Mass Transfer Performance for CO2 Absorption by 2-((2-aminoethyl

Publication Date (Web): November 19, 2017 ... Furthermore, an artificial neural network (ANN) model was applied to predict the mass transfer performan...
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Article Cite This: Energy Fuels 2017, 31, 14053−14059

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Mass-Transfer Performance for CO2 Absorption by 2‑(2Aminoethylamino)ethanol Solution in a Rotating Packed Bed Shuying Wu,† Liangliang Zhang,*,† Baochang Sun,† Haikui Zou,† Xiaofei Zeng,*,†,‡ Yong Luo,† Qiang Li,§ and Jianfeng Chen†,‡ †

Research Center of the Ministry of Education for High Gravity Engineering and Technology and ‡State Key Laboratory of Organic−Inorganic Composites, Beijing University of Chemical Technology, Beijing 100029, P. R. China § Xinjiang DunHua Co., Ltd., Kelamayi 834000, P. R. China ABSTRACT: The emission of CO2 is leading to serious global climate change, which has attracted increasing attention. In this work, a rotating packed bed (RPB) was employed as a highly effective reactor to intensify the CO2 absorption in an alkanolamine solution that mainly contained 2-(2-aminoethylamino)ethanol (AEEA). The effects of important operating conditions, such as high gravity level, amine solvent concentration, gas/liquid flow ratio, CO2 inlet concentration, absorption temperature, and CO2 loading in the amine solvent, on the gas-phase volumetric mass-transfer coefficient (KGa) and CO2 capture efficiency were investigated. The results indicated that the high gravity level and CO2 inlet concentration had significant effects on KGa, and the experimental value of KGa was found to be about 1.42−2.86 kmol·m−3·h−1·kPa−1 in the RPB, which is an order of magnitude higher than that in a conventional packed column. Furthermore, an artificial neural network (ANN) model was applied to predict the mass-transfer performance. The values predicted using the ANN model were in good agreement with the experimental data (±10%).

1. INTRODUCTION With increasing global energy consumption, carbon dioxide (CO2) emissions from industry and fossil-fuel-fired power plant bring about worrying environmental impacts, especially global warming,1 which greatly challenges the sustainable development of the world. Accordingly, intensive research has focused on reducing carbon emissions to the atmosphere, withh the aim of solving this thorny problem. In view of the large emission reductions needed from coal and gas power generation plants, which will remain a feature of the electricity mix for the foreseeable future, it is widely believed that CO2 capture and storage (CCS) is one a promising method for limiting future global temperature increases to 2 °C, which is the consensus and goal set in Paris Agreement.2,3 To date, several technologies have been widely investigated for CO2 capture on the laboratory and industrial scales, such as absorption,4 adsorption,5,6 membrane-based separation,7 biological separation,8 and cryogenic separation.9 Among these methods, absorption seems to be the established leading candidate for industrial CO2 capture technologies because of its high absorption reactivity, high selectivity, scale-up feasibility, and relatively low cost.10−12 Alkanolamines, which were discovered in the late 1920s by Bottoms,13 are the most commonly used absorption solvents. Among the alkanolamines, the monoethanolamine (MEA) scrubbing to capture CO2 was commercialized first and is regarded as a standard for the evaluation of the overall CO2 capture performance of various absorbents. However, the regeneration cost to capture CO2 from the flue gas of power plants is very high when MEA scrubbing is used because of its high regeneration temperature.14−16 Thus, increasing numbers of studies have focused on the research and development of new or mixed amine absorbents with high reaction rates, high CO2 capacities, and © 2017 American Chemical Society

low regeneration costs. For example, mixed amines, such as MEA/piperazine (PZ), MEA/diethylenetriamine (DETA),17 and DETA/PZ,18 have been found to provide improvements in either the average absorption rate or the saturated CO2 loading. It also has been found that 2-((2-aminoethyl)amino)-ethanol (AEEA) as a diamine offers high absorption rate combined with a high net cyclic capacity that is significantly higher than that of MEA.19 Moreover, the AEEA-rich solution can be desorbed at a lower temperature than the MEA solution, which favors the regeneration process and will greatly reduce the regeneration cost because less water is evaporated from the solution. An industrial plant with a CO2 capacity of 100000 tons using this AEEA-based absorbent in a conventional packed tower was established in Xinjiang, China, to capture the CO2 from the purge gas of a chemical plant. Over the past few decades, gas−liquid contact approaches such as packed towers, spray columns, bubble columns, and tray towers have been successfully developed for CO2 capture using alkanolamines in both bench-scale investigations and pilot-scale applications.20−22 However, the process intensification and performance improvement of these conventional gas− liquid mass-transfer processes still face great challenges, as mass-transfer limitations hinder CO2 capture performance and do not allow for reductions in device size.23−25 Gao et al.26 studied the removal of CO2 by MEA absorption in a microporous tube-in-tube microchannel reactor, demonstrating a new process intensification technique for CO2 capture with a relatively large throughput compared to those of conventional microchannel reactors. Zanfir et al.27 demonstrated that fallingReceived: October 5, 2017 Revised: November 17, 2017 Published: November 19, 2017 14053

