Application of machine learning to stress corrosion cracking risk assessment

Alamri, Aeshah Hassan

doi:10.1016/j.ejpe.2022.09.001

Cited by 22 publications

(5 citation statements)

References 67 publications

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“…n cross-validation, the available data is split into k equal-sized subsets, or “folds.” The model is then trained k times using a different fold as the validation set and the remaining k −1 folds as the training set. This allows the model to be trained and evaluated on different subsets of the data, which can help reduce the impact of overfitting and provide a more accurate estimate of the model’s performance (Anguita et al , 2012).…”

Section: Methodsmentioning

confidence: 99%

“…By reducing the cost and time of testing, machine learning (ML) can facilitate the design of alloys with specific properties. ML algorithms can provide a new approach to simulating different types of corrosion (Alamri, 2022;Scully and Miles, 2020). In recent years, many research studies have been conducted on the using of ML to predict the corrosion behavior of materials.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of hot corrosion behavior of Inconel 617 via machine learning

Rezaei

2023

ACMM

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Purpose This paper aims to study the feasibility of using machine learning in hot corrosion prediction of Inconel 617 alloy. Design/methodology/approach By examination of the experimental studies on hot corrosion of Inconel 617, a data set was built for machine learning models. Apart from the alloy composition, this paper included the condition of hot corrosion like time and temperature, and the composition of the saline medium as independent features, while the specific mass change is set as the target feature. In this paper, linear regression, random forest and XGBoost are used to predict the specific mass gain of Inconel 617. Findings XGBoost yields the coefficient of determination (R2) of 0.98, which was highest among models. Also, this model recorded the lowest value of mean absolute error (0.20). XGBoost had the best performance in predicting specific mass gain of the alloy in different times at temperature of 900°C. In sum, XGBoost shows highest accuracy in predicting specific mass gain for Inconel 617. Originality/value Using machine learning to predict hot corrosion in Inconel 617 marks a substantial progress in this domain and holds promise for simplifying the development and evaluation of novel materials featuring enhanced hot corrosion resilience.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of hot corrosion behavior of Inconel 617 via machine learning

Rezaei

2023

ACMM

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“…Merancang sebuah sistem merupakan solusi yang dapat mempercepat dan mempermudah deteksi seseorang menderita penyakit stroke, dimana sistem tersebut mengimplementasikan machine learning untuk melakukan klasifikasi data dari hasil rekam medis. Machine learning merupakan bagian dari kecerdasan buatan (AI) [6]. Machine learning bertujuan untuk merancang dan mengembangkan model matematika yang dapat dilatih tanpa pengetahuan lengkap tentang semua faktor eksternal yang mempengaruhi untuk memungkinkan keputusan dibuat secara cerdas [6].…”

Section: Pendahuluanunclassified

“…Machine learning merupakan bagian dari kecerdasan buatan (AI) [6]. Machine learning bertujuan untuk merancang dan mengembangkan model matematika yang dapat dilatih tanpa pengetahuan lengkap tentang semua faktor eksternal yang mempengaruhi untuk memungkinkan keputusan dibuat secara cerdas [6].…”

Section: Pendahuluanunclassified

Perancangan Klasifikasi Pasien Stroke Dengan Metode K-Nearest Neighbor

Yulianto,

Riadi,

Umar

2023

rabit

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Stroke merupakan penyakit yang ditandai gangguan fungsi otak yang di sebabkan kurangnya pasokan oksigen dan aliran darah ke otak sehingga mempengaruhi beberapa fungsi otak yang membuat penderita mengalami kesulitan dalam melakukan aktifitas. klasifikasi pasien stroke yang di temukan masih berupa catatan medis yang belum terintegrasi sehingga perlu waktu yang lebih lama untuk mendeteksi. Algoritma K-NN merupakan bagian dari algoritma machine learning yang dapat digunakan untuk mengklasifikasikan salah satu kasusnya yaitu klasifikasi pasien stroke. K-NN digunakan sebagai algoritma penentu kelas untuk memasukkan data baru yang diinputkan sesuai format. Berdasarkan hasil yang diperoleh penelitian ini mengarah pada perancangan sistem menggunakan Unified Model Language (UML) dan perancangan antarmuka pengguna sistem.

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“…ML methods in risk assessment from corrosion cracking are applied with big data to integrate the Internet of Things [24]. Several soft-computing techniques and their application in safety evaluation were suggested for strategic decision-making in hazard management [25].…”

Section: Introductionmentioning

confidence: 99%

Threat and Risk Analysis-Based Neural Network for a Chemical Explosion (TRANCE) Model to Predict Hazards in Petroleum Refinery

Gabhane

Rao

2023

Toxics

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Risk analysis and prediction is a primary monitoring strategy to identify abnormal events occurring in chemical processes. The accidental release of toxic gases may result in severe problems for people and the environment. Risk analysis of hazardous chemicals using consequence modeling is essential to improve the process reliability and safety of the refineries. In petroleum refineries: toluene, hydrogen, isooctane, kerosene, methanol, and naphtha are key process plants with toxic and flammable chemicals. The major process plants considered for risk assessment in the refinery are the gasoline hydrotreatment unit, crude distillation, aromatic recovery, continuous catalytic reformer, methyl–tert–butyl–ether, and kerosene merox units. Additionally, we propose a threat and risk analysis neural network for the chemical explosion (TRANCE) model for refinery incident scenarios. Significantly, 160 attributes were collected for the modeling on the basis of the significance of failure and hazardous chemical leaks in the refinery. Hazard analysis shows that the leakages of hydrogen and gasoline at the gasoline hydrotreatment unit, kerosene at the kerosene merox plant, and crude oil at crude-distillation units were areas of profound concern. The developed TRANCE model predicted the chemical explosion distance with an R2 accuracy value of 0.9994 and MSE of 679.5343.

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Application of machine learning to stress corrosion cracking risk assessment

Cited by 22 publications

References 67 publications

Prediction of hot corrosion behavior of Inconel 617 via machine learning

Prediction of hot corrosion behavior of Inconel 617 via machine learning

Perancangan Klasifikasi Pasien Stroke Dengan Metode K-Nearest Neighbor

Threat and Risk Analysis-Based Neural Network for a Chemical Explosion (TRANCE) Model to Predict Hazards in Petroleum Refinery

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