Guangcan Su

Postdoctoral Researcher · Northwestern University

LCA TEA Sustainability AI-augmented Workflows

I'm a postdoctoral researcher at Northwestern University working at the intersection of process simulation, sustainability assessment, and AI. My research combines life cycle assessment (LCA), techno-economic analysis (TEA), and spatial modeling to evaluate emerging technologies for carbon conversion, direct air capture, and clean energy systems — and to understand which of them actually make sense at scale.

Selected work

Carbon conversion, waste valorization, and AI-enabled assessment.

Recent publications span reactive carbon capture, electrochemical conversion, biochar systems, and data-driven tools for sustainable process design.

TEA, LCA, process simulation, and catalyst modeling visualization for dilute alloy electrocatalysts

Dilute alloy electrocatalysts for C-C coupling

Electrosynthetic ethylene production from a CO2 post-capture liquid.

Direct air capture liquid, electrochemical reactor, LCA, TEA, and process modeling visualization

Direct air capture

Electrosynthesis of CO from pH-shifted DAC liquid

Catalyst-support amide linkage design for post-capture carbon utilization.

Reactive carbon capture, syngas production, process simulation, LCA, and TEA modeling visualization

Reactive capture

Porous carbon supports for electrified syngas production

Integrated capture and conversion pathways for efficient syngas generation.

Biomass-to-methanol and activated carbon process simulation with LCA and TEA modeling visualization

Bioenergy systems

Carbon-negative methanol and activated carbon

Bagasse pyrolysis, activation, chemical looping, and methanol synthesis.

Integrated waste valorization process with biochar, methanol, LCA, TEA, and scenario modeling visualization

Waste valorization

Integrated methanol and biochar from wastes

A combined route for carbon sequestration, bioenergy, and waste valorization.

Machine learning model development for predicting biochar properties from biomass torrefaction

Machine learning

Predicting biochar properties from torrefaction

Data-driven models linking lignocellulosic feedstocks to biochar performance.