5 papers
Data-driven Bi-level Optimization of Thermal Power Systems with embedded Artificial Neural Networks
Talha Ansar, Muhammad Mujtaba Abbas, Ramit Debnath +2
Industrial thermal power systems have coupled performance variables with hierarchical order of importance, making their simultaneous optimization computationally challenging or inf…
From drift to adaptation to the failed ml model: Transfer Learning in Industrial MLOps
Waqar Muhammad Ashraf, Talha Ansar, Fahad Ahmed +3
Model adaptation to production environment is critical for reliable Machine Learning Operations (MLOps), less attention is paid to developing systematic framework for updating the…
Neural Network-enabled Domain-consistent Robust Optimisation for Global CO Reduction Potential of Gas Power Plants
Waqar Muhammad Ashraf, Talha Ansar, Abdulelah S. Alshehri +3
We introduce a neural network-driven robust optimisation framework that integrates data-driven domain as a constraint into the nonlinear programming technique, addressing the overl…
Domain-Informed Operation Excellence of Gas Turbine System with Machine Learning
Waqar Muhammad Ashraf, Amir H. Keshavarzzadeh, Abdulelah S. Alshehri +3
The domain-consistent adoption of artificial intelligence (AI) remains low in thermal power plants due to the black-box nature of AI algorithms and low representation of domain kno…
Domain Consistent Industrial Decarbonisation of Global Coal Power Plants
Waqar Muhammad Ashraf, Vivek Dua, Ramit Debnath
Machine learning and optimisation techniques (MLOPT) hold significant potential to accelerate the decarbonisation of industrial systems by enabling data-driven operational improvem…