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. 2019 Dec 24;28:105045. doi: 10.1016/j.dib.2019.105045

Scavenging of caffeine from aqueous medium through optimized H3PO4-activated Acacia mangium wood activated carbon: Statistical data of optimization

Mohammed Danish a,∗∗, Janine Birnbach b, Mohamad Nasir Mohamad Ibrahim c,∗, Rokiah Hashim d
PMCID: PMC6948124  PMID: 31921950

Abstract

The optimization data presented here are part of the study planned to remove the caffeine from aqueous solution through the large surface area optimized H3PO4-activated Acacia mangium wood activated carbon (OAMW-AC). The maximum adsorption capacity of the OAMW-AC for caffeine adsorption was achieved (30.3 mg/g) through optimized independent variables such as, OAMW-AC dosage (3.0 g/L), initial caffeine concentration (100 mg/L), contact time (60 min), and solution pH (7.7). The adsorption capacity of OAMW-AC was optimized with the help of rotatable central composite design of response surface methodology. Under the stated optimized conditions for maximum adsorption capacity, the removal efficiency was calculated to be 93%. The statistical significance of the data set was tested through the analysis of variance (ANOVA) study. Data confirmed the statistical model for caffeine adsorption was significant. The regression coefficient (R2) of curve fitting through the quadratic model was found to be 0.9832, and the adjusted regression coefficient was observed to be 0.9675.

Keywords: Activated carbon, Acacia mangium wood, Caffeine, Optimization, Response surface methodology

Specifications Table

Subject Chemical Engineering
Specific subject area Process chemistry and Technology
Type of data Table
Graph
Fig.
How data were acquired An experimental investigation based on the rotatable central composite design of response surface methodology approach. Using Stat-Ease Design-Expert Version 6.0.6 software.
Data format Raw
Analyzed
Filtered
Parameters for data collection Adsorbent and adsorbate contact time (min), adsorbent dosage (g/L), initial caffeine concentration (mg/L), and solution pH.
Description of data collection Based on the designed experiment for caffeine adsorption, thirty experiments were carried out and at the end of each experiment the residual concentration of the caffeine was analyzed using UV–Vis spectroscopy at λ-max 274 nm.
Data source location Institution: Bioresource research lab, School of industrial Technology, University Sains Malaysia, Penang 11800, Pulau Pinang, Malaysia
City/Town/Region: Georgetown, Penang
Country: Malaysia
Data accessibility With the article
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Value of the Data
  • •

    The data set reported in this article will help the researcher to understand the effect of operating parameters such as contact time, adsorbent dosage, initial concentration, and solution pH, on the adsorption capacity of wood based activated carbon (OAMW-AC) against caffeine molecules.

  • •

    The adsorption data of caffeine was analyzed through central composite design of RSM approach [[1], [2], [3], [4]]. Therefore, the data related to the optimization conditions and the determination of the effect of each parameter will be very understandable for Environmental science experts.

  • •

    The data modelling of the caffeine adsorption will help researchers to predict the effect of studied independent variables with different values on the adsorption capacity.

  • •

    This dataset will also be helpful to wastewater treatment industries for efficient removal of caffeine through OAMW activated carbon.

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1. Data

Based on the earlier reported results on caffeine adsorption [[5], [6], [7], [8]], it was observed that caffeine adsorption parameters such ascontact time, adsorbent dosage, initial concentration, and solution pH were not optimized by the previous researchers. In this data article, the optimized parameters with their statistical significance are reported. The experimental variables and their response with ranges and standard deviations are illustrated in Table 1. The dataset contains results of rotatable central composite design of design of experiment software version 6. The experiments were conducted in batch mode, after each experiment the residual caffeine concentrations were calculated using UV–Vis spectroscopy (Hitachi U2000) at λmax 274 nm. Table 2 describes the experimental plan for different combinations of independent variables and their corresponding results on adsorption capacity. As a result, the adsorption capacity varied from 3.7 to 40.0 mg/g with a standard deviation of 8.8 mg/g. Fig. 1 contains six contour plot, each plot depicts the change in adsorption capacity of OAMW-AC when two independent variables changes simultaneously, while other two independent variables kept constant. The adsorption capacity lines shown in the contour plot is above and below of the optimized independent variables, therefore, the values are less than the optimized response (adsorption capacity 30.3 mg/g).

