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一个新型紫外线 (UV) 光反应器的强大优化用于水消毒:神经网络方法

Mahla Seyedin¹, Amirhossein Hassanpour¹, Alireza Jalali¹

¹School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran.

|July 8, 2024

概括

本研究介绍了一种强大的优化算法,用于紫外线水消毒,集成遗传算法和多项式混乱扩展,以最大限度地降低不确定输入的风险,减少资源使用. 优化的设计提高了消毒性能,同时提高了光反应器的可靠性.

科学领域:

环境工程环境工程
水处理技术水处理技术
摄像机设计摄像机设计

背景情况:

优化紫外线水消毒对公共卫生至关重要.
光反应器的性能受到输入不确定性的重大影响.
需要强大的优化来减轻风险和资源浪费.

研究的目的:

为紫外线水消毒光反应器开发一个强大的优化算法.
为了最大限度地减少不确定的输入参数对光反应器性能的影响.
确定最佳的几何尺寸,以提高消毒效率和可靠性.

主要方法:

一个双目标遗传算法的集成与非侵入性的多项式混沌扩展 (PCE).
使用Sobol采样进行系统行为分析,并使用CFD数据训练人工神经网络替代模型.
优化一种具有外部反射器的新型UV光反应器,考虑不确定的输入 (UV灯功率,水传导率,扩散分数) 和几何变量.

主要成果:

最佳设计实现了3.95的日志减小值 (LRV),标准偏差 (SD) 为0.2.
变化系数 (CoV) 降至7%,表明对输入不确定性的敏感性降低.
强大的模型对各种流速的反射率变化表现出最小的灵敏度.

结论:

开发的强大的优化算法有效地提高了UV水消毒性能.
优化的光反应器设计显著降低了对输入不确定性的敏感性,提高了可靠性.
这种方法最大限度地减少了光反应堆建设和运行所需的物理和财务资源.

关键词:

人工神经网络的人工神经网络计算流体动力学计算流体动力学多项式混乱扩张的多项式.强大的优化优化.紫外线消毒水的消毒方法不确定性不确定性

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