Adaptive Neuro-Fuzzy inference system analysis on sorption studies of strontium and cesium cations onto a novel impregnated nano-zeolite

APT 2557

No. of Pages 14, Model 5G

7 January 2020 Advanced Powder Technology xxx (xxxx) xxx 1

Contents lists available at ScienceDirect

Advanced Powder Technology journal homepage: www.elsevier.com/locate/apt

2

Original Research Paper

6 4 7 5

Adaptive Neuro-Fuzzy inference system analysis on sorption studies of strontium and cesium cations onto a novel impregnated nano-zeolite

8

H.S. Hassan, O.A. Abdel Moamen ⇑, W.F. Zaher

9

Hot Lab. Center, Atomic Energy Authority of Egypt, P.O. No. 13759, Cairo, Egypt

11 10 12 1 2 4 8 15 16 17 18 19 20 21 22 23 24 25 26 27

a r t i c l e

i n f o

Article history: Received 21 January 2019 Received in revised form 6 November 2019 Accepted 24 December 2019 Available online xxxx Keywords: Impregnated zeolite Sr2+ Cs+ Sorption study Adaptive Neuro-Fuzzy Inference Systems (ANFIS)

a b s t r a c t In this research, a novel impregnated nano-zeolite (NAASMS-Z) was synthesized and characterized using different characterization techniques. Excellent properties, such as high specific surface area (
502.77 m2/g), low pore size (
8.92 Å) and the existence of numerous functional groups caused the efficient elimination of Sr2+ and Cs+ cations from aquatic systems. The sorption performance of the nanoparticles was enhanced by impregnation up to 60% in the aquatic media. The kinetic study indicated that the elimination process of both the concerned cations is controlled by external film mass transfer through the boundary within the first 30 min then controlled by intra-particle diffusion. The sorption equilibrium data suggested that the sorption process occurs on the heterogeneous sorbent surface. Parameters affecting the elimination of Sr2+ and Cs+ from a single metal sorption system, such as pH, initial contaminant concentration (Ci) and contact time (t), were investigated and optimized. A predictive model based on an Adaptive Neuro-Fuzzy Inference system (ANFIS) analysis was applied to evaluate the experimental parameters affecting the elimination of Sr2+ and Cs+ cations from aquatic system. A Mamdani-type FIS was employed to justify a collection of 16 rules (If-Then format) by means of centroid membership functions. The suggested fuzzy model revealed high predictive concert with high correlation coefficient (R2) and satisfactory deviation from the experimental data, affirming its appropriateness to predict Sr2+ and Cs+ elimination efficacy from the studied system. Rooted in experimental data and statistical analysis, the synthetized material was effective for treating contaminated aquatic solutions containing Sr2+ and Cs+ cations. Ó 2019 The Society of Powder Technology Japan. Published by Elsevier B.V. and The Society of Powder Technology Japan. All rights reserved.

29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

51 52

1. Introduction

53

Strontium-90 and cesium-137 cations are the essence longlived fission products with half-lives of 28.8 and 30.17 years, respectively [1]. Nuclear reactors accidents, testing of nuclear weapon, nuclear fuel reprocessing and nuclear waste repositories are the main routes in which radioactive Sr2+ and Cs+ cations are launched into the environment [2]. Chemically, cesium resembles potassium and sodium; it is mobile in numerous environments and can easily be predigested by several aquatic and terrestrial organisms. The swallowing, deposition and buildup of cesium radioisotopes in the soft tissues of the human body render an internal threat, particularly to the reproductive system [3]. Strontium behaves as a bi-valent cation that can substitute calcium in the bone matrix [4]. Thus, the elimination of Sr2+ and Cs+ cations from aquatic environments is an

54 55 56 57 58 59 60 61 62 63 64 65 66

⇑ Corresponding author. E-mail address: [email protected] (O.A. Abdel Moamen).

important issue in the environmental science and technology fields. There are diverse techniques for eliminating radioactive ions from aquatic systems, such as solvent extraction, evaporation, reduction, chemical precipitation, membrane filtration, biological process and ion exchange/sorption [2]. Amongst them, sorption, owing to its ease of operation, low-cost and effective performance, has received wide attention [5]. As sorption is generally executed on the surface of the sorbent, increasing the number of binding sites on the surface by impregnation using different functional groups, such as amine, carboxylic, phosphate, hydroxyl and sulfonic groups, is an auspicious approach for improving the sorption capacity. In other words, the number of functional groups plays a crucial role in determining the sorbent sorption capacity. Consequently, many researchers have emphasized modifying the sorbent surface to increase the number of functional groups. Amongst these modifications, synthesized zeolites have received much attention to be used as starting material owing to their welldefined porous structures and their high ionic exchange capacity

https://doi.org/10.1016/j.apt.2019.12.031 0921-8831/Ó 2019 The Society of Powder Technology Japan. Published by Elsevier B.V. and The Society of Powder Technology Japan. All rights reserved.

Please cite this article as: H. S. Hassan, O. A. Abdel Moamen and W. F. Zaher, Adaptive Neuro-Fuzzy inference system analysis on sorption studies of strontium and cesium cations onto a novel impregnated nano-zeolite, Advanced Powder Technology, https://doi.org/10.1016/j.apt.2019.12.031

67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85

APT 2557

No. of Pages 14, Model 5G

7 January 2020 2 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123

H.S. Hassan et al. / Advanced Powder Technology xxx (xxxx) xxx

to eliminate different ions such as Cs137, Sr90, Co60, Ag110, Zn2+, Cd2+, and As(V) [6–11]. Furthermore, micron-sized particles are replaced with nano-sized particles due to their high specific surface area and their interfacial activity renders them highly efficient sorbents [12–14]. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) is a branch of artificial intelligence (AI) that combines the learning abilities of artificial neural networks (ANN) and reasoning abilities of fuzzy system [15]. The AI methodology is a powerful, costless, timesaving and reliable technique that has been effectively utilized for numerous engineering applications. ANFIS is an influential instrument for mapping, modeling, problem-solving, forecasting, and data mining the input and output values relationship so as to depict nonlinear behavior in complex systems. The ANFIS model structure is composed of two main parts, viz., the premise and consequence part that are connecting to each other by fuzzy rules in the network form [15,16]. It is extensively accepted as a technology owing to its universal ability to simulate nonlinear variation, application in the expectation of the performance of many processes, and extrapolation rooted in historical data in numerous fields [15–19]. In this study, a novel nano 2-naphtyl amine 6:6azulene sodium methanesulfonate di sulphonic acid-impregnated zeolite (NAASMS-Z) was synthesized and characterized using diverse analytical techniques. Kinetic investigations were executed to gain insights into the anticipated capacity of the synthesized material and to identify the elimination reaction nature and controlling mechanism. The sorption equilibrium data of both the studied cations were interpreted using Freundlich and Langmuir isotherm models. Additionally, a Mamdani type of ANFIS was applied for predicting the elimination efficacy of Sr2+ and Cs+ cations from a single metal sorption system, and the comparison between the fuzzy and experimental data was also conducted. The effects of four significant variables, namely, pH, temperature, contact time and initial cation concentration were assessed, and the most affected parameter on the sorption process was sequenced. We concluded that the ANFIS model had a high predictive ability for the sorption of Sr2+ and Cs+ cations onto synthesized NAASMS-Z.

