Optimal Design of a Gas Antisolvent Recrystallization Process of

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Optimal Design of a Gas Antisolvent Recrystallization Process of Cyclotetramethylenetetranitramine (HMX) with Particle Size Distribution Model Sungho Kim, Shin Je Lee, Bumjoon Seo, Youn-Woo Lee, and Jong Min Lee* School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea ABSTRACT: Physical properties and characteristics of a solid particle highly depend on the particle shape and size. The Gas AntiSolvent (GAS) process is a recently developed process that can control the size and morphology of solid particles using supercritical fluids as an antisolvent to resrystallize desired products in solution. Though many lab-scale experiments with different choices of antisolvents exist, no study for the large-scale operation of such a system has been reported yet. This study proposes different design options of a GAS process for recrystallization of HMX (1,3,5,7-tetranitro-1,3,5,7-tetrazocane or cyclotetramethylenetetranitramine) using CO2 as an antisolvent. A detailed mathematical model for particle size distribution is integrated into the process flow sheet model. An optimal process which includes the particle size data of product and process specifications are proposed with economic evaluation and comparison of various alternative processes.

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INTRODUCTION The shape and size of a crystal are important properties for determining the quality of crystal products such as drugs and explosives. For high-energy explosives, fine particle sizes of a narrow distribution are required to improve its detonation performance, e.g., the ability to deliver an explosive’s energy in the intended way.1,2 While milling, spray drying, and solutionbased recrystallization are common choices for crystal synthesis, these conventional methods are not suitable for producing high-energy explosives owing to thermal degradation and safety issues. Furthermore, those methods show limited performance for controlling shape and size distribution.3,4 The Gas AntiSolvent (GAS) recrystallization process can be an alternative for producing microsized high-energy materials. It employs high-pressure gas or supercritical fluid as an antisolvent to precipitate dissolved products. First, a solution of raw product is fed into a chamber, followed by feeding the antisolvent. The antisolvent permeates the product solution and expands with the solution to cause precipitation of the target product by decreasing the solubility of the solution. Supercritical fluids are favored for an antisolvent in many GAS recrystallization processes because they can percolate other liquids easily and expand rapidly compared to other phases of any fluid.5 The GAS recrystallization process has several advantages in processing high-energy material compared to traditional methods. First, it can produce particles of desired shape by selecting the proper solvent, antisolvent, and reactor type. Second, it can control the size distribution of the product particles by manipulating process variables such as the flow rate of the antisolvent, pressure, and temperature. Third, the recrystallization is also much safer for processing explosive materials than using mechanical methods.6 Despite these advantages, there has been no study on process design for commercial-scale operation of the GAS process. Instead, previous works on GAS recrystallization are mainly © XXXX American Chemical Society

concerned with lab-scale experiments. There are a few studies on proposing mathematical models for the GAS recrystallization.7−10 These studies present models that explain thermodynamics on particle growth and phase equilibria. They suggest mathematical models for particular lab-scale experiments such as precipitation of phenanthrene from benzene 7 and beclomethasone dipropionateare (BDP) from acetone.9 They also simulate the particle size distribution of recrystallized product and validate with experimental results. However, these are limited to mathematical modeling of the recrystallization process itself and are not concerned with process design. Most of the simulation studies on the mathematical model of recrystallization processes have been carried out using generalpurpose numerical analysis software such as MATLAB. Some studies perform their simulations with particular process simulators especially suited for population balance equations, such as gPROMS/gSOLID8 and PARSIVAL.11 There has been research on the scale-up issues of the GAS process with pilot-scale plants.12−14 These studies analyze the effect of process parameters such as the antisolvent injection rate, initial concentration of solvent, and volume of the reactor. However, these are only focused on the recrystallizer unit. Separation and other processes still need to be considered within a unified process model for commercial-scale scale-up. For example, the operating temperatures of separation and recycle processes are limited due to the safety issue even for a trace amount of the HMX (1,3,5,7-tetranitro-1,3,5,7-tetrazocane or cyclotetramethylenetetranitramine) residue in the separated flow. Since the pressure and temperature of recycled CO2 affect the performance of the recrystallization process, determination of optimal operating conditions is related to the Received: March 3, 2015 Revised: October 19, 2015 Accepted: October 22, 2015

