一个基于Bi-LSTM的多子系统协作自适应软传感器,用于在废水处理过程中全球预测氨-度
Dong Li1, Chunhua Yang1, Yonggang Li1
1The School of Automation, Central South University, Changsha 410 083, China.
Water research
|February 29, 2024
概括
这项研究引入了一种新的Bi-LSTM软传感器,用于预测废水处理厂 (WWTP) 全球氨-度. 该方法通过提供准确的实时水质洞察力,提高了运营管理和效率.
科学领域:
- 环境工程 环境工程
- 水质监测 水质监测
- 人工智能在水处理中的应用
背景情况:
- 氨度是废水处理中的关键水质指标.
- 目前的检测方法仅限于废水监测,阻碍了全球流程优化.
- 准确的实时预测对于高效的废水处理厂 (WWTP) 管理至关重要.
研究的目的:
- 开发一个多子系统协作适应软传感器,用于全球氨度预测.
- 为了提高在WWTP中氨-监测的准确性和稳定性.
- 提高WWTP的运营管理和效率.
主要方法:
- 废水处理过程分为子系统;通过相互信息进行可变选择.
- 用于子系统预测的双向长短期内存 (Bi-LSTM) 网络.
- 集成跨子系统输出和基于PDF的动态移动窗口以实现稳定性.
主要成果:
- 拟议的Bi-LSTM软传感器在BSM1模型中准确预测了全球氨度.
- 该系统在各种天气条件 (阳光,雨,暴风雨) 中表现出有效性.
- 与传统方法相比,自适应软传感器显示出更高的性能.
结论:
- 开发的软传感器可以准确地预测全球氨-,优化WWTP操作.
- 该方法提高了WWTP的稳定性,处理效率,并降低了经济成本.
- 该方法为动态环境中的实时水质监测提供了强大的解决方案.
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