Fuzzy analytical hierarchy process (AHP) for multi-criteria selection in drying and harvesting process of microalgae system

• Main Authors: Tan, Jully, Low, Kok Yuan, Sulaiman, Nik Meriam Nik, Tan, Raymond Girard R., Promentilla, Michael Angelo B.
• Format: text
• Published: Animo Repository 2015
• Subjects: Microalgae > Harvesting; Microalgae > Drying; Energy crops > Harvesting; Energy crops > Drying; Chemical Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/3865
• Institution: De La Salle University

Over the decades, biofuels from agriculture crops have gained interest due to the awareness of environmental benefits with respect to greenhouse gases emissions and global warming. Among many types of crops, microalgae are considered to be the most promising new source of biomass compared with first and second-generation feedstocks. However, high energy requirement for harvesting and drying of the biomass, as well as the lack of cost-effective techniques for harvesting large quantities of microalgae, pose challenges to commercialization. In this work, we propose a systematic and simple methodology in the multi-criteria evaluation of alternatives for the harvesting and drying process. A fuzzy analytical hierarchy process (AHP) approach is used, where the pairwise comparison of the criteria and alternatives is done and expressed with triangular fuzzy numbers. In a case study, four alternatives each for the harvesting and drying process options are compared. Cost, environmental impacts and technology capability are used as the criteria in selecting of optimum option for harvesting and drying processes. From the pairwise comparison, filtration is found to be the best option for harvesting, compared with other options such as centrifugation, flotation and sedimentation with flocculation. On the other hand, sun drying process is found to be better than other drying alternatives, i.e., freeze drying, spray drying and drum drying. Fuzzy AHP allows the degree of confidence of the expert to be quantified. This method also allows the inconsistency of the judgments to be reconciled within the bounds of the fuzzy numbers. Copyright © 2015, AIDIC Servizi S.r.l.,.