H₂S removal by copper enriched porous carbon cuboids

Hydrogen sulfide (H2S) removal by adsorption from gas streams is crucial to prevent the environmental and industrial damage it causes. Amongst the nanostructures considered excellent candidates as sorbents, porous carbon has been studied extensively over the last years. In the present work we present a synthetic procedure for three high potential sorbents based on carbon cuboids, namely a low-surface-area copper-rich structure, a highly porous aggregate without metal addition, and lastly the same porous carbon decorated with copper. The properties and performance as catalysts of these three sorbents were evaluated by powder X-ray diffraction, X-ray photoelectron spectroscopy, thermal analysis, scanning electron microscopy with energy dispersive X-ray analysis, surface area determination through N2 adsorption and desorption, as well as by H2S adsorption measurements

Optimization of Adsorption-Based Natural Gas Dryers

Common approaches in designing natural gas dryers are based on empirical algebraic correlations and design heuristics. Such approaches fail to capture the process physics and are generally not optimal. In this work a new method for the design of natural gas dryers is presented. The method formulates a mixed integer nonlinear programming (MINLP) problem where the objective is to minimize the net present value of ensued costs (NPVC) of the drying system throughout its lifetime, while meeting all process constraints. Two process schemes based on common industrial conditions are considered, which differ in the source of the regeneration gas. Both schemes are shown to attain an optimal NPVC in the range of 4.5 to 5.4 $/MMSCF. When compared to conventional methods, this represents a reduction in the range of 17- 37%. The cost savings are primarily achieved from the optimization of the adsorption time, regeneration time, and the regeneration gas flow rate, thus illustrating the advantages of the proposed optimal design

Turbine Design and Optimization for a Supercritical CO2 Cycle Using a Multifaceted Approach Based on Deep Neural Network

Turbine as a key power unit is vital to the novel supercritical carbon dioxide cycle (sCO2-BC). At the same time, the turbine design and optimization process for the sCO2-BC is complicated, and its relevant investigations are still absent in the literature due to the behavior of supercritical fluid in the vicinity of the critical point. In this regard, the current study entails a multifaceted approach for designing and optimizing a radial turbine system for an 8 MW sCO2 power cycle. Initially, a base design of the turbine is calculated utilizing an in-house radial turbine design and analysis code (RTDC), where sharp variations in the properties of CO2 are implemented by coupling the code with NIST’s Refprop. Later, 600 variants of the base geometry of the turbine are constructed by changing the selected turbine design geometric parameters, i.e., shroud ratio (rs4r3), hub ratio (rs4r3), speed ratio (νs) and inlet flow angle (α3) and are investigated numerically through 3D-RANS simulations. The generated CFD data is then used to train a deep neural network (DNN). Finally, the trained DNN model is employed as a fitting function in the multi-objective genetic algorithm (MOGA) to explore the optimized design parameters for the turbine’s rotor geometry. Moreover, the off-design performance of the optimized turbine geometry is computed and reported in the current study. Results suggest that the employed multifaceted approach reduces computational time and resources significantly and is required to completely understand the effects of various turbine design parameters on its performance and sizing. It is found that sCO2-turbine performance parameters are most sensitive to the design parameter speed ratio (νs), followed by inlet flow angle (α3), and are least receptive to shroud ratio (rs4r3). The proposed turbine design methodology based on the machine learning algorithm is effective and substantially reduces the computational cost of the design and optimization phase and can be beneficial to achieve realistic and efficient design to the turbine for sCO2-BC

Synthesis of self-pillared zeolite nanosheets by repetitive branching

Hierarchical zeolites are a class of microporous catalysts and adsorbents that also contain mesopores, which allow for fast transport of bulky molecules and thereby enable improved performance in petrochemical and biomass processing. We used repetitive branching during one-step hydrothermal crystal growth to synthesize a new hierarchical zeolite made of orthogonally connected microporous nanosheets. The nanosheets are 2 nanometers thick and contain a network of 0.5-nanometer micropores. The house-of-cards arrangement of the nanosheets creates a permanent network of 2- to 7-nanometer mesopores, which, along with the high external surface area and reduced micropore diffusion length, account for higher reaction rates for bulky molecules relative to those of other mesoporous and conventional MFI zeolites

