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《工程(英文)》 >> 2022年 第9卷 第2期 doi: 10.1016/j.eng.2021.05.013

数字水务进展以及从汽车和飞机工业自动化发展中汲取的经验教训

a KWR Water Research Institute, Nieuwegein 3430 BB, Netherlands
b College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK
c Faculty of Engineering and Build Environment, National University, Bangi 43600, Malaysia

收稿日期: 2021-02-14 修回日期: 2021-04-11 录用日期: 2021-05-19 发布日期: 2021-07-23

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摘要

提供水务服务和涉水卫生服务是一项全球性关键挑战。水务设施服务和废水设施服务具有规模性、复杂性和关键性等特点,使其在规划和管理方面变得极为困难。数字化革命已深入人类生活的许多领域,水务行业也开始从数字化转型中受益。有效利用气象遥感和土壤湿度数据可以提高灌溉效率(即粮食产量),利用人工智能可以更好地检测管道网络的异常和故障,利用自然启发式优化方法可以改善系统的管理和规划,以及数字孪生和机器人技术的频繁利用,所有这些都展现了数字化技术在改善复杂水务系统管理上的巨大潜力。但是,这些技术也有附加风险,如网络安全风险、不正当使用、过度信任数字解决方案与自动化的性能和准确度等。本文明确了水务行业数字化技术的主要进展,并将取证工程原则应用到一些在自动化和数字化转型较为领先的领域,对其故障事件进行分析。新型数字化技术失误是引发汽车和飞机行业重大事故的潜在原因(如特斯拉自动驾驶汽车和波音737 MAX飞机),确定失误原因并将经验应用于水务行业的类似风险识别中,吸取教训,避免失败。重要发现表明:①自动化要求在回路系统配置人员;②人工操作者必须充分掌握技术,并接受技术培训;③在发生技术故障时,应当采取应急式人工干预;④虽然冗余传感器会增加成本,但能减少由传感器读数失误带来的风险;⑤必须考虑网络安全风险;⑥鉴于水务系统的自动化和互联性不断增强,必须考虑相关伦理问题。这些发现也指出了与水务行业数字化转型相关的主要研究领域。

参考文献

[ 1 ] World Health Organization. Progress on household drinking water, sanitation and hygiene 2000–2017: special focus on inequalities. New York: United Nations Children’s Fund (UNICEF), World Health Organization; 2019.

[ 2 ] 2018 UN world water development report, nature-based solutions for water [Internet]. New York: United Nations; 2018 Mar 19 [cited 2021 Jul 14.]. Available from: https://www.unwater.org/world-water-development-report2018-nature-based-solutions-for-water/. 链接1

[ 3 ] England and Wales, Apr 2019–Mar 2020 [Internet]. London: Water UK; 2020 [cited 2021 Jul 14]. Available from: https://discoverwater.co.uk/. 链接1

[ 4 ] Drinking water fact sheet 2019 [Internet]. Bezuidenhoutseweg: Vewin; 2019 [cited 2021 Jul 14]. Available from: https://vewin.nl/SiteCollectionDocuments/ Publicaties/Drinking%20water%20fact%20sheet%202019.pdf. 链接1

[ 5 ] Endsley MR. From here to autonomy: lessons learned from human– automation research. Hum Factors 2017;59(1):5–27. 链接1

[ 6 ] Banks VA, Plant KL, Stanton NA. Driver error or designer error: using the Perceptual Cycle Model to explore the circumstances surrounding the fatal Tesla crash on 7th May 2016. Saf Sci 2018;108:278–85. 链接1

[ 7 ] Johnston P, Harris R. The Boeing 737 MAX saga: lessons for software organizations. Software Qual Prof 2019;21(3):4–12. 链接1

[ 8 ] Makropoulos C, Savic´ DA. Urban hydroinformatics: past, present and future. Water 2019;11(10):1959. 链接1

[ 9 ] Sarni W, White C, Webb R, Cross K, Glotzbach R. Digital water: industry leaders chart the transformation journey. Report. London: International Water Association and Xylem Inc.; 2019. 链接1

[10] Shen C. A transdisciplinary review of deep learning research and its relevance for water resources scientists. Water Resour Res 2018;54 (11):8558–93. 链接1

[11] Savic D. 2019. Artificial intelligence. How can water planning and management benefit from it? IAHR White Paper Series, Issue 1/2019. Madrid: the International Association for Hydro-Environmental Engineering and Research; 2019.