Table 1.

Variables, ranges, standard deviation, and response design summary.

Name Units Type Std. Dev. Low High
Contact time min Factor 1 60 175
OAMW-AC dosage g/L Factor 0.6066 3 7
Initial caffeine concentration mg/L Factor 8.7 50 100
pH Factor 0.38 4 8
Adsorption capacity mg/g Response 8.7861 4.9 40.3
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Table 2.

Parameters and design layout for planned design of experiments.

Sdt Run Variables
 Response
Contact time (min) Adsorbent dose (g/L) Adsorbate concentration (mg/L) pH Adsorption capacity (mg/g)
15 1 60.0 7.0 100 8.0 12.6
9 2 60.0 3.0 50 8.0 14.4
17 3 2.5 5.0 75 6.0 13.3
28 4 117.5 5.0 75 6.0 11.1
30 5 117.5 5.0 75 6.0 11.4
13 6 60.0 3.0 100 8.0 30.1
7 7 60.0 7.0 100 4.0 12.3
10 8 175.0 3.0 50 8.0 14.0
22 9 117.5 5.0 125 6.0 21.9
24 10 117.5 5.0 75 10.0 13.3
20 11 117.5 9.0 75 6.0 7.4
29 12 117.5 5.0 75 6.0 13.7
25 13 117.5 5.0 75 6.0 13.5
3 14 60.0 7.0 50 4.0 6.4
21 15 117.5 5.0 25 6.0 4.9
14 16 175.0 3.0 100 8.0 29.5
1 17 60.0 3.0 50 4.0 14.4
11 18 60.0 7.0 50 8.0 6.3
26 19 117.5 5.0 75 6.0 13.5
27 20 117.5 5.0 75 6.0 13.3
8 21 175.0 7.0 100 4.0 13.1
23 22 117.5 5.0 75 2.0 12.0
4 23 175.0 7.0 50 4.0 6.4
2 24 175.0 3.0 50 4.0 14.8
12 25 175.0 7.0 50 8.0 6.4
6 26 175.0 3.0 100 4.0 30.0
18 27 232.5 5.0 75 6.0 13.6
19 28 117.5 1.0 75 6.0 40.3
5 29 60.0 3.0 100 4.0 30.2
16 30 175.0 7.0 100 8.0 12.9
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Fig. 1.

Fig. 1

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Contour plots showing change in the adsorption capacity of OAMW-AC with changing two variables simultaneously.

A correlation matrix of regression coefficient and a correlation matrix of factors (Pearson's r)were generated and displayed in Table 3 and Table 4,‘A’ is the contact time (min), ‘B’ is the adsorbent dose (g/L), ‘C’ is the adsorbate concentration (mg/L), and ‘D’ is the pH of the solution. Furthermore, the variance inflation factor (VIF) and the power at 5% alpha level for effect of ½, 1, and 2 standard deviations were determined (Table 5). The degrees of freedom can be found in Table 6. Additionally, the leverages derived from the (X’X)−1 are stated in Table 7. Fig. 2 shows the perturbation of the StdErr of design.

Table 3.

Correlation matrix of the regression coefficient.

