Onset of pulsing in trickle beds: Evaluation of current tools and state-of-the-art correlation

Authors

  • F. Larachi,

    Corresponding author
    1. Department of Chemical Engineering and CERPIC, Laval University, Ste-Foy, QC G1K 7P4, Canada
    • Department of Chemical Engineering and CERPIC, Laval University, Ste-Foy, QC G1K 7P4, Canada
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  • I. Iliuta,

    1. Department of Chemical Engineering and CERPIC, Laval University, Ste-Foy, QC G1K 7P4, Canada
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    • On leave from Department of Chemical Engineering, Faculty of Industrial Chemistry, University of Politehnica of Bucharest, Polizu 1, 78126 Bucharest, Romaina.

  • M. Chen,

    1. Department of Chemical Engineering and CERPIC, Laval University, Ste-Foy, QC G1K 7P4, Canada
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  • B. P. A. Grandjean

    1. Department of Chemical Engineering and CERPIC, Laval University, Ste-Foy, QC G1K 7P4, Canada
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Abstract

The transition between trickle flow and pulse flow regimes in cocurrent trickle-bed reactors is not properly predicted by existing phenomenological, semi-theoretical and empirical tools. Based on the most complete flow regime transition data base (700 measurements, 30 gas–liquid systems, 18 columns diameters, 38 packing materials, high pressure conditions, coalescing, non-coalescing and pseudoplastic non-Newtonian aqueous and organic liquids), a state-of-the-art explicit correlation of trickle-to-pulse flow changeover was derived relying on neural network modeling. Robustness of the proposed correlation was verified, and the limitations of the literature correlations and models were demonstrated through systematic statistical testing over the constructed data base. The overall result was a net improvement in predicting the trickle-to-pulse flow regime transition.

Abstract

La transition ruisselant-pulsé dans les réacteurs à lit fixe arrosé est mal estimée par tous les outils de prédiction développés à ce jour, qu'ils soient phénoménologiques, semi-théoriques ou simplement empiriques. Partant d'une vaste base de données expérimentale sur cette transition (700 mesures expérimentales, 30 couples gaz—liquide, 18 diamètres de colonne différents, 38 garnissages, des données à haute pression, des liquides coalescents, non-coalescents, pseudoplastiques nonNewtoniens, oganiques et aqueux), nous avons élaboré une corrélation explicite générale basée sur les réseaux neuronaux à couches. La robustesse de la corrélation proposée a été vérifiée, ainsi que les limitations des modèles et corrélations de la littérature discutées au moyen de tests statistiques. Le résultat global est un nouvel outil amélioré pour prédire le mieux possible l'ensemble des données expérimentales de la littérature publiées entre 1964 et 1997.

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