Isopropyl Alcohol Dehydration by Hot Gas Pressure Swing Adsorption

Apr 25, 2014 - Christopher M. Jones,. ‡ and Daniel R. Armstrong. §. †. The Dow Chemical Company, Engineering and Process Science, 2301 N. Brazosp...
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Isopropyl Alcohol Dehydration by Hot Gas Pressure Swing Adsorption: Experiments, Simulations, and Implementation Yujun Liu,*,† Shawn D. Feist,‡ Christopher M. Jones,‡ and Daniel R. Armstrong§ †

The Dow Chemical Company, Engineering and Process Science, 2301 N. Brazosport Boulevard, B-1603, Freeport, Texas 77541, United States ‡ The Dow Chemical Company, Engineering and Process Science, 845 Building, Midland, Michigan 48667, United States § The Dow Chemical Company, Water-Soluble Polymers, P.O. Box 150, Plaquemine, Louisiana 70765, United States S Supporting Information *

ABSTRACT: A full-scale hot gas pressure swing adsorption (HGPSA) process was designed by mathematical modeling for isopropyl alcohol dehydration. The mathematical model was validated against laboratory experiments. The experimental data were also used to obtain the transport properties used in the model by fitting the simulation results. After the designed full-scale hot gas pressure swing adsorption plant was manufactured and constructed, an Aspen Adsorption model was used to assist in the identification and troubleshooting of problems that arose during start-up. The Aspen package was also used to refine control strategies of the HGPSA unit. The use of the combined experimental and modeling tool approach enabled the successful start-up of the HGPSA plant. Sustainable operation was demonstrated to meet the isopropyl alcohol product specification and was able to be predicted by the Aspen Adsorption model. starch-based materials.21,22 Bannat et al. studied the separation of isopropyl alcohol from aqueous vapors using type A molecular sieves and biobased adsorbents such as natural palm stones, oak, and corncobs, and found that 3A molecular sieves achieve the best dehydration performance.22 Palm stones showed best performance among the bioadsorbents and outperformed 5A molecular sieves. Organic drying using 3A zeolites has been the most widely used adsorption process. Simo et al. experimentally studied the equilibrium and kinetics of water and ethanol adsorption/desorption on a 3A zeolite in the range of operating conditions corresponding to an industrial ethanol dehydration pressure swing adsorption (PSA) process. Both equilibrium isotherm data and kinetics parameters were obtained by using an adsorption breakthrough technique.23 Simo et al. also studied the dynamics and performance of an industrial ethanol dehydration PSA process by mathematical modeling and examined the effects of feed temperature, purge step time, feedwater concentration, and pressure ratio on the PSA process performance.24 The economic balance between PSA and membrane dehydration systems depends mainly on the scale of operation and the desired concentration specification.25 Membrane processes offer the best choice at very small scales; PSA processes are more economical at relatively large scales. There is, however, a significant range of intermediate scales in which not much difference in cost exists between these two processes. PSA systems are advantageous over membrane systems in the high organic purity region, while the opposite is true when the product water concentration requirement is less severe, e.g.

1. INTRODUCTION Isopropyl alcohol (IPA) has been widely used as a cleaning and rinsing agent in chemical, pharmaceutical, semiconductor, and electronics industries. Recycling and recovering IPA is critical from both environmental and economical points of view. IPA forms an azeotrope with water at 87.7 wt % IPA and 80.3 °C.1,2 This azeotrope makes it difficult to recover IPA by conventional distillation. Many techniques can be used for separation of azeotropic mixtures, such as azeotropic distillation, extractive distillation, pressure swing distillation, membrane pervaporation or vapor permeation, adsorptive separation, and hybrid distillation/membrane separation or distillation/adsorption.3−6 Both azeotropic and extractive distillation processes require the use of an azeotropic/extractive agent or entrainer to break the azeotrope between IPA and water.7−9 The addition of the azeotropic agent adds complexity and energy related operating costs to a process that is already very energy-intensive.4 In limited instances where the azeotropic composition changes with pressure, pressure-swing distillation can be a viable process alternative but has not been widely exploited commercially due to this limitation.3,5 Membrane pervaporation and vapor permeation have been extensively studied in the past couple of decades and are two commonly used commercial membrane operations.10−16 Both pervaporation and vapor permeation are best identified with dehydration of aqueous/organic mixtures to yield high purity organics, most notably ethanol, isopropyl alcohol, and ethylene glycol.17−20 They are more economical when drying organics containing less than 10 wt % water and when the specification on water concentration of the dehydrated organics is not very demanding, meaning that the specification is not extremely low. Several types of adsorbents have been investigated for breaking the isopropyl alcohol−water azeotrope. They include molecular sieves, silica gel, and biobased adsorbents such as © 2014 American Chemical Society

