Simultaneous Removal of Pb, Ni, Mn and Cr Heavy Metals with NA2EDTA: Modelling and Optimisation Using Box-Behnken Design

doi:10.21203/rs.3.rs-4978609/v1

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Simultaneous Removal of Pb, Ni, Mn and Cr Heavy Metals with NA₂EDTA: Modelling and Optimisation Using Box-Behnken Design

https://doi.org/10.21203/rs.3.rs-4978609/v1

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Phosphogypsum (PG) is considered a by-product, characterized as solid waste that occurs for manufacturing phosphoric acid by reacting of the sulfuric acid with the phosphate rock according to the wet method. Heavy metals in PG cause a series of drawbacks and negative impacts for global environment and create various restrictions in PG applications.

In this article, the Box-Behnken Design (BBD) technique was chosen for the simultaneous decrease of lead, nickel, manganese and chromium heavy metals from PG with Na₂EDTA which lead to optimizing factors such as Na₂EDTA concentration, liquid to solid (L/S) ratio and contact time and creating a mathematical model. The optimal process points were identified using the analysis of variance (ANOVA) and the surface response plots. Moreover, the physicochemical properties, before and after purification of PG were studied by XRF, XRD and FTIR methods.

Regarding the results found by applying the response surface design methodology, the coefficients of correlation (R²) of the experimental data and the second-order regression model for Pb, Ni, Mn and Cr were determined as 89.75%, 95.37%, 98.18% and 94.53%, respectively. Generally, high R-squared values state that the experimental data are compatible that the data predicted by the model

Under optimum conditions, Na₂EDTA concentration: 0.055 M, liquid/solid ratio: 20:1 ml/g, contact time: 157 minutes and the removal of lead, nickel, manganese and chromium were 46.65%, 42.31%, 67.02% and 77.9% respectively.

Phosphogypsum

Na2EDTA

Removal of Heavy Metals

Box-behnken experimental design

Phosphogypsum (PG) is an acidic character by-product residue produced in high volumes from the phosphatic fertilizer production industries by the wet manufacturing in order to produce the phosphoric acid (Rutherford et al. 1994). The chemical reaction is as shown in Eq. 1:

Ca₅(PO₄)₃F + 5H₂SO₄ + 10H₂O → 3H₃PO₄ + HF + 5CaSO₄⋅ 2H₂O (1)

In the wet method, at different reaction temperatures in the phosphoric acid manufacturing facility, phosphogypsum is generated in three different crystal structures: dihydrate (CaSO₄.2H₂O), hemihydrate (CaSO₄.1/2H₂O) and CaSO₄ that occurs as anhydrite (Rutherford et al. 1994). One metric ton manufacturing of phosphoric acid (H₃PO₄) generally produces between 4.5 and 5.5 tonnes PG (Yang et al. 2013; Contreras et al. 2018). European PG manufacturing is surmised 21 Mt/year accumulatively gathered from approximatley 30 various places, while the world manufacturing is 100–280 Mt per year (Gijbels et al. 2017).Generally, only 14% of the worldwide manufacturing is recycled, 28% is discharged into flora, fauna and water sources and 58% is being stored which can strongly cause major environmental pollutions (Carmichael et al. 1988). The usage and storage of PG is an important issue for several countries because it creates environmental problems such as water, soil and atmospheric pollution (Chernysh et al. 2021). There are reported events of land contamination, underground water resources pollution, and contamination of vegetable products owing to the presence of PG in many countries such as Brazil, China, Greece, Jordan, Kazakhstan, Poland, Russia, Spain, Turkey and USA (Villa et al. 2009; Nadymova 2016; Tovazhnyansky et al. 2013). Since PG waste is usually carried and disposed in slurry form, PG piles may be influenced by tidal changes and also dissoloution and leaching phenomenas of the elements naturally present in the PG may happen (Reijnders 2007).

PG, which consists essentially of gypsum, also includes P₂O₅, fluorine, radionuclides (Ra, U and Th) important heavy metals such as Cd, As, Pb, Ag, Ba, Cr, Pb, etc. (Hassoune et al. 2021). However, these elements exist on the "Essential and Potentially toxic elements (PTEs)" list of the Environmental Protection Agency (EPA) (Kandil et al. 2017). PG has been widely evaluated in agriculture as a soil additive and fertilizer in several countries for years (Hentati et al. 2015). Recently, Hentati et al. (2015) demonstrated that PG increased textural properties of the soil, calcium intake amount, and quality of crop yield. However, Nayak et al. (2011) reported that it may negatively effect soil quality, harm nutrient security, and also influence human health through the nutrient chain. Additionally, It was also indicated that heavy metals may easily enter food products through the soil and into the human body from food products when gradually be accumulated there and pose serious threats via soil (Yang et al. 2017). In order to manage the content of impurities in PG, researchers have offered methods such as leaching with water, wet sieving, conversion to hemihydrate or anhydrite depending on temperature, neutralization by adding a basic solution (Ca(OH)₂, NH₄OH …), or its effective treatment with acid (sulfuric, citric …) (Singh et al. 1996; Singh 2002; Kandil et al. 2017). Jarosinski et al. (1993) investigated the washing method to recover rare earth metals from PG by using aqueous sulfuric acid solution and then manufacturing of anhydrite form from the purified PG by recrystallisation using sulfuric acid solution with a high molarity. The results have demonstrated that the unwanted impurities were significantly decreased and the attained anhydrite can consequently be employed for plaster manufacturing.

