Abstract

In the previous study, greenhouse gas CO2 was successfully used as the precipitator to realize its carbonation by calcium ions in seawater with the help of magnesium oxide. In this study, the reaction process was firstly analyzed by a proposed reaction mechanism, and then the dynamic simulation of the gas–liquid–solid system was carried out via kinetic Monte Carlo simulation. Based on the reaction mechanism, the continuous experimental study was realized in a bubble column. The effects of air flow rate, carbon dioxide flow rate and temperature on the effectiveness evaluation indexes of decalcification efficiency, total mass transfer coefficient and carbon sequestration rate were studied. Finally, a bonnet tower with a diameter of 1 m and a height of 8 m was built to carry out the pilot test. In the laboratory experiments, the calcium removal rate reached 94%, the carbon sequestration rate reached 63.6%, and pure micron calcium carbonate products were obtained. The decalcification rate reached 95% in the pilot test, which is consistent with the results of the laboratory experiment.

HIGHLIGHTS

  • Reaction kinetics model in a gas–liquid–solid system is proposed.

  • The continuous experimental study was realized in a bubble column.

  • The basic data are provided for industrialization via laboratory and pilot tests.

Graphical Abstract

Graphical Abstract
Graphical Abstract

INTRODUCTION

With economic development, freshwater resources will become one of the biggest problems that plagues human development (José 2014). Seawater is considered as the most abundant raw material for freshwater production (Mahinpey et al. 2011). Now electrodialysis (Guillaume et al. 2018) and reverse osmosis (Lee et al. 2011) are being gradually developed as the environmentally friendly seawater desalination technologies. The large amount of calcium and magnesium ions in seawater will seriously affect the service life of the membrane (Tanaka et al. 2011).

Meanwhile, the development of new technologies for capturing and sequestrating the emitted CO2 is urgently required to restrain the effects of global warming. For this purpose, permanent fixation of CO2 in the form of stable and environmentally benign carbonate minerals, such as magnesite (MgCO3) and calcite (CaCO3), through a mineral carbonation process has gained attention recently (Bobicki et al. 2012; Lu et al. 2016; Arias et al. 2017; Ebrahimi et al. 2018).

The utilization of calcium ions in seawater to fix CO2 in the form of calcium carbonate in the presence of alkaline sources has been studied by our group, and shows the potential characteristics of high reaction rate and energy saving. Sodium hydroxide was firstly chosen as a base source, and it is interesting to find that OH would precipitate in the form of Mg(OH)2 and then Mg(OH)2 would combine with CO2 to form Mg2+ and meanwhile CaCO3 would precipitate, rather than OH directly reacting with to help the transformation of CO2 to CaCO3 (Zhao et al. 2013, 2016). Therefore, in order to reduce the operational cost, Mg(OH)2 and even the base residual, which is the brine pretreatment waste of soda ash plant mainly containing Mg (OH)2 and CaCO3, were successfully tested as the alkali source (Zhao et al. 2017).

It is further proposed that MgO can act as the alkali source and the produced Mg2+ and can precipitate in the presence of NaOH (Zhao et al. 2019). The experiments demonstrated the sequestration can proceed at high speed, and pure CaCO3 and MgCO3·3H2O can be produced as by-products, which can probably take place in a traditional chemical plant at minimal capital and energy expense.

Reaction kinetics is essential for the reactor and process analysis, design, simulation and control, as the state of the reactants and products over time can be obtained. Generally, the chemical reaction mechanism is firstly speculated and then a chemical reaction kinetics model is set up to make the dynamics simulation. The availability of the reaction mechanism can be effectively determined by comparing simulation results with the experimental results. Thus, the dynamic simulation is an indispensable part in the study of the chemical reaction mechanism.

Meanwhile, as an important gas–liquid mass transfer reactor, the absorption tower such as the spray tower has a good application prospect in carbon dioxide fixation because of its high mass transfer efficiency and gas–liquid contact time (Kavoshi et al. 2015). For example, Ma and colleagues used ammonia water as an absorbent to absorb carbon dioxide in a bubble reactor and achieved good results (Ma et al. 2016). Moller found low gas holdup and large bubbles limited the mass transfer between gas and liquid, by comparing the performance between different orifice modes of ejectors (Möller et al. 2017).

Therefore, a kinetic Monte Carlo (KMC) simulation was firstly carried out with the help of Java software to simulate the changes in Ca2+ and Mg2+ concentrations and alkalinity with reaction time in presence of MgO, and the reaction mechanism was revealed. Then a bubble column was chosen as a reactor, in which a distributor with a cross-flow sieve tray with void diameter of 0.01 mm was added to strengthen the gas–liquid contact process. Finally, a bonnet tower with a diameter of 1 m and a height of 8 m was built to carry out the pilot test.

