Zn(II) removal from wastewater by an alkali-activated material prepared from steel industry slags: optimization and modelling of a fixed-bed process

Figures & data

Table 1. The operational factors and their levels.

Factor	Coded Level
	−1.682	−1	0	1	1.682
$C_0$ (mg l^−1)	73.18	80.00	90.00	100.00	106.82
$Q$ (ml min^−1)	1.64	3.00	5.00	7.00	8.36
$m_{\mathrm{ads}}$ (g)	0.477	1.50	3.00	4.50	5.523

Figure 1. Illustration of the experimental setup.

Table 2. Results of the SSA, PV and PSD analyses. See ESI for a description of the analyses.

Sample name	Specific surface area (m^2/g)^a	Total pore volume (cm^3/g)^b	Cumulative pore volume (cm^3/g)^c	< 2 nm (%)^c	2–50 nm (%)^c	> 50 nm (%)^c
AAM after experiment 18	243	0.259	0.257	9	88	3
AAM after experiment 11	247	0.269	0.329	18	76	6
AAM after experiment 9	257	0.275	0.292	5	88	6
AAM after acid wash	172	0.252	0.302	9	88	3
AAM, unwashed	7	0.015	0.0177	4	91	5
BFS	1	0.0021	0.0038	5	77	18
LFS	3	0.006	0.0138	3	75	22

Note: ^a Calculated from the BET-isotherm.

^b Calculated from the experimental adsorption isotherm at $p/p_0=0.95$.

^c PSD evaluated using the Barret-Joyner-Halenda (BJH) formalism.

Table 3. The experimental design and the observed response values. The experiments were performed in the order given in the second column.

Exp.	Run Order	$C_0$ (mg l^−1)	$Q$ (ml min^−1)	mads (g)	EAP (%)	FBU	$q_{\mathrm{sat}}$ (mg g^−1)	$q_b$ (mg g^−1)	$l_{\mathrm{MTZ}}$ (cm)
1	8	80	3	1.5	37.54	0.21	14.40	3.01	2.54
2	4	100	3	1.5	36.63	0.24	12.71	3.05	2.08
3	2	80	7	1.5	34.52	0.18	12.46	2.18	3.74
4	9	100	7	1.5	35.27	0.13	10.81	1.42	4.15
5	12	80	3	4.5	46.80	0.48	14.46	6.87	3.41
6	17	100	3	4.5	57.22	0.53	12.62	6.64	2.50
7	5	80	7	4.5	43.89	0.40	13.31	5.31	4.08
8	16	100	7	4.5	42.24	0.33	14.05	4.68	5.02
9	18	73.18	5	3	41.63	0.29	15.79	4.60	3.48
10	10	106.82	5	3	41.20	0.31	11.48	3.61	3.44
11	14	90	1.64	3	49.90	0.41	17.25	7.03	2.56
12	7	90	8.36	3	36.87	0.21	11.46	2.45	4.99
13	11	90	5	0.477	28.07	0.06	13.18	0.81	3.12
14	6	90	5	5.523	46.83	0.48	13.42	6.42	4.42
15	15	90	5	3	51.11	0.32	10.57	3.43	2.85
16	1	90	5	3	39.67	0.34	12.89	4.36	3.64
17	13	90	5	3	39.32	0.28	12.96	3.64	3.94
18	3	90	5	3	43.79	0.31	12.28	3.75	3.68

Table 4. Summary of fit for the five studied responses.

Response	$R^2$	$Q^2$	Pregression	Plack of fit	Terms^a
FBU	0.986	0.964	0.000	0.881	7
$q_{\mathrm{b}}$	0.946	0.870	0.000	0.405	4
$l_{\mathrm{MTZ}}$	0.904	0.847	0.000	0.972	5
EAP	0.720	0.601	0.000	0.901	3
$q_{\mathrm{sat}}$	0.483	0.214	0.007	0.437	3

Note:^a Constant included.

Figure 2. The effects of factors to the responses according to Equations (16)–(19). The limits of the 95% confidence intervals are indicated by the curves with red dots.

Figure 3. Experimental BTCs from experiments 13, 14 and 18 with differing bed heights ($m_{\mathrm{ads}}$) other conditions being the same. $C_0\approx$ 90 mg/l and $Q\approx$ 5 ml/min. Effluent pH 4.9–5.5.

Figure 4. The BTC from experiment 18 and the curves fitted with the logistic, MDR, and Chu models. Equation (13) has been used as the objective function in nonlinear regression. $Q=$ 5 ml/min, $C_0=$ 90 mg/l and effluent pH $\approx$ 5.2.