Among the most important features that makes EDTA (ethylenediamine tetra acetic acid) a suitable option for removal are its especially include poor biodegradability, the high removal efficiencies of heavy metal (Tejowulan and Hendershot 1998), availability of suitable recycling methods (Udovic and Lestan 2007) and having minor effects on the soil microbial properties and enzyme activities compared to other agents such as hydrochloric acid (Udovic and Lestan 2012). EDTA is one of the most commonly used synthetic chelating agent with a high heavy metals extract rate (Jez and Lestan 2016). In earlier surveys, scientists have commonly used EDTA to remove heavy metals from sludge (Cheikh et al. 2010; Gitipour et al. 2016), cathode ray tube waste (Pant et al. 2014) and polluted surfaces like soils (Bilgin and Tulun 2016; Voglar and Lestan 2014). In general, most EDTA is denoted by the free acid (H₄Y) and the disodium salt is Na₂H₂Y.2H₂O form. H₄Y has a very low solubility in water, and therefore the disodium salt Na₂H₂Y·2H₂O, in which two of the acid groups are neutralized, is generally used when preparing the solution (Christian et al. 2015). Disodium ethylenediamine tetraacetate is a chelating agent, which is commonly utilized in laboratory and a wide range of industriesi fields (Tan et al. 2019). The molecular forms of EDTA complexes and disodium EDTA salt are presented in Fig. 1.

It is quite desired to investigate multivariant statistical solution to optimize the experimental model parameters. The Box Behnken design (RSM-BBD) approach, one of the response surface methodology is one of successfully applied multivariate statistical tools to optimize the operating parameters and various processes. This method includes multiple regression analysis, diagnostic testing with analysis of variance (ANOVA), and also the fitting of a polynomial equation to explain for whole possible parameter combinations and understandig their impact on the wanted responses. The most important advantage is to gain basic required information and determine the logical optimal conditions of experimental variables for the wanted responses with a smaller number of experiment execution (Raheem et al. 2022). Furthermore, the BBD tool saves cost and time with its high accuracy rate in identifying the impacts of the interaction between on one or more independent variables and estimated responses. Extensively, it gives the best result for the general optimization process, with regards to cost and avoiding experimental runs being conducted in extreme conditions (Mondal et al. 2017; Samuel et al. 2015).

There are no research articles in the literature around the topic of simultaneous removal of manganese, nickel, lead and chromium from phosphogypsum by ethylenediaminetetraacetic acid disodium salt using response surface methodology tool. This study investigates is to optimize parameters such as Na₂EDTA concentration, liquid/solid ratio and contact time for simultaneous decrease of manganese, nickel, lead and chromium from PG using RSM's Box-Behnken Design.

2.1 Materials

Phosphogypsum waste was employed in this investigates obtained from the Toros Tarım A.Ş in Mersin, Turkey. To ensure continuity, samples were taken from at least 10 different points and mixed. Firstly the PG was dried for 48h in 100 degree of celcius then dried samples using an automatic vibratory siever (Retsch) were sieved by a 38µm < PG < 75µm particle size siever. Heavy metal content before purification of phosphogypsum are as shown in Table 1. Na₂EDTA (AnalaR-BHD Chemical) as chelating agent for removal of heavy metals, H₂SO₄ (Sigma Aldrich) for the recovery of Na₂EDTA by precipitation, analytical balance (Radwag AS 220/C/2) for weighing samples, magnetic stirrer (Wisestir MSH-20A) for mixing and oven (Thermomac FCD-3000 Serials) for drying process were used.

Table 1

The major elements found in the PG
Element	As	Cd	Co	Cu	Hg	Ni	Mn	Pb	Cr
Content (ppm)	0.4	0.9	5.5	0.7	0.7	5.0	15.5	5.8	14.0

Actually, the chemical compound of PG in terms of elements is variable, depending on its ingredient in the natural phosphate ore and the manufacturing process of PG (Al-Hwaiti et al. 2010).

