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Residence time distribution (RTD) study of solids in a three-phase pilot-scale bubble column photoreactor has been carried out in order to provide data for the development of an artificial neural network model usable for process op- timisation. The experimental data indicated that the RTD of solids was a complex nonlinear function of gas and liquid ve- locities as well as the contacting pattern (co-current and countercurrent flow of gas and liquid). In this study, the solid par- ticle RTD data were modeled using feed forward artificial neural networks (ANN). The networks were trained with 250- sets of input-output patterns using back-propagation algorithm. The trained networks were tested using 50-sets of RTD data previously unknown to the networks. Out of several configurations, a 3-layered network with 6-neurons in its hidden layer yielded optimal results with respect to the validation data. The optimal model and empirical data exhibited good agreement with a correlation coefficient of 0.995.

Solids Residence Time Distribution in a Three-Phase Bubble Column Reactor: An Artificial Neural Network Analysis