SIMULATION AND EXPERIMENTAL PROCEDURE

Simulation algorithm

The core algorithm in this work is the dynamic Monte Carlo simulation (KMC) algorithm developed by Gillespie (1976). The algorithm does not distinguish between series, parallel reaction and reaction order, whereas the reaction rate constants of each reaction need to be determined in the algorithm. The principle of the algorithm is to decide the reaction probability according to the reaction rate constant, and then the time interval and the length of the reaction types for the next step is determined by generating random numbers which uniformly distributed from 0 to 1. Then, according to the chemical reaction rate constant and a random number, the reaction time is calculated and listed as below. Computing time is measured in Monte Carlo (MC) cycles (Gillespie 1976).
formula
(1)
formula
(2)
where,
  • is the reaction rate constant of reaction i;

  • is the number of particles of reactant j in reaction i;

  • is a random number uniformly distributed between 0 and 1.

According to the calculated and another random number , ai is determined by the following calculation (Gillespie 1976).
formula
(3)
formula
(4)

After the reaction types were determined, the number of particles of the reactants and products were adjusted accordingly.

Laboratory experiment

The preparation and chemical composition of used artificial seawater are as given in the previous literature (Al-Anezi & Hilal 2006). All the raw materials mentioned in this study are of analytical purity. The experimental device is shown in Figure 1(a). The reactor is a glass column with an inner diameter of 14.8 cm and a height of 160 cm, which is separated by trays into six sections. The temperature was controlled in the range from 30 °C to 80 °C by the distributed control system (DCS).

Figure 1

(a) Laboratory scale experimental equipment diagram and (b) pilot plant photographs. b-1: Pilot technological process. b-2: Seawater feed tank. b-3: Seawater tank and magnesium oxide storage tank. b-4: Gas–liquid contact tower. b-5: Air blower and carbon dioxide cylinder.

Figure 1

(a) Laboratory scale experimental equipment diagram and (b) pilot plant photographs. b-1: Pilot technological process. b-2: Seawater feed tank. b-3: Seawater tank and magnesium oxide storage tank. b-4: Gas–liquid contact tower. b-5: Air blower and carbon dioxide cylinder.

A fixed amount of magnesium oxide with seawater was firstly added to the mixer and stirred into a uniform suspension at a certain temperature controlled by the DCS. The amount of magnesium oxide is twice as much as the moles of calcium ions. Carbon dioxide and air supplied by a CO2 cylinder and air blower were mixed quantitatively in a gas collecting device. Then, the collected gas was piped into a distributor which was laid at the bottom of the reactor. Meanwhile, the suspension was pumped into the reactor from the top.

Pilot tests

The pilot test was conducted in a gas–liquid contact tower with the diameter of 1 m and the height of 8 m, which was also equipped with several storage tanks: seawater feed tank (Φ2,000 × 2,000 mm), seawater tank(Φ3,000 × 3,000 mm), magnesium oxide storage tank (Φ1,000 × 1,000 mm), mixing tank (SV-5), air blower (ZG-65) and carbon dioxide cylinder (40 L), as shown in Figure 1(b).

Calculation method

The decalcification and carbon sequestration rates are calculated using the following methods.

The carbonization efficiency of calcium ions was studied from the rates of calcium removal and carbon sequestration calculated by Equations (5) and (6) respectively.
formula
(5)
where is calcium ion concentration in raw seawater (g·L−1); is calcium ion concentration in seawater after decalcification (g·L−1).
formula
(6)
where V is the volume of seawater consumed per unit of time, L; MCa is the molar mass of calcium ions, 40 g·mol−1; is the volume concentration of , mol·L−1; is the volume concentration of , mol·L−1; is the ventilation flow rate of carbon dioxide, L·min−1; is the air ventilation flow rate, L·min−1; is the proportion of carbon dioxide in the air.

The following is the calculation method of total mass transfer coefficient.

DCO2 is the diffusion coefficient of carbon dioxide calculated by Equation (7).
formula
(7)
Substitute DCO2 into Equations (8) and (9) (Wang et al. 2017) to get and kg.
formula
(8)
formula
(9)
Plug DCO2 and into Equation (10) (Ma et al. 2016) and Equation (11) (Wang et al. 2017)
formula
(10)
formula
(11)
where k is the reaction rate constant of CO2 absorption which is obtained by lg k= 11.13–2,535/T, mol·L−1·s−1; is viscosity measured by a Ukrainian viscometer, mPa·s. H is Henry coefficient, Pa·cm−3·mol−1; T is temperature, K; Re is Reynolds number; R is the universal gas constant, 8.31 J·mol−1·K−1; Sh is Sherwood number; Sc is Schmidt number; d is diameter of tower, cm; h is height, cm; Π is 3.14; g is 9.81; Cint is calcium concentration, mol·L−1; kL is liquid film mass transfer coefficient, mol·Pa−1·cm−2·s−1; DCO2 is diffusion coefficient of carbon dioxide, cm2·s−1; ρ is density, kg·m−3; W is column circumference, cm; F is interface area, cm3; Q is flow rate, mL·s−1; μ is viscosity, mPa·s; MB is molar mass of seawater, kg·kmol−1; VA is molar volume of CO2, cm3·mol−1.
In the mass transfer process between gas phase and liquid phase, there are mass transfer resistance of gas film (1/kg) and mass transfer resistance of liquid film (1/kL). The total mass transfer resistance (1/KG) is generally calculated as Equation (12) (Wang et al. 2017):
formula
(12)
where kg and KG represent the gas film mass transfer coefficient, mol·Pa−1·cm−2·s−1, and total mass transfer coefficient, mol·Pa−1·cm−2·s−1; indicates the non-reactive liquid membrane mass transfer coefficient, cm·s−1; E indicates the reaction enhancement factor (Dugas & Rochelle 2009; Ma et al. 2016).

