TY - JOUR
T1 - Predictive analytics in the petrochemical industry
T2 - Research Octane Number (RON) forecasting and analysis in an industrial catalytic reforming unit
AU - Dias, Tiago
AU - Oliveira, Rodolfo
AU - Saraiva, Pedro
AU - Reis, Marco S.
N1 - Dias, T., Oliveira, R., Saraiva, P., & Reis, M. S. (2020). Predictive analytics in the petrochemical industry: Research Octane Number (RON) forecasting and analysis in an industrial catalytic reforming unit. Computers and Chemical Engineering, 139, [106912]. https://doi.org/10.1016/j.compchemeng.2020.106912
PY - 2020/8/4
Y1 - 2020/8/4
N2 - The Research Octane Number (RON) is a key parameter for specifying gasoline quality. It assesses the ability to resist engine knocking as the fuel burns in the combustion chamber. In this work we address the critical but complex problem of predicting RON using real process data in the context of a catalytic reforming process from a petrochemical refinery. We considered data collected from the process over an extended period of time (21 months). RON measurements are obtained offline, by laboratory analysis, with a significant delay and at much lower rates when compared to process measurements. The proposed workflow covers all the way from data collection, cleaning and pre-processing to data-driven modelling, analysis and validation for a real industrial refinery located in Portugal. The accuracy achieved with the best soft sensors open up perspectives for industrial applications and the results obtained also provide relevant information about the main RON variability sources.
AB - The Research Octane Number (RON) is a key parameter for specifying gasoline quality. It assesses the ability to resist engine knocking as the fuel burns in the combustion chamber. In this work we address the critical but complex problem of predicting RON using real process data in the context of a catalytic reforming process from a petrochemical refinery. We considered data collected from the process over an extended period of time (21 months). RON measurements are obtained offline, by laboratory analysis, with a significant delay and at much lower rates when compared to process measurements. The proposed workflow covers all the way from data collection, cleaning and pre-processing to data-driven modelling, analysis and validation for a real industrial refinery located in Portugal. The accuracy achieved with the best soft sensors open up perspectives for industrial applications and the results obtained also provide relevant information about the main RON variability sources.
KW - Big Data
KW - Catalytic reforming
KW - Predictive data analytics
KW - Research Octane Number
KW - Soft sensors
UR - http://www.scopus.com/inward/record.url?scp=85085205165&partnerID=8YFLogxK
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000555543100014
U2 - 10.1016/j.compchemeng.2020.106912
DO - 10.1016/j.compchemeng.2020.106912
M3 - Article
AN - SCOPUS:85085205165
VL - 139
SP - 1
EP - 15
JO - Computers & Chemical Engineering
JF - Computers & Chemical Engineering
SN - 0098-1354
M1 - 106912
ER -