Intercept A B C D A2 B2 C2
Intercept 1.000
A −0.000 1.000
B −0.000 −0.000 1.000
C −0.000 −0.000 −0.000 1.000
D −0.000 −0.000 −0.000 −0.000 1.000
A2 −0.535 −0.000 −0.000 −0.000 −0.000 1.000
B2 −0.535 −0.000 −0.000 −0.000 −0.000 0.143 1.000
C2 −0.535 −0.000 −0.000 −0.000 −0.000 0.143 0.143 1.000
D2 −0.535 −0.000 −0.000 −0.000 −0.000 0.143 0.143 0.143
AB −0.000 −0.000 −0.000 −0.000 −0.000 −0.000 −0.000 −0.000
AC −0.000 −0.000 −0.000 −0.000 −0.000 −0.000 −0.000 −0.000
AD −0.000 −0.000 −0.000 −0.000 −0.000 −0.000 −0.000 −0.000
BC −0.000 −0.000 −0.000 −0.000 −0.000 −0.000 −0.000 −0.000
BD −0.000 −0.000 −0.000 −0.000 −0.000 −0.000 −0.000 −0.000
CD −0.000 −0.000 −0.000 −0.000 −0.000 −0.000 −0.000 −0.000
D2 AB AC AD BC BD CD
D2 1.000
AB −0.000 1.000
AC −0.000 −0.000 1.000
AD −0.000 −0.000 −0.000 1.000
BC −0.000 −0.000 −0.000 −0.000 1.000
BD −0.000 −0.000 −0.000 −0.000 −0.000 1.000
CD −0.000 −0.000 −0.000 −0.000 −0.000 −0.000 1.000
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Table 4.

Correlation matrix of factors.

A B C D A2 B2 C2
A 1.000
B −0.000 1.000
C −0.000 −0.000 1.000
D −0.000 −0.000 −0.000 1.000
A2 −0.000 −0.000 −0.000 −0.000 1.000
B2 −0.000 −0.000 −0.000 −0.000 −0.111 1.000
C2 −0.000 −0.000 −0.000 −0.000 −0.111 −0.111 1.000
D2 −0.000 −0.000 −0.000 −0.000 −0.111 −0.111 −0.111
AB −0.000 −0.000 −0.000 −0.000 −0.000 −0.000 −0.000
AC −0.000 −0.000 −0.000 −0.000 −0.000 −0.000 −0.000
AD −0.000 −0.000 −0.000 −0.000 −0.000 −0.000 −0.000
BC −0.000 −0.000 −0.000 −0.000 −0.000 −0.000 −0.000
BD −0.000 −0.000 −0.000 −0.000 −0.000 −0.000 −0.000
CD −0.000 −0.000 −0.000 −0.000 −0.000 −0.000 −0.000
D2 AB AC AD BC BD CD
D2 1.000
AB −0.000 1.000
AC −0.000 −0.000 1.000
AD −0.000 −0.000 −0.000 1.000
BC −0.000 −0.000 −0.000 −0.000 1.000
BD −0.000 −0.000 −0.000 −0.000 −0.000 1.000
CD −0.000 −0.000 −0.000 −0.000 −0.000 −0.000 1.000
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Table 5.

VIF and power at 5% alpha level.

Term
 Std. Error VIF Ri2 Power at 5% alpha level for effect of
½ Std. Dev. 1 Std. Dev. 2 Std. Dev.
A 0.20 1.00 0.0000 20.9% 63.0% 99.5%
B 0.20 1.00 0.0000 20.9% 63.0% 99.5%
C 0.20 1.00 0.0000 20.9% 63.0% 99.5%
D 0.20 1.00 0.0000 20.9% 63.0% 99.5%
A2 0.19 1.05 0.0476 68.7% 99.8% 99.9%
B2 0.19 1.05 0.0476 68.7% 99.8% 99.9%
C2 0.19 1.05 0.0476 68.7% 99.8% 99.9%
D2 0.19 1.05 0.0476 68.7% 99.8% 99.9%
AB 0.25 1.00 0.0000 15.5% 46.5% 96.2%
AC 0.25 1.00 0.0000 15.5% 46.5% 96.2%
AD 0.25 1.00 0.0000 15.5% 46.5% 96.2%
BC 0.25 1.00 0.0000 15.5% 46.5% 96.2%
BD 0.25 1.00 0.0000 15.5% 46.5% 96.2%
CD 0.25 1.00 0.0000 15.5% 46.5% 96.2%
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Table 6.

Degrees of freedom for statistical evaluation.

