Catalyst Preparation Variables Research Articles

Influence of Catalyst Preparation Conditions on Coconut Shell-derived Solid Acid Catalyst Performance for Transesterification

The carbon-based solid acid catalyst is mostly synthesized through a series of incomplete carbonization and sulfonation process. This paper investigates the influence of catalyst preparation conditions on the catalyst performance in biodiesel conversion. Coconut-shell-derived solid acid catalyst was prepared through incomplete carbonization followed by sulfonation using concentrated sulphuric acid. The effect of catalyst preparation variables including carbonization temperature, carbonization time, sulfonation temperature and sulfonation time was evaluated using response surface methodology. The adequacy of the mathematical model represents the process was confirmed by using ANOVA analysis with R2 value of 0.9121 and p-value <0.0001. Optimal process conditions were obtained at carbonization temperature of 469.97 ºC, carbonization time of 3.37 h, sulfonation temperature of 99.14 ºC and sulfonation time of 7.11 h. At these conditions, the coconut shell-derived catalyst able to catalyse the transesterification reaction and achieved >80% FAME conversion. This study uncovers the potential of biomass valorisation into a useful end produc

Modeling and optimization of V2O5/TiO2 nanocatalysts for NH3-Selective catalytic reduction (SCR) of NOx by RSM and ANN techniques

In the present study, two statistical methods including the response surface method (RSM) and artificial neural network (ANN), were employed for modeling and optimization of selective catalytic reduction of NOx with NH3 (NH3-SCR) over V2O5/TiO2 nanocatalysts. The relationship between catalyst preparation variables, such as metal loading, impregnation temperature, and calcination temperature on NO conversion were investigated. The R2 value of 0.9898 was obtained for quadratic a RSM model, which proves the high agreement of the model with the experimental data. The results of Pareto analysis revealed that three factors including calcination temperature, V loading, and impregnation temperature have a considerable impact on the response. Deduced from the established RSM model, the order of influence on the NO conversion was as follows: calcination followed by V loading and impregnation temperature. The optimum condition of catalyst preparation for maximum NO conversion over V2O5/TiO2 nanocatalysts was predicted to be at 0.0051 mol of V loading, an impregnation temperature of 50 °C and a calcination temperature of 491 °C. Moreover, an ANN model was created by a feed-forward back propagation network (with the topology 4, 12 and 1) to model the relation between the selected catalyst preparation variables and NH3-SCR process temperature. The R2 values for training, validation as well as test sets, were 0.99, 0.9810 and 0.9733. These high values proved the accuracy of the AAN model in modeling and estimating the NO conversion over V2O5/TiO2 nanocatalysts. According to the ANN model, the relative significance of each variable on NO conversion is calcination temperature, process temperature loading, and impregnation temperature from high to low importance, respectively, corroborating the obtained results from RSM.

Statistical review of dry reforming of methane literature using decision tree and artificial neural network analysis

The aim of this work was to extract knowledge for dry reforming of methane (DRM) reaction from experimental data using data mining tools such as decision trees and artificial neural networks. An extensive database containing 5521 data points depending on 63 catalyst preparation and operational variables was constructed from 101 papers published between 2005 and 2014; the output variables were CH4 conversion, CO2 conversion and H2/CO ratio of the product stream. Then, the database, as a whole or as subsets for different base metals were analyzed using decision trees (DT) to develop heuristics for high performance and artificial neural networks (ANN) to determine relative importance of input variables and predict the performance under unstudied conditions; mostly CH4 conversion, which is the most frequently reported output variable, were used in analysis. The testing accuracy of the decision tree was about 80% leading to four heuristics (i.e. four possible courses of action) for high CH4 conversion over Ni based catalyst. The first decision point to separate these heuristics is the reaction temperature as can be expected. This is followed by the other variables such as support type, W/F and reduction temperature. ANN analysis revealed that operational variables have higher relative importance (55%) compared to catalyst preparation variables (45%). The most important operational variable was found to be the reaction temperature while the active metal and the support are the most important catalyst preparation variables. ANN model was also tested to predict the data, which was not seen by the model before, and the data in 65 papers out of 101 were predicted within 15% error while 76 papers had the error rate of less than 20%.

