Deep Structures for a Reformer Unit Soft Sensor

Deep Neural Network (DNN) based Soft Sensors (SSs) have been demonstrated as successful alternatives to other data-driven structures. Here, a dynamic DNN based SS is proposed for the estimation of the Research Octane Number (RON) for a Reformer Unit in a refinery. The SS is required to estimate the RON when the plant operates in two different working conditions. Nonlinear Finite Inputs Response (NFIR) models have been investigated. The regressors in the models have been selected according to a cross-correlation analysis between candidate inputs and the RON value. The performance of the proposed SSs has been compared with previously designed deep structures, based on different dynamic first level models, coupled with a fuzzy algorithm.