Optimization of process parameters for preparation of powdered activated coke to achieve maximum SO₂ adsorption using response surface methodology

Abstract

Powdered activated coke (PAC) is a good adsorbent of SO₂, but its adsorption capacity is affected by many factors in the preparation process. To prepare the PAC with a high SO₂ adsorption capacity using JJ-coal under flue gas atmosphere, six parameters (oxygen-coal equivalent ratio, reaction temperature, reaction time, O₂ concentration, CO₂ concentration, and H₂O concentration) were screened and optimized using the response surface methodology (RSM). The results of factor screening experiment show that reaction temperature, O₂ concentration, and H₂O (g) concentration are the significant factors. Then, a quadratic polynomial regression model between the significant factors and SO₂ adsorption capacity was established using the central composite design (CCD). The model optimization results indicate that when reaction temperature is 904.74°C, O₂ concentration is 4.67%, H₂O concentration is 27.98%, the PAC (PAC-OP) prepared had a higher SO₂ adsorption capacity of 68.15 mg/g while its SO₂ adsorption capacity from a validation experiment is 68.82 mg/g, and the error with the optimal value is 0.98%. Compared to two typical commercial activated cokes (ACs), PAC-OP has relatively more developed pore structures, and its S_BET and V_tot are 349 m²/g and 0.1475 cm³/g, significantly higher than the 186 m²/g and 0.1041 cm³/g of AC1, and the 132 m²/g and 0.0768 cm³/g of AC2. Besides, it also has abundant oxygen-containing functional groups, its surface O content being 12.09%, higher than the 10.42% of AC1 and 10.49% of AC2. Inevitably, the SO₂ adsorption capacity of PAC-OP is also significantly higher than that of both AC1 and AC2, which is 68.82 mg/g versus 32.53 mg/g and 24.79 mg/g, respectively.

Keywords

powdered activated coke (PAC) / SO₂ adsorption capacity / parameters optimization / response surface methodology

Introduction

SO₂ emissions from fossil fuel combustion and solid waste incineration greatly harm the environment and human health [¹–³]. At present, the most commonly used flue gas desulfurization (FGD) technology, namely wet FGD, which utilizes Ca-based absorbents for SO₂ removal, suffers from high operating costs for waste water treatment, high levels of water consumption, and issues with CO₂ leakage and secondary pollution [⁴,⁵]. Alternatively, the SO₂ adsorption technology using carbon-based adsorbents, such as activated carbon, activated coke (AC), and activated carbon fibers, has been regarded as a promising choice for the next-generation FGD technology owing to its advantages of water saving, sulfur recovery, and multi-pollutant removal [⁶–⁸]. This technology has been used in Europe and Japan for cleaning flue gas from both coal combustion and waste incineration [⁹].

Up to the present, however, this technology has not yet been applied in a wide-spread way in industry because of the complexity and high cost of preparation of adsorbent materials. Currently, the commonly used AC for SO₂ adsorption is granular AC (GAC) (F 5–9 mm) [¹⁰,¹¹] which is generally prepared by a two-step method: The materials are first carbonized in an inert atmosphere generally at a temperature below 800°C, then activated in an activating atmosphere at a temperature of approximately 700°C–1000°C. In addition, there is also a GAC forming process. Thus, the two-step method is a complex process with high operating costs, and hence, the price of GAC is naturally high. Furthermore, GAC products require a high hardness to avoid wear, and the utilization rate of pore structures and active sites inside the GAC are low [¹²].

To reduce the preparation cost and overcome the limitations of GAC, a process for the rapid preparation of powdered activated coke (PAC) from pulverized coal using a one-step rapid activated method in a flue gas atmosphere was proposed [¹³]. In previous studies, it was found that the O₂ concentration in the activating atmosphere during PAC preparation had a significant influence on the pore structure, and water vapor could assist O₂ in adjusting the PAC pore structure [¹⁴,¹⁵]. The PAC prepared by utilizing this method could be used for SO₂ and Hg adsorption from flue gas [¹⁶]. Moreover, a pilot test with PAC yields of approximately of 30–50 kg/h was conducted, and the experimental results further verified the feasibility of this method [¹⁷]. Furthermore, Li et al. [¹⁸] also reported that it is feasible to use flue gas to prepare AC as the FGD adsorbent, and finer coal particles improve the performance of the AC prepared.

