Jones, Gerald; Zhao, Jia; Ellis, Dylan; Sims, Ronald (Utah State University)
Faculty Advisor: Zhao, Jia (College of Science, Mathematics and Statistics Department); Sims, Donald (College of Engineering, Biological Engineering Department)
Algal blooms are a worsening issue across the United States. Algal blooms are formed by an overabundance of nutrients in the water such as Nitrogen and Phosphorus. To combat this, Utah State University is collaborating with the Central Valley Water Reclamation Center, the largest water treatment plant in Utah, with innovating ways to reduce Nitrogen and Phosphorus levels in wastewater before being discharged into the local bodies of water.
One process shows promise. The Rotating Algae Biofilm Reactor (RABR) consists of an attached biofilm growth that rotates through a growth substrate. As the biofilm grows, it assimilates the nutrients in the water, thereby transferring them from the water into the biofilm algae.
No referred literature exists on mathematically connecting RABR biofilm productivity with actual data. The purpose of this research is to formulate a model of the mass production of the RABR as a function of sunlight and rate of exposure. This process is done by combining an analytical and data-driven approach to discover the equation. Sparse Identification of Nonlinear Dynamics (SINDy) is a data-driven approach that represents a potential breakthrough in being able to identify and quantify the most important parameters influencing algae biofilm yield and productivity. This study will go through the process of creating an analytical model and then scrutinizing said model with real data and make necessary adjustments.
An analytical model using Bara and Bonneford's work on photoinhibition is provided as a baseline for this study. Some data-driven results will also be presented to address the fundamental characteristics of biofilm growth.
The biofilm harvested from the RABR has significant potential for bioproducts such as biofuel, medicine, bioplastics, and livestock feed. Across different industries, this robust model describing algae biofilm growth will provide a mathematical foundation for future algae-based biofilm technologies.