124

2. Experimental section

125

2.1. Materials

126

131

All the chemicals used in this study were of analytical reagent grade. Fumed silica (FS, Aldrich), sodium aluminate (SigmaAldrich), tetra-methyl-ammonium hydroxide (TMAOH, Merck), 2naphtha amine 6:6-C10H8CH2SO3Na disulphonic acid (Merck), and sodium hydroxide (NaOH, Winlab) were used for the impregnated nano-zeolite synthesis.

132

2.2. Impregnated zeolite synthesis

133

The synthesis of the organically functionalized nano-sized zeolite was executed via two main steps. The first step involved the synthesis of the nano-sized zeolite [12], and the second step involved the impregnation of nano 2-naphtyl amine6:6-azulene sodium methanesulfonate di sulphonic acid (NAASMS) particles onto the synthesized zeolite via precipitation technique. Initially, an alumina-silicate gel was synthesized by mixing silica and aluminate solutions in a molar ratio of 1.0Al2O3: 3.6SiO2: 0.044NaOH: 1.5(TMAOH): 0.88ethanol: 236H2O. The detailed preparation procedure and characterization of the synthesized nano-sized zeolite without impregnation are illustrated elsewhere [12]. A wet impregnation procedure was utilized with the aim of incorporating sulphonyl function groups onto the surface of zeolite to improve

127 128 129 130

134 135 136 137 138 139 140 141 142 143 144 145

the material quality for trapping the concerned contaminants from wastewater. The required amount of impregnated material was dissolved in bi-distilled water to which the required zeolite amount was added. The sample was maintained at 90 °C and stirred continuously until the water completely evaporated. This step led to the incorporation of sulphonyl salt into the cages and pores of the zeolite, preventing sulphonyl agglomeration. The sample was then dried overnight in an oven at 100 °C. The prepared material was further characterized via diverse characterization techniques.

146

2.3. Instrumentation

156

The residual concentrations of Sr2+ and Cs+ in aquatic solutions were determined by atomic absorption spectrophotometry (AAS, Buck Scientific, VGP 210) after suitable dilution. A pH meter (CG820 Schott Gerate pH meter, Germany) calibrated with standard buffer solutions was used for measuring the pH values in the aquatic phase. Fourier transform infrared spectroscopy (FTIR) (PerkinElmer, BX) was used to identify the absorption bands and chemical functional groups of the studied sorbent. The surface structure and elemental analysis of the sorbent were analyzed by scanning electron microscope with an energy dispersive X-ray spectrometry (SEM-EDXS) (JEOL, JEM-1000CX, USA). The degree of crystallinity of the prepared material was investigated using a powder X-ray diffraction technique (PXRD) (Philips, PW/1710). Thermal analysis of the prepared material was performed at a rate of heating of 10 °C/min to determine the thermal behavior of the synthesized material. Nitrogen desorption and adsorption isotherms were executed using a fully automated surface area analyzer (Nova instrument, Quantachrome Corporation, USA).

157

2.4. Sorption studies

175

An array of batch experiments was performed to study the equilibrium and kinetic sorptive behavior of Sr2+ and Cs+ cations onto the synthesized NAASMS-Z. These tests were executed in triplicate, and the average values are presented. Effect of parameters such as initial cation concentration (50–1500 mg/l), contact time (5– 180 min), pH (4–10) and temperatures (298, 303 and 313 K) were studied (by changing any one of the parameters and keeping the others constant) at solution volume to sorbent mass ratio of 500 ml/g. To control the pH during the sorption process, a buffer solution was used. At the termination of the sorption process, the sorbent was separated by centrifuging at 1000 rpm for 30 min. The sorbed amounts of Sr2+ and Cs+ (mg) per unit weight of NAASMS-Z (g) were calculated from the following equation:

176

ðC 0 V 0  C f V f Þ q¼ W

ð1Þ

where q is the sorption capacity (mg/g), C0 and Cf are the initial and final cation concentrations in the solution (mg/l), respectively; V0 and Vf are the initial and final (initial plus the volume of the added buffer) solutions (l), respectively; and W is the NAASMS-Z weight (g). The elimination efficacy (%E) was evaluated using the following equation:

ðC0 - Ce ÞX 100 %E ¼ C0

ð2Þ

147 148 149 150 151 152 153 154 155

158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174

177 178 179 180 181 182 183 184 185 186 187 188

189 191 192 193 194 195 196 197

198 200

where Ce is the equilibrium cation concentration.

201

2.5. Modeling using Adaptive Neuro-Fuzzy Inference System (ANFIS)

202

Fuzzy modeling is a widespread approach for modeling inputoutput relationships in complex nonlinear systems; this method establishes comparatively simple calculations on lingual terms

203

Please cite this article as: H. S. Hassan, O. A. Abdel Moamen and W. F. Zaher, Adaptive Neuro-Fuzzy inference system analysis on sorption studies of strontium and cesium cations onto a novel impregnated nano-zeolite, Advanced Powder Technology, https://doi.org/10.1016/j.apt.2019.12.031

204 205

APT 2557

No. of Pages 14, Model 5G

7 January 2020 3

H.S. Hassan et al. / Advanced Powder Technology xxx (xxxx) xxx 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234

instead of complex calculations. In the ANFIS, the Mamdani-type fuzzy inference system is utilized where the output of each rule can be a constant term or a linear combination of input variables plus a constant term. The main advantage of this method is its simplicity in the representation and the interpretation of fuzzy rules [20]. A general fuzzy system (Fig. 1) essentially includes four main parts, viz. [21,22] fuzzification, rule base and data base, fuzzy inference engine and defuzzification. First, the crisp inputs (numerical values) are taken, and their degrees are determined using elected membership functions to find appropriate fuzzy sets. Second, the produced fuzzified inputs are applied as the modernistic values of the fuzzy rules. A fuzzy rule with several modernistic values yields a single number afterward the modernistic assessment using the fuzzy operators, for instance, AND, OR and NOT. Combination is the third step in which the membership functions of all rule resultants are combined into a single fuzzy set and, consequently, the total output is obtained. Defuzzification is the final step in the fuzzy inference process, where the ultimate output of a fuzzy system is generated from the combined output of the fuzzy sets. One of the most widespread defuzzification methods is the centroid technique [23]. Triangular-shaped membership functions (trimf) were elected for all input and output variables. The advantages of the triangular-shaped membership functions are its ease of design and implementation and its applicability with few data. The triangular curve is a function of a vector, x, and relies on three scalar parameters, a, b, and c, as given by:

f ðx; xa; b; cÞ ¼

8 > < 0;

xa

ba > : cx cb

235

c6x6a a6x6b

ð3Þ

b6x6c

237

The above function can be modified to a compact form as in Eq. (4):

238

 x  a ; f ðx; xa; b; cÞ ¼ max min ba

240

c  x cb

;0



ð4Þ

239

242

The parameters a and c are located on the feet of the triangle, and the parameter b is located on the peak of the triangle. The steps of the ANFIS implementation are illustrated in supplementary file.

243

3. Results and discussion

247

3.1. Characterization

248

The results of the microscopy and spectroscopy analyses were employed to quantify the quality of the synthesized material in terms of the crystallite morphology, the absence of un-reacted alumino-silicate gel and silicon to aluminum ratio. To assess the quantitative analysis and the distribution of impregnated material over the zeolite particles, a chemical map analysis was obtained by SEM and EDXS (Fig. 2). Generally, zeolites are a group of alumina-silicate minerals that have the capability of accommodating a wide variety of cations, such as Na+ and K+ in their porous structures [7]. The SEM-EDXS data confirm that the main elements (Al, Si, O) exist in large quantities and are uniformly

249

Fig. 1. A schematic of Adaptive Neuro-Fuzzy Inference Systems (ANFIS).