Figure 5. Comparison of the curves fitted to the BTC data from experiment 18 using nonlinear regression with different objective functions. (a) Logistic model, (b) MDR model and (c) Chu model.

Table 5. Experimental $q_{\mathrm{b}}$ values and the values calculated from the fitted MDR equations. The type of regression is given in the column header.

Exp No	$q_{\mathrm{b},\exp }$ (mg/g)	$q_{\mathrm{b},\mathrm{calc}}$ lin. (mg/g)	${\displaystyle \frac{|q_{\mathrm{b},\mathrm{calc}}-q_{\mathrm{b},\exp }|}{q_{\mathrm{b},\exp }}\cdot 100\text{\%}}$	$q_{\mathrm{b},\mathrm{calc}}$ nonlin, Equation (13) (mg/g)	${\displaystyle \frac{|q_{\mathrm{b},\mathrm{calc}}-q_{\mathrm{b},\exp }|}{q_{\mathrm{b},\exp }}\cdot 100\text{\%}}$	$q_{\mathrm{b},\mathrm{calc}}$ nonlin, Equation (14) (mg/g)	${\displaystyle \frac{|q_{\mathrm{b},\mathrm{calc}}-q_{\mathrm{b},\exp }|}{q_{\mathrm{b},\exp }}\cdot 100\text{\%}}$
1	3.01	2.58	14.20	2.28	24.24	3.04	0.89
2	3.05	2.22	27.20	1.82	40.25	2.89	5.31
3	2.18	1.64	24.92	1.52	30.49	2.06	5.66
4	1.42	1.15	18.82	1.05	25.68	1.37	3.37
5	6.87	4.42	35.76	3.58	47.86	6.13	10.81
6	6.64	5.79	12.79	5.05	23.92	6.72	1.24
7	5.31	4.16	21.80	3.87	27.12	5.34	0.52
8	4.68	4.10	12.35	3.54	24.36	4.75	1.68
9	4.60	3.96	13.93	3.34	27.41	4.60	0.12
10	3.61	2.60	28.09	1.97	45.55	3.48	3.60
11	7.03	5.55	21.08	4.49	36.19	6.78	3.61
12	2.45	1.96	19.93	1.65	32.51	2.38	2.92
13	0.81	0.33	59.65	0.35	57.27	0.30	63.57
14	6.42	5.02	21.92	4.91	23.64	6.30	1.88
15	3.43	3.09	9.91	3.17	7.35	3.39	0.99
16	4.36	3.27	24.84	3.01	31.00	4.23	2.92
17	3.64	2.93	19.46	2.66	26.85	3.39	6.80
18	3.75	3.08	17.83	2.87	23.46	3.63	3.31

Table 6. Experimental $q_{\mathrm{sat}}$ values and the values calculated from the fitted MDR equations. The type of regression is given in the column header.

Exp No	$q_{\mathrm{sat},\exp }$ (mg/g)	$q_{\mathrm{sat},\mathrm{calc}}$ lin. (mg/g)	${\displaystyle \frac{|q_{\mathrm{sat},\mathrm{calc}}-q_{\mathrm{sat},\exp }|}{q_{\mathrm{sat},\exp }}\cdot 100\text{\%}}$	$q_{\mathrm{sat},\mathrm{calc}}$ nonlin, Equation(13) (mg/g)	${\displaystyle \frac{|q_{\mathrm{sat},\mathrm{calc}}-q_{\mathrm{sat},\exp }|}{q_{\mathrm{sat},\exp }}\cdot 100\text{\%}}$	$q_{\mathrm{sat},\mathrm{calc}}$ nonlin, Equation (14) (mg/g)	${\displaystyle \frac{|q_{\mathrm{sat},\mathrm{calc}}-q_{\mathrm{sat},\exp }|}{q_{\mathrm{sat},\exp }}\cdot 100\text{\%}}$
1	14.40	13.92	3.35	13.47	6.48	12.29	14.68
2	12.71	12.50	1.66	12.07	4.98	10.98	13.60
3	12.46	12.77	2.45	11.85	4.89	10.64	14.65
4	10.81	11.09	2.63	10.67	1.31	9.85	8.92
5	14.46	14.40	0.40	13.62	5.83	12.74	11.88
6	12.62	13.13	4.00	12.41	1.70	10.90	13.69
7	13.31	13.18	1.03	11.63	12.64	10.23	23.19
8	14.05	14.06	0.10	12.96	7.72	10.72	23.65
9	15.79	15.22	3.59	14.88	5.77	12.40	21.45
10	11.48	11.68	1.77	10.92	4.90	9.74	15.14
11	17.25	17.36	0.66	16.83	2.41	15.40	10.74
12	11.46	11.54	0.67	10.98	4.23	9.25	19.31
13	13.18	13.37	1.46	13.44	2.00	14.58	10.69
14	13.42	14.42	7.45	12.60	6.14	11.13	17.05
15	10.57	11.09	4.97	10.40	1.60	9.88	6.53
16	12.89	12.79	0.75	11.43	11.34	9.84	23.68
17	12.96	12.61	2.72	11.86	8.47	10.90	15.90
18	12.28	12.39	0.93	11.50	6.36	10.35	15.74

Table 7. Comparison of the maximum experimentally obtained column saturation capacity ($q_{\mathrm{sat}}$) in this study to column capacities reported for other adsorbents.

