Energy, decarbonization, and sustainability
Our research combines computational work on process design, optimization, fluid dynamics, and machine learning with experimental work in catalysis and renewable polymers. Students contribute to the development of:
- low and net zero carbon solutions that are able to ensure access to affordable, reliable, and sustainable energy for all
- pathways for industrial decarbonization and carbon dioxide removal technologies
Projects include:
- electrification with modern intermittent sources (wind and sun)
- carbon capture utilization and sequestration
- the development of novel chemicals and production pathways based on low carbon, renewable, and recycled feedstocks
- the design of novel catalysts
Sample research projects
Sunny Surprenant
The goal of Sunny Surprenant's research is to integrate well known chemistries used in petroleum-based polymers and bioconjugation to manipulate material properties and recyclability in biopolymer design. Agricultural proteins, such as keratin, whey, and zein, have recently been investigated as renewable feedstock in polymer manufacturing. These proteins, though brittle and dry on their own, have been shown to add to mechanical strength and possibly aid in biodegradability.
Advisor: Daphne Chan
Sampriti Chattopadhyay
Sampriti Chattopadhyay's current study addresses the challenge of determining the optimal retrofit decarbonization strategy for existing fossil-based refineries. Starting from a process flowsheet representing current operations in the industry, Chattopadhyay incorporates decarbonization alternatives into a comprehensive superstructure. By using a Mixed-Integer Linear Programming (MILP) model to optimize the superstructure, the research identifies a cost-effective decarbonization plan that enables meeting emission targets set by the company. The results provide valuable insights into the economic feasibility of retrofit electrification strategies.
Advisors: Ana Torres, Ignacio Grossmann
Rohan (Yuri) Sanspeur
Rohan (Yuri) Sanspeur is building automated workflows for materials discovery for green hydrogen. Sanspeur then streamlines the pipelines using surrogate models that predict first principles (DFT) properties. Those guide the materials discovery process.
Advisor: John Kitchin
Image source: Rohan Yuri Sanspeur, Javier Heras-Domingo, John R. Kitchin, and Zachary Ulissi, Journal of Chemical Information and Modeling 2023, 63(8), 2427-2437 DOI: 10.1021/acs.jcim.3c00142
Maya Bhat
Maya Bhat works on integrating Design of Experiments with machine learning techniques to accelerate catalyst discovery. Bhat works with experimentalists to guide future experimentation for optimization of systems and identification of underlying mechanisms that govern physical behaviors. Specifically, these methods have primarily been applied to high-throughput experimental setups for generating renewable hydrogen.
Advisor: John Kitchin
Image source: Bhat, Maya et al. High throughput discovery of ternary Cu–Fe–Ru alloy catalysts for photo-driven hydrogen production, Reaction Chemistry & Engineering 2023 8(7), 1738–1746 DOI: 10.1039/D3RE00059A
Chris Laliwala
Chris Laliwala is using superstructure optimization to design sustainable processes to recover rare earth elements (REE) from end-of-life products. As the US advances in the decarbonization of the economy, a sevenfold increase in REE demand is expected. Their recovery from electronic waste is potentially advantageous from both economic and environmental standpoints when compared to traditional mining. Laliwala's research aims to quantify these advantages and find the best optimal processing pathways.
Advisor: Ana Torres
Brook Wander
Brook Wander uses machine learning models to discover novel catalysts for the electrocatalytic conversion of carbon dioxide to value added products. Wander's primary project is a collaboration between Meta AI, the University of Toronto, and CMU that will combine high-throughput experiments and computational calculations to try to find a state-of-the-art catalyst. This research is important to address climate change because carbon dioxide utilization will make carbon capture more economically feasible.
Advisor: John Kitchin
Abdul Ahmed
Abdul Ahmed's research focuses on developing methodologies and frameworks that characterize and quantify the circularity of individual processes and supply chains. As a case study, he is currently evaluating the value chain of polyethylene terephthalate (PET), a common consumer single-use plastic and the main component of polyester textiles.
Advisor: Ana Torres
Unnatti Sharma
The catalytic electro-reduction reaction of carbon dioxide into chemicals is one of the most promising pathways to utilize the carbon that is captured by industries. Yet, this process is not well understood. The large number of possible products and reaction pathways that have been experimentally reported make process design, evaluation, and scale-up of these technologies a highly challenging task. The current focus of Unnatti Sharma's research is on building an optimization-based framework to theoretically determine the preferred reaction pathways and predict the product profile. The long-term purpose of this work is to be able to propose the catalyst and the process conditions that would make carbon dioxide utilization technologies economical and environmentally safe.
Advisors: Ana Torres, John Kitchin
Seolhee Cho
Seolhee Cho has developed a new optimization model for the expansion planning of reliable and resilient power transmission systems. A two-stage stochastic GDP model that aims to proactively plan for the capacity of transmission and re-dispatch power generation systems has been proposed, mitigating potential electricity supply disruptions caused by extreme weather events. The objective function is to minimize the total cost, which includes investment (for new installation and hardening), fixed/variable operating costs, and resilience-related penalties such as load shedding and curtailment penalties.
Advisor: Ignacio Grossmann
Energy, decarbonization, and sustainability faculty
Ignacio Grossmann
Rudolph R. and Florence Dean University Professor
Chemical Engineering
