Online monitoring of water quality in industrial wastewater treatment process based on near-infrared spectroscopy

IF 11.4 1区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research Pub Date : 2025-01-18 DOI:10.1016/j.watres.2025.123165

Cheng Peng , Zeming Wu , Shudi Zhang , Boran Lin , Lei Nie , Weilu Tian , Hengchang Zang

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Abstract

Water quality monitoring is one of the critical aspects of industrial wastewater treatment, which is important for checking the treatment effect, optimizing the treatment technology and ensuring that the water quality meets the standard. Chemical oxygen demand (COD) is a key indicator for monitoring water quality, which reflects the degree of organic matter pollution in water bodies. However, the current methods for determining COD values have drawbacks such as slow speed and complicated operation, which hardly meet the demand of online monitoring. This article firstly proposed a novel quantitative analysis method based on NIR spectroscopy and multi-preprocessing stacking, successfully enabling real-time online monitoring of COD values during industrial wastewater treatment. First, the existing swarm intelligence algorithm was enhanced to optimize the hyperparameters of various base models. Next, multiple spectral preprocessing techniques were innovatively combined with a stacking strategy to construct multi-preprocessing stacking models, enabling comprehensive extraction of effective spectral information. Finally, various combinations of base models were evaluated, leading to the selection of the multi-preprocessing stacking model with optimal performance. The results indicate that the model achieves excellent predictive performance and strong generalization ability. For equalization tank samples, the R² and RMSE values were 0.8640 and 326.6845 mg/L, respectively. For secondary settling tank samples, the R² and RMSE values were 0.8798 and 15.1917 mg/L, respectively. Compared to traditional single and stacking models, the RMSE was reduced by at least 12.75 % and 5.11 %, respectively. In addition, the method has a relatively low modeling cost and offers interpretability. This study presents an efficient and straightforward solution for the online monitoring of COD values in industrial wastewater treatment, laying a solid technical foundation for the efficient management of industrial wastewater and the protection of water resources and the ecological environment.

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基于近红外光谱的工业废水处理过程水质在线监测

水质监测是工业废水处理的关键环节之一，对检查处理效果、优化处理工艺、保证水质达标具有重要意义。化学需氧量（COD）是监测水体水质的关键指标，反映水体中有机物污染的程度。然而，现有的COD测定方法存在速度慢、操作复杂等缺点，难以满足在线监测的需求。本文首次提出了一种基于近红外光谱和多预处理叠加的新型定量分析方法，成功实现了工业废水处理过程中COD值的实时在线监测。首先，对已有的群智能算法进行改进，优化各种基模型的超参数；其次，创新地将多种光谱预处理技术与叠加策略相结合，构建多重预处理叠加模型，实现有效光谱信息的综合提取；最后，对各种基本模型组合进行了评价，从而选择出性能最优的多重预处理叠加模型。结果表明，该模型具有较好的预测性能和较强的泛化能力。均衡槽样品的R2和RMSE值分别为0.8640和326.6845 mg/L。二沉池样品的R2和RMSE分别为0.8798和15.1917 mg/L。与传统的单一模型和叠加模型相比，RMSE分别降低了至少12.75%和5.11%。此外，该方法具有相对较低的建模成本和可解释性。本研究为工业废水处理过程中COD值的在线监测提供了一种高效、直观的解决方案，为工业废水的高效管理、水资源和生态环境的保护奠定了坚实的技术基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Water Research 环境科学-工程：环境

CiteScore

20.80

自引率

9.40%

发文量

1307

审稿时长

38 days

期刊介绍： Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.