Robust Statistics for Soft Sensor Development in Cement Kiln
| Authors: | Lin Bao, CAPEC, Technical University of Denmark, Denmark
Recke Bodil, FLS Automation, Denmark
Renaudat Philippe, FLS Automation, Denmark
Knudsen Jørgen, FLS Automation, Denmark
Jørgensen Sten Bay, CAPEC, Technical University of Denmark, Denmark |
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| Topic: | 6.2 Mining, Mineral & Metal Processing |
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| Session: | Mineral Processing |
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| Keywords: | Regression analysis, Soft sensing, Statistics |
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Abstract
This paper presents a systematic approach of developing data-driven soft sensor using robust statistical technique. Data preprocessing procedures are described in detail. First, a template defined with a key process variable is used to handle missing data. Second, a univariate, followed by a multivariate approach, principal component analysis (PCA), is used to detecting outlying observations. Then, regression technique is employed to derive an inferential model. The proposed methodology is applied to a cement kiln system for realtime estimation of free lime, demonstrating improved performance over a standard multivariate approach.