DOI: 10.1021/acs.energyfuels.7b03002 Energy Fuels 2017, 31, 14053−14059

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Energy & Fuels film microreactors provide an intensification of CO2 absorption by increasing the interfacial area and minimizing wasting of the liquid reactant. However, the maximum throughputs of these reported technologies are much smaller than those of conventional gas−liquid reactors, making it difficult for these approaches to meet the demands of industrial applications. Additionally, these reactors are hard to scale up because of their specific structures. Consequently, it is urgent that efficient and scalable alternative technologies to overcome the above drawbacks be developed. As one of the cutting-edge process intensification approaches, HiGee (high-gravity) technology has received considerable attention. The key point of HiGee technology is that it relies on a simulated high-gravity environment created by the centrifugal force in a rotating packed bed (RPB).28,29 In an RPB, liquid is sprayed radially through the liquid distributor and then flows through the packing as a result of the centrifugal force under a high gravity field. The liquid spreads or is split into very fine liquid elements, including droplets, threads, and films, by the packing, which leads to the intensification of the mass-transfer performance. It has been shown that the volumetric mass-transfer coefficient in an RPB is 1−3 orders of magnitude higher than that in a conventional packed tower.30 Consequently, the total amount of packing required in an RPB can be greatly reduced, and the investment costs and size of the equipment can also be minimized.31−33 Thus, the RPB has been extensively explored in many industrial operations, including the absorption of H2S34,35 and SO2.36 Nevertheless, CO2 capture in an RPB using AEEA solution has not been reported, especially its process intensification and operating characteristics and a model of the mass-transfer performance in the RPB. The focus of this study is on the mass-transfer performance of the CO2 absorption process using AEEA solvent in an RPB. The performance of the process, in terms of the volumetric overall mass-transfer coefficient (KGa) and CO2 capture efficiency, was investigated experimentally under various conditions to evaluate the effects of operating variables, including high gravity level, gas/liquid ratio, inlet CO2 concentration, absorbent concentration, lean CO2 loading, and operating temperature. In addition, an artificial neural network (ANN) model was developed to predict the CO2 absorption performance. The accuracy of the ANN model was examined by comparing the model results with the experimental data. Finally, the process intensification characteristics of the RPB were evaluated by comparing the masstransfer coefficients in the RPB and in a packed tower.

Figure 1. Experimental setup for CO2 absorption.

edge of the RPB, whereas the absorbent as the dispersed phase was sprayed through four holes (each with a diameter of 1 mm) in the liquid distributor at the center of the RPB and moved outward under the centrifugal force. The gas and liquid streams underwent countercurrent contact in the RPB. Consequently, CO2 in the gas stream dissolved in the liquid stream reacted with the absorbent before the gas and liquid streams left the RPB from the gas outlet and liquid outlet, respectively. The RPB packed with stainless wire mesh had inner and outer diameters of 5 and 15 cm, respectively, and a height of 5.3 cm. Meanwhile, the surface area per unit volume of the packing was 650 m2/m3, and the porosity of the packing was 95%. 2.3. Sample Analysis. The CO2 concentrations in the gas phase at both the inlet and outlet were measured with an infrared CO2 analyzer (model GXH-3010F, Beijing Huayun Analytical Instrument Research Institute). The inlet and outlet concentrations of CO2 were separately monitored with analyzers in the range of 0−20 vol % with a resolution of 0.1% and in the range of 0−10 vol % with a resolution of 0.01%. The CO2 loadings in the absorbents and the concentrations of alkanolamine were measured by neutralization titration.37

3. RESULTS AND DISCUSSION The CO2−AEEA−H2O system is a reactive system. As AEEA is a diamine containing one primary amine group and one secondary amine group, the reactions within the system are very complicated. Based on the study of Ma’mum et al.,38,39 the mechanism of the absorption process is described by the following equations Diffusion of CO2 from the gas phase into the liquid phase

