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将预测转化为决策:利用变压器-长期短期记忆网络和自动控制来提高水处理效率和可持续性

Cheng Qiu^1,2, Qingchuan Li¹, Jiang Jing^1,2

¹Department of Material and Environmental Engineering, Chengdu Technological University, Chengdu 611730, China.

Sensors (Basel, Switzerland)

|April 28, 2025

概括

这项研究引入了一种新的变压器-LSTM模型,用于预测废水处理中的氨 (NH3-N). 该模型通过整合多个传感器数据输入来提高准确性并降低能源消耗.

科学领域:

环境工程环境工程
废水处理技术废水处理技术
环境科学中的人工智能

背景情况:

准确预测氨 (NH3-N) 度对于评估废水处理效率和环境影响至关重要.
序列批量反应堆 (SBR) 系统中复杂的动态和有限的测量对精确的NH3-N预测提出了挑战.
现有的方法经常与NH3-N动态的复杂的时间依赖性和多变量性质作斗争.

研究的目的:

开发SBR系统中NH3-N度的先进预测模型.
通过结合新的输入功能和混合AI架构来提高NH3-N预测的准确性和可靠性.
通过智能自动化来减少废水处理过程中的能源和时间消耗.

主要方法:

提出了一个创新的变压器长期短期存储器 (Transformer-LSTM) 网络模型,将变压器的远程依赖性捕获与LSTM的顺序模式建模集成在一起.
纳入的溶氧 (DO),电导率 (EC) 和氧化降解电位 (ORP),以及它们的变化速率和累积值,作为输入变量.
使用来自SBR系统的实验NH3-N数据集验证模型,并将性能与现有的先进方法进行比较.

主要成果:

变压器-LSTM模型显著优于现有方法,在根平均平方误差 (RMSE),平均绝对误差 (MAE) 和确定系数 (R2) 中表现出卓越的性能.
与实时传感器数据和自动控制的集成导致了水处理过程的重大改进.
与传统的固定加工循环相比,能源或时间消耗减少了26.9%.

结论:

拟议的变压器-LSTM模型提供了一个准确可靠的工具,用于预测SBR系统中的NH3-N度.
这种方法通过优化流程和减少资源消耗,有助于提高水处理的可持续性.
这些发现支持有效实施人工智能驱动的自动控制,以满足排放标准并改善废水管理.

关键词:

氨预测氨预测自动控制自动控制自动控制长期短期内存网络长期内存网络测序批量反应堆的批量反应堆变压器网络的变压器网络.

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