Please cite this article as: H. S. Hassan, O. A. Abdel Moamen and W. F. Zaher, Adaptive Neuro-Fuzzy inference system analysis on sorption studies of strontium and cesium cations onto a novel impregnated nano-zeolite, Advanced Powder Technology, https://doi.org/10.1016/j.apt.2019.12.031

244 245 246

250 251 252 253 254 255 256 257 258 259

APT 2557

No. of Pages 14, Model 5G

7 January 2020 4

H.S. Hassan et al. / Advanced Powder Technology xxx (xxxx) xxx

Fig. 2. (A) SEM micrograph of the impregnated zeolite with different magnifications; (B) EDXS mapping associated with the impregnated zeolite; and (C) maps of distribution of the elements Si, Al, O, Na, K, S, Cl and C.

260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295

spread throughout almost all particles; these elements are the chief building blocks of the zeolite structure. However, the other elements are observed in lower quantities and are less widespread (Fig. 2). The mapping images show the amount and location of different individually colour-coded elements. The carbon, potassium, sulfur and chlorine signals in the EDXS mapping also confirm the presence of impregnated material in the nano-zeolite sample. The synthesized material also has uniform conglomerated particle morphology with a narrow distribution of particles size and a mean diameter of approximately 30 nm, which denotes uniform particle growth throughout the crystallization process. White boundaries around the particles are attributed to the existence of a non-crystallized gel. (Fig. 3A) demonstrates that the characteristic peaks of the zeolite-Y structure before [12] and after anchoring were similar, indicating that the framework structure of the prepared material is preserved during the anchoring process; these results are comparable to the results of Treacy and Higgins [24]. As shown in this figure, after anchoring, a shift in the characteristic reflection of zeolite Y at 2h = 7.25° to 2h = 9.17° was observed. This result indicates that the synthesized zeolite experienced the anchor process. The synthesized material is highly crystalline, as proven by intense and narrow peaks without an elevated baseline. It has also been shown that the incorporated function groups are randomly distributed within the Y lattice as I331 > I220 > I311 [24]. (Fig. 3B) indicated a Si/Al ratio of approximately 2.38, which falls in the range of zeolite-Y. The differential thermal analysis (DTA) and thermo-gravimetric analysis (TGA) curves of the impregnated zeolite are shown in (Fig. 3C). The DTA profile shows that the prepared impregnated material has five dehydration steps at 56.10, 155.83, 258.73, 381.41 and 520.44, which are attributed to the variance in the bonding strength of water molecules attached to the aluminosilicate structure. The first endothermic region ranges from 50 to 165 °C with peaks at approximately 56.10 and 155.83 °C which correspond to the loss of physically adsorbed water molecules.

The weight loss in this region is approximately 4%. When increasing the temperature from 250 °C to 300 °C, a peak at approximately 258.73 °C is observed with a weight loss of approximately 4%. Additionally, peaks at approximately 381.41 and 520.44 are observed when the temperature was increased from 300 to 520 °C; these peaks may be due exclusively to the elimination of organic functional groups, such as 2-naphthyl amine, azulane, sodium methanesulfonate, causing a weight loss of approximately 10%. The total weight loss is approximately 56.5% of the initial weight of the prepared material, and the final weight loss occurred at 1000 °C. The FTIR spectrum possesses twenty-four absorption bands due to the internal vibrations in the tetrahedral and external linkages [12]. (Fig. 3D) shows a large broad band at 1105 cm1, which represents the asymmetric stretching vibration of bridging Si–O and Al–O ( OT? O, where T is Si or Al) inside the tetrahedral structure, while the band at 704 cm1 corresponds to the symmetric stretching vibration of Al–O and Si–O ( OTO ? ) in the tetrahedron. The sharp band at 465 cm1 corresponds to the bending mode of the Al–O– Si vibration [12]. The D6R vibration, which belongs to the vibration of the external linkage, is detected at approximately 500 cm1, while the water molecule vibration is noted at 1615 cm1 [12]. On the other hand, the hydroxyl groups related to the 3740–3745 cm1 and 2640–3680 cm1 bands were originally visualized as the only lattice-terminating OH groups on the external surface of the zeolite structure. The synthesized material contains resident functionalities including, 1037 cm1 (S=O) and 1105 cm1 (SO3H). The bands produced via the Bronsted acid sites, generated by the attack of the framework, and sulfonic acid groups were also detected at 2580–2522 cm1. The prepared impregnated synthesized material has a specific surface area of 502.77 m2/g. The increased surface of impregnated nanocrystalline alumino-silicate material (1.2 times higher than the synthesized zeolite specific surface area without impregnation) results in improved sorptive properties and additional surface area available for sorption and the reaction of molecules [25].

Please cite this article as: H. S. Hassan, O. A. Abdel Moamen and W. F. Zaher, Adaptive Neuro-Fuzzy inference system analysis on sorption studies of strontium and cesium cations onto a novel impregnated nano-zeolite, Advanced Powder Technology, https://doi.org/10.1016/j.apt.2019.12.031

296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331

APT 2557

No. of Pages 14, Model 5G

7 January 2020 H.S. Hassan et al. / Advanced Powder Technology xxx (xxxx) xxx

5

Fig. 3. (A) X-ray diffraction patterns of nano-zeolite before and after impregnation; (B) EDXS of impregnated zeolite showing elemental compositions and quantities (wt%); (C) TGA and DTA analysis; and (D) FTIR spectra.

343

The isoelectric point (pHiep) of the synthesized sorbent was determined using HCl or NaOH solutions (following the well-known salt addition technique) adjusted at various initial pH levels at room temperature. The final pH of solutions was measured after 24 h shaking to reach equilibrium. After this time, each resulting pH was recorded and the initial pH (pHo) vs. the difference amid the initial and final pH (pHf) values (pHo - pHf) was plotted (Figure omitted). The pH at which the sorbent surface charge is zero is called the isoelectric point (pHiep) and is usually used to quantify the electro-kinetic properties of sorbent surface. pHiep was found to be 4.9, implying that the net surface charge is negative at pH medium above 4.9, positive at pH medium below 4.9, and neutral at pH 4.9.

344

3.2. Sorption studies

345

3.2.1. pH effect study In the sorption process, the solution pH is one of the most important parameters that influence metal binding, as it not only

332 333 334 335 336 337 338 339 340 341 342

346 347

changes the sorbent surface charge but also changes the speciation of metal ions in the solution [2]. The formation of hydrolyzed species may lead in many cases to metal precipitation that can change the charge and the size of metal species, which, in turn, may influence their affinity for reactive groups on the sorbent. Thus, a study is performed to optimize the pH whereas the other parameters, such as the sorbent amount, initial cation concentration and temperature are fixed. (Fig. 4A and B) shows the cation elimination efficacy and the sorbed cations quantity as a function of pH. The primary cation concentration was 775 mg/l, and the volume solution to sorbent mass ratio was 500 ml/g. The experimental results indicated that Cs+ sorption increases while the pH reaches 6, then decreases and next increases again at pH 10, and the maximum sorption occurs at pH 6. The Sr2+elimination efficacy increases with increasing pH. The low Sr2+ and Cs+ elimination efficacy of the impregnated-zeolite at low pH (<6.0) is in agreement with previously reported studies of the investigated cation uptake by different zeolitic materials [7–12]. This result was attributed to the

Please cite this article as: H. S. Hassan, O. A. Abdel Moamen and W. F. Zaher, Adaptive Neuro-Fuzzy inference system analysis on sorption studies of strontium and cesium cations onto a novel impregnated nano-zeolite, Advanced Powder Technology, https://doi.org/10.1016/j.apt.2019.12.031

348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365

APT 2557

No. of Pages 14, Model 5G

7 January 2020 6

H.S. Hassan et al. / Advanced Powder Technology xxx (xxxx) xxx

Fig. 4. (A and B) Effect of pH on Sr2+ and Cs+ capacity and elimination efficacy (V/m = 500 ml/g, time 2 h, and T = 298 K); and (C, D) Effect of time on Sr2+ and Cs+ elimination (V/m 500 ml/g, T = 298 K and pH = 6).