2.2 Purıfıcatıon Method of PG

For batch leaching experiments, three different concentrations of Na₂EDTA (0.001 M, 0.005 M and 0.01 M) were used to decrease Pb, Ni, Mn and Cr heavy metals from phosphogypsum. Deionized water was used to prepare the solution at the determined concentrations. Experimental study was carried out at room temperature (~ 25°C), at mixing speed of 170 rpm, liquid/solid ratio of 10:1, 15:1 and 20:1, contact times of 60, 120 and 180 minutes. The filtered solids were then left in the oven at 50 ⁰C for overnight drying. The percentage of Pb, Ni, Mn and Cr removal efficiency (R) were assessed by using the Eq. 2:

R [%]=((C_i – C_f )/ C_i )*100 (2)

where C_i is the initial concentration of heavy metal as mg/kg in PG, and C_f is the final concentration of it after purification process.

2.3 Experimental Design for Optimum Working Conditions

It was optimized by the Box-Behnken design (BBD) to determine the major and interaction impacts on the percent removal of Pb, Ni, Mn and Cr by Na₂EDTA. The BBD experiment design was carried out by evaluating ANOVA with the help of Design Expert 12.0.3 software program. The Box-Behnken design was employed to optimise the removal process, with three factors: Na₂EDTA concentration, time and liquid/solid ratio. BBD was constructed using real and coded values in different levels three independent variables (-1, 0, + 1) as shown in Table 2. Each independent variable is coded according to Eq. 3.

𝑥_𝑖=(D_𝑖−D₀)/ΔD_i (3)

where xi denotes the dimensionless value of the variable, D_i is the real value for the variable, D₀ is the real value for the variable at the center point, and ΔD_i is the step change (Song et al. 2021).

Table 2

Three selected independent variables and levels of the Box-Behnken design
		Level
		Low	Central	High
Factor	Symbol	-1	0	+ 1
Na₂EDTA (mol/l)	A	0.001	0.05	0.1
Liquid/ Solid(ml/g)	B	10:1	15.1	20:1
Time (min.)	C	60	120	180

Here, Na₂EDTA concentration (mol/l), liquid/solid (ml/g) and time (min) are independent input parameters. Moreover, the number of experiments, needed to be applied for BBD Design, was designated using the Eq. 4.

Experiment number = 2k(k − 1) + C₀ (4)

where k is the number of variables, and C₀ is the number of center points (Guvenc and Varank 2020). A total of 17 experimental studies were performed for the three-level three-factor Box-Behnken experimental design. Moreover, center points provide to measure of the pure error of the curvature (Shahawy et al. 2021).

A quadratic polynomial model was employed to analyze and predict the ability to removal of heavy metals determined as shown in Eq. 5.

Y = β_o + \(\:\sum\:{{\beta\:}}_{İ}{X}_{İ}+\sum\:{{\beta\:}}_{İİ}{X}_{İ}^{2}+\sum\:\sum\:{{\beta\:}}_{İ\text{j}}{X}_{İ}{X}_{\text{j}}\) (5)

where Y is the predicted response (extraction recovery); Xi, Xj are the independent variables (Na₂EDTA concentration, liquid/solid ratio and time) that are known for each experimental run. The parameter β₀ is the model constant, β_i is the first-order main effect, β_iis are the quadratic coefficients, and β_ijs are the interaction coefficients (Reghioua et al. 2021).

The quality of the acquired models were designated by the analysis of variance (ANOVA) based on the coefficient of correlation (R²), probability value (p value) and Fisher method (F value), the response surface methodology (RSM) was drawn to improve and enhance the optimization of the processes.

2.4 Characterization and Analysis Method

X-ray diffraction (XRD) patterns were obtained by employing an Inel brand Equinox 1000 model X-Ray Diffractometer (XRD) device at 30 mA and 30 kV a equipped with cobalt (Co) anode. X-ray fluorescence used to define PG's chemical elements polarized energy dispersive X-rayfluorescence spectrometer (SPECTRO XLAB 2000) equipped with a400 W Rh end window tube and a Si(Li) detector with a resolution of 148 eV (1000 cps Mn Ka). Fourier Transform Infrared Spectroscopy (FTIR) analysis of the to investigate the composition of the products was performed using a Shimadzu FTIR-8400 spectrophotometer. Each 0.001 g of studied sample and 0.1 g of pure KBr were uniformly ground, placed into a mold, and then pressed into a transparent thin sheet on a hydraulic press, each spectrum was completed in the range of 400–4000 cm^− 1.

3.1 Optimization of Parameters with Box-Bhenken

To determine the combined impact of the three chosen parameters on the removal of heavy metals Pb, Ni Mn and Cr, the RSM method based on BBD was employed to determine the optimal values. Removal experiments were triplicated and the average values were reported. The experiments were designed as shown in Table 3.

