More Sustainable Processes: Engineering Clinic Project Tackles Sustainability in Chemical Engineering

In a world of perpetual innovation, researchers are constantly developing new chemicals for pharmaceutical and other industries. But what impact do these chemicals have on the environment? An engineering clinic project at Rowan aims to create a machine learning algorithm to predict novel chemical properties, separation mechanisms, environmental impacts, sustainability metrics and its life cycle.

"This experience has contributed to my research experience by providing a hands-on opportunity to apply computational design in chemical engineering. Working on this project has allowed us to bridge theoretical concepts with real-world applications, particularly in developing sustainable solutions." — Milo Barkow, chemical engineering major

The project, led by Kirti M. Yenkie, Ph.D. and Robert Hesketh, Ph.D., professors in the Department of Chemical Engineering, is a continuation of previous work where the team examined the environmental impact of chemicals from the pharmaceutical industry that end up in waste streams. If these materials could be better understood, engineers could design more chemicals or processes that use these materials more sustainably.

The project, sponsored by U.S. Environmental Protection Agency and supported by AstraZeneca, aims to create a machine learning algorithm that can predict this sustainability information. The goal is to trace the life cycle of a chemical, from its raw materials to a manufactured product, through its use and disposal. Where did the chemical ultimately end up — in a sanitation facility or was it recycled? This machine learning algorithm will help scientists determine any potential environmental impacts a chemical poses at the start of the design process, not after. Ultimately, the machine learning algorithm will be made publicly available. 

The algorithm will help manufacturers make more informed decisions — and highlight the pros and cons of various synthesis processes. This project will counter the assumption that all bio-based materials are inherently more sustainable by highlighting the longevity and qualities of certain materials and weighing them against comparable chemicals.

The project began in 2022 and will continue until 2025. In 2023, undergraduate clinic students won a grant from the American Institute of Chemical Engineers for their work on this project.

Student involvement includes collecting data which will act as inputs and outputs to the algorithm as well as learning about the processes of how chemicals are created. They also learned how to program the algorithm in Python, an invaluable industry skill that goes beyond what is covered in the chemical engineering curriculum. Graduate student Emmanuel Aboagye, a postdoctoral researcher now at Princeton, trained the team on machine learning algorithms.

In 2024, students Matthew Conway, Jared Longo, John Pazik and Milo Barkow worked on the project. Conway, ‘25, was awarded the prestigious 2024 Goldwater Scholarship, awarded to students pursuing research careers in the natural sciences, engineering and mathematics.

Also through this project, Yenkie and Hesketh designed K-12 outreach activities designed to teach younger students about the life cycle of materials through a popcorn-making exercise. Students were shown various methods of making popcorn, what materials were used and how much energy was consumed in each method. They then discussed which option was the most energy- and taste-efficient.