Predicting mass transfer in pilot scale external loop airlift reactors using neural networks

Abstract

Airlift reactors are a viable means for conducting large scale mass transfer operations. However, due to the difficulty experienced in understanding the complex behavioural characteristics of these reactors, the design of airlift reactors becomes very complicated and largely empirical. Existing correlations and traditional computational fluid dynamic modelling has proven to be mostly reactor dependent and not widely applicable thereby limiting their application. There is therefore a need to develop a model that does not require prior knowledge of relationships between parameters of these reactors but instead uses an alternate method to assist with the design of airlift reactors. An artificial neural network represents this method. The aim of this investigation was to build an artificial neural network using selected input data of Newtonian fluids in pilot scale external loop airlift reactors of varying designs in order to predict the mass transfer coefficient in other external loop airlift reactors with more general geometry. To achieve this, a large base of experimental data (663) was generated using glycerine-air and water-air systems in 5 configurations of external loop airlift reactors with 3 categories of sparger design. The data was modelled using the artificial neural network software, Predict (Version 3.30) by Neuralware. The Coefficient of Correlation for the neural network model was 0.98. The neural network model was tested with unseen external data from various sources of which the R values ranged from 0.91 to 0.99. Additional external data was evaluated with the superficial gas velocity out of the range of the experimental data from this investigation and with a very different design of sparger. The R values for this additional data were 0.85 and 0.67-0.85 respectively. To achieve good correlations it was found necessary to take into account the sparger design and pore size; the actual geometric dimensions of the reactors namely the riser and downcomer diameters and heights; the visual observations of the approximate bubble size and bubble flow patterns and static liquid height in addition to the more usual data of the area and aspect ratios; the fluid properties namely, surface tension, density and viscosity; the superficial gas velocity; the downcomer superficial liquid velocity; the riser gas holdup and the downcomer gas holdup. However, some parameters like the static liquid height although considered important appeared not to be.

By considering these as important input variables into the network, the artificial neural network was able to give excellent approximations for both seen and unseen data for some of the reactor configurations. However, the network also had the ability to pick up differences in the reactor configurations were it did not predict well, especially with respect to sparger design. An important conclusion arrived at in this investigation was the significant influence of the sparger and its design on the mass transfer. The sensitivity of the network to the sparger design means that a greater quantification of the influence of the sparger design is required.