Model 14
Residuals 15
 Lack Of Fit 10
 Pure Error 5
Corr Total 29
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Table 7.

Measures derived from (X’X)−1 matrix.

Std Leverage Point Type
1 0.5833 Fact
2 0.5833 Fact
3 0.5833 Fact
4 0.5833 Fact
5 0.5833 Fact
6 0.5833 Fact
7 0.5833 Fact
8 0.5833 Fact
9 0.5833 Fact
10 0.5833 Fact
11 0.5833 Fact
12 0.5833 Fact
13 0.5833 Fact
14 0.5833 Fact
15 0.5833 Fact
16 0.5833 Fact
17 0.5833 Axial
18 0.5833 Axial
19 0.5833 Axial
20 0.5833 Axial
21 0.5833 Axial
22 0.5833 Axial
23 0.5833 Axial
24 0.5833 Axial
25 0.1667 Center
26 0.1667 Center
27 0.1667 Center
28 0.1667 Center
29 0.1667 Center
30 0.1667 Center
Average 0.5000
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Fig. 2.

Fig. 2

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Perturbation plots for the statistical design.

The model was analyzed through a sequential model sum of squares (Table 8), a lack of fit test (Table 9) and model summary statistics (Table 10). The data of the analysis of variance is described in Table 11. There is a 0.01% chance that this model could occur due to noise and an 21.5% chance that the F-value of lack of fit occurs due to noise. The adeq. Precision for the design of experiment is 31.6. Table 12 shows the factors for the equation to predict the adsorption capacity and Table 13 represented the diagnostics case in statistical design. In addition to the normal plot of residuals. Fig. 3 illustrate the studentized residuals [a] depending on the predicted [b], run number [c], contact time [d], OAMW-AC dosage [e], initial caffeine concentration [f] and pH [g]. Fig. 4 shows the Outlier t [a], Cook's Distance [b] and leverage [c] against run number and the predicted against actual [d]. The box-cox plot for power transforms can be seen in Fig. 5.

Table 8.

Sequential model sum of squares.

Source Sum of Squares DF Mean Square F Value Prob > F
Mean 6961.63 1 6961.63
Linear 1775.47 4 443.87 32.73 <0.0001
2FI 85.18 6 14.20 1.06 0.4183
Quadratic 218.26 4 54.56 22.98 <0.0001
Cubic 28.68 8 3.58 3.62 0.0538
Residual 6.94 7 0.99
Total 9076.16 30 302.54
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Table 9.

Lack of fit tests.

Source Sum of Squares DF Mean Square F Value Prob > F
Linear 332.18 20 16.61 12.08 0.0058
2FI 247.00 14 17.64 12.83 0.0054
Quadratic 28.74 10 2.87 2.09 0.2151
Cubic 0.066 2 0.033 0.024 0.9765
Pure Error 6.88 5 1.38
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Table 10.

Model summary statistics.

Source Std. Dev. R-Squared Adjusted R-Squared Predicted R-Squared PRESS
Linear 3.68 0.8397 0.8140 0.7597 508.18
2FI 3.68 0.8799 0.8167 0.7923 439.13
Quadratic 1.54 0.9832 0.9674 0.9170 175.46
Cubic 1.00 0.9967 0.9864 0.9908 19.38
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Table 11.

Analysis of variance (ANOVA).

Source Sum of Squares DF Mean Square F value Prob > F
Model 2078.91 14 148.49 62.54 <0.0001
 A 0.042 1 0.042 0.018 0.8964
 B 1159.26 1 1159.26 488.21 <0.0001
 C 616.11 1 616.11 259.47 <0.0001
 D 0.060 1 0.060 0.025 0.8758
 A2 0.88 1 0.88 0.37 0.5517
 B2 211.85 1 211.85 89.22 <0.0001
 C2 0.76 1 0.76 0.32 0.5795
 D2 0.012 1 0.012 0.005 0.9445
 AB 0.25 1 0.25 0.11 0.7501
 AC 0.000 1 0.000 0.000 0.9745
 AD 0.16 1 0.16 0.067 0.7987
 BC 84.64 1 84.64 35.65 <0.0001
 BD 0.12 1 0.12 0.052 0.8234
 CD 0.010 1 0.010 0.000 0.9491
Residual 35.62 15 2.37
 Lack of Fit 28.74 10 2.87 2.09 0.2151
 Pure Error 6.88 5 1.38
Cor Total 2114.53 29
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Table 12.