RESULTS AND DISCUSSION

Simulation

Speculation of reaction mechanism

Based on the analysis on the concentration of residual ions in solution and the composition of solid precipitates, the main reactions for the CO2 sequestration by Ca2+ in the presence of MgO were discussed in the previous study work (Zhao et al. 2016). The change of concentration of Ca2+ and Mg2+ in solution and the composition of solid precipitates with reaction time at 80 °C and under the condition that the mole ratio of added MgO is four times that of calcium in water are shown in Figure 2.

Figure 2

The change of concentration of Ca2+ and Mg2+ in solution and the composition of solid precipitates with reaction time during the CO2 sequestration by Ca2+ in the presence of MgO.

Figure 2

The change of concentration of Ca2+ and Mg2+ in solution and the composition of solid precipitates with reaction time during the CO2 sequestration by Ca2+ in the presence of MgO.

It can be found that before the injection of CO2, alkali source MgO has already reacted with H2O, and Mg(OH)2 is created and ionized, which is generally called the digestion process of magnesium oxide as shown in Equations (13) and (14).
formula
(13)
formula
(14)
Once the gas CO2 is injected, Ca2+ would combine with to form CaCO3; meanwhile the concentration of Mg2+ increases. According to the simple thermodynamic analysis of standard molar reaction enthalpy change (kJ·mol−1) and standard Gibbs free energy change (kJ·mol−1), it is assumed that the reaction between MgO/Mg(OH)2 and CO2 occurs as listed in Equations (15)–(17).
formula
(15)
formula
(16)
formula
(17)
It is well known that CO2 will dissociate in the water and in this system its dissociation occurs with the help of OH, described as Equations (18)–(23).
formula
(18)
formula
(19)
formula
(20)
formula
(21)
formula
(22)
formula
(23)
With the generation of carbanion, calcium ions in the residual solution will decrease as CaCO3 precipitates as shown in Equation (24) and magnesium ions in the residual solution will increase as shown in Equations (16) and (17).
formula
(24)
However, when the calcium ions in the residual solution are not enough to form calcium carbonate, the form and dissolution of calcium carbonation (Equation (25)) will come to level off.
formula
(25)

Simulation procedure

Our KMC algorithm consists of the following steps, shown in Figure 3.

Figure 3

The simulation procedure.

Figure 3

The simulation procedure.

As shown in Figure 3, the whole procedure can be described as the following.

  • (a)

    Set the particle numbers of each ion according to its concentration and related reaction rates by Equations (2) and (3).

  • (b)

    Generate random numbers R1 and R2 uniformly distributed between 0 and 1.

  • (c)

    Update computing time from t to t+ Δt, Δt is defined by Equation (1).

  • (d)

    Perform the adsorption of CO2 according to its gas–liquid mass transfer model and calculate the particles of liquid CO2.

  • (e)

    The reactions are divided into three categories: fast reaction, controlled speed reaction and equilibrium reaction. Perform the KMC algorithm respectively:

  • For the controlled speed reactions, such as Equations (13), (16), (17), (21), (22) and (24), the reaction type i is firstly decided by Equations (3) and (4), and the number of particles of the reactant in the related reaction i is subtracted by 1 but those of the reaction products in the related reaction i are added by 1. For example, when Equation (13) is performed, if the number of particles of MgO and H2O is each greater than or equal to 1, the reaction can occur; then the number of particles of MgO and H2O is subtracted by 1 but the number of particles of Mg(OH)2 is added by 1: Mg(OH)2+1. If the number of particles of MgO or H2O is less than 1, the reaction cannot occur, and the KMC algorithm is skipped for the next step.

  • For the rapid reactions of Equations (18) and (20), and equilibrium reaction of Equations (14), (19), (23) and (25), if K > Ksp, the number of particles of the reactant in the related reaction i is subtracted by 1 but those of the reaction products in the related reaction i are added 1. If K < Ksp, the reverse reaction event happens: the number of the reactants increases by 1, and those of the products decrease by 1. If K = Ksp, skip the event.

  • (f)

    Repeat the algorithm until the concentrations of Ca2+ and Mg2+ reach equilibrium.

