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Updated: Sep 13, 2025

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Electrochemically and Bioelectrochemically Induced Ammonium Recovery
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机器学习驱动的动态预测和优化用于膜蒸系统中的氨回收
Ying Bi1, Minjian Li1, Muhammad Usman Farid1
1Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, New Territories, Hong Kong.
Water research
|July 27, 2025
概括
这项研究将机器学习与动态模拟相结合,通过膜蒸 (MD) 进行先进的氨回收. 它模拟复杂的运输动态,实现优化实时流程控制和增强资源可持续性.
科学领域:
- 环境工程 环境工程
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
背景情况:
- 氨回收对于环境保护和资源可持续性至关重要.
- 膜蒸 (MD) 为高纯度氨回收提供了一条途径,但其优化需要了解动态运输.
- 现有的模型与复杂的系统动态和概括性作斗争.
研究的目的:
- 在MD系统中开发出灵活而准确的氨运输模型.
- 将机器学习 (ML) 与动态模拟集成在一起,以改进流程建模.
- 为了实现实时过程控制和适应性优化氨回收.
主要方法:
- 理论分析确定了关键变量:温度,pH值和氨部分压力梯度.
- 开发了一个人工神经网络 (ANN),以模拟氨度变化的速率.
- 该ANN与第四阶Runge-Kutta (RK4) 算法相结合,用于实时预测.
主要成果:
- 在预测氨度变化方面,ANN模型实现了高准确度 (R2 = 0.8537).
- 综合模型成功预测了实时氨度和累积回收率.
- 基于ML的网格预测确定了即时MD性能的最佳操作参数.
结论:
- 这种混合方法增强了对MD中氨运输机制的理解.
- 该研究推进了对氨回收的实时过程控制和自适应优化.
- 这些发现促进了资源可持续性的理论见解和实际应用.
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