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DOI: 10.1021/acs.iecr.5b00841 Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

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Figure 1. Conceptual layout of GAS recrystallization process.

recrystallized HMX is separated at fine particle filters. The residue is sent to a storage tank and wasted. If the process is to run in a large scale, ancillary processes for the recycle of solvent and antisolvent should be considered. Furthermore, specifications for separation processes based on the production rate and the actual recovery rate of recrystallized product are also required. A conceptual layout of the GAS process of HMX is presented in Figure 1. HMX−acetone solution and scCO2 are fed into the recrystallizer, and recrystallization proceeds. After the recrystallization is complete, a mixture of recrystallized HMX product and other materials is produced from the reactor. A fabric filter is installed after the recrystallizer to separate recrystallized HMX from the product flow. To determine the filter specification, information on the particle size distribution of the recrystallized product is needed. In a lab-scale experiment, the size and morphology of the recrystallized particles are characterized by various analysis instruments such as a field emission scanning electron microscope (FE-SEM), particle size analyzer (PSA), and Fourier transform infrared spectrometer.17 However, for the modeling and optimization of a commercialscale plant with various operating conditions, results of the analysis from a few lab-scale experiments are not very useful. Instead, a mathematical model about particle size distribution can be applied. The model calculates the particle size distribution of the recrystallized HMX for a steady-state operation of the GAS recrystallization process with a given operating condition. After the recrystallized HMX product is recovered by the filter, residues are separated using flash separators and other equipment. Unlike a lab-scale experiment, a commercial-scale plant may require several steps of a separation process to accomplish high separation performance to recover process materials and remove impurities from raw HMX solution. Separated process materials can be recycled since no chemical reaction occurs in the residues during recrystallization.

final operating target of the recycle process. Moreover, the purity of acetone should be within the target for reuse since the purity affects the solubility of HMX−acetone solution. All these factors have to be integrated into a single model in a unified manner for commercial-scale design and flexible operation. The objective of this work is to develop an optimal design of a GAS recrystallization process using a mathematical model and a process flow sheet simulator. A mathematical model of GAS recrystallization is constructed to describe the particle size distribution of the recrystallized product. Lab-scale experimental results are used to validate the reliability of the mathematical model. A process flow sheet model is developed to include a custom GAS recrystallizer model, describe a filter unit for separation of HMX product using results of the custom model, and additional process equipment for recovery of solvent and antisolvent. The procedure of specifying each unit process is explained in detail. All the results are integrated to suggest an optimal design of the GAS process for the commercial-scale operation of the plant.

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GAS RECRYSTALLIZATION PROCESS OF HMX

This paper studies the GAS process to recrystallize HMX (1,3,5,7-tetranitro-1,3,5,7-tetrazocane or cyclotetramethylenetetranitramine) with acetone as a solvent and supercritical CO2 (scCO2) as an antisolvent. HMX is a high-energy explosive used in various industries. This material forms a white powder in the solid phase and is moderately soluble in organic solvents such as dimethyl sulfoxide (DMSO), cyclohexanone, and acetone but rarely dissolves in aqueous systems. The temperature, pressure, and flow rate of the antisolvent determine the physical properties of product particles. Since lower temperature has been found to be favored for producing the desired shape of HMX in a lab-scale experiment,15 the lowest condition in supercritical phase region is selected to maintain the supercritical phase before injection. The critical point of carbon dioxide is 31.1 °C at 7.38 MPa, and 35 °C is chosen as the operating condition for the process.16 As scCO2 is fed and mixed with the HMX solution, the volume of the mixed solution rapidly increases and precipitation occurs to produce recrystallized HMX particles. Then a mixture of HMX crystals, acetone, and CO2 is discharged from the chamber. The

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MODELING OF GAS RECRYSTALLIZATION PROCESS Simulation Strategy for GAS Recrystallizer. Whereas commercial chemical process simulators provide built-in B

DOI: 10.1021/acs.iecr.5b00841 Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

