An AI-Extended Prediction of Erosion-Corrosion Degradation of API 5L X65 Steel

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dc.catalogadorgrr
dc.contributor.authorEspinoza-Jara, Ariel Orlando
dc.contributor.authorWilk, Igor
dc.contributor.authorAguirre, Javiera
dc.contributor.authorWalczak, Magdalena
dc.date.accessioned2023-11-17T21:02:06Z
dc.date.available2023-11-17T21:02:06Z
dc.date.issued2023
dc.description.abstractThe application of Artificial Neuronal Networks (ANN) offers better statistical accuracy in erosion-corrosion (E-C) predictions compared to the conventional linear regression based on Multifactorial Analysis (MFA). However, the limitations of ANN to require large training datasets and a high number of inputs pose a practical challenge in the field of E-C due to the scarcity of data. To address this challenge, a novel ANN method is proposed, structured to a small training dataset and trained with the aid of synthetic data to produce an E-C neural network (E-C NN), applied for the first time in the study of E-C wear synergy. In the process, transfer learning is applied by pre-training and fine-tuning the model. The initial dataset is created from experimental data produced in a slurry pot setup, exposing API 5L X65 steel to a turbulent copper tailing slurry. To the previously known E-C scenario for selected values of flow velocity, particle concentration, temperature, pH, and the content of the dissolved Cu2+, new experimental data of stand-alone erosion and stand-alone corrosion is added. The prediction of wear loss by E-C NN considers individual parameters and their interactions. The main result is that E-C ANN provides better prediction than MFA as evaluated by a mean squared error (MSE) values of 2.5 and 3.7, respectively. The results are discussed in the context of the cross-effect between the proposed prediction model and the resulting estimation of relative contribution to E-C synergy, which is better predicted by the E-C NN. The E-C NN model is concluded to be a viable alternative to MFA, delivering similar prediction with better sensitivity to E-C synergy at shorter computation times when using the same experimental dataset.
dc.fechaingreso.objetodigital2023-11-17
dc.format.extent21 páginas
dc.fuente.origenWOS
dc.identifier.doi10.3390/lubricants11100431
dc.identifier.eissn2075-4442
dc.identifier.scopusidSCOPUS-ID: 85175021871
dc.identifier.urihttps://doi.org/10.3390/lubricants11100431
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/75334
dc.identifier.wosidWOS:001089554000001
dc.information.autorucEscuela de Ingeniería; Espinoza-Jara, Ariel Orlando; 0000-0002-1029-5813; 1086412
dc.information.autorucEscuela de Ingeniería; Walczak, Magdalena ; 0000-0003-2070-9458; 1007559
dc.issue.numero10
dc.language.isoen
dc.nota.accesoContenido completo
dc.publisherMDPI
dc.revistaLubricants
dc.rightsacceso abierto
dc.rights.licenseAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.es
dc.subjectErosion-corrosion wear
dc.subjectANN
dc.subjectMultifactorial analysis
dc.subjectSynthetic data
dc.subjectErosion-corrosion data
dc.subjectErosion data
dc.subjectCorrosion data
dc.subject.ddc620
dc.subject.deweyIngenieríaes_ES
dc.subject.ods07 Affordable and Clean Energy
dc.subject.odspa07 Energía asequible y no contaminante
dc.titleAn AI-Extended Prediction of Erosion-Corrosion Degradation of API 5L X65 Steel
dc.typeartículo
dc.volumen11
sipa.codpersvinculados1086412
sipa.codpersvinculados1007559
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