用AI筛选新型离子液体，高效低成本捕获工业废气中的二氧化碳
AI-Guided Discovery of Novel Ionic Liquid Solvents for Industrial CO2 Capture
- 基于图神经网络生成并预测离子液体的二氧化碳溶解度和粘度
- 筛选出36种可行候选物，可降低5-10%运行成本、最多节省10%初期投资
- 兼顾高捕集能力、低再生能耗与可合成性，适合炼油厂碳捕集应用
我们提出一种AI驱动的方法，用于发现适用于烟气—炼油排放源中二氧化碳捕集的新型离子液体（ILs）化合物。该方法聚焦于替代传统胺类溶剂的离子液体，成功识别出具备高工作容量、可控粘度、低再生能耗及可行合成路径的新候选物。流程包含五个阶段：首先通过阳离子与阴离子配对生成候选结构；其次利用基于图神经网络（GNN）的分子性质预测模型，估算其在不同温度与压力下的二氧化碳溶解度和粘度；随后通过范特霍夫模型将溶解度转化为工作容量与再生能量；再采用帕累托优化筛选最优候选集；最后根据可合成性进行过滤。共识别出36种可行候选物，可实现5%-10%运营成本（OPEX）节约和最高10%资本支出（CAPEX）减少，得益于更低的再生能耗和腐蚀性，为炼油行业提供一种全新的碳捕集策略。
原文摘要 · Abstract (English)
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.
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