Algae Meets Fungi: Microalgae-Fungi Co-Pelletization for Biofuel Production
Microalgae-fungi biofuel has significantly less CO2 emissions than fossil fuels, making it much more environmentally friendly. As well, unlike traditional biofuel, microalgae-fungi does not require large masses of agricultural land for production. Thus, microalgae-fungi is an optimal option for biofuel production. This is a cost-effective renewable energy source that can be used in place of regular gas in cars and other means of transportation. By determining the most effective fungi for biofuel production, the threat of the impending environmental damage from pollution can be diminished. This novel experiment determines which fungi: Aspergillus niger, Rhizopus stolonifer or Saccharomyces cerevisiae, is the most effective bioflocculant in the microalgae-fungi co-pelletization process for biofuel production. We hypothesize that when paired with the microalgae Chlorella vulgaris, Rhizopus stolonifer will be the most effective. It has a high lipid content which could enhance the overall production of biofuel. Furthermore, its negative charge will aid with attracting and neutralizing the C. vulgaris colloidal particles resulting in an easier and more efficient removal of microalgae particles. Through the process of bioflocculation, pelletization, esterification and transesterification, the most effective fungi paired with C. vulgaris was determined. This experiment was carried out thoroughly and precisely resulting in a cost-effective solution for the world's current pollution crisis.
Multiple Time-step Predictive Models for Hurricanes in the North Atlantic Basin Based on Machine Learning Algorithms
The cost of damage caused by hurricanes in 2017 is estimated to be over 200 billion dollars. Quick and accurate prediction of the path of a hurricane and its strength would be very valuable in alleviating these losses. Machine learning based prediction models, in contrast to models based on physics, have been developed successfully in many problem domains. A machine learning system infers the modeling function from a training dataset. This project developed machine learning based prediction models to forecast the path and strength of hurricanes in the North Atlantic basin. Feature analysis was performed on the HURDAT2 dataset, which contains paths and strengths of past hurricanes. Artificial Neural Networks (ANNs) and Generalized Linear Model (GLM) approaches such as Tikhonov regularization were investigated to develop nine hurricane prediction models. Prediction accuracy of these models was compared using a testing dataset, disjoint from the training dataset. The coefficient of determination and the mean squared error were used as performance metrics. Post-processing metrics, such as geodesic error in path prediction and the mean wind speed error, were also used to compare different models. TLS linear regression model performed the best of out the nine models for one and two time steps, while the ANNs made more accurate predictions for longer periods. All models predicted location and strength with greater than .95 coefficient of determination for up to two days. My models predicted hurricane path in under a second with accuracy comparable to that of current models.