Factors for the equation.

Factor Coefficient Estimate DF Standard Error 95% Cl Low 95% Cl High VIF
Intercept 12.75 1 0.63 11.41 14.09
A 0.042 1 0.31 −0.63 0.71 1.00
B −6.95 1 0.31 −7.62 −6.28 1.00
C 5.07 1 0.31 4.40 5.74 1.00
D 0.050 1 0.31 −0.62 0.72 1.00
A2 0.18 1 0.29 −0.45 0.81 1.05
B2 2.78 1 0.29 2.15 3.41 1.05
C2 0.17 1 0.29 −0.46 0.79 1.05
D2 −0.021 1 0.29 −0.65 0.61 1.05
AB 0.13 1 0.39 −0.70 0.95 1.00
AC 0.012 1 0.39 −0.81 0.83 1.00
AD −0.10 1 0.39 −0.92 0.72 1.00
BC −2.30 1 0.39 −3.12 −1.48 1.00
BD 0.088 1 0.39 −0.73 0.91 1.00
CD 0.025 1 0.30 −0.80 0.85 1.00
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Table 13.

Diagnostics case statistics.

Standard Order Actual Value Predicted Value Residual Leverage Student Residual Cook's Distance Outliner t Run order
1 14.40 15.50 −1.10 0.583 −1.102 0.113 −1.110 17
2 14.80 15.50 −0.70 0.583 −0.708 0.047 −0.696 24
3 6.40 5.77 0.63 0.583 0.633 0.037 0.619 14
4 6.40 6.28 0.12 0.583 0.121 0.001 0.117 23
5 30.20 30.15 0.046 0.583 0.046 0.000 0.045 29
6 30.00 30.21 −0.21 0.583 −0.214 0.004 −0.207 26
7 12.30 11.23 1.07 0.583 1.077 0.108 1.083 7
8 13.10 11.79 1.31 0.583 1.320 0.163 1.356 21
9 14.40 15.57 −1.17 0.583 −1.177 0.129 −1.194 2
10 14.00 15.18 −1.18 0.583 −1.185 0.131 −1.203 8
11 6.30 6.20 0.10 0.583 0.105 0.001 0.101 18
12 6.40 6.30 0.096 0.583 0.096 0.001 0.093 25
13 30.10 30.33 −0.23 0.583 −0.230 0.005 −0.223 6
14 29.50 29.99 −0.49 0.583 −0.490 0.022 −0.477 16
15 12.60 11.75 0.85 0.583 0.850 0.067 0.842 1
16 12.90 11.91 0.99 0.583 0.993 0.092 0.992 30
17 13.30 13.38 −0.083 0.583 −0.084 0.001 −0.081 3
18 13.60 13.55 0.050 0.583 0.050 0.000 0.049 27
19 40.30 37.77 2.53 0.583 2.547 0.605 3.266 28
20 7.40 9.97 −2.57 0.583 −2.580 0.621 −3.343 11
21 4.90 3.28 1.62 0.583 1.625 0.247 1.730 15
22 21.90 23.55 −1.65 0.583 −1.659 0.257 −1.773 9
23 12.00 12.57 −0.57 0.583 −0.570 0.030 −0.556 22
24 13.30 12.77 0.53 0.583 0.536 0.027 0.523 10
25 13.50 12.75 0.75 0.167 0.533 0.004 0.520 13
26 13.50 12.75 0.75 0.167 0.533 0.004 0.520 19
27 13.30 12.75 0.55 0.167 0.391 0.002 0.380 20
28 11.10 12.75 −1.65 0.167 −1.173 0.018 −1.189 4
29 13.70 12.75 0.95 0.167 0.675 0.006 0.663 12
30 11.40 12.75 −1.35 0.167 −0.960 0.012 −0.957 5
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Fig. 3.

