2025-2026 Faculty Research Grants

Hyun Kwon (Engineering). 

Development of an adaptive soft sensor for variable feed conditions in biofuel fermentation.

I have been actively engaged in the development of soft sensors, which has significant promise for monitoring fermentation processes in the biofuel industry. This project proposes a novel strategy for soft sensor development, incorporating adaptive/transfer learning algorithms to address variability in industrial feedstocks. While first-generation (1G) biofuel feedstocks, such as sugar and molasses, are widely used, second-generation (2G) feedstocks, including bagasse and straw, offer an eco-friendly alternative as they are non-edible and more sustainable. A key challenge is adapting soft sensors designed for feedstock variations.

To ensure the feasibility of industrial deployment, the soft sensor must demonstrate robust adaptability to dynamic environments. In collaboration with Dr. Celina Yamakawa and Dr. Elmer Rivera, a long-time coauthor with Dr. Kwon, we will develop soft sensors using experimental datasets from 1G feedstocks and apply adaptive/transfer learning algorithms to predict outcomes for additional datasets involving mixed 1G and 2G feedstocks.

The project will actively involve undergraduate researchers, with results presented at the AIChE conference and published in peer-reviewed journals. The ultimate goal is to secure external funding from the Department of Energy (DOE) to advance this innovative technology and further enhance its diverse  applicability such as environmental monitoring.