Table 3

Box-Behnken experimental design with three independent variables
Run	A:Na₂EDTA (M)	B:Liquid/solid (ml/g)	C:Time (min.)	Pb removal (%)	Ni removal (%)	Mn removal (%)	Cr removal (%)
1	0.0505	15	120	46.23	43.36	56.22	79.26
2	0.0505	15	120	45.25	46.66	59.12	80.12
3	0.0505	10	60	44.36	40.02	58.26	80.74
4	0.1	10	120	48.27	35	30.23	62.26
5	0.1	15	180	49.26	33.26	35.26	68.63
6	0.001	15	180	31.03	45.26	20.56	48.45
7	0.1	15	60	51.72	32.56	36.56	61.23
8	0.001	15	60	36.3	42.56	20.79	44.32
9	0.0505	10	180	46.55	42.82	62.21	78.23
10	0.0505	20	180	46.92	43.45	74.25	75.27
11	0.0505	15	120	51.72	41.66	62.23	72.23
12	0.0505	20	60	44.2	43.33	56.23	78.12
13	0.1	20	120	48.26	33.33	33.23	69.12
14	0.001	10	120	36.2	51.66	19.23	43.79
15	0.0505	15	120	46.25	43.26	61.36	70.01
16	0.0505	15	120	46.55	42.33	60.22	69.79
17	0.001	20	120	36.2	41.66	20.78	42.2

3.2 Variance Analysis

The accuracy and validity of the model may be assessed by F value and P-value. The P-value for each independent variable is used to determine the level of statistical significance of each variable related to the dependent variable. Fischer’s value should be bigger for a better conformance of the model with the experimental data A low p-value (< 0.05) and high F (> 1) show a better fit of the experimental data to the model results. The coefficient of regression (R²) is employed to state the degree of compatibility of experimental data with model results. Generally, the value of R² ranges between 0 to 1. The R² value must be higher than the value 0.80 for a reasonable fitting between experimental and predicted values (Vitale et al. 2019).

ANOVA is a collection of statistical models used to analyze how independent variables interact with each other and the effects of these interactions on the dependent variable (Segurola et al. 1999). Three separate regression models were created for the removal of Pb, Ni, Mn and Cr from PG. Statistical testing of the regression equations were controlled with the F test, and the fit of the model was evaluated with the quadratic model analysis of variance ANOVA. The ANOVA results of this study shown in Table 4.

Low p-values of the model are 0.0096, 0.0007, < 0.0001 and 0.0012 for Pb Ni, Mn and Cr, respectively. The p-value of 0.05 or lower is commonly considered a statistically significant result (Qu et al. 2017). Moreover, high F-values for Pb (6.81), Ni (16.03), Mn (41.95) and Cr (13.45) indicated that the model was significant. The sum of the square values of the Na₂EDTA concentration effect has high values for Pb (417.32), Ni (569.03), Mn (363.42) and Cr (850.37). These values have a high effect on the removal of heavy metals. Liquid/solid ratios and contact times have a low impact on heavy metal removal due to lower mean square values for each metal.

As it can be seen in Table 4 the high F-value, low p-value and sum of squares values obtained for the model authenticity indicated that the model was significant.

