Virtual Instruments Based on Stacked Neural Networks to Improve Product Quality Monitoring in a Refinery

In this paper, a virtual instrument for the estimation of octane number in the gasoline produced by refineries is introduced. The instrument was designed with the aim of replacing measuring hardware during maintenance operations. The virtual instrument is based on a nonlinear moving average model, implemented by using multilayer perceptron neural networks. Stacking approaches are adopted to improve the estimation performance of the instrument. Classical linear algorithms of model aggregation are compared in the paper with a nonlinear strategy, based on the neural combination of a set of first-level neural estimators. The validity of the proposed approach is verified by comparison with the performance of both linear and nonlinear modeling techniques. The designed virtual instrument has been implemented by a large refinery in Sicily, which supplied the data used during the design phase.