DEPARTMENT OF MATERIALAS AND METALLURGICAL ENGINEERING

This study investigates the potential of eggshell waste as a reinforcement material in aluminum matrices for kitchenware applications, aiming to enhance material properties. Composites were fabricated with 7%, 10%, and 13% eggshell reinforcement and subjected to tensile testing, Brinell hardness testing, Differential Scanning Calorimetry (DSC), Scanning Electron Microscopy (SEM), and Energy-Dispersive Spectroscopy (EDS) to assess mechanical, thermal, and microstructural properties. Tensile testing revealed a significant increase in Ultimate Tensile Strength (UTS) with 13% reinforcement, reaching 134.29 MPa, though ductility was reduced. SEM analysis of the 10wt% composite showed a finer textured structure but non-uniform particle distribution. EDS confirmed calcium presence, and showed reduced oxygen content. Brinell hardness exhibited a positive correlation between the weight percentage of eggshell in the aluminum composite, which showed that higher eggshell content within the tested range leads to increased hardness. DSC indicated that eggshell addition altered thermal characteristics, with the 13wt% composite showing a slightly higher melting temperature and changes in heat of fusion. These results demonstrate that eggshell reinforcement enhances the tensile strength, hardness and modifies the thermal behavior of aluminum.

Corrosive damage remains a critical issue across various industries, especially in remote oil and gas pipeline infrastructures.This study presents the design and implementation of an IoT- based wireless sensor network (WSN) integrated with machine learning Model (SVM) for corrosion monitoring and prediction. The system architecture involved deploying sensor nodes utilizing electromagnetic techniques for real-time corrosion data acquisition. These nodes communicated with an ESP32 microcontroller uipped ith wireless transmission capabilities to relay data to the ThingSpeak cloud platform for storage and visualization. Subsequently, MATLAB was used to preprocess the acquired data, enabling the training and validation of a supervised machine learning model for corrosion classification and prediction. With the help of the SVM model, corroded pipeline samples could be easily differentiated from a corrosion-free pipeline. 80% of the recorded data was used to train the algorithm, and the rest 20% was kept for testing the data without corrosion. The first graph displayed by the model shows that the resistance values from the corroded sample fluctuate only slightly over time Additionally, the chlorine level ranged between (1000–1500)ppm, showing emission of chlorine gas from the sample. There was a significant drop in resistance in the corrosion- free sample for the second graph, with values falling below 1000ohms and No chlorine data was indicated When the model was tested and validated, the model correctly classified 59 out of 60 test samples while one incorrectly indicating an accuracy of 98.33%.. When unseen samples were used, the model was still able to predict the presence of corrosion with almost the same amount of precision and gave results showing the state of the pipelines with a 50% chance of them being either corroded or not from a 40 sample prediction.. The results obtained affirm the effectiveness of both processes for corrosion monitoring in remote pipeline networks. The system’s autonomous operation, real-time data handling, and intelligent decision-making capabilities highlight its potential as a cost-effective and efficient alternative to traditional, labor-intensive methods. Moreover, its predictive capabilities enable proactive maintenance scheduling and safer operational planning, significantly reducing the risk of pipeline failure. This research thus lays a strong foundation for scalable, field-deployable corrosion monitoring systems leveraging modern IoT and AI tools

This study investigates the potential of okra (Abelmoschus esculentus) leaf extract as a green, eco-friendly corrosion inhibitor for low carbon steel in acidic environments. The research focuses on evaluating the inhibitory efficiency of the extract at different concentrations and exposure times using electrochemical methods, including potentiodynamic polarization and open circuit potential measurements. Surface characterization techniques, such as Scanning Electron Microscopy (SEM), were employed to analyze the steel surface morphology after exposure. Results indicated that the okra leaf extract significantly reduced the corrosion rate of low carbon steel, forming a protective layer on the metal surface. The inhibition efficiency increased with higher extract concentrations, demonstrating the potential of bioactive compounds in okra leaves to adsorb onto the steel surface and block corrosion sites. The study concluded that okra leaf extract can serve as an effective, environmentally safe corrosion inhibitor, providing a sustainable alternative to conventional chemical inhibitors. These findings highlight the applicability of plant-based extracts in corrosion control and open avenues for further research in green corrosion inhibition technologies

This study investigates the potential of eggshell waste as a reinforcement material in aluminum matrices for kitchenware applications, aiming to enhance material properties. Composites were fabricated with 7%, 10%, and 13% eggshell reinforcement and subjected to tensile testing, Brinell hardness testing, Differential Scanning Calorimetry (DSC), Scanning Electron Microscopy (SEM), and Energy-Dispersive Spectroscopy (EDS) to assess mechanical, thermal, and microstructural properties. Tensile testing revealed a significant increase in Ultimate Tensile Strength (UTS) with 13% reinforcement, reaching 134.29 MPa, though ductility was reduced. SEM analysis of the 10wt% composite showed a finer textured structure but non-uniform particle distribution. EDS confirmed calcium presence, and showed reduced oxygen content. Brinell hardness exhibited a positive correlation between the weight percentage of eggshell in the aluminum composite, which showed that higher eggshell content within the tested range leads to increased hardness. DSC indicated that eggshell addition altered thermal characteristics, with the 13wt% composite showing a slightly higher melting temperature and changes in heat of fusion. These results demonstrate that eggshell reinforcement enhances the tensile strength, hardness and modifies the thermal behaviour of aluminum

This project report presents the design, implementation, and performance evaluation of an innovative plastic pelletizing machine engineered to address the escalating challenge of plastic waste management. The core advancement of this machine lies in its hybrid preheating system, which synergistically combines a high-efficiency gas burner for rapid initial temperature elevation and a precision electric heater for sustained, uniform heat distribution. This dual approach significantly enhances the machine's ability to process a wide spectrum of waste plastics (PPT), optimizing melt homogeneity and minimizing thermal degradation. A detailed analysis of the machine's key components, including the automated feeding hopper, optimized extrusion barrel and screw design, multi-stage filtration system, precision pelletizing unit, and efficient cooling mechanism, is provided. Emphasis is placed on the dual preheating system's ability to achieve significant energy savings by leveraging the rapid heating capabilities of the gas burner and the precise control of the electric heater.The operational procedures, including material preparation, preheating, extrusion, pelletizing, and cooling, are outlined, along with critical safety considerations and maintenance protocols. The project concludes with a comprehensive assessment of the machine's potential applications in plastic recycling facilities and manufacturing industries, highlighting its contribution to sustainable plastic recycling practices through the production of high-quality, reusable pellets and its role in fostering a circular economy.