重新思考活性泥建模:对建模策略的批判性审查以及混合集成的作用
Jaydev Zaveri1, Guangyu Li1, Zijian Wang2
1School of Civil and Environmental Engineering, Cornell University, Ithaca, New York, USA.
概括
先进的人工智能 (AI) 模型为废水处理提供数据驱动的解决方案,补充传统方法. 混合模型将人工智能与机械理解和多omics数据集成在一起,有望提高可持续水解决方案的效率和主动管理.
科学领域:
- 环境工程环境工程
- 计算科学 计算科学
背景情况:
- 高效的废水处理对于联合国的可持续发展目标至关重要,面临着工业化和人口增长带来的挑战.
- 传统的机械 (白盒) 模型,如激活泥模型,提供结构化的见解,但与动态微生物相互作用和影响变异性作斗争.
- 现有的模型需要创新技术来减少能源消耗和化学品依赖.
研究的目的:
- 探索人工智能 (AI) 技术,特别是机器学习,用于废水处理建模.
- 解决白盒模型在捕捉复杂的动态和现实世界的可变性方面的局限性.
- 倡导混合建模方法,将机械学理解与人工智能结合起来.
主要方法:
- 审查传统的机械模型技术.
- 探索人工智能 (AI) 和机器学习 (ML) 的数据驱动建模方法.
- 建议一个混合建模框架,整合机械学和人工智能方法.
主要成果:
- 人工智能模型擅长识别大型数据集中的复杂模式,但需要高质量的数据,缺乏可解释性.
- 将机械框架与人工智能相结合的混合模型显示了增强预测能力和稳健性的潜力.
- 为了改善监测和决策,建议将多领域数据纳入混合数字双胞胎的前景框架.
结论:
- 混合建模为克服废水处理中传统和纯粹基于人工智能的方法的局限性提供了一个有希望的途径.
- 在混合数字双胞胎框架中整合多个omics数据可以显著提高废水处理效率.
- 未来的研究应该专注于开发和验证这些综合混合模型的实际应用.
相关概念视频
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