Pt-CeO2/SiO2 catalyst for CO oxidation in humid air at ambient temperature

CO self-poisoning and slow surface kinetics pose major challenges to a CO oxidation catalyst that should work at ambient temperature. Furthermore, the presence of moisture would cause passivation of the catalyst. A highly active ceria promoted Pt catalyst (4%Pt-12%CeO2/SiO2; conversion ≥ 99% at low (< 500 ppm) and high (> 2500 ppm) CO concentrations was developed for CO oxidation at ambient temperature in humid air. Catalyst preparation variables such as Pt and CeO2 loading, ceria deposition method, drying and calcination conditions for the ceria and Pt precursors were optimized experimentally. The activity was correlated with surface properties using CO/H2 chemisorption, O2-H2 titration, X-ray diffraction and BET surface area analysis. The method of CeO2 deposition had a significant impact on the catalytic activity. CeO2 deposition by impregnation resulted in a catalyst that was three times more active than that prepared by deposition precipitation or CeO2 grafting. O2-H2 titration results revealed that the close association of ceria and Pt in the case of CeO2 deposition by impregnation resulted in higher activity. The catalyst support used was also crucial as a silica supported catalyst was five times more active than an alumina supported catalyst. The particle size and pore structure of the catalyst support were also crucial as the reaction was diffusion controlled. The drying and calcination conditions of the ceria and Pt precursors also played a crucial role in determining the catalytic activity. The Pt-CeO2/SiO2 catalysts with Pt > 2.5 wt% and CeO2 > 15 wt% were highly active (TOF > 0.02 s−1) and stable (conversion ≥ 99% after 15 h) at ambient conditions.

Optimization of Pd-B/γ-Al2O3 catalyst preparation for palm oil hydrogenation by response surface methodology (RSM)

Response surface methodology was used to design and evaluate the experimental variables for Pd-B/γ-Al2O3 catalyst preparation. The catalyst was prepared by impregnation and chemical reduction. Thirteen different samples of the catalyst were prepared at different KOH concentrations and annealed at various temperatures, before applying them in palm oil hydrogenation. Hydrogenation was performed on a 0.12% Pd-B/γ-Al2O3 catalyst at a temperature of 393 K, hydrogen pressure of 500 kPa and agitation of 500 rpm for 1 h. The iodine value (IV) and trans fatty acids (TFAs) content responses were measured for each hydrogenated palm oil sample. The predicted models were verified for both responses and found to be statistically adequate. An optimization study was performed on the catalyst preparation variables for minimizing both IV and TFAs content. The Pd-B/γ-Al2O3 prepared under optimized conditions exhibited 47% higher conversion and 22% lower trans-isomerization selectivity than Escat 1241 commercial catalyst. The Pd-B/γ-Al2O3 catalyst preparation variables have a noticeable effect on palm oil hydrogenation conversion and trans-isomerization selectivity.

Knowledge extraction for water gas shift reaction over noble metal catalysts from publications in the literature between 2002 and 2012

In this work, a database (containing 4360 experimental data points) on water gas shift reaction (WGS) over Pt and Au based catalysts was constructed using the data obtained from the published papers between the years 2002 and 2012. Then, the database was analyzed using three data mining tools to extract knowledge in three areas: Decision trees to determine the empirical rules and conditions that lead to high catalytic performance (high CO conversion); artificial neural networks (ANNs) to determine the relative importance of various catalyst preparation and operational variables and their effects on CO conversion; support vector machines (SVMs) to predict the outcome of unstudied experimental conditions. It was concluded that, all three models were quite successful and they complement each other to extract knowledge from the past published works and to deduce useful trends, rules and correlations, which are not easily comprehensible by the naked eyes.

Developing global reaction rate model for CO oxidation over Au catalysts from past data in literature using artificial neural networks

In this work, the literature for CO oxidation kinetics over Au based catalysts was analyzed using artificial neural networks to test the possibility of developing global reaction rate models representing the entire literature. A database was constructed using the data obtained from nineteen papers published between the years 1997 and 2011; then, the reaction rate was modeled as a function of catalyst preparation and operational variables by using neural networks. Next, global reaction rate equations in the form of power law were developed for each support type by the help of the neural network model, and the order of reaction with respect to each reactant and the parameters of Arrhenius relation were estimated. These power law models were successfully validated by using the information reported in the literature; hence, it was concluded that they can be used for the initial estimation of the reaction rates in the absence of more specific rate equations.