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Figure 2. Schematic description of GAS recrystallization process.

models for various process units with reaction kinetics and thermodynamics, solid particle processes are still difficult to simulate using the general-purpose process flow sheet simulator because of the mathematical rigor required to estimate the particle size distribution. However, the particle size distribution would be beneficial to determine process equipment size and operating conditions for various quality demands. This work models the recrystallization process using population balance and compare with lab-scale experiments. Mathematical Model of GAS Recrystallization. A mathematical model of GAS recrystallization including a population balance, mass balance, and thermodynamic equations is presented.10 The study uses the model to describe a GAS process using toluene as a solvent and CO2 as an antisolvent to refine phenanthrene. The model estimates the particle size distribution for a given injection rate of antisolvent and other process conditions using recrystallization kinetics, the Peng−Robinson equation of state, and mass balance equations. Many other studies on GAS process modeling are based on the model.7,18 Figure 2 schematically describes the GAS recrystallization process. In Muhrer’s model, the population density function of recrystallized particles is expressed as ∂n ∂n n d(NLvL) +G + =0 NLvL dt ∂t ∂L

Table 1. Nomenclature notation n G L NL NV Np QA vL kv vP m3 f i,α φ zi,α G k2 S f 0P,P ΔL̅i (Gn)i+(1/2)

dt

where n is the population density function [m ], G is the particle growth rate [m/s], L is the characteristic length of particle [m], NL is the molar holdup of each component in the liquid phase [mol], and vL is the molar volume of solute in the liquid phase [m3/mol] (see Table 1). This population density function is defined as the number of crystals with characteristic length between L and (L + dL) at time t. The following equations describe the mass balance in the recrystallizer. dt d(NLxS + NVyS ) dt

= QA =0

units [m−4] [m/s] [m] [mol] [mol] [mol] [mol/s] [m3/mol]

d(NLx P + Np)

(1) −4

d(NLxA + NVyA )

description population density function particle growth rate characteristic length of particle molar holdup of each component in liquid phase molar holdup of each component of vapor phase molar holdup of each component of solid phase molar flow rate of antisolvent molar volume of liquid phase volume shape factor, π/6 molar volume of solid phase (product) third moment of population density function fugacities of component i in each phase fugacity coefficient mole fraction of component i in phase α particle growth rate parameter of growth correlation, 3.75 × 10−6 ratio of fugacities of solute in liquid and solid phases fugacity at reference pressure finite volume grid for size discretization cell-face fluxes at control volume boundaries

=0

[m3/mol] [m3/m3] [N/m2]

[m/s] [m/s]

[N/m2] [m]

(4)

NV and Np are the molar holdups of each component of the vapor and solid phases in the recrystallizer, respectively, and QA is the flow rate of antisolvent. Np is a function of the third moment coefficient of density function, i.e., skewness of particle size distribution, which is widely used for describing symmetry of distribution. The molar holdup Np in eq 4 is given as Np =

(2)

NLvLk vm3 vP

(5)

where vL is the molar volume of the liquid phase, kv is the volume shape factor which is π/6 for a spherical shape. and m3 is the third moment of the population density function, n.

(3) C

DOI: 10.1021/acs.iecr.5b00841 Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

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∫0

∞

L3n dL

ϕ(θ ) =

(6)

(7)

fS,L = fS,V

(8)

The initial condition of eq 13 is chosen as n(0, L) = 1010 n(0, L) = 0

(9)

To calculate f, the Peng−Robinson equation of state is used to find the fugacity coefficient φ with given temperature, pressure, and mole fraction (z) of the components (i) in each phase (α) of the system. The particle growth rate G can be described in terms of the supersaturation ratio, S. An empirical correlation between G and S is used in this study. The order of the growth rate is set as 2 since the value of the growth order in this correlation is between 1 and 2 and the GAS process is sensitive to the supersaturation ratio. G = k 2S2

S=

(S > 0)

(10)

fP,L ( x̲ , p , T ) fP,P (p , T )

(6.0 ≤ L ≤ 10.0 μm)

(17)

(L < 6.0 μm, L > 10.0 μm)

