Status : Verified
| Personal Name | Condor, Billriz E. |
|---|---|
| Resource Title | Enhancing accumulation of carbohydrates using microalga Chlorella vulgaris FSP-E for high-gravity bioethanol fermentation |
| Date Issued | July 2021 |
| Abstract | Bioethanol production from microalgae has been long considered a potential alternative to conventional fossil fuels. However, several bottlenecks with the technology's efficiency and yield have continued to inhibit the further development and commercialization of microalgal bioethanol. To address these inefficiencies, this thesis investigates several techniques and strategies to improve the overall production process. First, the cultivation process of microalgae strain, Chlorella vulgaris FSP-E, was optimized to produce feedstock with highly desirable characteristics. The biomass production and carbohydrate accumulation of microalgae cultures were optimized using well-established methods, namely response surface methodology and artificial neural networks. Both models were able to reliably estimate the resulting carbohydrate content of the harvested biomass using light intensity, nitrogen starvation duration, and initial sodium nitrate concentration as inputs. In this study, cultivation at optimal conditions was able to produce biomass with 59.53 ± 0.52% carbohydrate content. Next, the hydrolysis of microalgae carbohydrates was also optimized for higher biomass loading of up to 200 g/L. Several fermenting microorganisms were screened for bioethanol fermentation using microalgae biomass, namely Saccharomyces cerevisiae and Zymomonas mobilis. Lastly, high-gravity fermentation of microalgae biomass was able to produce a final ethanol concentration of 42.86 g/L representing a 0.214 g ethanol per g biomass yield, a value higher than most literatures on bioethanol fermentation. |
| Degree Course | MS Energy Engineering |
| Language | English |
| Keyword | microalgae; bioethanol; artificial neural network; response surface methodology |
| Material Type | Thesis/Dissertation |
Preliminary Pages
860.37 Kb
Category : C - Confidential information of a third-party is embedded.
Access Permission : Limited Access