Artificial neural network (ANN) for evaluating permeability decline in permeable reactive barrier (PRB)

Artificial neural networks (ANNs) were developed which enable evaluation of long-term permeability losses that occur in permeable reactive barriers (PRBs) used in groundwater remediation. The network architectures consist of non-changing input and output layer(s) while the optimal hidden layer types and structures were determined through trial-and-error. Fluid residence time within the PRB, pressure drop, inlet volumetric flow rate, dynamic viscosity of fluid, average porosity, average particle size and the length of the reactor were selected as the input parameters to estimate the output parameter, namely, permeability. Of all experimental data available for each ANN structure, 70 % was used for training, 15 % for validation and the remaining 15 % for testing the ANN. The ANN structures were developed using a combination of soft computing techniques and mathematical association of varying physical parameters. Predictions obtained from the optimized ANN structures were compared with linear and non-linear regression models to assess their performance. The results indicate that ANN performs significantly better than the regression models and ANN modelling is a promising tool for the simulation and assessment of the permeability decline in PRBs.