(18)

The purpose of this model is to predict the particle size distribution of the product. Since the particle size distribution of the initial seeds are not measurable, initial seeds of the uniform distribution are assumed instead of specifying the initial nucleation at the first injection of antisolvent. The calculation results of the population density may vary depending on the initial condition. We simulated the particle growth with various initial distributions such as uniform distribution and normal distribution. The simulated results showed that the initial distribution does not affect the final distribution much and yielded similar results regardless. Thus, the assumption of uniform distribution of the initial seeds is reasonable. On the other hand, the simulation results were sensitive to the size range of initial seeds, which is available information from the lab-scale experiment. The obtained solution is the number of crystals at each node of characteristic length. The values at each node represent the population density of particles in the length grid between the (i)th and (i + 1)th nodes. Figure 3 shows the calculated sample

The fugacities in the liquid and vapor phases are expressed as fi , α = zi , αφi , αp

(16) 21

The vapor−liquid equilibrium of the system follows isofugacity relationships between the fugacities of the solvent (S) and antisolvent (A) in each phase, described in eqs 7 and 8. fA,L = fA,V

|θ | + θ 1 + |θ |

(11)

The supersaturation term S is the ratio of fugacities of the solute in liquid and solid. k2 is an empirical coefficient in the growth rate model. The fugacity of the solid is a function of the molar volume of 0 the solid as shown in eq 9. f P,P is the fugacity at the reference pressure, p0, which is 1 bar. ⎡ fP,P = f P0, P exp⎢ ⎣

∫p

p

0

⎤ vP dP ⎥ RT ⎦

(12)

Solving these equations simultaneously, the population density function n can be obtained as a function of time t and characteristic length L. Simulation of Particle Size Distribution of Recrystallized Product. A semidiscrete high-resolution finite volume scheme is applied to solve the population balance equation.19,20 This scheme is discrete in space while it is continuous in time. Then eq 3 is modified as Figure 3. Simulation result of GAS recrystallization from population density function.

∂ni 1 n d(NLvL) + ((Gn)i + (1/2) − (Gn)i − (1/2)) + ∂t ΔLi̅ NLvL dt (13)

=0

particle size distributions from the initial state to 60 s. The mean value (m) and standard deviation (σ) of the particle size distribution result from the GAS process model were 17.50 (μm) and 3.35 (μm). The simulation result showed a particle size distribution similar to the result from actual lab-scale experiment as shown in Figure 4. In Figure 4, the right axis indicates the population density distribution for each particle size and the left indicates the cumulative particle size distribution over all the length range. The mean value (m) and standard deviation (σ) of the particle size distribution result from the experiment were 18.36 (μm) and 3.50 (μm). Figure 5 shows a schematic diagram of the apparatus. The equipment consists of a recrystallizer, separation units, several

where ΔL̅i is the finite volume grid for size discretization. (Gn)i+(1/2), the cell-face flux at the control volume boundaries, is obtained by ⎛ ⎞ ΔLi̅ (Gn)i + (1/2) = Gi + (1/2)⎜⎜ni + ϕ(θi+)(ni + 1 − ni)⎟⎟ 2ΔLi̅ − (1/2) ⎝ ⎠ (14)

θi+ =

ni − ni − 1 + ε ni + 1 − ni + ε

(15)

The limiting function ϕ is D

DOI: 10.1021/acs.iecr.5b00841 Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

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Figure 4. Experimental data of GAS recrystallization process.

Figure 5. Schematic diagram of experimental apparatus.

