Experimental and modelling waste rice husk ash as a novel green corrosion inhibitor under acidic environment

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

Section: The Results Of Actual Func Tional Groups Characterization Re...supporting

confidence: 76%

Section: Gas Chromatography Mass Spectroscopy Analysismentioning

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

“…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.

“…The previous works demonstrate that GCI is the solution to address the toxicity issue of the existing inorganic corrosion inhibitor. Furthermore, white tea [10] and rice husk ash [11] have been used to protect the steel in an acidic environment with lower toxicity and high thermal resistance.…”

Section: Introductionmentioning

confidence: 99%

Development of saga (Abrus precatorius) seed extract as a green corrosion inhibitor in API 5l Grade B under 1m HCL solutions

Riastuti

Soedarsono

Aribowo

et al. 2022

Self Cite

The critical role of newly green corrosion inhibitors shows the disruption of cathodic and anodic reactions at the metals and solution interface. The object of this study is the development of Saga as a corrosion inhibitor to mitigate the effect of corrosive HCl 1M оn mild steel. The inhibitor was extracted using methanol to prepare various concentrations. Fourier transform infrared (FTIR) spectroscopy was used to determine the functional group of the inhibitor. The electrochemical impedance spectroscopy aided by the potentiodynamic polarization was utilized to evaluate the inhibitor’s effectiveness. Optical emission spectroscopy (OES) was implemented to determine the percentage of elements in mild steel. Based on the FTIR results, C=O, -OH, C=C, benzene, and C-O are accountable for the inhibitor to donate its lone pair of an electron to the 3-d orbital of iron metal. Increasing the inhibitor concentration decreases the capacitive double layer to elevate the inhibitor resistance. The higher inhibitor resistance of 29.33 Ω cm-1 increases as the concentration increases due to the depression of Cdl 420.16 µF cm2 at 10 ml inhibitor solution. Parallelly, it increases the inhibition efficiency at 65.58 %, slightly lower than the PP measurement of nearly 88 %. The higher value of adsorption/desorption constant, Kads, at 2.9 L mol-1 shows the strength of the inhibitor, which lowers the value of Gibbs free energy (ΔGads). The Saga inhibitor is considered a chemisorption inhibitor ΔGads –36.87 kJ/mol. The value demonstrates the formation of dative covalent bonding, which promotes the transferred electron from the inhibitor to the substrate. On the other hand, the Saga inhibitor abides by the Langmuir adsorption isotherm as the R2 value is 0.99.