new system will be tested in pilot plants and industrial settings. Process industries are in dire need of an improved alarm system. Operators are often overwhelmed by alarms and are unable to perform their main responsibilities. Risk is a measure that is well understood and highly valued. Hence, the developed methodology is expected to find widespread utility in process industries. By developing technologies and tools that ensure smooth, accident-free operation of industrial plants, this program will contribute directly to the productivity of the industrial sector. Thus it will help to save billions of dollars in losses caused by abnormal situations in industries. Along with the development of tools and methods, a number of highly qualified personnel will be trained in this program. The HQP will be trained on developing the tools as well as its implementation to real plants; thus the HQP will be valuable assets for the concerned industry. Finally, the developed methods will be useful for risk assessment in general with applications outside process facilities, e.g. for health monitoring, economic activities and natural disasters.
Program
Discovery Grants Program - Individual
Location
Newfoundland and Labrador, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
RGPIN-2019-04122
Name nom
Ahmed, Salim
Department département
Engineering and Applied Science, Faculty of - Engineering and Applied Science, Faculty of
Organization id
89
Institution établissement
Memorial University of Newfoundland
Province en
Newfoundland and Labrador
Province fr
Terre-Neuve-et-Labrador
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2019
Award amount
28000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1511
Committee name en
Materials and Chemical Engineering
Committee name fr
Génie chimique et des matériaux
Area of application code
300
Area of application group en
Energy resources
Area of application group fr
Ressources d'énergie
Area of application en
Energy resources (including production, exploration, processing, distribution and use)
Area of application fr
Ressources d'énergie (production, exploration, traitement, distribution et utilisation de l'énergie)
Research subject code
9999
Research subject group en
Not available
Research subject group fr
Not available
Research subject en
Non disponible
Research subject fr
Non disponible
Application title
Probabilistic predictive warning system for process operations
Keyword
Alarm philosopy; Dependency; Event warning; Model prediction; Probabilistic method; Process operations; Risk; Uncertainty; Warning system
Application summary
This program aims to develop a probabilistic predictive approach to the design and management of early warning systems used in process industries. The special features and novelty of the proposed approach is in the use of probabilistic models for alarm annunciation and the use of risk in every stage of warning design, namely, allocation, annunciation, prioritization and management. In addition, predictions will be used for issuing warnings. Industries significantly benefit from using predictions for control. On the other hand, considerable resources are used for risk assessment. However, current alarm systems are not risk-based; use of predictions is also restricted. Research efforts are required to make plant alarm systems an effective tool in preventing accidents. We plan to develop a comprehensive early warning system based on quantified risk and prediction of events from plant data. The current plant alarm systems use a deterministic setting where abnormality is described without any reference to its likelihood. Our objective is to develop tools to describe unwanted events in terms of likelihood and perform diagnosis by considering a set of possible combinations of failures that explain the observations. Methodologies will be developed to quantify uncertainties with plant models which arise from assumptions on model structures and noises in process data. Uncertainties associated with risk models will also be developed. Correlations and dependencies among events as well as their consequences will be quantified and taken into account in the design of warning system. We also want to propose an alarm design philosophy to complement the existing standards in incorporating risk. A prototype alarm system will be developed that will be integrate-able with the existing plant alarm systems. The new system will be tested in pilot plants and industrial settings. Process industries are in dire need of an improved alarm system. Operators are often overwhelmed by alarms and are unable to perform their main responsibilities. Risk is a measure that is well understood and highly valued. Hence, the developed methodology is expected to find widespread utility in process industries. By developing technologies and tools that ensure smooth, accident-free operation of industrial plants, this program will contribute directly to the productivity of the industrial sector. Thus it will help to save billions of dollars in losses caused by abnormal situations in industries. Along with the development of tools and methods, a number of highly qualified personnel will be trained in this program. The HQP will be trained on developing the tools as well as its implementation to real plants; thus the HQP will be valuable assets for the concerned industry. Finally, the developed methods will be useful for risk assessment in general with applications outside process facilities, e.g. for health monitoring, economic activities and natural disasters.
Memorial University of Newfoundland - $28,000 Grant (… | Canada Spends