Data-Driven Soft Sensor Approach for Quality Prediction in a Refining Process
In the petrochemical industry, the product quality reflects the commercial and operational performance of a manufacturing process. However, real-time measurement of product quality is generally difficult. Online prediction of quality using readily available, frequent process measurements would be be...
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| Veröffentlicht in: | IEEE transactions on industrial informatics 2010-02, Vol.6 (1), p.11-17 |
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| creator | Wang, D. Jun Liu Srinivasan, R. |
| description | In the petrochemical industry, the product quality reflects the commercial and operational performance of a manufacturing process. However, real-time measurement of product quality is generally difficult. Online prediction of quality using readily available, frequent process measurements would be beneficial in terms of operation and quality control. In this paper, a novel soft sensor technology based on partial least squares (PLS) regression is developed and applied to a refining process for quality prediction. The modeling process is described, with emphasis on data preprocessing, multivariate-outlier detection and variables selection. Enhancement of PLS strategy is also discussed for taking into account the dynamics in the process data. The proposed approach is applied to data from a refining process and the performance of the resulting soft sensor is evaluated by comparison with laboratory data and analyzer measurements. |
| doi_str_mv | 10.1109/TII.2009.2025124 |
| format | Article |
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However, real-time measurement of product quality is generally difficult. Online prediction of quality using readily available, frequent process measurements would be beneficial in terms of operation and quality control. In this paper, a novel soft sensor technology based on partial least squares (PLS) regression is developed and applied to a refining process for quality prediction. The modeling process is described, with emphasis on data preprocessing, multivariate-outlier detection and variables selection. Enhancement of PLS strategy is also discussed for taking into account the dynamics in the process data. The proposed approach is applied to data from a refining process and the performance of the resulting soft sensor is evaluated by comparison with laboratory data and analyzer measurements.</description><identifier>ISSN: 1551-3203</identifier><identifier>EISSN: 1941-0050</identifier><identifier>DOI: 10.1109/TII.2009.2025124</identifier><identifier>CODEN: ITIICH</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Chemical industry ; Data analysis ; Data preprocessing ; Laboratories ; Least squares method ; Least squares methods ; Manufacturing industries ; Manufacturing processes ; Mathematical models ; On-line systems ; Outliers ; partial least squares ; Petrochemicals ; Preprocessing ; Quality control ; quality prediction ; Refining ; refining process ; Regression ; Sensors ; soft sensor ; Strategy</subject><ispartof>IEEE transactions on industrial informatics, 2010-02, Vol.6 (1), p.11-17</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Feb 2010</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c355t-294d81a9e21544528ca70080e74dd84990d3ca9ce55bc99f318cde62da2a32a53</citedby><cites>FETCH-LOGICAL-c355t-294d81a9e21544528ca70080e74dd84990d3ca9ce55bc99f318cde62da2a32a53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5184939$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5184939$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wang, D.</creatorcontrib><creatorcontrib>Jun Liu</creatorcontrib><creatorcontrib>Srinivasan, R.</creatorcontrib><title>Data-Driven Soft Sensor Approach for Quality Prediction in a Refining Process</title><title>IEEE transactions on industrial informatics</title><addtitle>TII</addtitle><description>In the petrochemical industry, the product quality reflects the commercial and operational performance of a manufacturing process. However, real-time measurement of product quality is generally difficult. Online prediction of quality using readily available, frequent process measurements would be beneficial in terms of operation and quality control. In this paper, a novel soft sensor technology based on partial least squares (PLS) regression is developed and applied to a refining process for quality prediction. The modeling process is described, with emphasis on data preprocessing, multivariate-outlier detection and variables selection. Enhancement of PLS strategy is also discussed for taking into account the dynamics in the process data. The proposed approach is applied to data from a refining process and the performance of the resulting soft sensor is evaluated by comparison with laboratory data and analyzer measurements.</description><subject>Chemical industry</subject><subject>Data analysis</subject><subject>Data preprocessing</subject><subject>Laboratories</subject><subject>Least squares method</subject><subject>Least squares methods</subject><subject>Manufacturing industries</subject><subject>Manufacturing processes</subject><subject>Mathematical models</subject><subject>On-line systems</subject><subject>Outliers</subject><subject>partial least squares</subject><subject>Petrochemicals</subject><subject>Preprocessing</subject><subject>Quality control</subject><subject>quality prediction</subject><subject>Refining</subject><subject>refining process</subject><subject>Regression</subject><subject>Sensors</subject><subject>soft sensor</subject><subject>Strategy</subject><issn>1551-3203</issn><issn>1941-0050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kUtPwzAMxyMEEmNwR-JScYFLh_Nqm-O08Zg0xGPjHIXUhUxbO5IWad-eTJs4cODih_yzZftPyDmFAaWgbuaTyYABqGiYpEwckB5VgqYAEg5jLCVNOQN-TE5CWADwHLjqkcexaU069u4b62TWVG0ywzo0Phmu174x9jOpYvLSmaVrN8mzx9LZ1jV14urEJK9YudrVH7HQWAzhlBxVZhnwbO_75O3udj56SKdP95PRcJpaLmWbMiXKghqFjEohJCusyQEKwFyUZSGUgpJboyxK-W6VqjgtbIkZKw0znBnJ--RqNzfu-NVhaPXKBYvLpamx6YLOBc8ymhdZJK__JWmWx2cJlomIXv5BF03n63iHVpQqxVTGIgQ7yPomBI-VXnu3Mn6jKeitEDoKobdC6L0QseVi1-IQ8ReXNB7KFf8BAzuB3w</recordid><startdate>201002</startdate><enddate>201002</enddate><creator>Wang, D.</creator><creator>Jun Liu</creator><creator>Srinivasan, R.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Chemical industry Data analysis Data preprocessing Laboratories Least squares method Least squares methods Manufacturing industries Manufacturing processes Mathematical models On-line systems Outliers partial least squares Petrochemicals Preprocessing Quality control quality prediction Refining refining process Regression Sensors soft sensor Strategy |
| title | Data-Driven Soft Sensor Approach for Quality Prediction in a Refining Process |
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