Modeling corrosion growth for complex systems such as the oil refinery system is a major challenge since the corrosion process of oil and gas pipelines are inherently stochastic and depends on many factors including exposures to environmental conditions, operating conditions, and electrochemical reactions. Moreover, the number of sensors is usually limited, and sensor data are incomplete and scattering, which hinders the capability of capturing the corrosion growth behaviors. Therefore, this paper proposes Multi-sensor Corrosion Growth Model with Latent Variables to predict the corrosion growth process in oil refinery piping. The proposed model is a combination of the hierarchical clustering algorithm and the vector autoregression (VAR) model. The clustering algorithm aims to find the hidden (i.e., latent) data clusters of the measured time series data, from which the time series from the same cluster will be included in the VAR model to predict the corrosion depth from multiple sensors. The model can capture the relationship between sensor time series data and identify latent variables. A real case study of an oil refinery system, in which in-line inspection (ILI) data were collected, was utilized to validate model. Regarding corrosion growth prediction, the paper compared the prediction accuracy of VAR model with other three forms of power law model, which is widely accepted to expect the time-dependent depth of corrosion such as power function (PF), PF with initiation time of corrosion (PFIT), and PF with initiation time of corrosion and covariates (PFCOV). The results showed that VAR model has the lowest prediction error based on the mean absolute percentage error (MAPE) evaluation for test data. Finally, the proposed model is believed to be useful for dealing with a complex system that has a variety of corrosion growth behaviors, such as the oil refinery system, as well as it can be applied in other real-time applications.
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A High-resolution Parameter Extraction Algorithm for Multiple Clusters Channels
Multi-path components (MPCs) in wireless channels
generally occur in clusters, i.e., groups of MPCs that have similar
delay/angle characteristics. However, when those clusters are
widely separated and have significantly different power, highresolution
parameter extraction (HRPE) algorithms based on
serial interference cancellation, such as CLEAN, can miss some
of the weaker clusters because they concentrate the path search
in the strongest cluster. This effect can occur particularly in
the presence of calibration error and/or diffuse scattering. To
solve this problem, we propose a heuristic modification, Regional
CLEAN (R-CLEAN), that employs cluster identification in the
Fourier domain and limits the number of MPCs per cluster. We
first demonstrate the effect, and the effectiveness of our proposed
algorithm, on synthetic channels with calibration error or diffuse
scattering. We then demonstrate it with a THz Multiple-Input-
Multiple-Output (MIMO) measurement at 145 - 146 GHz. The
proposed optimization and algorithm can thus be an essential
step towards evaluating channels with multiple clusters.
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- NSF-PAR ID:
- 10342404
- Date Published:
- Journal Name:
- IEEE SPAWC 2022
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
