SustainAI Group Co-Organizes X2026 Symposium on AI-Driven Process Systems
The SustainAI Group is pleased to announce the symposium “AI-Driven Process Systems: Bridging Chemistry, Control, and Sustainability” at X2026, the joint Canadian Society for Chemistry (CSC) and Canadian Society for Chemical Engineering (CSChE) Conference and Exhibition, to be held in Toronto from May 24–28, 2026.
Together with colleagues Chris DeGroot and Ahmed AlSayed, Dr. Liu is co-organizing this symposium to highlight how artificial intelligence and machine learning are advancing process systems engineering, from modeling and control to optimization and sustainability.
The symposium will showcase recent developments in several areas, including AI-driven process control and plant-wide optimization, hybrid and physics-informed machine learning models, digital twins and predictive maintenance, soft sensing and spectroscopy-based monitoring, and applications in molecular- and reaction-level engineering for energy systems, water and wastewater treatment, carbon capture, and related fields.
We warmly welcome abstract submissions from researchers and practitioners in both academia and industry, in Canada and internationally, and look forward to an exciting exchange of ideas in Toronto.
X2026 Conference: https://www.cheminst.ca/conference/x2026/
Program Overview: https://www.cheminst.ca/conference/x2026/program/program-overview/
Details:
AI-Driven Process Systems: Bridging Chemistry, Control, and Sustainability (SC)
Organizers
Tianlong (Taylor) Liu, Western University
Christopher DeGroot, Western University
Ahmed AlSayed, Western University
Description:
This symposium explores the integration of AI into process systems engineering to advance understanding, automation, optimization, and sustainability in chemical engineering and chemistry. It will feature recent advances in AI-driven process control, hybrid modeling, physics-informed machine learning, digital twins, and predictive maintenance, with applications in molecular- and reaction-level engineering for energy systems, water/wastewater treatment, carbon capture, etc. Topics include plant-wide optimization via reinforcement learning and model predictive control, soft sensing and spectroscopy-based monitoring, and embedding reaction kinetics, thermodynamics, and stoichiometry into AI tools to bridge lab-scale chemistry and industrial-scale operations. This symposium will embody the “Erasing Boundaries” theme and bring together researchers and practitioners from both societies, fostering interdisciplinary collaboration between molecular science and process engineering. Contributions from both academia and industry are welcome, with a focus on diverse
