Development of white tea extract as green corrosion inhibitor in mild steel under 1 M hydrochloric acid solution

Corrosion in API 5L steel under 1M HCl is a common issue; hence, creating a more effective and naturally-based inhibitor is critical. In this research, Syzygium Cumini leaf extract (SCLE) was used as a new green corrosion inhibitor under acidic conditions. The inhibition properties of the novel cumini extract were thoroughly characterized using potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS), Fourier-transform infrared spectroscopy (FTIR), and atomic force microscope (AFM). The results show that the cumini inhibitor has excellent corrosion inhibition with 93 % inhibition efficiency. The adsorption behavior of the inhibitor follows the Langmuir Adsorption Isotherm due to the nearness of R2 to unity. The potentiodynamic and electrochemical measurements demonstrate the mixed type of corrosion inhibitor. Thermodynamic calculation of ΔGads is – 18.41 kJ mol-1 showing the physical adsorption process between the inhibitor and metals. Further inspection of ΔHads at ‒58.93 kJ mol-1 considers releasing energy during adsorption. The FTIR results agree with the increased growth of passive layers due to the adsorption of polyphenol and flavonoids on metals. Remarkably, the adsorption peak at 3266.59 cm-1 corresponds to the adsorption of –OH. The peak at 1612.56 and 1698.4 cm-1 is attributed to C=C and C=O functional groups. The above functional groups serve as adsorption centers to reduce the corrosion effect. The surface treatment of AFM indicated a good relationship with the functional group characterization and confirmed the significant corrosion rate reduction. This work can be used as a benchmark to develop a natural plant as a corrosion inhibitor.

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Effect of syzygium cumini leaf extract as a green corrosion inhibitor on API 5l carbon steel in 1M HCL

Riastuti

Mashanafie

Rizkia

et al. 2022

“…Without RCA, the business impact of material loss consumes the company's time and finances, especially in the oil and gas (O&G) industry. Some mitigation strategies have been implemented, including using a green corrosion inhibitor [8][9][10][11], failure analysis (FA) [12], and Risk-Based Inspection (RBI) based on AI [6]. Amongst the strategies, the blending implementation of FA and AI is critical to preventing uncertainty-induced risk.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Risk analysis of Ex-spool 16” mol: an insight of machine learning and experimental result

Aditiyawarman

Soedarsono

Kaban

et al. 2022

Self Cite

The paper reports the development of a Risk-Based Inspection (RBI)-Machine Learning perspective. The Optical Emission Spectrometry (OES), Tensile and Hardness Test, Scanning Electron Microscope (SEM), Energy Dispersive X-Ray Spectroscopy (EDS), Sulfate Reducing Bacteria Check, and X-Ray Diffraction (XRD) was used to analyze the root cause of the pipeline’s failure. Corrosion attack shows at the cross-section microstructure based on SEM results. Carbon, Manganese, Phosphorous, and sulfur’s chemical composition is dramatically lower than the standard API 5L Grade X42. Siderite and hematite dominate the composition of the corroded area as a result of CO2 dissolving in water. In contrast, hematite is generated due to the pipe and outdoor atmosphere reaction. Severe local wall thinning of the sand abrasion causes the degradation of the material’s mechanical properties and increases the corrosion rate. This result amplifies by the development of Machine Learning (ML) of Pearson Multicollinear Matrix and Supervised ML (Random Forest, Support Vector Machine, and Linear Regression) to estimate the corrosion degradation of the material. The source of datasets provided by ILI inspection includes the calculated PoF Remaining Useful Life (RuL) as input data, while Probability of Failure (PoF) prediction serves as output data. The Random Forest shows superior predictions of 92.18 %, with the lowest validation loss of 0.0316. The modeling result confirms the experimental outcome. This work demonstrates the implementation strategy to reduce the analysis time, minimize human bias, and serve as a reliable reference tool and guideline to maintain the integrity of the subsea pipelines.

“…In addition, it develops and induces internal corrosion defects in pipelines [7] despite the variation of operating pressure, the characteristic of fluids, and the injection inhibitors system. Several approaches have been implemented to address the risk of corrosion, such as cathodic protection [8], coatings [9], selection of materials [10], and injection of corrosion inhibitors [11][12][13]. The primary objective of handling corrosion (corrosion management) is to understand how to reduce the corrosion rate.…”

Section: Introductionmentioning

confidence: 99%

Unraveling the study of liquid smoke from rice husks as a green corrosion inhibitor in mild steel under 1 M HCl

Kaban

Mayangsari

Anwar

et al. 2022

The work provides the more comprehensive development of Liquid Smoke from Rice Husks Ash (RHA). Notably, the study focuses on the interaction between the primary molecules of inhibitor and mild steel, including thermodynamic calculation and surface treatment upon addition of inhibitor. The electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization (PP) characterization were utilized to evaluate the anticorrosion of RHA. The Raman Spectroscopy pre and post-addition of RHA’s inhibitor were used to compare the adsorbed functional group of inhibitors. Moreover, the thermodynamic calculation of the inhibitor’s adsorption determines the types of adsorption of the inhibitor. As a result of the adsorption process, the Scanning Electronic Microscope-Energy Dispersive X-Ray (SEM-EDX) aided by The Atomic Force Microscopy (AFM) and Contact Angle Test was implemented to unveil the surface treatment and the change of elemental composition after the addition of an 80 ppm inhibitor. The PP and EIS results show a significant depression of the current density at ‒2.75 μA·cm2 in 80 ppm solution with the highest inhibition efficiency of 99.82 %. The superior inhibition correlates to the adsorption of Si–OH, C–C, C–O–C, >C=O, complex structure, and –OH at wavenumber 458, 662, 1095, 1780, and 3530 cm-1. The LS shows a significant surface area of protection of 0.9982 and high adsorption constant (Kads) at 11.648. The calculated ΔGads of ‒6.59 kJ/mol unveils the chemisorption in nature. At the same time, a combination of 20 and 80 ppm solution is predicted adsorbed horizontally to reduce the contact between the solution and substrate, as shown in SEM and AFM results. It also increases the contact angle and their corresponding hydrophobicity