Artificial intelligence to predict inhibition performance of pitting corrosion

Boukhari, Yakoub; Boucherit, Mohamed Nadir; Zaabat, M.; Amzert, Sid‐Ahmed; Brahimi, K.

doi:10.4314/jfas.v9i1.19

Cited by 12 publications

(10 citation statements)

References 26 publications

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“…While the adjusted R 2 is given by the following equation: (6) where, N is number of data, P is number of variables and R 2 is the coefficient of determination given in Eq. 4.…”

Section: Predictive Performancesmentioning

confidence: 99%

“…In recent years, machine learning algorithms are becoming increasingly more important in modelling of complex phenomena where many factors determine the outcome of the process. It is so difficult, even impossible, to predict target outputs with simple statistical models [6]. One of the major advantages of machine learning algorithm is that it does not need the explicit knowledge of chemical and physical behavior of phenomena [7].…”

Section: Introductionmentioning

confidence: 99%

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Application and Comparison of Machine Learning Algorithms for Predicting Mass Loss of Cement Raw Materials due to Decarbonation Process

Boukhari¹

2020

RIA

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The purpose of this work is to predict the mass loss of cement raw materials during the decarbonation process. The mass loss is influenced by the interaction of several parameters such as chemical composition of raw material, particle size, temperature range of decarbonation and time exposed. Therefore, predicting mass loss based on experimental parameters data is often challenging. For this reason, various machine learning algorithms such as deep networks using autoencoder DN-AE, artificial neural networks optimized by particle swarm optimization PSO-ANN, ANN optimized by ant colony optimization ACO-ANN and ANN are proposed to predict the mass loss. In this research, all models have been applied successfully to predict the mass loss with high accuracy. The results obtained have shown the superiority of DN-AE compared to PSO-ANN, ACO-ANN and ANN. In addition, PSO-ANN and ACO-ANN have a better performance than the individual use of ANN. The values of adjusted R2 indicate that 99.11%, 98.66%, 98.27% and 97.03% of data are explained by DN-AE, ACO-ANN, PSO-ANN and ANN respectively with scatter index (SI) less than 0.1 and maximum error less than 3.32%. Finally, the results justify that all models proposed can be employed to predict the mass loss as alternative tools.

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“…While the adjusted R 2 is given by the following equation: (6) where, N is number of data, P is number of variables and R 2 is the coefficient of determination given in Eq. 4.…”

Section: Predictive Performancesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application and Comparison of Machine Learning Algorithms for Predicting Mass Loss of Cement Raw Materials due to Decarbonation Process

Boukhari¹

2020

RIA

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“…Some works have already demonstrated the efficiency of these models in corrosion systems using different conditions. For example, in the prediction of corrosion inhibition in pipeline steel [ 16 , 17 , 18 ], the resistance of dental metallic [ 19 ], to determine inhibitor efficiency applied in aluminium [ 20 ], and others [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Modelling of Behavior for Inhibition Corrosion of Bronze Using Artificial Neural Network (ANN)

Millán-Ocampo

Parrales

Martı́nez

et al. 2018

Entropy

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Abstract:In this work, three models based on Artificial Neural Network (ANN) were developed to describe the behavior for the inhibition corrosion of bronze in 3.5% NaCl + 0.1 M Na 2 SO 4 , using the experimental data of Electrochemical Impedance Spectroscopy (EIS). The database was divided into training, validation, and test sets randomly. The parameters process used as the inputs of the ANN models were frequency, temperature, and inhibitor concentration. The outputs for each ANN model and the components in the EIS spectrum (Z re , Z im , and Z mod ) were predicted. The transfer functions used for the learning process were the hyperbolic tangent sigmoid in the hidden layer and linear in the output layer, while the Levenberg-Marquardt algorithm was applied to determine the optimum values of the weights and biases. The statistical analysis of the results revealed that ANN models for Z re , Z im , and Z mod can successfully predict the inhibition corrosion behavior of bronze in different conditions, where what was considered included variability in temperature, frequency, and inhibitor concentration. In addition, these three input parameters were keys to describe the behavior according to a sensitivity analysis.

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“…The activation energy varied between 31–635 and 88–471 J/mol, which represents higher thermal sensitivity in corrosion process with respect to low pH and high dissolved oxygen conditions. Recently, there has been some progress in using artificial intelligence to foresee corrosion of materials via different algorithms including the ANFIS model (Akano, Fakindele, Mgbemere, & Amechi, ; Boukhari, Boucherit, Zaabat, Amzert, & Brahimi, ; Vignesh et al, ; Xu, Ran, & Chen, ). Yet, the conducted works are still in development phase (mostly in the material science).…”

Section: Introductionmentioning

confidence: 99%

Modeling corrosion trends in tin‐free steel and tinplate cans containing tomato paste via adaptive‐network‐based fuzzy inference system

Ganje

Jafari

Amanjani

et al. 2017

J Food Process Engineering

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Corrosion process in metallic cans is one of the most significant issues, which is of utmost importance in food packaging; meanwhile, it can be studied through both economical and hygienic aspects. Designing a method to determine this process in an acidic food like tomato paste might be a good standard to monitor this phenomenon in food products. Accordingly, in this article, tin‐free steel (TFS) and tinplate (TIN) cans containing tomato paste were incubated at three different temperatures (25, 35, and 45 °C) and three filling percentages (85, 90, and 95%). Corrosion rate was then studied via image processing method within six months, thereupon corrosion modeling was carried out using adaptive‐network‐based fuzzy inference system (ANFIS). Time, temperature and filling percentage were considered as the input parameters, on the other hand the corrosion rate was assumed as the output. Multiple training cycles and various membership functions were used in the form of trial and error to have the models optimized. Having the models been examined, trapezoidal membership functions with 3–3–3 functions for TFS cans and 2–2–2 functions in Tin cans were determined as the best models. High coefficients of determination (R2 = 0.97) between experimental and predicted values by optimum models presented a robust method for monitoring corrosion in metallic cans. Practical applications Food products might be preserved in metallic cans for more than 2 years without any significant changes in organoleptic properties. One disadvantage is that cans are more susceptible to corrosion compared with other packaging materials. The absence of appropriate methods and the high costs of analytical instruments are major problems encountered in evaluating the corrosion process. On the other hand, developing straightforward and modern techniques may be an efficient solution in this area. This study was devoted to the prediction of the corrosion process using two prevalent can plates employed in the food industry. ANFIS modeling was implemented in tomato paste, which exhibits acidic and corrosive behavior. ANFIS modeling may be an onset for further research in this area to engineer and manufacture intelligent sensors by which on‐line monitoring in complex and nonlinear interactions is undertaken.

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Artificial intelligence to predict inhibition performance of pitting corrosion

Cited by 12 publications

References 26 publications

Application and Comparison of Machine Learning Algorithms for Predicting Mass Loss of Cement Raw Materials due to Decarbonation Process

Application and Comparison of Machine Learning Algorithms for Predicting Mass Loss of Cement Raw Materials due to Decarbonation Process

Modelling of Behavior for Inhibition Corrosion of Bronze Using Artificial Neural Network (ANN)

Modeling corrosion trends in tin‐free steel and tinplate cans containing tomato paste via adaptive‐network‐based fuzzy inference system

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