In this study, we developed a predictive model for quantitatively assessing the copper ion (Cu(II)) removal performance of 001*7 resin in a liquid solid fluidized bed reactor. The model was developed using the dimensional analysis approach based on dynamic experimental data. Experiments were conducted to evaluate the effects of operational variables (adsorbent dosage, initial solution concentration, and superficial liquid velocity) on the Cu(II) removal performance. The model predictions were in good agreement with the experimental data, thereby indicating that the model could accurately describe the removal of Cu(II) by fluidized adsorption. A model that obtains better predictions could help to optimize the operational variables in implementations of the fluidized adsorption technique for wastewater treatment. The model showed that increasing the 001*7 resin dosage and decreasing the superficial liquid velocity could achieve a higher Cu(II) uptake capacity. In order to verify the theoretical rationality of the model, we clarified the effect of the superficial liquid velocity on the Cu (II) removal. A larger superficial liquid velocity yielded a higher Reynolds number which could enhanced the fluidized adsorption, but led to a higher void fraction which could weaken the adsorption. Thus, the effect of the superficial liquid velocity on the Cu(II) removal was modeled as a combination of the Reynolds number and void fraction.