Design and Control of Stand-Alone Hydrogen Production Systems

Article pubs.acs.org/IECR

Design and Control of Stand-Alone Hydrogen Production Systems with Maximum Waste Heat Recovery Wei Wu* and Cheng-Yi Wang Department of Chemical Engineering, National Cheng Kung University, Tainan 70101, Taiwan, R.O.C. ABSTRACT: Three types of steam and autothermal reforming systems using methanol or ethanol are addressed in the Aspen Plus environment. Regarding the stand-alone energy design, the external energy supply is completely replaced by the waste heat recovery system. The optimal operating condition not only ensures the maximum hydrogen yield, but the maximum waste heat recovery can also be achieved. An autothermal reforming system using methanol is verified to achieve the lower hydrogen production cost as well as lower energy loss than other designs. To explore a simplistic single-input single-output (SISO) control structure, the closed-loop autotune variation (ATV) tests and the PID control settings are demonstrated by Aspen Dynamics. Finally, simulations show that the water flow manipulation could reject small unknown disturbances to ensure the no offset and stable output regulation. was recently investigated. Ouzounidou et al.7 provided experimental and simulated studies of the integrated and fully automated power system based on methanol as a primary fuel. Choi and Stenger8 studied the optimization of an integrated methanol reformer using several heat exchangers subject to maximum hydrogen yield and/or maximum economic profit. Moreover, Wang and Wang9 showed that the autothermal reforming system is optimized through thermodynamic equilibrium and exergetic analyses. Stamps and Gatzke10 showed that a PEMFC stack coupled to a methanol reformer with aid of a specific auxiliary power could effectively meet desired power targets. Li et al.11 developed dynamic models to validate the operation of hydrogen generation using steam reforming of methanol in a packed-bed reactor. Through experimental tests and simulation, Vadlamudi and Palanki12 studied the steadystate operating conditions of a miniaturized methanol reformer that can produce sufficient hydrogen to meet the hydrogen demand from fuel cell systems. Recently, Wu and Pai13 studied a proton exchange membrane fuel cell (PEMFC) which is directly connected to fuel processing units, and Nieto Degliuomini et al.14,15 used the plant-wide control design to analyze the performance of an integrated system which is composed of a PEMFC power system and ethanol fueled processing units. By their approaches, fuel processing units were simulated in HYSYS environment working in steady-state mode. The heat integration is applied to enhance the heat recovery, but the external energy supply system is necessary. Inspired by the review given above, a compact and standalone design of a hydrogen production system using liquid fuels is a new approach for fuel cell applications. To address the waste heat recovery design, the optimization of fuel processing units involves a specific criterion for addressing maximum waste heat recovery in order to reduce heat loss. Under the same fuel

1. INTRODUCTION Fuel reforming systems are a commonly used and economically competitive method for hydrogen production. Approximately 90% of all hydrogen is currently produced by steam reforming of natural gas, but decentralized hydrogen production plants using liquid fuels may become part of the future energy network. Methanol or ethanol is a liquid at room temperature and it can be formed either from natural gas or from biomass,1 so it can be treated as candidate fuel for small-scale reformers. Methanol has certain advantages compared to other hydrocarbons due to the low reforming temperature, ease of handling, and relatively high hydrogen−carbon ratio. Similarly, ethanol is also a potentially attractive feedstock because of its availability, nontoxicity, and handling safety. Although bioethanol may be the best candidate to be the liquid fuel of tomorrow, the hydrogen production system using the steam reforming of methanol or ethanol has been widely investigated.2 On the basis of different kinetics, specific operating conditions, and economics, there has been no answer to determine the best hydrogen production system using methanol or ethanol until now. From the aspect of kinetics, the reforming processes using different catalysts could affect the hydrogen yield, conversion efficiency, and selectivity of undesired products. Hung et al.3 provided optimal operating conditions by adjusting H2O/ethanol and O2/ethanol ratios to improve catalytic activity. In terms of catalyst utilization, Tang et al.4 showed that the autothermal reformer has superior performance over the steam reformer. From the aspect of reactor design, a compact and high-performance fuel reformer system through the optimization of reactor volume and reaction pathway was developed.5 From the aspect of energetic and economic performance, Damen et al.6 provided comparisons of different process integrations for electricity and hydrogen production. It is noted that the integrated gasification combined cycle (IGCC) plant with a shift reactor and a physical absorption unit is the most likely candidate for electricity production with strongly reduced CO2 emission. The stand-alone fuel cell power system associated with fuel processing units for stationary and potentially mobile applications © XXXX American Chemical Society

