587 - 597 |
Neural network analysis of void fraction in air/water two-phase flows at elevated temperatures Malayeri MR, Muller-Steinhagen H, Smith JM |
599 - 610 |
Development of an artificial neural network correlation for prediction of overall gas holdup in bubble column reactors Shaikh A, Al-Dahhan M |
611 - 620 |
Long-term prediction of nonlinear hydrodynamics in bubble columns by using artificial neural networks Lin HY, Chen W, Tsutsumi A |
621 - 643 |
Prediction of the gas-liquid volumetric mass transfer coefficients in surface-aeration and gas-inducing reactors using neural networks Lemoine R, Fillion B, Behkish A, Smith AE, Morsi BI |
645 - 652 |
Modelling of the flow behavior of activated carbon cloths using a neural network approach Faur-Brasquet C, Le Cloirec P |
653 - 662 |
Reinforcing the phenomenological consistency in artificial neural network modeling of multiphase reactors Tarca LA, Grandjean BPA, Larachi F |
663 - 674 |
Neural network multi-criteria optimization image reconstruction technique (NN-MOIRT) for linear and non-linear process tomography Warsito W, Fan LS |
675 - 695 |
Neural network approach to support modelling of chemical reactors: problems, resolutions, criteria of application Molga EJ |
697 - 713 |
Using hybrid neural networks in scaling up an FCC model from a pilot plant to an industrial unit Bollas GM, Papadokonstadakis S, Michalopoulos J, Arampatzis G, Lappas AA, Vasalos IA, Lygeros A |
715 - 721 |
Solving differential equations with unsupervised neural networks Parisi DR, Mariani MC, Laborde MA |