Following the process simulation workflow outlined in above, PET conversion of 94.75% was attained, resulting in a TPA yield of 95.5%, PET conversion of 94.75% and an EG yield of 64.27%. The remaining unreacted material accounted for just 5.25%. Based on stoichiometry, the expected produced quantities were 864.58 kg for TPA and 322.92 kg for EG. The selectivity values, as presented in Table 3, indicate a selectivity of 0.799 for TPA and 0.201 for EG. These findings demonstrate that the reaction predominantly produces the desired hydrolyzed product (TPA) while minimizing the unwanted byproduct of EG. The high selectivity for TPA underscores the efficiency and cost-effectiveness of the process. It is important to note that achieving this high PET conversion and TPA yield typically requires elevated temperature of 240 ºC and 32 bar pressure over an extended 2 hours’ duration, water to PET (5:1) and catalyst to PET (1:75).
3.1. Sensitivity of TPA Yield and PET conversion to Key Reaction Parameters
Effect of Reaction Temperature: The analysis showed a strong correlation between temperature, reaction time, TPA yield, and PET conversion in the depolymerization process. At lower temperatures (30-70 °C), PET depolymerization proceeded at a very slow rate, requiring more time to achieve the desired level of depolymerization. TPA yield and PET conversion rates gradually increased, indicating that thermal energy is essential for initiating the process. This agrees with, Ügdüler et al. (2020) that observed that temperatures below 50 °C lacked sufficient thermal energy to initiate PET hydrolysis.
Beyond a specific temperature threshold (150 °C), a significant rise in PET conversion and TPA yield occurred due to rapid reaction kinetics, achieving maximum conversion in a shorter timeframe. This threshold suggested a breach in the activation energy barrier, leading to an accelerated reaction rate. TPA yield closely followed the temperature increase, showing a gradual rise initially followed by a more pronounced ascent. The temperature of 240 °C was identified as the point of maximum TPA yield, highlighting the critical impact of temperature on reaction efficiency.
Conversely, operating at temperatures exceeding 240 °C favoured secondary reactions, such as thermal oxidative degradation of PET and intermolecular dehydration of ethylene glycol (EG). These secondary reactions, occurring at elevated temperatures, could complicate subsequent purification processes as was observed by Liu et al. (2012). The findings underscore the intricate relationship between temperature, reaction kinetics, and the overall efficiency of the PET depolymerization process as shown in Fig. 4.
Effect of PET particle size: During a 2-hour reaction at 240 ºC, it was observed that as particle size is decreased, both PET conversion and TPA yield showed increased significantly (Fig. 5). These results were expected since smaller particle size PET feedstocks provide a larger reaction surface area, resulting in a faster reaction rate and higher PET conversion rates. Accordingly, by lowering the particle size, saw a comparable improvement in PET conversion as also reported by Ügdüler et al. (2020) and Liu et al. (2009). For instance, 18.5 mm particles produced a PET conversion and TPA yield of about 97% and 96% respectively, whereas 140 mm particles had a significantly lower PET conversion rate and TPA yield of 45% and 47% respectively. This finding is crucial for the practical application of PET neutral hydrolysis in industrial settings because it raises the possibility of a partial cost reduction for energy-intensive grinding required to produce smaller particle sizes.
Effect of Reaction Time: Investigating the impact of reaction time on the depolymerization process, within the time frame of 5 to 60 minutes at various temperature ranges between 200 and 250 degrees Celsius. With a PET/Zn(Ac)2 ratio of 1:70 and pressure of 50 bar, the simulation was run. Fig. 6 shows the results of PET depolymerization carried out at temperatures above 200 ºC with the yield of TPA and the depolymerization of PET both showed an appreciable increase with increasing reaction durations especially with a notable increase in yield between 15 and 45 minutes. It is noticed that, at a short reaction time of 10 min, there is incomplete hydrolysis of PET into its monomers. This resulted in a lower yield of terephthalic acid of (61.19%). More depolymerization, or the breaking down of more PET polymer chains into monomers, usually results from longer reaction times. By extending the reaction time, purer monomers are produced by ensuring that the depolymerization step is completed. Similarly, Liu et al. (2012) observed an increase in TPA yield of about 73% when reaction time was increased to 60 min from 5 min at 220 ºC.
Effect of Reaction Pressure: Echoing the findings on the effect of temperature on PET conversion and TPA yield, PET depolymerization demonstrated sensitivity to pressure variations. Higher pressure conditions consistently yielded higher TPA concentrations and PET conversion at a constant temperature of 240 ºC, indicating a direct influence of pressure on reaction efficiency. It is observed that at a low pressure of 10 bar, reaction did not proceed as efficiently as a low percentage PET conversion and TPA yield of 47.23% and 48.58% respectively were obtained, while an increase in pressure enhanced the contact between the reactants and the catalyst, potentially leading to a more efficient depolymerization reaction. The results underscore the importance of pressure as a parameter for optimizing TPA production (Fig. 7). Mishra et al. (2003) reported an increase in the rate of depolymerization with pressure. However, the pressure effect was highly dependent on the reaction temperature with increased TPA yield observed at higher temperatures.
