Developing A Model Approximation Method and Parameter Estimates for Solid State Reaction Kinetics

The James S. Markiewicz Solar Energy Research Facility was built to research solar chemistry and currently being used to research the change in metal oxides such as iron or magnesium oxide that act as a medium for the production of hydrogen from water. This is significant because hydrogen can be used in vehicles equipped with appropriate fuel cells and due the decreased cost of producing hydrogen with this method. The shrinking core model which governs this process has proved difficult to solve due to the high number of unknown constants and its non-linearity. We detail in this work the implementation of less common heuristics, mainly Particle Swarm Optimization. This technique was used because of its wide unbiased search for the possible constants. The development and method we are using to solve these unknown constants will be shown

Developing A Model Approximation Method and Parameter Estimates for Solid State Reaction Kinetics

The James S. Markiewicz Solar Energy Research Facility was built to research solar chemistry and currently being used to research the change in metal oxides such as iron or magnesium oxide that act as a medium for the production of hydrogen from water. This is significant because hydrogen can be used in vehicles equipped with appropriate fuel cells and due the decreased cost of producing hydrogen with this method. The shrinking core model which governs this process has proved difficult to solve due to the high number of unknown constants and its non-linearity. We detail in this work the implementation of less common heuristics, mainly Particle Swarm Optimization. This technique was used because of its wide unbiased search for the possible constants. The development and method we are using to solve these unknown constants will be shown

Implementing Machine Learning Techniques in Financial Modeling

The data science competition forum Kaggle, in conjunction with Two Sigma, proposed a financial modeling competition open to the public. The challenge is to predict an anonymous time-varying financial instrument based on anonymous features given in the dataset. To accomplish this task, we will demonstrate several machine learning techniques and show how well they perform in the prediction of the class variable. These techniques include Ridge Regression, Extreme Gradient Boosting, and Extremely Randomized Trees. We will review each of the techniques, and then show the results of how they worked independently and together