Although many studies have been conducted on the one-step rapid activated method for PAC preparation, there still exist some shortcomings. The effect of oxygen-coal equivalent ratio and reaction time have not yet been taken into account and an optimum preparation condition, under which PAC is prepared with the highest SO₂ adsorption capacity, has not yet been given. Therefore, in this paper, a total of six parameters are considered in the rapid preparation of PAC, which are oxygen-coal equivalent ratio, reaction temperature, reaction time, O₂ concentration, CO₂ concentration, and H₂O (g) concentration. Besides, the Jinjie bituminous coal was used as the raw material to prepare PAC using a drop-tube reactor (DTR). The SO₂ adsorption capacities of the PACs prepared for 2 h which were determined in a fixed bed SO₂ adsorption system were the response values, and significant factors affecting this process were determined in the screening experiments. Moreover, a quadratic regression model between the significant factors and the response value was established in the central composite design. Furthermore, in model optimization, the preparation condition of the optimal SO₂ adsorption property of PAC was predicted, which was experimentally verified and compared correspondingly.

Materials and methods

Experimental materials

In this paper, the experimental material is the pulverized coal of Jinjie bituminous coal (JJ-coal) with a particle size of 60–90 mm which is first dried at 105°C for 8 h, and then crushed, ground, and sieved. The analysis results of the JJ-coal are shown in Table 1.

Experimental system and setup

The experimental system used to prepare the PAC was illustrated in Fig. 1, which was composed of the gas system, the micro feeder, the DTR, the PAC collector, and the vent treatment system. The pulverized JJ-coal was continuously fed into the DTR by the micro feeder using nitrogen (N₂-1) as the carrier gas at a flow rate of 6 L/min. The gas system consisted of N₂, O₂, and CO₂, and each flow was controlled by the mass flow controller. The composition of the reaction atmosphere was the simulated flue gas from the gas system. Water vapor was introduced into the simulated flue gas from a steam generator, and the temperature of the pipe extending from the steam generator was maintained at 120°C to avoid steam condensation. The DTR was a stainless steel tube with an inner diameter of 80 mm and a length of 2000 mm. The maximum temperature that the DTR could reach was 1200°C, and the length of the constant temperature zone was 1200 mm. The PAC was collected in a container at the bottom of the DTR through a water-cooled sampling tube. After filtering, the vent gas was released into the atmosphere.

O₂ concentration, CO₂ concentration, and H₂O (g) concentration of reaction atmosphere were regulated by the gas system. The oxygen-coal equivalent ratio (similar to the excess oxygen coefficient in combustion) was regulated by changing the amount of feed when the reaction atmosphere was fixed. The reaction temperature was regulated by the settling furnace temperature control system. The residence time was adjusted by replacing the water-cooled feed tubes, as depicted in Fig. 1.

Determination of SO₂ adsorption capacity of PAC

The SO₂ adsorption capacity of the PAC was tested using a fixed bed SO₂ adsorption system [¹⁹], whose detailed description and adsorption condition were presented in the Electronic Supplementary Material (ESM). In addition, the SO₂ adsorption capacity for 2 h of all samples were used as the evaluation standard.

Design of experiment (DOE)

The DOE was divided into two parts using the response surface methodology (RSM), which was an effective and commonly used method for factor screening and operating condition optimization of multi-factor influencing target value [²⁰]. This method has been successfully applied in engineering problems involving parameter optimization [²¹–²³], product yield [²⁴,²⁵], and removal rate [²⁶,²⁷], etc. The experimental process was implemented according to the flowchart of DOE (displayed in Fig. 2) with the help of design-expert software. Part one was the screening experiments (SE) by which significant factors were determined from six factors, while part two was the central composite design (CCD) by which the quadratic polynomial regression model between significant factors and response was established, and optimization results were obtained.

Results and discussion

Screening experiments

Six factors with their levels, as presented in Table 2, were studied in SE using the two-level factorial design, and a IV resolution two-level factorial design was selected with 16 runs. The experimental conditions and corresponding results were tabulated in Table 3.

