Area of Research

Research in this theme focuses on developing state-of-the-art Machine Learning and Deep Learning models for analyzing and learning from time-dependent data. It includes designing methods for forecasting, anomaly detection, and capturing temporal patterns, trends, and dependencies across domains such as energy, healthcare, finance, and climate science. The work also emphasizes end-to-end ML operations best practices, including scalable pipelines, model continuous integration (CI), and continuous deployment (CD), to ensure reliable real-world implementation and monitoring.

Understanding the energy, exergy, and environmental aspects of chemical processes is crucial for sustainable and efficient design. Our lab conducts comprehensive analysis using advanced methodologies to evaluate the energy and exergy efficiency, environmental impact, and economic viability of various industrial processes. These analyses provide valuable insights for optimizing process performance, identifying opportunities for improvement, and promoting sustainable practices 

We leverage the power of artificial intelligence and machine learning to develop advanced algorithms for fault diagnosis and detection in industrial processes. By utilizing real-time data and process dynamics, our research enables the development of intelligent systems capable of detecting anomalies, diagnosing faults, and improving the reliability and safety of industrial operations.

We are at the forefront of research on biomass utilization for biohydrogen production. Our team explores novel techniques and reactor designs to efficiently convert biomass into hydrogen gas, a clean and renewable energy carrier. By harnessing the potential of biomass, we aim to contribute to the development of sustainable energy solutions and reduce dependency on fossil fuels.

Our lab specializes in the development of efficient and environmentally friendly methods for acid gas removal from biogas and natural gas. By utilizing ionic liquids, we aim to enhance the purification processes, ensuring the production of clean and high-quality energy sources while minimizing environmental impact.

Our lab focuses on the application of advanced process control techniques based on process dynamics. By understanding the complex dynamics of industrial processes, we develop control strategies that optimize stability, productivity, and energy efficiency. Our research contributes to the field of process control by leveraging cutting-edge methodologies such as model predictive control, adaptive control, and multivariable control.