Comparative Studies of Response Surface Methodology (RSM) and Predictive Capacity of Artificial Neural Network (ANN) on Mild Steel Corrosion Inhibition using Water Hyacinth as an Inhibitor

Okewale, Akindele; Omoruwuo, Felix; Adesina, Olusola Adedayo

doi:10.1088/1742-6596/1378/2/022002

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…It is very expensive and time consuming to perform experiments repeatedly to obtain better results. 29 RSM, a statistical and mathematical technique, can be used to solve this problem. The data and results in a mathematical model are influenced by various process parameters, to analyze and optimize the affecting factors, generally, a second-order equation is developed by the model.…”

Section: Experimental Methodsmentioning

confidence: 99%

Comparison of response surface methodology with artificial neural network for prediction of the tensile properties of friction stir-processed surface composites

Butola

Singari

Murtaza

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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In the present work, nanoboron carbide is integrated in the aluminum matrix using friction stir processing: by varying process parameters, that is, tool pin profile, tool rotational speed and tool traverse speed, based on Taguchi L16 design of experiment. A self-assembled monolayer is successfully developed on the substrate to homogeneously and uniformly distribute the reinforcement particles. Response surface methodology and artificial neural network models are developed using ultimate tensile strength and total elongation as responses. Percentage absolute error between the experimental and predicted values of ultimate tensile strength and total elongation for the response surface methodology model is 3.537 and 2.865, respectively, and for artificial neural network is 2.788 and 2.578, respectively. For both the developed models experimental and forecasted values are in close approximation. The artificial neural network model showed slightly better predictive capacity compared to the response surface methodology model. From the scanning electron microscopy micrograph, it is evident that throughout the matrix B4C reinforcement particles are well distributed also; with increasing tool rotational speed grain size decreases up to 1200 r/min; on further increasing the tool rotational speed particles starts clustering.

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Section: Experimental Methodsmentioning

confidence: 99%

Comparison of response surface methodology with artificial neural network for prediction of the tensile properties of friction stir-processed surface composites

Butola

Singari

Murtaza

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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show abstract

“…ANN frequently contains the following three layers: the input layer, hidden layer, and output layer. 24 The input data were imported to the neural network as training input data, and the corrosion current density is predicted as an output parameter. The architecture of the network is chosen with three units in the input layer (the pH, temperature (°C), and chloride concentration (M)), one unit in the output layer for predicting the corrosion current density of carbon steel (μA cm −2 ), and five units for the hidden layer.…”

Section: Investigation Schemementioning

confidence: 99%

“…15,16 The studies on the corrosion prediction using the response surface methodology [16][17][18][19][20][21][22][23] as well as those that compare the ease of use and efficiency of these techniques applied in various industries. 24,25 However, to the best of our knowledge, there is no studies study comparing the optimality of these two methods for predicting corrosion lifetime, especially soil corrosion. Therefore, comparing and finding an optimal method to predict the corrosion rate of soil environments based on the agents is necessary to further develop corrosion science.…”

mentioning

confidence: 99%

Comparison of Response Surface Methodologies and Artificial Neural Network Approaches to Predict the Corrosion Rate of Carbon Steel in Soil

Nguyen¹,

Choi²,

Yong‐Sang³

2022

J. Electrochem. Soc.

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Soil corrosion is a critical problem that has recently interested many scientists. Several soil factors affect the corrosion rate of carbon steel, and they can all be relevant at the same time, thus making it difficult to maintain conditions across soil corrosion studies. There are currently two potential methods for predicting corrosion rates in a complex environment such as soils: the response surface methodology (RSM) and artificial neural network (ANN). RSM is the method using statistical to design experiment, while ANN predicts the corrosion rate through training based on human brain systems. In this study, these two methods were implemented to predict the corrosion rate of carbon steel considering three factors, such as pH, temperature, and chloride. The prediction of corrosion rate is successful in both methods, and they have their own advantages and disadvantages, thus it must be based on the conditions of laboratory advance to decide between the methods.

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“…Scorch is visually recognized only after curing has taken place and the foam slab is split open to expose the core [12]. This means adjustments during production are not a smart decision as experts only make adjustments prior to scorch occurrence.…”

Section: Introductionmentioning

confidence: 99%

Strategic Feature Selection for Enhanced Scorch Prediction in Flexible Polyurethane Form Manufacturing

Omoruwou,

Ojugo,

Ilodigwe

2024

J. Comput. Theor. Appl.

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The occurrence of scorch during the production of flexible polyurethane is a significant issue that negatively impacts foam products' resilience and generally jeopardizes their integrity. The likelihood of foam product failure can be decreased by optimizing production variables based on machine learning algorithms used to predict the occurrence of scorch. Investigating technology is required because prevention is the best approach to dealing with this problem. Hence, machine learning algorithms were trained to predict the occurrence of scorch using the thermodynamic profile of polyurethane foam, which is made up of recorded production variables. A variety of heuristics algorithms were trained and assessed for how well they performed, namely XGBoost, Decision trees, Random Forest, K-nearest neighbors, Naive Bayes, Support Vector Machines, and Logistic Regression. The XGboost ensemble was found to perform best. It outperformed others with an accuracy of 98.3% (i.e., 0.983), followed by logistic regression, decision tree, random forest, K-nearest neighbors, and naïve Bayes, yielding a training accuracy of 88.1%, 66.7%, 84.2%, 87.5%, and 67.5% respectively. The XGBoost was finally used, yielding 2-distinct cases of non(occurrence) of scorch. Ensemble demonstrates that it is quite capable and is an effective way to predict the occurrence of scorch.

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Comparative Studies of Response Surface Methodology (RSM) and Predictive Capacity of Artificial Neural Network (ANN) on Mild Steel Corrosion Inhibition using Water Hyacinth as an Inhibitor

Cited by 10 publications

References 9 publications

Comparison of response surface methodology with artificial neural network for prediction of the tensile properties of friction stir-processed surface composites

Comparison of response surface methodology with artificial neural network for prediction of the tensile properties of friction stir-processed surface composites

Comparison of Response Surface Methodologies and Artificial Neural Network Approaches to Predict the Corrosion Rate of Carbon Steel in Soil

Strategic Feature Selection for Enhanced Scorch Prediction in Flexible Polyurethane Form Manufacturing

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