The Pareto chart and main effect plots for the response of SO₂ adsorption capacity, as demonstrated in Fig. 3, were obtained by analyzing the statistical data in Table 3. According to Fig. 3(a), X₂, X₄ and X₆ have significant effects on the SO₂ adsorption capacity of PAC, in which the effect of X₂ is the most obvious, followed by X₄ and X₆. X₁, X₃ and X₅ are non-significant factors, whose influential degree is X₁>X₅>X₃. According to Fig. 3(b), since the absolute value of the slope of the line is proportional to the magnitude of the main effect, the effect of each factor can be easily ascertained from these plots. Therefore, highly similar results can be found where X₂, X₄ and X₆ are significant factors; X₁, X₃ and X₅ are non-significant factors; and the influential degree is X₂>X₄>X₆>X₁>X₅>X₃. Consequently, it can be concluded that X₂, X₄ and X₆ play key roles in PAC preparation, while X₁, X₃ and X₅ have no significant effects.

Central composite design (CCD)

X₂, X₄ and X₆ were found to be significant factors. Therefore, a CCD with 5 levels and 3 factors were used for optimization of the conditions for preparation of PAC. A total of 17 runs with 8 cube points, 6 star points, and 3 center points were executed according to CCD as presented in Fig. 4. The distance of each star point from the center was determined as a = 2^3/4 = 1.68 [²²]. According to Fig. 3(b) and specific experimental conditions, X₁ = 0.3, X₃ = 4 s, and X₅ = 12% were selected. The values and levels of the factors used in CCD are presented in Table 4, and the experimental matrix and results of CCD are presented in Table 5.

For most industrial problems, a second-degree polynomial model between factors and response, as expressed in Eq. (1), can be established through CCD.

(1)

where S is the response; Y_i and Y_j are the variables, and the range of i is from 1 to k, while j is from 2 to k; b₀ is the intercept coefficient of the model; b_i is the linear coefficient; b_ii is the quadratic coefficient; and b_ij is the second order term coefficient.

The experimental data coming from CCD as listed in Table 5 were statistically analyzed, and the model of SO₂ adsorption capacity was developed. Similar to Eq. (1), the model of S was

(2)S=-\mathrm{612.86}+\mathrm{1.39}{{\displaystyle Y}}_{\mathrm{1}}+\mathrm{19.87}{{\displaystyle Y}}_{\mathrm{2}}+\mathrm{0.41}{{\displaystyle Y}}_{\mathrm{3}}-\mathrm{0.01}{{\displaystyle Y}}_{\mathrm{1}}{{\displaystyle Y}}_{\mathrm{2}}+\mathrm{1.23}\times {\mathrm{10}}^{-\mathrm{3}}{{\displaystyle Y}}_{\mathrm{1}}{{\displaystyle Y}}_{\mathrm{3}}-\mathrm{0.13}{{\displaystyle Y}}_{\mathrm{2}}{{\displaystyle Y}}_{\mathrm{3}}-\mathrm{7.58}\times {\mathrm{10}}^{-\mathrm{4}}{{\displaystyle Y}}_{\mathrm{1}}^{\mathrm{2}}-\mathrm{0.67}{{\displaystyle Y}}_{\mathrm{2}}^{\mathrm{2}}-\mathrm{1.55}\times {\mathrm{10}}^{-\mathrm{2}}{{\displaystyle Y}}_{\mathrm{3}}^{\mathrm{2}}.

Table 6 shows the analysis of variance (ANOVA) of this model. The “R²” of 0.9892 suggests that the fitting of the model is very appropriate and accurate. “Lack of fit” indicates a chance that the model fails to represent data in the experimental domain at the points which are not included in the regression. Not significant “Lack of fit” is good, and is expected by the model, which can accurately predict the experimental results. “Adequate precision” measures the ratio of signal to noise, for which greater than 4 is desirable. Here it is 27.451, indicating that this model is sufficiently accurate to navigate the design space.

In addition, the predicted values of this model calculated by Eq. (2) and the actual values from the experiments are exhibited in Fig. 5, from which the data calculated by the model fit the experimental data well. In conclusion, this model (Eq. (2)) can accurately predict the experimental data.