CuAu/SiO2 catalysts for the selective oxidation of propene to acrolein: the impact of catalyst preparation variables on material structure and catalytic performance

CuAu/SiO2 catalysts are active and selective for the synthesis of acrolein from propene by selective oxidation using a mixture of H2 and O2 as oxidant. The catalyst synthesis method (impregnation or deposition) and the activation procedure (reduction, calcination or reduction followed by calcination and the choice of reducing agent) all affect the structure of the catalyst and therefore the performance in catalysis. For a material prepared by coimpregnation of HAuCl4 and Cu(NO3)2, reduction with hydrogen leads to a catalyst with a significant level of CuAu alloy, whilst reduction with NaBH4 solution leads to small particles but with negligible interaction between copper and gold. Subsequent high temperature calcination of these materials destroys any CuAu phase but retains a Cu–Au interaction as observed by Transmission Electron Microscopy. Preparation of the catalyst by a two-step deposition method gives smaller particles than coimpregnation when reduced by H2 but a different alloy phase is observed by EXAFS. Calcination of this material gives a catalyst which combines high activity with excellent selectivity to acrolein. Characterisation of the materials using various techniques allows the nature of the active sites to be understood.

Tartaric acid–Ni supported catalysts obtained from hydrotalcite-like compounds: Effects of catalyst preparation variables on enantioselectivity

The catalytic properties of tartaric acid–nickel supported catalysts obtained from hydrotalcite-like compounds have been assessed in the enantioselective hydrogenation of methyl acetoacetate. Ni particle size above a minimum threshold of ca. 20nm was found to influence enantioselectivity. For materials with similar Ni crystallite size, e.e. progressively lowers as Mg is incorporated to Ni/Al, whereas incorporation of Zn improves e.e. for all Ni/Zn ratios. In general, activity is higher on Ni/Mg/Al than on Ni/Zn/Al series. Moreover, the synthetic method to obtain hydrotalcite-like compounds affects the catalytic properties of the resultant catalysts. For a series of materials of the same composition, the urea hydrolysis method leads to catalysts with enantiodifferentiation ability, whereas the coprecipitation method does not. Variables of reaction during chiral modification, such as pH and tartaric acid concentration, proved not to have a major effect on enantioselectivity. Furthermore, no direct correlation between the amount of tartaric acid adsorbed onto, or Ni leached from the surface, and the enantiodifferentiation ability of the catalysts was found.

Investigation of water gas-shift activity of Pt–MO x–CeO 2/Al 2O 3 (M = K, Ni, Co) using modular artificial neural networks

The water gas shift activity of promoted Pt–CeO 2/Al 2O 3 catalysts were investigated in this work. The catalysts were prepared by incipient to wetness impregnation and tested using a microflow reaction system. It was found that K has beneficial effects under product-containing feed compositions while Co and Ni promoters worsen catalyst performance. The reaction temperature and feed H 2O/CO ratio positively affect the catalytic activity, whereas CO 2 and H 2 addition to the feed decreases CO conversion, as expected. The experimental results were also modeled using modular neural networks, at which the catalyst preparation and operational (reaction) variables were used together in the same network because they are interacting but processed differently because they are dissimilar in their form (i.e. categorical versus continuous) and their effects on catalytic activity. It was concluded that the effects of catalyst preparation and operational variables and their relative importance could be comprehended more accurately by using this approach, which may be also employed in other similar systems.

Neural network Analysis of Selective CO Oxidation over Copper-Based Catalysts for Knowledge Extraction from Published Data in the Literature

In this work, a database containing 1337 data points for selective CO oxidation over Cu-based catalysts was constructed from 20 research publications and used for knowledge extraction by artificial neural networks. The experimental CO conversions reported in each publication were successfully predicted by a neural network trained using the data from the remaining 19 publications unless that one publication contained unique variables. The effects and relative significances of the catalyst preparation variables (such as Cu loading, second metal additive, support type, and preparation method) and operating variables (such as reaction temperature, feed composition, and feed flow rate/catalyst weight ratio) were also determined quite successfully by the artificial neural networks. We conclude that neural network modeling can be used to extract valuable experience and knowledge accumulated in the published data and can help researchers to plan new experiments in a more effective manner.