2. EXPERIMENTAL SECTION

CO2 (g) ⇌ CO2 (l)

2.1. Chemicals. 2-(2-Aminoethylamino)ethanol (AEEA) was supplied by Dalian University of Technology and diluted with deionized water prepared from our own laboratory. Both CO2 and N2 gas were purchased from Beijing Ruyuanruquan Technology Co., Ltd., Beijing, China, and had purities of 99.9%. 2.2. Experimental Procedure. The experimental setup for CO2 absorption is shown in Figure 1. The CO2 removal experiments were performed as follows: CO2 gas from the gas cylinder was first mixed with N2 gas to simulate stack gas. The flow rates of the CO2 and N2 gases were controlled with gas flow meters to adjust the CO2 content in the gas mixture within the range of 8−20 vol %. The feed alkanolamine aqueous solution was prepared by adding a predetermined amount of alkanolamine to deionized water, and this solution was then pumped into the liquid inlet. The gas mixture stream as the continuous phase flowed inward from the outer

(1)

Formation of protonated alkanolamine AEEA + H3O+ ⇌ AEEAH+ + H 2O

(2)

Formation of diprotonated alkanolamine +

AEEAH+ + H3O+ ⇌ HAEEAH+ + H 2O

(3)

Formation of carbamate 2AEEA + 2CO2 (l) + 2H 2O ⇌ AEEACOOp− + AEEACOOs− + H3O+ 14054

(4) DOI: 10.1021/acs.energyfuels.7b03002 Energy Fuels 2017, 31, 14053−14059

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Energy & Fuels Formation of protonated carbamate K Ga =

AEEACOOp− + AEEACOOs− + H3O+ ⇌ +HAEEACOOP− + +HAEEACOOs− + H 2O

AEEACOOp + AEEACOOs + 2CO2 (l) + 2H 2O (6)

In eqs 4−6, subscripts p and s denote bonding to the primary and secondary amine groups, respectively. The protonation in the solution occurs instantaneously, and the formations of primary (p) and secondary (s) carbamates are also simultaneous.37 Compared with the reactions of MEA,19 the reactions between AEEA and CO2 are much faster, causing the physical mass transfer inside the liquid film to be the limiting process of the entire CO2 capture. According to two-film theory, the mass flux per unit volume (NCO2a) can be written in terms of the overall gas-side mass-

⎡ y (1 − yin ) ⎤ ⎥ × 100% η = ⎢1 − out ⎢⎣ yin (1 − yout ) ⎥⎦

2

gravity level = ω 2r /g

(13)

where ω is the angular speed of the RPB, r is the geometric average radius of the packing, and g is the acceleration of gravity (9.8 m·s−2). Figure 2 shows the dependence of KGa and the CO2 capture efficiency on the high gravity level at different inlet CO2

(7)

2

(12)

3.1. Effects of High Gravity Level on KGa and CO2 Capture Efficiency. A high gravity level means the multiple of centrifugal acceleration in RPB, which is equal to the acceleration of gravity. The numerical gravity level can be calculated using the equation

transfer coefficient (KGa) as * ) NCO2a = K Ga × P(yCO − yCO

(11)

where yin and yout denote the molar concentrations of gas-phase CO2 entering and leaving the RPB, respectively. Additionally, the CO2 capture efficiency (η) can be expressed as



⇌ 2(−OOCAEEACOO−) + 2H3O+

πPH(rout 2

⎡ y (1 − yCO ,out ) 2 ⎢ln CO2,in × − rin 2) ⎢⎣ yCO ,out (1 − yCO ,in ) 2 2

⎛ y yCO ,out ⎞⎤ CO2 ,in 2 ⎟⎥ + ⎜⎜ − ⎟⎥ 1 y 1 y − − ⎝ CO2 ,in CO2 ,out ⎠⎦

(5)

Formation of dicarbamate −

GI

where P represents the total pressure and yCO2 and y*CO2 are the mole fraction and equilibrium mole fraction of CO2 in the gas phase, respectively. Considering a differential volume with a cross-sectional area of 2πrH and a radial thickness of dr in the RPB, the mass balance can be expressed as ⎞ ⎛ y CO2 ⎟ NCO2a × 2πrH dr = G I d⎜⎜ ⎟ 1 − y ⎝ CO2 ⎠

(8)

where GI is the inert-gas molar flow rate and H is the axial height of the packing. Substitution of eq 7 into eq 8 gives ⎞ ⎛ y * ) × 2πrH dr = G I d⎜ CO2 ⎟ K Ga × P(yCO − yCO ⎜1 − y ⎟ 2 2 ⎝ CO2 ⎠

