Development of Computational-Based Tools and Modules for Chemical Engineering Education

The CACHE Corporation is a 501(c)(3) not-for-profit organization whose purpose is to promote cooperation among universities, industry and government in the development and distribution of computer-related and/or technology-based educational aids for the chemical engineering profession. For more than fifty years, CACHE has played a key role in the development and dissemination of educational tools and modules to the chemical engineering profession.

Project Goal

This program seeks to fund 12-month-long projects (maximum budget of $5000 per project) that focus on the development of novel computational-based educational modules or tools.

Educational Modules and Tools

Title	Principal Investigator	Institution	Final Report	Other Links	Year
Integrating Machine Learning based Sustainability Evaluation for Chemicals and Processes in the Undergraduate Curriculum	Kirti M. Yenkie, Aditya Dilip Lele	Rowan			2025
The Industrially-Situated Redox Flow Battery Virtual Laboratory	Milo Koretsky, Graham Leverick	Tufts			2025
Preparing Chemical Engineering Faculty for Rapid Instructional Interventions using AI-Generated Resources	David L. Silverstein	Mississippi			2025
Interactive online visualization-based data science modules for chemical and biomolecular engineers	Srinivas Rangarajan	Lehigh			2022
Development of interactive coding tutorials for introducing computational systems biology	Chris Kieslich	Auburn			2022
QRProblems.org: Open-Sourced System for Variable Parameter Quantitative and Qualitative Engineering Problems	Amanda Malefyt	Trine		QR-ED.org	2022
Modernizing the ChE Curriculum via Interactive Visual Aids for Traditional Courses	Roman Voronov	NJIT	VORONOV_COMSOL_APPS_REPORT.pdf	web page	2020
Development of Open Access Version of Applied Numerical Computing Course	Ashlee N. Ford Versypt	Buffalo	Final Report-Development of Open Access Version of Applied Numerical Computing Course.pdf	GitHub	2020
Coding Concepts and Chemical Engineering Analysis With Interactive Jupyter Notebooks	Matthew D. Stuber	Connecticut	CACHE_FinalReport.pdf	GitHub	2020