Transesterification of sunflower oil catalyzed by flyash-based solid catalysts

Flyash-based base catalyst was used in the transesterification of sunflower oil with methanol to methyl esters in a heterogeneous manner. Catalyst preparation variables such as, the KNO 3 loading amount and calcination temperature were optimized. The catalysts were characterized by powder XRD. The catalyst prepared by loading of 5 wt.% KNO 3 on flyash followed by its calcination at 773 K has exhibited maximum oil conversion (87.5 wt.%). The influence of various reaction parameters such as % catalyst loading, methanol to oil molar ratio, reaction time, temperature, reusability of the catalyst on the catalytic activity was investigated. K 2O derived from KNO 3 might be an essential component in the catalyst for its efficiency.

Evidence-Based Design and Optimisation of Titania Photocatalysts via Artificial Neural Network Analysis

This study deals with the development of artificial neural networkfor optimal titania-based photocatalyst design using over 700 data cases from the literature. In spite of the variability in intrinsic error across laboratories and continents over a 20-year span, feed-forward ANNs relating catalyst preparation variables to photocatalyst properties displayed good predictive capabilities with correlation coefficients generally greater than 0.92. Calcination temperature and dopant concentration exhibited strong negative connection weights to the optophysical properties of the catalyst (surface area, crystallite size, band-gap energy and point of zero charge) while dopant oxidation number and ionic radius have positive connection weights although the optimal ANN ensemblefor each photocatalystproperty contains different numberof neuronsin the hiddenlayer. A global ANN connecting both catalyst preparation variables and reaction conditions as inputs optimised the relationship to photoactivity with a 16-neuron hidden layer with calcination temperature, dopant concentration, molecular weight of the organic substrate and photocatalyst loading having a negative effect on photoactivty while the most important positive influence was provided by the initial concentration of the organic pollutant and dopant ioinic radius. Due to the large spectrum of input variables accommodated by this ANN, it may beusedasa meaningfulguideinthedesignofnew photocatalystsforspecific applications. Thereliabilityof this optimal ANN architecture is demonstrated with test data which has excellent parity plot with the predicted values.

Development of chromium-free iron-based catalysts for high-temperature water-gas shift reaction

Chromium-free iron-based catalysts were prepared and studied in regard to their performance in the high-temperature water-gas shift reaction (HTS). The effects of various catalyst preparation variables (i.e., Fe/promoter ratio, pH of precipitation medium, calcination and reduction temperatures) and preparation methods were investigated. Aluminum is a potential chromium replacement in HTS catalysts. Further improvement in WGS activity of Fe–Al catalysts can be achieved by the addition of small amounts of copper or cobalt. Catalysts were characterized using BET surface area measurements, temperature-programmed reduction (TPR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). As a textural promoter, aluminum and chromium prevent the sintering of iron oxides and stabilize magnetite phase by retarding its further reduction to FeO and metallic Fe. The promotional effect of Cu is found to be strongly dependent on the preparation method.

Low-Temperature Oxidation of CO in Smoke: A Review

Abstract The low-temperature catalytic oxidation of CO has been reviewed, targeting its possible application to cigarette smoke. The treatment of CO in smoke by using a filter-packed catalyst is extremely complicated by the presence of a variety of chemically active gaseous compounds, a particulate phase, the high velocity of pulsing smoke flow, and ambient temperature. The relevant mechanisms of catalysis and the catalyst preparation variables that could help to overcome these problems are considered. Possible contributors to the overall kinetics that must include variety of diffusion processes were briefly discussed. The chemisorption of O2, CO and CO2 on Pd, Pt and Au and on partially reducible supports, surface reactions and oscillations of the CO oxidation rate were analyzed. The effects of the surface structure and electronic properties of the catalyst support, preparation conditions and presence of a second transition metal on the projected CO oxidation activity of the catalysts in smoke are also discussed. The reviewed catalyst preparation approaches can solve the low-temperature catalyst activity problem. However, more work is required to stabilize this activity of an air-exposed catalyst to provide a necessary shelf life for a cigarette. The greatest challenge seems to be a particular phase - exclusive selectivity that would not contradict with the necessary fast diffusion of gases through the catalyst pores.

