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School of Civil and Environmental Engineering
Ph.D. Thesis Defense Announcement
Enabling Large-Scale Microalgal Biomass Production in Open Raceway Ponds
By: Thomas Kelly Igou
Advisor:
Dr. Yongsheng Chen
Committee Members:
Dr. John Crittenden (CEE)
Dr. Ching-Hua Huang (CEE)
Dr. Ameet Pinto (CEE)
Dr. Zhaohui Tong (ChBE)
Date & Time:
December 5th, 2022; 12pm
Location:
Daniel Lab 303; https://gatech.zoom.us/j/95434213831?pwd=cjBDUlVnekJjRk5DNDRheThRdU8yQT09
Microalgal biotechnology holds great potential for renewable biofuels, bioproducts and carbon capture applications due to its unparalleled photosynthetic efficiency, metabolic diversity, and ability to utilize sunlight, atmospheric carbon, non-arable land and non-potable water. Outdoor open raceway pond (ORP) cultivation offers the most energy efficiency of any microalgal production system due to its use of naturally available sunlight and passive aeration. However, ORPs are inherently exposed to predators and highly dynamic environmental conditions that fluctuate both diurnally and seasonally, resulting in lower productivities in addition to operational and analytical complexity. This dissertation focuses on large-scale microalgal cultivation, chemical crop protection, online monitoring, and predictive productivity modeling. Pilot-scale, year-round outdoor ORP experiments conducted at Georgia Tech are part of the Unified Field Studies of the Algae Testbed Public-Private-Partnership (ATP3 UFS), which generated the largest ORP dataset available to-date.
ORP studies investigate replicate testbed operations in Georgia, Florida, Arizona, California and Hawaii, gathering a total of 598 productivities across 32 ORPs to evaluate impacts of geography and seasonality on biomass productivity and suggest improved deployment scenarios. Chemical crop protection strategies are developed for targeted elimination of zooplankton predators by electron transport chain inhibitors, preventing the risk of pond crashes and spontaneous biomass deterioration. Field implementation of low-cost, remotely-monitored sensors provides millions of environmental and water chemistry records including temperature, pH, photosynthetically active radiation, total dissolved solids and dissolved oxygen. Monitoring data then serves as a platform for development of data-driven models for accurate productivity prediction without cumbersome analytical techniques or physical interaction with ORPs, providing commercial operators inexpensive supervision and forecasting capabilities. The image-based deep learning model developed here may provide a potential inexpensive tool to address management of other dynamic, complex systems. The results of this dissertation could de-risk and widen the scope of ORP deployment, potentially accelerating the commercialization of large-scale microalgal cultivation for biofuels and bioproducts.