Figure 2. Effects of high gravity level on KGa and CO2 capture efficiency. (9)

Thus, the value of KGa can be derived from the expression K Ga =

GI πPH(rout 2 − rin 2)



yCO

2

concentrations for a gas/liquid ratio of 150 L/L and a 25 wt % alkanolamine solution at ambient temperature (about 17 °C). When the high gravity level was varied from 22 to 87, KGa increased obviously, and then the increasing trend slowed and KGa tended to remain constant when the high gravity level was over 87. This is because the effectiveness of process intensification with an RPB depends highly on the liquid flow pattern, especially the liquid forms. It can be observed by visual experiments that a number of liquid forms exist in the packing: pore flow (at gravity levels of 15−60), droplet flow (at gravity levels beyond 100), and film flow, which exists on the packing surface and coexists with both the pore flow and droplet flow.40,41 As the high gravity level increases, the absorbent in the RPB obtains a higher centrifugal acceleration, which gives rise to enhancement of cutting and breaking effects. These

⎞ ⎛ y 1 CO2 ⎟ d⎜⎜ ⎟ * 1 y − − yCO ⎝ CO2 ⎠ 2 (10)

by integrating the equation from rin to rout, where rin and rout are the inner and outer radii, respectively, of the packing. Because of the fast reaction between CO2 and the aqueous amine solution, the absorbed CO2 in the solution is consumed immediately in the process. Therefore, yCO * 2 is very close to zero and is usually negligible. Then, KGa can be obtained by integrating the eq 10 from y = yin to y = yout, giving 14055

DOI: 10.1021/acs.energyfuels.7b03002 Energy Fuels 2017, 31, 14053−14059

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Energy & Fuels effects contribute to thinner liquid films and smaller liquid droplets, eventually generating large gas−liquid contact areas and high interface refresh rates, which favor mass transfer. Thus, KGa first increases with increasing high gravity level. The film thickness decreases as the high gravity level increases until a point (87 in the present work) and then approaches a constant state at higher gravity levels, so that the high gravity level has little effect on KGa afterward. It has been reported that the liquid film thicknesses in RPBs achieving high gravity fields are usually about 100 μm.42 Furthermore, a high gravity level means more energy consumption, resulting in higher operating costs. Based on KGa and energy savings, the optimal gravity level of 87 was therefore used in subsequent experiments. 3.2. Effects of Gas/Liquid Ratio on KGa and CO2 Capture Efficiency. Figure 3 illustrates the effects of the

Figure 4. Effects of inlet CO2 concentration on KGa and CO2 capture efficiency.

mass-transfer rate per unit driving force, and the increase of the driving force with the CO2 partial pressure leads to a decrease of KGa. As a whole, an increase of the CO2 partial pressure was found to have negative effects on KGa and the CO2 capture efficiency, which is similar to many experimental results reported in the literature.43,44 3.4. Effects of Absorbent Concentration on KGa and CO2 Capture Efficiency. The absorbent concentration is closely related to the value of KGa, as shown in Figure 5. Based

Figure 3. Effects of gas/liquid ratio on KGa and CO2 capture efficiency.

gas/liquid ratio on KGa and the CO2 capture efficiency with a high gravity level of 87 and an absorbent concentration of 25 wt % at ambient temperature. The gas/liquid ratio was controlled by changing the liquid flow rate under a fixed gas flow rate of 2 m3/h. The curves show that KGa decreased with increasing gas/ liquid ratio. Because an increase in gas/liquid ratio results in a lower distribution of liquid per unit volume of packing, this weakens the driving force in the liquid phase and leads to a decrease of the mass-transfer coefficient. However, a smaller gas/liquid ratio means more absorbent feeding, which can lead to an increase of the amount of absorbent cycled and subsequent energy costs for absorbent regeneration, as the gas amount and gravity level are particular values. 3.3. Effects of Inlet CO2 Concentration on KGa and CO2 Capture Efficiency. Considering that industrial stack gas contains 10−20 vol % CO2, inlet CO2 concentrations of 8, 12, 16, and 20 vol % were studied with a high gravity level of 87, a gas/liquid ratio of 150 L/L, and an absorbent concentration of 25 wt % at ambient temperature. As shown in Figure 4, an increased inlet concentration of CO2 led to the decrease of both KGa and the CO2 capture efficiency, but a high CO2 capture efficiency of more than 92% of the industrial stack gas concentration could still be achieved. An increase of the CO2 partial pressure theoretically enhances the reaction rate of CO2 with AEEA and allows more CO2 molecules to travel from the gas bulk to the gas/liquid interface according to two-film theory. This is beneficial for reducing the mass-transfer resistance of the gas phase. However, KGa represents the