366 367 368 369 370 371 372 373 374 375 376 377 378

379

competition between H+ and cations for the exchange sites of the zeolite. Additionally, in acidic media, the solubility of the zeolite constituents is notable, so there will be a comparatively small number of available sites [12,26]. The enhanced elimination efficacy with increasing pH value (˃6) is related to the hydroxyl group at the surface of the zeolite that might be coordinated by either two similar or different atoms (two Al or two Si or one Al and one Si); this effect causes the presence of dissimilar surface hydroxyl group properties and thus the elimination affinity is enhanced [27]. The relationship between the sorption capacity and elimination efficacy as a function of pH was found to fit the following relations: For Sr2+

%E ¼ 0:2084 pH 3  5:1466pH 2 þ 34:82pH  39:89 3

381

q ¼ 0:8074pH  19:943pH þ 169:8 pH  154:6

382

For Cs+

383

3

2

2

%E ¼ 0:518pH  11:677pH þ 85:77pH  112:2 385 386 387 388 389 390

3

ð5Þ

2

q ¼ 2:007pH  45:247pH þ 332:18pH  434:7

ð6Þ

The quantity of sorbed cations onto the sorbent surface is substantially relies on several factors, such as ionic radius, hydrated ionic radius and hydration energy of cations. Cs+ cations have a larger ionic radius (1.67 Å) than Sr2+ cations (1.26 Å); however, the hydrated radius of Cs+ cations (2.26 Å) is smaller than that of Sr2+

cations (4.12 Å). Thus, the free Cs+ cations should have a larger diffusion coefficient than the free Sr2+ cations in dilute solutions. These differences lead to the prediction of faster diffusion of free Cs+ cations through the sorption onto the synthesized material. The hydration energy (Eh) was ranked as follows: Eh(Cs+) = –68 Kcal  Eh(Sr2+) = –346 Kcal. Decreases in hydrated ionic radius and hydration energy enables the cations to shed their hydration shell upon entering the sorbent interlayers [1].

391

3.2.2. Effect of contact time and temperature In this section, cation (Sr2+ and Cs+) sorption by the synthesized sorbent was investigated as a function of time of contact, and the results are shown in (Fig. 4C and D). The results indicate that cation sorption is fast at the beginning, and after 30 min, the sorption is balanced. At the beginning of the sorption process, the changes in the sorption amount occurs rapidly since more sorbing sites are accessible; after 30 min, the process reaches equilibrium, and the sorbing sites are nearly saturated. As shown in (Fig. 4C and D), the sorption amount was increased by increasing the temperature which indicates that the sorption process is endothermic.

399

3.2.3. Kinetic analysis Generally, the sorption process occurs according to diffusion control, chemical reactions and mass transfer via chemical or physical forces. The obtained kinetic parameters are useful for forecast-

410

Please cite this article as: H. S. Hassan, O. A. Abdel Moamen and W. F. Zaher, Adaptive Neuro-Fuzzy inference system analysis on sorption studies of strontium and cesium cations onto a novel impregnated nano-zeolite, Advanced Powder Technology, https://doi.org/10.1016/j.apt.2019.12.031

392 393 394 395 396 397 398

400 401 402 403 404 405 406 407 408 409

411 412 413

APT 2557

No. of Pages 14, Model 5G

7 January 2020 7

H.S. Hassan et al. / Advanced Powder Technology xxx (xxxx) xxx 414 415 416 417 418 419

420 422 423 424 425 426 427

ing the rate interpretation and sorption process modeling. Therefore, pseudo-1st-order (PFO) and pseudo-2nd-order (PSO) [27] models were utilized for Sr2+ and Cs+ sorption onto the synthesized zeolite. The difference between the experimental data and the predicted model’s values is represented by the residual error. The nonlinear PFO model is expressed as:

qt ¼ ð1  ek1 t Þ qe

430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445

qt ¼ qe

ðK 2 :tÞ ð1 þ K 2 :tÞ

; K 2 ¼ K 2 : qe

ð8Þ

In these models, qt and qe (mg g1) are the sorbate concentration in the solid phase at time t and the sorption equilibrium, respectively; K1 and K2 are the rate constants for PFO and PSO sorption, respectively. Nonlinear regression was used to reduce the fitting errors, and the results are listed in Table 1. The visual examination of the obtained results, Figs. (S2, S3) and (Fig. 5), demonstrates that the PSO kinetic provided better fitting than the PFO kinetic model. Residual error analysis graphs were scrutinized, and it is clear from (Fig. 5(A–D)) that the smallest residual error values are obtained from the PSO model. This finding indicates that a chemical reaction occurs amid the sorbent surface and the sorbate via covalent forces or sharing electrons until the active sites on the zeolite surface are fully occupied with cations. The PSO parameters examination as a function of time (Fig. 5E and F) designates that

Table 1 Regression parameters of Sr2+ and Cs+ elimination using kinetic model based on reaction order. Cs

+

Sr

2+

The pseudo-first-order qe, mg/g Temperature, K 298 K 303 K 313 K

376.236 378.673 379.104

343.56 355.56 359.699

K1 298 K 303 K 313 K

0.32182 0.36432 0.45126

0.28514 0.4315 0.45671

R2 298 K 303 K 313 K

0.835 0.861 0.716

0.922 0.91 0.923

The pseudo-second-order qe, mg/g 298 K 303 K 313 K

385.82 387.85 390.22

355.22 361.68 364.24

K2 298 K 303 K 313 K

  qsr ¼ 0:1123 T2 þ 8:4675 ðTÞ þ 210:73

R2 ¼ 0:997

K 2 ¼ 3  105 T2 þ 0:0024T  0:0385

R2

0.0015 0.00246 0.00377

0.00202 0.00538 0.0095

R 298 K 303 K 313 K

0.974 0.966 0.911

0.985 0.956 0.901

qe, exp 298 K 303 K 313 K

381.51 384.15 385.51

352.23 363.69 369.75

2

ð9Þ

446 447 448

449 451 452

ð7Þ

The variance between the experimental and the qe values from the utilized model confirms the inadequacy of the model, even when the residual error exhibits a high values [28]. The PSO model was examined to assess the efficacy of experimental data and is represented as:

428

the parameters increase with increasing temperature and the relation between them is as follows: For Sr2+

¼ 0:999 For Cs

ð10Þ

+

  qCs ¼ 0:0099 T 2 þ 0:771ðT Þ þ 370:12 ¼ 0:999

454 455

456

R

2

ð11Þ

458 459

  K 2 ¼ 7  106 T2  0:0002ðTÞ R2

þ 0:0018 ¼ 0:999

ð12Þ

461

Inserting Eqs. (9), (10) and (11), (12) into the applicable PSO equation, the subsequent empirical equations indicate the sorbed Sr2+ and Cs+ amounts as a function of temperature and time. For Sr2+