[12] De Souza GG, Costa MA, Libânio M. Predicting water demand: a review of the methods employed and future possibilities. Water Supply 2019;19 (8):2179–98. 链接1

[13] Zhu S, Piotrowski AP. River/stream water temperature forecasting using artificial intelligence models: a systematic review. Acta Geophys 2020;68 (5):1433–42. 链接1

[14] Cobb F, Carper KL. Forensic engineering. 2nd ed. Boca Raton: Taylor & Francis; 2000. 链接1

[15] Gagg C. Forensic engineering: the art and craft of a failure detective. Los Angeles: CRC Press; 2020. 链接1

[16] Lewis PMR, Reynolds K. Forensic engineering: a reappraisal of the Tay Bridge disaster. Interdiscip Sci Rev 2002;27(4):287–98. 链接1

[17] Larsen A. Aerodynamics of the Tacoma Narrows Bridge—60 years later. Struct Eng Int 2000;10(4):243–8. 链接1

[18] FAO. Water for sustainable food and agriculture—a report produced for the G20 Presidency of Germany. Report. Rome: Food and Agriculture Organization of the United Nations; 2017. 链接1

[19] Mulla DJ. Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. Biosyst Eng 2013;114 (4):358–71. 链接1

[20] Sonka S. Big data and the Ag sector: more than lots of numbers. Int Food Agribus Man 2014;17(1):1–20. 链接1

[21] Jung J, Maeda M, Chang A, Bhandari M, Ashapure A, Landivar-Bowles J. The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems. Curr Opin Biotechnol 2021;70:15–22. 链接1

[22] Ramatsabana P, Tanner J, Mantel S, Palmer A, Ezenne G. Evaluation of remotesensing based estimates of actual evapotranspiration over (diverse shape and sized) Palmiet Wetlands. Geosciences 2019;9(12):491. 链接1

[23] Bonthuys J. Beyond the farm gate: FruitLook unlocks bigger picture: featurewater and agriculture. Water Wheel 2017;16(5):26–9. 链接1

[24] Jarmain C, Goudriaan R, Naude R. Eight years later and FruitLook continues to grow. Agriprobe 2018;15(2):44–7. 链接1

[25] Reis S, Seto E, Northcross A, Quinn NWT, Convertino M, Jones RL, et al. Integrating modelling and smart sensors for environmental and human health. Environ Model Softw 2015;74:238–46. 链接1

[26] Solomatine DP, Ostfeld A. Data-driven modelling: some past experiences and new approaches. J Hydroinform 2008;10(1):3–22. 链接1

[27] Nicklow J, Reed P, Savic D, Dessalegne T, Harrell L, Chan-Hilton A, et al. State of the art for genetic algorithms and beyond in water resources planning and management. J Water Res Plan Man 2010;136(4):412–32. 链接1

[28] Maier HR, Kapelan Z, Kasprzyk J, Kollat J, Matott LS, Cunha MC, et al. Evolutionary algorithms and other metaheuristics in water resources: current status, research challenges and future directions. Environ Modell Softw 2014;62:271–99. 链接1

[29] Romano M, Boatwright S, Mounce S, Nikoloudi E, Kapelan Z. AI-based event management at United Utilities. IAHR Hydrolink 2020;4:104–8. 链接1

[30] Romano M, Kapelan Z, Savic´ DA. Automated detection of pipe bursts and other events in water distribution systems. J Water Resour Plan Manage 2014;140 (4):457–67. 链接1

