Waveband Selection with Equivalent Prediction Performance for FTIR/ATR Spectroscopic Analysis of COD in Sugar Refinery Waste Water

Xie, Jun; Sun, Donglei; Cai, Jiaxiang; Cai, Fuhong

doi:10.32604/cmc.2019.03658

Cited by 5 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3 Non-contact water quality monitoring techniques have the potential to address these challenges, but research on this topic is still limited to seven peerreviewed publications. [4][5][6][7][8][9][10] In a pioneering study, Russell et al, 2003 measured the light reflection intensity of an 880 nm laser on 200 wastewater samples in the laboratory. 5 They obtained promising results for the estimation of oxygen demand (COD) (R 2 = 0.79) and suspended solids (R 2 = 0.83), but the subsequent field trials were unsatisfactory.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards non-contact pollution monitoring in sewers with hyperspectral imaging

Lechevallier,

Villez,

Felsheim

et al. 2024

Environ. Sci.: Water Res. Technol.

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

“…3 Non-contact water quality monitoring techniques have the potential to address these challenges, but research on this topic is still limited to seven peer-reviewed publications. 4–10…”

Section: Introductionmentioning

confidence: 99%

Towards non-contact pollution monitoring in sewers with hyperspectral imaging

Lechevallier,

Villez,

Felsheim

et al. 2024

Environ. Sci.: Water Res. Technol.

View full text Add to dashboard Cite

show abstract

“…(Gruber et al, 2006). Non-contact water quality monitoring techniques have the potential to address these challenges, but have mostly been studied in laboratory settings (Gitelson, Stark and Dor, 1997;Russell et al, 2003;Natesan, 2013;Agustsson et al, 2014;Xie et al, 2019;Xing et al, 2019;Huang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Towards non-contact pollution monitoring in sewers with hyperspectral imaging

Lechevallier¹,

Villez²,

Felsheim³

et al. 2023

Preprint

View full text Add to dashboard Cite

Monitoring water quality in sewers is challenging, particularly because state-of-the-art technologies require contact with the raw wastewater. The presence of fat, oil, grease and solids makes automated grab sampling difficult and causes sensor fouling. To overcome these limitations, non-contact methods based on light reflectance, such as hyperspectral imaging (HSI), are gaining attention. However, HSI has never been tested for raw wastewater. To assess its accuracy for measuring pollution, we developed a laboratory setup and performed targeted experiments with a combination of raw and diluted wastewater, as well as synthetic turbidity stock solutions. We measured seven pollution variables: chemical oxygen demand, turbidity, dissolved organic compounds, ammonium, total nitrogen, phosphate, and sulphates. We used automated pixel selection and partial least squares regression to retrieve pollution information from the hyperspectral images. Our results, based on 144 samples, suggest that HSI can estimate pollution levels with a precision in the range of state-of-the-art absorbance spectroscopy methods. Additionally, we found that the combination of pixel and wavelength selection, enabled by the hyperspectral data structure, significantly influences the performance of partial least square modelling. Overall, our findings indicate that HSI is a promising technology for non-contact monitoring of water quality in raw wastewater.

show abstract

“…In remote sensing, the water body's spectral feature serves as the primary indicator in the identification and extraction of the riverway [2]. In the visible waveband, the water body has less light absorption and higher reflectivity [3]. However, physical and chemical characteristics of different water body segments may cause measurement variations, thereby increasing the difficulty of water body extraction.…”

Section: Introductionmentioning

confidence: 99%

Urban Riverway Extraction from High-Resolution SAR Image Based on Blocking Segmentation and Discontinuity Connection

2020

View full text Add to dashboard Cite

An urban riverway extraction method is proposed for high-resolution synthetic aperture radar (SAR) images. First, the original image is partitioned into overlapping sub-image blocks, in which the sub-image blocks that do not cover riverways are regarded as background. Sub-image blocks covering riverways are then filtered using the iterative adaptive speckle reduction anisotropic diffusion (SRAD) that introduces the relative signal-to-noise ratio (RSNR). The filtered images are segmented quickly by the Sauvola algorithm, and the false riverway fragments are removed by the area and aspect ratio of the connected component in the segmentation results. Using the minimum convex hull of each riverway segment as the connection object, the seeds are automatically determined by the difference between adjacent pyramid layers, and the sub-image block riverway extraction result is used as the bottom layer. The discontinuity connection between river segments is achieved by multi-layer region growth. Finally, the processed sub-image blocks are stitched to get the riverway extraction results for the entire image. To verify the applicability and usefulness of the proposed approach, high-resolution SAR imagery obtained by the Gaofen-3 (GF-3) satellite was used in the assessment. The qualitative and quantitative evaluations of the experimental results show that the proposed method can effectively and completely extract complex urban riverways from high-resolution SAR images.

show abstract

Waveband Selection with Equivalent Prediction Performance for FTIR/ATR Spectroscopic Analysis of COD in Sugar Refinery Waste Water

Cited by 5 publications

References 0 publications

Towards non-contact pollution monitoring in sewers with hyperspectral imaging

Towards non-contact pollution monitoring in sewers with hyperspectral imaging

Towards non-contact pollution monitoring in sewers with hyperspectral imaging

Urban Riverway Extraction from High-Resolution SAR Image Based on Blocking Segmentation and Discontinuity Connection

Contact Info

Product

Resources

About