The correlated kinetic constants

Some relative reaction rate constants ki are obtained from the previous literature and some equilibrium constants Ki are calculated by HSC 6.0 The correlated kinetic constants of the main reactions are shown in Table 1.

Table 1

Elementary reactions and corresponding kinetic parameters used in this work

Steps(i)Elementary reationsn (kg·s−1), k (s−1·Pa−1)or KReferences
MgO + H2O → Mg(OH)2 k1 = 4.5167 × 1010 Rollason & Plane (2001)  
Mg(OH)2⇌Mg2+ + 2OH K2 = 4.8318 × 109 HSC6.0 
 n3 = 3.2097 × 107 × (61.3272 − c) Calculated by authors 
MgO + CO2 + H2O → Mg(HCO3)2 k4 = 1.1630 Smithson & Bakhshi (1973)  
Mg(OH)2 + CO2 → Mg(HCO3)2 k5 = 1.1630 Smithson & Bakhshi (1973)  
H2O⇌H+ + OH K6 = 0.2508488 Perry et al. (1984)  
H2O + CO2(aq)⇌H+ +  K7 = 4.786009566 × 107 HSC6.0 
+ H+ K8 = 7.57313428 × 1011 HSC6.0 
OH + CO2(aq) →  k9 = 152,230 Pohorecki & Moniuk (1988)  
10  + CO2 + H2k10 = 1.362628711 Zhao & Zhu (2007)  
11 H2O + + OH K11 = 3.5 × 1011 HSC6.0 
12 Ca2+ + → CaCO3 k12 = 6,126.1306 He et al. (2008)  
13 CaCO3⇌Ca2+ +  K13 = 3,360.0 Perry et al. (1984)  
Steps(i)Elementary reationsn (kg·s−1), k (s−1·Pa−1)or KReferences
MgO + H2O → Mg(OH)2 k1 = 4.5167 × 1010 Rollason & Plane (2001)  
Mg(OH)2⇌Mg2+ + 2OH K2 = 4.8318 × 109 HSC6.0 
 n3 = 3.2097 × 107 × (61.3272 − c) Calculated by authors 
MgO + CO2 + H2O → Mg(HCO3)2 k4 = 1.1630 Smithson & Bakhshi (1973)  
Mg(OH)2 + CO2 → Mg(HCO3)2 k5 = 1.1630 Smithson & Bakhshi (1973)  
H2O⇌H+ + OH K6 = 0.2508488 Perry et al. (1984)  
H2O + CO2(aq)⇌H+ +  K7 = 4.786009566 × 107 HSC6.0 
+ H+ K8 = 7.57313428 × 1011 HSC6.0 
OH + CO2(aq) →  k9 = 152,230 Pohorecki & Moniuk (1988)  
10  + CO2 + H2k10 = 1.362628711 Zhao & Zhu (2007)  
11 H2O + + OH K11 = 3.5 × 1011 HSC6.0 
12 Ca2+ + → CaCO3 k12 = 6,126.1306 He et al. (2008)  
13 CaCO3⇌Ca2+ +  K13 = 3,360.0 Perry et al. (1984)  

n: mass transfer flux per unit time; c: concentration of CO2 in liquid phase, kg·m−3.

Simulation results

The simulation program ran 3.23 × 109 MC cycles. In order to reduce computer's memory footprint, data were recorded every 107 cycles, and a total of 323 sets of data were output, including ‘machine time, MT’, ‘Ca2+ particle number’, ‘Mg2+ particle number’ and ‘total alkalinity’, where the total alkalinity is calculated using the intermediate variables of OH, and in the program.

Figure 4(a) shows the simulation results of Ca2+ and Mg2+ particle number along with the change of machine time. It can be seen that Ca2+ particle count drops rapidly when the machine time is 0–5 MT, and then it goes down like a ladder within 5–35 MT, which is speculated to be due to the nature of the KMC algorithm for only a reaction can occur at each time step, and there are multiple ways for the generation of , the restriction of number of particles leading to the step-down. Within 5–35 MT, the reduction rate of Ca2+ particle number gradually decreases, and finally drops to the lowest point at about 35 MT. After that, the concentration remains unchanged, but it does not reach the theoretical value of 0, nor does it reach the value at the dissolution equilibrium of CaCO3.

Figure 4

(a) Changes of Ca2+ and Mg2+ particle numbers with machine time in simulation results, (b) comparison of Ca2+ and Mg2+ concentration between simulation results and experimental results and (c) comparison of total alkalinity between simulation results and experimental results.

Figure 4

(a) Changes of Ca2+ and Mg2+ particle numbers with machine time in simulation results, (b) comparison of Ca2+ and Mg2+ concentration between simulation results and experimental results and (c) comparison of total alkalinity between simulation results and experimental results.

Mg2+ particle number increased rapidly within 0–5 MT, and then the particle number of Mg2+ did not increase.

In the KMC algorithm, the MT generated by the random number of CPU is not one-to-one correspondence with the natural reaction time, so the machine time needs to be corresponded with the natural reaction time. By comparison, 1 MT is equal to 0.2143 min.

