TY  - CONF
SN  - 9781538607909
PB  - Institute of Electrical and Electronics Engineers Inc.
Y1  - 2017///
VL  - 2018-J
EP  - 60
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047415109&doi=10.1109%2fICBDAA.2017.8284107&partnerID=40&md5=09efacc858ac8ebc53780725fc57be19
A1  - Hassanudin, S.N.
A1  - Aziz, I.A.
A1  - Jaafar, J.
A1  - Qaiyum, S.
A1  - Zubir, W.M.A.M.
AV  - none
KW  - Corrosion rate; Crude oil; Desalination; Distillation; Linear regression; Losses; Model predictive control; Predictive analytics; Risk perception
KW  -  Analysis of data; Artificial neural network; Artificial neural network algorithm; Crude distillation units; Crude oil refining; Desalter; Multiple linear regression analyse; Multiple linear regression analyses (MLRA); Oil refining process; R programming
KW  -  Neural networks
TI  - Predictive analytic dashboard for desalter and crude distillation unit
SP  - 55
ID  - scholars8535
N2  - 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.
N1  - cited By 4; Conference of 2017 IEEE Conference on Big Data and Analytics, ICBDA 2017 ; Conference Date: 16 November 2017 Through 17 November 2017; Conference Code:134594
ER  -