The model expressed in Eq. (2) can be represented in 3D figures (as shown in Fig. 6) by using the design-expert software. These 3D figures can be used to describe the values predicted and help to show the combined effect of the three significant factors on PAC preparation. Figure 6(a) shows the combined effect of temperature and O₂ concentration, from which it can be seen that when the temperature was increased, the SO₂ adsorption capacity of the PAC prepared was also improved progressively while at higher temperatures, the capacity was reduced. The reason for this is that when the temperature is low, the volatile release of pulverized coal is insufficient, so is the activation reaction; however when the temperature is too high, the reaction is too intense, which will cause excessive ablation and structural collapse [²⁸]. There is a similar trend for O₂ concentration. At the maximum SO₂ adsorption capacity, if both the parameters were increased or decreased, there was a considerable decrease in SO₂ adsorption capacity, which implies a high significance of these parameters on PAC preparation. Similarly, Fig. 6(b) also reveals that both the parameters of temperature and H₂O (g) concentration also have a great combined effect on PAC preparation. Figure 6(c) shows the combined effect of O₂ and H₂O (g) concentration, from which it can be observed that neither a low O₂ and H₂O (g) concentration nor a high O₂ and H₂O (g) concentration is conducive to PAC preparation. A low O₂ and H₂O (g) concentration will result in inadequate activation in PAC preparation, which will lead to the fact that the PAC prepared cannot form developed pore structure while a high O₂ and H₂O (g) concentration will lead to excessive activation, serious burning and destruction of pore structure, thus resulting in a low SO₂ adsorption capacity for the PAC prepared.

Optimization of process parameters

Response optimization could help to identify the optimal parameters that will give rise to an optimal response. Here, three significant parameters were evaluated in order to get the optimal condition under which the PAC prepared (PAC-OP) had the maximum SO₂ adsorption capacity, i.e., the maximum SO₂ adsorption capacity was the optimization objective. The optimization criteria of all parameters were listed in Table 7. The final optimal values of the parameters were found to be Y₁ = 904.74°C, Y₂ = 4.67%, and Y₃ = 27.98%. Under the optimum condition, the SO₂ adsorption capacity calculated was 68.15 mg/g.

To verify the optimization result, a verification test was conducted under the optimal condition. The result predicted and the experimental result were listed in Table 8. It can be noted that the error between the experimental and the result predicted was 0.98%, which was within acceptable limits, thus the response surface model and the optimization result were verified to have good performance and reliability.

Characterization and comparison

An ASAP 2020 gas absorption analyzer (Micrometrics, USA) was used to analyze the pore structures of PAC-OP and two commercial activated carbons (AC1 and AC2). The functional groups on their surface were determined by the transmission FTIR spectra and the XPS spectra. In addition, their SO₂ adsorption capacities were also determined by using the method described in Section 2.3.

Textural characterization

Figure 7(a) depicts the N₂ adsorption-desorption isotherms of PAC-OP and two ACs, while Fig. 7(b) their related pore size distributions. Table 9 lists the pore structure parameters of the three samples. In addition, the detailed BET analysis methods were presented in ESM.

As can be seen from Fig. 7(a) and Table 9, the pore structure of PAC-OP was more developed than that of the two ACs. The S_BET of PAC-OP is 349 m²/g, which is higher than 186 m²/g of AC1 and 132 m²/g of AC2, respectively. Besides, the V_tot of PAC-OP is 0.1475 cm³/g, which is also higher than 0.1041 cm³/g of AC1 and 0.0768 cm³/g of AC2, respectively. Moreover, PAC-OP has more developed microporous structure, whose microporosity (V_mic/V_tot) is 76.2%, higher than 50.4% of AC1 and 46.4% of AC2, respectively.

As can be seen from Fig. 7(b) and Table 9, the micropores of PAC-OP and AC1 consist mostly of ultra-micropores, whose ultra-microporosity (V_{mic<1 nm}/V_mic) were 71.5% and 70.3%, respectively. In addition, the ultra-micropores of PAC-OP centered on 0.59 nm and 0.68–0.80 nm, and that of AC1 centers on 0.86 nm. Relatively, the ultra-microporosity of AC2 is only 41.3% with pore diameters centered on 0.59 nm.

Surface functional groups

Functional groups on the surface of PAC-OP and the two ACs were determined by the transmission FTIR spectra and the XPS spectra. The transmission FTIR spectra of PAC-OP, as shown in Fig. 8(a), is similar to the other two ACs, in which the types of functional groups corresponding to each waveband and the main peak positions are both marked. Moreover, the detailed congruent relationship is presented in ESM, of which, the strong absorption band in the waveband of 1800–1300 cm^–1 and 1300–1000 cm^–1 are mainly attributed to the stretching vibrations of C=O groups and C-O groups, respectively [¹⁴,²⁹], which indicates that there are abundant oxygen-containing functional groups on their surface.

