An artificial neural network toward simulating the treatment of wastes in multiphase reactors by ozonation

Abstract

The simulation of the performance of multiphase reactors is a difficult task with a high degree of uncertainties, due to the complex flow regimes, and multiple mass-transfer and reactive processes. A dimensionless artificial neural network (ANN) is developed to simulate the remediation of muds, heavily polluted with organic wastes. The mud is pretreated with synthetic seawater (SW) and anionic surfactant sodium dodecyl sulphate (SDS), then is placed inside a semi-batch bubble flow column, where the organic species are oxidized with the injection of an ozone-rich gas. The process performance is evaluated by measuring the removal efficiency of the total organic carbon (TOC) in dried mud. Experimental data are employed to calibrate a knowledge-based model (KBM), used as a tool for data regularization, and generation of simulated results to be used for ANN. A feed-forward dense multilayer ANN is developed, using a group of dimensionless variables as inputs and the KBM-simulated TOC removal efficiency in the liquid and solid phase of mud, as outputs. The interpretation of ANN results with the Shapley Additive explanation (SHAP) method shows that the TOC removal efficiency is enhanced when (i) the initial TOC concentration in solid phase increases; (ii) the mud is pre-treated with seawater/surfactant solution leading to the increase of the initial TOC concentration in liquid phase, and decrease of the initial TOC concentration in solid phase; (iii) the ozone concentration of injected gas increases; (iv) the Reynolds number in liquid phase takes on moderate values, facilitating the ozone dissolution, its reaction with dissolved TOC, and the mixing of phases; (v) the SDS concentration is quite high and, due to foaming, the apparent liquid column height, and the retention time of gas increase.