UV-photodegradation of R6G dye in three-phase fluidized bed reactor: Modeling and optimization using adaptive neuro-fuzzy inference system and artificial neural network

Abstract

The development of an efficient photoreactor is still the most challenging task in the photocatalytic process. The main challenges include the determination of the optimal hydrodynamic conditions required for the efficient photodegradation of biorecalcitrant pollutants. In this study, a TiO2-ZnO/BAC composite catalyst was applied to investigate the hydrodynamic characteristics of a three-phase fluidized bed reactor in the UV-photodegradation of rhodamine 6G dye. Artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) were used for modeling and optimization of the photodegradation process. From the preliminary study, 70° column inclination angle and 0.028 ms−1 superficial gas velocity, showed good axial solid distribution and average gas holdup. Subsequently, 0.019 ms−1 gas flow rate at 70° column inclination angle was found to be the optimum condition for the removal of rhodamine 6G (97.0 %). Moreover, solid distribution was found to be a dominant limiting factor in the photodegradation process as compared to gas holdup. Meanwhile, sensitivity analysis showed that all the input parameters (lamp position, inclination angle, and superficial gas velocity) were above 10%, confirming a strong influence on the process. The ANN-trainlm and ANFIS-hybrid with R-values of 0.9911 and 0.9546, respectively confirmed that the predicted model fits well with the experimental data. Furthermore, the inclination angle of 70° can be important in solar photoreactors to attain a relatively efficient tilt angle when using solar energy.