Operational optimization of a simulated atmospheric distillation column using support vector regression models and information analysis

Like any other production processes, atmospheric distillation of crude oil is too complex to be accurately described with first principle models, and on-site experiments guided by some statistical optimization method are often necessary to achieve the optimum operating conditions. In this study, the design of experiment (DOE) optimization procedure proposed originally by Chen et al. (1998) and extended later by Chu et al. (2003) has been revised by using support vector regression (SVR) to build models for target processes. The location of future experiments is suggested through information analysis which is based on SVR models for the performance index and observed variables and reduces significantly the number of experiments needed. A simulated atmospheric distillation column (ADC) is built with Aspen Plus (version 11.1) for a real operating ADC. Kernel functions and parameters are investigated for SVR models to represent suitably the behavior of the simulated ADC. To verify the effectiveness of the revised DOE optimization procedure, three case studies are carried out: (1) The modified Himmelblau function is minimized under a circle constraint; (2) the net profit of the simulated ADC is maximized with all the 15 controlled variables free for adjusting in their operational ranges; (3) the net profit of the simulated ADC is maximized with fixed production rates for the three side-draws.

► Improve a design of experiment optimization method using support vector regression.
► Model a simulated atmospheric distillation column with support vector regression.
► Optimize the operational conditions of a simulated atmospheric distillation column.
► Build a simulated atmospheric distillation column with Aspen Plus.