A new transient thermal fouling probe for cross flow tubular heat exchangers

Perez, Laetitia; Ladevie, Bruno; Tochon, Patrice; Batsale, Jean Christophe

doi:10.1016/j.ijheatmasstransfer.2008.05.029

Cited by 22 publications

(10 citation statements)

References 13 publications

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“…The convective heat exchanger coefficient and fouling thickness in a heat exchanger was determined via transient state estimation by comparing the global response time of a system in fouling and no fouling conditions (Perez, Ladevie, Tochon, & Batsale, 2009). For this, a transient fouling probe was developed using a sensitivity function and the probe was evaluated theoretically and experimentally.…”

Section: Temperature and Heat Transfer Parametersmentioning

confidence: 99%

Detection methods of fouling in heat exchangers in the food industry

Wallhäußer¹,

Hussein²,

Becker³

2012

Food Control

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Section: Temperature and Heat Transfer Parametersmentioning

confidence: 99%

Detection methods of fouling in heat exchangers in the food industry

Wallhäußer¹,

Hussein²,

Becker³

2012

Food Control

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“…The second method is to use heat flow meters as diagnostic sensors. Perez et al [11] installed a heat flowmeter on the water wall to monitor the ash deposition and slag formation and monitored the change of heat flow to diagnose the ash deposition. The third method is direct observation and diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Soot Blowing Optimization for Frequency in Economizers to Improve Boiler Performance in Coal-Fired Power Plant

Shi

Wen

et al. 2019

Energies

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Because of the present ineffective method of soot blowing on a boiler’s heating surface in a coal-fired power plant, and to improve the economic benefit of the boiler in the power plant, weigh the improvement of boiler efficiency and steam loss brought by soot blowing, and ensure the safe operation of the unit, an optimization model of soot blowing on the boiler’s heating surface is established. Taking the economizer of the 300 MW coal-fired power plant unit as the research object, the measurement data and basic thermodynamic calculation data of the Distributed Control System (DCS) of the thermal power plant are used to calculate the fouling rate of the heated surface in real time. By analyzing the multi-group fouling rate under the same working conditions, the incremental distribution of the same measuring point at different times is obtained, and the expectation is obtained according to the distribution curve. The state of heating of the heated surface at a time in the future is predicted by the known initial cleaning state. By analyzing the trend of the fouling rate and combining the soot blowing optimization model, a set of soot blowing optimization strategies are proposed. The method proposed in this manuscript can be applied to the guidance of boiler soot blowing operation.

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“…Fouling deposition is influenced by various parameters like Reynolds number [2], salt content, age of milk [3], protein concentration [4], and pH [5]. To achieve these goals different approaches were made like monitoring heat transfer measurands [10,11], electric parameters [12][13][14], or acoustical quantities for fouling detection [15][16][17]. When fouling occurs heat transfer resistance and production costs are increased wile thermal efficiency is decreased and unsupervised cleaning will take place [9].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, it is of great importance for dairy industry to determine fouling presence and monitor its progress and cleaning success. To achieve these goals different approaches were made like monitoring heat transfer measurands [10,11], electric parameters [12][13][14], or acoustical quantities for fouling detection [15][16][17]. Another approach is to feed measured values to a computational or numerical method that then states fouling presence [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Detection of dairy fouling: Combining ultrasonic measurements and classification methods

Wallhäußer¹,

Hussein²,

Hussein³

et al. 2013

Engineering in Life Sciences

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Fouling and cleaning in heat exchangers are severe and costly (up to 0.3% of gross national product) issues in dairy and food processing. Therefore, reducing cleaning time and cost is urgently needed. In this study, two classification methods [artificial neural network (ANN) and support vector machine (SVM)] for detecting protein and mineral fouling presence and absence based on ultrasonic measurements were presented and compared. ANN is based on a multilayer perceptron feed forward neural network, whereas SVM is based on clustering between fouling and no fouling using a hyperplane. When both fouling types (1239 datasets) were combined, ANN showed an accuracy of 71.9% while SVM displayed an accuracy of 97.6%. Separate fouling detection of mineral/protein fouling by ANN/SVM was comparable: dependent on fouling type detection accuracies of 100% (protein fouling, ANN and SVM), and 98.2% (SVM), and 93.5% (ANN) for mineral fouling was reached. It was shown that it was possible to detect fouling presence and absence offline in a static setup using ultrasonic measurements in combination with a classification method. This study proved the applicability of combining classification methods and fouling measurements to take a step toward reducing cleaning costs and time.

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A new transient thermal fouling probe for cross flow tubular heat exchangers

Abstract: International audienc

Cited by 22 publications

References 13 publications

Detection methods of fouling in heat exchangers in the food industry

Detection methods of fouling in heat exchangers in the food industry

Soot Blowing Optimization for Frequency in Economizers to Improve Boiler Performance in Coal-Fired Power Plant

Detection of dairy fouling: Combining ultrasonic measurements and classification methods

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