(383l) Modeling and Optimization of Electrified and Low-Carbon Process Technologies: A Systems-Level Perspective

I am a PhD-trained chemical engineer with expertise in multi-scale process modeling, reactor engineering, and techno-economic analysis, with applications spanning electrified chemical manufacturing, CO₂-to-fuel systems, and energy-efficient separations. My work bridges first-principles modeling, dynamic optimization, and system-level integration to support the development and deployment of novel process technologies.

At New York University, I have led efforts to develop customized models for advanced steam cracking reactors, integrating kinetic, heat, and mass transfer, and economic considerations to evaluate their performance under varying electricity grid conditions. I also contributed to the ARPA-E RENewthanol project, modeling CO₂ electrolysis systems and downstream separation trains under dynamic power inputs, combining phenomenological modeling with surrogate-based optimization.

My doctoral work at IIT Madras focused on developing generalized process synthesis frameworks and cryogenic energy storage systems, where I applied mixed-integer nonlinear programming to optimize hybrid heat/work integration. Across projects, I have collaborated closely with experimental teams, using models to inform pilot-scale design, material selection, and process feasibility under realistic operating constraints.

Proficient in Python, MATLAB, Aspen Plus, and first-principles simulation, I aim to apply modeling as a unifying language between R&D, engineering design, and scale-up. I bring strong communication and leadership skills, demonstrated through mentoring graduate and undergraduate students, and service as session chair and reviewer in AIChE and international PSE conferences.

Research Interests

• First-principles modeling of novel reactors and separation systems
• Modelling and optimization for low-carbon chemical pathways
• Dynamic and steady-state process modeling of electrified manufacturing
• Integration of modeling with TEA and environmental metrics
• Digital design tools (Python, Aspen, MATLAB) and hybrid modeling (AI/PINNs)
• Flexible and resilient process design under grid variability

Key Words

Process Electrification, Energy System Modelling and Integration, Process Design and Analysis, First-principles modeling, Dynamic and steady-state simulation, Process intensification