Catalytic gasification of algal biomass for hydrogen-rich gas production: Parametric optimization via central composite design. (15th February 2018)

• Record Type: Journal Article
• Authors: Raheem, Abdul; Ji, Guozhao; Memon, Asif; Sivasangar, Siva; Wang, Wei; Zhao, Ming; Taufiq-Yap, Yun Hin.
• Is Part Of: Energy conversion and management. Volume 158(2018)
• Page Start: 235
• Page End: 245
• Publication Date: 2018-02-15
• Subjects: Gasification -- Tri-metal oxide catalyst -- Chlorella vulgaris -- Hydrogen generation -- Parametric optimization; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2017.12.041
• Languages: English
• ISSNs: 0196-8904

Abstract

Graphical abstract: Highlights: Conversion of algal biomass via catalytic gasification was investigated. H2 production was optimized using central composite design. Higher temperatures and catalyst favored H2 yield and reduction of tar and char. H2 fraction peaked at 18.77 mol kg-biomass −1 at optimized operating conditions. Abstract: This study presents the optimization of algal biomass catalytic gasification for hydrogen-rich gas production. Three operating parameters under varying conditions of temperature (700–900 °C), catalyst loading (5–20 wt%) and reaction time (15–40 min) were optimized. The central composite design (CCD) was used to perform optimization and to investigate the influence of operating parameters on response variables (fractions of H2, CO, CO2 and CH4 ). The results indicated the temperature and catalyst loading as the most significant reaction parameters influencing H2 production and reducing the tar produced during the gasification process. The highest H2 fraction of 48.95 mol% (18.77 mol kg-biomass −1 ) was obtained at an optimum condition at 851 °C with catalyst loading of 16.4 wt% and reaction time of 28.8 min. The high R 2 values of 0.98, 0.97, 0.95, and 0.90 corresponding to the response variables (H2, CO, CO2 and CH4 ) showed paramount estimation of model.