of the filter, the number of filter bags, and other operating conditions such as the residence time for 99% separation of the recrystallized product from the flow are determined. Since the custom PSD model calculates the particle size distribution of the recrystallized HMX in the product flow and provides the result as a stream property of the product flow, the fabric filter design tool can determine the required specifications for a desired separation performance. The residence time is determined by the pressure difference, flow rate, and empirical equation in the filter design tool. The residence time of the filter t is calculated by

pressure regulators, backpressure regulators, and a pressure controller since the recycle is not included in the lab experiment. In this experiment, 20 mL of 2.17 wt % HMX− acetone solution was fed to a 350 mL size reactor. scCO2 at 100 bar and 35 °C was used as antisolvent and injected until the chamber was full. After 60 s, the exhaust valve below the recrystallizer chamber was opened and the product mixture was discharged. HMX was then recoverd by a fabric filter. To enhance the conversion of raw HMX to recrystallized product and flush the chamber, additional scCO2 was injected. About 80% of raw HMX was converted into recrystallized HMX product and recovered by the fabric filter unit while the residues were wasted. The entire processing takes approximately 5 min. Equations 1−18 were imported into the process flow sheet simulator, ASPEN Plus 7.3, by using ASPEN Custom Modeler 7.3. The custom model functions have been integrated as a unit block into the process simulator. This model provides the particle size distribution of recrystallized HMX product to the product stream of the process flow sheet. Other process variables such as temperature, pressure, and flow rate are calculated by the process simulator. Recovery of Recrystallized HMX Product. Recrystallized HMX product is discharged from the recrystallizer with solvent and antisolvent. A fabric filter including several filter bags is used to separate the product from the discharge stream. Based on particle size distribution (PSD) data from the custom GAS recrystallizer model, design specifications such as the pore size

t=

ΔP CKV0 2

(19)

1000 d p2

(20)

K=

where ΔP is the pressure difference [bar], C is the dust concentration [g/m3], K is the dust resistance coefficient [m/ (g/m2)(m/s)], dp is the average particle size [m], and V0 is the air to cloth ratio, defined as V0 =

Q Abag Nbag

(21)

Q is the volumetric flow rate of the gas [m /s], Abag is the area of the filter surface of the bag [m2], and Nbag is the number of filter bags. 3

E

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liquefaction and pumping process. Cooling water with simple heat exchangers can provide enough cooling effect for the multistage gas compression process, while the liquefaction and pumping process requires an additional cooling cycle. On the other hand, the liquefaction and pumping process can remove large compressors in the multistage compression process. As the amount of recycled CO2 is large, additional refrigerant cycle and compressors can significantly affect the economy of the entire process. For the scenario including the multistage compression process, two compressors are used to liquefy gas CO2 and then the CO2 is pressurized to supercritical phase with a liquid pump. The optimal compression ratio of each compressor is determined to minimize the compressor duty. As a rule of thumb in process design, the pressure drop at the intercooling unit was set to be 0.1 bar. For the scenario using the liquefaction and pumping process, a propane refrigerant cycle is coupled with separated gas CO2 for condensation. The remaining HMX in the mixture after filtering is assumed to be dissolved completely in the acetone and moves with acetone during further separation but does not affect acetone solubility. This is because the concentration of HMX in the mixture after filtering is very low. We also considered process options without recycle of each process materials for the economic analysis. Scenario 1: To Recycle Both Acetone and CO2. Figures 6 and 7 show a schematic procedure of recycling both acetone and CO2. In this scenario a distillation column is used to separate both components. Separated CO2 gas is pressurized through multistage compression units or liquefaction−pumping units to the initial condition of the reactor inlet flow of antisolvent (100 bar, 35 °C) for recycle. This scenario requires almost twice as many equilibrium stages of the separation column as the other scenarios. Scenario 2: To Recycle Acetone Only. In this scenario the key component is acetone. Before the column separation, a simple flash vessel is installed, as shown in Figure 8, to decrease the CO2 content in acetone. By using the flash vessel, 83.6% of CO2 from acetone is removed. Separated acetone from the flash vessel is sent to a separation column to remove the remaining CO2. Since CO2 is not recycled but is disposed of in this option, this process does not include CO2 compression or liquefaction units. Scenario 3: To Recycle CO2 Only. The third scenario is to recover CO2 and dispose of acetone. This scenario has the same structure with separation units as scenario 2, but has additional CO2 compression or liquefaction−pumping units.