Received: Revised: Accepted: Published: 8599

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Figure 1. Schematic of the HGPSA laboratory apparatus. PT: pressure transducers; TE: thermocouples; FT: flow transducer (controller).

this work is, therefore, to present a comprehensive pressure swing adsorption (PSA) process development effort for IPA dehydration from laboratory experimentation and mathematical modeling to the implementation of the commercial-scale process. The use of the commercial software Aspen Adsorption to identify problems and solutions during the PSA plant startup and to optimize the performance of the successfully started PSA process are also described.

higher than 0.5 wt %. PSA systems typically have a greater capacity than membrane systems of similar cost. Furthermore, membrane process capacity drops significantly when organic product purity increases, while PSA processes can maintain much more capacity. In general, membrane systems require less engineering, and their capital costs are less than or similar to PSA systems. However, the PSA operational life is generally longer than membrane systems, particularly the polymeric membranes. For example, polymeric membrane modules are typically recommended to be replaced in 1−3 years, while the 3A zeolite in the PSA system can last more than 5 years or even longer.25−27 The application of hybrid processes that combine distillation and adsorption or distillation and membrane separation have also been studied.1,4,22 Such hybrid processes may enable operating cost savings but do not always deliver capital cost savings. Lower operating costs result mainly from a lower energy demand, and relatively higher capital costs result from increased process complexity and high membrane prices. The hybrid systems can bring economic advantages when used at relatively large scales over long periods but they are not profitable in the case of small isopropyl alcohol dehydration systems.28 Among all the open literature on isopropyl alcohol dehydration, a systematic approach to process development including all aspects of experimentation, simulation, scale-up, and implementation is rarely reported. This is particularly true for commercial-scale process implementations. The objective of

2. BACKGROUND In many production-scale chemical plants, solvents such as isopropyl alcohol are used for product washing. In the example described in this work, the washing effluent is about 5000 kg/h and contains about 35 mol % water in IPA. The IPA in the washing effluent is recovered and reused to reduce raw material and wastewater treatment costs. Water concentration in IPA affects the required wash volume of IPA, washing time, energy intensity of the process, and the plant capacity. As water content in the IPA solvent is reduced, required volumes of IPA are also reduced and wash cycle times are lowered. For the specific process of interest, the target water concentration in the recovered IPA was 1.65 mol % or less. Through a thorough evaluation of all potential processes, a hot-gas PSA (HGPSA) process was chosen due to its ability to meet the tight water specification, robust operation, and avoidance of entrainer or azeotropic agents. The term “hot-gas” is prefixed to PSA to point out that the feed stream requires evaporation and superheating before it enters the adsorbent 8600

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with different feed flow rates, water concentrations, and volumetric purge to feed ratios. The operating conditions are given in Table 2. It is noted that in Runs 2 and 4 a purge to

beds. It was determined that a simple, two-bed PSA unit would be needed, and the HGPSA process should only involve the basic PSA cycle steps, such as pressurization, feed, blowdown and purge, etc. The design of the HGPSA system started with laboratory experiments, which were used to validate the process feasibility and to obtain design data. The data were then used to estimate the transport properties to be used in a HGPSA mathematical model, which was used to design the full-scale HGPSA process. An Aspen Adsorption model was also developed for the fullscale PSA system and was used to assisting in problem diagnoses during plant startup.

Table 2. Operating Conditions of HGPSA Experiments total cycle time (s) pressurization step (s) feed step (s) blowdown step (s) purge step (s) volumetric feed flow rate (m3/min STP) feed mole fraction feed vapor temperature (°C) adsorption pressure (kPa) purge pressure (kPa) volumetric purge to feed ratio

3. EXPERIMENTAL SECTION Figure 1 is a flowsheet of the HGPSA experimental system. The HGPSA unit consisted of two 5.08 cm I.D. and 3.66 m tall adsorbent beds packed with 3A molecular sieves, a vaporizer, a superheater, two water-cooled condensers, two condensate tanks, and two recirculation pumps. The pumps were used to pump product and regeneration effluents out of the condensate tanks. The columns were steam traced to keep process vapor from condensing on the walls of the column. Four thermocouples were evenly distributed in each of the adsorbent beds with tips of the thermocouples at the centerline of the beds. A rotary-vane vacuum pump was used for pressure control. Camile software was used for data acquisition and control of the HGPSA experiments. Table 1 lists the details of the PSA beds and adsorbent properties.

