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This abstract describes the smart grid management system is an emerging technology that utilizes machine learning algorithms for efficient energy distribution. The paper presents an overview of the architecture, benefits, and challenges of smart grid management systems. The paper also discusses various machine learning algorithms used in smart grid management systems such as neural networks, decision trees, and Support Vector Machines (SVM). The advantages of using machine learning algorithms in smart grid management systems include increased energy efficiency, reduced energy wastage, improved reliability, and reduced costs. The challenges in implementing machine learning algorithms in smart grid management systems include data security, privacy, and scalability. The paper concludes by discussing future research directions in smart grid management systems based on machine learning algorithms.

The eco-friendly inhibitor is used to safeguard the environment as well as the industrial equipments where corrosive acids are used for cleaning purposes. The inhibition ability of Atalantia monophylla leaves (AML) on the deterioration of mild steel (MS) in acid medium was investigated by mass loss and electrochemical techniques. The inhibition capability raised with rising the concentration of AML extract from weight loss method, immersion period and temperature studies. Electrochemical studies data explained in which AML inhibitor performed like cathodic and anodic type of inhibitor in corrosive medium. The adsorbed layer formed on the metal due to AML extract fulfilled the Langmuir adsorption isotherm. The formation of the protective film on the MS surface was acknowledged by SEM, EDS and FTIR. All the techniques proved AML as acceptable corrosion inhibitor in 1N HCl medium.