The pressure drop is an important design variable of the fabric filter bags. The resistance to gas flow through the filter is related to the pressure drop, which can be estimated by an empirical equation such as Darcy’s law or by measuring the pressures of the inlet and the outlet of the filter. The dust resistance coefficient K depends on the characteristics of the particle such as the particle size. The resulting filter specifications are listed in Table 2. Table 2. Required Specifications of Filter design input conditions flow rate of input feed mixture (m/s) pressure drop in a filter bag (bar) calculated required filter specs number of filter bags required filter pore diameter (μm) required residence time (h)

0.015 0.02 78 18 0.59

Separation and Recycle of Antisolvent and Solvent. In the operation of a commercial-scale process, acetone and CO2 should be separated and recycled because these two species have moderate solubilities. Higher purity of separated material is required if the material flow is to be recycled. If the separation is not perfect, several problems may occur. Residual acetone in CO2 may cause malfunction of the compressor in the recycle process since acetone would be liquefied prior to CO2. On the other hand, if CO2 remains in acetone, then the solubility of HMX in acetone would decrease to lower the productivity. Therefore, a separation process using a distillation column with an additional flash vessel is suggested. Since separating both two components with high purity is costly for commercial-scale operation, options for the separation are proposed for comparison. Since a trace amount of HMX may reside in the mixture, lower temperature is favored for the safety issues. Process designs for each scenario are presented in Figures 6, 7, 8, 9, and 10. Conditions of the residue stream after the filtration in this design are 100 bar and 0 °C. Because of the Joule−Thomson effect at valves after the recrystallizer, the pressure and temperature of the stream are decreased. However, due to pressure regulators, the pressure of the residue stream is kept at 100 bar for constant operation. A multistage gas compression process and liquefaction and pumping of CO2 via a cooling process are used in the scenarios for recycle of CO2 gas. The multistage gas compression process can be used to reduce operating cost compared to the

Figure 6. Process flow sheet of separation process 1 with multistage compression. F

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Figure 7. Process flow sheet of separation process 1 with liquefaction−pumping.

Figure 8. Process flow sheet of separation process 2.

The process flow sheet of this scenario is shown in Figures 9 and 10.

CAPEX is the sum of equipment costs which is determined by the capacity of the process. In this study, the major reason for CAPEX differences among the scenarios is the process equipment of the CO2 recycle process via compression or liquefaction−pumping. Since the flow rate of CO2 is much higher than those of other components such as acetone, the capacity of compression units for CO2 is much larger than those of separation units.

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ECONOMIC EVALUATION OF EACH SCENARIO Method for Choosing an Optimal Process Scenario. To choose an optimal process among these scenarios, economic evaluation of capital expenditure (CAPEX) and operating expenditure (OPEX) for each scenario using process model and simulation results is performed. G

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Figure 9. Process flow sheet of separation process 3 with multistage compression.

Figure 10. Process flow sheet of separation process 3 with liquefaction−pumping.

OPEX is the sum of cost elements for process operation, various feedstocks, and utilities. Major differences in OPEX among the candidate scenarios arise from the additional refrigerant cycle and cost of disposed reagents. Also, since the recrystallization process does not involve chemical reactions, all the chemicals except HMX can be recycled. Decisions between recycle or disposal of the used chemicals should be made based on comparing those cost elements. Recycle of each process material will decrease the OPEX, but increase the CAPEX instead. The basis for economic evaluation is listed in Table 3. Cooling water at 30 °C is used as the cooling utility for

intercooling. Based on producing 100 tons of recrystallized HMX per year, the lab-scale experimental conditions, and basis data of 2014 in Table 4, economic evaluation of each scenario is performed. Table 4. Cost Basis for Economic Analysis cost data22−24 raw acetone ($/ton) raw liquid CO2 ($/ton) utility (coolant) ($/ton) electricity ($/MWh)

Case: Recycle of CO2. Table 5 shows that recycle of CO2 is recommended rather than disposal. This can be found by comparing the results of scenario 1 and scenario 2. Recycle of used CO2 increases the total CAPEX by $5,656,900 (148%) and $3,318,100 (86%) for each process since recycling requires a multistage compression process. However, from the viewpoint of OPEX, it reduces the cost of feedstock of CO2 compared to scenario 2. Since the amount of CO2 used in

