This document is an expanded abstract.
Localized corrosion is quite common and at the same time difficult to monitor in oil/gas metallic pipelines. If it is not detected in a timely manner, localized corrosion can result in a pipeline failure and consequently economic, environmental and even human losses.
This abstract briefly presents a case study in optimization-based layout design of sensors and human inspection for a pipeline under localized corrosion. In the case study, the data for the corrosion is synthetically generated. This data, which is assumed to be stochastic in nature, is used in an approach to maximize a measure of detection and maximize a utility function of cost.
Localized corrosion, such as pitting, refers to a gradual loss of material in a small area of a metallic surface and can eventually result in failure in pipelines. Corrosion depends on several factors including pH and temperature of the operating fluid in the pipeline. Corrosion is considered to be a stochastic process with its growth to be quite difficult to predict. Corrosion is a considerably slow degradation process at the early operation of a pipeline. In particular, corrosion exacerbates with time in aged pipelines (Okeniyi et al., 2014).
One possible approach for predicting corrosion is by collecting data about the condition of the pipeline’s interior surface using a layout of sensors. Since there is a large number of factors affecting the conditions of corrosion, it is necessary to obtain a layout of sensors that is robust to damage realizations (Kishawy and Gabbar, 2010). In contrast to extant literature (Alduraibi et al., 2016; Zhang and Zhou, 2014), this abstract considers a bi-objective optimization-based case study for sensor layout design.
This case study involves a pipeline segment that is subjected to corrosion. The goal is to construct a bi-objective optimization model that will result in identifying a layout design for external sensors including human inspection that could best detect corrosion-based damages.