run 2

run 3

run 4

0.0187

1020 115 395 210 300 0.0172 0.017

0.0358

0.354 121 310.3 17.2 2.5

0.354 121 310.3 62.1 0.81

0.442 121 310.3 17.2 0.86

0.354 121 310.3 17.2 1.8

feed ratio of less than one was chosen. Although this has been shown to not be sustainable over long-term operation, the conditions were chosen to test the ability of the system to respond to upsets and how long the system could sustain running at transient state conditions. This will be discussed further later in this paper.

4. MATHEMATICAL MODELING 4.1. Mathematical Models. A mathematical model of the HGPSA process was developed and validated against experimental data. Upon successful verification, the model was used to functionally design the full-scale HGPSA process. The model was a multicomponent, nonisothermal model and is similar to the ones published elsewhere.29,30 It consisted of component and total mass balance equations, an energy balance equation, a linear driving force equation for the mass transfer between the vapor phase and adsorbent phases, a temperatureindependent Langmuir model for the adsorption isotherms, and correlations for the physical properties such as the vapor phase heat capacities and heat of adsorption. Mass transfer was accounted for using an effective mass-transfer coefficient with the Linear Driving Force (LDF) model. Heat transfer was accounted for using an overall heat transfer coefficient in the energy balance equation. Details of the model equations and boundary, as well as initial conditions, are available in the Supporting Information (SI). This model applies some common assumptions that are generally utilized in PSA simulations, which include the following:29−33 • Ideal gas law • Negligible column pressure drop • Thermal equilibrium between gas and solid phases • Plug flow • Negligible axial and radial dispersion • Negligible axial heat conduction • Temperature-independent adsorbent properties • Linear pressure histories during pressure change steps, i.e., pressurization and blowdown steps. 4.2. Adsorption Isotherms. The adsorption isotherms of water vapor on 3A molecular sieve were obtained from the database of Aspen Adsim, a previous version of the Aspen Adsorption package. These data were regressed into a temperature-independent Langmuir model shown as eq 1.

Table 1. Bed Characteristics and Physical Properties bed diameter (m) bed length (m) bed void fraction adsorbent particle density (kg/m3) heat capacity of adsorbent (kJ/kg·K) heat tracing temperature (°C)

run 1

0.0508 3.66 0.4 1200 1.0 120

A standard 4-step Skarstrom cycle was used, which includes concurrent feed pressurization, feed or adsorption, countercurrent blowdown, and countercurrent purge. A liquid feed stream of IPA and water, which exists as a liquid at room temperature, was vaporized in the steam-heated total vaporizer and superheated to the desired temperature by the steamheated superheater. The resulting gas stream was used to pressurize the adsorbent bed during the pressurization step and passed through the adsorbent bed during the adsorption step, where water is selectively adsorbed. The adsorbent bed was depressurized to the purge pressure during the countercurrent blowdown step. During the purge step, a portion of the dried IPA product stream leaving the adsorbent bed was expanded via pressure drop across a control valve and sent to the desorbing bed as purge gas. The remaining portion of the product stream was condensed and collected. The desorption effluent stream was also condensed and collected. The washing effluent sample from a Dow plant was used as the feed. The water mole fraction in the IPA water mixture was 0.354−0.42. Samples were taken from the condensed product effluent during each cycle. Water concentration analysis was performed using a coulometric Karl Fischer titration technique. Four runs were selected for their proximity to the full-scale design conditions and compared with the outputs of rigorous numerical simulations. The four experiments were conducted 8601

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The model was solved using a finite-difference algorithm coupled with a time-adaptive differential and algebra equation solver.35 It is noteworthy that the accuracy of this model has been validated with extensive experimental data obtained from a bench-scale PSA system.33 To compare simulation results with those of the experiments, the volume-averaged mole fraction of the water in the dried IPA composite during cyclic steady state was calculated using eq 2. The mass concentrations in the condensed streams were then calculated according to the mole fraction assuming complete condensation.

