Neural Network Modeling of Heavy Metal Sorption on Lignocellulosic

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NEURAL NETWORK MODELING OF HEAVY METAL SORPTION ON LIGNOCELLULOSIC BIOMASSES: EFFECT OF METALLIC ION PROPERTIES AND SORBENT CHARACTERISTICS Didilia I. Mendoza-Castillo, Nellie Villalobos-Ortega, Adrian Bonilla-Petriciolet, and Juan-Carlos Tapia-Picazo Ind. Eng. Chem. Res., Just Accepted Manuscript • Publication Date (Web): 17 Dec 2014 Downloaded from http://pubs.acs.org on December 18, 2014

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Industrial & Engineering Chemistry Research

NEURAL NETWORK MODELING OF HEAVY METAL SORPTION ON LIGNOCELLULOSIC BIOMASSES: EFFECT OF METALLIC ION PROPERTIES AND SORBENT CHARACTERISTICS D.I. Mendoza-Castillo, N. Villalobos-Ortega, A. Bonilla-Petriciolet *, J.C. Tapia-Picazo Instituto Tecnologico de Aguascalientes, Aguascalientes, Mexico, C.P. 20256

ABSTRACT. This study reports the application of a neural network approach for modeling and analyzing the sorption performance of different lignocellulosic wastes, namely jacaranda fruit, plum kernels and nutshell, for the removal of heavy metal ions (Pb2+, Cd2+, Ni2+ and Zn2+) from aqueous solutions. This ANNs model was used to determine the relevance and importance of both sorbent and pollutant characteristics on the metal sorption kinetics and isotherms. Results of this study highlighted the role of acidic functional groups, lignin composition of tested biomasses, and the pollutant molecular weight in the sorption of heavy metals. The nutshell biomass showed the best sorption properties for heavy metal removal, where its monolayer sorption capacities ranged from 1.0 to 7.0 mg/g. In summary, this study highlights the capabilities of ANNs-based models for analyzing and understanding complex but relevant sorption processes for environmental protection and wastewater treatment.

KEYWORDS: Heavy metal removal, Lignocellulosic biomasses, Sorption, Artificial Neural Network, Water treatment

*

Corresponding author: [email protected], (52)4499105002

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1. INTRODUCTION Lignocellulosic biomasses include a variety of wood, plants, crops and agro-based wastes and by-products, and they have emerged as a promising worldwide source of raw materials for solving environmental pollution problems caused by both inorganic and organic toxic compounds. Different studies reviewed the use and application of lignocellulosic-based sorbents for water and wastewater treatment [1-3]. In particular, raw lignocellulosic biomasses showed competitive sorption properties for the removal of a wide variety of metallic species from aqueous solution [1,2,4,5]. These natural sorbents are attractive for heavy metal removal because they imply low cost, great local availability and simple pre-processing. It has been estimated that several thousands of million tons of these natural sorbents are annually generated worldwide [6] and, consequently, they are considered as renewable and ecofriendly. The chemical composition of lignocellulosic biomasses includes cellulose and hemicellulose (50 – 60%) and lignin (20 – 35%) in different proportion depending on the type and source of the raw material [2,7,8]. These biomasses comprise a variety of functional groups that may interact with heavy metals in aqueous solution via the substitution of hydrogen ions with the metal ions present in the solution, or the donation of an oxygen electron pair to form complexes [1,2,9,10]. In particular, the metal sorption capacities of raw lignocellulosic sorbents are mainly attributed to the presence and content of the acidic functional groups [1]. Metal ions are thought to bind to carboxylic, phenolic, hydroxylic and carbonyl groups via complexation and ion exchange. These functional groups are present in cellulose, hemicellulose, lignin and pectin, which are the main components of lignocellulosic biomasses [9,11]. On the other hand, the removal performance of any sorbent also depends on the operating conditions and the properties of pollutant to be removed. The efficacy of sorption process for heavy metal removal is affected by pH, temperature, the type of metal ion species in solution and its concentration, the type, size and dosage of sorbent, etc. These operating parameters determine the metal sorption rate, selectivity and sorption capacities. In addition, the affinity of a metal ion for the binding sites of the sorbent used in the removal process depends on the physicochemical properties of the metallic species. In fact, the preference/selectivity of lignocellulosic sorbents for the removal of metal ions has been related to the electronegativity, the covalent index, the hydrated ionic radii, among other physicochemical properties of the pollutant to be removed [12,13]. Based on these facts, the sorption of metal ions on lignocellulosic biomasses has been recognized as a complex process involving strong and highly nonlinear interactions between the physicochemical properties of the pollutant to be removed, the sorbent characteristics and the process operating conditions.