The simulation results of the Ca2+ and Mg2+ concentrations with time are shown in Figure 4(b) and can be compared with experimental data. We can obviously draw the conclusion that the simulation results of Ca2+ are in accordance with the experimental data; at about 7 min, Ca2+ concentration basically reached the lowest value which can elucidate the mechanism proposed in Table 1. The simulation results of Mg2+ concentration change are close to the experimental results, and Mg2+ concentration increases gradually during 10 min. It is inferred that the difference of simulation results with the experimental results could be because the simulation only considers the ideal adsorption of CO2 into the system, but does not take into account the actual gas–liquid mass transfer in the CO2 bubbling process, and there is no consideration of MgO or Mg(OH)2 particles morphology and particle number for the influence of the reaction rate. The total number of solid particles is considered as a fixed value at the beginning of the reaction; as the reaction progresses, MgO and Mg(OH)2 are consumed gradually, so the particle number will substantially reduce, resulting in the decrease of the total contact area with CO2, and thus the lower apparent reaction rate.

The comparison of total alkalinity between the simulation results and the experimental results is shown in Figure 4(c). The simulation results are basically consistent with the experimental data. The increase of alkalinity in this system mainly depends on the reaction between MgO, Mg(OH)2 and CO2 or OH produced by the ionization of Mg(OH)2.

The differences between experimental results and simulation results during 1–7 min may also be caused by consideration of only the ideal CO2 adsorption, ignoring the actual gas–liquid mass transfer during CO2 bubbling and the change of the specific surface area of MgO and Mg(OH)2. Meanwhile, sampling and analysis preparation time is required for the offline analysis of the basic ions in the experiments, and probably there is still going on the solvation of the CO2, thus causing certain deviation between the experimental result and the simulation value. After 10 min, Mg(OH)2 completely disappeared in the system. At this point, CO2 and and were in the state of reaction equilibrium and dissolution equilibrium, so the total alkalinity would not continue to increase.

Based on the above analysis, the dynamic simulation by using the KMC algorithm is basically consistent with the experimental results.

Laboratory experiment

Based on the above mechanism studies, it can be concluded that the reaction between magnesium oxide and carbon dioxide theoretically depends on the reaction between gas (CO2) and solid (MgO), rather than the hydrolysis of magnesium oxide and the ionization of magnesium hydroxide. Therefore, it is proposed in this paper that there is no need for digestion after adding magnesium oxide. Moreover, the column spray tower is used to enhance the mass transfer rate between the vapor, liquid and solid, thus reducing the reaction time and realizing continuous reaction. In this part, the influence of air flow, temperature and carbon dioxide concentration on the decalcification rate and carbon sequestration rate were investigated. Meanwhile, the corresponding total mass transfer coefficients were calculated.

Influence of air flow

The influence of air flow on the decalcification rate was firstly investigated, in which the flow rate of CO2 was maintained but the air flow rate was increased.

Figure 5(a) shows the change of calcium removal rate under different air flow rates. As the air flow increased from 4 L·min−1 to 8 L·min−1, the calcium removal rate gradually decreased. Keon et al. (2020) found that the formation of bubbles is unstable under large gas flow, and the rising speed of large bubbles in solution is faster than that of small bubbles. Then it is speculated that a reduction in the carbon dioxide ratio is not conducive to the gas–liquid exchange, as the proportion of carbon dioxide decreases and air flow increases. The surface area of the mass transfer interface was calculated by taking the tray aperture as the average diameter of the bubble to calculate the total mass transfer coefficient (KG).

Figure 5

Variation diagram under different air flow rates (4 L·min−1, 6 L·min−1, 8 L·min−1). (a) Changes of decalcification rate; (b) changes of total mass transfer coefficient; (c) changes of carbon sequestration rate.

Figure 5

Variation diagram under different air flow rates (4 L·min−1, 6 L·min−1, 8 L·min−1). (a) Changes of decalcification rate; (b) changes of total mass transfer coefficient; (c) changes of carbon sequestration rate.

As shown in Figure 5(b), KG decreases with the increase of air flow, which can also be explained from Equation (12); when 1/kL is fixed, the increases of 1/kg of CO2 with increasing air gas flow, which is caused by the decrease of CO2 proportion, would lead to the decrease of KG. And it also verifies that the mass transfer is reduced but the total gas–liquid contact area increases with the increase of air flow. Thus, the decalcification rate decreases with increasing the air flow, as shown in Figure 5(a). To further verify this conclusion, the carbon sequestration rate was calculated, as shown in Figure 5(c).

From Figure 5(c), it can be seen that carbon sequestration rate decreases gradually with the increase of air flow rate. It is speculated that excessive air flow leads to shorter residence time, and the reduction of carbon dioxide proportion is not conducive to absorption.

The effect of temperature

The influence of temperature was then investigated, since theoretically the temperature will affect gas–liquid mass transfer and the reaction between gas and solid.

