Department of Electrical Engineering

Permanent URI for this communityhttps://repository.cuilahore.edu.pk/handle/123456789/18

Browse

Has files: YesSubject: search.filters.subject.FA22

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    Estimation of Syngas Production in Biomass Using Machine Learning Algorithms
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Nabeel Arshad; FA22-REE-006; Dr.Aamer Bilal Asghar; LHR TP 9499
    Renewable energy technologies can be beneficial for the economic development of any country on the planet. Also, with the present situation in the energy sector, the high cost of fuel, the use of renewable energy resources has seemed to have gained importance. The energy resources that pertain to solar, biomass, and wind energies are clean energy friendly to the environment. In the modern world, fossil fuels meet the requirements of the transportation sector in large volume and bring several negative impacts viz., air pollution, noise, and global warming. Besides, the fast decline of below-ground petroleum resources that arise with the rapid consumption of fossil fuels is accompanied by another grave problem for the transportation sector. Usually, the gasifier's temperature is greater than 1000°C. Before coal is put into the gasifier, it must go through a process called pyrolysis, a sequence of intricate physical and chemical reactions that occur without oxygen or air and gradually increase in temperature from 150 to 700 degrees Celsius. High molecular weight charcoal and volatile substances, such as CO, H2, H2O, CO2, and CH4, are the process byproducts. In this research work, machine learning algorithms such as Artificial Neural Network (ANN), Random Forest (RF), GBR, and XGB will be used to increase the efficiency of biomass gasification using AI-based machine learning algorithms. A dataset of biomass converting into syngas and other useful products is obtained to estimate the output of syngas, H2, CH4, and composition of different gases based on input parameters, namely carbon, temperature, sulfur, oxygen, nitrogen and ash, etc. The algorithms have an advantage in their capacity to train a model using a dataset consisting of numerical values and achieve an optimal fit point by incorporating efficiency parameters. Root Mean Square Error (RMSE) is a frequently used metric to estimate the precision of predictions. The RMSE values will be computed for each scenario. The approach will be experimented with under temperature variations, and other relevant constraints.
  • No Thumbnail Available
    Item
    Design and Development of an Intelligent Control System for Multifunctional Agricultural Simulator
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Muhammad Afzaal; FA22-REE-005; Dr. Fawad Azeem; LHR TP 9370
  • No Thumbnail Available
    Item
    Landslide Monitoring using Solar Energy Powered Wireless Sensor Network and Machine Learning Techniques
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Malka Arooj; FA22-REE-003; Dr. Muhammad Farooq-i-Azam; LHR TP 9372
    This research examines the case of landslide monitoring and how energy-autonomous Wireless Sensor Networks (WSN) integrated with Machine Learning (ML) technologies enhanced achievement of implementation attributes of accuracy and efficiency gains. These include the ability of constantly generating power through solar energy, and the ability of a ML algorithm to predict since this is the basic building block of this more encompassing architecture. Hence, the main objective is to explore the traditional and the new models of landslide monitoring, their problems that might be encountered and the ways of early recognition and prediction. In the context of the study, a high-level self-operating autonomous system is proposed to for efficient monitoring of landslides using solar energy powered WSNs and ML techniques. In proposed approach passive networking technique is used to enhance the operation and longevity of the nodes. Passive networks have led to a limited energy consumption which has a figure of 69.76% as compared to the 100 % that is expected with traditional methodologies. Different types of ML models were employed to obtain more realistic outcomes, and Gradient Boosting Machines (GBM) was more effective than the other ML models used in this thesis. To enhance the results of accuracy, precision, recall, F1 score and Matthews Correlation Coefficient (MCC) a series of techniques like ensemble learning, feature scaling, hyper parameter tuning were applied on the GBM. However, it was only the Chi-square that significantly enhanced the findings; the percentage stood at 86. 42% accuracy, 89. 23% precision, 85. 93% recall, F1 score of 87.55% and MCC value of 72.69%. In further development of the integrated defining methodologies, more attempts at ML models along with optimization strategies should be researched. In enhancing the practical use of the system this proposed model needs to take place in varied geographical and environmental contexts to increase its robustness as well as its applications.