María Jesús Jiménez-Come

Pitting corrosion behaviour of austenitic stainless steel using artificial intelligence techniques

Abstract Different artificial intelligent tools have been used to model pitting corrosion behaviour of EN 1.4404 austenitic stainless steel. Samples from this material have been subjected to polarization tests in different chloride solutions using different precursor salts: NaCl and MgCl2. The aim of this work is to compare the results obtained from the different classification models using both solutions studying the influence of them. Furthermore, in order to determine pitting potential values ( E pit ), different environmental conditions have been tested varying chloride ion concentration, pH value and temperature. The techniques used try to find the relation between the environmental parameters studied and the status pitting corrosion of this alloy. Several classification techniques have been used: Classification Trees (CT), Discriminant Analysis (DA), K-Nearest-Neighbours (K-NN), Back-Propagation Neural Networks (BPNN) and Support Vector Machine (SVM). The results obtained show the good correlation between experimental and predicted data for all the cases studied demonstrating the utility of artificial intelligence for modelling pitting corrosion problem.

A two-stage procedure for forecasting freight inspections at Border Inspection Posts using SOMs and support vector regression

The number of goods which passes through a border inspection post (BIP) may cause important congestion problems and delays in the port system, having an effect in the level of service of the port. Therefore, a prediction of the daily number of goods subject to inspection in BIPs seems to be a potential solution. This study proposes a two-stage procedure to better predict freight inspections. In the first stage, a Kohonen self-organising map (SOM) is employed to decompose the whole data into smaller regions which display similar statistical characteristics. In the second stage, support vector regression (SVR) is used to forecast the different homogeneous regions individually. The results obtained are compared with the single SVR technique. The experiment shows that SOM–SVR models outperform the single SVR models in the inspection forecasting. The application of the proposed technique may become a supporting tool for the prediction of the number of goods subject to inspection in BIPs of other international seaports or airports, and provides relevant information for decision-making and resource planning.

A Comparison of Forecasting Methods for Ro-Ro Traffic: A Case Study in the Strait of Gibraltar

The objective of this article is to predict volumes of Ro-Ro (Roll-on, Roll-off) freight in order to apply this prediction as a decision making tool in logistics planning and port organization. This tool can help to improve supply chain performance in a Ro-Ro terminal. Seasonal ARIMA (SARIMA) and Artificial Neural Networks (ANNs) were the forecasting methods used in this study. A resampling procedure was applied in order to find out the best model from a statistical point of view using multiple comparison methods. The results have been very promising (R=0.9157; d=0.9546; MSE=0.0195)

Breakdown potential modelling of austenitic stainless steel

The breakdown potential is a crucial factor in the study of pitting corrosion resistance of stainless steel. This work aims to demonstrate the advantage of different chemometric techniques to estimate the breakdown potential of austenitic stainless steel. In order to predict pitting corrosion behaviour of stainless steel, a total of 60 samples of this alloy were subjected to electrochemical tests varying chloride ion concentration, pH and temperature. The experimental values of the breakdown potential, in addition to the tested environmental factors, were used to construct the predictive models based on support vector machines and artificial neural networks. A multiple‐comparison study based on statistic tests was applied to determine the optimal configuration for each technique. According to the results, support vector machines became a suitable and reliable technique to be applied in the modelling of the breakdown potential of austenitic stainless steels. This technique outperformed the models based on artificial neural networks and provided a useful tool to compare the pitting corrosion resistance of stainless steel in different environmental conditions without recourse to polarization tests. Therefore, this model presented a relevant meaning in science and engineering applications. Copyright

A two‐stage forecasting approach for short‐term intermodal freight prediction

The forecasting of the freight transportation, especially the short-term case, is an important topic in the daily supply chain management. Intermodal freight transportation is subject to multiple complex calendar effects arising in the port environment. The use of prediction methods provides information that may be helpful as a decision-making tool in the management and planning of operations processes in ports. This work addresses the forecasting problem on a daily basis by a novel two-stage scheme combination to offer reliable predictions of fresh freight weight on Ro-Ro (roll-on/roll-off) transport for 7 and 14 days ahead. The study compares daily forecasting with a weekly forecasting approach. The applies database preprocessing and Bayesian regularization neural networks (BRNN) in Stage I. In Stage II, an ensemble framework of the best BRNN models is used to enhance the Stage I forecasting. The results show that the models assessed are a promising tool to predict freight time series for Ro-Ro transport.

