Soft Sensor Transferability between Lines of a Sulfur Recovery Unit

被引：4

作者：

Curreri, F. ^{[1
]}

Patane, L. ^{[2
]}

Xibilia, M. G. ^{[2
]}

机构：

[1] Univ Palermo, Dipartimento Matemat & Informat, Via Archirafi 34, I-90123 Palermo, Italy

[2] Univ Messina, Dipartimento Ingn, I-98166 Messina, ME, Italy

来源：

IFAC PAPERSONLINE | 2021年 / 54卷 / 07期

关键词：

transferable soft sensor; nonlinear model; recurrent neural network; monitoring; prediction; inferential model; REGRESSION; PREDICTION; ENSEMBLE;

D O I：

10.1016/j.ifacol.2021.08.415

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Soft Sensors (SSs) are mathematical models that allow real-time estimation of hard-to-measure variables as a function of easy-to-measure ones in an industrial process, emulating the behavior of existing sensors when they are, for instance, taken off for maintenance. The Sulfur Recovery Unit (SRU) from a refinery is taken in exam. Recurrent Neural Networks (RNN) can capture the nonlinearity of such process but present a high complexity training and a very time-consuming structure optimization. For this reason, strategies to use pre-existing models are here examined by testing the transferability of the SSs between two parallel lines of the process. Copyright (C) 2021 The Authors.

引用

页码：535 / 540

页数：6

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