Figure 5. Effects of absorbent concentration on KGa and CO2 capture efficiency.

on the mechanism of mass transfer between AEEA and CO2, AEEA−CO2 is an activated system, and the reaction takes place on the interface between the gas and liquid phases. Thus, absorbents present in high concentrations enhance mass transfer and increase the liquid-side volumetric mass-transfer coefficient, leading to an increase of the overall mass transfer. However, the carbonation reaction will reach the reaction limit under specific conditions, so that KGa tends to remain constant at relatively high absorbent concentrations. For AEEA, high concentrations will increase the viscosity of the absorbent, which is likely to offset the benefit of increased free amine molecules and lead to reductions in KGa and the CO2 capture efficiency. Moreover, anincreased concentration of alkanolamine solution will aggravate the corrosion of the equipment in long-term operations. Thus, a critical absorbent concentration 14056

DOI: 10.1021/acs.energyfuels.7b03002 Energy Fuels 2017, 31, 14053−14059

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Energy & Fuels value can be observed in the curves, and 25 wt % alkanolamine solution was obtained as the optimized value. 3.5. Effects of Lean CO2 Loading on KGa and CO2 Capture Efficiency. In practical industry, absorbents are regenerated in regeneration units and then cycled to the absorption units; thus, absorbents are usually lean solutions containing some absorbed CO2. It is meaningful to investigate the effects of lean CO2 loadings on KGa and the CO2 capture efficiency as a reference for the regeneration section. Figure 6

Figure 7. Effects of temperature on KGa and CO2 capture efficiency.

collisions, which accelerates the reaction. On the other hand, according to the reaction thermodynamics, high temperature also gives rise to the reverse reaction. When the gas pressure and concentration of CO2 are determined, the dissolution of CO2 in the absorbent thermodynamically decreases with increasing temperature, impeding mass transfer. Initially, the acceleration of the absorption reaction is dominant over the decrease of the dissolution of CO2 and the reverse reaction. Thus, initially, KGa increases obviously as the temperature increases. Then, the benefits of increasing temperature to improve the absorption rate offsets the adverse factors as the effects of the dissolution of CO2 in the absorbent and the reverse reaction rate tend to strengthen, resulting in a slow increase of KGa. Therefore, the effect of temperature on KGa is a combined outcome. 3.7. Comparison of KGa between an RPB and a Packed Column. As CO2 capture using AEEA solution was studied using a packed column in a previous work,45 Table 1 presents a

Figure 6. Effects of lean CO2 loading on KGa and CO2 capture efficiency.

shows the KGa values and CO2 capture efficiencies for different CO2 loadings of the absorbent. For the fresh absorbent (with a small amount of CO2 absorbed from the environment, with a loading of 0.026 mol of CO2/mol of amine), the CO2 capture efficiency is above 92%. For the solution with a CO2 loading of 0.274 mol of CO2/mol of amine (nearly 26% of the saturated loading), the effectiveness of CO2 capture is somewhat weakened, but the absorbent can still achieve more than 80% CO2 capture efficiency. However, when the CO2 loading reaches 0.541 mol of CO2/mol of amine (nearly half of the saturated loading), the CO2 capture efficiency reduces to 60− 73%. A lower CO2 loading means more free amine in solution, resulting in a greater absorption capacity. According to vapor− liquid equilibrium (VLE) theory, the mole fraction in equilibrium with the bulk concentration (yCO * 2) decreases as the CO2 loading decreases, increasing the driving force (yCO2 − y*CO2) for CO2 to transfer from the gas phase into the liquid phase and then resulting in a higher KGa value and a greater CO2 capture efficiency. 3.6. Effects of Temperature on KGa and CO2 Capture Efficiency. The absorbent temperature is also an important parameter influencing the CO2 capture efficiency, as can be observed in Figure 7. Absorbent with a dissolved CO2 content of 0.333 mol of CO2/mol of amine was investigated at a high gravity level of 87 and an inlet CO2 concentration of 12 vol %. As shown in Figure 7, KGa increased over the temperature range from ambient temperature to 323 K, and the increasing trend became much slower when the temperature was above 303 K. This can be explained from two aspects: the reaction of CO2 with the absorbent and the dissolution of CO2 in the absorbent. On one hand, the AEEA−CO2 system involves a reversible and exothermic reaction. Increasing temperature activates the reactant molecules and causes more effective