Received: March 24, 2013 Revised: July 20, 2013 Accepted: September 19, 2013

A

dx.doi.org/10.1021/ie400937t | Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

Industrial & Engineering Chemistry Research

Article

Figure 1. Stand-alone hydrogen production systems: (a) steam alcohols-to-H2 processor; (b) autothermal alcohols-to-H2 processor.

adjust the inlet temperature of the pressure swing adsorption (PSA), TPSA,in. The PSA is used to separate gas species from a mixture of gases under pressure to produce the high purity hydrogen (99.95+%). The burner is connected to the waste gas stream of the PSA to produce high-temperature flue gas. For an autothermal alcohols-to-H2 processor, Figure 1b shows that an autothermal reformer (ATR) as an adiabatic PFR takes the place of the SR in Figure 1b. Notably, the flue gas from the burner is completely used to adjust the inlet temperature of the ATR, TATR,in. Moreover, the corresponding kinetics, main reactions, and calculations are introduced as follows. 2.1. Steam Reformer. When the pure ethanol and water are fed into the SR, the reactions of steam reforming of ethanol over Co3O4/ZnO catalyst at atmospheric pressure and temperature in the 600−800 K range are shown as

processing configuration and optimization algorithms, it is verified that an autothermal reforming system using methanol can provide the lower hydrogen production cost than other designs due to lowest energy loss. On the basis of the prescribed optimal operating conditions, the Aspen Dynamics models are established within a SISO control framework. Through the closed-loop autotune variation (ATV) tests and the PID control settings, the closed-loop simulations show that the no offset and stable output regulation can be ensured by manipulating the water flow.

2. ALCOHOLS-TO-H2 PROCESSOR To construct a compact hydrogen production system without external energy providers, steam and autothermal reforming systems are depicted by Figure 1a and b, respectively. Alcohol such as methanol or ethanol is considered as a low-carbon liquid fuel. Both system configurations are similar because of the same number of mixer, heat exchanger, pressure swing adsorption (PSA), and burner. However, both designs with regard to kinetics, operating temperatures, and waste heat recovery framework are quite different. For the steam alcoholsto-H2 processor, Figure 1a shows that the pure methanol or ethanol is mixed with a preheated water flow. The outlet stream of the mixer is heated by a heat exchanger (E-101) up to a desired inlet temperature of the steam reformer (SR), TSR,in, where SR is considered as a plug flow reactor (PFR) with heating jacket. A low-temperature water-gas-shift (WGS) reactor as an adiabatic PFR is directly connected to the outlet stream of the SR to reduce CO and produce hydrogen. Another heat exchanger (E-102) is used to preheat the inlet water flow and

C2H5OH → CH3CHO + H 2 (rEt,1), 0 ΔH723K = 71 kJ/mol

(1)

C2H5OH → CO + CH4 + H 2 (rEt,2), 0 ΔH723K = 52.9 kJ/mol

(2)

CO + H 2O ↔ CO2 + H 2 (rEt,3), 0 ΔH723K = −34.5 kJ/mol

(3)

CH3CHO + 3H 2O → 2CO2 + 5H 2 (rEt,4), 0 ΔH723K = 127.4 kJ/mol

B

(4)