Adsorbent		$m_{\mathrm{ads}}$ (g)	$h$ (cm)	$Q$ (ml/min)	$C_0$ (mg/l)	pH	$q_{\mathrm{sat}}$ (mg/g)	Ref.
Aluminosilicate based materials	AAM from steel industry slags	3	1.7	1.64	90	5.5–6.0	17.27	This study
	Red loess	30		0.5	500	6.70	2.69	[45]
	Australian zeolite	40	12	8	5	6.0	1.19	[46]
	Ausralian zeolite with iron-coating	40	12	8	5	6.0	1.66	[46]
	Functionalized Zeolites	33.25	98	13	24	5	5.91^a	[47]
	Yellow loess	30		0.5	500	6.58	7.55	[45]
Biomass based materials	Biosorbent composed of non-viable Pseudomonas putida trapped in agar beads	-	23	0.5	60	4.3	0.117	[48]
	Raw chicken feathers	5	8	5	20	5	1.73	[2]
	Stratified bone char	2	-	6	200	5	41.1	[26]
	Green coconut shells washed with NaOH	600	160	200	200	6–13	25.76^b	[49]
	Commercial bone char	13	-	4	60	5	3.04	[8]
	Chicken bone biochar	9.1	-	0.021	50	-	178	[27]

Note: ^aBreakthrough capacity (definition of the breakthrough point unclear, probably C/C₀ = 0.1).

^b Zn at least partly removed by precipitation as Zn(OH)₂. Value reported for C/C₀= 0.5.

Punrattanasin P, Sariem P. Adsorption of copper, zinc, and nickel using loesses as adsorbents by column studies. Pol J Environ Stud. 2015;24:1267–1275.

 Web of Science ®Google Scholar

Nguyen TC, Loganathan P, Nguyen TV, et al. Simultaneous adsorption of Cd, Cr, Cu, Pb, and Zn by an iron-coated Australian zeolite in batch and fixed-bed column studies. Chem Eng J. 2015;270:393–404.

 Web of Science ®Google Scholar

Moreno-Pérez J, Bonilla-Petriciolet A, Rojas-Mayorga CK, et al. Adsorption of zinc ions on bone char using helical coil-packed bed columns and its mass transfer modeling. Desalin Water Treat. 2016;57:24200–24209.

 Web of Science ®Google Scholar

Meringer A, Liffourrena AS, Heredia RM, et al. Removal of copper and/or zinc ions from synthetic solutions by immobilized, non-viable bacterial biomass: batch and fixed-bed column lab-scale study. J Biotechnol. 2021;328:87–94.

 PubMed Web of Science ®Google Scholar

Aguayo-Villarreal IA, Bonilla-Petriciolet A, Hernández-Montoya V, et al. Batch and column studies of Zn2+ removal from aqueous solution using chicken feathers as sorbents. Chem Eng J. 2011;167:67–76.

 Web of Science ®Google Scholar

Hernández-Hernández LE, Bonilla-Petriciolet A, Mendoza-Castillo DI, et al. Antagonistic binary adsorption of heavy metals using stratified bone char columns. J Mol Liq. 2017;241:334–346.

 Web of Science ®Google Scholar

Raulino GSC, Vidal CB, Lima ACA, et al. Treatment influence on green coconut shells for removal of metal ions: pilot-scale fixed-bed column. Environ Technol. 2014;35:1711–1720.

 PubMed Web of Science ®Google Scholar

Gordillo-Ruíz FA, Sánchez-Ruíz FJ, Mendoza-Castillo DI, et al. Dynamic fuzzy neural network for simulating the fixed-bed adsorption of cadmium, nickel, and zinc on bone char. Int J Environ Sci Technol. 2018;15:915–926.

 Web of Science ®Google Scholar

Park J-H, Cho J-S, Ok YS, et al. Competitive adsorption and selectivity sequence of heavy metals by chicken bone-derived biochar: batch and column experiment. J Environ Sci Health A. 2015;50:1194–1204.

 PubMed Web of Science ®Google Scholar