Direct syngas hydrogenation over a Co–Ni bimetallic catalyst: Process parameter optimization
Introduction
The projected depletion of crude oil resources and the potential contribution of fossil energy resources to greenhouse gas emissions drew attention to the need to develop renewable energy technologies. Fischer–Tropsch synthesis (FTS) is one of the most technologically feasible processes for producing transportation fuels from a range of feed stocks such as natural gas, coal, municipal solid waste, and biomass (Enger and Holmen, 2012, Ramasamy et al., 2014). Efforts to synthesize hydrocarbons through the catalytic hydrogenation of carbon monoxide (CO) date back to 1902, when Sabatier and Senderens synthesized hydrocarbons from CO and hydrogen (H2) (Calderone et al., 2011).
Catalysts made up of cobalt (Co) and iron (Fe) are the most commonly used catalysts in the FTS process. Co-based catalysts are generally more active and more selective to linear long chain hydrocarbons, and are typically more resistant to deactivation by water, but require a complex matrix of downstream unit operations, such as cracking and isomerization, to generate the finished fuel, which can be very expensive (Jahangiri et al., 2014). Fe-based catalysts can be operated under wider ranges of temperatures and H2/CO ratios without a significant rise in methane (CH4) selectivity; these catalysts are inexpensive compared to Co. However, Fe catalysts generate high levels of gasoline range products and C2–C5 olefinic compounds, and they rapidly deactivate to make costs unfavorable from the commercial point of view (de Smit and Weckhuysen, 2008). The tendencies of the nickel (Ni) catalyst to generate high levels of CH4 via methanation chemistry and form volatile carbonyls resulting in the continuous loss of metal at the FTS operating conditions made Ni unsuitable for the commercial FTS process (Dry, 2004a p. 533; Enger and Holmen, 2012). Recently, Rytter et al. (2010) identified Ni as a suitable replacement for rhenium (Re) as a reduction and activity promoter to their Co catalyst in the FTS process. They reported the presence of Ni has a profound positive impact on the catalytic activity, as well as the start of run activity, steady-state level, and deactivation rate.
For several decades, researchers have struggled with optimizing FTS catalysts to control product selectivity and overcome catalyst deactivation. FTS performance depends on the catalyst composition, preparation methods, treatment parameters, and reaction operating parameters (Calderone et al., 2011). Recently, several groups have explored using bimetallic catalysts to control selectivity and suppress deactivation. Most studies using bimetallic catalysts have focused on a combination of the conventional FTS metals (Co, Ni, Fe, and Ru) (Feyzi et al., 2013; Calderone et al., 2011), both bimetallic and ternary, with respect to the alloy formation, metal support interaction, promoter effect, and catalyst synthesis and activation techniques to selectively generate liquid fuel mixtures with a high carbon yield in fewer reaction steps.
One current interest is the generation of small olefin compounds that can be oligomerized to the targeted fuel or chemical compounds (Torres Galvis and de Jong, 2013). As discussed previously, Fe catalyst always exhibits high levels of olefin composition, but also generates high levels of CO2, along with carbon formation on the catalyst. Literature also points out that a Co-manganese- (Mn) based catalyst can have the potential to generate a high olefin composition with low levels of CO2 and CH4; this is still in the early stages of research (Zhou et al., 2015, Liang et al., 1998). This information, combined with some of our preliminary results from the Co–Ni bimetallic catalyst on the CO hydrogenation, led us to further explore and understand the ability of Co–Ni to generate hydrocarbon compounds containing high levels of a small olefin fraction. In this paper, the bimetallic Co–Ni catalyst activity and the product selectivity towards small olefins from the syngas (CO+H2) conversion with respect to the catalyst metal loading and reaction temperature will be discussed based on the experimental results. In addition, this paper will introduce a dual bed configuration to convert oxygenates generated in the CO hydrogenation process to produce targeted hydrocarbon compounds.
Section snippets
Catalyst preparation
The catalyst preparation uses cobalt nitrate and/or nickel nitrate and Davisil Silica 645 (purchased from Sigma-Aldrich). The catalysts are prepared by incipient wetness impregnation of silica (SiO2) support (60/100 mesh, pre-calcined at 500 °C). The appropriate quantities of a cobalt nitrate solution and a nickel nitrate solution were combined with enough deionized water to bring the total volume of the impregnation solution to 90% of the water adsorption pore volume of the silica support to
Catalyst characterization
The phase structure of the unreduced catalysts (calcined at 350 °C) was analyzed using XRD. The powder XRD patterns of the Ni and Co oxides at different compositions supported on SiO2 is shown in Fig. 2. The monometallic Ni catalyst comprises a primarily cubic NiO phase and the monometallic Co catalyst comprises a primarily cubic Co3O4 phase. For the Co–Ni bimetallic catalyst, a cubic NiCo2O4 phase dominates. In the Co–Ni bimetallic catalyst, nickel oxides and cobalt oxides are potentially
Conclusion
Co–Ni bimetallic catalysts show promise for converting syngas produced from biomass and other low-value material, such as municipal solid waste (via gasification technologies), to valuable compounds. There are many improvements that can be made to the Co–Ni bimetallic catalysts by optimizing the catalyst preparation procedure, such as calcination temperatures, catalyst loadings, different promoters, and other operational parameters particularly focused on reducing the CH4 composition with a
Acknowledgements
The Pacific Northwest National Laboratory is operated by the Battelle Memorial Institute for the U.S. Department of Energy under contract no. DE-AC05-76RL01830. This work was supported by the U.S. Department of Energy’s Bioenergy Technology Office. The authors wish to express thanks to Robert A. Dagle and Michael A. Lilga for the valuable technical discussions, Colin D. Smith for the XRD analysis, and Xiaohong Shari Li for the BET analysis.
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