2.1 Materials
The black liquor used in the study was obtained from a laboratory scale system for cellulose production and it was based on pulping of corn stalks. The corn stalks come from unprotected corn plantations and were gathered from the Iasi region, Romania with the permission of the farmers, following the national rules for agricultural waste collection. In a typical pulping experiment, about 400 [g] oven dried stalks were used. The material was pulped with 48 [g] NaOH and 3600 [cm3] distilled water (corresponding to 12% NaOH alkali charge and a solid to liquid ratio of 1:9). Following reactor closing, heating was started to reach a temperature of 120 [°C] (25 [min]) which was maintained for 40 [min] After pulping time was completed, a sample of the liquid phase was withdrawn from the pulping reactor, cooled to room temperature and filtered to remove any remaining solids. The resulted liquor had a characteristic darkish brown color, a relatively high alkalinity (pH =11) a conductivity of 24.5 [mS/cm] and organic load (COD = 40 [g O2/L]). The solid content of the black liquor was further determined according to the TAPPI test method (TAPPI T650, 1989). Organic to inorganic ratio was determined taking into account the ash content values (TAPPI 625 cm – 85).
Common commercial TiO2 powder (M-1319) supplied at FCC purity grade by Mayam (http://www.mayam.eu) was used as such during this study. The powder was characterized by SEM and EDX analysis and, in a previous study, was successfully applied for photochemical degradation of methylene blue (Atomi et al. 2018).
The experiments were performed using irregular shape particles of active carbon supplied by Buzău Romcarbon Company (Romania), active carbon that was characterized in the study of (Secula et al. 2011) : specific microporous volume 0.48 [cm3/g], total microporous volume 0.66 [cm3/g], mean pore size 1.62 [nm], BET surface 1403 [m2/g], external surface 38 [m2/g] and total surface 631 [m2/g]. Prior to the experimental study, the particles were classified by sieving, the average diameter ranging among 2.5 and 3.15 [mm].
2.2 Equipment
The UV light source was a Biocomp UV-lamp with wavelength of 253.7± 0.8 [nm]. An analog UV light sensor GUVA S12SD was used to measure the intensity of incident UV radiation. The UV-vis spectra and the absorbance values were recorded using a JASCO V-550 UV-Vis spectrophotometer. The chemical oxygen demand (COD), [mg O2/L] was measured using a standard Hach-Lange kit LCK 114. A BHG Hermle Z 229 Centrifuge: 220 V, 50 Hz, 1.1 A, 240 W, maximum number of revolutions 15000 rpm was also used.
2.3 Experimental design
In order to determine the optimal parameters for BL decolorization three representative independent variables were considered for each method, as depicted in Table 1. These parameters and their limits were selected based the data provided by literature (Peralta-Zamora et al. 1998). Following the design of experiments approach (DOE) proposed by Box and Hunter (Box and Hunter 1957) a minimum number of experiments were statistically programmed as presented in Table 2, where ηACD (%) represents the decolorization efficiency for the active carbon decolorization and ηPCD (%) for the TiO2 promoted photochemical decolorization. In table 2 both the coded and the decoded variables were presented, the notations being the same as one used in Table 1. The bold columns from Table 2 indicate the experimental results obtained with the identified values by the DOE approach.
Table 1
Designated variables and their variation range for the chosen decolorization methods
|
Active carbon decolorization (ACD)
|
Independent variables
|
Measure units
|
Notation
|
Range
|
Symbol
|
from
|
to
|
Active carbon concentration
|
[g/L]
|
[AC]x1
|
5
|
50
|
[AC]
|
Dilution
|
ratio
|
[Ct]x2
|
1:100
|
1:200
|
[Dil]
|
Contact time
|
[min]
|
[Dil]x3
|
10
|
30
|
[Ct]
|
|
TiO2 promoted photochemical decolorization (PCD)
|
Independent variables
|
Measure units
|
Notation
|
Range
|
Symbol
|
from
|
to
|
TiO2 concentration
|
[g/L]
|
[TiO2]x1
|
1
|
2
|
[TiO2]
|
UV path length
|
[cm]
|
[It]x2
|
5
|
25
|
[hUV]
|
Irradiation time
|
[min]
|
[hUV]x3
|
15
|
60
|
[It]
|
Table 2
Experiment planning and results
No.