The influence of the Zn(AC)2 to PET ratio on the PET depolymerization process: Sensitivity was carried out utilizing Zn(Ac)2 to PET ratios ranging from 1:50 to 1:90, as well as in the absence of catalyst, at temperatures between 200 and 250 °C and reaction times between 15 min and 120 min to examine the effect of Zn(Ac)2 concentration on depolymerization. With the exception of 250 °C, when PET conversion and TPA yield were low because of the absence of Zn(Ac)2, it was observed that both PET conversion and TPA production increased with increasing catalyst to PET rations between 200 and 240 °C. While Campanelli et al. (1994) reported only modest increase in depolymerization due to presence of zinc acetate, Güçlü et al. (2003) noted that high water to PET ratios could mask the effect of the catalyst. Güçlü et al. (2003) observed significant depolymerization in presence of zinc acetate. Figure 8 shows that increasing the catalyst to PET ratio resulted in a corresponding gradual increase in both TPA yield and PET conversion. The presence of catalysts provides a different, lower-activation-energy reaction pathway, which speeds up the depolymerization step. Faster PET breakdown as a result is advantageous for industrial processes. The trend is consistent regardless of the residence time as shown below. The decline to 30.29% PET conversion and 37.12% TPA yield shown to the left of Fig. 8 is due to absence or total catalyst consumption at 250 °C as was similarly reported by Liu et al. (2012). The catalyst to PET ratio increases from right to left.
3.2. Optimizing the PET Hydrolysis Process via Response Surface Modelling
The optimal conditions for the hydrolysis of polyethylene terephthalate (PET) were determined by utilizing the Box-Behnken design in Response Surface Methodology. Four independent variables were examined, including reaction temperature, pressure, time, and water to PET ratio. TPA yield and PET conversion were the two dependent variables. These four variables were represented mathematically to estimate the PET yield using a regression model. Analysis of variance was used to evaluate the dependability of the model.
A very high TPA yield of 96.23% and PET conversion of 94.96% were attained under optimal operating conditions (temperature, time, water to PET mass ratio, and pressure of 225 °C, 67.5 min, 5.5:1, and 30 bar respectively) as highlighted in Table 3.
Table 3 Box-Behnken design matrix for experimental TPA yields (%)
Run
|
Factor1
|
Factor 2
|
Factor 3
|
Factor 4
|
Response 1
|
Response 2
|
|
A: Temp. (ºC)
|
B: Time (mins)
|
C: Pressure (bar)
|
D:Water/Pet Ratio
|
% PET conversion
|
Y: % TPA Yield
|
1
|
165
|
120
|
30
|
1
|
69.76
|
72.09
|
2
|
225
|
30
|
30
|
1
|
76.45
|
78.07
|
3
|
165
|
15
|
30
|
1
|
42.05
|
52.14
|
4
|
165
|
67.5
|
10
|
1
|
70.89
|
71.21
|
5
|
225
|
67.5
|
30
|
1
|
79.92
|
82.65
|
6
|
300
|
67.5
|
50
|
5.5
|
87.01
|
92.56
|
7
|
300
|
67.5
|
30
|
5.5
|
71.48
|
85.87
|
8
|
150
|
120
|
50
|
5.5
|
43.01
|
44.16
|
9*
|
225
|
67.5
|
30
|
5.5
|
94.96
|
96.23
|
10
|
165
|
67.5
|
30
|
5.5
|
74.23
|
77.44
|
11
|
150
|
67.5
|
30
|
5.5
|
66.96
|
72.08
|
12
|
300
|
30
|
20
|
5.5
|
77.76
|
79.08
|
13
|
150
|
67.5
|
10
|
5.5
|
43.24
|
45.43
|
14
|
165
|
67.5
|
50
|
5.5
|
68.9
|
72.11
|
15
|
165
|
120
|
10
|
5.5
|
75.32
|
81.06
|
16
|
300
|
120
|
30
|
5.5
|
84.89
|
88.72
|
17
|
300
|
15
|
30
|
5.5
|
90.26
|
93.37
|
18
|
165
|
15
|
10
|
5.5
|
37.67
|
50.2
|
19
|
165
|
67.5
|
10
|
5.5
|
67.88
|
72.29
|
20
|
165
|
15
|
20
|
5.5
|
34.11
|
43.11
|
21
|
225
|
15
|
30
|
5.5
|
85.96
|
94.13
|
22
|
165
|
15
|
50
|
5.5
|
72.43
|
75.14
|
23
|
300
|
67.5
|
30
|
10
|
71.43
|
75.67
|
24
|
225
|
67.5
|
10
|
10
|
46.87
|
53.22
|
25
|
165
|
67.5
|
30
|
10
|
60.96
|
62.76
|
26
|
165
|
67.5
|
20
|
10
|
79.67
|
83.12
|
27
|
165
|
120
|
20
|
10
|
72.01
|
74.21
|
28
|
165
|
120
|
30
|
5.5
|
64.11
|
71.42
|
29
|
165
|
67.5
|
50
|
10
|
72.43
|
73.06
|
Creation of a regression model: A non-linear regression model (Equation 5) that captures the correlation between the coded values of the four independent components and the TPA yield (Y) response was developed based on the experimental runs from the Box-Behnken design in Table 3.
