2025 AIChE Annual Meeting

Session: Machine Learning and Analytics for Sustainability

Papers in this session represent the manipulation of data and/or the development of analytics (i.e., the interpretation and communication) of data for the purpose of evaluating or guiding efforts toward sustainability. Various machine learning methods, novel in their application or results, are of interest.

Chair

Rui Shi, The Pennsylvania State University

Co-Chair

Kirti Yenkie, Rowan University

Presentations

08:00 AM

(510a) A Customized Large Language Model for Creating Circular Reaction Networks for Discovering Sustainable Chemical Processes
Avan Kumar, Sunghoon Kim, George Stephanopoulos, Bhavik Bakshi

08:18 AM

(510b) A Machine Learning and Data Driven Holistic LCA Approach for Early-Stage Sustainability Assessment
Harriet Appiah, Matthew Conway, Brendan Weil, Marcella McMahon, Jahnvi Patel, Andres Castellar-Freile, Robert Hesketh, Kirti Yenkie

08:36 AM

(510c) Scenario-Based Environmental and Economic Impact Assessment By Pinn-Based Modular Digital Twin for Sustainable Electroplating
Mahboubeh Moghadasi, Yinlun Huang

08:54 AM

(510d) Generating Widely Applicable Systems-Scale Insights into Fermentation Titer-Rate-Yield
Sarang Bhagwat, Christopher V. Rao, Huimin Zhao, Vijay Singh, Corinne Scown, Jeremy Guest

09:12 AM

(510e) Predictive Modeling of Hydrogen YIELD from Biomass-Derived BIO-Oils Using Machine Learning
Anjali Dahat, Diwakar Z. Shende, Kishor M. Bhurchandi, Kailas L. Wasewar

09:30 AM

(510f) Predicting Aqueous and Organic Solubilities with Machine Learning: A Workflow for Identifying Organic Co-Solvents
Maurycy Krzyzanowski

09:48 AM

(510g) Accelerating Direct Air Carbon Capture Design Optimization with Fourier Neural Operators
Jonathan Gallagher, Zhao Pan, Roberto Guglielmi, Yunli Wang, Yi Zhang, Apurav Tambe

10:06 AM

(510h) Achieving More Accurate Global Warming Potential Prediction Based on Multimodal Machine Learning: A Case Study of the Chemical Industry
Kai Zhao, Shanying Hu