Statistical Product Selectivity Modeling and Optimization for γ-Al2O3-Supported Cobalt Catalysts-Based Fischer–Tropsch Synthesis

• Published in: Catalysis letters 2021-11, Vol.151 (11), p.3273-3286
• Main Authors: Amirov, Nurlan, Vakhshouri, Amir Reza
• Format: Article
• Language: English
• Subjects: Adequacy; Alkenes; Aluminum oxide; Catalysis; Catalysts; Chemical synthesis; Chemistry; Chemistry and Materials Science; Cobalt; Fischer-Tropsch process; Hydrocarbons; Industrial Chemistry/Chemical Engineering; Mathematical models; Metal particles; Methane; Model reduction; Multiple objective analysis; Optimization; Organometallic Chemistry; Paraffins; Particle size; Physical Chemistry; Polynomials; Pore size; Quadratic equations; Selectivity; Transitional aluminas
• ISSN: 1011-372X; 1572-879X
• DOI: 10.1007/s10562-021-03557-0

The statistical selectivity models were developed for four different Fischer–Tropsch synthesis product range, including methane (CH 4 ), light olefins (C 2 =C 4 ), light paraffins (C 2 –C 4 ), and long-chain hydrocarbons (C 5+ ), based on the experimental data obtained over thirteen γ-Al 2 O 3 supported cobalt-based catalysts with different cobalt particle and pore sizes. The input variables consist of cobalt metal particle size and catalyst pore size. The cubic and quadratic polynomial equations were fitted to the experimental data, however, the mathematical models were subjected to model reduction for the enhancement of model adequacy, which was investigated through ANOVA. The multi-objective optimization revealed that the maximum C 5+  selectivity (84.150%) could be achieved at the cobalt particle size and pore sizes of 14.764 and 23.129 nm, respectively, while keeping the selectivity to other hydrocarbon products minimum. Graphic Abstract