Table 1. Comparison of Column Characteristics and KGa Values between the RPB and the Packed Column packed column type of packing amine G (m3·h−1) L (L·h−1) KGa (kmol·m−3·h−1·kPa−1)

θ rings 20% AEEA + 2% MDEA 1.6−2.8 15−30 0.15−0.2642

RPB wire mesh 15−30% AEEA 2 10−20 1.42−2.86

comparison of KGa between the RPB in this work and the packed column in that work.45 Specifically, Shen et al.45 studied the mass-transfer performance of the AEEA−CO2 system in a packed column using θ-ring packing with a diameter of 8 cm and a height of 120 cm, which was much higher than the packing in the RPB. In both situations, the gas/liquid ratio was the same, ranging from 100 to 200 L/L, and the absorbent concentrations were similar as well. As can be observed, the KGa values calculated from the RPB are within the range of 1.42−2.86 kmol·m−3·h−1·kPa−1, which are an order of magnitude higher than those calculated for the packed column, namely, 0.15−0.26 kmol·m−3·h−1·kPa−1. As the mass transfer is greatly intensified in the RPB, it is feasible to apply an RPB to CO2 capture using AEEA solution to scale up the process and reduce the equipment size. 14057

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Energy & Fuels

4. ARTIFICIAL NEURAL NETWORK (ANN) MODEL OF KGa 4.1. General Description of ANN Model. It is of great importance to predict the mass-transfer performance of CO2 capture using alkanolamine solution in an RPB under practical industrial conditions. The gas-phase volumetric mass-transfer coefficient was chosen to quantify the mass transfer between the gas and liquid in the RPB. Because of the number of influencing factors and the complexity of gas/liquid mixing, mass-transfer and reaction processes in the high-gravity-based RPB, the relationships between the performance parameters and the influencing factors are highly nonlinear and implicit. Therefore, an artificial neural network model was employed to predict the mass-transfer performance. An ANN model evolves from artificial intelligence applications and is able to recognize the underlying linear and nonlinear relationships among input and output data.46 Figure 8 shows a schematic structure of an ANN model for KGa prediction. A general ANN model contains an input layer,

Figure 9. Comparison of predicted and experimental KGa values.

5. CONCLUSIONS In this work, the mass transfer between CO2 and AEEA solution in an RPB was studied. The influencing factors of high gravity level, gas/liquid ratio, inlet CO2 concentration, absorbent concentration, lean CO2 loading, and temperature were investigated, all of which were found to have significant effects on the gas-phase volumetric mass-transfer coefficient (KGa) and CO2 capture efficiency. Within the range of this experimental investigation, the high gravity level and temperature had obviously positive effects, whereas the gas/liquid ratio and lean CO2 loading had negative effects on KGa and the CO2 capture efficiency. Optimal operation for a high gravity level of 87 and an absorbent concentration of 25 wt % was obtained as well. The CO2 capture efficiency in the RPB was found to be more than 90% in most situations, and an order-of-magnitudehigher KGa was achieved in the RPB compared with a packed column, showing good CO2 capture performance. Additionally, the values of KGa predicted using an ANN model were in good agreement with experimental data, with a deviation of ±10%.



Figure 8. Schematic structure of an ANN model for KGa prediction.

AUTHOR INFORMATION

Corresponding Authors

*Tel.: +86-10-64443134. Fax: +86-10-64434784. E-mail: zhll@ mail.buct.edu.cn. *Tel.: +86-10-64443134. Fax: +86-10-64434784. E-mail: [email protected].

one or more hidden layers, and an output layer. The layers are connected by transfer functions, and the neurons among different layers are adjusted by weight and bias. Then, the data collected are split into two parts: the training data and the testing data. Finally, it is necessary to modify the parameters of the network to obtain an optimized ANN model using the training data and to verify the model with the testing data. 4.2. ANN Model of KGa for the AEEA−CO2 System. In the present work, a back-propagation (BP) algorithm based on an error-correction learning rule, which is a classical training algorithm, was applied to the AEEA−CO2 system. After the validity of the developed ANN model was confirmed, the prediction of KGa for the AEEA−CO2 system was carried out using the experimental data obtained in this work, with 79 data sets (75%) for training and 27 data sets (25%) for testing. The main influencing factors, such as high gravity level, gas/liquid ratio, and inlet CO2 concentration, were selected as input parameters, whereas KGa was chosen as the output variable. The finally selected network was found for a network topology of 6−11−1. The parity plot in Figure 9 shows that the predicted results are in good agreement with the experimental values, with deviations of less than 10%, which is an acceptable range for mass-transfer studies.