Table 3. Raw Material Requirement for a Single Operation of Recrystallization Process requirement raw acetone (kg) raw liquid CO2 (tons) raw HMX (kg)

1822 75 0.163 98.4

64.331 2251 1.427 H

DOI: 10.1021/acs.iecr.5b00841 Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

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Industrial & Engineering Chemistry Research Table 5. CAPEX, OPEX, and Detailed List of Cost Elements scenario 1 total CAPEX (USD) recrystallizer (%) fabric filter (%) flash vessel (%) column (%) compressor (%) heat exchanger (%) liquid pump (%) annual OPEX (USD/year) feedstock (%) operation energy (%) utility (%)

scenario 3

with multistage compression

with liquefaction−pumping

scenario 2

with multistage compression

with liquefaction−pumping

9,562,800 23.00 13.85 − 6.89 55.61 0.65 − 752,428 − 66.05 33.95

7,152,400 30.76 18.52 − 9.21 26.02 0.57 14.92 857,920 − 68.59 31.41

3,834,300 57.37 34.55 1.2 6.88 − − − 1,356,938 90.68 7.83 1.50

9,556,100 23.02 13.86 0.57 6.26 55.63 0.65 − 769,515 13.27 58.80 27.93

7,145,600 30.78 18.54 0.76 8.37 26.03 0.57 14.93 875,054 11.67 62.05 26.28

Figure 11. Full process flow diagram of GAS recrystallization.

suggested options. Based on the conclusion, an integrated process model which includes the GAS recrystallizer, separation of HMX product, and recovery of solvent and antisolvent are presented in Figure 11.

the process is 35 times larger than the amount of acetone, the benefit of recycling CO2 becomes larger by compensating the increased CAPEX during the process lifetime. The results show that the annual OPEX of scenario 2 is more than twice the annual OPEX of other scenarios. They also show that the cost of CO2 in the scenario 2 accounts for 90.68% of the annual OPEX, increasing the annual OPEX greatly. Case: Recycle of Acetone. To investigate the effect of recycling acetone, the results of scenario 1 and scenario 3 are compared. In case of the recycle of acetone, CAPEX is slightly increased by $6,700 (0.07%) . This means installation of a flash vessel eliminates the CAPEX benefits of using a smaller column. However, the annual OPEX is decreased by $17,087 (2.22%) since the scenario 1 can reduce the cost of acetone. Case: Process Options for Recycle of CO2. Among the two options for recycle of CO2, the liquefaction−pumping process is preferred compared to the multistage compression in both scenarios. The total CAPEX of each scenario is reduced by $2,410,400 (25.22%) and $2,410,500 (25.20%) for each scenario when the liquefaction−pumping process is chosen. Although the OPEX is increased by $105,492 (14.02%) and $105,539 (13.71%), the liquefaction−pumping process is favored considering the entire economics. Considering all these factors, scenario 1, i.e., recycling both components with liquefaction−pumping of separated CO2 is found to be the most cost-effective process scenario among the

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CONCLUSION

A design for the GAS recrystallization process for a large-scale operation is presented. This study integrates a population balance model for recrystallization into a process flow sheet environment and provides an optimal design of an integrated process for large-scale operation with economic evaluation. One of the key features of the suggested approach is that the PSD of the recrystallization process is readily available for given conditions such as temperature and pressure to yield more accurate estimates for process design and performance because the PSD from the recrytallizer subsequently affects product separation and recycle of residues. Several separation and recycle process options are suggested and the most economic option is chosen. The results are put together to establish an integrated process model for the commercial-scale GAS recrystallization process. I

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Industrial & Engineering Chemistry Research

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AUTHOR INFORMATION

Corresponding Author

*Tel.: +82-2-8801504. Fax: +82-2-8881604. E-mail: jongmin@ snu.ac.kr. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the Agency for Defense Development and the High Energy Material Research Center in South Korea. The Energy Efficiency & Resources Core Technology Program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resources from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 2012T100201687). This research was supported by “Development of sensor-based virtual plant engineering technology for the support of plant O&M”, funded by the Ministry of Trade, Industry & Energy(10048341).

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DOI: 10.1021/acs.iecr.5b00841 Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX