The main objective of this work is to fit results from lead biosorption by untreated and chemically treated olive stone (OS) using two models: means of full factorial design methodology and fuzzy neural network. Concretely, OS was modified by three chemical agents: HNO3, H2SO4 and NaOH, in order to improve its biosorption capacity. The examined operational variables were: concentration of chemical agent (0.1–2 M), pH (3–5) and initial lead concentration (50–250 mg/L), and the studied response was biosorption capacity (mg/g). Results obtained from full factorial design methodology showed that all these factors considerably affected the studied response. Experimental results were fitted by a second-order equation showing the influence of each factor and their interactions. While the application of a fuzzy neural network model allowed to predict the results for the dependent variables as a function of the operating conditions used with errors less than 5% in all cases. Observed results were different when the biosorbent was treated with acid treatment or with basic one, although for all treatments the highest biosorption capacity was obtained with a concentration 2 M. Finally, models were compared and it is showed that ANFIS model predicted better experimental data with higher R2 values.
