High throughput screening of promoted bimetallic catalysts supported on alumina for production of light olefins via Fischer-Tropsch synthesis with artificial neural network and response surface methodology

The production of light olefins via Fischer-Tropsch Synthesis (FTS) was investigated by both experimental and modeling studies. Experimental studies were conducted by employing promoted FeCo/alpha-Al2O3 (FeCo) bimetallic catalysts. The Response Surface Methodology (RSM) and Artificial Neural Network (ANN) methods were used in modeling. Specifically, effects of the types and concentration of promoters on the catalytic performances of alpha-Al2O3 supported iron-cobalt bimetallic catalysts are investigated by using Ce, Ni, La, Ho, Ga Cu, Mn, and Zn used as promoters. A total of 49 catalysts were prepared by co-impregnation method and tested at 310 degrees C and 1 bar in a high-speed catalyst performance test system (HT-CPA). The FTS performances of catalysts showed that both the types and amounts of the promoters can affect light olefin production. The light olefin (C-2(=)-C-3(=)) production of unpromoted FeCo catalyst was about 3.87 x 10(-3) (mol C/g active metal. h). The best performances were obtained from FeCo catalysts promoted with Ho, Cu, and Zn. Presence of these promoters increased olefin production up to similar to 84 %, 76 % and 70 %, respectively. Performance parameters (MSE and R-2) of RSM and ANN models revealed that both methods can be used to support experimental studies and provide a procedure for optimizing catalyst formulations yielding the desired product selectivity in FTS processes. The RSM outcomes aligned with experimental findings, suggest that that Ho, Cu, and Zn can serve as suitable promoters for the light olefin production in FTS. ANN model attained higher performance parameters (R-2 = 0.95 and MSE = 2E-7) than RSM model (R-2 = 0.85 and MSE = 3.4E-7). This indicates that ANN model, which designed as one hidden layer including 7 neurons, can provide better predictions and a fitting capacity. Therefore, it may be used in further optimization and sensitivity analysis approaches. Alignment degree between the experimental and modeling results demonstrates that the proposed methodology can provide a reliable and efficient way to guide experiments to optimize catalyst formulations through identification of potential promoters and their amounts to be used.