[31] Myrans J, Everson R, Kapelan Z. Automated detection of fault types in CCTV sewer surveys. J Hydroinf 2019;21(1):153–63. 链接1

[32] Chen AS, Evans B, Djordjevic´ S, Savic´ DA. A coarse-grid approach to representing building blockage effects in 2D urban flood modelling. J Hydrol 2012;426-427:1–16. 链接1

[33] Ghimire B, Chen AS, Guidolin M, Keedwell EC, Djordjevic´ S, Savic´ DA. Formulation of a fast 2D urban pluvial flood model using a cellular automata approach. J Hydroinform 2013;15(3):676–86. 链接1

[34] Guidolin M, Chen AS, Ghimire B, Keedwell EC, Djordjevic´ S, Savic´ DA. A weighted cellular automata 2D inundation model for rapid flood analysis. Environ Model Softw 2016;84:378–94. 链接1

[35] Mala-Jetmarova H, Sultanova N, Savic D. Lost in optimisation of water distribution systems? A literature review of system operation. Environ Model Softw 2017;93:209–54. 链接1

[36] Mala-Jetmarova H, Sultanova N, Savic D. Lost in optimisation ofwater distribution systems? A literature review of system design. Water 2018;10(3):307. 链接1

[37] Quintiliani C, Vertommen I, Laarhoven KV, Vliet JVD, van Thienen P. Optimal pressure sensor locations for leak detection in a Dutch water distribution network. Environ Sci Proc 2020;2(2):40. 链接1

[38] Ostfeld A, Uber JG, Salomons E, Berry JW, Hart WE, Phillips CA, et al. The battle of the water sensor networks (BWSN): a design challenge for engineers and algorithms. J Water Res Plan Man 2008;134(6):556–68. 链接1

[39] Conejos Fuertes P, Martínez Alzamora F, Hervás Carot M, Alonso Campos JC. Building and exploiting a digital twin for the management of drinking water distribution networks. Urban Water J 2020;17(8):704–13. 链接1

[40] Savic DA, Morley MS, Khoury M. Serious gaming for water systems planning and management. Water 2016;8(10):456. 链接1

[41] Chen Y, Han D. Water quality monitoring in smart city: a pilot project. Autom Construct 2018;89:307–16. 链接1

[42] Mirats Tur JM, Garthwaite W. Robotic devices for water main in-pipe inspection: a survey. J Field Robot 2010;27(4):491–508. 链接1

[43] Parrott C, Dodd TJ, Boxall J, Horoshenkov K. Simulation of the behavior of biologically-inspired swarm robots for the autonomous inspection of buried pipes. Tunn Undergr Space Technol 2020;101:103356. 链接1

[44] Badue C, Guidolini R, Carneiro RV, Azevedo P, Cardoso VB, Forechi A, et al. Selfdriving cars: a survey. Expert Syst Appl 2021;165:113816. 链接1

[45] Herkert J, Borenstein J, Miller K. The Boeing 737 MAX: lessons for engineering ethics. Sci Eng Ethics 2020;26(6):2957–74. 链接1

[46] Sgobba T. B-737 MAX and the crash of the regulatory system. J Space Saf Eng 2019;6(4):299–303. 链接1

[47] Ulrich L. Top 10 tech cars: 2018. IEEE Spectr 2018;55(4):30–41. 链接1

[48] Cysneiros LM, Raffi M, do Prado Leite JCS. Software transparency as a key requirement for self-driving cars. In: Proceedings of 2018 IEEE 26th International Requirements Engineering Conference (RE); 2018 Aug 20–24; Banff, AB, Canada. New York: IEEE; 2018.. p. 382–7. 链接1

[49] Hassanzadeh A, Rasekh A, Galelli S, Aghashahi M, Taormina R, Ostfeld A, et al. A review of cybersecurity incidents in the water sector. J Environ Eng 2020;146 (5):03120003. 链接1

[50] Doorn N. Artificial intelligence in the water domain: opportunities for responsible use. Sci Total Environ 2021;755:142561. 链接1

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