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• W2016029772 abstract "A large number of experimental data points (7374) obtained in our laboratory as well as from the literature, covering wide ranges of reactor geometry (reactor diameter and type, impeller diameter and gas distribution scheme), physicochemical properties (liquid and gas density and molecular weight, liquid viscosity and surface tension, diffusivity) and operating variables (superficial gas velocity, temperature, pressure, mixing speed, liquid height and mixtures) were used to develop empirical as well as back-propagation neural network (BPNN) correlations in order to predict the hydrodynamic and mass transfer parameters in gas–liquid agitated reactors (ARs). The empirical and BPNN correlations developed were incorporated in a calculation algorithm for predicting the gas holdup (ɛG), volumetric mass transfer coefficients (kLa), Sauter mean bubble diameter (dS), gas–liquid interfacial area (a) and liquid-side mass transfer coefficient (kL) in ARs, operating in surface-aeration, gas-inducing and gas-sparging modes. The algorithm was used to predict the effects of liquid viscosity and hydrogen mole fraction in the feed gas (H2 + N2) on the hydrodynamic and mass transfer parameters for the soybean oil hydrogenation process conducted in a large-scale gas-sparging agitated reactor (7000 kg soybean oil capacity). The predictions showed that increasing the liquid-phase viscosity, mimicking the evolution of the hydrogenation of soybean oil in a batch reactor, decreased ɛG and increased dS, resulting in a decrease of a. The decrease of the gas holdup with increasing the liquid-phase viscosity was related to the increase of gas bubble coalescence in the reactor. Increasing liquid-phase viscosity, however, decreased kL as well as kLa values for both H2 and N2 within the range H2 mole fraction (0–1) used. This kL behavior indicated that the effect of viscosity on kL is more significant than that of dS, since kL was reported to be proportional to dS. The predictions also showed that increasing the H2 mole fraction in the feed to the reactor decreased ɛG and increased dS, resulting in a decrease of a and an increase of kL as well as kLa for both H2 and N2 within the range of liquid-phase viscosity used (0.0023–0.0047 Pa s). The decrease of the gas holdup with increasing the H2 mole fraction in the feed gas was attributed to the decrease of the density (momentum) of the gas mixture. The increase of kL values with increasing the H2 mole fraction in the feed gas was related to the increase of dS. The predicted kLa values indicated that the mass transfer behavior in the large-scale gas-sparging reactor proposed for soybean oil hydrogenation was controlled by the mass transfer coefficient, kL. Also, under similar conditions, kLa values for H2 in soybean oil when using the gaseous mixture (H2 + N2) were lower than those obtained for H2 (as a single-component); and kL values for H2 were consistently greater than those of N2 within the ranges of the operating conditions used in the simulation." @default.
• W2016029772 created "2016-06-24" @default.
• W2016029772 creator A5022842482 @default.
• W2016029772 creator A5049481941 @default.
• W2016029772 date "2005-11-01" @default.
• W2016029772 modified "2023-10-10" @default.
• W2016029772 title "An algorithm for predicting the hydrodynamic and mass transfer parameters in agitated reactors" @default.
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• W2016029772 cites W1965516353 @default.
• W2016029772 cites W1967164263 @default.
• W2016029772 cites W1967216476 @default.
• W2016029772 cites W1972599164 @default.
• W2016029772 cites W1973220533 @default.
• W2016029772 cites W1973602979 @default.
• W2016029772 cites W1974419363 @default.
• W2016029772 cites W1977875010 @default.
• W2016029772 cites W1989442564 @default.
• W2016029772 cites W1990807772 @default.
• W2016029772 cites W1996992374 @default.
• W2016029772 cites W1998543272 @default.
• W2016029772 cites W2003231951 @default.
• W2016029772 cites W2003317009 @default.
• W2016029772 cites W2005160835 @default.
• W2016029772 cites W2005697148 @default.
• W2016029772 cites W2006275446 @default.
• W2016029772 cites W2006839746 @default.
• W2016029772 cites W2008999502 @default.
• W2016029772 cites W2010092091 @default.
• W2016029772 cites W2011763020 @default.
• W2016029772 cites W2012220899 @default.
• W2016029772 cites W2013785205 @default.
• W2016029772 cites W2031829811 @default.
• W2016029772 cites W2036004827 @default.
• W2016029772 cites W2038276425 @default.
• W2016029772 cites W2038619670 @default.
• W2016029772 cites W2039158618 @default.
• W2016029772 cites W2045625640 @default.
• W2016029772 cites W2045857804 @default.
• W2016029772 cites W2046775419 @default.
• W2016029772 cites W2046919415 @default.
• W2016029772 cites W2049222712 @default.
• W2016029772 cites W2051612289 @default.
• W2016029772 cites W2051881854 @default.
• W2016029772 cites W2056134413 @default.
• W2016029772 cites W2058139728 @default.
• W2016029772 cites W2058661050 @default.
• W2016029772 cites W2060457856 @default.
• W2016029772 cites W2064579685 @default.
• W2016029772 cites W2067931584 @default.
• W2016029772 cites W2072811205 @default.
• W2016029772 cites W2081702635 @default.
• W2016029772 cites W2085629398 @default.
• W2016029772 cites W2095816427 @default.
• W2016029772 cites W2102349865 @default.
• W2016029772 cites W2155329316 @default.
• W2016029772 cites W2163450069 @default.
• W2016029772 cites W2164122658 @default.
• W2016029772 cites W2164296567 @default.
• W2016029772 cites W2199413629 @default.
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• W2016029772 doi "https://doi.org/10.1016/j.cej.2005.08.015" @default.
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