通过热力学和数据驱动的建模阐明全尺度 struvite 恢复系统性能驱动因素
Samuel Enrique Aguiar1, Rishabh Puri2, Dong Hoon Sim2
1Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, United States; Jacobs Solutions Inc., Tempe, AZ, United States.
Water research
|December 12, 2025
概括
侧流斯特鲁维特回收系统可以改善的去除,但性能各不相同. 数据驱动的模型准确地预测了的转化和产量,从而实现了运营改进.
科学领域:
- 环境工程 环境工程
- 恢复水资源的水资源回收
- 化学工程是化学工程的重要组成部分.
背景情况:
- 侧流 struvite 恢复通过减少内部循环来增强生物去除.
- 尽管采用率增加,但斯特鲁维特回收系统的性能不一致.
- 现有的模型无法预测或诊断操作中断.
研究的目的:
- 开发新的数据驱动模型来评估和预测斯特鲁维特回收反应堆的性能.
- 确定影响转化和 struvite 产量的关键因素.
- 举例说明如何运营调整可以提高的去除和回收.
主要方法:
- 分析了五年来从一个全尺寸的流化床斯特鲁维特结晶反应堆的历史数据.
- 开发了数据驱动的模型,包括流化床循环流和组成.
- 利用特征重要性分析来识别关键性能驱动因素.
主要成果:
- 数据驱动模型显著优于传统模型,实现R2=0.83的转换和R2=0.95的收益率.
- 料的度和化学剂量率是转换的关键.
- 在回收流中的颗粒的度对产量至关重要.
- 污水中的流损失可能很大 (高达P质量流量的25%).
结论:
- 数据驱动的建模为理解和预测 struvite 恢复性能提供了一种强大的方法.
- 优化回收流的组成和运行参数对于最大限度地回收至关重要.
- 精确的建模可以指导操作变化,以提高去除效率并减少结构损失.
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