Isopropyl alcohol was treated as an inert that does not adsorb onto the adsorbent. qi* =

IP1e IP2 / T Pyi 1 + IP3e IP4 / T Pyi

(1)

The model parameters are as follows: IP1 = 5.90 × 10−5 mol/kg· kPa IP2 = 6636.6 K−1 IP3 = 6.081 × 10−6 kPa−1

t

ya ,avg =

IP4 = 6622.5 K−1

The adsorption isotherms at different temperatures are displayed in Figure 2. The adsorption equilibrium capacities

∫0 a yw , a ,LP ua ,LPAdt t

∫0 a ua ,LPAdt

(2)

5. MODEL VALIDATION AND OVERALL MASS AND HEAT TRANSFER COEFFICIENTS The ultimate goal of the experimental and simulation efforts was to obtain the mass and heat transfer coefficients for water adsorption on 3A molecular sieve so that it could be used for designing a full-scale IPA HGPSA unit. In doing so, the mass and heat transfer coefficients were first obtained by matching the simulation and experimental results of Run 1 in Table 2, i.e. by comparing the IPA product concentrations, as well as the temperature profiles and temperature swings. Then these coefficients were used in the model to simulate other experiments for validation. The best fit coefficients were as follows: • Mass transfer coefficient, k = 0.0121 s−1 • Heat transfer coefficient, h = 0.01 kJ/m2·s·K. Figure 3 compares the Run 1 temperature profiles at the end of the adsorption and purge steps obtained from experiment

Figure 2. Adsorption equilibrium isotherms of water vapor on 3A molecular sieve.

calculated from this model are slightly higher than those reported by Simo et al.23 The likely reasons for the difference are that (1) Simo et al. measured the water adsorption equilibrium capacities using a fixed bed adsorption breakthrough technique with 3A zeolites of different particle sizes (3.6 and 1.8 mm) and (2) they used water and ethanol mixture as feed, not pure water vapor. The equilibrium data obtained by the breakthrough technique, normally used for obtaining mixture isotherms, are generally less than the “true” equilibrium capacity of water due to effects of particle size, residence time, coadsorption of ethanol, and other nonidealities of the fixed bed experiments. 4.3. Heat of Adsorption and Heat Capacities. Another important parameter in the model is the heat of adsorption (ΔH.) A constant heat of adsorption was used in the IPA HGPSA dehydration process simulations. The value of the constant heat of adsorption was an average of the heats of adsorption at different loadings that were calculated from the adsorption isotherms using the Clausius−Clapeyron equation.34 The average heat of adsorption is ΔH = −57.6 kJ/mol and is essentially the same as the value reported by Simo et al.23 The heat capacity of the adsorbed water was assumed to be the same as its liquid form and is 0.0765 kJ/mol·K. The heat capacity coefficients used in the model are given in Table S1 of the SI.

Figure 3. Comparison of temperature profiles at the end of adsorption and purge steps during cyclic steady state.

and simulation. The model predicts the adsorption step temperature profile very well, while the prediction for the purge step is good in trend but only fair in accuracy. One likely reason for this discrepancy is that the model uses a constant mass transfer coefficient throughout, while in reality the mass transfer coefficient is loading dependent. During the purge step, the water loading decreases due to desorption. The overall lower loading leads to a smaller mass transfer coefficient and thus less desorption and in turn less temperature drop in the experiment. The constant mass transfer coefficient used in the 8602

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beds. Although the industrial process was not planned to be run in this fashion, understanding how the HGPSA process responds under a worst case scenario such as insufficient purge is beneficial for operational and troubleshooting knowledge. A simple sensitivity study was conducted on the simulated product purity to mass transfer coefficient by using Run 1. When the mass transfer coefficient was reduced 10% to 0.0109 s−1, the water concentration in the IPA product increased 72% from 774 to 1331 ppmw. When the mass transfer coefficient was increased 10% to 0.0133 s−1, the water concentration in IPA product decreased 30.2% to 540 ppmw. It is seen that the simulation results were quite sensitive to the mass transfer coefficient and an accurate estimation of the mass transfer coefficient could impact the simulation results significantly. Because the simulation results were quite sensitive to the mass transfer coefficient, the fact that the simulation was able to match the experimental results quite well indicated that the assumed mass transfer coefficient was sufficient for the HGPSA scale-up efforts.

