Energy modeling, fabrication and evaluation of a small-scale natural convection solar dryer for microalgae biofuel production

• Main Author: Lopez, Neil Stephen A.
• Format: text
• Language: English
• Published: Animo Repository 2011
• Subjects: Biomass energy; Microalgae; Mechanical Engineering
• Online Access: https://animorepository.dlsu.edu.ph/etd_masteral/4087; https://animorepository.dlsu.edu.ph/context/etd_masteral/article/10925/viewcontent/CDTG005071_P.pdf
• Institution: De La Salle University

One of the most promising sources of biofuel is microalgae. Microalgae are unicellular photosynthetic organisms that have a high photosynthetic efficiency (10 to 20%) compared to other plant species (0.5%) (Huntley, et al., 2007). However, the large initial moisture content of microalgae (80 to 90%, wet-basis) causes problems during oil extraction. According to Halim, et al. (2011), the lipid yield of dried microalgae is 33% higher than wet microalgae using hexane extraction. The study aims to predict the drying performance of microalgae biomass as a function of solar dryer design. The best combination of direct radiation, convective heat input and air flow rate was determined. A computational model for predicting the drying air temperature and relative humidity was solved using a spreadsheet program. The results of both models were validated by means of actual experimentation with a fabricated solar dryer and ANSYS Computational Fluid Dynamics simulation. Statistical analysis of the results showed that direct radiation (Qr) is more efficient than the convective heat input in increasing the drying rate. Qr allows the biomass surface temperature to get over the drying airs wet bulb temperature, giving rise to a significant increase in the drying rate. High air velocity results to inefficient drying. The computational model predicted the temperature and relative humidity of the drying air with an error percentage of 4.3% and 11.8%, respectively. ANSYS-CFD simulation was congruent with the assumptions used in the computational model. The regression equation generated from the characterization experiment was able to predict the average drying rate with an error percentage of 10%. The computational model was able to predict the temperatures and relative humidity of the drying chamber with an error percentage of 4.3% and 11.8%, respectively. Overall, the results show that drying rate is maximum at low air flow rate, high direct radiation and high convective heat input. Also, based on the measured available energy inside the solar dryer from actual experimentations, a microalgae dry mass yield of 800g/m2-hr which will be able to produce 180mL of oil per hour on a good sunny day was computed.