462

 qt ¼

 3  10

5

    2 T 2 þ 0:0024 ðT Þ  0:0385  0:1123 ðT 2 Þ þ 8:4675 ðTÞ þ 210:73 ð tÞ

464 465

466

1 þ ð3  105 ðT 2 Þ þ 0:0024 ðTÞ  0:0385Þð tÞ

ð13Þ

For Cs+ qt ¼

463

468 469

ð7  106 ðT 2 Þ  0:0002 ðTÞ þ 0:0018Þ 



2  0:0099 ðT 2 Þ þ 0:771 ðTÞ þ 370:12 ð tÞ

470

1 þ ð7  106 ðT 2 Þ  0:0002 ðTÞ þ 0:0018Þ  ð  0:0099 ðT 2 Þ þ 0:771 ðTÞ þ 370:12Þð tÞ

ð14Þ

472

Rooted in the rate constants of k2 at different temperatures, the activation energy of Sr2+ and Cs+ sorption onto the synthesized material was obtained according to the integrated Arrhenius formula [29], which is shown as:

473

ln K 2 ¼ lnA 

Ea RT

ð15Þ

where Ea is the process activation energy (kJ/mol), T is the absolute temperature (K), A is the pre-exponential factor (g/mg h) and R is the ideal gas constant (8.314 J/mol K). The activation energy value can be calculated from the slope of the plot of lnK2 versus 1/T. From the results (Fig. S4), it can be shown that the activation energies are 79.006 and 64.9441 kJ/mol for Sr2+ and Cs+, respectively, which is greater than the energies corresponding to the chemisorption boundary (above 40 kJ/mol) [28]. The resistance effect of bulk diffusion can be ignored when the system is properly mixed (adequate velocity), and the film diffusion resistance is mostly active in sorption control within a preliminary step of the sorption process. A typical model that considers intra-particle diffusion is Fick’s second law, which can be represented as [24–30]:

  @q D @ @q ¼ 2 R2 @t R @R @R

ð16Þ

Eq. (16) depicts the diffusion through spherical sorbent particles. Symbols R and D signify the medium radius of the zeolite particle (cm) and the intra-particle diffusion coefficient (cm2/min), respectively. Vermeulen proposed an approximate solution of Eq. (16) to yield the following integral form as [30]:

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi   Dp2 qt ¼ qe : 1  exp :t R2

ð17Þ

Please cite this article as: H. S. Hassan, O. A. Abdel Moamen and W. F. Zaher, Adaptive Neuro-Fuzzy inference system analysis on sorption studies of strontium and cesium cations onto a novel impregnated nano-zeolite, Advanced Powder Technology, https://doi.org/10.1016/j.apt.2019.12.031

474 475 476

477 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493

494 496 497 498 499 500 501

502

504

APT 2557

No. of Pages 14, Model 5G

7 January 2020 8

H.S. Hassan et al. / Advanced Powder Technology xxx (xxxx) xxx

Fig. 5. Residual errors for non-linear regression of kinetic removal data for (A) Cs+ in PFO model; (B) Cs+ in PSO order model; (C) Sr2+ in PFO model; (D) Sr2+ in PSO model and (E and F) the amount sorbed of Cs+ and Sr2+ as a function of time and temperature, respectively. 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523

where R is the mean radius of the sorbent particles. Consistent with Vermeulen’s approximation plotted in (Fig. 6A– D), the fitted results indicated that diffusion plays a chief role in the sorption rate of Sr2+ and Cs+ cations onto the synthesized material. The Vermeulen model implies that the diffusion of Sr2+ and Cs+ becomes very complicated when the zeolite surface becomes covered with Sr2+- and Cs+ -occupied sorption sites, which are more easily accessible. The results show a dependence of the diffusion coefficient (D) on the temperature (Table 2). Increasing the system temperature leads to an increase in the D value due to the weakening of the solution viscosity and the increasing cation mobility which consequently enhance the sorptive capacity of the sorbent. Because the value of D falls within the range of 109 to 1017 m2/s, the system is chemisorption. To strengthen the hypothesis that Sr2+ and Cs+ elimination is diffusion-controlled, the intra-particle diffusion model rooted in the Weber and Morris theory was used, in which the sorption varies proportionally with the square root of time as follows:

pffiffi qt ¼ K D : t þ I

524

ð18Þ

526

where KD is the intra-particle diffusion rate constant (mg/g min0.5). I, the intercept of stage i, provides an idea regarding the boundary layer thickness, i.e., the larger the intercept, the greater the boundary layer effect. (Fig. 7) shows the analysis of the experimental data compared with the intra-particle model. The plots of qt vs. t0.5 show the presence of two portions signifying two different stages during the sorption process viz. external mass transfer followed by intraparticle diffusion signified that the cations were transported from the solution to the external surface of zeolite particles thru film diffusion. Then, cations were entered into zeolite particles by intraparticle diffusion through pores where intra-particle diffusion is rate-determining step [27]. As shown in (Fig. 7A–D), it could be inferred that external surface sorption (stage 1) was completed before 30 min after which the stage of intra-particle diffusion (stage 2) started and continued from 30 to 180 min and retained in the pores of zeolite. The values of the rate constants and the

527

Please cite this article as: H. S. Hassan, O. A. Abdel Moamen and W. F. Zaher, Adaptive Neuro-Fuzzy inference system analysis on sorption studies of strontium and cesium cations onto a novel impregnated nano-zeolite, Advanced Powder Technology, https://doi.org/10.1016/j.apt.2019.12.031

528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543

APT 2557

No. of Pages 14, Model 5G

7 January 2020 H.S. Hassan et al. / Advanced Powder Technology xxx (xxxx) xxx

9

Fig. 6. Vermeulen kinetic model for Sr2+ and Cs+ sorption.

Table 2 Regression parameters of Sr2+ and Cs+ elimination using kinetic model based on reaction mechanism. +

Vermeulen model qe, mg/g D  1010, m2/s

R

2

Intra-particle model KD

I

R

2

RC (%)

2+

Cs

Sr

298 K 303 K 313 K 298 K 303 K 313 K 298 K 303 K 313 K

387.33 388.44 390.22 1.02 2.32 2.99 0.911 0.901 0.899

359.33 368.44 376.44 1.22 2.62 3.99 0.888 0.901 0.902

298 K 303 K 313 K

First stage second stage 71.69 0.057 74.91 0.0617 77.15 0.074

First stage second stage 63.47 0.016 66.84 0.049 67.94 0.395

298 K 303 K 313 K

29.67 383.12 31.19 384.23 52.32 385.12

29.06 352.02 34.34 363.09 84.81 364.80

298 K 303 K 313 K

0.9872 0.9813 0.9425

0.9772 0.9001 0.9713 0.9712 0.8956 0.9015

298 K 303 K 313 K

7.66 98.91 8.03 98.92 13.41 98.69

0.8921 0.9140 0.9170

8.09 97.96 9.32 98.54 22.64 97.42

determination coefficients are summarized in Table 2. The larger slopes of the first straight line reveal that the rate of cations elimination is higher at the initial stage because of the availability of large surface area and active sorption sites on it. The lower slopes of the second line appear owing to reduced concentration gradients which cause the diffusion of cations in the pores of sorbent take longer time, accordingly reducing the rate of cations elimination. The results propose that intra-particle diffusion is not the sole rate determining step and the extant sorption process is cooperatively controlled by film diffusion and an intra-particle diffusion mechanism. From the tabulated values, it is shown that the intercept increases as the temperature increases, which means that as the temperature increase the boundary layer effect will be greater due to the decreased tendency of the metal ion to escape from the sorbent surface to the solution phase as the nature of sorption is chemisorption [27–31]. However, only an intercept value might not be sufficient in the examination of the rate-limiting step, as it only provides information on the magnitude of the external mass transfer excluding intra-particle diffusion. The qe value should then be incorporated into the intercept to permit a better examination, which is proposed as a relative coefficient parameter (RC), which is calculated as [32]:

I RCð%Þ ¼ X100 qe

ð19Þ

Please cite this article as: H. S. Hassan, O. A. Abdel Moamen and W. F. Zaher, Adaptive Neuro-Fuzzy inference system analysis on sorption studies of strontium and cesium cations onto a novel impregnated nano-zeolite, Advanced Powder Technology, https://doi.org/10.1016/j.apt.2019.12.031

544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567

568 570

APT 2557

No. of Pages 14, Model 5G

7 January 2020 10

H.S. Hassan et al. / Advanced Powder Technology xxx (xxxx) xxx

Fig. 7. Evaluation of the controlling sorption mechanism using intra-particle diffusion model for Sr2+ and Cs+ sorption.

571 572 573 574 575 576 577 578 579 580 581 582

The higher RC (%) values indicate that the external mass transfer step is a rate-limiting step, while the lower RC (%) values indicate that the intra-particle diffusion step was the ratelimiting step. From Table 2, an increase in the temperature caused an increase in the RC (%) values for both of the studied cations at the initial stage. This increase indicated that the external mass transfer became more significant at higher temperature. For the second stage regarding the two studied cations, no general trend could be observed on the determination of rate limiting step and the relationship between RC (%) and temperature was found to be parabolic with the maximum taken place at 303 K (Fig. 7E).

3.2.4. Equilibrium investigations Equilibrium data denotes to the affinity of the material for both the studied cations, where the sorbed amount increase by increasing the initial ion concentration. Equilibrium sorbed strontium and cesium plot at various equilibrium ion concentrations indicated that the data curved upward (sorbent affinity type isotherm) (Fig. S5), where marginal sorption energy increases with increasing the surface concentration of the studied cations. This phenomenon is assigned for the strong intermolecular attraction amid the sorbent layers and solute-sorbent complexation reactions [33–35]. Equilibrium isotherm data were non-linearly fitted to Langmuir and Freundlich isotherm models to scrutinize the nature of cesium

Please cite this article as: H. S. Hassan, O. A. Abdel Moamen and W. F. Zaher, Adaptive Neuro-Fuzzy inference system analysis on sorption studies of strontium and cesium cations onto a novel impregnated nano-zeolite, Advanced Powder Technology, https://doi.org/10.1016/j.apt.2019.12.031

583 584 585 586 587 588 589 590 591 592 593 594

APT 2557

No. of Pages 14, Model 5G

7 January 2020 H.S. Hassan et al. / Advanced Powder Technology xxx (xxxx) xxx 595 596 597 598

and strontium elimination. Langmuir isotherm model is valid for monolayer sorption onto a surface comprising a determinate number of identical sites. Non-linear Langmuir sorption equation can be expressed as:

599 601 602 603 604 605 606 607 608 609 610 611

Q 0 :b:C e qe ¼ 1 þ b:C e

ð20Þ

o

where Q is the monolayer sorption capacity (mg/g), b is the Langmuir constant. The results of non-linear fitting of the experimental data illustrates the high correlation coefficients values (R2) that indicate that this model offer a good experimental data representation. The Freundlich isotherm model is an empirical formula employed to ascribe the interaction between the sorbed molecules and heterogeneous systems and proposes that the sorption energy declines exponentially on the completion of the sorption centers of a sorbent. The non-linear Freundlich form is expressed as:

612 614

qe ¼ K f :C e 1=n

615

where Kf is the constant (mg/g) that describes a relative sorption capacity of the sorbent and n is the constant indicative of the sorption process intensity.

616 617

ð21Þ

11

The visual examination of the results, Fig. S5, proposes that Sr2+ and Cs+ sorption onto the impregnated zeolite obeys Freundlich isotherm model. Table S.1 shows the results of fitting the experimental data to both the two studied models; it is obviously shown that the correlation coefficients of Freundlich isotherm model have high values signifying that the heterogeneity surface of the synthesized zeolite. Moreover, the residual error plots of both models (Fig. S5.B) were scrutinized and it is clear that the smallest residual errors are obtained from Freundlich isotherm model. It is found that the n values is >1, which affirms a stronger Sr2+ and Cs+ tendency to bind to the zeolite sites [34].

618

3.3. Implementation of adaptive Neuro-Fuzzy inference system model

629

FL modeling was performed using MATLAB 2011a and developed using four input variables including the initial cation concentration (mg/g), pH, contact time (min) and system temperature (K) with ranges considered between [50, 1500], [5,11], [5, 120] and [298, 303], respectively. After executing the training process, the construction of an ANFIS model was obtained, as shown in Fig. (S.6). The training process terminated when the obtained data were associated with the training epoch that exhibits a minimum error. The consumed com-

630

Fig. 8. Surface plots for batch studies (a) pH versus time; (b) concentration versus pH; (c) temperature versus pH; and (e) time versus concentration for Cs+.

Please cite this article as: H. S. Hassan, O. A. Abdel Moamen and W. F. Zaher, Adaptive Neuro-Fuzzy inference system analysis on sorption studies of strontium and cesium cations onto a novel impregnated nano-zeolite, Advanced Powder Technology, https://doi.org/10.1016/j.apt.2019.12.031

619 620 621 622 623 624 625 626 627 628

631 632 633 634 635 636 637 638

APT 2557

No. of Pages 14, Model 5G

7 January 2020 12

H.S. Hassan et al. / Advanced Powder Technology xxx (xxxx) xxx

Fig. 9. Surface plots for batch studies (A) pH versus time; (B) concentration versus pH; (C) temperature versus pH; and (D) time versus concentration for Sr2+.

639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660

putation time to complete the training progress was approximately 0:00:22 sec using a PC memory of 3.00 GB RAM. The output variable of the fuzzy model is the elimination efficacy (%E) of both Sr2+ and Cs+. The membership function parameters for each input were determined throughout the training process. Fig. (S.7) illustrates how the ANFIS operates in various layers. The relationship between the input and the output variables was generated by 16 fuzzy model rules, and accordingly, the three-dimensional surfaces of the fuzzy model rules were formed, as illustrated in Figs. 8 and 9. Fig. 10A and B shows that there is a small deviation between the experimental and predicted data, obviously confirming the satisfactory performance of the ANFIS. Moreover, correlation coefficient values (R2) of over 0.99 and the obtained residual error plots demonstrate good agreement between the experimental and predicted data attained from the model. The list of experimental and predicted fuzzy data besides the input variables in each run is presented in Table 3. The experimental data were fed to the ANFIS-based model via the use of the ‘‘exhsrch” function. This function performs an extensive search within the accessible inputs to elect the most significant input on the elimination of the concerned cations. The data were organized as follows: the first four columns contained input

candidates (initial cation concentration, pH, contact time and temperature), and the final column contained output data (cation elimination efficacy). The left-most input variable had the least RMSE or, in other words, the most significance with respect to the output, as illustrated in (Fig. 10C and D) for Sr2+ and Cs+, respectively. As shown in (Fig. 10C and D), the order of the experimental parameters affecting the Sr2+ and Cs+ elimination efficacy was pH > temperature > time > initial cation concentration. These results clearly indicated that the parameters ‘‘pH” and ‘‘temperature” were the most influential inputs on the elimination efficiencies of Sr2+ and Cs+. As formerly mentioned in Section 3.2.1, the maximum Sr2+ and Cs+ elimination efficacy was observed at an initial pH of 10.0, and the sorption capacity was decreased approximately 2 times by lowering the initial pH to 4.0. Thus, consistent with the ANFIS results, the elimination efficacy of Sr2+ and Cs+ was primarily pH-dependent.