model, which was used to predict the temperature profile during the feed step, may overpredict the amount of desorption, an endothermic process, and thus could result in an overprediction of the temperature decrease. Nevertheless, these simulations results were considered satisfactory for the purposes of this work. Figure 4 shows the Run 1 water vapor concentration and water loading profiles at the end of adsorption and purge steps

6. FULL-SCALE HGPSA DESIGN BY SIMULATION The plant HGPSA process was required to dehydrate wet IPA containing 35 mol % water to a dry IPA containing less than 1.65 mol % water. In designing the large-scale HGPSA unit, most of the operating conditions were kept the same as those used in the laboratory experiments. The adsorbent bed diameter was determined by keeping the feed superficial velocity close to what was used in the experiments. In the laboratory experiments, the feed superficial velocity ranged from 4.6 to 9.6 cm/s. A superficial feed velocity of 6.3 cm/s was chosen for the plant HGPSA. This led to bed diameter of 2.286 m. The half cycle time or the adsorption step time was set at 10 min. The feed pressurization, blowdown, and purge step times were set to 1.5, 3.5, and 5.0 min, respectively. The mathematical model was used to determine the height of the full-scale HGPSA beds that met the dry IPA water specification. The mass transfer coefficient fitted from the experiments was used in the simulations. Because the full-scale HGPSA would operate essentially at adiabatic conditions, the overall heat transfer coefficient was set to zero when simulating the full-scale HGPSA process. In addition, a volumetric purge to feed ratio of 1.2 was used for the full-scale process simulation. The final bed height was determined to be 4.88 m. With beds of these dimensions and under the designed operating conditions, the simulated full-scale HGPSA would yield of IPA with 1325 ppmw water (0.44 mol %), well below the specified 1.65 mol %. The final full-scale HGPSA design and its simulated performance are summarized in Table 4. Figure 5 displays the simulated water vapor concentration and water loading profiles at the end of the adsorption step in a cyclic steady state cycle. The water vapor concentration profile shows a spreading mass transfer zone. The adsorption of water occurs in the bed section of z/L = 0.5−0.97. The water loading

Figure 4. Cyclic steady water vapor concentration and loading profiles at the end of adsorption and purge steps of Run 1.

during the cyclic steady state. The bed capacity factor (BCF), defined as the bed capacity used during the cyclic steady state (measured at the end of adsorption step) compared to the maximum bed capacity corresponding to the feed conditions, was calculated to be 98.2%.31 The difference between the water loading profiles at the end of the adsorption and purge steps indicates the working capacity of the adsorbent bed. Table 3 gives the average water concentrations in the dried IPA product from experiments and simulations for all four runs. For Run 1, the model predicted a water concentration of 774 ppmw, which is very close to that measured experimentally (764.1 ppmw). For Run 3, the model predicted a water concentration of 1324 ppmw that is higher than that obtained from experiment (471.4 ppmw). However, the predicted water concentrations of Runs 2 and 4 are much higher than those obtained from experiments. Runs 1 and 3 used a volumetric purge to feed ratio of greater than one whereas in Runs 2 and 4 the volumetric purge to feed ratios were less than one (see Table 2). It has been reported that a PSA process with a volumetric purge to feed ratio of less than unity would not reach a cyclic steady state without significant breakthrough.31,33 It is most likely that Runs 2 and 4 did not reach cyclic steady state, which led to the discrepancy between the simulation and experimental results. Runs with purge to feed ratios of less than unity were chosen to push the experimental system to give an idea of how long the process could be run under upset conditions in which an insufficient amount of purge was available for desorption of water from the

Table 3. Summary of Operating Variables and Simulation Resultsa experimental IPA product water concentration simulated IPA product water concentration BCF (%) a

run 1

run 2

run 3

run 4

764.1 ppmw 774 ppmw 98.2

1490 ppmw 10.5 wt % 99.9

471.4 ppmw 1324 ppmw 98.5

1.46 wt % 12.8 wt % 99.8

Run conditions are in Table 2. 8603

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a relatively large BCF is desirable because it generally means smaller PSA beds compared to those with a smaller BCF.