Table 4

ANOVA results of quadratic models
Source	Sum of Squares	df	Mean Square	F-value	p-value
Pb Removal (%)
Model	504.71	9	56.08	6.81	0.0096	significant
A-Na₂EDTA	417.32	1	417.32	50.69	0.0002
B-Sıvı/katı	0.0050	1	0.0050	0.0006	0.9810
C-Süre	0.9941	1	0.9941	0.1208	0.7384
AB	0.0000	1	0.0000	3.037E-06	0.9987
AC	1.97	1	1.97	0.2398	0.6393
BC	0.0702	1	0.0702	0.0085	0.9290
A²	74.16	1	74.16	9.01	0.0199
B²	2.48	1	2.48	0.3007	0.6005
C²	3.58	1	3.58	0.4346	0.5308
Residual	57.62	7	8.23
Lack of Fit	31.31	3	10.44	1.59	0.3252	not significant
Pure Error	26.32	4	6.58
Total	562.33	16
R² = 0.8975, R² (adjusted) = 0.7658, R² (predicted) = 0.0361, Adeq Precision = 7.2028
Ni Removal (%)
Model	622.29	9	69.14	16.03	0.0007	significant
A-Na₂EDTA	569.03	1	569.03	131.94	< 0.0001
B-Sıvı/katı	0.2278	1	0.2278	0.0528	0.8248
C-Süre	0.1984	1	0.1984	0.0460	0.8363
AB	1.53	1	1.53	0.3537	0.5708
AC	9.86	1	9.86	2.29	0.1743
BC	1.80	1	1.80	0.4163	0.5393
A²	21.57	1	21.57	5.00	0.0604
B²	2.81	1	2.81	0.6513	0.4462
C²	14.66	1	14.66	3.40	0.1078
Residual	30.19	7	4.31
Lack of Fit	15.38	3	5.13	1.39	0.3687	not significant
Pure Error	14.81	4	3.70
Total	652.48	16
R² = 0.9537, R² (adjusted) = 0.8942, R² (predicted) = 0.5873, Adeq Precision = 13.6405
Mn Removal (%)
Model	5431.16	9	603.46	41.95	< 0.0001	significant
A-Na₂EDTA	363.42	1	363.42	25.26	0.0015
B-Sıvı/katı	26.50	1	26.50	1.84	0.2169
C-Süre	52.22	1	52.22	3.63	0.0984
AB	0.5256	1	0.5256	0.0365	0.8538
AC	0.2862	1	0.2862	0.0199	0.8918
BC	49.49	1	49.49	3.44	0.1060
A²	4925.88	1	4925.88	342.39	< 0.0001
B²	0.2451	1	0.2451	0.0170	0.8998
C²	29.93	1	29.93	2.08	0.1924
Residual	100.71	7	14.39
Lack of Fit	78.92	3	26.31	4.83	0.0812	not significant
Pure Error	21.79	4	5.45
Total	5531.87	16
R² = 0.9818, R² (adjusted) = 0.9584, R² (predicted) = 0.7656, Adeq Precision = 18.2050
Cr Removal (%)
Model	2774.60	9	308.29	13.45	0.0012	significant
A-Na₂EDTA	850.37	1	850.37	37.09	0.0005
B-Sıvı/katı	0.0120	1	0.0120	0.0005	0.9824
C-Süre	4.76	1	4.76	0.2076	0.6625
AB	17.85	1	17.85	0.7786	0.4068
AC	2.67	1	2.67	0.1166	0.7428
BC	0.0289	1	0.0289	0.0013	0.9727
A²	1889.88	1	1889.88	82.43	< 0.0001
B²	6.54	1	6.54	0.2854	0.6098
C²	27.63	1	27.63	1.20	0.3086
Residual	160.49	7	22.93
Lack of Fit	58.99	3	19.66	0.7748	0.5658	not significant
Pure Error	101.50	4	25.38
Total	2935.08	16
R² = 0.9453, R² (adjusted) = 0.8750, R² (predicted) = 0.6244, Adeq Precision = 10.1035

The Lack of Fit test was performed to evaluate the adequacy of the model. The Lack of Fit F-values for Pb, Ni, Mn, and Cr are 1.59, 1.39, 4.83, and 0.77, respectively. Model Lack of fit values were determined to be insignificant for all four treatments. Lack of fit p value was ≥ 0.05 for each element, making lack of fit not significant for all four models. The lack of fit test due to pure error is insignificant and the non-significance of this term indicates the accuracy of the model (Sahu et al. 2017). Additionally, the lack of fit value being insignificant is an indicator that the model shows a high level of predictability performance (Guvenc and Varank 2020).

Regression coefficients (R²); Pb (0.8975), Ni (0.9537), Mn (0.9818) and Cr (0.9453) obtained indicated that Pb (0.1025%), Ni (0.0463%), Mn (0.0182%), and Cr (0.0547%) were not sufficiently described by the model due to error. Statistical analysis of the regression model, which showed low correlation values for Pb removal, indicates that this process negatively affects both the linear and quadratic model by the Na₂EDTA concentration.

Moreover, the adjusted R² and predicted R² values of the model are Pb (0.7658 − 0.0361), Ni (0.8942 − 0.5873), Mn (0.9584 − 0.7656) and Cr (0.8750 − 0.6244). The adjusted and predicted R² values of the model for the four elements are in reasonable conformity. Therefore, this quadratic model can be used to predict in the best possible values of factors at maximum simultaneous extraction of Pb, Ni, Mn and Cr.

Adeq Precision values were also found to be high. Adeq Precision measures the signal to noise ratio and it is desirable that the ratio be above 4 (Yuana et al. 2019). Ratios of 7.2028, 13.6405, 18.2050 and 10.1035 for Pb, Ni, Mn and Cr, respectively, showed appropriate and adequate signal. Thus, this model showed that it could be employed to study and create the design space.

For the linear model, the adjusted and predicted R² values were low, although the p-value of Pb removal was significant. However, there was Pb removal. Therefore, the findings obtained for each element indicate that the model obtained is valid for the experimental study. Although the p value of Pb removal was significant, the adjusted R² and predicted R² values were low for the linear model.

