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Predictive analytics in the petrochemical industry
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Orientador(es)
Resumo(s)
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.
Descrição
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
Palavras-chave
Big Data Catalytic reforming Predictive data analytics Research Octane Number Soft sensors General Chemical Engineering Computer Science Applications