02494nas a2200205   4500008004100000022001400041245009400055210006900149260006900218300001000287490000800297520166700305653004101972653002102013653006202034653002502096653002202121100002502143856012002168       2005                        eng d  a1385-894700aGeneral kinetic model framework for the interpretation of adiabatic calorimeter rate data0 aGeneral kinetic model framework for the interpretation of adiaba  aPO BOX 564, 1001 LAUSANNE, SWITZERLANDbELSEVIER SCIENCE SAcJUN  a67-780 v1103 a<p>Notwithstanding the variety and complexity of the reactions studied by adiabatic calorimeters like ARC, the data interpretation techniques are not general enough. Traditional thermokinetic analysis primarily lumps a complex multi-step reaction into a single overall reaction and ignores possible thermal effects in some of the possible side reactions. With detailed chromatographic/mass spectrometric analysis of the headspace gases and the condensed phase residues, the pressure profile becomes an additional source of identification of the mechanism and the kinetics of the overall reaction. Finally, in the context of new multiphase catalytic processes of greater efficiency and environment friendliness and with reference to the storage of potentially incompatible fluid mixtures in metallic containers, ARC studies of heterogeneous reaction systems are becoming part of the mandatory safety evaluations. With a few additional measurements a proper kinetic interpretation of the ARC data on such systems seems possible. The paper presents a general model that was shown to be easily adaptable to a number of published reactions of various complexities referred to above. Standard thermal hazard characteristics like the onset temperature, adiabatic temperature rise, self-heat rate, time-to-maximum rate, pressure-temperature profile, etc. could be accurately calculated by the model and these compared closely with the experimental data. It is hoped that the model would be useful as a general-purpose tool for the interpretation of adiabatic calorimetric data for the purpose of process hazard assessment. (c) 2005 Elsevier B.V. All rights reserved.</p>10aaccelerating rate calorimeters (ARC)10aKinetic modeling10aMOC (material of construction) incompatibility in storage10amulti-step reactions10arunaway reactions1 aBhattacharya, Arijit  uhttps://library.ncl.res.in/content/general-kinetic-model-framework-interpretation-adiabatic-calorimeter-rate-data-002141nas a2200181   4500008004100000022001400041245008800055210006900143260006900212300001200281490000700293520143400300653003401734653001601768653002701784100002501811856012301836       2005                        eng d  a0255-270100aModeling a continuous multistage liquid phase cyclohexane oxidation reactor network0 aModeling a continuous multistage liquid phase cyclohexane oxidat  aPO BOX 564, 1001 LAUSANNE, SWITZERLANDbELSEVIER SCIENCE SAcMAY  a567-5790 v443 a<p>A model is presented for a continuous multistage liquid phase cyclohexane oxidation reactors-in-series network, which uses, unlike previous efforts, a closed form rate model derived on the basis of the well-known free-radical kinetic mechanism of the oxidation reaction leading to a more generalized representation of the oxygen dependence of the rate. The model calculates the required transport and hydrodynamic parameters by one of the best available set of correlations shown earlier to be successfully used in cyclohexane oxidation in a well-designed laboratory reactor. Process sensitivities with regard to variables such as air rate, residence time, head pressure, inlet air composition and sparger configuration have been predicted. Some of these trends compared very well with the limited published experimental data in a three (351) agitated and sparged tank-in-series reactor system, thus partially validating the model. The model has highlighted a fairly generalized way of correlating performance data from a given reactor, namely in terms of a yield-conversion characteristic which can change depending on the mass transfer efficiency and the effective kinetics. Hence, it can aid in plant monitoring and optimization. It has also been shown how to use the same as an aid in preliminary scale-up studies based on laboratory or pilot plant reactor performance data. (C) 2004 Elsevier B.V. All rights reserved.</p>10acontinuous multistage reactor10aCyclohexane10aLiquid phase oxidation1 aBhattacharya, Arijit  uhttps://library.ncl.res.in/content/modeling-continuous-multistage-liquid-phase-cyclohexane-oxidation-reactor-network-002044nas a2200157   4500008004100000022001400041245013100055210006900186260007100255300001400326490000700340520136200347100002501709700002101734856013101755       2006                        eng d  a0021-956800aRe-tooling Benson's group additivity method for estimation of the enthalpy of formation of free radicals: C/H and C/H/O groups0 aRetooling Bensons group additivity method for estimation of the   a1155 16TH ST, NW, WASHINGTON, DC 20036 USAbAMER CHEMICAL SOCcJUL  a1169-11810 v513 a<p>Enthalpy of formation of free radicals is often required for the discrimination of reaction mechanisms (for complex processes such as combustion, hydrocarbon oxidation, or decomposition of peroxides and hydroperoxides) involving both molecular species and free radicals. Despite the expanding availability of the experimental measurements, the database for the enthalpy of formation of radicals is still not comprehensive. One of the simplest, efficient, and reliable methods for estimating the quantity for organic radicals from their molecular structure is the group-additivity method due to Benson and co-workers. Perusal of the literature shows that the group-additivity values (GAV) for many radical groups have remained either undetermined or obtained by assignment to the corresponding molecular groups without justification. In this paper, we devise simple methodologies to evaluate GAVs for a number of new oxygen-containing radical groups and re-estimate several alkyl and oxygen-containing radical groups using experimental data on the radical enthalpy of formation. The validity of these GAVs has been established by comparing the predicted enthalpies with experimental data. Finally, some of these updated GAVs were used in estimating the enthalpy change associated with the typical elementary steps in a peroxide decomposition reaction.