Influence of pretreatment conditions on the catalytic behavior and structure of Fe‐Co‐Mn / MgO FTS nanocatalyst: Modeling and optimization using RSM

Abstract

A co-precipitation procedure was employed for the preparation of supported Fe-Co-Mn oxide nanocatalysts. The prepared samples were evaluated for the hydrogenation of carbon monoxide to produce light olefins. After determining the optimum reductant agent type the influence of the other pretreatment factors including both reduction temperature and time, and the optimum flow of reductant agent was studied using response surface methodology (RSM). The modeling process for the selected responses was performed followed by the optimization of reduction parameters was also carried out using the RSM method and historical data design type of DOE. It was found that the pretreatment conditions greatly affect the catalytic performance. The optimization results show that H 2 reductant agent with the reduction temperature of 399 C, reduction time of 12 hours, and H 2 reductant agent flow of 37.52 mL/min are the best pretreatment conditions for achieving the highest catalyst activity and selectivity toward light olefins and lower selectivity toward methane concurrently. The structural properties of prepared specimens were characterized by XRD, TEM, XPS, TGA, DSC, SEM, EDS, and BET. The characterization results reveal that the structural characteristics of the samples were changed under various pretreatment conditions.

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

“…It has been found that RSM is the most beneficial and potent procedure for optimizing and modeling various processes 47,48 . In our reported works we have also used the RSM method for modeling and optimization of preparation and pretreatment variables for light olefin production 49,50 . The RSM method is also a practical way to detect the most impressive variables and communication between factors; in this way, suitable models for the selected responses are achieved by fitting the variables.…”

“…47,48 In our reported works we have also used the RSM method for modeling and optimization of preparation and pretreatment variables for light olefin production. 49,50 The RSM method is also a practical way to detect the most impressive variables and communication between factors; in this way, suitable models for the selected responses are achieved by fitting the variables. In the present work, we attempted to investigate the influence of operational factors using the RSM method.…”

An investigation of the catalytic performance of Fe–Mn/Al2O3 nanocatalyst for light olefins production using RSM method and kinetic study

“…By regulating the factors, the appropriate model is achieved from the favorable response. The RSM technique was also used in our previous works for CO hydrogenation reaction on the Fe–Co–Mn catalysts and Co–Ce bimetallic system 37–40 . In the current work, this technique (RSM) and the historical data design type of DOE are used in order to perform the optimization and modeling processes on the Fe–Mn/Al 2 O 3 nanocatalyst.…”

“…The RSM technique was also used in our previous works for CO hydrogenation reaction on the Fe-Co-Mn catalysts and Co-Ce bimetallic system. [37][38][39][40] In the current work, this technique (RSM) and the historical data design type of DOE are used in order to perform the optimization and modeling processes on the Fe-Mn/Al 2 O 3 nanocatalyst. Empirical data were fitted to polynomial equations by using the multiple regression analysis as follows:…”