Article

The corresponding kinetic models of four reactions (rEt,1, ..., rEt,4) at a reference temperature of 773 K are expressed by power-law rate expressions:16 ⎛ 70 ⎛ 1 1 ⎞⎞ rEt,1 = 2.1 × 104 exp⎜⎜ − ⎜ − ⎟⎟⎟PC H OH 773 ⎠⎠ 2 5 ⎝ R ⎝ TSR rEt,2

⎛ 130 ⎛ 1 1 ⎞⎞ = 2.0 × 10 exp⎜⎜ − − ⎜ ⎟⎟⎟PC H OH 773 ⎠⎠ 2 5 ⎝ R ⎝ TSR

Table 1. Major Specifications of Steam Alcohols-to-H2 Processors

(5)

3

(6)

⎛ 70 ⎛ 1 1 ⎞⎞ rEt,3 = 1.9 × 104 exp⎜⎜ − ⎜ − ⎟⎟⎟ 773 ⎠⎠ ⎝ R ⎝ TSR ⎛ ⎜ PC2OPH2 × ⎜PCOPH2O − 4577.8 ⎜ exp T − 4.33 ⎝ SR

(

)

⎞ ⎟ ⎟ ⎟ ⎠

(8)

1. 2. 3. 4. 5.

SR unit WGS reactor E-101 E-102 Jacket

EtOH-to-H2 Processor Co3O4/ZnO: 10 kg CuO/ZnO/Al2O3: 0.8 kg

7.04 2.35 314.3 1509.9 780.0

ATR unit WGS reactor E-101 E-102

catalyst (kgcat)

volume (m3)

CuO/ZnO: 5 kg CuO/ZnO/Al2O3: 0.8 kg

2.35 2.35

UA (W/K)

2003.1 1541.6

Cat

° = −41.2 kJ/mol ΔĤ 298K (15)

The corresponding rate of reaction is expressed by18 ⎛ 47400 ⎞⎛ PCO2PH2 ⎞ rWGS = 82.2 exp⎜ − ⎟ ⎟⎜PCOPH2O − KWGS ⎠ ⎝ RTWGS ⎠⎝

CO + H 2O ↔ CO + 2H 2 (rMe,2),

(16)

where TWGS is the temperature of the WGS reactor and KWGS is the equilibrium constant of the WGS reaction shown by

(10)

° = 90.1 kJ/mol ΔH298K

ln(KWGS) =

(11)

5693.5 + 1.077 ln(TWGS) TWGS + 5.44 × 10−4TWGS − 1.125 × 10−7TWGS2 49170 − − 13.148 TWGS2 (17)

The corresponding kinetic models of three reactions (rMe,1, ..., rMe,3) are expressed by17

2.3. Pressure Swing Adsorption. On the basis of assumptions of isothermal energy balance and the Peng− Robinson equation of state for thermodynamic calculation, the pressure swing adsorption (PSA) is simulated as a “black box” with a 90% H2 recovery and 99.95% H2 purity in the product in the Aspen Plus simulation environment.19 As for fuel cell applications, the pressure swing adsorption (PSA) technology was connected to the WGS reactor in order to achieve 99.95+% purity of hydrogen by using a solid adsorbent, e.g., activated carbon. Moreover, the outlet composition of the PSA is expressed by20

(12)

⎛ 1.1 × 105 ⎞ rMe,2 = 5 × 103 exp⎜ − ⎟ RTWGS ⎠ ⎝ ⎞ ⎛ PCO2PH2 ⎟ × ⎜PCOPH2O − exp(4 × 104 /RTWGS − 3.3) ⎠ ⎝ (13)

⎛ 1.12 × 105 ⎞ rMe,3 = 9.97 × 104 exp⎜ − ⎟ RTSR ⎠ ⎝

2.35 2.35 175.1 839.6 449.9

CO + H 2O ← → CO2 + 2H 2 ,

(9)