Figure 6(a) shows the change of calcium removal rate at different temperatures. By comparing the five temperatures, it is found that the conversion rate at 30 °C is lower than 75%, but the decalcification rate can increase to 90% at 40 °C, and then the temperature brings little increase when it continues to increase. Based on kinetic theory (Liu et al. 2013), the increase in temperature will accelerate the thermodynamic movement of gases and the percentage of activated molecules; then the reaction rate increases between CO2 and MgO. As the temperature rises, the gas molecules become too active, causing them to escape from the liquid. Therefore, 50 °C can be chosen as the optimal reaction condition. As shown in Figure 6(b), the total mass transfer coefficient first increases and then decreases with the rise of temperature. The mass transfer coefficient is highest at 50 °C. But the carbon sequestration rate decreases with increasing temperature (as can be seen in Figure 6(c)), and it can be explained that high temperatures lead to the escape of CO2 molecules from seawater and a decrease in the solubility of carbon dioxide (Abuín et al. 2014).

Figure 6

Effect of different temperatures on (a) calcium removal rate, (b) total mass transfer coefficient and (c) carbon sequestration rate.

Figure 6

Effect of different temperatures on (a) calcium removal rate, (b) total mass transfer coefficient and (c) carbon sequestration rate.

Influence of carbon dioxide concentration

The influence of carbon dioxide concentration was then investigated by changing the CO2 flow rate while the flow rate of air was kept constant, since the concentration of CO2 may affect gas–liquid mass transfer and the adsorption between gas and solid.

The effect of carbon dioxide flow rate on calcium removal rate is shown in Figure 7(a). From Figure 7(a), we can see that the decalcification rate is the highest when the carbon dioxide flow rate is 60 L·h−1, and the decalcification rate is lowest when the carbon dioxide flow rate is 80 L·h−1. The change of carbon dioxide flow has no effect on the total mass transfer coefficient, as listed in Figure 7(b). From Figure 7(c), it can be seen that carbon sequestration rate decreases with the increase of carbon dioxide flow rate. It is inferred that the change of carbon dioxide has little effect on the gas–liquid mass transfer, mainly because the increase of carbon dioxide flow has little change on the total gas volume, and the change of kg can be ignored. But higher flow rate leads to lower adsorption of CO2, and then there is an optimal CO2 flow concentration.

Figure 7

Effects of different CO2 flow rates on (a) calcium removal, (b) the total mass transfer coefficient and (c) carbon sequestration rate.

Figure 7

Effects of different CO2 flow rates on (a) calcium removal, (b) the total mass transfer coefficient and (c) carbon sequestration rate.

The detailed process analysis under optimal experimental condition

According to the above comparation of experimental conditions, the process was analyzed in detail under the optimal experimental condition, in which the air flow rate was 4 L·min−1, CO2 flow rate was 60 L·h−1 and the temperature was 50 °C

The alkali oxide of MgO is difficult to dissolve, but from the simulation it is speculated that MgO reacts with CO2 to form Mg2+. Therefore, the concentration of Mg2+ in solution with operation time was measured and compared with the theoretically maximum Mg2+ concentration, as it is the sum of the magnesium in the original seawater and MgO, shown in Figure 8(a). As can be seen, the magnesium ion concentration stabilizes after 40 minutes of operation time. After stabilization, the Mg2+ concentration reaches its theoretically maximum concentration, indicating magnesium oxide is completely ionized after experimental stabilization.

Figure 8

(a) Magnesium ion concentration change diagram and magnesium ion maximum concentration comparison chart and (b) comparison of crystal culturing for 0 h and 24 h under optimal conditions.

Figure 8

(a) Magnesium ion concentration change diagram and magnesium ion maximum concentration comparison chart and (b) comparison of crystal culturing for 0 h and 24 h under optimal conditions.

After the samples were collected, crystals were allowed to grow for 24 hours under the optimal conditions to see whether the decalcification changed. From Figure 8(b), the decalcification rate has a small increase to 95% after crystal growing time of 24 h. Thus, it is concluded that the gas–liquid–solid contact time in the column is sufficient to ensure their complete reaction.

Figure 9(b) shows the electron microscope image of the solid after the reaction at different magnification. The particles are about 5 microns in size and have the shapes of particles, columnar and spindles. In order to determine the composition of the solid phase after the reaction, X-ray diffraction (XRD) is used and the diffraction pattern is shown in Figure 9(a).

Figure 9

(a) XRD diffraction pattern and (b) transmission electron microscopy images of calcium carbonate.

Figure 9

(a) XRD diffraction pattern and (b) transmission electron microscopy images of calcium carbonate.

