Electronic Technology for Wastewater Treatment and Clean Water Production
Abstract
:1. Introduction
2. Current Practices in Water Infrastructure
3. The Future of Infrastructure Sensing and Processing
- The sensing of other specialized properties, such as optical density, color, pH, conductivity, as well as sensor fusion and soft sensoring, which combine multiple measurable properties to indirectly construct measurements for other hardly measurable properties.
- High speed measurement. High sample-rate pressure meters are, as of yet, rarely used, but are rapidly becoming mainstream as the costs are now similar to the placement of pressure sensors. Data transmission is getting simpler and faster and data storage is generally not considered to be a significant hurdle. High speed pressure data can reveal transmission-line parameters for a given segment in terms of the flexibility of the pipe, or possibly even leak detection at a distance.
- Smart pipes, employing sensors integrated in the mains material are quickly becoming a reality [13], the detection of stress-strain relations, bending effects, crack detection, excavation activities nearby, tampering and other anomaly detection will be easily possible using these systems.
- Pipe inspection technologies can be used as well but are generally costly. Combining inspection data with other failure data, replacement, geographical, and load-history data with artificial intelligence seems a promising tool to help in this area and could improve failure prediction massively.
4. Call for Insight
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Scozzari, A.; Mounce, S.; Han, D.; Soldovieri, F.; Solomatine, D. (Eds.) ICT for Smart Water Systems: Measurements and Data Science; Springer: Berlin/Heidelberg, Germany, 2021; Volume 102. [Google Scholar]
- Geelen, C.V.C.; Yntema, D.R.; Molenaar, J.; Keesman, K.J. Burst Detection by Water Demand Nowcasting Based on Exogenous Sensors. Water Resour. Manag. 2021, 35, 1183–1196. [Google Scholar] [CrossRef]
- Qi, Z.; Zheng, F.; Guo, D.; Maier, H.R.; Zhang, T.; Yu, T.; Shao, Y. Better Understanding of the Capacity of Pressure Sensor Systems to Detect Pipe Burst within Water Distribution Networks. J. Water Resour. Plan. Manag. 2018, 144, 04018035. [Google Scholar] [CrossRef]
- Soldevila, A.; Blesa, J.; Tornil-Sin, S.; Fernández-Cantí, R.M.; Puig, V. Sensor placement for classifier-based leak localization in water distribution networks using hybrid feature selection. Comput. Chem. Eng. 2018, 108, 152–162. [Google Scholar] [CrossRef][Green Version]
- Fuchs-Hanusch, D.; Steffelbauer, D. Real-world Comparison of Sensor Placement Algorithms for Leakage Localization. Procedia Eng. 2017, 186, 499–505. [Google Scholar] [CrossRef]
- Geelen, C.V.C.; Yntema, D.R.; Molenaar, J.; Keesman, K.J. Optimal Sensor Placement in Hydraulic Conduit Networks: A State-Space Approach. Water 2021, 13, 3105. [Google Scholar] [CrossRef]
- Dingemans, M.M.; Baken, K.A.; Van Der Oost, R.; Schriks, M.; Van Wezel, A.P. Risk-based approach in the revised European Union drinking water legislation: Opportunities for bioanalytical tools. Integr. Environ. Assess. Manag. 2018, 15, 126–134. [Google Scholar] [CrossRef] [PubMed]
- Karengera, A.; Bao, C.; Riksen, J.A.; van Veelen, H.P.J.; Sterken, M.G.; Kammenga, J.E.; Murk, A.J.; Dinkla, I.J. Development of a transcription-based bioanalytical tool to quantify the toxic potencies of hydrophilic compounds in water using the nematode Caenorhabditis elegans. Ecotoxicol. Environ. Saf. 2021, 227, 112923. [Google Scholar] [CrossRef] [PubMed]
- Saeys, Y.; Inza, I.; Larrañaga, P. A review of feature selection techniques in bioinformatics. Bioinformatics 2007, 23, 2507–2517. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Mesquida, M.F.; Prof, S.; Margarida, H.; Ramos, S. Digital Twin in Water Distribution Networks Energy Engineering and Management. Master’s Thesis, Universitat Politècnica de Catalunya, Barcelona, Spain, 2021. [Google Scholar]
- Wéber, R.; Hős, C. Efficient Technique for Pipe Roughness Calibration and Sensor Placement for Water Distribution Systems. J. Water Resour. Plan. Manag. 2020, 146, 04019070. [Google Scholar] [CrossRef]
- Hossain, S.; Hewa, G.A.; Chow, C.W.K.; Cook, D. Modelling and Incorporating the Variable Demand Patterns to the Calibration of Water Distribution System Hydraulic Model. Water 2021, 13, 2890. [Google Scholar] [CrossRef]
- Tran, V.Q.C.; Le, D.V.; Yntema, D.R.; Havinga, P.J.M. A Review of Inspection Methods for Continuously Monitoring PVC Drinking Water Mains. IEEE Internet Things J. 2021, 1–20. [Google Scholar] [CrossRef]
- Lead in Drinking-Water Background; WHO Guidelines for Drinking-Water Quality; WHO: Geneva, Switzerland, 1996; Volume 2, p. 7.
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Yntema, D.R.; Geelen, C.V.C. Electronic Technology for Wastewater Treatment and Clean Water Production. Water 2022, 14, 1276. https://doi.org/10.3390/w14081276
Yntema DR, Geelen CVC. Electronic Technology for Wastewater Treatment and Clean Water Production. Water. 2022; 14(8):1276. https://doi.org/10.3390/w14081276
Chicago/Turabian StyleYntema, Doekle R., and Caspar V. C. Geelen. 2022. "Electronic Technology for Wastewater Treatment and Clean Water Production" Water 14, no. 8: 1276. https://doi.org/10.3390/w14081276