3.3 Mathematical Model Fitting

The four empirical models were generated from both experiment data and quadratic polynomial equations. To analyze the empirical association between predicted the removal of heavy metals and effective variables i.e., the combined effects of A (Na₂EDTA concentration), B (liquid/solid ratio), and C (contact time), quadratic polynomial equations were expressed as follows: (6)–(9);

Pb removal (Y₁) = 47.20 + 7.22A + 0.0250B – 0.3525C – 0.0025AB + 0.7025AC +

0.1325BC – 4.20A² – 0.7688B² – 0.9237C² (6)

Ni removal (Y₂) = 43.45–5.87A – 0.9662B + 0.7900C + 2.08AB – 0.500AC –

0.6700BC – 3.52A² + 0.4767B² – 1.53C² (7)

Mn removal (Y₃) = 59.83 + 6.74A + 1.82B + 2.56C + 0.3625AB – 0.2675AC +

3.52BC – 34.20A² + 0.2413B² + 2.67C² (8)

Cr removal (Y₄) = 74.28 + 10.31A – 0.0388B + 0.771C + 2.11AB + 0.8175AC –

0.0850BC – 21.19A² + 1.25B² + 2.56C² (9)

In the case of removing Mn, Ni, Pb and Cr, A, B, C, AB, AC, BC, A², B² and C² would be our significant model terms.

3.4 Analysis of Residual Plots

Figure 2 shows the comparative effects of all independent variables on the removal efficiency of Pb, Ni, Mn and Cr with the perturbation plot.

The Na₂EDTA concentration showed a sharp curvature (parameter A). This have shows that the removal efficiency of Pb, Ni, Mn, Cr is very responsive to this factor. The liquid/solid ratio (B) and contact time (C) curve are less responsive for Pb, Ni, Mn, Cr removal efficiency. In addition, the Na₂EDTA concentration term of the quadratic model was significant for Pb (0.0002), Ni (0.0003), Mn (0.0015) and Cr (0.0005) (p < 0.01), indicating that Na₂EDTA concentration is an significant factor influencing the removal efficiency.

The normality of the data was assessed by means of the normal probability plot in Fig. 3 indicating that the data points fall either on or near the straight line. This plot indicates if the residuals normally distributed. The points should lay along a roughly straight line in a normal distribution (Saha et al. 2018).

Residual values are randomly distributed at the top and bottom of the normal distribution line and may be said to be located quite close to the line.

The adequacy of the model can be evaluated by implementing the definition plots of the predicted values against the actual values. According to Fig. 4, the predicted versus true plot converges along a straight line, showing that the quadratic regression model is satisfactory. The points lay along a straight line and can be assumed to be normally distributed as a result.

The normal distributions of the residuals express the trueness of the presumptions, and the independence of the residuals (Natarajan and Ponnaiah 2017).

It has been shown that the predicted values and experimental data are in perfect conformity with each other, and the resulting response surface model fits well with the regression model, which demonstrates its adequacy for successful prediction of Mn, Cr, Ni and Pb removal. This observation indicated that the experimental results for this study are quite admissible. Moreover, the R² value being greater than 0.80 is adequate to verify the suitability between the experimental and predicted values (Ölmez 2009).

The scattering nature of these plots is illustrated by the plots in Fig. 5, which are suitable for the corresponding optimization of the model. In the graph, a random distribution is expected around the zero line.

It did not clearly show a pattern, which confirms the constant variance assumption. In the case of element removals, all data points were determined to be within admissible ranges for Pb (31.03–51.72%), Ni (32.56–51.66%), Mn (19-74.25%), and Cr (42.2-80.74%), while all data points indicate a good fit of the model.

3.5 Response Surface Plots

Three-dimensional (3D) response surface plots, which are graphical diagrams of regression equations were used to show the mutual influence of two factors while all other factors were held at constant levels (Hadiani et al. 2018). Analysis of the plotted response surfaces allows identification of areas where the tested input parameters mutually produced the most advantageous response (Choińska-Pulita et al. 2018). 3D surface plots showed that A (Na₂EDTA concentration), B (liquid/solid ratio) and C (contact time) were important variables to achieve high removal percentage.

Figure 6 shows the relationship of Na₂EDTA concentration and liquid/solid ratio on the removal efficiency of Pb Ni, Mn and Cr elements. At this point, the contact time was kept constant at 120 minutes.

The removal efficiency of Mn was reduced from 20.78–19.23% with a decrease in the liquid/solid ratio of Pb, Ni, Mn and Cr from 20 to 10 ml/g, while the removal efficiencies were increased for Ni from 50.25% to %51.66 and for Cr from 42.2–43.79% and a linear effect was noticed for Pb. The removal efficiency were increased for Pb from 36.2–48.26%, for Ni from 31.12–50.25% with an increase in the Na₂EDTA concentration in Pb, Ni, Mn and Cr experiments from 0.001 M to 0.1 M, while the removal efficiency of Mn decreased from 33.23–20.78%, and for Cr from 69.12–42.2%.

Figure 7 shows the interaction between Na₂EDTA concentration and contact time for Pb Ni, Mn and Cr elements and their impact on the removal efficiency. At this point, the liquid/solid ratio was kept constant at 15 ml/mol.

