Simon Piché

Flooding capacity in packed towers : Database, correlations, and analysis

Experimental results on the flooding capacity of randomly dumped packed beds were collected from the literature to generate a working database. The reported measurements were first used to review the accuracy of existing predictive tools in that field. A total of 14 correlations were extracted from the literature and cross-examined with the database. Many limitations regarding the level of accuracy and generalization came to light with this investigation. Artificial neural network modeling was then proposed to improve the broadness and accuracy in predicting the flooding capacity, which is an important design parameter for packed towers. A combination of six dimensionless groups, namely, the Lockhart-Martinelli parameter (X); the liquid Reynolds (Re L ), Galileo (Ga L ) and Stokes (St L ) numbers; the packing sphericity (Φ); and one bed number (S B ) outlining the tower dimensions were used as the basis of the neural network correlation. With an initial database containing 1019 measurements, the correlation yielded an absolute average relative error (AARE) of 16.1% and a standard deviation of 20.4%. Another database containing over 100 measurements on the flooding capacity was used to validate the correlation. The prediction based on these results yielded an AARE of 11.6% and a standard deviation of 13.7%. Through a sensitivity analysis, the Stokes number in the liquid phase was found to exhibit the strongest influence on the prediction, while the liquid velocity, gas density, and packing shape factor were determined to be the leading physical properties defining the flooding level. As a matter of fact, the neural correlation remains in accordance with the design recommendations and trends reported in the literature.

Improved Liquid Hold-up Correlation for Randomly Packed Towers

The state-of-the-art tools for the evaluation of the total liquid holdup in gas-liquid counter-current randomly dumped packed beds are critically evaluated by thoroughly interrogating a wide hydrodynamic database. This database, consisting of ca. 1,500 experiments on liquid hold-up below the flooding point, represents an important portion of the non-proprietary information released in the literature since the 1930s. Providing access to diversified information, it is dedicated to embracing wide-ranging temperature and gas density levels, and packing shapes extending from classical ones to modern third generation packings. Furthermore, a total of eleven correlations on the total liquid hold-up extracted from the literature are cross-examined using the database. Many limitations regarding the level of accuracy and generalization come to light with this investigation. Artificial neural network modelling and dimensional analysis are then proposed to improve the accuracy in predicting the total liquid hold-up in the pre-loading and the loading regions of packed beds. A combination of five dimensionless groups, comprising the liquid Reynolds (ReL), Froude (FrL), and Ohnesorge (OhL) numbers as well as the gas Froude (FrG) and Stokes (StG) numbers are used as the basis of the correlation. The correlation yields an absolute average relative error of ca. 14% for the whole database and remains in accordance with trends reported in the literature.

Two-fluid model for counter-current dumped packing-containing columns

The objective of this work is to develop a fully predictive two-fluid mechanistic model for gas-liquid counter-current random packing wherein the inter-relationship between the irrigated presure drop, the total liquid holdup and the packing fractional wetted area is for the first time quantitatively highlighted.The approach is designed to approximate the actual two-phase flow topography in random packings using two inclined and interconnected slits consisting of a dry slit solely fed by gas, and gas-liquid slit fed by liquid and remaining gas. Model validation has been performed using literature data relative to the packing wetted area, pressure drop and liquid holdup obtained under various operational contexts.

A unified approach to the hydraulics and mass transfer in randomly packed towers

New robust correlations and mechanistic model of macroscopic fluid dynamic and gas–liquid mass transfer characteristics for randomly packed towers were developed based on first principles, neural network computing and dimensional analysis (artificial neural network and dimensional analysis, ANN–DA). These tools concerned the loading and flooding capacities, the total liquid hold-up, the irrigated pressure drop, the local volumetric liquid-side, kLa, and gas-side, kGa, mass transfer coefficients, the overall volumetric, KLa and KGa, mass transfer coefficients, and the packing fractional wetted area. Validation of these tools was performed by interrogating a broad experimental database including over 10,750 measurements published in the literature over the past seven decades. The fully-predictive mechanistic model proved powerful in forecasting the tower hydraulics below the loading point without requiring any adjustable parameter. On the other hand, the ANN–DA correlations proved highly powerful in correlating the tower fluid dynamics and gas–liquid mass transfer regardless of the operating flow regime. These approaches were also benchmarked with respect to the comprehensive Billet and Schultes (Trans. Industr. Chem. Eng. 77 (1999) 498) phenomenological approach and the classical Onda et al. (J. Chem. Eng. Japan 1 (1968) 56) mass transfer correlations.

