Title of article :
Optimization of Cu oxide catalyst for methanol synthesis under high CO2 partial pressure using combinatorial tools Original Research Article
Author/Authors :
Kohji Omata، نويسنده , , Masahiko Hashimoto، نويسنده , , Yuhsuke Watanabe، نويسنده , , Tetsuo Umegaki، نويسنده , , Satoshi Wagatsuma، نويسنده , , Gunji Ishiguro، نويسنده , ,
Muneyoshi Yamada، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2004
Abstract :
In one-stage dimethyl ether synthesis from syngas, a hybrid catalyst with Cu-based oxide and solid acid is used. An active catalyst for methanol formation with high resistant to carbon dioxide is essential for high efficiency of the process. Besides Cu, Zn and Al, four promising additives were selected and the composition of the seven elements was optimized. Combinatorial tools with high-throughput screening under pressure using 96 well microplates and a data mining technique was used to discover a new catalyst with high tolerance for carbon dioxide. The activity of the catalyst optimized under 30% CO2 (Cu0.459Zn0.184Al0.175Cr0.000B0.181Zr0.001Ga0.000O1.179) was 270 g-MeOH/(kg-cat. h) at 1 MPa, 498 K, and H2/CO/CO2/N2=43/22/30/5, which is much higher than 190 g-MeOH/(kg-cat. h) of the catalyst optimized under 5% CO2 (Cu0.472Zn0.124Al0.165Cr0.000B0.207Zr0.026Ga0.006O1.215).
Keywords :
Methanol synthesis , Artificial neural network , High-throughput screening , Combinatorial catalysis
Journal title :
Applied Catalysis A:General
Journal title :
Applied Catalysis A:General