Corrosion of brass subjected to cast-off cooking oil blended with diesel

With the aim of leaving a smaller ecological footprint and to develop fuels which will change the energy needs of the future in a sustainable manner, the present study aims at investigating the corrosion phenomenon of brass using biodiesel fuel from cast-off cooking oil (CCO) in various blending ratios with commercial diesel (5 %, 10 % and 20 %) on brass. The mechanism of corrosion of brass in biodiesel has not been adequately investigated, and in the literature there is still a drop in the knowledge of the corrosion of brass from which many diesel engine parts are made. The corrosion rate of brass has been evaluated by mass loss and electrochemical methods. As a complementary technique, conductivity was monitored before and after each test. Surface morphology was examined by optical microscope. The surface morphology of brass samples in NaCl was coated in dark deposits that indicated corrosion. Some pitting was found in O99, and no significant change was found in oil-diesel blends. There is a positive correlation between the corrosivity and conductivity of the test media. The wettability studies also assisted in determining the non-corrosive nature of biodiesel.

A transfer-learning approach for corrosion prediction in pipeline infrastructures

Pipeline infrastructures, carrying either gas or oil, are often affected by internal corrosion, which is a dangerous phenomenon that may cause threats to both the environment (due to potential leakages) and the human beings (due to accidents that may cause explosions in presence of gas leakages). For this reason, predictive mechanisms are needed to detect and address the corrosion phenomenon. Recently, we have seen a first attempt at leveraging Machine Learning (ML) techniques in this field thanks to their high ability in modeling highly complex phenomena. In order to rely on these techniques, we need a set of data, representing factors influencing the corrosion in a given pipeline, together with their related supervised information, measuring the corrosion level along the considered infrastructure profile. Unfortunately, it is not always possible to access supervised information for a given pipeline since measuring the corrosion is a costly and time-consuming operation. In this paper, we will address the problem of devising a ML-based predictive model for internal corrosion under the assumption that supervised information is unavailable for the pipeline of interest, while it is available for some other pipelines that can be leveraged through Transfer Learning (TL) to build the predictive model itself. We will cover all the methodological steps from data set creation to the usage of TL. The whole methodology will be experimentally validated on a set of real-world pipelines.

Corrosion Behavior in External Surface of API 5L X52 Underground Pipelines in Buzurgan Region/ Missan province

External corrosion of underground pipeline is one of the most common damage mechanismsassociated with the soil environment particularly in ageing pipelines. This study investigatesthe external corrosion phenomenon for exporting crude oil pipeline (28'') in Buzurgan region /Missan province. Corrosion rate was measured by using weight loss method, and Tafelextrapolation. Scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS)were also used in this work. During the field survey, soil sample was taken from Buzurganregion (collected as close as possible to buried pipes at 1.2 m) for tests were carried out for soilthat included: pH, moisture content, resistivity and chemical composition (XRF, XRD, andsoluble salts). It was found that the environment of the areahas a low soil resistivity value(870) ohm.cm. On other hand, the samples that were immersed in Buzurgan soil showed thethat highest damage was by pitting corrosion due to the high soluble salts content particularlychlorides (7388 )ppm and sulfates (2570) ppm.

Green Corrosion Inhibitors from Agri-Food Wastes: The Case of Punica granatum Extract and Its Constituent Ellagic Acid. A Validation Study

Giving a “new life” to wastes should be the golden rule for all production processes in the forthcoming future, aiming at making them more sustainable and environmentally friendly. In the corrosion science field, the ambitious circular economy paradigm has recently led to the employment of extracts from plants (and, in less extent, from agri-food wastes) as green inhibitors against corrosion of metals. However, in spite of the number of scientific papers published in the field, a deep revision of the scientific approach is needed both in the execution of experiments and in the critical analysis of the results. Starting from some discrepancies in published data, the corrosion inhibition effect induced by a well-characterized methanolic extract from wastes of fermented Punica granatum and by its main component (ellagic acid, EA) was validated. The corrosion behaviour of Armco® pure iron in the presence of small concentrations of ellagic acid and extract (containing ca. 10 µM and 100 µM EA) was studied by combining results from mass loss tests, at free corrosion potential, and from polarization tests, by linearly sweeping the potential applied to the metal substrate. Experiments were carried out both in acidic medium (typical for a general corrosion phenomenon) and in near-neutral chloride bearing solution (characteristic of a localized pitting corrosion phenomenon). Neat conflicts with already published data have been identified first in the solubility of the inhibitor and then in the inhibition efficiency (around 40% in a 0.05 M HCl). The very limited solubility in aqueous environment was identified as the main drawback, hindering any possible exploitation of ellagic acid and pomegranate extract as promising green corrosion inhibitors. Results point to the necessity to establish clear and rigorous good laboratory practices to follow while reporting results on such complex matrices like vegetable extracts.