Recovery of materials from end-of-life products- Circular Economy
The current extract-make-use-dispose paradigm throughout product supply chains has enormous environmental and socioeconomic impacts, including climate change, biodiversity loss, depletion of natural resources, and pollution. Policymakers and industry practitioners have suggested Circular Economy (CE) to solve these challenges. CE aims to re-design current processing/ consumption practices to eliminate waste and pollution, circulate products and materials at their highest value, and regenerate nature. Although conceptually simple, the development of CE networks is hindered by the lack of scientific guidance on how to implement and evaluate the effectiveness of CE initiatives.
The goal of this research is to advance the state of the art in designing and evaluating CE networks. Specifically, we intend to develop mathematical models for different CE initiatives and multi-agent networks with a particular focus on the recovery/ reuse of plastic and electronic waste.
Selected Publications:
Process Design, Simulation and Optimization of Critical Mineral and Rare Earth Element Recovery Systems
Selected Publications:
Software
- Generalized Superstructure Framework (AIChE Journal, 2026):
Code: ;
Documentation: ).
- Chemical Precipitator (Systems and Control Transactions, 2025)
Code:
Documentation:
Electrification, Renewable Energy, Decarbonization of the Chemical Industry, Green Hydrogen
Research focuses on decarbonizing the chemical industry and energy systems through renewable integration, green hydrogen, and biomass conversion. We develop optimization frameworks to identify cost‑effective retrofit strategies for existing infrastructure, balancing emissions reduction with capital costs, electricity prices, and policy constraints.
Key challenges include managing intermittent renewable supply, matching generation with demand, and designing flexible power‑to‑x systems. We also study storage and time‑of‑use strategies, including optimal battery operation under degradation constraints, to minimize costs while enabling deep decarbonization.
Selected Publications:
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Data-Driven Modeling
This line of research is motivated by the need to discover interpretable and mathematically explicit models directly from data. We are currently investigating Symbolic Regression via Kaizen Programming (KP) as a systematic, data-driven framework for learning physically meaningful models without requiring a priori specification of the functional form.
In contrast to conventional black-box machine learning approaches that prioritize predictive accuracy at the expense of transparency, symbolic regression simultaneously performs model structure identification and parameter estimation by searching over the space of candidate analytical expressions. The resulting models are expressed as closed-form equations that map input descriptors to target outputs, enabling direct physical interpretability