Data-Based Design of Multi-Model Inferential Sensors

TL;DR

This paper introduces two innovative methods for designing multi-model inferential sensors that enhance predictive accuracy in nonlinear industrial processes while maintaining linearity, demonstrated on a petrochemical refinery unit.

Contribution

The paper proposes two novel approaches for multi-model inferential sensor design that outperform existing methods in accuracy and robustness.

Findings

Substantial performance improvements over existing sensors.

Effective application demonstrated on a real-world refinery unit.

Enhanced predictive accuracy with linear sensor structures.

Abstract

This paper deals with the problem of inferential (soft) sensor design. The nonlinear character of industrial processes is usually the main limitation to designing simple linear inferential sensors with sufficient accuracy. In order to increase the inferential sensor predictive performance and yet to maintain its linear structure, multi-model inferential sensors represent a straightforward option. In this contribution, we propose two novel approaches for the design of multi-model inferential sensors aiming to mitigate some drawbacks of the state-of-the-art approaches. For a demonstration of the developed techniques, we design inferential sensors for a Vacuum Gasoil Hydrogenation unit, which is a real-world petrochemical refinery unit. The performance of the multi-model inferential sensor is compared against various single-model inferential sensors and the current (referential) inferential sensor used in the refinery. The results show substantial improvements over the state-of-the-art design techniques for single-/multi-model inferential sensors.