ORCID

Liangliang Zhang: 0000-0002-6812-6860 Baochang Sun: 0000-0002-3435-1250 Haikui Zou: 0000-0003-0681-9036 Yong Luo: 0000-0001-8300-5277 Notes

The authors declare no competing financial interest.



ACKNOWLEDGMENTS This work was financially supported by the National Key R&D Program of China (No. 2017YFB0603300) and the Fundamental Research Funds for the Central Universities (JD1706). We are thankful to Prof. Yongchun Zhang of Dalian University of Technology for providing the AEEA-based absorbent, and to Prof. Shaoyun Chen of Dalian University of Technology for useful discussions on the topic of absorbent improvement and the industrial applications of AEEA-based absorbent.

■ 14058

NOMENCLATURE g = acceleration of gravity, m·s−2 G = gas flow rate, m3·h−1 DOI: 10.1021/acs.energyfuels.7b03002 Energy Fuels 2017, 31, 14053−14059

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Energy & Fuels GI = inert-gas flow rate, m3·h−1 H = axial length of the packing KGa = gas-phase volumetric mass-transfer coefficient L = liquid flow rate, L·h−1 N = rotation speed, rpm r = geometric average radius of the packing, m rin = inner radius of the packing rout = outer radius of the packing Yin = inlet mole fraction of CO2 Yout = outlet mole fraction of CO2

(30) Zhang, L. L.; Wu, S. Y.; Liang, Z. Z.; Zhao, H.; Zou, H. K.; Chu, G. W. Chin. J. Chem. Eng. 2017, 25, 175−179. (31) Zhang, L. L.; Wang, J. X.; Xiang, Y.; Zeng, X. F.; Chen, J. F. Ind. Eng. Chem. Res. 2011, 50, 6957−6964. (32) Guo, K.; Wen, J. W.; Zhao, Y.; Wang, Y.; Zhang, Z. Z.; Li, Z. X.; Qian, Z. Environ. Sci. Technol. 2014, 48, 6844−6849. (33) Lin, C. C.; Su, Y. R. Sep. Purif. Technol. 2008, 61, 311−316. (34) Qian, Z.; Li, Z. H.; Guo, K. Ind. Eng. Chem. Res. 2012, 51, 8108−8116. (35) Li, W. M.; Li, Z. H.; Hao, G. J.; Zhang, W. S.; Zeng, D.; Guo, K. J. Chem. Ind. Eng. 2014, 2, 26−30 (in Chinese). (36) Bai, S.; Chu, G. W.; Li, S. C.; Zou, H. K.; Xiang, Y.; Luo, Y.; Chen, J. F. Environ. Eng. Sci. 2015, 32, 806. (37) Ma’mun, S.; Jakobsen, J. P.; Svendsen, H. F.; Juliussen, O. Ind. Eng. Chem. Res. 2006, 45, 2505−2512. (38) Ma’mun, S.; Dindore, V. Y.; Svendsen, H. F. Ind. Eng. Chem. Res. 2007, 46, 385−394. (39) Wang, T.; Liu, F.; Ge, K.; Fang, M. X. Chem. Eng. J. 2017, 314, 123−131. (40) Burns, J. R.; Ramshaw, C. Chem. Eng. Sci. 1996, 51, 1347−1352. (41) Guo, K. A Study on Liquid Flowing inside the HiGee Rotor. Ph.D. Dissertation, Beijing University of Chemical Technology, Beijing, 1996. (42) Zhao, B. T.; Tao, W. W.; Zhong, M.; Su, Y. X.; Cui, G. M. Renewable Sustainable Energy Rev. 2016, 65, 44−56. (43) Sheng, M. P.; Sun, B. C.; Zhang, F. M.; Chu, G. W.; Zhang, L. L.; Liu, C. G.; Chen, J. F.; Zou, H. K. Energy Fuels 2016, 30, 4215− 4220. (44) Lin, C. C.; Chen, B. C. Chem. Eng. Res. Des. 2011, 89, 1722− 1729. (45) Shen, H. S.; Zhang, Y. C.; Chen, S. Y.; Jiang, P. Mod. Chem. Ind. 2010, 30, 70−73 (in Chinese). (46) Fu, K. Y.; Chen, G. Y.; Sema, T.; Zhang, X.; Liang, Z. W.; Idem, R.; Tontiwachwuthikul, P. Chem. Eng. Sci. 2013, 100, 195−202.

