DR. O. I. OMOIFO

In order to address the growing global energy demand and reduce the environmental impact from operating gas turbine power plants, the performance and how to improve existing gas turbine power plants need to be studied. In view of this, this research work aims at carrying out the thermodynamic and environmental analyses of Azura Edo Power Plant for design and off-design conditions. Ebsilon Software is a commercially accepted energy and mass balance tool for power plant modelling. The performance of the Azura Edo Power Plant at design and off-design conditions were modelled using Ebsilon Software and the simulation exercise was validated. Energy, exergy and environmental analyses were conducted using operating data collected from the power plant to evaluate the thermal efficiencies, heat rate, energy losses and exergy destruction of each major component of the power plant and carbon dioxide emission rates with the aid of MATLAB software. The effects of ambient air temperature on the thermodynamic and environmental performances were carried out. Preliminary analyses of the effect of integrating an inlet air cooling system and heat recovery steam generator (HRSG) for future consideration were conducted.

Precision agriculture has emerged as a vital approach for improving agricultural efficiency and sustainability by leveraging advanced technologies. This project focuses on developing a machine learning model that utilizes historical data for precision agriculture applications. The system aims to assist farmers in making data-driven decisions by predicting suitable crops, forecasting crop yields, and detecting potential crop diseases. The proposed solution integrates wireless sensor networks to collect environmental parameters such as rainfall, temperature, humidity, Potassium, Nitrogen and soil pH, which are combined with historical agricultural data. Machine learning models, including Voting Classifier (Support Vector Machines, Gaussian Naïve Bayes, Random Forest) and Linear regression model, are trained and deployed to analyze the data for actionable insights. The Voting classifier model achieved a 99.3% accuracy in predicting suitable crops, while the Linear regression model provided yield forecasts with an R² score of 0.91. The Convolutional Neural Network (CNN) detected crop diseases with an accuracy of 93.8%. The system’s implementation demonstrates the effectiveness of combining historical data with machine learning techniques to enhance precision agriculture practices. By providing accurate and timely information, this solution helps optimize crop selection, improve resource allocation, and mitigate the risk of crop diseases. Recommendations for integration with mobile applications, weather data integration and farmer education and training are proposed to further enhance the system’s usability and impact. This project offers a promising step toward sustainable and smart farming practices, contributing to food security and agricultural productivity.