Unconventional advanced oxidation technique: Evaporation liquid rate and phenolic compounds degradation evaluation and modelling/optimization process with CFD, RSM and ANNs. (15th September 2021) - Brandão, Yana B.; Dias, Fernando F.S.; Oliveira, Dinaldo C.; Zaidan, Lea E.M.C.; Teodosio, Jailson R.; Oliveira, Julierme G.C.; Benachour, Mohand
Graphical abstract: Highlights: Effect of air excess/natural gas flow on the energy efficiency of the DiCTT process. Control of the liquid evaporation/temperature rate from the combustion of natural gas. Energy efficiency to prevent the release of phenol for the environment by DiCTT. Reuse of natural fuel for increase the phenol degradation and TOC conversion. Modeling and optimization of unconventional advanced oxidation process for the phenol. Abstract: This study aims to develop unconventional advanced oxidation processes for managing phenolic effluents generated from thermochemical oxidation using Direct Contact Thermal Treatment ( DiCTT ) for liquid water reuse. The DiCTT system uses a stainless-steel reactor described by the combustion of natural gas and production of hydroxyl radicals ( OH ), which ensures its compact installation and application to offshore oil-exploration platforms where natural gas is accessible and space is limited. The oxidation of phenolic compounds is evaluated under atmospheric pressure, which controls the rate of evaporation of the phenol to the environment during the combustion step, and thus helps avoid incineration in the liquid phase. The efficiency of the process is studied as a function of three independent variables: natural gas flow rate ( QGN, 2–4 m 3 . h −1 ), air excess ( E, 10–50%), and recycling rate of the combustion gases ( QRG, 0–100%). Optimal conditions identified for complete phenol degradation (>99%) and total organic carbon (TOC) conversion (>30%) for a given rate of evaporation for the liquid phase (less than11%) and effluent temperature (70–78 °C) were QGN  = 4 m 3. h −1, E  = 10%, and QRG  = 100%. For modelling and optimization of results, Computational Fluid Dynamics (CFD), Response Surface Method ( RSM ) and Artificial Neural Network ( ANN ) technique were used. These techniques have shown to be very promising in the prediction of contaminant degradation and in the optimization of this type of mechanism.