Predicted product selectivities conversion

Figure 7 Predicted product selectivities as a function of conversion for three different laboratory catalyst characterization test units.

Since a CSTR operates at or close to uniform conditions of temperature and composition, its kinetic and product parameters can usually be predicted more accurately and controlled with greater ease. The CSTR can often be operated at a selected conversion level to optimize space-time yield, or where a particular product parameter is especially favored. 

Again, points on the curve were the measured acrolein production rates, and the line is the predicted production rate based on the current and the stoichiometry according to eq 9. At higher conversions, we observed significant amounts of CO2 and water, sufficient to explain the difference between the acrolein production and the current. It should be noted that others have also observed the electrochemical production of acrolein in a membrane reactor with molybdena in the anode. The selective oxidation of propylene to acrolein with the Cu—molybdena— YSZ anode can only be explained if molybdena is undergoing a redox reaction, presumably being oxidized by the electrolyte and reduced by the fuel. By inference, ceria is also likely acting as a catalyst, but for total oxidation. 

FCC catalyst testing prior to use in commercial reactors is essential for assuring acceptable performance. Purely correlative relations for ranking catalysts based on laboratory tests, however, can be erroneous because of the complex interaction of the hydrodynamics in the test equipment with the cracking kinetics. This paper shows how the catalyst activity, coke-conversion selectivity and other product selectivities can be translated from transient laboratory tests to steady state risers. Mathematical models are described which allow this translation from FFB and MAT tests. The model predictions are in good agreement with experimental data on identical catalysts run in the FFB, MAT and a laboratory riser. 

As a result of these reactions a mixture of C0-I-H2+C02-I-CH4+H20 is obtained which complies with thermodynamic predictions, and tend to effect complete equilibrium among all the components of the product gas. Conversions close to the equilibrium values can be achieved with considerable ease over supported Ni catalysts. To favour propane oxidation according to reaction (1) a selective catalytic material must be used. For practical purposes, nickel is usually impregnated on a suitable porous support which provides thermal stability at working temperatures [2]. But selectivity of a catalyst may depend on various other factors like composition, concentration of active component, physical and structural parameters. The effect of these parameters on the behaviour in propane oxidation of the Ni supported on mullite has been studied in our previous papers [3,4]. 

Linear alkyl benzene (LAB) is manufactured by catalytic dehydrogenation of C10-C13 n-parafifins, followed by alkylation with benzene. High product selectivity, and reasonable catalyst life, in the dehydrogenation reaction, are obtained at the expense of conversion, by adjusting reaction parameters. Proper choice of reaction parameters is thus of paramount importance in this reaction. The present study, was carried out with n-decane, as model feed, and a promoted Pt/ALOs catalyst. A composite Box-Wilson experimental design was adopted to develop an empirical model for predicting monoene yield as a function of reaction conditions. Further, the model was used for determination of optimum reaction parameters. 

How can tracer methods help us in solving these two problems We know that reactor performance, as measured by conversion of the limiting reactant or by product selectivity, is a function of kinetics, flow pattern and mixing pattern in the reactor. The flow and mixing phenomena in various reactor geometries are complex, and we are currently unable to characterize them completely (at an economical cost). The only reactors that we know how to design, predict their performance and scale up with confidence, are those that behave as the two ideal reactor types, i.e. the plug flow (PFR) and the continuous flow stirred tank reactor (CSTR). 

Catalysis opens reaction pathways that are not accessible to uncatalysed reactions. It should be self-evident that thermodynamics predict whether a reaction can occur. So, catalysis influences reaction rates (and as a consequence selectivities), but the thermodynamic equilibrium still is the boundary. Catalysis plays a key role in chemical conversions, although it is fair to state that it is not applied to the same degree in all sectors of the chemical industry. While in bulk chemicals production catalytic processes constitute over 80 % of the industrially applied processes, in fine chemicals and specialty chemicals production catalysis plays a relatively modest role. In the pharmaceutical industry its role is even smaller. It is the opinion of the authors that catalysis has a large potential in these areas and that its role will increase drastically in the coming years. However, catalysis is a multidisciplinary subject that has a lot of aspects unfamiliar to synthetic chemists. Therefore, it was decided to treat catalysis in a separate chapter. 

However, the detailed description of the FT product distribution together with the reactant conversion is a very important task for the industrial practice, being an essential prerequisite for the industrialization of the process. In this work, a detailed kinetic model developed for the FTS over a cobalt-based catalyst is presented that represents an evolution of the model published previously by some of us.10 Such a model has been obtained on the basis of experimental data collected in a fixed bed microreactor under conditions relevant to industrial operations (temperature, 210-235°C pressure, 8-25 bar H2/CO feed molar ratio, 1.8-2.7 gas hourly space velocity, (GHSV) 2,000-7,000 cm3 (STP)/h/gcatalyst), and it is able to predict at the same time both the CO and H2 conversions and the hydrocarbon distribution up to a carbon number of 49. The model does not presently include the formation of alcohols and C02, whose selectivity is very low in the FTS on cobalt-based catalysts. 

For the range of industrially relevant conditions, the developed model could accurately predict both the observed CO conversion and the products distribution up to n = 49, in terms of total hydrocarbons, n-paraffins, and a-olefins. In particular, using thirteen adaptive parameters, the model is able to describe the typical deviations of the product distribution from the ASF model, i.e., the methane high selectivity, the low selectivity to C2 species, and the change of the slope of the ASF plot with growing carbon number. Accordingly, the present model can be applied to identify optimized process conditions that are suitable to grant the desired conversion with the requested products distribution. 

The tuneable solvent capability of SCCO2 offers the potential for a subtle control of reactions in order to achieve higher selectivities and improved reaction rates. Furthermore, the separation of extractives or, in the case of a synthesis, of reactants, products, and catalysts by simple decompression could be facilitated. The low solubility of many metal complexes and catalysts usually is an obstacle to their exploitation in SCCO2-based processes. For instance, the solubility of a homogeneous catalyst needs to be sufficiently high to ensure participation of all active metal centers during a catalyzed reaction. In particular for reactions, solubility properties are difficult to predict, because the component composition is continuously changed with conversion. 

DFT studies on the facial selectivities of six Johnson-Claisen rearrangements (Scheme 5) analogous to those used in the synthesis of gelsemine have reproduced experimental results in five out of the six cases, but have predicted formation of the same product (21) in all six reactions. The selectivity in these cases has been attributed to a combination of steric repulsions between vinylic proton H(l) and allylic proton H(7) or H(14), and electrostatic attractions between C(l) and the oxetane hydrogens C(5)-H and C(16)-H. Both of these factors, however, apparently predicted the non-observed product in the conversion of (22) into (23)22... 

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