Modeling Pb (II) adsorption from aqueous solution by ostrich bone ash using adaptive neural-based fuzzy inference system

Abstract

To evaluate the performance of Adaptive Neural-Based Fuzzy Inference System (ANFIS) model in estimating the efficiency of Pb (II) ions removal from aqueous solution by ostrich bone ash, a batch experiment was conducted. Five operational parameters including adsorbent dosage (C(s)), initial concentration of Pb (II) ions (C(o)), initial pH, temperature (T) and contact time (t) were taken as the input data and the adsorption efficiency (AE) of bone ash as the output. Based on the 31 different structures, 5 ANFIS models were tested against the measured adsorption efficiency to assess the accuracy of each model. The results showed that ANFIS5, which used all input parameters, was the most accurate (RMSE = 2.65 and R(2) = 0.95) and ANFIS1, which used only the contact time input, was the worst (RMSE = 14.56 and R(2) = 0.46). In ranking the models, ANFIS4, ANFIS3 and ANFIS2 ranked second, third and fourth, respectively. The sensitivity analysis revealed that the estimated AE is more sensitive to the contact time, followed by pH, initial concentration of Pb (II) ions, adsorbent dosage, and temperature. The results showed that all ANFIS models overestimated the AE. In general, this study confirmed the capabilities of ANFIS model as an effective tool for estimation of AE.

Adsorption of Pb(II) and Hg(II) ions from aqueous single metal solutions by using surfactant-modified ostrich bone waste

“…This phenomenon may be explained by the following reasons: at low Hg(II)/B-Detergent ratio, the Hg(II) ions adsorption performs on the high-energy sites, but with increasing the Hg(II)/B-Detergent ratio the higher energy sites are saturated and adsorption commences on the lower energy sites, concluding in a decrease in adsorption efficiency. Thus, the pH curves are shifted to alkaline pH, the similar results have been previously published in the literature [8,20,24,33]. The observed lower uptake in an acidic medium may be attributed to the partial protonation of the active sites (the dominant active sites of B-Detergent are -SO 3 H, -P-OH, and ≡Ca-O-H species) and the competition of H + with metal ions.…”

“…11 and 12. When the initial Pb(II) and Hg(II) ions concentration were increased, the removal efficiencies of metal ions by B-Detergent reached to 73 and 47%, respectively, at 25˚C for 24 h. The increase in the loading capacities of sorbents with increasing Pb(II) and Hg(II) concentrations is due to the interaction between metal ions and adsorbents which provides the vital driving force to defeat the resistances to the mass transfer of metal ions between the aqueous and the bone samples [24]. However, the removal of HMs in this study was in the order of Hg(II) < Pb(II), with the same trend of hydrated radius as Hg(II) (0.234 nm) < Pb(II) (0.254 nm) [51].…”

“…These animal by-products can be used as adsorbent for contaminants removal due to naturally, readily available, high adsorption capacity, and low cost [18][19][20][21][22][23]. From the economical point of view, ostrich bone can be a viable alternative to commercial adsorbents, such as activated carbon or ion exchange resins, for treatment of metal contaminated wastewater [24,25]. The ostrich bone used as naturally and abundantly available adsorbent in this work is an animal by-product produced in large amounts in many parts of Iran.…”

“…Pristine ostrich bone contains more than 80% organic compounds which could block the inorganic phase and suitable active sites of the bone (i.e. hydroxyapatite is known to bind metals strongly [18][19][20][21][22][23][24]30], which were shown earlier to promote heavy-metal-ion adsorption through preferential ion exchange [24,31,32]). So in this work various processes of pretreatment are designed to ostrich bone with the aim of enhancing the metal adsorption capacity by opening up the physical structure and framework to give more active sites and/or by obtaining suitable chemical modification of the available functional groups.…”

Functionalized copper oxide–zinc oxide nanocomposite: synthesis and genetic programming model of dye adsorption

“…neural network [33][34][35] which is a powerful tool for prediction, but it is not usually able to produce practical prediction equations [36]. Adaptive neuro-fuzzy inference system [37][38][39][40] and least square support vector machine [41,42] are some other methods of modeling that were used in this field. However, no reported work was found to use genetic programming (GP) approach for modeling of dye removal in the field of adsorption process.…”