Improvement of light olefins production over the Co‐Ni‐Mn nano‐catalyst: modeling and optimization of process by RSM

Abstract

The co-precipitated Co-Ni-Mn nano-catalysts were evaluated for carbon monoxide hydrogenation. The influence of Al 2 O 3 , SiO 2 , Zeolite, Active carbon, MgO supports and then the effect of best chosen support loading on the catalytic behavior and structure of ternary Co-Ni-Mn nano-catalysts were studied. The Co-Ni-Mn/20 wt%SiO 2 sample has shown highest selectivity toward light olefins production. Furthermore the influence of process conditions was investigated. Evaluation tests were performed under process conditions of P = 1-10 bar, T = 523-623 K, and H 2 /CO = 0.67-3. The Response Surface Methodology (RSM) method was used for modeling and optimization process and the best conditions for catalyst evaluation were determined (T = 598.16 K, P = 1 atm and H 2 /CO = 2.19). Under these conditions the highest selectivity of light olefins, higher CO conversion % and lower selectivity toward methane were achieved. Catalysts characteristics were investigated using XRD, BET, TEM, TGA, DSC, SEM, XPS, and TPR techniques.

Design and Deconstruction of Chemical Process Flow Based on Deep Learning

“…As can be seen from Figure 3, there are three basic features of this process design [16]. e first is the professionalism of the researchers.…”

Fischer–Tropsch synthesis over the Co–Ni/Al2O3 nanocatalyst: influence of process variables, modeling and optimization using response surface methodology

“…In our previous works we investigated the impact of reaction conditions on the catalytic performance of different prepared catalytic systems. [29][30][31][32] In the present work, after investigating the effect of operational factors on catalytic performance, modeling and optimization of the considered parameters were performed using the response surface methodology (RSM) procedure. This investigation was performed using both experimental and RSM procedures, so it is possible to observe the effect of various process parameters on the catalytic behavior independently (by the experimental method) and also the effect of all of these parameters on the catalytic performance concurrently (via the RSM procedure).…”

“…In the current work we investigated the influences of process variables on the catalytic behavior of Co–Ni/Al 2 O 3 ‐impregnated nanocatalyst for lower olefin production. In our previous works we investigated the impact of reaction conditions on the catalytic performance of different prepared catalytic systems 29‐32 . In the present work, after investigating the effect of operational factors on catalytic performance, modeling and optimization of the considered parameters were performed using the response surface methodology (RSM) procedure.…”