Optimization of the remediation of oil-drilling cuttings by combining artificial neural networks with knowledge-based models

In the present study, a feed-forward dense multilayer artificial neural network (ANN) trained with backpropagation algorithm (BP) is used to determine the kinetic parameters governing the performance of the oil-drilling cuttings (ODC) ozonation. Ozonation tests of ODC, pre-treated with surfactant (SDS) and diluted with synthetic seawater, are conducted on a semi-batch bubble flow reactor. The performance of ozonation experiments on semi-batch bubble flow reactors is evaluated by measuring the removal efficiency of the total organic carbon (TOC). The experimental datasets are employed to calibrate two mathematical models of increasing complexity: a tank-in-series (TSM), and a computational fluid dynamics (CFM) model, both combining multiphase transport with reactive processes, involving a high number of unknown parameters, and being able to provide numerous simulated datasets for various values of dimensionless parameters. These data are employed for the training and validation of the neural network. The trained neural network is then interpreted through the Shapley additive explanation (SHAP) method to provide insights about the experimental parameters.