# Data-Based Design of Multi-Model Inferential Sensors

arXiv: https://arxiv.org/abs/2308.02872 · HTML: https://mlsanity.com/paper/2308.02872/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 36 references that are in the map.
- First posted on arXiv 5 Aug 2023.

## Builds on (most cited first, 3 of 3)

- [Recurrent Neural Network Training with Convex Loss and Regularization Functions by Extended Kalman Filtering](https://mlsanity.com/paper/2111.02673/) (2021)
- [Intelligent sampling for surrogate modeling, hyperparameter optimization, and data analysis](https://mlsanity.com/paper/2306.04066/) (2023)
- [Data-based Design of Inferential Sensors for Petrochemical Industry](https://mlsanity.com/paper/2106.13503/) (2021)

## Abstract (arXiv)

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.