Modeling and active constrained optimization of C5/C6 isomerization via Artificial Neural Networks

Abstract

Optimizing oil refinery processes for fuel efficiency and product quality is becoming more important under increasingly tighter environmental regulations. In this paper, we consider the problems of offline and active optimization of the C5/C6 isomerization process using only process inputs and output key performance indicators (KPIs). For offline optimization, we simulate thousands of process configurations and study the impact of optimizing for one KPI (e.g., yield) on other KPIs (e.g., octane number). Surprisingly, for our choices of optimization variables, minimizing energy consumption is the least detrimental on other KPIs. Moreover, an artificial neural network (ANN) model significantly outperforms baseline models in predicting simulated data. For active optimization, we show that our easy-to-use and extensible method can find optimal feasible parameter configuration in as few as 30 experiments, enabling operators to optimize their processes without the need for a model of the refinery process.