⎛ 8.4 × 104 ⎞ rMe,1 = 1.5 × 103 exp⎜ − ⎟CCH3OHC H2O RTSR ⎠ ⎝

UA (W/K)

catalysts at atmospheric pressure and temperature in the 400−600 K range is

CH3OH + H 2O ↔ CO2 + 3H 2 (rMe,1),

CH3OH ↔ CO + 2H 2 (rMe,3),

SR unit WGS reactor E-101 E-102 Jacket

1. 2. 3. 4.

where TSR is the reformer temperature, Pi (i = C2H5OH, CO, H2O, ...) is corresponding partial pressure, and R is the universal gas constant. If the pure methanol in place of ethanol is fed into the same SR, the reactions of steam reforming of methanol over CuO/MnO catalysts at atmospheric pressure and temperature in the 600−700 K range are shown as

° = −41.2 kJ/mol ΔH298K

1. 2. 3. 4. 5.

MeOH-to-H2 Processor CuO/MnO: 4 kg CuO/ZnO/Al2O3: 0.8 kg

Table 2. Major Unit Specifications for Autothermal MeOH-to-H2 Processor

(7)

⎛ 98 ⎛ 1 1 ⎞⎞ rEt,4 = 2.0 × 105 exp⎜⎜ − ⎜ − ⎟⎟⎟(PCH3CHOPH2O3) 773 ⎠⎠ ⎝ R ⎝ TSR

° = 49 kJ/mol ΔH298K

volume (m3)

catalyst (kgcat)

yH ,PSA,out = SPSA

(14)

(18)

2

2.2. Water-Gas-Shift Reactor. Since the outlet stream of the SR contains a large amount of CO and other components, a water-gas-shift (WGS) reactor with packed catalyst is connected to convert part of the components, CO and H2O, into CO2 and H2. The WGS reaction over CuO/ZnO/Al2O3

yi ,PSA,out = (1 − SPSA )

yi ,PSA,in ∑ yi ,PSA,in

i = CH3OH, CO, CO2 , H 2O C

, (19)



Article

Figure 2. Autothermal MeOH-to-H2 processor with respect to TATR,in at different S/C: (a) hydrogen production rate; (b) corresponding UA.

and the outlet composition of the PSA waste gas is shown by yi ,PSA,in , yi ,PSA,out = ̃ ∑ yi ,PSA,in + (1 − SPSA ) × yH ,PSA,in 2

i = H 2 , CH3OH, CO, CO2 , H 2O

(20)

where SPSA (=99.95%) represents the hydrogen purity from the PSA. 2.4. Burner. The burner could produce high temperature flue gas as a self-sufficient waste heat source. Assumptions include the following: (i) H2 fed into the burner is adjustable, (ii) all components of the burner are completely burned, and (iii) the Aspen module is considered as an adiabatic and stoichiometric reactor. For the steam alcohols-to-H2 processor, the air flow is directly fed into the burner in which the combustion reactions are shown as follows:

Figure 3. Optimization of autothermal MeOH-to-H2 processor. D



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Figure 4. Comparisons of three hydrogen processors with respect to S/C: (a) hydrogen yield; (b) ratio of H2 to CO2; (c) hydrogen production costs.

C2H5OH + 3O2 → 2CO2 + 3H 2O, ° = −1279 kJ/mol ΔĤ 298K

CH4 + 2O2 → CO2 + 2H 2O, ° = −803.1 kJ/mol ΔĤ 298K

(21)

E

(22)



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Notably, the combustion reactions in the EtOH-to-H 2 processor include eqs 21−24, and the combustion reactions in the MeOH-to-H2 processor include eqs 23−25. For the autothermal alcohols-to-H2 processor, the air flow is fed into the ATR and the unreacted oxygen and other components are fed into the burner with the same combustion reactions shown in eqs 21−25. 2.5. Autothermal Reformer. The autothermal reforming of methanol over CuO/ZnO catalysts is proceeding in the ATR, where the following methanol partial oxidation reaction is added in the steam reforming of methanol

