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This work covers the effectiveness of the White tea extract as a green corrosion inhibitor and is correlated to the strength and stability bonding between the phenolic molecule and the Fe atoms in mild steel and how this interaction can be studied by altering the concentration and temperature. White tea has received considerable attention due to its capability as a corrosion inhibitor and has been extensively studied using electrochemical techniques. However, accurate and systematic functional group identification and surface modification have been missing. Our study sought to demonstrate the quantitative measurement of electrochemical impedance spectroscopy (EIS) complemented by the FTIR (Fourier transform infrared spectroscopy), Total Phenolic Test, and Raman Spectroscopy. The SEM (Scanning Electronic Microscope)/EDX (Energy-Dispersive X-Ray Spectroscopy), and AFM (Atomic Force Microscope) were used to study the surface modification. The EIS results show that the optimum inhibition efficiency was 96 % in a solution of 80 ppm at 60 °C. Acetone 70 % was used to extract White tea and gives 14.17±0.25 % phenolic compound. Spectroscopic studies show -OH, Aromatic C=C, C=O and C-O-C become major contributors in the adsorption process and are found on the surface of metals as corrosion protection. Meanwhile, the thermodynamic calculation shows the White tea was adsorbed chemically. The nearness of R2 to 1 shows the adsorption agrees with the Langmuir adsorption isotherm. Eventually, the surface modification revealed that phenol molecules are responsible to reduce the corrosion rate at 16.38×10-3 mpy. Our results are expected to provide a guideline for future research in White tea as a green corrosion inhibitor

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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

Inspection and Maintenance methods development have a pivotal role in preventing the uncertainty-induced risks in the oil and gas industry. A key aspect of inspection is evaluating the risk of equipment from the scheduled and monitored assessment in the dynamic system. This activity includes assessing the modification factor's Probability of Failure (PoF) and calculating the equipment's remaining useful life (RUL). The traditional inspection model constitutes a partial solution to grouping the vast amount of real-data inspection and observations at equal intervals. This literature review aims to offer a comprehensive review concerning the benefit of Machine Learning (ML) in managing the risk while incorporating time-series forecasting studies and an overview of Risk-Based Inspection (RBI) methods (e.g. quantitative, semi-quantitative, and qualitative). A literature review with a deductive approach is used to discuss the improvement of the clustering Gaussian Mixture Model (GMM) to overcome the non-circular shape data that may show in the K-Means models. Machine Learning classifiers such as Decision Trees, Logistic Regression, Support Vector Machines, K-nearest neighbours, and Random Forests were selected to provide a platform for risk assessment and give a promising prediction towards the actual condition and their severity level of equipment. This work approaches complementary tools and grows interest in embedded artificial intelligence in Risk Management systems and can be used as the basis of more robust guidance to organize complexity in handling inspection data, but further and future research is required.