Greek Symbols

η = CO2 capture efficiency, % ω = angular speed, rad·min−1



REFERENCES

(1) Matthews, D.; Solomon, S. Science 2013, 340, 1523. (2) Metz, B.; Davidson, O.; Coninck, H. D.; Loos, M.; Meyer, L. Carbon Dioxide Capture and Storage; Intergovernmental Panel on Climate Change (IPCC): Geneva, Switzerland, 2005. (3) Shen, S. F.; Yang, Y. N. Energy Fuels 2016, 30, 6585−6596. (4) Singh, D.; Croiset, E.; Douglas, P. L.; Douglas, M. A. Energy Convers. Manage. 2003, 44, 3073−3091. (5) Aaron, D.; Tsouris, C. Sep. Sci. Technol. 2005, 40, 321−348. (6) Siriwardane, R. V.; Shen, M.-S.; Fisher, E. P.; Losch, J. Energy Fuels 2005, 19, 1153−1159. (7) Zhang, Y.; Wang, Z.; Wang, S. C. Chem. Lett. 2002, 31, 430−431. (8) Goli, A.; Shamiri, A.; Talaiekhozani, A.; Eshtiaghi, N.; Aghamohammadi, N.; Aroua, M. K. J. Environ. Manage. 2016, 183, 41−58. (9) Tan, Y. T.; Nookuea, W.; Li, H. L.; Thorin, E.; Yan, J. Y. Appl. Therm. Eng. 2017, 123, 721−733. (10) Dou, B. L.; Song, Y. C.; Liu, Y. G.; Feng, C. J. Hazard. Mater. 2010, 183, 759−765. (11) Rochelle, G. T. Science 2009, 325, 1652−1654. (12) Wang, M.; Lawal, A.; Stephenson, P.; Sidders, J.; Ramshaw, C. Chem. Eng. Res. Des. 2011, 89, 1609−1624. (13) Astarita, G.; Savage, D. W.; Bisio, A. Gas Treating with Chemical Solvents; John Wiley & Sons: New York, 1983. (14) Figueroa, J. D.; Fout, T.; Plasynski, S.; McIlvried, H.; Srivastava, R. D. Int. J. Greenhouse Gas Control 2008, 2, 9−20. (15) Olajire, A. A. Energy 2010, 35, 2610−2628. (16) Pires, J. C. M.; Martins, F. G.; Alvim-Ferraz, M. C. M.; Simões, M. Chem. Eng. Res. Des. 2011, 89, 1446−1460. (17) Zhu, D.; Fang, M.; Lv, Z.; Wang, Z.; Luo, Z. Energy Fuels 2012, 26, 147−153. (18) Chang, Y. C.; Leron, R. B.; Li, M. H. J. Chem. Thermodyn. 2013, 64, 106−113. (19) Ma’mun, S.; Svendsen, H. F.; Hoff, K. A.; Juliussen, O. Energy Convers. Manage. 2007, 48, 251−258. (20) Yeh, J. T.; Pennline, H. W.; Resnik, K. P. Energy Fuels 2001, 15, 274−278. (21) Kuntz, J.; Aroonwilas, A. Ind. Eng. Chem. Res. 2008, 47, 145− 153. (22) Chen, P. C.; Shi, W.; Du, R.; Chen, V. Ind. Eng. Chem. Res. 2008, 47, 6336−6343. (23) Mangalapally, H. P.; Hasse, H. Energy Procedia 2011, 4, 1−8. (24) Mac Dowell, N. M.; Shah, N. Comput. Chem. Eng. 2015, 74, 169−183. (25) Mac Dowell, N.; Samsatli, N. J.; Shah, N. Int. J. Greenhouse Gas Control 2013, 12, 247−258. (26) Gao, N. N.; Wang, J. X.; Shao, L.; Chen, J. F. Ind. Eng. Chem. Res. 2011, 50, 6369−6374. (27) Zanfir, M.; Gavriilidis, A.; Wille, C.; Hessel, V. Ind. Eng. Chem. Res. 2005, 44, 1742−1751. (28) Asendrych, D.; Niegodajew, P.; Drobniak, S. Chem. Process Eng. 2013, 34, 269−282. (29) Green, A. Chem. Ind. 1998, 5, 168−172. 14059

DOI: 10.1021/acs.energyfuels.7b03002 Energy Fuels 2017, 31, 14053−14059