Salim Ahmed

Associate Professor

Memorial University of Newfoundland

Canada

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About

Salim Ahmed is an Associate Professor at Memorial University of Newfoundland, Canada. His research focuses on machine learning applications in process systems, including fault detection, risk assessment, and data-driven modeling. Recent articles highlight innovations in early detection methodologies for chemical processes and the integration of physical knowledge with machine learning techniques.

Articles (14)

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2025

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2024

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2024

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2023

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2023

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2021

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2021

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2021

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2021

Collaborators (5)

Peter Vanberkel

Dalhousie University

CANADA

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Faisal Khan

Professor & Director

Texas A&M Engineering

UNITED STATES

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Md. Tanjin Amin

Lecturer

Texas A&M University

UNITED STATES

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He Wen

University of Alberta

CANADA

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Syed Imtiaz

Associate Professor

Memorial University of Newfoundland

CANADA

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Salim Ahmed

Research Interests

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Articles (14)

Machine learning for early detection of distillation column flooding

A robust neural network model for fault detection in the presence of mislabelled data

A hierarchical Bayesian network-based semi-mechanistic model for handling data variabilities in dynamical process systems

Assessment of Situation Awareness Conflict Risk between Human and AI in Process System Operation

Integrating process dynamics in data-driven models of chemical processing systems

Risk assessment of human-automation conflict under cyberattacks in process systems

A methodical approach for knowledge-based fire and explosion accident likelihood analysis

A method for the early prediction of abnormal conditions in chemical processes combined with physical knowledge and the data-driven model

Autonomous Fault Diagnosis and Root Cause Analysis for the Processing System Using One-Class SVM and NN Permutation Algorithm

A methodology for time-varying resilience quantification of an offshore natural gas pipeline

Artificial intelligence and machine learning for process safety: Points to ponder

An Active Learning Polynomial Chaos Kriging metamodel for reliability assessment of marine structures

A data-driven Bayesian network learning method for process fault diagnosis

Risk-based fault detection and diagnosis for nonlinear and non-Gaussian process systems using R-vine copula

Collaborators (5)

Peter Vanberkel

Faisal Khan

Md. Tanjin Amin

He Wen

Syed Imtiaz

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