多代理大型语言模型框架:解锁优化废水处理操作的新可能性
Samuel Rothfarb1, Mikayla Friday1, Xingyu Wang1
1School of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut, 06269, USA.
Environmental research
|March 16, 2025
概括
多代理大型语言模型 (LLM) 通过整合各种数据来更好地做出决策,提供先进的废水处理厂 (WWTP) 控制. 这种人工智能方法提高了运营效率和适应能力,超出了传统方法.
科学领域:
- 环境工程 环境工程
- 人工智能的人工智能
- 废水处理 废水处理
背景情况:
- 废水处理厂 (WWTP) 面临着由于复杂,动态的相互作用而面临的运营挑战.
- 像激活泥模型 (ASM) 和机器学习算法 (MLA) 这样的传统模型与非结构化,多式联运的WWTP数据作斗争.
研究的目的:
- 探索多代理,装备工具的大型语言模型 (LLM) 的潜力,以增强WWTP操作.
- 展示LLM如何改善流程控制,决策和实时适应能力.
主要方法:
- 使用专业的LLM代理合作的多代理框架.
- 整合各种数据流进行全面分析.
- 应用LLM来应对复杂的WWTP运营挑战,以污泥积聚案例研究为例.
主要成果:
- 与传统方法相比,多代理LLM在处理复杂的WWTP数据方面表现出卓越的能力.
- 该框架能够提供明智的决策,并提高工厂运营的实时适应性.
- 一个关于污泥积聚的案例研究强调了相对于传统方法的实际好处.
结论:
- 多代理的LLM为先进的WWTP管理提供了可扩展和适应的解决方案.
- 由LLMs促进的AI驱动的决策支持是优化废水处理的关键创新.
- 诸如计算成本和人工智能风险等挑战可以通过验证和人类监督来管理.
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