661

4. Conclusion

677

In this work, a faujasite zeolite of type Y was synthesized and impregnated with NAASMS to be used as a potential sorbent for

678

Please cite this article as: H. S. Hassan, O. A. Abdel Moamen and W. F. Zaher, Adaptive Neuro-Fuzzy inference system analysis on sorption studies of strontium and cesium cations onto a novel impregnated nano-zeolite, Advanced Powder Technology, https://doi.org/10.1016/j.apt.2019.12.031

662 663 664 665 666 667 668 669 670 671 672 673 674 675 676

679

APT 2557

No. of Pages 14, Model 5G

7 January 2020 13

H.S. Hassan et al. / Advanced Powder Technology xxx (xxxx) xxx

(A)

(B)

100 yCs+ = 1.0468x -5.2305

7 Cs+

Sr2+

5

90

Residual error

Eliminaon efficacy from fuzzy model

R² = 0.9948

80 Cs+

Sr2+

3

1

-1 0

1

70

2

3

4

5

6

7

8

9

10 11 12 13 14 15 16 17

Experiment number -3

ySr2+ = 1.013x -1.8121 R² = 0.9931 60

-5

60

70

80

90

100

Eliminaon efficacy from experimental data

(C) Cs+

(D) Sr2+

Fig. 10. (A) Comparison amid the experimental data of Sr2+ and Cs+ elimination efficacy (%); (B) the predicted data using fuzzy inference model; (C) input variable’s influence on Cs+; and (D) Sr2+ sorption.

Table 3 Experimental data and predicted response of fuzzy logic model for elimination efficacy of Sr2+ and Cs+ (%) along with four independent variables. Experiment number

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Time (min)

10 120 10 120 10 120 10 120 10 120 10 120 10 120 10 120

PH

5 5 11 11 5 5 11 11 5 5 11 11 5 5 11 11

Conc. (mg/g)

50 50 50 50 1500 1500 1500 1500 50 50 50 50 1500 1500 1500 1500

Temp. (K)

298 298 298 298 298 298 298 298 303 303 303 303 303 303 303 303

Elimination efficacy (%) Experimental data Sr2+ Cs+

Fuzzy model Cs+

Sr2+

77.90 81.62 86.85 89.87 73.70 75.61 84.75 88.18 91.90 99.58 99.66 99.91 76.91 84.67 87.97 93.12

76.21 80.02 84.22 88.33 72.11 73.22 84.33 87.91 90.76 99.22 99.41 98.67 75.83 83.87 86.62 93.02

68.83 73.22 74.92 80.98 64.87 70.51 74.87 80.02 75.65 82.81 84.98 90.99 72.02 80.83 83.84 88.62

68.33 74.93 75.92 81.87 65.71 72.37 75.20 80.98 76.53 83.92 85.03 91.69 73.60 81.06 84.23 89.18

Please cite this article as: H. S. Hassan, O. A. Abdel Moamen and W. F. Zaher, Adaptive Neuro-Fuzzy inference system analysis on sorption studies of strontium and cesium cations onto a novel impregnated nano-zeolite, Advanced Powder Technology, https://doi.org/10.1016/j.apt.2019.12.031

APT 2557

No. of Pages 14, Model 5G

7 January 2020 14

H.S. Hassan et al. / Advanced Powder Technology xxx (xxxx) xxx

711

Sr2+ and Cs+ elimination from a single metal sorption aquatic systems. The elimination reaction of both the concerned cations is endothermic and Cs+ is having higher elimination than Sr2+. The elimination proceeds via a chemical reaction through sharing electrons amid metal cations and synthesized zeolite and the sorption rate is proportional to the number of active sites. The elimination is controlled by two consecutive mechanisms, the initial is external film mass transfer through boundary layer that controls surface sorbent coverage and the second is intra-particle diffusion. The isotherm studies showed that Freundlich isotherm is better fitted than Langmuir isotherm having a correlation coefficient of 0.9964 and 0.9995 for Sr2+ and Cs+, respectively. The experimental results showed a reliance of the diffusion coefficients on the temperature. The ANFIS model can be utilized to forecast the sorption rate rooted in the input variables including the contact time, temperature, pH, and initial cation concentration. The ANFIS model with triangular membership function (trimf) for all input variables and a linear relation for its output gives an acceptable predictive model. To validate the model, the predicted values are compared to the measured values using residual error analysis. The results from the ANFIS showed that the sorption of Sr2+ and Cs+ was mainly affected by the pH and temperature. This study proved that the NAASMS-Z could be used as a promising sorbent for the elimination of the concerned cations from a single metal sorption aquatic system. It is recommended to study the practicality of applying the obtained results from this work and to investigate the regeneration-reuse of the material. Moreover, the waste nano particles are removed from the solution after sorption process with considerable difficulties due to their high dispersion. For overcoming this difficulty, it is also recommended to study the ability of nano particles to be either magnetized or immobilized with another matrix.

712

Declaration of Competing Interest

713 715

The authors state that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest.

716

Appendix A. Supplementary material

717 718

Supplementary data to this article can be found online at https://doi.org/10.1016/j.apt.2019.12.031.

719

References

680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710

714

720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742

[1] R.M. Cornell, Adsorption of cesium on minerals: a review, J. Radioanal. Nucl. Chem. 171 (1992) 483–500. [2] O.A. Abdel Moamen, H.A. Ibrahim, N. Abdelmonem, I.M. Ismail, Thermodynamic analysis for the sorptive removal of cesium and strontium ions onto synthesized magnetic nano zeolite, Microporous Mesoporous Mater. 223 (2016) 187–195. [3] R. Kamaraj, S. Vasudevan, Evaluation of electrocoagulation process for the removal of strontium and cesium from aqueous solution, Chem. Eng. Res. 93 (2015) 522–530. [4] J. Terra, E.R. Dourado, J.G. Eon, D.E. Ellis, G. Gonzalez, A.M. Rossi, The structure of strontium-doped hydroxyapatite: an experimental and theoretical study, Phys. Chem. Chem. Phys. 11 (2009) 568–577. [5] A. Celekli, S. Birecikligil, F. Geyik, H. Bozkurt, Prediction of removal efficiency of Lanaset Red G on walnut husk using artificial neural network model, Bioresour. Technol. 103 (2012) 64–70. [6] A.E. Osmanlioglu, Treatment of radioactive liquid waste by sorption on natural zeolite in Turkey, J. Hazard. Mater. 137 (2006) 332–335. [7] A.M. El-Kamash, Evaluation of zeolite A for the sorptive removal of Cs+ and Sr2+ ions from aqueous solutions using batch and fixed bed column operations, J. Hazard. Mater. 151 (2008) 432–445. [8] A.M. El-Kamash, A.A. Zaki, M. Abd El Geleel, Modeling batch kinetics and thermodynamics of zinc and cadmium ions removal from waste solutions using synthetic zeolite A, J. Hazard. Mater. 127 (2005) 211–220.

