Neural network models for atmospheric residue desulfurization using real plant data with novel training strategies

Abstract

Oil refining industry has been using processes to convert heavy oil into high-value materials due to environmental regulations and decreasing demand for heavy oil. Atmospheric Residue Desulfurization (ARDS) process is important as a pre-treatment process for removing impurities from heavy oil. In this paper, neural network (NN) models are developed to predict the impurity removal amounts in the ARDS process using real plant data. To improve the reliability and reflect the chemical reaction characteristics, three training strategies are proposed: (1) restricting NN weights to positive values, (2) penalizing imbalances for two identical reactor trains, and (3) updating NNs with the latest 4-week data in a receding manner. The trained models satisfy the chemical consistency and provide well-balanced predictions. The model with a pre-defined aging factor structure can learn the catalyst aging status. The maximum percent error in the predicted values is smaller than or equal to 9.20%.