His research interests include optimization-based control of complex process networks, computational multiscale modeling of process systems in sustainable and smart manufacturing, cyber-physical systems and network communication analysis in large-scale processes, and system identification using process data analytics and machine learning with application to chemical and energy systems. In particular he is interested in the control and automation of sustainable production of chemicals and energy in combined cycles, and secure estimation and control of complex networks in modern cyber-physical systems and smart process manufacturing.
Research:
- Control and automation in Process Industry 4.0: A framework to utilize cyber-physical systems, internet of things, and cloud computing in chemical processes and energy systems
- Sustainable process control
- Adaptive robust model predictive control: A framework to address the model uncertainty through online system identification using process data analysis
- Synthesis of advanced control structures for complex processes using sensor networks
- Multiscale modeling and uncertainty quantification of catalytic systems for energy applications
- Optimization-based control of complex process networks
The research in our lab focuses on developing a theoretical framework and the corresponding computational tools needed for
- Controlling complex process networks,
- Designing cyber-physical systems for smart process manufacturing,
- Applied artificial intelligence in process operation,
- Utilizing efficient network communication in process systems, and
- Advancing system identification using machine learning and process data analytics
