Data-driven Soft Sensors in the process industry

被引:1385
|
作者
Kadlec, Petr [1 ]
Gabrys, Bogdan [1 ]
Strandt, Sibylle [2 ]
机构
[1] Bournemouth Univ, Computat Intelligence Res Grp, Smart Technol Res Ctr, Poole BH12 5BB, Dorset, England
[2] Evon Degussa AG, D-45128 Essen, Germany
关键词
Soft Sensors; Process industry; Data-driven models; PCA; ANN; PRINCIPAL-COMPONENT ANALYSIS; PROCESS FAULT-DETECTION; PARTIAL LEAST-SQUARES; BATCH PROCESSES; NEURAL-NETWORKS; MISSING DATA; QUANTITATIVE MODEL; ADAPTIVE-CONTROL; DIAGNOSIS; IDENTIFICATION;
D O I
10.1016/j.compchemeng.2008.12.012
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity. already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work. (C) 2009 Elsevier Ltd. All rights reserved.
引用
收藏
页码:795 / 814
页数:20
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