Table 3. Hydrogen Benefits in Three Types of Hydrogen Processors EtOH-to-H2 processor at S/C|I = 2 maximum hydrogen yield H2/CO2 H2 cost

H2 +

CO +

MeOH-to-H2 processor at S/C|II = 0.5

72.1%

79.2% (=23.77/30)

2.163 2.377 8.16 USD/mol 5.86 USD/mol

autothermal MeOH-to-H2 processor at S/C|III = 0.75 81.8% 2.455 5.67 USD/mol

1 O2 → H 2O, 2

° = −242 kJ/mol ΔĤ 298K

1 O2 → CO2 , 2

° = −283 kJ/mol ΔĤ 298K

CH3OH +

(23)

CH3OH +

° = −193 kJ/mol ΔH298K

(24)

The corresponding kinetic models is expressed by

3 O2 → CO2 + 2H 2O, 2

° = −667 kJ/mol ΔĤ 298K

1 O2 → CO2 + 2H 2 (rMe,4), 2 (26) 16

⎛ 8.5 × 104 ⎞ rMe,4 = 1.3 × 104 exp⎜ − ⎟CCH3OHCO2 RTSR ⎠ ⎝

(25)

(27)

Figure 5. Optimization and steady-state simulation of steam alcohols-to-H2 processor: (a) EtOH-to-H2 processor; (b) MeOH-to-H2 processor. F



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Figure 6. Optimization and steady-state simulation of autothermal MeOH-to-H2 processor.

Notably, their kinetic models for the autothermal reforming of methanol are described by eqs 12−14 and 22. In general, the operating temperature range is from 600 to 900 K. For simplicity, the kinetic model for the autothermal reforming of ethanol is omitted. 2.6. Simulation. Regarding the Aspen Plus simulation of both H2 processors, the pressure drops of all PFRs are fixed at 0.1 atm, kinetic models are referred to as the temperature dependence of the Langmuir−Hinshelwood−Hougen−Watson (LHHW) model, and the Peng−Robinson model describes

Table 4. Energy Recovery in Hydrogen Processors

flue gas flow rate Tflue,out TSR,out TSR,in TATR,in (Tin,max) waste heat loss

EtOH-to-H2 processor

MeOH-to-H2 processor

2.3 × 103 kg/h 635 K 634 K 600 K

1.5 × 103 kg/h 519 K 518 K 500 K

1.2 × 106 kJ/h

3.8 × 105 kJ/h

autothermal MeOH-to-H2 processor 1.1 × 103 kg/h 321 K

833.9 K 1.8 × 105 kJ/h

Figure 7. Open-loop tests of autothermal MeOH-to-H2 processor with ±6% step changes of water flow rate: (a) responses of hydrogen production rate; (b) corresponding TATR,in; (c) corresponding Tflue,out; (d) corresponding TPSA,in. G



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Figure 8. Dynamic simulation of the autothermal MeOH-to-H2 processor using a water flow manipulator.

Figure 9. PID control of the autothermal MeOH-to-H2 processor: (a) responses of ATV tests; (b) tuning parameter options and results.

Tflue,out, is affected by both the inlet temperature of the SR (TSR,in) and the outlet temperature of the SR (TSR,out). Since the residual heat for a heating jacket or the inlet temperature of a heating jacket depends on the temperature effect of TSR,in, the waste heat recovery performance is restricted by the temperature cross between Tflue,out and TSR,out. For the autothermal alcohols-to-hydrogen processor, the heating

thermodynamic properties of species. The heat integration design is compact which is composed of the water feed flow as a cold stream and the high-temperature flue gas from the burner as a hot stream. Therefore, the specifications of heat exchangers would dominate operating temperatures, e.g., TSR,in, TPSA,in, and TATR,in. Remark 1: For the steam alcohols-to-hydrogen processor, the outlet temperature of flue gas from the heating jacket, H



Article

Figure 10. Set point tracking tests for autothermal MeOH-to-H2 processor: (a) responses of hydrogen production rate; (b) corresponding TATR,in; (c) corresponding Tflue,out; (d) manipulated water flow rate.

jacket is removed and TATR,in is completely adjusted by a heat exchanger.

