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Online monitoring of water quality in industrial wastewater treatment process based on near-infrared spectroscopy
Water Research ( IF 11.4 ) 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
Water Research ( IF 11.4 ) 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
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.
中文翻译:
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,对于检查处理效果、优化处理技术、确保水质达标具有重要意义。化学需氧量 (COD) 是监测水质的关键指标,它反映了水体中有机物的污染程度。然而,目前测定COD值的方法存在速度慢、操作复杂等缺点,难以满足在线监测的需求。本文首先提出了一种基于 NIR 光谱和多前处理堆叠的新型定量分析方法,成功地实现了工业废水处理过程中 COD 值的实时在线监测。首先,对现有的 swarm intelligence 算法进行了增强,以优化各种基础模型的超参数。接下来,创新性地将多光谱预处理技术与堆叠策略相结合,构建多预处理堆叠模型,实现有效光谱信息的综合提取。最后,评估了基础模型的各种组合,从而选择了性能最优的多预处理堆叠模型。结果表明,该模型取得了优异的预测性能和较强的泛化能力。对于均衡池样品,R2 和 RMSE 值分别为 0.8640 和 326.6845 mg/L。对于二次沉淀池样品,R2 和 RMSE 值分别为 0.8798 和 15.1917 mg/L。与传统的单一和堆叠模型相比,RMSE 分别降低了至少 12.75 % 和 5.11 %。此外,该方法的建模成本相对较低,并且具有可解释性。 本研究为工业废水处理中 COD 值在线监测提出了一种高效、直接的解决方案,为工业废水的高效管理以及水资源和生态环境的保护奠定了坚实的技术基础。
更新日期:2025-01-18
中文翻译:
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,对于检查处理效果、优化处理技术、确保水质达标具有重要意义。化学需氧量 (COD) 是监测水质的关键指标,它反映了水体中有机物的污染程度。然而,目前测定COD值的方法存在速度慢、操作复杂等缺点,难以满足在线监测的需求。本文首先提出了一种基于 NIR 光谱和多前处理堆叠的新型定量分析方法,成功地实现了工业废水处理过程中 COD 值的实时在线监测。首先,对现有的 swarm intelligence 算法进行了增强,以优化各种基础模型的超参数。接下来,创新性地将多光谱预处理技术与堆叠策略相结合,构建多预处理堆叠模型,实现有效光谱信息的综合提取。最后,评估了基础模型的各种组合,从而选择了性能最优的多预处理堆叠模型。结果表明,该模型取得了优异的预测性能和较强的泛化能力。对于均衡池样品,R2 和 RMSE 值分别为 0.8640 和 326.6845 mg/L。对于二次沉淀池样品,R2 和 RMSE 值分别为 0.8798 和 15.1917 mg/L。与传统的单一和堆叠模型相比,RMSE 分别降低了至少 12.75 % 和 5.11 %。此外,该方法的建模成本相对较低,并且具有可解释性。 本研究为工业废水处理中 COD 值在线监测提出了一种高效、直接的解决方案,为工业废水的高效管理以及水资源和生态环境的保护奠定了坚实的技术基础。