The regression model's goodness-of-fit is highlighted by the values of R2 and adjusted R2 being very close to unity. Additionally, as an earlier study by Rai et al. (2016) had indicated, a difference between the adjusted R2 of 0.9983 and the predicted R2 of 0.9960 less than 0.2 proves the model’s reliability. The required threshold of 4 is also exceeded for the acceptable precision, which calculates the signal-to-noise ratio. This demonstrates that, given the specified design space, the regression model is appropriate for predicting the response variable in this case, the TPA yield. The regression model can thus be used for optimization purposes (Table 4).
Table 4 Fit statistics of the regression model
Statistic
|
Value
|
Standard Deviation
|
0.7271
|
Mean
|
59.82
|
Coefficient of Variation
|
1.22%
|
Adequate Precision
|
123.0957
|
Predicted R²
|
0.996
|
Table 5 below displays the results of the response surface model's analysis of variance (ANOVA). When a term's corresponding p-value is less than 0.05, it is considered statistically significant. Moreover, a term's larger F-value indicates that it has a significant influence on the response of the model. Except for (BC, BD, CD, and D2), it can be concluded that every main effect and most of the interaction effects in the regression model represented by Equation 5 above demonstrated statistical significance.
The linear correlation graph (Fig. 9(a)) shows a high degree of correlation between observed and expected response variables while a substantial percentage of the response variances are satisfactorily explained by the non-linear model. The residuals are well distributed between -1.5 and 2.5 (Fig. 9(b)).
Table 5 ANOVA for the non-linear regression response surface model
Source of variation
|
Sum of Squares
|
df
|
Mean Square
|
F-value
|
p-value
|
Model*
|
8671.59
|
14
|
619.4
|
1171.61
|
<0.0001
|
A-Temperature
|
3981.8
|
1
|
3981.8
|
7531.67
|
<0.0001
|
B-Time
|
2341.65
|
1
|
2341.64
|
4429.29
|
<0.0001
|
C-Pressure
|
6.54
|
1
|
6.54
|
12.37
|
0.0034
|
D-Water/PET
|
12.08
|
1
|
12.08
|
22.85
|
0.0003
|
AB
|
184.42
|
1
|
184.42
|
348.83
|
<0.0001
|
AC
|
23.91
|
1
|
23.91
|
45.23
|
<0.0001
|
AD
|
26.16
|
1
|
26.16
|
49.49
|
<0.0001
|
BC
|
0.1722
|
1
|
0.1722
|
0.3258
|
0.5772
|
BD
|
0.0289
|
1
|
0.0289
|
0.0547
|
0.8185
|
CD
|
0.99
|
1
|
0.99
|
1.87
|
0.1927
|
A2
|
1185.67
|
1
|
1185.67
|
2242.72
|
<0.0001
|
B2
|
1119.53
|
1
|
1119.53
|
2117.61
|
<0.0001
|
C2
|
14.28
|
1
|
14.28
|
27.01
|
0.0001
|
D2
|
0.011
|
1
|
0.011
|
0.0209
|
0.8872
|
Residual
|
7.4
|
14
|
0.5287
|
|
|
Lack of fit**
|
5.59
|
10
|
0.5594
|
1.24
|
0.4523
|
Pure error
|
1.81
|
4
|
0.4519
|
|
|
Total
|
8678.99
|
28
|
|
|
|
* Significant; ** Not significant
Analysis of response surfaces: A chemical reaction is impacted by a variety of interrelated elements in addition to single ones. Making three-dimensional response surfaces is helpful to better understand these interaction effects. Equation 8 above defines these surfaces, which show the relationship between two components while holding the others constant. Fig. 10(a) visually shows the interaction between reaction temperature (factor A) and reaction time (factor B) in determining TPA yield. The yield tended to exhibit an increase in tandem either other factor as either the reaction temperature or duration increased until attaining an optimum. Matching the contour plot (Fig. 10(b) with the 3D response surface plot clearly shows the optimum reaction time and temperature of 67.5 min and 225 °C respectively with the Water to PET ratio and reaction pressure fixed at 5.5:1 and 30 bar respectively. Increasing TPA yield is shown as progressively from blue to green to yellow to pink to red in both Fig. 10(a) and Fig. 10(b).
The principal effects of each independent component are depicted in Fig. 11. In general, the distinct parameters caused noticeable variances in the TPA yield and PET conversion. Through an examination of the responses at both extremes and the middle point (shown by the intersection in Fig, 10(left) and Fig. 10(right) in the investigated range, it was clear that reaction temperature and reaction time had a greater impact on yield and conversion than pressure and the mass ratio of PET to water. It is noted that in the regression model (Equation 8), the coefficients for the main effects A and B were comparatively bigger than those for the main effects C and D.