The removal efficiency of Cr was decreased from 48.45–44.32% by decreasing the contact time from 180 to 60 minutes, while the removal efficiency were increased for Pb from 31.03–36.3% and from 45.26–49.23% for Ni, a linear effect is showed for Mn. The removal efficiency were increased for Pb from 31.03–49.26%, for Mn from 20.56–35.26% and for Cr from 48.45–68.63% with an increase in the Na₂EDTA concentration of Pb, Ni, Mn and Cr from 0.001 M to 0.1 M, while the removal efficiency of Ni decreased from 45.26–32.56%.

Figure 8 shows the effect of interaction of liquid/solid ratio and contact time on the removal efficiency of Pb Ni, Mn and Cr elements. At this point, the Na₂EDTA concentration was kept constant at 0.05 M.

The removal efficiency of Pb, Ni, Mn and Cr shows a linear effect between the liquid/solid ratio (10–20 ml/g) and time (60–180) minutes.

3.6 Confirmation for the Optimum Model

It was performed to designate the optimum values of process variables such as Na₂EDTA concentration, liquid/solid ratio and contact time for simultaneous removal of lead, nickel, manganese and chromium by implementing models obtained from the experimental results. The optimal reaction conditions and corresponding predictive values for the independent variables are as follows: Na₂EDTA concentration is 0.055 M, liquid/solid ratio is 20 ml/g, and contact time is 157 minutes.

These independent variables are common values for all tested process parameters. It showed that under optimal conditions, Pb, Ni, Mn, and Cr predicted by the model should result in removal values of 46.63%, 42.29%, 67.04% and 77.92%, respectively. To demonstrate the accuracy of the model, additional experiments were performed under optimal conditions. The actual results of removal efficiencies obtained in the experimental study are Pb (46.65 ± 0.02%), Ni (42.31 ± 0.02%), Mn (67.02 ± 0.02%) and Cr (77.9 ± 0.02%). This showed sufficient consistency between the actual value and the predicted values (p > 0.05) (Yuana et al. 2019).

3.7 Fourier transformation infrared spectroscopy (FTIR) analysis

In order to investigate the preservation of the structure of PG during the application of Na₂EDTA at different concentrations, the FTIR analysis spectra of phosphogypsum before and after purification the maximum removal of Pb, Cr, Ni and Mn are given in Fig. 9.

The fundamental vibrational modes of phosphogypsum were assigned to the peaks in FTIR spectra. The peaks observed at approximately 3556 cm^− 1 and 3402 cm^− 1 are attributed to O-H stretching vibrations of the cristalized water in PG. The peaks at approximately 1689 and 1620 cm^− 1 are characterized the O-H bending vibrations of the crystallized water. Two bands at 601 and 671 cm^− 1 are attributed to asymmetric vibration mode of SO₄²⁻. As a result FTIR peaks were compatible with those in previous literature (Chernysh et al. 2018). Obviously, Na₂EDTA is suitable for PG solid waste containing Mn, Ni, Cr and Pb contaminations.

3.8 X-ray diffraction (XRD) analysis

The morphologıcal crystalinity alteratıon in phosphogypsum phase in 2 steps (before and after) of purification and the maximum removal for Pb, Cr, Ni and Mn are given in Fig. 10.

The XRD pattern shows that the main component of the PG sample was gypsum (CaSO₄·2H₂O) with a small amount of brushite (CaHPO₄·2H₂O) and quartz (SiO₂). After using Na₂EDTA, it was determined that the calcium sulfate dihydrate, silica and calcium phosphate phases in the phosphogypsum dissoluted, however no phase transition happen.

3.9 Recoverability of Na₂EDTA

Since there are still a significant quantity of authentic EDTA in the aqueous solution after the removal procedure, it is economically important to recover and reuse this chelator. In different studies, various procedures have been offered to recover EDTA and metals from the heavy metal complex of EDTA after the extraction procedure (Peters 1999).

The pH value of the solution after the extraction process was measured to be 4.69, and the pH value was reduced to 2.0 by adding sulphuric acid (H₂SO₄) drop-by-drop via the precipitation method. After, filtration was done. The Na₂EDTA remaining on the filter paper was washed once with distilled water and left to dry in an oven at 50 ⁰C for overnight. With this study, 53.4% of Na₂EDTA were recovered. The FTIR spectra of fresh Na₂EDTA and Na₂EDTA recovered by precipitation are shown in Fig. 11.

