Digital Water Developments and Lessons Learned from Automation in the Car and Aircraft Industries

作     者：Dragan Savic Dragan Savi?

作者机构：KWR Water Research InstituteNieuwegein 3430 BBNetherlands College of EngineeringMathematics and Physical SciencesUniversity of ExeterExeter EX44QFUK Faculty of Engineering and Build EnvironmentNational UniversityBangi 43600Malaysia 

出 版 物：《Engineering》 (工程（英文）)

年 卷 期：2022年第8卷第2期

页      面：35-41页

核心收录：

学科分类：08[工学] 0815[工学-水利工程] 

主　　题：Digitalization Automation Water sector Potential risks Lessons 

摘      要：The provision of water and sanitation services is a key challenge *** size,complexity,and critical nature of the water and wastewater infrastructure providing such services make the planning and management of these systems extremely *** the digital revolution in many areas of our lives,the water sector has begun to benefit from digital *** utilization of remotely sensed weather and soil moisture data for more efficient irrigation(i.e.,for food production),better detection of anomalies and faults in pipe networks using artificial intelligence,the use of nature-inspired optimization to improve the management and planning of systems,and greater use of digital twins and robotics all exhibit great potential to change and improve the ways in which complex water systems are ***,there are additional risks associated with these developments,including—but not limited to—cybersecurity,incorrect use,and overconfidence in the capability and accuracy of digital solutions and *** paper identifies key advances in digital technology that have found application in the water sector,and applies forensic engineering principles to failures that have been experienced in industries further ahead with automation and digital *** iden-tifying what went wrong with new digital technologies that might have contributed to high-profile acci-dents in the car and aircraft industries(e.g.,Tesla self-driving cars and the Boeing 737 MAX),it is possible to identify similar risks in the water sector,learn from them,and prevent future *** key findings show that:①Automation will require“humans in the loop;②human operators must be fully aware of the technology and trained to use it;③fallback manual intervention should be available in case of tech-nology malfunctioning;④while redundant sensors may be costly,they reduce the risks due to erroneous sensor readings;⑤cybersecurity risks must be considered;and⑥ethics iss