(Tflue,out − TSR,out)|I,II ≥ ΔTmin|I,II

where FidH2 is denoted as the theoretical H2 production rate, the decision variable S/C|i represents the feed ratio of design i, which is bounded by eq 24, and ΔTmin|i is the minimum temperature difference between Tflue,out and TSR,out. If ΔTmin|i exists, the corresponding Tflue,out can be minimized. It implies that the maximum waste heat recovery is achieved. In our approach, ΔTmin|i is assumed to be equal to 1 °C. To address the maximum waste heat recovery of design III, the following optimization algorithm for maximizing TATR,in is expressed as

3. OPTIMIZATION For steam and autothermal alcohols-to-H2 processors, the EtOH-to-H2 processor, the MeOH-to-H2 processor, and the autothermal MeOH-to-H2 processor are specified as designs I, II, and III, respectively. The corresponding specifications are shown in Tables 1 and 2. In our approach, the sequential quadratic programming (SQP) method is adopted to solve the optimization problems in the Aspen Plus environment. The optimization strategies with regard to minimum waste heat loss or maximum waste heat recovery are addressed as follows. 3.1. Maximum Heat Recovery. The following optimization algorithm for maximizing the hydrogen yield of designs I and II is described by max Ji = S/C|i

FH2 FHid2

,

max TATR,in UA

(31)

subject to UA ≤ UA u

(32)

It was assumed that TATR,in is adjusted by changing the heat capacity of exchanger (UA), and UAu represents an ultimate bound of UA. If the maximum inlet temperature of the ATR (Tin,max) is achieved, i.e., TATR,in → Tin,max, by increasing UA, then the corresponding Tflue,out would be close to the minimum value. It implies that the maximum waste heat recovery at design III is achieved. Figure 2a shows that the largest hydrogen yield appears at the prescribed inlet temperature (Tin,max) at

i = I, II (28)

subject to 0.4 ≤ S/C|I ≤ 1.5 1.5 ≤ S/C|II ≤ 5

(30)

(29) I



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Figure 11. Autothermal MeOH-to-H2 processor with ±10° C perturbations in the inlet temperature of water flow: (a) responses of hydrogen production rate; (b) corresponding TATR,in; (c) corresponding Tflue,out; (d) manipulated water flow rate.

hydrogen yield, the optimal operating conditions, S/C|III = 0.75 and Tin,max = 833.9 K, are determined. 3.2. Comparisons. To verify the performance of the three illustrated hydrogen processors with the same low-cost catalyst and reactor geometry, three references including hydrogen yield, the ratio of H2 to CO2 (H2/CO2), and the cost of H2 production are given. Parts a and b of Figure 4 show that the autothermal MeOH-to-H2 processor can ensure the largest hydrogen yield and the ratio of H2/CO2 at prescribed S/C than other designs. Referring to the Web site of ICIS21 to access chemical pricing information, the international prices of methanol and ethanol are quoted about 435 and 770 USD/ton, respectively. Figure 4c shows the cost of H2 production (USD/kmol H2) at different S/C when the methanol and ethanol flows are all 10 kmol/h. By comparisons of the three designs shown in Table 3, the autothermal MeOH-to-H2 processor can provide the best hydrogen costs due to highest hydrogen yield and lower price. On the basis of the above optimization algorithms, the above designs with optimal operating conditions using Aspen Plus are shown in Figures 5 and 6, respectively. Regarding the waste heat recovery performance, the corresponding Tflue,out and flue gas flow rate are shown in Table 4. Notably, the autothermal MeOH-to-H2 processor can ensure a lowest flue gas temperature and lowest waste heat loss as well.

