Study of the evolution of a semi‐open cooling circuit

Boucherit, Mohamed Nadir; Amzert, Sid Ahmed; Arbaoui, Fahd; Sari, A.; Tebib, D.

doi:10.1108/00035590610678901

Cited by 6 publications

(8 citation statements)

References 15 publications

(18 reference statements)

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“…In a previous paper, it was shown that for materials used in an open cooling circuit, the choice of the carbon content of steel should take into account the chloride quantity of the medium to which it will be exposed (Boucherit and Tebbib, 2005). Even if the steel does not exhibit localised attack after several years (Boucherit et al , 2006), preventive measures must be considered because localised corrosion occurs rapidly and insidiously at a microscopic level, and can be stimulated by numerous thermohydraulic factors.…”

Section: Introductionmentioning

confidence: 99%

Pitting corrosion in presence of inhibitors and oxidants

Boucherit

Amzert²,

Arbaoui³

et al. 2008

Anti-Corrosion Methods and Materials

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PurposeThe purpose of this paper is to illustrate the usefulness of inhibitors for the prevention of localised corrosion of carbon steel in a low‐aggressive medium. The efficiencies of two inorganic non‐toxic inhibitors are compared, associated with an oxidant.Design/methodology/approachMany experiments were conducted. For each experiment, a solution was prepared with different concentrations of pitting agent, inhibitor and oxidant. The performance was then estimated by the pitting potential taken from the voltammograms of carbon steel obtained with each solution.FindingsThe results show that the efficiency of molybdate and tungstate were comparable. The presence of iodate, which plays an oxidizing role, can be synergistic to the inhibitor but harmful if the concentration ratio is not adequate.Practical implicationsThe interest in the use of an oxidant is that it makes it possible to reduce the inhibitor concentration, which limits the pH increase and prevents scale deposition.Originality/valueThis work provides useful guidance in the localised corrosion prevention of a semi‐open cooling circuit subject to seasonal sand‐storms. The obtained results from the many experiments carried out were compiled using neural networks for performance prediction.

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Section: Introductionmentioning

confidence: 99%

Pitting corrosion in presence of inhibitors and oxidants

Boucherit

Amzert²,

Arbaoui³

et al. 2008

Anti-Corrosion Methods and Materials

Self Cite

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“…Then we used the collected electrochemical data to evaluate different artificial neural network structures. All electrochemical experiments were carried out with the same material: a 0.2 per cent carbon steel used in the secondary cooling circuit of a nuclear installation (Boucherit et al , 2006). The preparation of the metal samples for the electrochemical experiments is detailed in a previous publication (Boucherit and Tebib, 2005).…”

Section: Methodsmentioning

confidence: 99%

Modelling input data interactions for the optimization of artificial neural networks used in the prediction of pitting corrosion

Boucherit¹,

Amzert

Arbaoui

et al. 2019

ACMM

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Purpose This paper aims to predict the localized corrosion resistance by the application of artificial neural networks. It emphasizes the importance to take into account the relationships between the physical parameters before presenting them to the network. Design/methodology/approach The work was conducted in two phases. At the beginning, the authors executed an experimental program to measure pitting corrosion resistance of carbon steel in an aqueous environment. More than 900 electrochemical experiments were conducted in chemical solutions containing different concentrations of pitting agents, corrosion inhibitors and oxidant reagents. The obtained results were collected in a table where for a combination of the experimental parameters corresponds a pitting potential Epit obtained from the corresponding electrochemical experiment. In the second step, the authors used the experimental data to train different artificial neuron networks for predicting pitting potentials. Findings In this step, the authors considered the relationships that the chemical parameters are likely to have between them. Two types of relationships were taken into account: chemical equilibria which are controlled by the pH and the synergistic relationships that some corrosion inhibitors may have when they are in the presence of a chemical oxidant. Originality/value This comparative study shows that adjusting the input data by considering the physical relationships between them allows a better prediction of the pitting potential. The quality of the prediction, quantified by a regression factor, is qualitatively confirmed by a statistical distribution of the gap between experimental and calculated pitting potentials.

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“…This work is part of a research programme on the behaviour of localized corrosion of carbon steel used in the secondary cooling circuit of a nuclear reactor (Boucherit et al, 2006). The work is based on gathering experimental data obtained through electrochemical measurements.…”

Section: Introductionmentioning

confidence: 99%

Pitting corrosion prediction from cathodic data: application of machine learning

Boucherit

Arbaoui

2021

ACMM

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Purpose To constitute input data, the authors carried out electrochemical experiments. The authors performed voltammetric scans in a very cathodic potential region. The authors constituted an experimental table where for each experiment we note the current values recorded at a low polarization range and the pitting potential observed in the anodic region. This study aims to concern carbon steel used in a nuclear installation. The properties of the chemical solutions are close to that of the cooling fluid used in the circuit. Design/methodology/approach In a previous study, this paper demonstrated the effectiveness of machine learning in predicting the localized corrosion resistance of a material by considering as input data the physicochemical properties of its environment (Boucherit et al., 2019). With the present study, the authors improve the results by considering as input data, cathodic currents. The reason of such an approach is to have input data that integrate both the surface state of the material and the physicochemical properties of its environment. Findings The experimental table was submitted to two neural networks, namely, a recurrent network and a convolution network. The convolution network gives better pitting potential predictions. Results also prove that the prediction by observing cathodic currents is better than that obtained by considering the physicochemical properties of the solution. Originality/value The originality of the study lies in the use of cathodic currents as input data. These data contain implicit information on both the chemical environment of the material and its surface condition. This approach appears to be more efficient than considering the chemical composition of the solution as input data. The objective of this study remains, at the same time, to seek the optimal neuronal architectures and the best input data.

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Study of the evolution of a semi‐open cooling circuit

Cited by 6 publications

References 15 publications

Pitting corrosion in presence of inhibitors and oxidants

Pitting corrosion in presence of inhibitors and oxidants

Modelling input data interactions for the optimization of artificial neural networks used in the prediction of pitting corrosion

Pitting corrosion prediction from cathodic data: application of machine learning

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