Use of extended Kalman filtering in detecting fouling in heat exchangers

Jónsson, Guðmundur; Lalot, Sylvain; Palsson, Olafur P.; Desmet, Bernard

doi:10.1016/j.ijheatmasstransfer.2006.11.025

Cited by 82 publications

(46 citation statements)

References 11 publications

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“…A non-linear physical state model for online detection of fouling in heat exchangers was applied (Jonsson, Lalot, Palsson, & Desmet, 2007). A cross-flow heat exchanger was split in cells and described with inlet and outlet temperatures and mass flow rates of hot and cold fluid which got using extended Kalman filtering.…”

Section: Computational and Numerical Methods For Fouling Modelling Anmentioning

confidence: 99%

Detection methods of fouling in heat exchangers in the food industry

Wallhäußer¹,

Hussein²,

Becker³

2012

Food Control

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Section: Computational and Numerical Methods For Fouling Modelling Anmentioning

confidence: 99%

Detection methods of fouling in heat exchangers in the food industry

Wallhäußer¹,

Hussein²,

Becker³

2012

Food Control

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“…The study demonstrated in addition, the benefit of including models into the design phase and introduced a modified linear model that aided the determination of the optimum design parameters. Various other methods exist which include the neural networks (Riverol and Napolitano, 2005), wavelets (Ingimundardóttir and Lalot, 2011), linear parameter varying (LPV) models (Mercère et al, 2013), fuzzy observers (Delmotte et al, 2008), physical model (Gudmundsson et al, 2009), and Extended Kalman filters (Jonsson et al, 2007). Moreover, Wen et al (2017) employed a multi-resolution wavelet neural network approach for the prediction of fouling resistance of a plate heat exchanger.…”

Section: Models For Predicting Foulingmentioning

confidence: 99%

Fouling Phenomenon and Its Effect on Heat Exchanger: A Review

Ajayi¹,

Ogbonnaya²

2017

Frontiers in Heat and Mass Transfer

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Heat exchangers as a heat transfer device has gained wide applications across different levels of domestic and industrial set-ups. Various studies have been carried out to study, analyse and predict its performance. However, one major phenomenon that limits heat exchanger performance is attributed to fouling. Based on this, various studies and approaches have focused on reduction, elimination and mitigation of fouling. This study therefore focused on this. It reviewed several attempts that have been carried out to understand and mitigate the incidents of fouling in heat exchangers. The study found that despite the existing models developed towards understanding fouling, there is no single model that has provided an accurate prediction of fouling in all the different types of heat exchangers. Also, majority of existing results have limited applications and some of the solutions are only peculiar to particular type of heat exchanger. Further to this, majority of the study results only pointed to small scale laboratory test schemes and there is absence of in-situ measurements that predicts actual (real life) service performance.

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“…Traditionally, FDD in plant utilities is focused on the condition of the equipments responsible for the utility generation and distribution, without studying the effects of the faults plant-wide [26][27]. Recently though, [28] acknowledged a malfunctioning utility as a cause for plantwide disturbances and estimated its economical effects.…”

Section: Fault Detection and Diagnosis Of Plant Utilitiesmentioning

confidence: 99%

Importance of auxiliary systems for process fault detection and diagnosis

Cecı́lio

Chen

Thornhill

2011

2011 19th Mediterranean Conference on Control &Amp; Automation (MED)

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Abstract-In industrial processes, fault detection and diagnosis is traditionally applied separately to process and equipment without relating the information on both systems. This paper shows the interaction during fault propagation between the process and its auxiliary systems, i.e. mechanical, electrical and utility systems. Evidence is given with an industrial case study. The relevance of this finding is demonstrated by showing the advantages of including information from electrical and mechanical data in a root cause diagnosis test.

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Use of extended Kalman filtering in detecting fouling in heat exchangers

Cited by 82 publications

References 11 publications

Detection methods of fouling in heat exchangers in the food industry

Detection methods of fouling in heat exchangers in the food industry

Fouling Phenomenon and Its Effect on Heat Exchanger: A Review

Importance of auxiliary systems for process fault detection and diagnosis

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