different feed ratios of S/C. The corresponding profiles of UA are shown in Figure 2b. Notably, TATR,in = Tin,max can be found if UA → UAu. UAu is too large to be realized. Tin,max cannot be achieved either. By our approach, a feasible operating line (purple line) shown in Figure 2b is used to determine proper UA and TATR,in. Furthermore, the optimization algorithm for maximizing the hydrogen yield of design III is expressed by max JIII =

S/C|III

FH2 FHid2

(33)

subject to

0.5 ≤ S/C|III ≤ 3 TATR,in = Tin,max

(34)

where the decision variable S/C|III represents the feed ratio of design III which is bounded by eq 34. Tin,max is obtained by solving eq 31. In Figure 3, the increase of water flow could improve the methanol conversion if S/C|III ≤ 0.75, but the higher value of S/C|III (e.g., S/C|III > 0.75) induces the rapid decay of Tin,max because the waste heat supply from the burner is insufficient. By the above optimization algorithms for maximizing J



Article

Figure 12. Autothermal MeOH-to-H2 processor with ±3% perturbations in the inlet temperature of methanol flow: (a) responses of hydrogen production rate; (b) corresponding TATR,in; (c) corresponding Tflue,out; (d) corresponding control by manipulating water flow rate.

affected by manipulating the water feed flow. Compared to the methanol flow manipulation, it is an economic and easy choice. Figure 8 shows that a feedback control framework is added in this dynamic system in the Aspen Dynamics environment. In our approach, a closed-loop ATV test is used to determine the ultimate period and gain when the hydrogen production rate and water flow rate are treated as the controlled output and manipulated input, respectively. The default value of the relay output amplitude is 5%. Figure 9a shows that the ATV test is finished after several (4−6) cycles. Figure 9b shows that the PID controller settings are determined by the Ziegler−Nichols tuning rule. For the test of set point tracking using PID control, Figure 10a shows that the no offset set point tracking can be achieved and the undesired oscillation can be suppressed by the large and rapid control actions in Figure 10d. The corresponding temperature profiles of TATR,in and Tflue,out are depicted in Figure 10b and c, respectively. For the tests of disturbance rejection, Figure 11a shows that the stable, no offset output regulation is achieved while ±10 °C step changes of inlet temperature of water flow are considered. The corresponding control action by manipulating water flow rate is depicted in Figure 11d. Moreover, Figure 12a shows that the stable, no offset output regulation is achieved while ±3% step changes of inlet temperature of methanol flow are considered.

4. PROCESS CONTROL Aspen Dynamics are employed to model the autothermal MeOH-to-H2 processor with prescribed steady-state initials. According to the optimal inlet conditions by design III, Figure 6 shows that three inlet flow rates for methanol, water, and oxygen are kept at a fixed ratio, e.g., CH3OH:H2O:O2 = 10:7.5:5. For a feasible dynamic manipulation, the impractical UAu should be restricted by the purple line in Figure 2b. Whereas Tin,max = 833.9 K cannot be achieved, TATR,in = 799.4 K could be determined by an acceptable value of UA (=463.2 K/W) for heat exchanger (E-101). The steady-state simulation shows that the hydrogen production rate would be changed from 24.55 to 22.36 kmol/h. Moreover, the open-loop tests of the system dynamics with respect to ±6% step changes of water flow are shown in Figure 7. Owing to the position of water feed flow near the PSA unit, the step changes of water feed flow may cause undesired transient responses of hydrogen production rate, which is depicted by Figure 7a. Notably, the increase of water feed flow indirectly reduces the hydrogen production rate because temperatures of TATR,in and TPSA,in, which are depicted by Figure 7b and d, respectively, decrease. The temperature of Tflue,out shown in Figure 7c raises, but waste heat loss increases. It is verified that the hydrogen production rate can be K