|
Coded variable
|
Decoded ACD
|
Decoded PCD
|
ηACD (%)
|
ηPCD
(%)
|
x1
|
x2
|
x3
|
[AC]x1
|
[Ct]x2
|
[Dil]x3
|
[TiO2]x1
|
[It]x2
|
[hUV]x3
|
|
|
1
|
1
|
1
|
1
|
50
|
30
|
200
|
2
|
60
|
25
|
83.08
|
19.83
|
2
|
−1
|
1
|
1
|
5
|
30
|
200
|
1
|
60
|
25
|
57.40
|
20.62
|
3
|
1
|
−1
|
1
|
50
|
10
|
200
|
2
|
15
|
25
|
69.74
|
16.88
|
4
|
−1
|
−1
|
1
|
5
|
10
|
200
|
1
|
15
|
25
|
33.54
|
18.41
|
5
|
1
|
1
|
−1
|
50
|
30
|
100
|
2
|
60
|
5
|
81.22
|
33.90
|
6
|
−1
|
1
|
−1
|
5
|
30
|
100
|
1
|
60
|
5
|
52.27
|
36.63
|
7
|
1
|
−1
|
−1
|
50
|
10
|
100
|
2
|
15
|
5
|
60.68
|
18.50
|
8
|
−1
|
−1
|
−1
|
5
|
10
|
100
|
1
|
15
|
5
|
21.48
|
20.45
|
9
|
α
|
0
|
0
|
54.8
|
20
|
150
|
2.11
|
37.5
|
15
|
80.53
|
19.39
|
10
|
−α
|
0
|
0
|
0.176
|
20
|
150
|
0.89
|
37.5
|
15
|
13.99
|
24.90
|
11
|
0
|
α
|
0
|
27.5
|
32.15
|
150
|
1.5
|
64.8
|
15
|
78.58
|
19.52
|
12
|
0
|
−α
|
0
|
27.5
|
7.85
|
150
|
1.5
|
10.15
|
15
|
65.70
|
17.23
|
13
|
0
|
0
|
α
|
27.5
|
20
|
210.75
|
1.5
|
37.5
|
27.15
|
72.65
|
18.61
|
14
|
0
|
0
|
−α
|
27.5
|
20
|
89.25
|
1.5
|
37.5
|
2.85
|
74.24
|
35.01
|
15
|
0
|
0
|
0
|
27.5
|
20
|
150
|
1.5
|
37.5
|
15
|
68.88
|
18.74
|
16
|
0
|
0
|
0
|
27.5
|
20
|
150
|
1.5
|
37.5
|
15
|
69.28
|
19.52
|
2.4 Experimental procedure
The BL was used as such, underprivileged of any pH chemical regulations, at room temperature. Bi-distilled water was used to reach the required dilution ratio for each experiment. In order to avoid settling (and to ensure a constant exposure of the mixture) the slurry was stirred constantly during all the experiments involving the presence of TiO2 powder or active carbon particles.
In the case of active carbon decolorization, for a well-defined period of time, 100 [mL] samples of specifically diluted BL solutions were mixed with the adequate amount of active carbon, according to the data presented in Table 2. Disposable disc filters 0.45 [µm] were used for particles separation.
In the case of TiO2 promoted photochemical decolorization, 50 [mL] samples of BL solutions (with 1:100 dilution ratio) were mixed with the adequate amount of TiO2 and placed below the UV source for the corresponding period of time. After irradiation, the TiO2 powder was separated from the solutions using a centrifugal separator. The required UV path length that gives the intensity of the incident UV radiation was attained by changing the distance between the UV source and the sample under study.
2.5 Chemical assays
The BL decolorization was checked by measuring the absorbance of the solution given by the lignin content at 280 [nm] (UV280) as presented in Fig. 1. The correlation between the chemical oxygen demand (COD) and the absorbance was determined at different dilution ratio in order to establish a calibration curve that validates the accuracy of decolorization efficiency calculations. The coefficient of determination (R2) was 0.97, in accordance with the literature reported values (Torrades et al. 2011). The efficacy of BL decolorization was calculated using the following equation:
where and are the absorbance’s recorded before and after each experiment.
2.6 Software and algorithm
The MINITAB package (Minitab Institute, USA) was chosen to implement the response surface method algorithm. In addition, the process was optimized with a second method represented by DE, an efficient metaheuristic approach, that was successfully used (simple or in combination with other approaches) for optimization and modelling of a wide range of systems, from robot control (Neri and Mininno 2010), water quality monitoring (Yazdi 2018), adsorption processes (Bleotu et al. 2018). Examples of DE application in chemical engineering can be found in (Dragoi and Curteanu 2016). The DE based software used was developed in (Drăgoi et al. 2012) in combination with artificial neural networks (ANNs) and applied for predicting the liquid crystalline property of some organic compounds. Distinctively, in this work, the ANN is replaced by the model determined with MINITAB package, the DE variant (SADE) performing only the process optimization part.
DE is inspired from the Darwinian principle of evolution (Storn and Price 1995) and it works with a population of potential solutions that it is evolved (through a series of steps that include mutation, crossover and selection) until a stop criterion is reached. After the potential solutions (which will be further referred as individuals) are initialized (using a random based procedure), the individuals undergo a mutation procedure. DE has many mutation variants and, in this work, two differential terms combined with a randomly selected based vector was used (this is also known as the rand/2 version). Eq. 2 describes the mutation equation used.
where a is the base vector, F is the scaling factor (one of the control parameters of DE), and b, γ are the differential terms. The differential term is created by subtracting a randomly selected vector with another one.
After that, the features of the mutated and current individuals are combined to create a new population called trial. This is the crossover step and the variant used in this work is the binomial crossover.
In the next step, the trial and the current population undergo a one-to-one comparison, the best individuals being selected to form the next generation. The measure used to determine the best individuals is represented by the fitness function and for the current work, the fitness function represents the regression model of the process generated by the Minitab software.
One of the main characteristics of the SADE version is represented by the use of self-adaptability to determine the values of the control parameters. In this manner, the difficult task of manually setting the optimal values for the control parameters is automatized by including them into the algorithm itself. A simplified schema of approach used in this work is presented in Fig. 2.