</p>1 aBhattacharya, Arijit1 aShivalkar, Sagar  uhttps://library.ncl.res.in/content/re-tooling-bensons-group-additivity-method-estimation-enthalpy-formation-free-radicals-ch-002243nas a2200217   4500008004100000022001400041245010900055210006900164260006900233300001200302490000800314520138400322653001801706653003101724653003301755653002501788653002801813653003001841100002501871856012901896       2008                        eng d  a1385-894700aGeneral model for analyzing data on the rate of reactive dissolution of poly-disperse particulate solids0 aGeneral model for analyzing data on the rate of reactive dissolu  aPO BOX 564, 1001 LAUSANNE, SWITZERLANDbELSEVIER SCIENCE SAcAPR  a347-3600 v1373 a<p>A model has been presented which successfully simulates the experimentally observed integral batch reactive dissolution rate data while accounting for the poly-dispersity of the solid particulate charge. This allowed avoiding arbitrary assumptions about the particulate inventory and size independence of the mass transfer coefficient. There is also no need for a priori identification of the controlling regime, which can change from kinetic to mass transfer during the process, apart from the possibility of the shift with the specified process and operating conditions. The model was applied with equal ease to a simple isothermal reaction, an exothermic reaction with runaway potential and to a phase-transfer catalysis reaction with a complex mechanism. The model was shown to predict the time required for a specified extent of conversion of the particulate reactant or the rate of consumption of a key liquid reactant under a variety of process and operating conditions, like temperature, liquid reactant and the catalyst concentration, particle size, poly-dispersity of the charge and agitation speed. Such information is useful in reactor design and scale-up. Where dissolution is accompanied with a runaway reaction, the model can predict quantities of interest in hazard assessment and should aid safe reactor design. (c) 2007 Elsevier B.V. All rights reserved.</p>10akinetic model10aparticle size distribution10apoly-disperse solid reactant10areactive dissolution10areactive thermal hazard10asolid-liquid PTC reaction1 aBhattacharya, Arijit  uhttps://library.ncl.res.in/content/general-model-analyzing-data-rate-reactive-dissolution-poly-disperse-particulate-solids-002041nas a2200205   4500008004100000022001400041245006000055210006000115260006900175300001200244490000800256520132300264653003601587653002101623653002701644653001801671653003001689100002501719856009101744       2008                        eng d  a1385-894700aKinetic modeling of liquid phase autoxidation of cumene0 aKinetic modeling of liquid phase autoxidation of cumene  aPO BOX 564, 1001 LAUSANNE, SWITZERLANDbELSEVIER SCIENCE SAcAPR  a308-3190 v1373 a<p>A new kinetic model for the liquid phase autoxidation of cumene has been developed utilizing the existing knowledge about the traditional free-radical mechanism involving the initiation of the free-radicals, the chain propagation and transfer and the various modes of radical termination. Unlike previous work, in the re-organized reaction network an important cross-termination step replaces an often used but less likely one and a new derivation of the rate model has been provided. A base set of rate parameters for the elementary steps within this reaction network were chosen, many of them were same or very similar to those published in the literature, with a few critical ones re-estimated for correct match with directly observed kinetic data reported in the literature on cumene oxidation in bench scale reactors. Embedding this kinetic sub-model within a simple reaction engineering model for a single air-sparged continuous cumene oxidator, the liquid oxidate composition at the reactor exit could be predicted that compared closely with some limited published data from an industrial reactor. It is hoped that the kinetic model presented here would be a useful tool in the analysis and design of other autoxidation reactors as well with minor adaptations. (c) 2007 Elsevier B.V. All rights reserved.</p>10aair-sparged continuous oxidator10acumene oxidation10afree-radical mechanism10akinetic model10aliquid phase autoxidation1 aBhattacharya, Arijit  uhttps://library.ncl.res.in/content/kinetic-modeling-liquid-phase-autoxidation-cumene-001885nas a2200205   4500008004100000022001400041245012200055210006900177260007600246300001200322490000700334520106400341653002101405653002801426653002601454653001801480653002701498100002501525856012901550       2012                        eng d  a0001-154100aConverting ab initio energies to enthalpies of formation of free radicals. I. new atom equivalents for alkyl radicals0 aConverting ab initio energies to enthalpies of formation of free  aCOMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USAbWILEY-BLACKWELLcFEB  a600-6090 v583 a<p>A simple method is presented to convert ab initio computed total energies to the standard enthalpy of formation (Delta H-f(o)) of a large number of saturated alkyl radicals (especially those that are relatively highly branched), for which experimental data are scarcely available. For this purpose a new set of radical atom-equivalents (AEQ) and their unique combinations were defined and the energy values of the latter assigned. The theory level and the basis set requirement for the quantum chemistry calculation of the molecular energy were found to be moderate. The Delta H-f(o) predictions appear to be quite accurate with reference to limited available experimental data and are better than values calculated by the group-additivity and the difference methods. The strategy provides an inexpensive way of harnessing the power of computational chemistry and combining it with the organization and insight from the group-additivity method sans any empirical corrections. (C) 2011 American Institute of Chemical Engineers AIChE J, 58: 600-609, 2012</p>10aatom-equivalents10acomputational chemistry10aenthalpy of formation10afree radicals10agoup additivity method1 aBhattacharya, Arijit  uhttps://library.ncl.res.in/content/converting-ab-initio-energies-enthalpies-formation-free-radicals-i-new-atom-equivalents-0