期刊
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
卷 147, 期 -, 页码 192-213出版社
ELSEVIER
DOI: 10.1016/j.psep.2020.09.009
关键词
Corrosion sensor; Structural health monitoring; Humidity sensor; pH sensor; Sewer pipelines; H2S detection; Data processing
资金
- Australian Research Council Centre Discovery Early Career Researcher Award (DECRA) [DE180100688]
- Australian Academy of Science, on behalf of the Department of Industry, Science, Energy and Resources
- Australian Government under the National Innovation and Science Agenda
Corrosion, causing gradual destruction of materials, is crucial for sewage pipelines due to its significant financial, health, and safety costs. Research efforts have focused on understanding effective parameters leading to corrosion in sewer pipes and monitoring infrastructure conditions, resulting in reduced equipment damage. Microbiologically induced corrosion and data analysis techniques have also been explored.
Corrosion is known as the gradual destruction of materials, leading to structural integrity loss and deteriorates the surface function. Regarding sewage pipelines, corrosion is vital due to its substantial financial, health, and safety costs for society, and it is considered as one of the biggest problems facing water and wastewater infrastructure. Also, it is the primary cause of chemical property alteration, efficiency loss, life span reduction, etc. To overcome the resulting problems, various researches have been performed to understand not only the effective parameters leading to corrosion in sewer pipes but also monitoring the infrastructure conditions. Studies have depicted that developments in sensing systems to detect effective parameters in pipe corrosion such as temperature, H2S, and pH, have significantly reduced damage to the industrial equipment of sewage pipelines caused by corrosion. This paper presents a critical review of the effective factors resulting in sewer pipeline corrosion and discusses advanced sensing systems utilized for relevant monitoring. Also, microbiologically induced corrosion and effective factors are individually discussed. Moreover, various data analysis techniques adopted to evaluate outputs of the sensors for corrosion prediction have been explored. Finally, recommendations and future directions for improving sensing accuracy and robustness are detailed. (C) 2020 Published by Elsevier B.V. on behalf of Institution of Chemical Engineers.
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