Soft sensors for estimating a fraction of key components in the products of a Low-temperature isomerization process equipped with a deisohexanizer distillation column are developed. Real plant data from the refinery distributed control system (DCS) are used. A considerable attention is paid to data selection, selection of key input variables, data processing and analysis.
Dynamic soft sensor models for estimating the fraction of 2,2-Dimethylbutane, 2,3-Dimethylbutane, 2-Methylpentane and 3-Methylpentane in the products of the process are developed based on linear and nonlinear regression techniques. The development of a linear dynamic Finite Impulse Response (FIR), Autoregressive with Exogenous Inputs (ARX), Output Error (OE), as well as Nonlinear Dynamic Autoregressive with Exogenous Inputs (NARX) and Hammerstein-Wiener (HW) models are presented. Developed models were evaluated by validation techniques including Root Mean Square Error (RMSE), Absolute Error (AE), Final Prediction Error (FPE) and FIT coefficients.
The results show that the developed soft sensors can reliably estimate the content of key components and thus replace process analysers in the case of failure. This will contribute to the operation stability of the column, the rise in product quality, the reduction in energy consumption as well as the improvement of whole isomerization process. Also, the developed soft sensors can be applied in the Advanced Process Control scheme of the deisohexanizer column.