Current Projects

Current Projects

Surrogate Modeling for Real-Time Optimization of Hydroprocessing Units

Surrogate Modeling UnitThis researcg focuses on building AI-based surrogate models for plant-scale hydroprocessing units, including hydrocrackers and hydrotreaters. Using real industrial datasets, I trained models to approximate complex plant behavior under varying operating conditions. The models are designed to aid in optimization and predictive control applications, enabling faster decision-making and improved energy efficiency. (Souvik) 

 

AI-Driven Digital Twins for Bioelectrochemical Systems

MFC-PIMLThis research aims to develop an AI-powered digital twin for bioelectrochemical systems (BES) to optimize energy production, wastewater treatment, and bioremediation. By integrating microbial, electrochemical, and operational data, the digital twin will simulate and predict BES behavior using deep learning, hybrid modeling, and convolutional neural networks. Real-time updates and reinforcement learning will enable adaptive control and performance optimization, while explainable AI will clarify decision-making. The system will be scaled to pilot applications through industrial collaboration. (Miguel)

Physics Informed Modelling of Great Lakes Water Surface Temperature

LSTM Lake Temp

This research explores the use of Long Short-Term Memory (LSTM) networks combined with physics-based constraints to predict lake surface temperatures across buoy locations in the Great Lakes. The model leverages historical meteorological and hydrological inputs to learn temporal dynamics, while physics-informed loss functions ensure consistency with thermodynamic principles. (Souvik)

 

Physics-Informed and Interpretable Modelling of Hydrocracking Kinetics

PIML Hydrocracking Kinetics

This research integrated domain knowledge of chemical kinetics with neural ordinary differential equations (Neural ODEs) to model hydrocracking reactions in petroleum refining. The PINO model was trained on synthetic and industrial data, capturing non-linear dynamics and temperature-dependent behaviors of multi-lump reaction networks. By incorporating Arrhenius kinetics into the training process, the model achieved both physical consistency and high predictive accuracy. (Souvik)

AI enhanced Predictive Maintenance for Wastewater Treatment Equipment

This research focuses on developing AI-enhanced predictive maintenance (PdM) strategies for critical wastewater treatment plant (WWTP) equipment such as decanter centrifuges and turbo blowers. By leveraging machine learning models and real-time operational data, the project aims to predict equipment failures, optimize maintenance schedules, and minimize unplanned downtime. The approach will improve system reliability, reduce operational costs, and support smarter, data-driven maintenance decisions in WWTPs. (Xiangyuan)

Machine Learning Aided Polymer Design

Large Language Models for Process Engineering

We are leveraging LLMs to process and interpret complex engineering data, facilitate automated documentation, and support decision-making processes in engineering design and operations. We are intetested in exploring the use of LLMs in predictive maintenance, process optimization, and knowledge management within engineering systems.