MONITORING

This study aimed to design and implement a low-cost microcontroller-based system for monitoring the energy consumption of individual workshop machines, addressing the limitations of conventional centralized metering systems that fail to provide machine- specific data. The literature review examined previous work on energy monitoring technologies, including commercial, open-source, and academic systems, highlighting the growing role of the Internet of Things (IoT) in enabling real-time data acquisition and remote monitoring. It emphasized the need for affordable, scalable, and educationally adaptable solutions for developing regions, where technical expertise and financial resources are limited. The research adopted an experimental design methodology involving hardware and software integration. The system was built using Arduino Nano and ESP32 microcontrollers, ZMPT101B voltage and SCT-013 current sensors, an LCD display, and a ThingSpeak IoT cloud interface. Mathematical modeling was applied to compute voltage, current, power, energy, and cost, while SolidWorks was used for casing design. Calibration and testing were conducted under varying load conditions to assess accuracy, response time, and data stability. Data were logged both locally on an SD card and remotely on the cloud for redundancy and analysis. Results indicated that the system achieved high accuracy within ±1% for voltage and ±5% for current, with an overall efficiency of 95% and IoT data transfer uptime of 98%. The developed prototype successfully provided real-time monitoring, stable performance, and reliable data transmission. The study concluded that the Arduino-based energy monitoring system is a cost-effective, scalable, and efficient solution suitable for educational, domestic,

The integrity and efficient operation of pipelines are critical for the safe transportation of oil, gas, and other fluids. Traditional pipeline monitoring methods often fall short in providing comprehensive and real-time data essential for proactive maintenance and risk management. This project explores the integration of smart technologies in pipeline pigging systems to enhance pipeline monitoring and management. Smart pigging involves the use of intelligent inspection tools that traverse the pipeline, collecting high-resolution data on internal conditions, including corrosion, cracks, and other anomalies. By leveraging advanced sensors, data analytics, and real-time communication technologies, smart pigging systems offer unprecedented insights into pipeline health, enabling predictive maintenance and timely interventions. The implementation of these systems can significantly reduce the risk of leaks and ruptures, thereby ensuring environmental safety and operational efficiency. This study reviews the latest advancements in smart pigging technology, examines case studies of successful implementations, and discusses the challenges and future directions in the field of smart pipeline monitoring. In summary, the implementation of smart pipeline monitoring using pigging systems has delivered substantial benefits in terms of anomaly detection, maintenance optimization, operational efficiency, safety, and environmental protection. The device was fully tested and proven to perform optimally by taking temperature readings of the pipeline and converted it to pressure through the use of a programmable microcontroller. Pipeline leaks and failures can easily be detected by this prototype model when there is an increase and decrease in the flow rate of the fluid