Fig. 3

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Plot of the studentized residuals [a] depending on, predicted value of adsorption capacity [b], run number [c], contact time [d], OAMW-AC dosage [e], initial caffeine concentration [f] and solution pH [g].

Fig. 4.

Fig. 4

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Outlier t [a], Cook's Distance [b] and leverage [c] against run number and the predicted against actual [d].

Fig. 5.

Fig. 5

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Box-Cox plot for power transforms.

Finally, the optimum independent variables for caffeine adsorption, outcome response as adsorption capacity, and propagation of error in the results due to deviations in the independent variables are represented in Fig. 6. The descriptive plot for propagation error in the adsorption capacity owing to deviations in the independent variables, considering two variables at a time, is represented through six plots as shown in Fig. 7.

Fig. 6.

Fig. 6

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Adsorption capacity optimization output for selected parameters taken within the range.

Fig. 7.

Fig. 7

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The propagation of error in the adsorption capacity of OAMW-AC.

2. Experimental design, materials, and methods

The Experimental Design was calculated through the software Design Expert (version 6.0.6 Stat-Ease Inc. Minneapolis, USA). The activated carbon was produced from wood sawdust of Acacia mangium by the method described by Danish et al., 2014 [9]. The flow diagram of the experiment conducted to generate this data set is shown in Fig. 8. Effect of contact time on the caffeine adsorption was studied at the time interval of 2.5 min, 60 min, 117.5 min, 175 min, and 232.5 min. The initial concentration of caffeine varies at 25.00 (±0.35) mg/L, 50.00 (±1.92) mg/L, 75.00 (±2.73) mg/L, 100.00 (±1.71) mg/L, and 125.00 (±3.99) mg/L; and the effect of pH on OAMW-AC were studied at five different pH levels: 2.0 (±0.08), 4.0 (±0.15), 6.0 (±0.11), 8.0 (±0.08), and 10.0 (±0.10) for caffeine; by using 50 mg, 150 mg, 250 mg, 350 mg, and 450 mg in 50 mL of caffeine solution. The solutions of caffeine were prepared by diluting a stock solution (0.5 g in 1 L flask). Each solution was measured by a UV–Vis spectrometer at λ-max (maximum wavelength) 274 nm before the adsorption of caffeine to determine the exact initial concentration. Thirty experiments were conducted under the conditions which are shown in Table 2, after the adsorption had occurred, the OAMW-AC was filtrated, and the caffeine concentration was determined again. The adsorption capacity qe (mg/g) was calculated using the following equation [[10], [11], [12]]:

qe=(ci−ce)cAC (1)

where, Ci is the initial concentration of caffeine (mg/L), Ce the concentration of caffeine after adsorption (mg/L) and CAC the dosage of added OAMW-AC (g/L). For the calibration, five standards were measured within the linear range of 0.1–0.8 at the same wavelength. The average of the linear regression coefficient for all conducted calibrations was 0.999.

Fig. 8.

Fig. 8

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Flow diagram of optimization experiments.

Acknowledgments

Authors are thankful to Malaysian Institute of Chemical and Bioengineering Technology (MICET), Universiti Kuala Lumpur (UniKL), and School of Chemical Sciences and School of Industrial Technology, Universiti Sains Malaysia (USM) for providing research facilities during the experiment. Special thanks also for Prof. Dr. Farook Adam for letting us using his furance. We are also acknowledging USM Research University Grant: 1001/PKIMIA/801170 and DAAD RISE fellowship to Janine Birnbach.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.dib.2019.105045.

Contributor Information

Mohammed Danish, Email: mdanish@unikl.edu.my.

Mohamad Nasir Mohamad Ibrahim, Email: mnm@usm.my.

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Multimedia component 1
mmc1.xlsx (25.1KB, xlsx)
Multimedia component 2
mmc2.xml (311B, xml)

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