PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
An Integrative Systems Model for Oil and Gas Pipeline Data Prediction and Monitoring Using a Machine Intelligence and Sequence Learning Neural Technique
Version 1
: Received: 9 August 2018 / Approved: 9 August 2018 / Online: 9 August 2018 (15:02:06 CEST)
How to cite:
Onukwugha, C.G.; Osegi, E.N. An Integrative Systems Model for Oil and Gas Pipeline Data Prediction and Monitoring Using a Machine Intelligence and Sequence Learning Neural Technique. Preprints2018, 2018080194. https://doi.org/10.20944/preprints201808.0194.v1
Onukwugha, C.G.; Osegi, E.N. An Integrative Systems Model for Oil and Gas Pipeline Data Prediction and Monitoring Using a Machine Intelligence and Sequence Learning Neural Technique. Preprints 2018, 2018080194. https://doi.org/10.20944/preprints201808.0194.v1
Onukwugha, C.G.; Osegi, E.N. An Integrative Systems Model for Oil and Gas Pipeline Data Prediction and Monitoring Using a Machine Intelligence and Sequence Learning Neural Technique. Preprints2018, 2018080194. https://doi.org/10.20944/preprints201808.0194.v1
APA Style
Onukwugha, C.G., & Osegi, E.N. (2018). An Integrative Systems Model for Oil and Gas Pipeline Data Prediction and Monitoring Using a Machine Intelligence and Sequence Learning Neural Technique. Preprints. https://doi.org/10.20944/preprints201808.0194.v1
Chicago/Turabian Style
Onukwugha, C.G. and Emmanuel Ndidi Osegi. 2018 "An Integrative Systems Model for Oil and Gas Pipeline Data Prediction and Monitoring Using a Machine Intelligence and Sequence Learning Neural Technique" Preprints. https://doi.org/10.20944/preprints201808.0194.v1
Abstract
Oil and gas pipeline vandalism is a recurrent problem in oil rich zones of Nigeria and its West African neighbors and remains a challenge for multinationals to set ahead control measures to avert possible damages to operations both in infrastructure and business profit margins. In this paper, an integrative systems model comprising of a machine intelligence technique called Hierarchical Temporal Memory (HTM) and a sequence learning neural network called the Online-Sequential Extreme Learning Machine (OS-ELM) is proposed for monitoring and prediction of pipeline pressure data. The system models the continual prediction of pipeline oil/gas pressure signals useful for secure monitoring and control to avert acts of vandalism in oil and gas installations. The HTM uses a spatial pooler operated in temporal aggregated fashion and is defined as HTM-SP. The OS-ELM technique uses an explicit hierarchical training scheme so that the best cost estimates may be found after a stipulated number of trial runs. We study the performance of three OS-ELM neural activations: the sigmoid (sig), sinusoidal (sin) and radial basis function (rbf) activations. The results indicate improvement factors of 1.297, 1.297 and 1.300 of the HTM-SP over the OS-ELM sigmoid, sinusoidal and radial basis activations respectively.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
