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

Timo Korpela; Olli Suominen; Yrjö Majanne; Ville Laukkanen; Pentti Lautala

doi:10.1016/j.conengprac.2016.07.002

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

Timo Korpela, Olli Suominen, Yrjö Majanne, Ville Laukkanen, Pentti Lautala

Research output: Contribution to journal › Article › Scientific › peer-review

20 Citations (Scopus)

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 MW_th 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.

Original language	English
Pages (from-to)	101-115
Number of pages	15
Journal	Control Engineering Practice
Volume	55
DOIs	https://doi.org/10.1016/j.conengprac.2016.07.002
Publication status	Published - 21 Jul 2016
Publication type	A1 Journal article-refereed

Keywords

Data reconciliation
Diagnostics
Estimation
Gross error detection
Monitoring
Power plant

Publication forum classification

Publication forum level 2

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering
Applied Mathematics
Computer Science Applications

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10.1016/j.conengprac.2016.07.002

Cite this

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title = "Robust data reconciliation of combustion variables in multi-fuel fired industrial boilers",

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",

author = "Timo Korpela and Olli Suominen and Yrj{\"o} Majanne and Ville Laukkanen and Pentti Lautala",

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AU - Korpela, Timo

AU - Suominen, Olli

AU - Majanne, Yrjö

AU - Laukkanen, Ville

AU - Lautala, Pentti

PY - 2016/7/21

Y1 - 2016/7/21

N2 - 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.

AB - 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.

KW - Data reconciliation

KW - Diagnostics

KW - Estimation

KW - Gross error detection

KW - Monitoring

KW - Power plant

U2 - 10.1016/j.conengprac.2016.07.002

DO - 10.1016/j.conengprac.2016.07.002

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SN - 0967-0661

VL - 55

SP - 101

EP - 115

JO - Control Engineering Practice

JF - Control Engineering Practice

ER -

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

Abstract

Keywords

Publication forum classification

ASJC Scopus subject areas

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