Pilot scale test

The pilot test device was designed and developed on the basis of the small test. As shown in Table 2, the optimal conditions of the small test were compared with those of the pilot test. Based on the limitation of the site and equipment of the pilot test, the scale of the pilot plant is 244 times that of the laboratory. Through the comparison, it was found that the air flow increased by 333.33 times, the sea water flow increased by 330.12 times, and the carbon dioxide flow increased by only 150 times. This is because a higher gas volume greatly increases the total gas–liquid contact area in the pilot scale, and the height of the tower increases the gas–liquid contact time and makes the reaction more complete, so a better effect is achieved.The samples were allowed to stand for 24 hours for analysis of calcium removal rate at different operation times, as shown in Figure 10.

Table 2

Pilot test data sheet

Temperature °CCO2 flux m3·h−1Air flux m3·h−1Seawater flux m3·h−1Magnesium oxide flux m3·h−1
Optimal conditions for small trials 50 0.36 0.498 
Optimal conditions for pilot test 50 80 15 1.44 
Temperature °CCO2 flux m3·h−1Air flux m3·h−1Seawater flux m3·h−1Magnesium oxide flux m3·h−1
Optimal conditions for small trials 50 0.36 0.498 
Optimal conditions for pilot test 50 80 15 1.44 
Figure 10

Changes of calcium removal rate in different operation time after 24 h standing in pilot test.

Figure 10

Changes of calcium removal rate in different operation time after 24 h standing in pilot test.

As can be seen, the decalcification rate reached 90% at 6 minutes and was maintained at 95% after 10 minutes, indicating that the seawater has filled the tower and aerated steadily within 10 minutes. The results of the pilot test were consistent with those of laboratory tests.

CONCLUSION

In this study, based on the reaction mechanism, the dynamic simulation of the reaction process was carried out, and then continuous laboratary experiments were done to establish the optimal experimental conditions and its pilot tests. The main conclusions are as follows.

  • i.

    A simulation model of the decalcification reaction was established, and the simulation of the gas–liquid–solid reaction kinetics process was realized through the KMC algorithm. Through simulation, the relationship of the concentration of Ca2+ and Mg2+ ions, and the total alkalinity with reaction time were obtained. Through a comparison, the simulation results were basically consistent with the experimental results.

  • ii.

    In the laboratory experiments, the optimal operating conditions were obtained from the three aspects of temperature, carbon dioxide flow and air flow. The calcium removal rate reached 94% and the carbon sequestration rate reached 63.6% under the considered optimal experimental condition. It can continuously produce micron-sized calcium carbonate with uniform particle size whose crystal type is aragonite.

  • iii.

    On the basis of the laboratory test, the pilot test was expanded in the pilot bonnet tower by about 300 times. The results showed that the absorption effect of gas and liquid was better, and the calcium removal rate was consistent with the results of laboratory test.

  • iv.

    Continuous decalcification and carbon sequestration experiments were carried out in the column and bonnet tower for the first time, which greatly saves raw materials and can achieve decalcification efficiency of about 95% at lower temperatures. The produced calcium carbonate has high purity and can be continuously produced as a product.

ACKNOWLEDGEMENTS

The work is supported by the Tianjin Science and Technology Project (20JCZDJC00450), Natural Science Foundation of Hebei Province (B2020202029), Chinese Postdoctoral Science Foundation (2017M611142), Science and Technology Project of Hebei Province (17273101D), and Science and Technology Project of Quanzhou (2019C109).

DATA AVAILABILITY STATEMENT

All relevant data are included in the paper or its Supplementary Information.

