Tumer, Abdullah ErdalEdebali, Serpil2024-02-232024-02-232018978-1-5386-6974-7https://hdl.handle.net/20.500.12452/1720314th Symposium on Neural Networks and Applications (NEUREL) -- NOV 20-21, 2018 -- Belgrade, SERBIAIn this study, multiple-linear regression (MLR) model was used to predict the efficiency of two commercial resins, Amberjet 1200H and Diaion CR11, used for the removal of Cr (III) from aqueous solutions. The effects of descriptors used in the experiments (pH, amount of resin, temperature, contact time and concentration) on the removal were investigated with 36 different laboratory studies. The removal efficiency was calculated. Two regression models were developed with MLR analysis which is used to describe the effects of experiment parameters. The performances of both models developed to determine the removal efficiency of these sorption systems were found satisfactory. Statistical results indicated that Amberjet 1200H was more effective than Diaion CR11 for the removal of Cr(III).eninfo:eu-repo/semantics/closedAccessMlr MethodModelingOptimizationRemoval EfficiencySorptionConference Object2-s2.0-85060916296WOS:000457745100012