Intelligent modeling and experimental study on methylene blue adsorption by sodium alginate-kaolin beads

As tighter regulations on color in discharges to water bodies are more widely implemented worldwide, the demand for reliable inexpensive technologies for dye removal grows. In this study, the removal of the basic dye, methylene blue, by adsorption onto low-cost sodium alginate-kaolin beads was investigated to determine the effect of operating parameters (initial dye concentration, contact time, pH, adsorbent dosage, temperature, agitation speed) on dye removal efficiency. The composite beads and individual components were characterized by a number of analytical techniques. Three models were developed to describe the adsorption as a function of the operating parameters using regression analysis, and two powerful intelligent modeling techniques, genetic programming and artificial neural network (ANN). The ANN model is best in predicting dye removal efficiency with R2 = 0.97 and RMSE = 3.59. The developed model can be used as a useful tool to optimize treatment processes using the promising adsorbent, to eliminate basic dyes from aqueous solutions. Adsorption followed a pseudo-second order kinetics and was best described by the Freundlich isotherm. Encapsulating the kaolin powder in sodium alginate resulted in removal efficiency of 99.56% and a maximum adsorption capacity of 188.7 mg.g−1, a more than fourfold increase over kaolin alone.