Table 4. Full-Scale HGPSA Design and Performance operating parameters bed diameter (m) bed height (m) volumetric feed flow rate (m3/min) feed water mole fraction adsorption pressure (kPa) purge pressure (kPa) feed temperature (°C) volumetric purge to feed ratio PSA cycle adsorption step (min) blowdown (min) purge (min) repressurization (min) performance product water concentration (ppmw) BCF (%)

design and performance 2.286 4.88 22.5 0.35 310.3 17.2 121 1.2

7. FULL-SCALE HGPSA IMPLEMENTATION, STARTUP, AND ASPEN ADSORPTION ANALYSIS 7.1. Initial Startup. A full-scale HGPSA unit was constructed according to the previously described design. An extra 0.305 m was added to the designed bed height to cover possible feed flow fluctuations. The plant HGPSA system has exactly the same flowsheet as the laboratory HGPSA apparatus as shown in Figure 1, except the initial plant design combined the feed vaporizer and superheater into a single heat exchange unit that served as both vaporizer and superheater. After the construction was completed, an initial startup was conducted. A couple of needed areas for improvement in performance were identified: • The combined vaporizer/superheater was either unable to fully vaporize IPA and water feed at adequate rates or unable to provide enough superheat at lower feed rates. It was observed that when the flow rate exceeded approximately 1/3 design rates, the knockout pot immediately downstream of the vaporizer/superheater exhibited significant accumulation of liquid. • The system was not capable of sustaining bed temperatures above the process vapor’s dew point. Bed temperatures dropped gradually over time, eventually falling below the dew point temperature of the IPA and water mixture at various points within each bed. On the basis of the above findings, corrective actions were determined. An Aspen Adsorption simulation was developed that rigorously modeled the HGPSA operating conditions used during the initial startup. This simulation was used to analyze the two areas for improvement listed above. 7.2. Aspen Adsorption Simulation and Analysis. 7.2.1. Model. Aspen Adsorption is a flowsheet-based dynamic modeler by AspenTech. It offers many options for modeling heat and mass balances, the mass transfer rate, and the equilibrium isotherms of an adsorption process. Any combina-

10 3.5 5.0 1.5 1325 98.4

Figure 5. Cyclic steady state water vapor concentration and loading profiles at the end of adsorption step of the designed PSA beds.

profile indicates that much of the adsorption capacity of the bed is utilizedthe bed capacity factor (BCF) is 98.4%. A PSA system that produces a product within the specification and has

Figure 6. Aspen Adsorption HGPSA-IPA dehydration simulation flowsheet. 8604

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tion of the models can be used. Its selection should be based on the type of application and required degree of rigor. A model of the full-scale IPA HGPSA process was developed with Aspen Adsorption. Figure 6 shows the screenshot of the graphical representation of the IPA HGPSA model. Note that the model does not include any of the auxiliary equipment. The model simulates only the PSA process and takes the superheated isopropyl alcohol and water mixture at its feed composition. It also does not simulate the vacuum pump and reject condenser but sets up the pressures within the model. As shown in Figure 6, a single-bed approach was used. This makes the model setup simpler and improves the simulation speed while retaining the accuracy of the final results. The models for the full-scale IPA HGPSA process were selected as below: • Material balance − The model used was Convection Only. This means that the dispersion caused by molecular diffusion and turbulent mixing was ignored. • Momentum balance − accounted by the Ergun Equation. • Kinetic Model − Mass transfer resistance was accounted by the Solid Film mechanism with a Lumped Resistance assumption. The linear Lumped Resistance Model was used. • Energy Balance − modeled by the Non-Isothermal with Gas and Solid Conduction Model. The heat of the adsorbed water phase was ignored and the heat of adsorption was assumed constant. The gas thermal conductivity was also assumed to be constant. • Isotherm model − Aspen Adsorption has an isotherm model library. To avoid using a user subroutine to define the isotherms, the four-parameter Langmuir 2 model in the library was used to represent the adsorption isotherms of water on 3A molecular sieve. The model assumes the exact same form as eq 1. The feed temperature, pressure, and mole fraction of each component, 0.35 for water and 0.65 for IPA, were specified in the Configure Table for the feed block, F1. The feed flow rate was specified in the Configure Table for valve VF1. Processes with different feed rates, ranging from 1800 to 5987 kg/h, were simulated. The adsorbent bed characteristics were specified in the Specify Table within the Configure Form for the adsorbent bed, Bed 1, as shown in Table S2 in the SI. Other adsorbent bed conditions were also specified in this table, along with the adsorption isotherm model parameters and physical and transport properties of the adsorbent bed and column. The plant HGPSA process utilized a Skarstrom cycle with added pressure equalization steps. The pressure equalization steps were added to the full-scale PSA process in an attempt to improve the IPA recovery. With the pressure equalization steps, part of the IPA that would normally get lost in the blowdown step in one column is used to pressurize the other column. The cycle configuration was specified within the Cycle Organizer and contained the following steps: 1. Feed. In this step, the vaporized wet IPA was fed to Bed 1 to produce dry IPA. The feed pressure and temperature were 310.3 kPa and 121 °C, respectively. A portion of the dry IPA was sent back to the other bed to purge the bed at the desorption pressure. Different step times were used, ranging from 6 to 10 min. 2. Pressure equalization (from high to middle). In this step, the valve connecting the top of the two beds was set open and allowed the pressures of two beds to equalize.

