Integrating Smart Bio-Panels and Machine Learning for Enhanced Microalgae Cultivation and Carbon Reduction
DOI:
https://doi.org/10.70112/ajeat-2024.13.2.4252Keywords:
Microalgae, Photobioreactor (PBR), Biofuels, Carbon Dioxide Removal, Sustainable EnergyAbstract
As the world becomes increasingly dependent on fossil fuels, it faces growing environmental and economic challenges, particularly with carbon emissions and energy sustainability. One promising solution involves using photosynthetic microalgae, which can absorb carbon dioxide and convert sunlight into energy-rich materials, such as biofuels. Microalgae can grow on land that is unsuitable for conventional farming and can utilize various types of water, including seawater, making them an eco-friendlier option. A critical technology for large-scale algae cultivation is the photobioreactor (PBR), a controlled system designed to promote algae growth by regulating factors such as light, temperature, and nutrients. Recent innovations are integrating PBRs with smart bio-panels, which capture solar energy, generate electricity, and simultaneously facilitate carbon dioxide removal from the atmosphere. Machine learning tools, such as Support Vector Machines (SVM), are also being employed to predict algal growth and optimize conditions for enhanced productivity. However, microalgae utilize only a small portion of sunlight for photosynthesis, and traditional cultivation methods can result in energy inefficiencies and increased salinity due to water evaporation. To enhance algae cultivation, researchers are exploring methods to capture more sunlight, including the use of specialized lighting systems or genetically engineered algae strains. These advancements could make microalgae a more efficient and sustainable source of biofuels, bioplastics, and other valuable products, contributing to the resolution of both energy and climate issues. Microalgae offer a renewable, carbon-neutral alternative to fossil fuels and could play a vital role in addressing global energy needs while minimizing the environmental impact of conventional energy sources. By integrating advanced technologies in cultivation, renewable energy production, and carbon capture, microalgae farming presents a sustainable approach to tackling energy and climate challenges, offering economic and environmental benefits.
References
M. A. Borowitzka and N. R. Moheimani, “Sustainable biofuels from algae,” Mitigation and Adaptation Strategies for Global Change, vol. 18, no. 1, pp. 13-25, 2013.
E. G. Nwoba, D. A. Parlevliet, D. W. Laird, K. Alameh, and N. R. Moheimani, “Light management technologies for increasing algal photobioreactor efficiency,” Algal Research, vol. 39, p. 101433, 2019.
N. R. Moheimani, D. Parlevliet, M. P. McHenry, P. A. Bahri, and K. de Boer, “Past, present and future of microalgae cultivation developments,” in Biomass and Biofuels from Microalgae: Advances in Engineering and Biology, pp. 1-18, 2015.
C. Posten and G. Schaub, “Microalgae and terrestrial biomass as sources for fuels - a process view,” Journal of Biotechnology, vol. 142, no. 1, pp. 64-69, 2009.
K. Kumar, S. K. Mishra, A. Shrivastav, M. S. Park, and J. W. Yang, “Recent trends in the mass cultivation of algae in raceway ponds,” Renewable and Sustainable Energy Reviews, vol. 51, pp. 875-885, 2015.
Y. Dote, S. Sawayama, S. Inoue, T. Minowa, and S. Y. Yokoyama, “Recovery of liquid fuel from hydrocarbon-rich microalgae by thermochemical liquefaction,” Fuel, vol. 73, no. 12, pp. 1855-1857, 1994.
K. de Boer, N. R. Moheimani, M. A. Borowitzka, and P. A. Bahri, “Extraction and conversion pathways for microalgae to biodiesel: A review focused on energy consumption,” Journal of Applied Phycology, vol. 24, pp. 1681-1698, 2012.
M. A. Borowitzka, “Commercial production of microalgae: Ponds, tanks, tubes, and fermenters,” Journal of Biotechnology, vol. 70, no. 1-3, pp. 313-321, 1999.
P. Metzger and C. Largeau, “Botryococcus braunii: A rich source for hydrocarbons and related ether lipids,” Applied Microbiology and Biotechnology, vol. 66, pp. 486-496, 2005.
P. Mercer and R. E. Armenta, “Developments in oil extraction from microalgae,” European Journal of Lipid Science and Technology, vol. 113, no. 5, pp. 539-547, 2011.
N. R. Moheimani and M. P. McHenry, “Developments of five selected microalgae companies developing ‘closed’ bioreactor biofuel production systems,” International Journal of Innovation and Sustainable Development, vol. 7, no. 4, pp. 367-386, 2013.
R. L. White and R. A. Ryan, “Long-term cultivation of algae in open-raceway ponds: Lessons from the field,” Industrial Biotechnology, vol. 11, no. 4, pp. 213-220, 2015.
F. Lehr and C. Posten, “Closed photo-bioreactors as tools for biofuel production,” Current Opinion in Biotechnology, vol. 20, no. 3, pp. 280-285, 2009.
C. Y. Chen, K. L. Yeh, R. Aisyah, D. J. Lee, and J. S. Chang, “Cultivation, photobioreactor design and harvesting of microalgae for biodiesel production: A critical review,” Bioresource Technology, vol. 102, no. 1, pp. 71-81, 2011.
T. Ishika, N. R. Moheimani, and P. A. Bahri, “Sustainable saline microalgae co-cultivation for biofuel production: A critical review,” Renewable and Sustainable Energy Reviews, vol. 78, pp. 356-368, 2017.
E. Eroglu, S. M. Smith, and C. L. Raston, “Application of various immobilization techniques for algal bioprocesses,” in Biomass and Biofuels from Microalgae: Advances in Engineering and Biology, pp. 19-44, 2015.
W. Zhang, J. Li, T. Liu, S. Leng, L. Yang, H. Peng, et al., “Machine learning prediction and optimization of bio-oil production from hydrothermal liquefaction of algae,” Bioresource Technology, vol. 342, p. 126011, 2021.
J. Li, X. Zhu, Y. Li, Y. W. Tong, Y. S. Ok, and X. Wang, “Multi-task prediction and optimization of hydrochar properties from high-moisture municipal solid waste: Application of machine learning on waste-to-resource,” Journal of Cleaner Production, vol. 278, p. 123928, 2021.
L. Sheng, X. Wang, and X. Yang, “Prediction model of biocrude yield and nitrogen heterocyclic compounds analysis by hydrothermal liquefaction of microalgae with model compounds,” Bioresource Technology, vol. 247, pp. 14-20, 2018.
G. Anastopoulos, Y. Zannikou, S. Stournas, and S. Kalligeros, “Transesterification of vegetable oils with ethanol and characterization of the key fuel properties of ethyl esters,” Energies, vol. 2, no. 2, pp. 362-376, 2015.
R. R. Kumar, A. K. Singh, and S. Kumar, “IoT-based water quality monitoring system using pH, turbidity, and temperature sensors with Raspberry Pi,” Journal of Engineering Science and Technology, vol. 15, no. 3, pp. 1763-1775, 2020.
A. Abdullah, M. M. Rahman, A. Almogren, et al., “Computer Vision Based Deep Learning Approach for the Detection and Classification of Algae Species Using Microscopic Images,” Water, vol. 14, no. 14, p. 2231, 2022.
S. S. Iqbal, et al., “Smart Home Automation Using IoT and Raspberry Pi,” International Journal of Advanced Research in Computer Science, vol. 10, no. 2, pp. 9, 2019.
R. K. Singh, A. K. Sharma, and S. Kumar, “Development of IoT-based smart energy monitoring system using Raspberry Pi and Arduino,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 9, no. 2, pp. 12-24, 2020.
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