Computational intelligence modelling of methylene blue adsorption by metal-organic frameworks

Computational intelligence models for wastewater treatment utilise mathematical algorithms to simulate and optimise the processes involved in the removal of pollutants from wastewater, aiding in the design and operation of the wastewater treatment plant. The aim of this work was to compare the efficiency of computational models in modelling the dynamic adsorption of dyes. Metal-organic frameworks (MOFs) are considered as adsorbents because of their excellent properties. Further, the dataset used for this study was sourced from literature pertaining to the adsorption of methylene blue (MB), which is one of the commonly found dyes in wastewater. Adsorption parameters considered for the study are initial concentration, bed height, flow rate, pH and time, for the continuous adsorption process. The efficacy of the selected computational models was compared by employing various statistical metrics. Moreover, the results show the efficiency of artificial neural network and radial basis function neural network models is superior to other models based on R2 and mean square error, which were in the range of 0.95-0.999 and 10(-3)-10(-5), respectively. In summary, the computational intelligence models serve as the best tools for the prediction of the adsorption performance of MOFs for MB. GRAPHICAL ABSTRACT