对燃料电池和流电池的生成性AI驱动过程计算.
Rishi Garg1, Vasudev Majhi2, Vinay Chamola3
1Department of Chemical Engineering, Birla Institute of Technology & Science Pilani, Pilani Campus, Pilani, Rajasthan 333031, India.
ACS omega
|March 2, 2026
概括
本研究介绍了一种使用大语言模型 (LLM) 的生成性AI框架,以改进电化学能源系统建模. 人工智能方法提高了燃料电池和电池的精度和效率,减少了人类的努力.
科学领域:
- 计算科学与工程 计算科学与工程
- 电化学能源系统 电化学能源系统
- 科学建模中的人工智能.
背景情况:
- 像PEMFC,SOFC和VRFB这样的电化学系统涉及复杂的,多尺度的运输动力学.
- 机械解决方案提供了忠实性,但是繁的;数据驱动模型 (ANN,DRL) 缺乏稳定性.
- 现有的方法在物理准确性和计算效率之间的平衡中扎.
研究的目的:
- 为电化学过程计算开发一个生成性人工智能辅助的计算框架.
- 将大型语言模型 (LLM) 与检索增强生成 (RAG),物理约束提示和工具集成推理集成.
- 为了提高复杂的电化学能源系统建模的准确性,稳定性和效率.
主要方法:
- 使用了由LLMs编排的生成性AI框架.
- 员工检索-增强生成 (RAG) 用于知识整合.
- 嵌入了受物理限制的提示和工具集成的推理计算.
- 在PEMFC和VRFB模拟的合成和工业Aspen Plus数据集上进行评估.
主要成果:
- 实现了PEMFC偏振曲线分解的低RMSE (9.6mV合成,7.8mV阿斯数据).
- 在模拟中显著减少了约束违规 (48%/42%降至1.2%/0.5%).
- 实现了VRFB优化的高能效 (79.1%合成,74.9%阿斯数据),减少了人力投入 (85%).
结论:
- 生成性人工智能框架在各种数据集中展示了稳定性,并提高了电化学建模的准确性.
- 与传统方法相比,这种方法可以显著降低计算负担和人力成本.
- 这一框架显示了与数字双胞胎的集成,故障检测和电化学系统中先进的过程控制的前景.
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