Article

(10) Stamps, A. T.; Gatzke, E. P. Dynamic modeling of a methanol reformerPEMFC stack system for analysis and design. J. Power Sources 2006, 161, 356. (11) Li, M.; Duraiswamy, K.; Knobbe, M. Adsorption enhanced steam reforming of methanol for hydrogen generation in conjunction with fuel cell: Process design and reactor dynamics. Chem. Eng. Sci. 2012, 67, 26. (12) Vadlamudi, V. K.; Palanki, S. Modeling and analysis of miniaturized methanol reformer for fuel cell powered mobile applications. Int. J. Hydrogen Energy 2011, 36, 3364. (13) Wu, W.; Pai, C.-C. Control of a heat-integrated proton exchange membrane fuel cell system with methanol reforming. J. Power Sources 2009, 194, 920. (14) Nieto Degliuomini, L.; Biset, S.; Luppi, P.; Basualdo, M. S. A rigorous computational model for hydrogen production from bioethanol to feed a fuel cell stack. Int. J. Hydrogen Energy 2012, 37, 3108. (15) Nieto Degliuomini, L.; Zumoffen, D.; Basualdo, M. Plant-wide control design for fuel processor system with PEMFC. Int. J. Hydrogen Energy 2012, 37, 14801. (16) Uriza, I.; Arzamendi, G.; Lopez, E.; Llorca, J.; Gandia, L. M. Computational fluid dynamics simulation of ethanol steam reforming in catalytic wall microchannel. Chem. Eng. J. 2011, 167, 603. (17) Amphlett, J. C.; Creber, K. A. M.; Davis, J. M.; Mann, R. F.; Peppley, B. A.; Stokes, D. M. Hydrogen production by steam reforming of methanol for polymer electrolyte fuel cells. Int. J. Hydrogen Energy 1994, 19, 131. (18) Choi, Y.; Stenger, H. G. Water gas shift reaction kinetics and reactor modeling for fuel cell grade hydrogen. J. Power Sources 2003, 124, 432. (19) Mivechian, A.; Pakizeh, M. Hydrogen recovery from Tehran refinery off-gas using pressure swing adsorption, gas absorption and membrane separation technologies: Simulation and economic evaluation. Korean J. Chem. Eng. 2013, 30, 432. (20) Rajesh, J. K.; Gupta, S. K.; Rangaiah, G. P.; Ray, A. K. Multiobjective optimization of industrial hydrogen plants. Chem. Eng. Sci. 2001, 56, 999. (21) http://www.icispricing.com/il_home.asp.

By the above simulation results, a SISO control framework by manipulating the water feed low rate could cope with slight set point changes and small unknown perturbations.

5. CONCLUSIONS In this paper, the steam or autothermal reforming system is developed as a compact and stand-alone hydrogen production system in the Aspen Plus environment. On the basis of the optimization strategy for maximizing hydrogen yield subject to constraints of maximum waste heat recovery, an autothermal reforming system using methanol with respect to hydrogen production costs and waste heat loss is superior to other designs. To explore a simplistic control structure, a PID controller via ATV tests is adopted. The closed-loop control tests show that the small unknown perturbations that appeared in the hydrogen production rate can be asymptotically eliminated by manipulating water feed flow. Although the water flow manipulation could ensure the no offset and stable output regulation, the whole control system robustness is weak if large set point changes or significant disturbances are considered. Therefore, the multivariable control strategy is recommended in the future study.

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AUTHOR INFORMATION

Corresponding Author

*Tel.: +886 6 2757575. Fax: +886 6 2344496. E-mail: weiwu@ mail.ncku.edu.tw. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors would like to thank the National Science Council of the Republic of China for financially supporting this research under Contract No. NSC 101-2211-E-006-218.

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REFERENCES

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