Logo

Predictive analytic dashboard for desalter and crude distillation unit

Hassanudin, S.N. and Aziz, I.A. and Jaafar, J. and Qaiyum, S. and Zubir, W.M.A.M. (2018) Predictive analytic dashboard for desalter and crude distillation unit. 2017 IEEE Conference on Big Data and Analytics, ICBDA 2017, 2018-J . pp. 55-60.

Full text not available from this repository.

Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Desalter and crude distillation unit is an equipment used for desalting of salt and other impurities to minimize corrosion during the crude oil refining process. This study presents the predictive analysis of data from desalter and crude distillation unit. Artificial Neural Network (ANN) algorithm is used with R programming language for the forecasting. The corrosion rate was identified by using Multiple Linear Regression Analysis (MLRA). The objective was to develop a predictive analysis model by incorporating ANN and MLRA using parameters from the desalter, the crude distillation unit data and the corrosion rate. ANN is used to forecast data while MLRA is used to find the corrosion rate. A dashboard system was developed to visualize the propose analysis. The proposed predictive analytical model was validated within the proposed dashboard system. This predictive dashboard is to aid the corrosion engineer to make decision on replacing pipeline on estimated time to avoid financial losses and risk. © 2017 IEEE.

Item Type:Article
Impact Factor:cited By 0; Conference of 2017 IEEE Conference on Big Data and Analytics, ICBDA 2017 ; Conference Date: 16 November 2017 Through 17 November 2017; Conference Code:134594
Uncontrolled Keywords:Corrosion rate; Crude oil; Desalination; Distillation; Distillation equipment; Forecasting; Linear regression; Losses; Neural networks; Pipeline corrosion; Predictive analytics; Risk perception, Corrosion engineers; Crude distillation units; Crude oil refining; Desalter; Financial loss; Multiple linear regression analyses (MLRA); Predictive Analytic, Big data
ID Code:21775
Deposited By: Ahmad Suhairi
Deposited On:14 Aug 2018 00:45
Last Modified:14 Aug 2018 00:45

Repository Staff Only: item control page