利用废水表面图像和水质数据进行污水水质预测的脑启发多模式方法

IF 6.1 2区 环境科学与生态学 Q2 ENGINEERING, ENVIRONMENTAL
Junchen Li, Sijie Lin, Liang Zhang, Yuheng Liu, Yongzhen Peng, Qing Hu
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引用次数: 0

摘要

通过数据驱动的分析来有效预测出水水质,这对污水处理的稳定运行具有重大意义。在本研究中,我们旨在开发一种预测出水 COD 和 NH3 水平的综合方法。然后,我们收集了 COD、DO、pH、NH3、EC、ORP、SS 和水温等关键参数的数据,以及污水表面图像,形成了约 40246 个点的数据集。然后,我们利用这些数据提出了一个与 CNN-LSTM 网络(BITF-CL)集成的脑启发图像和时间融合模型。这一创新模型将污水图像与水质数据进行了协同,从而提高了预测精度。因此,与传统方法相比,BITF-CL 模型减少了 23% 以上的预测误差,即使不使用溶解氧和 SS 传感器数据,其性能仍可与传统技术媲美。因此,这项研究为污水处理提供了一个经济高效的精确预测系统,展示了大脑启发模型的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Brain-inspired multimodal approach for effluent quality prediction using wastewater surface images and water quality data

Brain-inspired multimodal approach for effluent quality prediction using wastewater surface images and water quality data

Efficiently predicting effluent quality through data-driven analysis presents a significant advancement for consistent wastewater treatment operations. In this study, we aimed to develop an integrated method for predicting effluent COD and NH3 levels. We employed a 200 L pilot-scale sequencing batch reactor (SBR) to gather multimodal data from urban sewage over 40 d. Then we collected data on critical parameters like COD, DO, pH, NH3, EC, ORP, SS, and water temperature, alongside wastewater surface images, resulting in a data set of approximately 40246 points. Then we proposed a brain-inspired image and temporal fusion model integrated with a CNN-LSTM network (BITF-CL) using this data. This innovative model synergized sewage imagery with water quality data, enhancing prediction accuracy. As a result, the BITF-CL model reduced prediction error by over 23% compared to traditional methods and still performed comparably to conventional techniques even without using DO and SS sensor data. Consequently, this research presents a cost-effective and precise prediction system for sewage treatment, demonstrating the potential of brain-inspired models.

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来源期刊
Frontiers of Environmental Science & Engineering
Frontiers of Environmental Science & Engineering ENGINEERING, ENVIRONMENTAL-ENVIRONMENTAL SCIENCES
CiteScore
10.90
自引率
12.50%
发文量
988
审稿时长
6.1 months
期刊介绍: Frontiers of Environmental Science & Engineering (FESE) is an international journal for researchers interested in a wide range of environmental disciplines. The journal''s aim is to advance and disseminate knowledge in all main branches of environmental science & engineering. The journal emphasizes papers in developing fields, as well as papers showing the interaction between environmental disciplines and other disciplines. FESE is a bi-monthly journal. Its peer-reviewed contents consist of a broad blend of reviews, research papers, policy analyses, short communications, and opinions. Nonscheduled “special issue” and "hot topic", including a review article followed by a couple of related research articles, are organized to publish novel contributions and breaking results on all aspects of environmental field.
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