REFERENCES

Abuín
A. G.
Díaz
D. G.
Navaza
J. M.
2014
CO2 capture by pyrrolidine: reaction mechanism and mass transfer
.
AIChE Journal
60
(
3
),
1098
1106
.
Al-Anezi
K.
Hilal
N.
2006
Effect of carbon dioxide in seawater on desalination: a comprehensive review
.
Separation and Purification Reviews
35
(
3
),
223
247
.
Arias
B.
Alonso
M.
Abanades
C.
2017
CO2 capture by calcium looping at relevant conditions for cement plants: experimental testing in a 30 kW(th) pilot plant
.
Industrial & Engineering Chemistry Research
56
(
10
),
2634
2640
.
Bobicki
E. R.
Liu
Q. X.
Xu
Z. H.
2012
Carbon capture and storage using alkaline industrial wastes
.
Progress in Energy & Combustion Science
38
(
2
),
302
320
.
Ebrahimi
A.
Saffari
M.
Hong
Y.
2018
Mineral sequestration of CO2 using saprolite mine tailings in the presence of alkaline industrial wastes
.
Journal of Cleaner Production
188
,
686
697
.
Guillaume
D.
Sergey
M.
Sami
G.
2018
How electrodialysis configuration influences acid whey deacidification and membrane scaling
.
Journal of Dairy Science
101
(
9
),
7833
7850
.
He
S. L.
Wang
W.
Huang
D.
Liu
Z.
2008
Crystallization kinetics of CaCO3 in saturated NaCl brine using Ca2+ ion-selective electrode
.
Journal of Salt Science and Chemical Industry
05
,
7
11
.
Kavoshi
L.
Rahimi
A.
Hatamipour
M. S.
2015
CFD modeling and experimental study of carbon dioxide removal in a lab-scale spray dryer
.
Chemical Engineering Research & Design
98
,
157
167
.
Keon
B.
Gang
S. G.
Nam S
N.
Young-Il
L.
Jong
W. B.
Dong
H. L.
2020
Bubble characteristics in pressurized bubble column associated with micro-bubble dispersion
.
Chemical Engineering Journal
386
,
121339
.
Lee
K. P.
Arnot
T. C.
Mattia
D.
2011
A review of reverse osmosis membrane materials for desalination – development to date and future potential
.
Journal of Membrane Science
370
(
1–2
),
1
22
.
Liu
Y. X.
Zhong
X. H.
Chen
X. M.
2013
Transport Process in Near Equilibrium State, General Course of College Physics·Heat Science
, 2nd edn.
Peking University Press
,
China
.
Lu
S. Q.
Lan
P. Q.
Wu
S. F.
2016
Preparation of nano CaCO3 from phosphogypsum by gas-liquid-solid reaction for CO2 sorption
.
Industrial & Engineering Chemistry Research
55
(
38
),
10172
10177
.
Ma
S. C.
Chen
G. D.
Zhu
S. J.
Han
T. T.
Yu
W. J.
2016
Mass transfer of ammonia escape and CO2 absorption in CO2 capture using ammonia solution in bubbling reactor
.
Applied Energy
162
,
354
362
.
Mahinpey
N.
Asghari
K.
Mirjafari
P.
2011
Biological sequestration of carbon dioxide in geological formations
.
Chemical Engineering Research & Design
89
(
9
),
1873
1878
.
Möller
F.
Seiler
T.
Lau
Y. M.
Weber
Mf.
Weber
Mk.
Hampel
U.
Schubert
M.
2017
Performance comparison between different sparger plate orifice patterns: hydrodynamic investigation using ultrafast X-ray tomography
.
Chemical Engineering Journal
316
,
857
871
.
Perry
R. H.
Green
D. W.
Maloney
J. O.
1984
Perry's Chemical Engineers’ Handbook
.
McGraw-Hill
,
New York, USA
.
Pohorecki
R.
Moniuk
W.
1988
Kinetics of reaction between carbon dioxide and hydroxyl ions in aqueous electrolyte solutions
.
Chemical Engineering Science
43
(
7
),
1677
1684
.
Rollason
R. J.
Plane
J. M. C.
2001
A kinetic study of the reactions of MgO with H2O, CO2 and O2: implications for magnesium chemistry in the mesosphere
.
Physical Chemistry Chemical Physics
3
(
21
),
4733
4740
.
Smithson
G. L.
Bakhshi
N. N.
1973
Kinetics and mechanism of carbonation of magnesium oxide slurries
.
Industrial & Engineering Chemistry Process Design & Development
12
(
1
),
99
106
.
Tanaka
N.
Nagase
M.
Higa
M.
2011
Preparation of aliphatic-hydrocarbon-based anion-exchange membranes and their anti-organic-fouling properties
.
Journal of Membrane Science
384
(
1–2
),
27
36
.
Wang
L. D.
An
S. L.
Yu
S. H.
2017
Mass transfer characteristics of CO2 absorption into a phase-change solvent in a wetted-wall column
.
International Journal of Greenhouse Gas Control
64
,
276
283
.
Zhao
Y. N.
Zhu
G. C.
2007
Thermal decomposition kinetics and mechanism of magnesium bicarbonate aqueous solution
.
Hydrometallurgy
89
(
3–4
),
217
223
.
Zhao
Y. Y.
Yuan
J. S.
Zhang
J. J.
Xie
L.
Ji
Z. Y.
Su
M.
Chen
J. X.
2013
A different approach for seawater decalcification pretreatment using carbon dioxide as precipitator
.
Desalination
322
(
4
),
151
158
.
Zhao
Y. Y.
Cao
H. N.
Xie
Y. H.
Yuan
J. S.
Ji
Z. Y.
Zhang
Y.
2016
Mechanism studies of a CO2 participant softening pretreatment process for seawater desalination
.
Desalination
393
,
166
173
.
Zhao
Y. Y.
Zhang
Y.
Liu
J.
Gao
J. Y.
Ji
Z. Y.
Guo
X. F.
Liu
J.
Yuan
J. S.
2017
Trash to treasure: seawater pretreatment by CO2 mineral carbonation using brine pretreatment waste of soda ash plant as alkali source
.
Desalination
407
,
85
92
.
Zhao
Y. Y.
Wu
M. F.
Guo
X. F.
Zhang
Y.
Ji
Z. Y.
Wang
J.
Liu
J.
Liu
J. L.
Wang
Z. R.
Chi
Q. F.
Yuan
J. S.
2019
Thorough conversion of CO2 through two-step accelerated mineral carbonation in the MgCl2-CaCl2-H2O system
.
Separation and Purification Technology
210
,
343
354
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).