Preparation of high loading silica supported nickel catalyst: simultaneous analysis of the precipitation and aging steps

The precipitation and aging steps used for the preparation of high loading silica supported nickel catalysts were simultaneously analyzed with the aid of statistical design of experiments. Empirical models relating catalyst final properties and preparation variables were developed. It was found that the precipitation step determines the final catalyst nickel content, and that at low nickel concentrations high metallic area per gram of reduced nickel may be attained with the precipitated precursor without performing the aging step. When it is performed, the aging step has a strong influence on final catalyst properties, such as specific surface area, metallic area per gram of nickel and degree of reduction. Therefore, catalyst aging may be an effective method to control dispersion, reducibility and active phase accessibility. The importance of the aging step was shown to increase with the aging temperature, which is linked to the larger rates of silicate formation at high temperatures. A semi-empirical model was developed to describe the final catalyst specific surface area as a function of the rate of silicate formation. This model is able to describe changes of the catalyst surface area fairly well and allow the proper manipulation of preparation conditions in order to maximize the catalyst specific area, leading to maximum catalyst activities.

Catalyst Preparation Variables That Affect the Creation of Active Sites for HDS on Co/Mo/Al2O3Catalytic Materials

In a recent paper (J. Catal.158, 411, 1996) we demonstrated that the intensity of a specific peak (pK ∼6) in proton affinity distributions (PADs) measured for a series of Co-Mo/Al2O3catalysts could be correlated to the variation in HDS activity within the series. PADs access information about species on the catalyst's surface from analyses of proton binding data collected using potentiometric titration. If the species determined at the solid/aqueous interface are present after catalyst activation and are active during catalytic testing, then this methodology can provide a simple, surface sensitive procedure for catalyst characterization which should correlate with other characterization techniques. To test this hypothesis we provide herein results from more conventional catalyst characterization techniques in order to corroborate the PAD results. The techniques of temperature-programmed reduction, X-ray photoelectron spectroscopy, and laser Raman spectroscopy were used to provide additional characterization of Co-Mo/Al2O3catalysts in their oxide state. PADs were obtained for this catalyst system in their oxide and sulfided states. The HDS activity for each member of this series was measured using hydrodesulfurization of thiophene as a test reaction. The effect of cobalt loading, molybdenum content, presulfidation conditions, and pH during catalyst preparation were the variables used. Based on the experimental data we propose that the active site for HDS activity is a Co-Mo interaction species consisting of molybdate octahedra and a dispersed Co which could also be octahedrally coordinated. This species can be transformed reversibly during the processes of sulfidation and reoxidation and its formation is controlled by pH.

Impact of Catalyst Metal−Acid Balance in n-Hexadecane Hydroisomerization and Hydrocracking

The reaction pathways and kinetics of n-hexadecane hydroisomerization and hydrocracking were determined in the presence of each of three platinum-containing dual-function catalysts: (a) Pt on a proprietary zeolite (Pt/Ζ), (b) Pt on silica−alumina (Pt/Si−Al), and (c) Pt on MCM-41 (Pt/MCM-41). The reaction networks were used to interpret differences in isomerization selectivity. The low isomerization selectivity observed in the presence of Pt/Si−Al was shown to be a consequence of changes in both relative isomerization/cracking rates and reaction pathways. Using the classical bifunctional reaction scheme, the changes in pathway were hypothesized to be consistent with changes in the relative concentrations of metal and acid sites (i.e., the metal−acid balance). On the basis of a recently proposed model of dual-function catalysis, the different observed pathways were subsequently shown to be those expected in two limiting case of the metal−acid balance. The simplified quantitative picture given here provides a preliminary basis for relating catalyst preparation variables to catalyst performance for dual-function catalysts.

Preparation of alumina catalyst supports and NiMo/Al2O3 catalysts

Several alumina catalysts were prepared to investigate effects of catalyst preparation variables on pore structure, including sintering time and temperature, acid type, and fiber type and loading. Each catalyst was characterized with a porosimeter and sorptometer in terms of pore size distribution, average pore diameters, pore volumes, densities and surface area. Bimodal NiMo catalysts were effectively prepared without losing surface area by using a combined method of coextrusion and fiber incorporation.

Hydrogenation of xylenes over supported Pt catalysts. Influence of different variables on their catalytic activity

In this work we studied and compared the catalytic activity of various supported Pt systems on the gas-phase hydrogenation of o-, m- and p-xylene. We analyzed the influence of different catalyst preparation variables (solvent, precursor, salt, support and catalyst reduction temperature used) on the catalytic activity and selectivity towards the corresponding cis-and trans-dimethylcyclohexanes.