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基于循环神经网络的污水处理过程氨氮软测量研究

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本文提出了一种利用循环神经网络进行污水处理过程模拟的方法,旨在开发适用于序批式反应器(SBR)处理生活污水的软测量模型.结合易于获取的其他变量的实测值,该模型实现了污水处理重要水质指标的实时监测.通过收集生活污水处理单元的实测数据,并与模型预测结果进行比较研究,验证了该模型在实际运行环境中的有效性.本研究的软测量模型为SBR工艺过程水质预测提供了一种有效的低成本解决方案.通过重要指标的软测量,可以改善运行策略,提高处理效果,为污水处理提供可持续发展的路径.
Ammonia nitrogen soft measurement in wastewater treatment process based on recurrent neural networks
This study proposes a method for simulating the wastewater treatment process using recurrent neural networks and aims to develop a soft measurement model applicable to the sequencing batch reactor for treating domes-tic wastewater.The model combines the measured values of other easily obtainable variables to achieve real-time monitoring of important water quality indicators in wastewater treatment.By collecting measured data from the domes-tic wastewater treatment unit and comparing it with the model's predicted results,the effectiveness of the model in practical operating environments has been validated.The soft measurement model developed in this study provides an effective and cost-efficient solution for predicting water quality in the SBR process.Through soft measurement of im-portant indicators,operational strategies can be improved,and treatment efficiency can be enhanced,thereby offering a sustainable pathway for wastewater treatment.

Recurrent neural networksSBRSoft measurement

代先锋、袁玥、曹强

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广安市前锋区环境监测站,四川 广安 638019

爱土工程环境科技有限公司,北京 100020

循环神经网络 SBR 软测量

2024

环境与发展
内蒙古自治区环境科学研究院,内蒙古环境检测中心站

环境与发展

影响因子:0.326
ISSN:1007-0370
年,卷(期):2024.36(6)