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Robust data reconciliation of combustion variables in multi-fuel fired industrial boilers

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Pages (from-to)101-115
Number of pages15
JournalControl Engineering Practice
Volume55
DOIs
Publication statusPublished - 21 Jul 2016
Publication typeA1 Journal article-refereed

Abstract

This paper introduces an application of simultaneous nonlinear data reconciliation and gross error detection for power plants utilizing a complex but computationally light first principle combustion model. Element and energy balances and robust techniques introduce nonlinearity and the consequent optimization problem is solved using nonlinear optimization. Data reconciliation improves estimation of process variables and enables improved sensor quality control and identification of process anomalies. The approach was applied to an industrial 200 MWth fluidized bed boiler combusting wood, peat, bark, and slurry. The results indicate that the approach is valid and is able to perform in various process conditions. As the combustion model is generic, the method is applicable in any boiler environment.

Keywords

  • Data reconciliation, Diagnostics, Estimation, Gross error detection, Monitoring, Power plant

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