[9] H.A. Ibrahium, A.M. El-Kamash, M. Hanafy, N.M. Abdel, Monem, Examination of the use of synthetics zeolite Na-AX blends as backfill material in a radioactive waste disposal facility, J. Chem. Eng. 144 (2008) 67–74. [10] R.O. Abdel Rahman, H.A. Ibrahim, N.M. Abdel Monem, Long-term performance of zeolite Na A-X blend as backfill material in near surface disposal vault, Chem. Eng. J. 149 (2009) 143–152. [11] Guadalupe C. Velazquez-Peñaa, M. Solache-Ríosa , M.T. Olguina , Cheikh Fallc , As(V) sorption by different natural zeolite frameworks modified with Fe, Zr and FeZr, Microporous Mesoporous Mater. 273 (2019) 133–141. [12] O.A. Abdel Moamen, I.M. Ismail, N. Abdelmonem, R.O. Abdel Rahman, Factorial design analysis for optimizing the removal of cesium and strontium ions on synthetic nano-sized zeolite, J. Taiwan Inst. Chem. Eng. 55 (2015) 133–144. [13] M. Wang, Developing bioactive composite materials for tissue replacement, Biomat. 24 (2003) 2133–2151. [14] D.S. Moraes, R.S. Angélica, C.E.F. Costa, G.N. Rocha Filho, J.R. Zamian, Bentonite functionalized with propyl sulfonic acid groups used as catalyst in esterification reactions, Appl. Clay Sci. 51 (2011) 209–213. [15] Maryam Dolatabadi, Marjan Mehrabpour, Morteza Esfandyari, Hosein Alidadi, Mojtaba Davoudi, Modeling of simultaneous adsorption of dye and metal ion by sawdust from aqueous solution using of ANN and ANFIS, Chemom. Intell. Lab. Syst. 181 (2018) 72–78. [16] M.A. Ahmadi, S.R. Shadizadeh, New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept, Fuel 102 (2012) 716–723. [17] A.R. Boga, M. Ozturk, I.B. Topcu, Using ANN and ANFIS to predict the mechanical and chloride permeability properties of concrete containing GGBFS and CNI, Compos B: Eng. 45 (2013) 688–696. [18] M. Ghaedi, R. Hosaininia, A.M. Ghaedi, A. Vafaei, F. Taghizadeh, Adaptive neuro-fuzzy inference system model for adsorption of 1,3,4-thiadiazole-2,5dithiol onto gold nanoparticales-activated carbon, Spectrochim. Acta Part A. 131 (2014) 606–614. [19] S. Mandal, S.S. Mahapatra, R.K. Patel, Neuro fuzzy approach for arsenic (III) and chromium (VI) removal from water, J. Wat. Proc. Eng. 5 (2015) 58–75. [20] K. Aghajani, H. Tayebi, Adaptive Neuro-Fuzzy Inference system analysis on adsorption studies of Reactive Red 198 from aqueous solution by SBA-15/CTAB composite spectrochim, Acta Mol. Biomolec. Spectr. A. 171 (2017) 439–448. [21] H. Javadian, M. Ghasemi, M. Ruiz, A. Sastre, S. Asl, M. Masomi, Fuzzy logic modeling of Pb (II) sorption onto mesoporous NiO/ZnCl2-Rosa Canina-L seeds activated carbon nanocomposite prepared by ultrasoundassisted coprecipitation technique, Ultrason. Sonochem. 40 (2018) 748–762. [22] K. Yetilmezsoy, B. Ozkaya, M. Cakmakci, Artificial intelligence-based prediction models for environmental engineering, Neural Net. W. 21 (2011) 193–218. [23] M. Ghaedi, A.M. Ghaedi, F. Abdi, M. Roosta, A. Vafaei, A. Asghari, Principal component analysis-adaptive neuro-fuzzy inference system modeling and genetic algorithm optimization of adsorption of methylene blue by activated carbon derived from Pistacia khinjuk, Ecotox. Environ. Saf. 96 (2013) 110–117. [24] M.M.J. Treacy, J.B. Higgins, B. Higgins, Collection of Simulated XRD Powder Patterns for Zeolites, Elsevier, 2001. [25] T.M. Salama, I. Othman, M. Sirag, G.A. El-Shobaky, Comparative study of molybdenum oxide in NaY zeolite prepared by conventional impregnation and vapor-phase deposition techniques, Micropor Mesopor Mater. 95 (2006) 312– 320. [26] C. Oliveira, I. Petkowicz, A. Smaniotto, C. Pergher, Magnetic zeolites: a new adsorbent for removal of metallic contaminants from water, Water Res. 38 (2004) 3699–3704. [27] R.O. Abdel Rahman, O.A. Abdel Moamen, M. Hanafy, N.M. Abdel, Monem, Preliminary investigation of zinc transport through zeolite-X barrier: Linear isotherm assumption, Chem. Eng. J. 185 (2012) 61–70. [28] O.A. Abdel Moamen, H.S. Hassan, E.A. El-Sherif, Binary oxide composite adsorbent for copper, nickel and zinc cations removal from aqueous solutions, Desalin Water Treat. 82 (2017) 219–233. [29] M.S. Gasser, H.S. Mekhamer, R.O. Abdel Rahman, Optimization of the utilization of Mg/Fe hydrotalcite like compounds in the removal of Sr(II) from aqueous solution, J. Environ. Chem Eng. 4 (2016) 4619–4630. [30] X. Gaoa, M. Bid, K. Shia, Z. Chaia, W. Wua, Sorption characteristic of uranium (VI) ion onto K-feldspar, Appl. Radiat. Isot. 128 (2017) 311–317. [31] M. Horsfall, A.I. Spiff, Effects of temperature on the sorption of Pb2+ and Cd2+ from aqueous solution by Caladium bicolor (Wild Cocoyam) biomass, Electron J. Biotechn. 5 (2005) 162–169. [32] K.L. Tan, B.H. Hameed, Insight into the adsorption kinetics models for the removal of contaminants from aqueous solutions, J. Taiwan Inst. Chem. Eng. 74 (2017) 25–48. [33] R. Apiratikul, P. Pavasant, Sorption of Cu2+, Cd2+, and Pb2+ using modified zeolite from coal fly ash, Chem. Eng. J. 144 (2008) 245–258. [34] M. Ghaly, Farida M.S.E. El-Dars, M.M. Hegazy, R.O. Abdel Rahman, Evaluation of synthetic Birnessite utilization as a sorbent for cobalt and strontium removal from aqueous solution, Chem. Eng. J. 284 (2016) 1373–1385. [35] R.O. Abdel Rahman, O.A. Abdel Moamen, N. Abdelmonem, I.M. Ismail, Optimizing the removal of strontium and cesium ions from binary solutions on magnetic nano-zeolite using response surface methodology (RSM) and artificial neural network, (ANN), Environ Res J. 173 (2019) 397–410.

Please cite this article as: H. S. Hassan, O. A. Abdel Moamen and W. F. Zaher, Adaptive Neuro-Fuzzy inference system analysis on sorption studies of strontium and cesium cations onto a novel impregnated nano-zeolite, Advanced Powder Technology, https://doi.org/10.1016/j.apt.2019.12.031

743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825