Deep-learning based monitoring of FOG layer dynamics in wastewater pumping stations

Moreno-Rodenas, Antonio; Duinmeijer, Alex; Clemens, F.H.L.R.

doi:10.1016/j.watres.2021.117482

Cited by 17 publications

(6 citation statements)

References 23 publications

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“…Current imaging technology can detect the water level in sewer videos, 30,31 measure the water flow 32 and detect fat layer accumulation. 33 To what extent hyperspectral information could improve this, e.g., by better distinguishing pipe material from water surfaces or by facilitating superior particle-image-velocimetry, should be investigated in future studies.…”

Section: Environmental Science: Water Research and Technology Papermentioning

confidence: 99%

Towards non-contact pollution monitoring in sewers with hyperspectral imaging

Lechevallier,

Villez,

Felsheim

et al. 2024

Environ. Sci.: Water Res. Technol.

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Section: Environmental Science: Water Research and Technology Papermentioning

confidence: 99%

Towards non-contact pollution monitoring in sewers with hyperspectral imaging

Lechevallier,

Villez,

Felsheim

et al. 2024

Environ. Sci.: Water Res. Technol.

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“…Finally, an interesting question is whether we could extract hydrodynamics information from the hyperspectral data-cubes. Current technology can detect the water level in sewer videos (Nguyen et al, 2009;Ji et al, 2020), measure the water flow (Leitão et al, 2018) and detect fat layer accumulation (Moreno-Rodenas, Duinmeijer and Clemens, 2021). To what extent hyperspectral information could improve this, e.g.…”

Section: Potential For Real-world Applications Of Hyperspectral Image...mentioning

confidence: 99%

Towards non-contact pollution monitoring in sewers with hyperspectral imaging

Lechevallier¹,

Villez²,

Felsheim³

et al. 2023

Preprint

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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.

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“…However, in fact, these facilities produce a lot of sludge and emit a lot of greenhouse gases, necessitating additional optimization [81]. This research [82] demonstrates how to simultaneously optimise the dissolved oxygen and chemical dosage in a wastewater treatment plant (WWTP) using the unique technique known as multi-agent deep reinforcement learning (MADRL). From a life-cycle perspective, the incentive function was created to achieve sustainable optimization.…”

Section: Usage Of Iot and Artificial Intelligence For Effective Water...mentioning

confidence: 99%

Smart Water Resource Management Using Artificial Intelligence—A Review

Somayaji

Nallakaruppan²,

Chengoden³

et al. 2022

Sustainability

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Water management is one of the crucial topics discussed in most of the international forums. Water harvesting and recycling are the major requirements to meet the global upcoming demand of the water crisis, which is prevalent. To achieve this, we need more emphasis on water management techniques that are applied across various categories of the applications. Keeping in mind the population density index, there is a dire need to implement intelligent water management mechanisms for effective distribution, conservation and to maintain the water quality standards for various purposes. The prescribed work discusses about few major areas of applications that are required for efficient water management. Those are recent trends in wastewater recycle, water distribution, rainwater harvesting and irrigation management using various Artificial Intelligence (AI) models. The data acquired for these applications are purely unique and also differs by type. Hence, there is a dire need to use a model or algorithm that can be applied to provide solutions across all these applications. Artificial Intelligence (AI) and Deep Learning (DL) techniques along with the Internet of things (IoT) framework can facilitate in designing a smart water management system for sustainable water usage from natural resources. This work surveys various water management techniques and the use of AI/DL along with the IoT network and case studies, sample statistical analysis to develop an efficient water management framework.

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Deep-learning based monitoring of FOG layer dynamics in wastewater pumping stations

Cited by 17 publications

References 23 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

Smart Water Resource Management Using Artificial Intelligence—A Review

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