Using Artificial Neural Network to Predict the Pressure Drop in a Rotating Packed Bed

Abstract

Although rotating beds are good equipments for intensified separations and multiphase reactions, but the fundamentals of its hydrodynamics are still unknown. In the wide range of operating conditions, the pressure drop across an irrigated bed is significantly lower than dry bed. In this regard, an approach based on artificial intelligence, that is, artificial neural network (ANN) has been proposed for prediction of the pressure drop across the rotating packed beds (RPB). The experimental data sets used as input data (280 data points) were divided into training and testing subsets. The training data set has been used to develop the ANN model while the testing data set was used to validate the performance of the trained ANN model. The results of the predicted pressure drop values with the experimental values show a good agreement between the prediction and experimental results regarding to some statistical parameters, for example (AARD% = 4.70, MSE = 2.0 × 10⁻⁵ and R² = 0.9994). The designed ANN model can estimate the pressure drop in the countercurrent flow rotating packed bed with unexpected phenomena for higher pressure drop in dry bed than in wet bed. Also, the designed ANN model has been able to predict the pressure drop in a wet bed with the good accuracy with experimental.

Keywords:

• artificial neural network model
• pressure drop
• rotating packed bed

ACKNOWLEDGEMENTS

The authors are grateful to Shiraz University for supporting this research.

Notes

*A metal sponge-like material made of 85% nickel and 15% chromium.

**rpm = round per minute.

*Best architecture among 50 executions of various trained networks.