Nickel Phosphide Nanoparticles for Selective Hydrogenation of SO2 to H2S

Highly mesoporous SiO2 encapsulated NixPy crystals, where (x, y) = (5, 4), (2, 1), and (12, 5) were successfully synthesized by adopting thermolytic method using oleylamine (OAm), trioctylphosphine (TOP) and trioctylphosphine oxide (TOPO). The Ni5P4@SiO2 system shows the highest reported activity for the selective hydrogenation of SO2 towards H2S at 320 oC (96 % conversion of SO2 and 99 % selectivity to H2S) which was superior to the activity of the commercial CoMoS@Al2O3 catalyst (64 % conversion of SO2 and 71 % selectivity to H2S at 320 oC). The morphology of the Ni5P4 crystal was finely tuned via adjustment of the synthesis parameters receiving a wide spectrum of morphologies (hollowed, macroporous-network and SiO2 confined ultra-fine clusters). Intrinsic characteristics of the materials were studied using XRD, HRTEM/STEM-HAADF, EDX, BET, H2-TPR, XPS, and experimental and calculated 31P MAS ssNMR towards establishing the structure-performance correlation for the reaction of interest. Characterization of the catalysts after the SO2 hydrogenation reaction proved the preservation of the morphology, crystallinity and Ni/P ratio for all the catalysts

Ni2P Nanoparticles Embedded in Mesoporous SiO2 for Catalytic Hydrogenation of SO2 to Elemental S

Highly active nickel phosphide nano clusters (Ni2P) confined in mesoporous SiO2 catalyst were synthesized by a two-step process targeting tight control over the Ni2P size and phase. The Ni precursor was incorporated into the MCM-41 matrix by one-pot synthesis, followed by the phosphorization step which was accomplished in oleylamine with trioctylphosphine at 300 oC so to achieve the phase transformation from Ni to Ni2P. For benchmarking, Ni confined by the mesoporous SiO2 (absence of phosphorization) and 11 nm Ni2P nanoparticles (absence of SiO2), were also prepared. From the microstructural analysis, it was found that the growth of Ni2P nano clusters was restricted by the mesoporous channels, thus forming ultrafine and highly dispersed Ni2P nano clusters ( n-Ni2P > u-Ni@m-SiO2 > c-Ni2P in the selective hydrogenation of SO2 to S. In particular, u-Ni2P@m-SiO2 exhibited an SO2 conversion of 94 % at 220 oC and ~99 % at 240 oC, which is higher than the 11 nm stand-alone Ni2P particles (43 % at 220 oC and 94 % at 320 oC), highlighting the importance of the role played by SiO2 in stabilizing ultrafine nanoparticles of Ni2P. The reaction activation energy Ea over u-Ni2P@m-SiO2 is ~33 kJ/mol, which is lower than over n-Ni2P (~36 kJ/mol) and c-Ni2P (~66 kJ/mol), suggesting that the reaction becomes energetically favored over the ultrafine Ni2P nano clusters

Ni2P Nanoparticles Embedded in Mesoporous SiO2 for Catalytic Hydrogenation of SO2 to Elemental S

Highly active nickel phosphide nano clusters (Ni2P) confined in mesoporous SiO2 catalyst were synthesized by a two-step process targeting tight control over the Ni2P size and phase. The Ni precursor was incorporated into the MCM-41 matrix by one-pot synthesis, followed by the phosphorization step which was accomplished in oleylamine with trioctylphosphine at 300 oC so to achieve the phase transformation from Ni to Ni2P. For benchmarking, Ni confined by the mesoporous SiO2 (absence of phosphorization) and 11 nm Ni2P nanoparticles (absence of SiO2), were also prepared. From the microstructural analysis, it was found that the growth of Ni2P nano clusters was restricted by the mesoporous channels, thus forming ultrafine and highly dispersed Ni2P nano clusters ( n-Ni2P > u-Ni@m-SiO2 > c-Ni2P in the selective hydrogenation of SO2 to S. In particular, u-Ni2P@m-SiO2 exhibited an SO2 conversion of 94 % at 220 oC and ~99 % at 240 oC, which is higher than the 11 nm stand-alone Ni2P particles (43 % at 220 oC and 94 % at 320 oC), highlighting the importance of the role played by SiO2 in stabilizing ultrafine nanoparticles of Ni2P. The reaction activation energy Ea over u-Ni2P@m-SiO2 is ~33 kJ/mol, which is lower than over n-Ni2P (~36 kJ/mol) and c-Ni2P (~66 kJ/mol), suggesting that the reaction becomes energetically favored over the ultrafine Ni2P nano clusters