Figure 8(b) shows the XPS spectra of PAC-OP and the two ACs, in which the carbon and oxygen peaks can be distinctly observed. However, the nitrogen and sulfur peaks are relatively weak. Therefore, they are not marked. The reason for this is that the peak strength is closely related to element content. The surface carbon concentration of 87.01% on the surface of PAC-OP is lower than that of both AC1 (88.29%) and AC2 (88.89%), respectively, while the surface oxygen concentration of 12.09% is higher than that of both AC1 (10.42%) and AC1 (10.49%), respectively, which indicates that PAC-OP mainly contains C like any other ACs, but it has more abundant oxygen-containing functional groups on its surface than ACs. It has been reported that the major active sites which promote SO₂ adsorption are mainly distributed in the oxygen-containing functional groups on the surface of ACs [⁴].

SO₂ adsorption capacity

Figure 9 shows the SO₂ adsorption breakthrough curves and the SO₂ cumulative adsorption capacity of PAC-OP and two ACs within two hours, in which Fig. 9(a) demonstrates that the adsorption performance of SO₂ by PAC-OP is obviously better than that by AC1 and AC2 whereas Fig. 9(b) indicate that the SO₂ adsorption capacity for 2 h of PAC-OP is 68.82 mg/g, higher than 32.53 mg/g of AC1 and 24.79 mg/g of AC2, respectively. This is mainly attributed to the fact that the PAC-OP has relatively more developed pore structures and abundant oxygen-containing functional groups on its surface, compared with AC1 and AC2.

Regeneration adsorption

To understand the influence of regeneration times on adsorption of the three samples (PAC-OP, AC1, and AC2), four times regeneration adsorption experiments were conducted. The detailed descriptions of the regeneration system and process are presented in ESM.

SO₂ adsorption capacities of PAC-OP, AC1, and AC2 versus regeneration number are depicted in Fig. 10 which illustrates that the SO₂ adsorption capacities of the three samples exhibit a general decrease as the regeneration number increases and that of PAC-OP decreases from the original 68.8 mg/g to 60.5 mg/g in the 4th regeneration, a decrease of 12.1%, that of AC1 decreases from 32.5 mg/g to 28.5 mg/g, a decrease of 12.3%, and that of AC2 decreases from 24.8 mg/g to 16.6 mg/g, a decrease of 33.1%. It is found that PAC-OP and AC1 have a similar decline, while AC2 has a higher decline, i.e., PAC-OP and AC1 have a high recycling value, and AC2 has a low recycling utilization rate. Besides, after multiple regeneration cycles, PAC-OP still has a high SO₂ adsorption performance, which is still significantly better than the two ACs.

Conclusions

The rapid preparation process of the PAC under the atmosphere of flue gas is affected by multiple factors. At present, six influencing factors are considered. Factor screening design and CCD in RSM are adopted to screen and optimize the influencing parameters. The following conclusions can be reached:

The degree of influence of the six influencing factors on the preparation process of PAC is temperature>oxygen concentration>water vapor concentration>equivalent ratio>carbon dioxide concentration>residence time, of which, temperature, oxygen concentration, and water vapor concentration are significant factors while the other three are secondary ones.

When the temperature is 904.74°C, the oxygen concentration is 4.67% and the water vapor concentration is 27.98%, the PAC prepared has the maximum SO₂ adsorption capacity, being 68.15 mg/g, and the error with the experimental value (68.82 mg/g) is only 0.98%, which indicates that the predicted value of the model is consistent with the experimental value, verifying the feasibility and reliability of the RSM.

Compared to two typical ACs, PAC-OP has relatively more developed pore structures and abundant oxygen-containing functional groups, and its SO₂ adsorption capacity for 2 h is also significantly higher than that of ACs. After 4 times of regeneration, the SO₂ adsorption capacity of PAC-OP can still reach 87.9% of its initial adsorption capacity, which is superior to the two ACs.

Compared with ACs, PAC-OP prepared from powdered JJ-coal has a more developed pore structure and a higher SO₂ adsorption capacity. Moreover, after repeated regeneration, PAC-OP still has a higher SO₂ adsorption capacity. Therefore, it can be used as a new SO₂ adsorbent. Besides, the one-step process for rapid preparation of PAC is also a suitable process.

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