Artificial Neural Network Modeling of Mixing Efficiency in a Split-Cylinder Gas-Lift Bioreactor for Yarrowia lipolytica Suspensions

The mixing efficiency in a split-cylinder gas-lift bioreactor has been analyzed for Yarrowia lipolytica cells suspensions. Based on the experimental results, three different approaches for modeling have been applied to predict the mixing time depending on yeast concentration, aeration rate, as well as position on the riser or downcomer regions height. These approaches are represented by: an algorithm mixing differential evolution (DE) with artificial neural networks (ANNs), named hSADE-NN, regression, and the Multilayer Perceptron module from IBM SPSS. In the hSADE-NN, ANN models the process, while DE simultaneously optimizes the topology and the internal parameters of the ANN, so that an optimal model is obtained. It was found from simulations that ANNs are able to model the targeted process with a high degree of efficiency (average absolute relative error less than 8.5%), a small difference among the two ANN-based approaches being observed. Additionally, a sensitivity analysis was performed for determining the model inputs influence on the mixing time.