The infrared spectrum of fresh Na₂EDTA with first time recycled Na₂EDTA showed similar bands. Accordingly, the region between 3600 − 3000 cm^− 1 (3518, 3402, 3387, 3032 and 3016) are assigned to the existence of hydroxyl groups due to O–H and N-H stretching vibration. The vibrational bands located near 2700 − 2500 cm^− 1 (2669 and 2584) are characteristic of formation of dimers for carboxylic acid and are attributed to the C–O stretching and O–H bending absorption modes. The bands existed at 1681 and 1627 cm^− 1 are related to carboxylic groups. The peaks observed at 1481 and 1419 cm^− 1 are associated with the symmetrical carboxyl stretching band, while the bands at 1396, 1319 and 1311 cm^− 1 are also attributed to carboxyl groups. The absorption bands between 900 − 700 cm^− 1 (771 and 709) originated from vibrations modes of C-H stretching and bending. These data are consistent with previous studies (Goel et al. 2009, Lanigan and Pidsosny 2007)

The impact of using Na₂EDTA as a chelating agent in Mn, Ni, Pb and Cr extractions has been shown by FTIR and XRD analyzes which demonstrate that chelating process did not negatively alter or influence the physical and chemical of PG.

While choosing the quadratic model, four empirical models were created. determined by ANOVA analysis. Three-dimensional response surface plots were drawn using these regression models. These plots indicated a more effective on the removal of heavy metal ions the Na₂EDTA concentration than liquid/solid ratio and contact time.

According to the findings, optimized parameters were defined as contact time of 157 minutes, liquid/solid ratio of 20.0 ml/g and Na₂EDTA concentration 0.055 M. Under these conditions, the estimated removal efficiency of the model was determined as Cr (77.92%) > Mn (67.04%) > Pb (46.63%) > Ni (42.29%), while the removal efficiency obtained during the investigation was Cr (77.92%) > Mn (67.04%) > Pb (46.63%) > Ni (42.29%). Accordingly, the regression models and optimum conditions obtained in the study are compatible with the experimental results.

With this study, the levels of heavy metal contents from PG heaps are at a level that will shed light on the studies to be done in this area and contribute to the use of resources.

Author Contribution

EÇ conducted experimental studies and wrote the main manuscript textSE drafted the work and approved the version to be publishedBoth authors reviewed the manuscript

Acknowledgement

AcknowledgementsThis study is financially supported by The Scientific and Technological Research Council of Turkey (TÜBİTAK, Project No: 118C085) and conducted by the co-operation of Ankara University and Toros Agri R&D Center within the scope of TÜBİTAK 2244 Industrial PhD Fellowship Program. The authors would like to thank the Earth Sciences Application and Research Center (YEBİM) from Ankara University for XRF and XRD analyses.

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No competing interests reported.

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Simultaneous Removal of Pb, Ni, Mn and Cr Heavy Metals with NA₂EDTA: Modelling and Optimisation Using Box-Behnken Design

Status:

Version 1

Abstract

Figures

1. INTRODUCTION

2. MATERIALS AND METHODS

2.1 Materials

2.2 Purıfıcatıon Method of PG

2.3 Experimental Design for Optimum Working Conditions

2.4 Characterization and Analysis Method

3. RESULTS AND DISCUSSION

3.1 Optimization of Parameters with Box-Bhenken

3.2 Variance Analysis

3.3 Mathematical Model Fitting

3.4 Analysis of Residual Plots

3.5 Response Surface Plots

3.6 Confirmation for the Optimum Model

3.7 Fourier transformation infrared spectroscopy (FTIR) analysis

3.8 X-ray diffraction (XRD) analysis

3.9 Recoverability of Na₂EDTA

4. Conclusions

Declarations

Author Contribution

Acknowledgement

References

Additional Declarations

Supplementary Files

Status:

Version 1

Simultaneous Removal of Pb, Ni, Mn and Cr Heavy Metals with NA2EDTA: Modelling and Optimisation Using Box-Behnken Design

Status:

Version 1

Abstract

Figures

1. INTRODUCTION

2. MATERIALS AND METHODS

2.1 Materials

2.2 Purıfıcatıon Method of PG

2.3 Experimental Design for Optimum Working Conditions

2.4 Characterization and Analysis Method

3. RESULTS AND DISCUSSION

3.1 Optimization of Parameters with Box-Bhenken

3.2 Variance Analysis

3.3 Mathematical Model Fitting

3.4 Analysis of Residual Plots

3.5 Response Surface Plots

3.6 Confirmation for the Optimum Model

3.7 Fourier transformation infrared spectroscopy (FTIR) analysis

3.8 X-ray diffraction (XRD) analysis

3.9 Recoverability of Na2EDTA

4. Conclusions

Declarations

Author Contribution

Acknowledgement

References

Additional Declarations

Supplementary Files

Status:

Version 1

Simultaneous Removal of Pb, Ni, Mn and Cr Heavy Metals with NA₂EDTA: Modelling and Optimisation Using Box-Behnken Design

3.9 Recoverability of Na₂EDTA