Prediction of HETP for randomly packed towers operation: integration of aqueous and non-aqueous mass transfer characteristics into one consistent correlation

Abstract Height equivalent to a theoretical plate (HETP) calculations, essential for the design of randomly packed distillation columns were extracted from the open literature to generate a working database including over 2350 measurements (only total molar reflux data). The merging of mass transfer characteristics from non-aqueous and aqueous separation experiments has instigated the generation of a consistent correlation predicting HETP. Based on results presented elsewhere for absorption and stripping conditions (Ind. Eng. Chem. Res. 41 (2002) 4911), a set of artificial neural network (ANN) correlations for the gas–liquid interfacial area ( a w ) and the pure local mass transfer coefficients ( k γ , γ=G or L) was proposed with the following dimensionless structures: a w / a T = f ( Re L , Fr L , Eo L , I , χ , K ) and Sh γ = f ( Re γ , Fr γ , Sc γ , χ ). The gas–liquid interfacial area and the pure local mass transfer coefficients were extracted using a reconciliation procedure which combined actually measured interfacial areas with pseudo-interfacial areas inferred from the actually measured volumetric mass transfer coefficients ( k L a w , K L a w , k G a w , K G a w —absorption and stripping) and HETP (distillation). The neural network weights of the two a w and k γ correlations were adjusted using a least-squared composite criterion simultaneously over the six mass transfer parameters’ databases. The optimized set of ANN correlations yielded an average absolute relative error (AARE) of 21.3% for the 2357 HETP measurements available. Likewise, the measured interfacial area and volumetric mass transfer coefficients (3770 data) were correlated with an AARE of approximately 26.5%, which undeniably proves the intimate correspondence of absorption and distillation mass transfer characteristics in randomly packed towers. HETP predictions remain as well in accordance with the physical evidence reported in the literature.

Loading Capacity in Packing Towers – Database, Correlations and Analysis

Experimental results on the flooding capacity of randomly dumped packed beds were collected from the literature to generate a working database. The reported measurements were first used to review the accuracy of existing predictive tools in that field. A total of 14 correlations were extracted from the literature and cross-examined with the database. Many limitations regarding the level of accuracy and generalization came to light with this investigation. Artificial neural network modeling was then proposed to improve the broadness and accuracy in predicting the flooding capacity, which is an important design parameter for packed towers. A combination of six dimensionless groups, namely, the Lockhart−Martinelli parameter (χ); the liquid Reynolds (ReL), Galileo (GaL) and Stokes (StL) numbers; the packing sphericity (φ); and one bed number (SB) outlining the tower dimensions were used as the basis of the neural network correlation. With an initial database containing 1019 measurements, the correlation yield...

Advances in Chemical Oxidation of Total Reduced Sulfur from Kraft Mills Atmospheric Effluents

Chemical oxidation techniques in use for the reduction of malodorous total reduced sulfur (TRS) emissions in the kraft mills atmospheric effluents were reviewed with an emphasis on recent industrial improvements in chlorine dioxide (ClO2) oxidation of TRS as well as on laboratory developments of an iron-based chemistry process. The ClO2 approach was implemented successfully at the industrial scale in two Québec kraft mills. The approach consisted in mixing the non-condensable gases (NCG) containing the TRS with gaseous chlorine dioxide obtained either as a residue from a bleach plant vent stream or through vaporization of fresh solution. Full-scale tests have shown that the amount of chlorine dioxide injected or mixed in the NCG was sufficient to reduce the TRS load below the 10 ppmv-regulated levels in a cost efficient way as compared with incineration. A prospective approach validated in laboratory conditions and using the iron redox chemistry for alkaline oxidative scrubbing of TRS is being investigated at Laval University to reduce the odor pollution and to convert TRS into valuable sulfur. Two configurations were evaluated, one consisting of homogeneous Fe(III) sequestered in trans-1,2-diaminocyclohexanetetraacetic acid (cdta) chelates and another of heterogeneous Fe(III) as Fe/Ce oxides-hydroxides mixtures. The relative performances, advantages and weaknesses of the various chemical oxidation processes were discussed. In addition, the fundamentals of the alkaline oxidative scrubbing of TRS using the iron-based alkaline approach were summarized in terms of the gas-liquid thermodynamic equilibria and of the homogeneous and heterogeneous iron redox reactions.