Environmental impact prediction of microalgae to biofuels chains using artificial intelligence: A life cycle perspective

• Main Authors: Mayol, A. P., San Juan, Jayne Lois G., Sybingco, E., Bandala, Argel A., Dadios, Elmer P., Ubando, Aristotle T., Culaba, Alvin B., Chen, W. H., Chang, J. S.
• Format: text
• Published: Animo Repository 2020
• Subjects: Algal biofuels—Refining; Environmental impact analysis; Life cycle costing; Mechanical Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1382; https://animorepository.dlsu.edu.ph/context/faculty_research/article/2381/type/native/viewcontent
• Institution: De La Salle University

Biofuels derived from microalgae is an emerging technology that can supply fuel demand and alleviate greenhouse gas emissions. However, exclusively producing biofuels from microalgae remains to be commercially unsustainable because of its high investment and operating costs. A promising opportunity to address this are algal bio-refineries. Nonetheless, there is still a need to verify the environmental sustainability of this system along its entire process chain, from raw material acquisition to end-of-life. This study utilizes a life-cycle perspective approach to assess the sustainability of the algal bio-refinery and developed environmental impact prediction model using artificial intelligence, particularly adaptive neuro fuzzy inference system. Results will indicate the environmental impacts of a bio-refinery system identifying its major hotspots on different environmental impact categories. Results show that in the investigated proposed algal bio-refinery, the transesterification process had a huge contribution on the overall environmental impact having over 51.5 % of the total weight. In addition, ANFIS results showed the correlation of input parameters with respect to the environmental impact of the system. The model also indicated that there is a perfect correlation between the two parameters. The model and its accuracy should be further validated with the use of real data. © 2020 Institute of Physics Publishing. All rights reserved.