AI-Guided Discovery of Novel Ionic Liquid Solvents for Industrial CO2 Capture

TL;DR

This paper introduces an AI-driven pipeline to discover novel ionic liquids optimized for CO2 capture, significantly reducing operational and capital costs for industrial applications.

Contribution

It presents a comprehensive five-stage AI-based method for identifying and optimizing ionic liquids as efficient, cost-effective CO2 capture solvents, with validated candidate compounds.

Findings

Identified 36 feasible ionic liquid candidates for CO2 capture.

Potential 5-10% OPEX savings through lower regeneration energy.

Up to 10% CAPEX reductions due to reduced corrosivity.

Abstract

We present an AI-driven approach to discover compounds with optimal properties for CO2 capture from flue gas-refinery emissions' primary source. Focusing on ionic liquids (ILs) as alternatives to traditional amine-based solvents, we successfully identify new IL candidates with high working capacity, manageable viscosity, favorable regeneration energy, and viable synthetic routes. Our approach follows a five-stage pipeline. First, we generate IL candidates by pairing available cation and anion molecules, then predict temperature- and pressure-dependent CO2 solubility and viscosity using a GNN-based molecular property prediction model. Next, we convert solubility to working capacity and regeneration energy via Van't Hoff modeling, and then find the best set of candidates using Pareto optimization, before finally filtering those based on feasible synthesis routes. We identify 36 feasible candidates that could enable 5-10% OPEX savings and up to 10% CAPEX reductions through lower regeneration energy requirements and reduced corrosivity-offering a novel carbon-capture strategy for refineries moving forward.