CEDAR Research Overview

In our CEDAR project, students and faculty will conduct a rich variety of research projects that advance the circular economy, that incorporate data science methods, including data mining, modeling, and machine learning, to solve engineering and science problems. The research is broad and a wide variety of projects within the context of applied data science methods applied to solving problems towards sustainability will be studied.

The research can be loosely organized around three themes all leveraging data science and analytics techniques, namely:

(a) Atom and energy efficiency. A problem with many current manufacturing and production methods is that they produce a significant amount of waste, both material waste and energy waste. There is a strong need to develop methods that minimize the production of waste, as well as minimize lost energy.

(b) Upcycling. Rather than just recycling materials, upcycling takes waste materials to create high-value products. Upcycling leads to even greater economic and environmental impacts, since materials are simply not downgraded, but can be upgraded to products having greater worth.

(c) Benign by design. A sustainable process minimizes toxic effects on the environment and health. De-signing processes that minimize toxicity is at the heart of this research thrust. WPI has many faculty that work on developing green, sustainable processes.

CEDAR research projects can be found here.