This step was set to event driven and it stopped when the pressure in the head of the Bed 1 (TD2) was less than or equal to 200 kPa. 3. Blowdown. This step was time driven and the step time was set to 200 s. 4. Purge with dried IPA. The purge pressure was set 17.2 kPa and the volumetric purge to feed ratio was 1.2. The step time was to be controlled by Step 1, i.e., it had the same step time with Step 1. 5. Pressure equalization (from low to middle). This step was set to be controlled by Step 2. 6. Feed repressurization. This step was set to event driven. It stopped when the Bed 1 pressure was equal to or greater than 308 kPa. 7.2.2. Ambient Heat Loss Analysis. The simulation was used to investigate the heat loss from the HGPSA columns to the environment when there was no heat tracing implemented as discussed in Section 7.1. This was done by specifying the heat transfer coefficient for the heat transfer between the PSA beds and the atmosphere which had a temperature of 298 K (see Table S2 in the SI). The simulation predicted 55670 kJ/h of heat loss to the environment. This was significantly higher than the amount of superheat that could be carried into the beds at a lower feed flow rate (35610 kJ/h assuming 10 °C superheat at 1800 kg/h). The combined vaporizer/superheater was not designed to deliver enough superheat at the low flow rates to overcome the ambient heat loss. This problem was mitigated at higher flow rates as the kJ/h superheat increased proportional to flow rate; however, running for extended periods between 1350 and 1800 kg/h feed was a legitimate operating condition for the system and thus required additional corrective actions. It was determined that installation of electric heat tracing on the beds and process lines would provide a robust mechanism for managing ambient heat loss regardless of the feed flow rate. The tracing would be turned on automatically by the control system to maintain column skin temperature set points. More extensive insulation of the beds and process lines was also added to the system. A new heat exchanger was also installed upstream of the original combined vaporizer/superheater and served solely as a vaporizer. The original vaporizer/superheater remained in service and served as a superheater only. This new configuration not only ensured full vaporization of feed mixture within the range of all designed flow rates but also provided sufficient superheat to the feed vapor entering the HGPSA beds. Together with the added heat tracing and more extensive insulation, the HGPSA beds were able to always sustain above the vapor’s dew point, eliminating vapor condensation and thus ensuring normal operation. 7.2.3. Cyclic Steady-State Transition Effects. The simulation was also used to evaluate the effects of making step changes in feed flow rate, specifically reduction from 5987 to 1800 kg/h. This was done by simulating the HGPSA process to its cyclic steady state with the feed flow rate of 5987 kg/h then using the end-of-process bed profiles as the initial conditions for the next simulation with the flow rate of 1800 kg/h. Results showed that if the cycle time remained the same, a net removal of water occurred from the adsorbent bed as the system transitioned from the first cyclic steady-state condition to the next. The resulting loss of water also carried its associated heat of adsorption, resulting in a net loss of heat from the beds. The simulation showed that the transition happened quickly enough that bed temperatures would approach and descend past the 8605

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dew point temperatures. To mitigate this effect, a control strategy was implemented that adjusts the cycle time as a function of feed rate to the HGPSA. Doing so minimizes the net removal/addition of water during transitions from one feed flow rate to another. 7.3. Successful Startup. After the changes discussed in the previous section were implemented, the HGPSA was brought back online. The Aspen Adsorption simulations were again used to simulate the flow rate ramping and determination of optimal cycle and step times. The model was also used to predict the bed temperature changes and the HGPSA process performance. Because of the addition of the heat tracing, there was essentially no heat loss to the environment. Therefore, the simulation assumed adiabatic operation, i.e., no heat transfer to environment. An analysis of IPA yield from the HGPSA plant at feed rate of 4990 kg/h was performed and compared with predictions by the Adsorption model. IPA yield is defined as the percentage of IPA fed to the HGPSA that is recovered in the anhydrous IPA stream. Plant IPA yield calculated using streamflow rates and stream composition analysis was 83%. Adsorption model predicted IPA yield of 89% under the same conditions at cyclic steady state. Water concentration in the dried IPA product stream was analyzed by a plant gas chromatography (GC) with a detection limit of 500 ppmv and was measured as nondetectable. The Adsorption model predicted the water concentration to be 1.98 ppmv. Due to the limitation of the plant GC’s detection limit (500 ppmv), it was difficult to determine exactly how close the prediction was to the actual concentration. However, comparison between the simulation results and plant data, particularly the IPA yields, indicated the Aspen Adsorption model can satisfactorily predict the HGPSA performance, at least under the plant application standard.