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Although several authors have focused on the modeling and prediction of sorption data using different process parameters (e.g., initial pollutant concentration, agitation rate, temperature, pH, particle size, mass-volume ratio), to the best of our knowledge, no studies have analyzed the nonlinear relationships between the sorbent characteristics and the sorbate properties on the removal of heavy metal ions using lignocellulosic biomasses. The main reason for this lack of studies is that traditional approaches used for sorption modeling and conventional kinetic and isotherms equations (e.g., Langmuir and Freundlich isotherms) are not suitable to analyze and assess the complex interactions involved in heavy metal removal. Note that different removal mechanisms can take place simultaneously according to the nature of the active sites of the lignocellulosic biomass and the process operating conditions and, unfortunately, the identification and quantification of the metal-sorbent interactions via experimentation are difficult [14]. This context also limits the developing of chemico-physical mechanistic models, which are based on hypothesized chemicophysical reactions among the binding sites and the sorbate in solution, where the model complexity required for providing a satisfactory correlation performance implies exhaustive sorption studies [15]. Therefore, alternative modeling tools should be used for the correlation and prediction of heavy metal sorption processes using these natural sorbents. In particular, artificial neural networks (ANNs) are black-box models capable of identifying complex and highly nonlinear relationships between several parameters and variables involved in the process under study [16,17]. ANNs-models have been recognized as an alternative and reliable tool for modeling sorption and biosorption processes [18]. These models have been applied in sorption field with promising results, e.g. [16,17,19-24]. In particular, ANNs can be employed for modeling and analyzing the influence of both sorbent characteristics and metal properties on the performance of lignocellulosic sorbents but using less experimental data than those required for developing mechanistic models. Therefore, in this study an ANNs approach has been used for modeling the sorption of heavy metals on lignocellulosic biomasses using relevant characteristics and properties of both the sorbent and the metallic pollutant. Specifically, three different lignocellulosic biomasses (i.e., raw plum kernels, nutshell and jacaranda fruit) have been used to assess their efficacy for the removal of Pb2+, Cd2+, Ni2+ and Zn2+ ions from aqueous solution. ANNs were used as a black box model for the analysis of the nonlinear relationships between the physicochemical properties of the metal to be removed, the biomass characteristics and the sorption operating conditions. Note that traditional sorption models are not suitable to perform this nonlinear analysis. This ANNs-based model and a sensitivity analysis have been applied to analyze the relevance and impact of both sorbent and pollutant characteristics on the modeling of metal sorption kinetics and isotherms. In summary, this 3 ACS Paragon Plus Environment


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study highlights the capabilities of ANNs-based models for analyzing and understanding complex, but relevant, sorption processes for environmental protection and wastewater treatment.

2. METHODOLOGY 2.1 Lignocellulosic biomasses used as heavy metal sorbents Three lignocellulosic biomasses have been used as sorbents of heavy metal ions in aqueous solutions. In particular, the biomasses obtained from jacaranda fruit (JF), nutshell (NS) and plum kernels (PK) have been applied in metal sorption studies. These lignocellulosic materials were washed with deionized water at 50 °C until a constant pH was obtained. Later, all biomasses were dried, crushed and sieved to obtain sorbent particle sizes from 20 to 35 meshes. These sorbent particles were used in the sorption experiments of heavy metal ions. Elemental analysis of all biomasses was performed for determining the carbon, hydrogen, oxygen and nitrogen contents using a LECO analyzer. The pH of zero charge (pHPZC) of all sorbents was determined at batch conditions using the pH drift method [25]. Specifically, NaCl solutions (i.e., 0.01, 0.1 and 1.0 N) with initial pH between 3 and 8 were mixed with tested sorbents at 30 °C for 24 h. The sorbents were separated from the solution by filtration and the pH of filtrate was measured under continuous deaeration using N2, which was used to remove dissolved gases and to stabilize the pH by preventing the dissolution of CO2 and also for avoiding uncertainties in pH measurements for the determination of pHPZC [25]. Results obtained at different NaCl concentrations exhibited a common intersection point, which was then considered for the calculation of pHPZC. Note that this point is the only one where the surface charge is independent of the electrolyte concentration [25]. On the other hand, the specific surface area was estimated using the methylene blue sorption isotherms and the concentration of acidic groups of lignocellulosic sorbents was estimated using the Boehm titration method [26]. The cellulose, hemicellulose and lignin contents of these biomasses were quantified following procedures reported in literature [27]. Finally, SEM micrographs of these biomasses were recorded and the functional groups of all sorbents were identified using FTIR spectra, which were recorded at the wavenumber range of 4000 – 600 cm-1 with a 4 cm-1 resolution.

2.2 Performance evaluation of the lignocellulosic biomasses for the heavy metal removal PK, NS and JF biomasses were used to obtain kinetics and sorption isotherms of Pb2+, Cd2+ Ni2+ and Zn2+ in aqueous solution at batch conditions. Metal solutions were prepared using nitrate salts of each metal (analytical grade) and deionized water. All kinetic and equilibrium sorption experiments were performed at 30 °C, pH of 5.0 ± 0.1 and using a sorbent dosage of 1 mg/mL. 4 ACS Paragon Plus Environment

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Based on the fact that solution pH is a relevant parameter for the sorption of heavy metal ions using lignocellulosic materials, the removal experiments of heavy metals were performed at pH 5 to reduce the degree of protonation of acidic functional groups of all tested sorbents. Note that it is assumed that acidic sites of these biomasses are responsible of the heavy metal removal. This assumption is consistent with results reported in previous studies [28,29]. The operating conditions used for sorption experiments were identified as optimal via preliminary studies. In particular, kinetic experiments were performed using metal solutions with initial concentrations of 40 and 100 mg/L and samples were taken from t = 1 to 24 h. Sorption rates were calculated for all metal ions and biomasses using both the pseudo-first and pseudo-second order kinetic models; while the controlling steps affecting the sorption kinetics of heavy metal ions was studied using an analysis of qt − t1/2 where qt is the metal sorption capacity at time t. On the other hand, the sorption isotherms were obtained using metal initial concentrations from 20 to 250 mg/L and an equilibrium time of 24 h. Monolayer adsorption capacities (qmon) and sorption equilibrium constants (KL) were calculated using the Langmuir isotherm model. After performing sorption experiments, all sorbent samples were removed from the solution by filtration and the metal concentration was determined. Metal concentrations were measured by atomic absorption spectroscopy using a Perkin Elmer AAnalyst 100 spectrometer. These metal quantifications were made with an air-acetylene flame and a hollow cathode lamp. All sorption experiments were performed by triplicate to determine its reproducibility and the average values were used in data analysis. Metal sorption capacities (q, mg/g) of the lignocellulosic biomasses were calculated using a mass balance

q=

(C0 − Ct )V

(1)

m

where Co and Ct are the initial and final concentrations of metal ions given in mg/L, V is the solution volume in L, and m is the amount of lignocellulosic biomass used in the sorption process given in g, respectively.

2.3 An artificial neuronal network model for analyzing the sorption of heavy metals on lignocellulosic biomasses An ANNs approach was used for modeling and analyzing the sorption kinetics and isotherms of heavy metals on selected biomasses. This ANNs model was used as the starting point to determine the relevance and importance of both sorbent and pollutant characteristics on the heavy metal removal. Herein, it is convenient to remark that the application of traditional sorption models such as Langmuir, Freundlich or conventional kinetic equations for this nonlinear analysis is not feasible 5 ACS Paragon Plus Environment


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and straightforward. These models are used to adjust a set of experimental data, which correspond usually to a specific pollutant and fixed operating conditions (i.e., temperature and pH). On the other hand, ANNs models can adjust and model simultaneously all the experimental data set using the characteristics and properties of both metal ions and biomasses, and the sorption operating conditions as input variables. These capabilities allow to predict the performance of sorbents at other scenarios not included in the experimental stage. Based on these facts, ANNs offer advantages for analyzing the nonlinear interactions of selected variables in the metal sorption process. Briefly, a neural network consists of a group of artificial neurons that are ordered into input, hidden and output layers. These artificial neurons are interconnected to each other via connection weights. Mathematically, the net input Yij of the neuron j in the layer i is given by ni −1

Yij = ∑ wijkVi −1,k + θ ij

(2)

Vij = g(Yij )

(3)

k =1

where wijk is the connection weight, Vik is the neuron input and θij is the neuron bias. An activation function g(Yij) is used to calculate the neuron output Vij given the set of neuron inputs. Traditionally, the sigmoid function is used as the activation function for the neurons of hidden layers, while an identity function (i.e., Vij = Yij) can be employed in the output layer. The values of w and θ for each neuron of the ANNs model should be determined via a training process. This training stage is performed using the input data and target output values obtained from the system to be studied. Theoretically, the ANNs approach is capable of modeling complex relationships between inputs and outputs or finding patterns in selected data if a proper training process and ANNs architecture are employed in data modeling. The classical backpropagation algorithm for ANNs training has been used in this study where the ANNs toolbox of MATLAB® was employed with its default parameter values. The proposed ANNs model includes the sorbent characteristics and the properties of metallic ions as input for data analysis of sorption kinetics and isotherms. Specifically, the input data are the experimental sorption data (i.e., C0 and t for kinetics or Ce for isotherms), the sorbent characteristics (i.e., the specific surface area, the content of cellulose, hemicellulose and lignin, and the concentration of acidic groups) and the characteristics of the heavy metal ion (i.e., molecular weight, hydrated ionic radii, electronegativity and hydration energy). These input variables are uncorrelated and have been used for the experimental data modeling because they have been recognized as factors that may influence the performance of heavy metal removal using lignocellulosic biomasses at batch conditions. Tables 1 and 2 show the values of pollutant and sorbent characteristics used for the ANNs modeling of sorption data. The output values of ANNs 6 ACS Paragon Plus Environment

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model were the sorption capacities (i.e., qt or qe) of all heavy metal ions on lignocellulosic sorbents. In particular, three ANNs models were considered for sorption data analysis where different combinations of input variables were used, i.e.: a) ANNs model using sorption data and sorbent characteristics, b) ANNs model using sorption data and pollutant characteristics and c) ANNs model using sorption data, sorbent and pollutant characteristics. The proposed ANNs models are reported in Figure 1 and the experimental sorption data were employed for training (70%), validation (15%) and testing (15%) of the ANNs models. In particular, 169 experimental kinetic data and 92 isotherm data were used in this modeling stage. Trial and error calculations were performed for identifying the proper ANNs architecture for modeling the heavy metal sorption on lignocellulosic biomasses. Each topology was repeated five times to avoid random data correlation caused by the random initialization of training process. Thus, these ANNs models included an input layer, one hidden layer with 10 neurons, and one output layer. Preliminary results showed that a high number of hidden neurons (i.e., > 10) may cause overfitting of the ANNs models. In addition, this ANNs topology offered the best performance for sorption data modeling with the lowest CPU time, which was < 1 second for sorption data correlation of both kinetic and isotherms. A sensitivity analysis was performed using the ANNs model for identifying those characteristics of sorbent and pollutant that have a significant effect on heavy metal removal using lignocellulosic materials.

3. RESULTS AND DISCUSSION Chemical composition and general physicochemical properties of lignocellulosic biomasses used in this study are reported in Table 1. Results of ultimate analysis showed that all biomasses are mainly composed of carbon (C) and oxygen (O) and these elemental compositions agree with the values reported by other authors for similar biomasses, which may have C and O contents of 48 – 52 % and 40 – 46 %, respectively [30-33]. In particular, the ratio O/C of PK, NS and JF is 0.87, 0.78 and 0.98, respectively, which are typical values for lignocellulosic materials [34]. These biomasses show significant variations in cellulose (29 – 50 %) and lignin (26 – 40 %) contents, while the hemicellulose percentage (21 – 25 %) is similar in all sorbents. Note that both PK and NS are mainly constituted by lignin with cellulose and hemicellulose as secondary constituents, while JF showed a higher content of cellulose. The values of pH and pHpzc indicate the acid character of all biomasses, which may facilitate the removal of anions in aqueous solution depending on the operating conditions. These results of surface chemistry are consistent with data obtained from Boehm’s titration, which confirmed the acidic group dominance at the sorbent surfaces. The acid character of lignocellulosic biomasses can be attributed to functional groups that have hydrogen atoms, which may act as electron acceptors [35]. On the other hand, the surface areas of raw 7 ACS Paragon Plus Environment


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biomasses ranged from 23 to 33 m2/g where JF has the highest surface area followed by NS and PK, respectively. Finally, Figure 2 shows the corresponding SEM micrographs of tested sorbents where it is evident that their morphologies are different. However, the textures of all raw biomasses are micro-rough. Figure 3 shows the sorption kinetics for all tested heavy metal ions and lignocellulosic sorbents. Kinetic analysis indicated that there is a fast sorption on the external surface of these sorbents. Figure 4 provides the kinetic sorption rates where the values of rate constants k1 and k2 ranged from 0.42 to 3.98 h-1 and from 0.52 to 4.3 g/mg h, respectively, for tested operating conditions. Overall, sorption rates of Pb2+ were higher than obtained for other metallic ions. This may be due to this metal has the major electronegativity and the lowest ionic radius and, consequently, these properties could favor the rate of mass transfer during removal process. It is convenient to note that the pseudo-second order model offered the best performance for data modeling of sorption kinetics; see results reported in Table 3. On the other hand, the intraparticle diffusion analysis is reported in Figure 5. The multi-linearity behavior of the plots qt – t0.5 indicates that several steps may be involved in the sorption mechanism of heavy metal ions on tested biomasses. Note that the sharp section of these plots indicates a relevant role of the external surface of the biomasses in heavy metal removal. In particular, the functional groups available in the external surface of lignocellulosic materials may play an important role for heavy metal removal. As expected, the intraparticle diffusion is not the rate limiting stage for the sorption of heavy metal ions on these natural sorbents, which have a low surface area. Sorption isotherms obtained in heavy metal removal are reported in Figure 6. Figure 7 shows the monolayer sorption capacities and equilibrium constants calculated with Langmuir model and its relationship with respect to the metal ion, sorbent type and biomass acidity. Overall, metal sorption capacities ranged from 1.0 to 7.0 mg/g for these biomasses, where all sorbents showed the highest metal uptakes for Pb2+. The metal sorption capacities of NS are higher than those obtained for JF and PK. In fact, NS biomass appears to have a more effective interaction of its functional groups and heavy metal ions at the solution. Note that there is no clear relationship between monolayer metal uptakes and biomass acidity (Figure 7c), which indicate again the complex nature of heavy metal sorption on this type of natural sorbents. The metal removal mechanism may differ quantitatively and qualitatively depending on the type of lignocellulosic material, its source and pre-processing. These removal mechanisms may take place simultaneously and it is difficult to distinguish between the single steps [36]. The role of functional groups on heavy metal removal depends on the quantity of sites, its accessibility, chemical state and affinity between the active site and the metal ion [36]. For illustration, Figure 1 8 ACS Paragon Plus Environment

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shows the FTIR spectra of all sorbents before and after heavy metal removal. There are not significant differences in the FTIR characteristic absorption bands of raw sorbents, which indicates that the functional groups on the surface of all biomasses are practically the same. Specifically, FTIR spectra show intense bands located between ~ 3325 and 1020 cm-1, which are associated with the presence of different functional groups of lignocellulosic materials, i.e., aromatic compounds, carbonyl groups, hydroxyl groups and ethers [37]. The broad absorption band around 3325 cm-1 represents typical –OH stretching vibrations of hydroxyl groups [38]. These groups may include aliphatic primary and secondary alcohols and absorbed water found in hemicellulose and cellulose [39,40]. The strong bands located at 2915 – 2842 cm-1 correspond to symmetric and asymmetric – CH stretching of aliphatic groups [38,39,41]. The peak at ~ 1720 cm-1 is associated with carbonyl (C=O) stretching vibrations of aldehyde group [41-43]. This peak could be due to the acetyl groups in the hemicellulose or lignin structure [37]. The absorption band at ~ 1650 cm-1 could be attributed to C=O stretching vibration belonging to conjugated carbonyl of lignin [44]. The peaks located at 1590 and 1510 cm-1 represent the C=C stretching vibrations in aromatic rings of lignin [45,46]. The bands observed at 1460, 1420, and 1360 cm-1 can be attributed to symmetrical C-H bending vibrations in methylene and methyl groups [43,46]. On the other hand, the peaks at ~1225 and 1330 cm-1 might represent the phenolic hydroxyl groups in lignin [37,47]. Finally, the band located at ~1020 cm-1 can be assigned to –CO stretching vibration in hydroxyl groups and/or symmetrical angular deformation of ether-type structures [45,46]. Note that after heavy metal removal, some asymmetrical stretching vibrations were shifted and there is an increase of the intensity of dominant peaks in comparison with those peaks obtained for raw materials. For example, this behavior can be observed at ~3325, 1720, 1225 and 1020 cm-1. These changes may suggest that there are binding processes taking place on the sorbent surface [48]. It appears that the functional groups involved in the sorption process of heavy metals are carboxyl, hydroxyl and carbonyl groups. According to literature [48-50], these functional groups may interact with heavy metal ions via the following reactions 2(≡S-OH) + M2+ → (≡S-O)2M + 2H+

(4)

2(≡S-COOH) + M2+ → (≡S-COO)2M + 2H+

(5)

2(≡S-C6H5-OH) + M2+ → (≡S-C6H5-O)2M + 2H+

(6)

where M2+ corresponds to the heavy metal ion involved in the removal process and S represents the biomass surface. At low pH, the functional groups of lignocellulosic sorbents are protonated and, consequently, restrict the approach of cations due to repulsive forces. As the solution pH increases, the degree of protonation of these functional groups decreases and the functional groups become negatively charged. On the other hand, the heavy metal removal using lignocellulosic materials 9 ACS Paragon Plus Environment


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might involve an ion exchange process between the metal species and Ca2+ ions. This exchange process has been documented for others biomasses containing pectin and hemi-celluloses, which show carboxyl groups linked together by calcium bridges [29,51]. Figure 7d shows that Ca2+ was released during the heavy metal removal using tested biomasses; however, this ion exchange appears to have a minor contribution in heavy metal sorption. Herein, it is convenient to remark that the sorption of heavy metals on lignocellulosic sorbents may also include the ion entrapment in inter- and intrafibrillar capillaries and spaces of structural lignin and polysaccharide networks [11]. These phenomena may occur besides the well-known electrostatic interactions and ion exchange processes that involve the functional groups of the sorbent surface. Finally, JF and NS showed the highest metal uptakes and surface area. But, the differences of the sorption capacities for each metal and these sorbents are not proportional to the changes in the surface area. In summary, these results confirmed that the metal sorption on these biomasses is a multivariable process, which is influenced by the physicochemical properties of both the sorbent and pollutants besides the process operating conditions. Table 4 shows the results of the three ANNs models used for the correlation of sorption kinetics and isotherms. For comparing ANNs models, the Mean Relative Error (MRE) between experimental (qexp) and predicted (qcalc) sorption data was calculated using

MRE =

1 ndat qiexp − qicalc ∑ q exp ndat i =1 i

(7)

MRE values of the different models are also reported in Table 4. Results showed that the ANNs model that used both the sorbent and pollutant characteristics as input variables offered the best performance for modeling the sorption kinetics and isotherms data. In fact, this ANNs model is capable of representing satisfactorily the sorption experimental data and its R values are higher than those obtained for the other ANNs models. Figures 8 - 10 show the results obtained for sorption data modeling with the best ANNs architecture. In particular, Figure 10 shows that this black box model has a proper convergence performance during sorption data modeling. In addition, this ANNs-based model is useful to estimate the relative contributions of the properties of both lignocellulosic sorbent and pollutant on heavy metal removal. Therefore, the ANNs-based sensitivity analysis indicated the relative significance of input variables in ANNs-based kinetic and isotherm modeling. These results are reported in Table 5 for the best ANNs models. Overall, ANNs-based data analysis indicated that the metal uptakes of tested biomasses were mainly affected by the lignin composition and surface chemistry of the raw lignocellulosic material and the molecular weight and hydration energy of the metal ion to be removed. Lignin has been identified as the main component in lignocellulosic biomasses responsible for the metal sorption 10 ACS Paragon Plus Environment

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process [52]. As stated, it contains several functional groups capable of binding heavy metals, e.g. phenolic hydroxyl groups, methoxyl groups and carbonyl groups [2]. Note that results of ANNsbased sensitivity analysis indicate that no clear difference between cellulose and hemicellulose composition could be made in terms of sorption kinetics and isotherms using these biomasses and tested heavy metals. On the other hand, the metal ion properties affect diffusion process and the sorbent–pollutant interactions. In particular, lignocellulosic biomasses showed the best metal uptakes for metal ions with the highest molecular weight and lowest hydration energy. These results are consistent with findings reported in literature, e.g. Nassar [13]. Finally, it is convenient to remark that the ANNs model has been useful for performing the analysis of nonlinear interactions of input variables on both metal sorption kinetics and isotherms. Overall, ANNs model showed best R values than those obtained with traditional kinetic and isotherm models besides this black box model is useful for the simultaneous correlation of all experimental data and it can be used for predicting the sorption performance of lignocellulosic biomasses at other operating conditions. In summary, ANNs-based models offer advantages for data analysis and modeling of complex sorption processes.

4. CONCLUSIONS This study introduces the application of an ANNs-based approach for analyzing and understanding the influence of both sorbent characteristics and metal properties on the removal performance of lignocellulosic biomasses obtained from plum kernels, nutshell and jacaranda fruit. Data analysis of experimental kinetics and isotherms confirmed that the metal sorption on these lignocellulosic biomasses is a multivariable process, which is influenced by the physicochemical properties of both the biomass and heavy metal ion besides the process operating conditions. In particular, ANNs modeling showed that the biomass lignin content, concentration of acidic groups and the metal molecular weight and hydration energy seem to be the most relevant parameters that affect the heavy metal removal using lignocellulosic sorbents. On the other hand, the physicochemical characterization and FTIR results indicated that the carboxyl, hydroxyl and carbonyl functional groups of lignocellulosic biomasses could play a relevant role in the sorption process of heavy metal ions. Finally, the biomass obtained from nutshell showed the best sorption capacities for heavy metal removal and it appears a promising lignocellulosic sorbent for its application in wastewater treatment.

REFERENCES



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[50] Reddy, D.H.K.; Ramana, D.K.V.; Seshaiah, K.; Reddy, A.V.R. Biosorption of Ni(II) from aqueous phase by Moringa oleifera bark, a low cost biosorbent, Desalination 2011, 268, 150. [51] Iqbal, M.; Saeed, A.; Zafar, S.I. FTIR spectrophotometry, kinetics and adsorption isotherms modeling ion exchange, and EDX analysis for understanding the mechanism of Cd2+ and Pb2+ removal by mango peel waste, J. Hazard. Mater. 2009, 164, 161. [52] Velazquez-Jimenez, L.H.; Pavlick, A.; Rangel-Mendez, J.R. Chemical characterization of raw and treated agave bagasse and its potential as adsorbent of metal cations from water, Ind. Crop. Prod. 2013, 43, 200.


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Table captions. Table 1. Results of physicochemical characterization of plum kernels, nut shell and jacaranda fruit used as sorbents of heavy metal ions. Table 2. Metal ion properties used for the ANNs-based modeling of the heavy metal removal using lignocellulosic biomasses. Table 3. Kinetic rates for the sorption of heavy metals on lignocellulosic biomasses. Table 4. Results of ANNs models used for the correlation of sorption kinetics and isotherms of heavy metal removal using lignocellulosic biomasses. Table 5. Sensitivity analysis of the best ANNs models for the data correlation of sorption kinetics and isotherms of heavy metal removal using lignocellulosic biomasses.



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Table 1. Property Component analysis (wt %) Cellulose Hemicellulose Lignin Soluble compounds Elemental analysis (wt %) C H O N Acidity constant, mmol/g Surface area, m2/g pH pHpzc

Lignocellulosic biomass Plum kernels Nut shell Jacaranda fruit 34.44 22.60 40.49 2.47

29.54 25.87 40.50 4.09

50.16 21.46 26.58 1.67

52.00 6.20 45.20 0.30 1.02 23.00 4.39 5.20

52.25 5.15 40.54 0.24 0.87 26.00 4.73 5.31

48.20 4.40 47.20 0.20 1.14 33.00 4.85 5.60


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Table 2. Metal ion Pb2+

Cd2+

Ni2+

Zn2+

207.20

112.40

58.69

65.39

Electronegativity

1.90

1.70

1.90

1.60

Hydrated ionic radii, Å

4.01

4.26

4.25

4.30

Hydration energy, kJ/mol

-1485

-1809

-2106

-2047

Property Molecular weight, g/mol



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Table 3. Biomass

Co, mg/L

Kinetic model 1

Metal

R2

Fobj 2

Pb2+ 0.820 0.152 Cd2+ 0.993 0.020 Ni2+ 0.845 0.122 Zn2+ 0.982 0.061 100 Pb2+ 0.925 0.049 Cd2+ 0.892 0.047 Ni2+ 0.994 0.023 Zn2+ 0.978 0.011 40 Pseudo-second order Pb2+ 0.939 0.056 Cd2+ 0.978 0.031 Ni2+ 0.943 0.044 Zn2+ 0.939 0.114 100 Pb2+ 0.977 0.030 Cd2+ 0.986 0.006 Ni2+ 0.985 0.042 0.984 0.011 Zn2+ NS 40 Pseudo-first order Pb2+ 0.990 0.011 Cd2+ 0.605 0.292 Ni2+ 0.995 0.003 Zn2+ 0.923 0.058 100 Pb2+ 0.977 0.006 Cd2+ 0.825 0.037 Ni2+ 0.967 0.025 Zn2+ 0.766 0.215 40 Pseudo-second order Pb2+ 0.978 0.025 Cd2+ 0.848 0.778 Ni2+ 0.966 0.026 Zn2+ 0.984 0.013 100 Pb2+ 0.979 0.006 Cd2+ 0.972 0.006 Ni2+ 0.966 0.038 Zn2+ 0.895 0.101 JF 40 Pseudo-first order Pb2+ 0.908 0.069 Cd2+ 0.844 0.048 Ni2+ 0.860 0.031 Zn2+ 0.831 0.045 100 Pb2+ 0.937 0.020 Cd2+ 0.967 0.007 0.938 0.014 Ni2+ Zn2+ 0.987 0.008 40 Pseudo-second order Pb2+ 0.985 0.017 Cd2+ 0.977 0.007 Ni2+ 0.988 0.003 Zn2+ 0.972 0.007 100 Pb2+ 0.991 0.002 Cd2+ 0.977 0.005 Ni2+ 0.978 0.529 0.960 0.029 Zn2+ 1 − k1t Model: a) Pseudo-first order model qt = qte (1 − e ) and b) Pseudo-second order model q t PK

2

40

ndat  q exp − q calc  Fobj = ∑  i exp i  qi i =1  

E, %

Pseudo-first order

2


12.40 ± 8.59 4.53 ± 3.12 11.86 ± 6.26 6.58 ± 7.01 7.37 ± 4.33 7.02 ± 4.59 4.53 ± 3.90 2.90 ± 2.84 7.75 ± 4.90 6.12 ± 2.87 7.02 ± 3.87 11.51 ± 5.83 3.98 ± 5.57 2.44 ± 1.71 6.53 ± 4.43 3.54 ± 1.86 2.66 ± 3.13 0.02 ± 0.03 1.90 ± 0.89 8.32 ± 4.07 2.79 ± 1.07 6.57 ± 3.39 5.00 ± 3.64 15.34 ± 9.13 4.66 ± 3.99 12.56 ± 8.48 5.24 ± 3.37 3.89 ± 1.99 2.31 ± 1.85 2.45 ± 1.74 5.72 ± 5.08 10.89 ± 5.44 9.28 ± 3.96 7.73 ± 3.11 6.09 ± 3.03 7.18 ± 3.73 4.61 ± 2.96 2.78 ± 1.37 3.46 ± 3.16 2.68 ± 2.25 4.13 ± 2.98 2.98 ± 1.30 1.76 ± 1.20 2.77 ± 1.73 1.53 ± 0.74 2.44 ± 1.06 2.43 ± 1.39 5.38 ± 3.74 =

qte2 k 2t 1+ qte k 2t

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Table 4. Sorption data Kinetics

Isotherms

ANNs model Sorbent characteristics Metal ion properties Sorbent characteristics + Metal ion properties Sorbent characteristics Metal ion properties Sorbent characteristics + Metal ion properties


R 0.82 0.60 0.99 0.67 0.73 0.97

MRE 0.4772 0.5308 0.0639 0.2064 0.4360 0.0902


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Page 22 of 33

Table 5. ANNs Model Sorption kinetics

Input variable Sorbent characteristics

1 Surface area

2 Lignin composition

Sorption Isotherms

Metal ion properties Sorbent characteristics

Hydration energy Lignin composition

Metal ion properties

Molecular weight

Molecular weight Concentration acidic groups Hydration energy

Rank of relative importance 3 4 Concentration of Cellulose composition acidic groups Hydrated ionic radii Electronegativity of Surface area Hemicellulose composition Hydrated ionic radii Electronegativity


5 Hemicellulose composition Cellulose composition

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Figure captions. Figure 1. ANNs models used for data correlation of heavy metal removal using lignocellulosic biomasses. Input variables: a) Sorbent characteristics, b) Metal ion properties and c) Sorbent characteristics + Metal ion properties. Figure 2. SEM micrographs and FTIR spectra of biomasses obtained from plum kernels, nut shell and jacaranda fruit before and after sorption of heavy metal ions. Figure 3. Sorption kinetics for the removal of a) Pb2+, b) Cd2+, c) Ni2+ and d) Zn2+ ions on lignocellulosic biomasses. Figure 4. Calculated sorption rates for the heavy metal removal using lignocellulosic biomasses. Figure 5. Intraparticle diffusion analysis for the sorption of a) Pb2+, b) Cd2+, c) Ni2+ and d) Zn2+ ions on lignocellulosic biomasses. Initial metal concentration: 40 mg/L (filled symbols) and 100 mg/L (empty symbols). Figure 6. Sorption isotherms of a) Pb2+, b) Cd2+, c) Ni2+ and d) Zn2+ ions on lignocellulosic biomasses. Figure 7. a) Langmuir monolayer sorption capacities, b) sorption equilibrium constants, c) qm versus biomass acidity, and d) calcium released in the aqueous solution for the sorption of heavy metals on lignocellulosic biomasses. Figure 8. Results for the ANNs-modeling of sorption kinetics of heavy metal ions on lignocellulosic biomasses. Figure 9. Results for the ANNs-modeling of sorption isotherms of heavy metal ions on lignocellulosic biomasses. Figure 10. Numerical performance of the best ANNs-model for data correlation of a) kinetics and b) isotherms of sorption of heavy metal ions on lignocellulosic biomasses.



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a) 1. Kinetic or isotherm input data 2. Surface area 3. Acidity 4. Cellulose, % 5. Hemicellulose, % 6. Lignin, %

Output variable for kinetic or isotherm

Input neurons

Hidden Output neurons neurons

b) 1. Kinetic or isotherm input data 2. Molecular weight 3. Electronegativity 4. Hydrated ionic radii 5. Hydration energy

c)


Input neurons

Hidden Output neurons neurons

1. Kinetic or isotherm input data 2. Surface area 3. Acidity 4. Cellulose, % 5. Hemicellulose, % 6. Lignin, % 7. Molecular weight 8. Electronegativity 9. Hydrated ionic radii 10. Hydration energy


Input neurons

Hidden neurons

Output neurons

Figure 1.


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Page 25 of 33

Plum kernels

Cd2+ Pb2+ Ni2+ Zn2+ Raw

4000

3300

2600

1900

1200

500

2600

1900

1200

500

1900

1200 -1

500

Nut shell

Transmittance, %

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Cd2+ Pb2+ Ni2+

Zn2+

Raw 4000

3300

Jacaranda fruit

Cd2+ Pb2+ Ni2+ Zn2+ Raw

4000

3300

SEM micrographs

2600

Wavelength, cm

Figure 2. 25 ACS Paragon Plus Environment


1.0

Ct /Co

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

a)

1.0

0.8

0.9

0.6

0.8

0.4

0.7

0.2

0.6 PK

0.0 0

NS 4

ANNs model

JF 8

12

16

0 0.9

0.8

0.8

0.7

0.7

0.6

0.6 0

4

8

12

16

4

8

12

16

1.0

0.9

0.5

b)

0.5

20

c)

1.0

Page 26 of 33

0.5 20 0 Time, h

20

d)

4

Figure 3.


8

12

16

20

Pseudo-first order model

k1, h-1 k2, g/mg h

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Pseudo-second order model

Page 27 of 33

Metal ion

Biomass

Figure 4.


Metal ion

Biomass


4.8

Intraparticle diffusion

a)

3.0

3.8

2.4

2.9

1.8

1.9

1.2

1.0


b)

0.6 PK

qt, mg/g

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 28 of 33

NS

JF

0.0

0.0 0

1.8

0.9

1.8

2.7

3.6

4.5


c)

0 1.5

1.4

1.2

1.1

0.9

0.7

0.6

0.4

0.3

0.0 0

0.9

1.8

2.7

3.6

0.0 0 4.5 Time 0.5, h

0.9

1.8

2.7

3.6

4.5

2.7

3.6

4.5


d)

0.9

Figure 5.


1.8

Page 29 of 33

7.0

6.0

a)

b)

4.8

5.6 4.2

PK

3.6

2.8

NS

2.4

JF

1.4

qe, mg/g

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1.2

ANNs model

0.0

0.0 0

6.0

50

100

150

200

0

250

5.0

c)

4.8

4.0

3.6

3.0

2.4

2.0

1.2

1.0

0.0 0

50

100

150

200

50

100

150

200

250

50

100

150

200

250

d)

0.0 250 0 Ce, mg/L

Figure 6.



qm, mg/g

a)

Metal ion

Biomass

Metal ion

Biomass

KL, L/mg

b)

8

c)

NS JF

6 4

m

qm, mg/g

PK

2 0 0.850

0.950 1.050 Acidity, mmol/g

1.150

d)

Ca2+, mg/L

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Metal ion Figure 7. 30 ACS Paragon Plus Environment

Biomass

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Validation: R=0.99118

Training: R=0.99861 Output ~= 0.97*Target + 0.051

4 Data Fit Y=T

3.5 3 2.5 2 1.5 1 0.5 1

2

3

4 Data Fit Y=T

3.5 3 2.5 2 1.5 1 0.5

4

1

Target Test: R=0.98229 Data Fit Y=T

3 2.5 2 1.5 1 0.5 1

2

3

4

agclheLipElrEnctq All: R=0.99499

4 3.5

2

Target

Output ~= 0.99*Target + 0.03

ANNs-calculated sorption capacity, mg/g

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


3

4 Data Fit Y=T

3.5 3 2.5 2 1.5 1 0.5

4

Target

1

2

Target

Experimental sorption capacity, mg/g Figure 8.


3

4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ANNs-calculated sorption capacity, mg/g


Experimental sorption capacity, mg/g

Figure 9.


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agclheLipElrEnctq

1

a)

10

Train Validation Test Best

0

Mean squared error

10

-1

10

-2

10

-3

10

0

5

10

15

20

25

Best Validation Performance iter 5 is 0.10575 at epoch 17

b)

2

10 Train Validation Test Best

1

10

Mean squared error

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0

10

-1

10

-2

10 0

5

10

15

23 Epochs Epochs

Figure 10.


20

Neural Network Modeling of Heavy Metal Sorption on Lignocellulosic

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