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Quantitative analysis of product quality of naphtha reforming process under uncertain process conditions

Abstract Soft sensors have been used for estimation of research octane number (RON) of gasoline and to realize stability in process operation. In this study, a novel framework is proposed by incorporating uncertainty analysis in the conventional soft-sensor design for RON prediction. Ensemble learning method, i.e., boosting, and artificial neural networks (ANN) were used to realize highly efficient soft sensor. The individual impact of uncertainty of each process variable on RON was analyzed using Sobol technique and Fourier Amplitude Sensitivity Test (FAST). The accumulative effect of uncertainties on RON was investigated through nonintrusive polynomial chaos expansion (PCE). For realizing reliability in online application of the predictive distribution of RON, control limits were derived using a Just-in-Time method, i.e., k-d tree. The proposed method was validated through process data where the results were found suitable for industrial applications.