Austenitic Stainless Steel EN 1.4404 Corrosion Detection Using Classification Techniques

Different methods of classification have been used in this paper to model pitting corrosion behaviour of austenitic stainless steel EN 1.4404. This material was subjected to electrochemical polarization tests in aqueous environment of varying chloride ion concentration (from NaCl solutions), pH values and temperature in order to determine values of critical pitting potentials (Epit) for each condition tested. In this way, the classification methods employed try to simulate the relation between Epit and those various environmental parameters studied. Different techniques have been used: Classification Trees (CT), Discriminant Analysis (DA), K-Nearest-Neighbours (K-NN), Backpropagation Neural Networks (BPNN) and Support Vector Machine (SVM). These models have generally been regarded as successful. They have been able to give a good correlation between experimental and predicted data. The analysis of the results becomes useful to plan improvement in the austenitic stainless steel protection and to avoid critical conditions expositures of this material.

Prediction of pitting corrosion status of EN 1.4404 stainless steel by using a 2-stage procedure based on support vector machines

The excellent properties of EN 1.4404 have made this material one of the most popular types of austenitic stainless steel used for many applications. However, in aggressive environments, this alloy may suffer corrosion. Electrochemical analyses have been extensively used in order to evaluate pitting corrosion behaviour of stainless steel. These techniques may be followed by microscopic analysis in order to determine the resistance of the passive layer. This step requires the human interpretation, and therefore, subjectivity may be included in the results. This work aims to solve this drawback by the development of an automatic model with the capability to predict pitting corrosion status of this material. A combined model based on support vector machines (SVMs) is presented in this work. With the aim to improve the prediction performance, the model considers the breakdown potential values estimated by itself at a first stage. The performance is evaluated based on receiver operating characteristic (ROC) curves. The area under the curve (AUC) and accuracy results (0.998 and 0.952, respectively) demonstrate the utility of the proposed model as an efficient and accurate tool to predict pitting behaviour of EN 1.4404 automatically.

Hybrid Approaches of Support Vector Regression and SARIMA Models to Forecast the Inspections Volume

The constant growth of air and maritime traffic of goods creates the need of increasing the number, the reliability and the security of inspections at the European borders. In this context of high security, this work applies a two-step procedure based on the hybridization of SARIMA and Support Vector Regression to forecast the inspection volume at the Border Inspection Post of Port of Algeciras Bay. Three hybrid approaches are proposed and two prediction horizons are evaluated. Based on several performance indexes to assess the goodness-of-fit of the models, the hybrid approaches perform better than the SARIMA and SVR models used separately. Hence, the study shows that the hybrid methodology improves the single methods. The experimental results can provide relevant information for resource planning and may become a decision-making tool in the inspection process of other European BIPs.

A two-stage model based on artificial neural networks to determine pitting corrosion status of 316L stainless steel

Abstract Motivated to reduce the costs incurred by corrosion in material science, this article presents a combined model based on artificial neural networks (ANNs) to predict pitting corrosion status of 316L austenitic stainless steel. This model offers the advantage of automatically determining the pitting corrosion status of the material. In this work, the pitting corrosion status was predicted, with the environmental conditions considered, in addition to the values of the breakdown potential estimated by the model previously, but without having to use polarization tests. The generalization ability of the model was verified by the evaluation using the experimental data obtained from the European project called “Avoiding Catastrophic Corrosion Failure of Stainless Steel”. Receiver operating characteristic space, in addition to area under the curve (AUC) values, was presented to measure the prediction performance of the model. Based on the results (0.994 for AUC, 0.980 for sensitivity, and 0.956 for specificity), it can be concluded that ANNs become an efficient tool to predict pitting corrosion status of austenitic stainless steel automatically using this two-stage procedure approach.