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

Korpela, Timo; Suominen, Olli; Majanne, Yrjö; Laukkanen, Ville; Lautala, P.

doi:10.1016/j.conengprac.2016.07.002

Cited by 20 publications

(3 citation statements)

References 28 publications

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“…A value of c w = 2.9846 is used to obtain 95% asymptotic efficiency on the standard distribution. 65 Table 5 shows the material balance for the same set of measurements as presented before in Table 4 but after applying the proposed robust data reconciliation approach. As can be observed, the material balance is satisfied.…”

Section: Resultsmentioning

confidence: 99%

“…For a robust estimation in case of outliers, the traditional least-squares objective function is replaced by a Welsch estimator defined by with tuning parameter c w . A value of c w = 2.9846 is used to obtain 95% asymptotic efficiency on the standard distribution Table shows the material balance for the same set of measurements as presented before in Table but after applying the proposed robust data reconciliation approach.…”

Section: Resultsmentioning

confidence: 99%

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Iterative Real-Time Optimization Scheme for Optimal Operation of Chemical Processes under Uncertainty: Proof of Concept in a Miniplant

Hernández

Dreimann

Vorholt

et al. 2018

Ind. Eng. Chem. Res.

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Real-time optimization (RTO) has gained growing attention during the past few years as a useful approach to boost process performance while safety and environmental constraints are satisfied. Despite the increasing acceptance of RTO in traditional industries such as petrochemical and refineries, its application to novel chemical processes remains limited. This can be partially explained by the fact that only inaccurate models are available and the performance of the traditional RTO scheme suffers in the presence of plant-model mismatch. During the past few years, the so-called modifier-adaptation schemes for real-time optimization have been gaining popularity as an efficient tool to handle plant-model mismatch. So far, there are only few published works regarding experimental implementations. In this contribution, a reliable RTO scheme that is able to deal with model uncertainty and measurement noise is applied to a novel transition metal complex catalyzed process that is performed in a continuously operated miniplant. The experimental results show that the proposed scheme is able to drive the process to an improved operation despite significant plant-model mismatch demonstrating the applicability of the method to real processes.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Iterative Real-Time Optimization Scheme for Optimal Operation of Chemical Processes under Uncertainty: Proof of Concept in a Miniplant

Hernández

Dreimann

Vorholt

et al. 2018

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

“…Instead, in [20], data reconciliation was used in synergy with PCA for monitoring and sensor fault detection in a modelled ammonia synthesis plant. In [21], data reconciliation improved the estimation of process variables and enabled improved sensor quality control and identification of process anomalies.…”

Section: Introductionmentioning

confidence: 99%

Data-Reconciliation Based Fault-Tolerant Model Predictive Control for a Biomass Boiler

et al. 2017

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This paper presents a novel, effective method to handle critical sensor faults affecting a control system devised to operate a biomass boiler. In particular, the proposed method consists of integrating a data reconciliation algorithm in a model predictive control loop, so as to annihilate the effects of faults occurring in the sensor of the flue gas oxygen concentration, by feeding the controller with the reconciled measurements. Indeed, the oxygen content in flue gas is a key variable in control of biomass boilers due its close connections with both combustion efficiency and polluting emissions. The main benefit of including the data reconciliation algorithm in the loop, as a fault tolerant component, with respect to applying standard fault tolerant methods, is that controller reconfiguration is not required anymore, since the original controller operates on the restored, reliable data. The integrated data reconciliation-model predictive control (MPC) strategy has been validated by running simulations on a specific type of biomass boiler-the KPA Unicon BioGrate boiler.

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Data-driven framework for boiler performance monitoring

2016

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

Cited by 20 publications

References 28 publications

Iterative Real-Time Optimization Scheme for Optimal Operation of Chemical Processes under Uncertainty: Proof of Concept in a Miniplant

Iterative Real-Time Optimization Scheme for Optimal Operation of Chemical Processes under Uncertainty: Proof of Concept in a Miniplant

Data-Reconciliation Based Fault-Tolerant Model Predictive Control for a Biomass Boiler

Data-driven framework for boiler performance monitoring

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