reduction of feed flow rate occurred while the cycle time remained unchanged, a net loss of heat from the beds happened quickly enough that bed temperatures descended past the dew point temperatures. This led to condensation in the adsorbent bed and eventually to an inability to sustain operation. Corrective actions were taken by adding a separate feed vaporizer in addition to the existing superheater, heat-tracing the adsorbent beds, and implementing a control strategy that adjusted the cycle time as a function of feed rate. The full-scale HGPSA was started successfully after the corrective actions and produced a dry IPA product with nondetectable water (< 500 ppmw). The Adsorption model also predicted the full-scale HGPSA performance trends and was determined to be a valuable tool for process development and troubleshooting.

8. CONCLUSIONS A commercial-scale HGPSA for IPA dehydration was developed and implemented. The development process started with laboratory experiments to prove the feasibility of the HGPSA process to dehydrate a wet IPA feed containing 35 mol % water to less than the required 1.65 mol %. The experimental data were also used to validate a rigorous mathematical model and to obtain the overall mass transfer (0.0121 s−1) and heat transfer (0.01 kJ/m2·s·K) coefficients for the water−3A molecular sieve adsorption system under the operating conditions. The model with the obtained transport properties predicted the experimental results well when a volumetric purge to feed ratio greater than unity was used. The model was then used to design a commercial-scale IPA-HGPSA system, of which the bed diameter (2.286 m) was decided by the plant feed flow rate and by choosing a superficial feed velocity (6.3 cm/s) with the range of laboratory experiments (4.6−9.6 cm/ s). The height of the adsorbent bed was designed to be 4.88 m for a dry IPA water concentration of 0.5 mol %. The full-scale HGPSA was constructed in the plant with an extra 0.305 m added to the adsorbent bed height for possible feed flow fluctuations. An Aspen Adsorption model was developed to assist in identifying improvements to and troubleshooting of the process. The Adsorption simulations indicated a 10 °C feed vapor superheating was not enough to offset the heat loss from the insulation-only adsorbent beds to the environment at lower feed rates, resulting in bed temperatures dropping below the dew point. The simulations also indicated that when a sizable but not unlikely step change

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ASSOCIATED CONTENT

S Supporting Information *

HGPSA mathematical model equations and initial and boundary conditions, Tables S1 and S2 as mentioned in the text. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We gratefully acknowledge support and help by Nathan P. Francis, Walter R. Cooney Jr., and the HGPSA plant operators. We also gratefully acknowledge Lanny Robbins for his pioneering work in HGPSA processes at The Dow Chemical Company. NOMENCLATURE A = cross sectional area of PSA adsorbent bed, m BCF = bed capacity factor, % h = overall heat transfer coefficient, kJ/m2·s·K ΔH = isosteric heat of adsorption, kJ/mol IP1 = adsorption isotherm parameter, mol/kg·kPa IP2 = adsorption isotherm parameter, K−1 IP3 = adsorption isotherm parameter, kPa−1 IP4 = adsorption isotherm parameter, K−1 k = mass transfer coefficient, s−1 L = bed length, m P = pressure, kPa qi* = equilibrium amount adsorbed, mol/kg t = time, s T = temperature, °C u = interstitial velocity, m/s y = gas phase mole fraction z = axial position in the column, m

Greek Letters

ε = interstitial void fraction in the column

Subscripts

a = adsorption or feed avg = average f = feed i = index LP = light product w = water 8606

dx.doi.org/10.1021/ie500171v | Ind. Eng. Chem. Res. 2014, 53, 8599−8607

Industrial & Engineering Chemistry Research

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Article

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dx.doi.org/10.1021/ie500171v | Ind. Eng. Chem. Res. 2014, 53, 8599−8607