I NTERNATIONAL J OURNAL OF C HEMICAL R EACTOR E NGINEERING Volume 7
2009
Article A59
Optimal Design, Modeling and Simulation of an Ethanol Steam Reforming Reactor
∗
Luis E. Arteaga∗
Luis M. Peralta†
Yannay Casas‡
Daikenel Castro∗∗
Universidad Central de Las Villas,
[email protected] Universidad Central de Las Villas,
[email protected] ‡ Universidad Central de Las Villas,
[email protected] ∗∗ Universidad Central de Las Villas,
[email protected] ISSN 1542-6580 c Copyright 2009 The Berkeley Electronic Press. All rights reserved. †
Optimal Design, Modeling and Simulation of an Ethanol Steam Reforming Reactor∗ Luis E. Arteaga, Luis M. Peralta, Yannay Casas, and Daikenel Castro
Abstract The optimum design, modeling and simulation of a fixed bed multi-tube reformer for the renewable hydrogen production are carried out in the present paper. The analogies between plug flow model and a fixed bed reactor are used as design patterns. The steam reformer is designed to produce enough hydrogen to feed a 200kW fuel cell system (>2.19molH/s) and considering 85% of fuel utilization in the cell electrodes. The reactor prototype is optimized and then analyzed using a multiphysics and axisymmetric model, implemented on FEMLABM(R) where the differential mass balance by convection-diffusion and the energy balance for convection-conduction are solved. The temperature profile is controlled to maximize hydrogen production. The catalyst bed internal profiles and the effect of temperature on ethanol conversion and carbon monoxide production are discussed as well. KEYWORDS: reactor modeling, optimization, process simulation
∗
The authors wish to acknowledge to editor and unknown referees. Arteaga acknowledges the professor Miguel Laborde and the researchers of the Catalytic Productions Laboratory of Universidad de Buenos Aires and the CYTED (Projects 3303 & 332205).
Arteaga et al.: Design, Modeling and Simulation of an Ethanol Steam Reformer
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1. INTRODUCTION. Nowadays catalytic steam reforming coupled to fuel cell systems (FCS) is a new interest topic to obtain hydrogen, power and heat from non-fossil hydrocarbons or green alcohols. The mentioned above is based fundamentally on the low emissions and high efficiency levels obtained from the operation of FC systems and hydrogen combustion engines as well [1-3]. The use of bioethanol and biomass gasification for hydrogen production appears as the more attractive alternatives because of their renewable characteristics (it is part of a CO2 neutral cycle), high yield, easy handling, transportation, biodegradability and low CO2 emissions [4]. Among the technologies for hydrogen production from bioethanol, the steam reforming process (allothermal), is the most effective (thermal efficiency near to 85%), cheap and broadly used [4,5]. The ethanol steam reforming (ESR) is a highly endothermic process (∆H = +173.75 kJ/mol) [6], because of this, the development of novel heat integrated reactors using mathematical tools is a very efficient way to diminish the heat losses for the process and to maximize the hydrogen production. The heat exchangers type reactors, which facilitate the exchange of energy with a high level of efficiency, are very suitable devices for the ESR process and due to this they are discussed in the present paper [7]. The feasibility of hydrogen production from bioethanol steam reforming at temperatures higher than 500K has been proved from previous thermodynamic studies [8,9]. Furthermore these studies have shown that the increment of temperature and water/ethanol feed molar ratio (RAE), increase the hydrogen production, while high pressures reduces considerably the total yield. The bioethanol steam reforming is an endothermic reaction, so an amount of heat has to be supplied from an external source; it could be represented in the simplest case by the following stoichiometry equation:
C2 H 5 OH + 3H 2 O ⇔ 2CO2 + 6H 2
ΔHº = +173.5 kJ mol -1
Due to the high endothermic character of the ESR, catalytic materials are needed to reduce the activation energy and to produce hydrogen in a select way. At this time a wide range of catalytic materials has been investigated, but there is a special interest on the transition metals due to their stability at the reforming conditions [1]. A number of Ni-based catalysts supported on different oxides have been reported as very active and selective for the steam reforming of bio-ethanol, being demonstrated that the catalyst supports plays an important role. In the present paper a Ni/Al2O3 formulation is used, because of the stability, selectivity and cheapness reported for this catalyst [10, 11]. Although the Al2O3 acidic sites
Published by The Berkeley Electronic Press, 2009
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Vol. 7 [2009], Article A59
promotes the coke production and the activity reduction, they can be used when are doped with alkali (Na, K) [12]. Although many papers reports the process performance using different catalyst for the ESR, there are a little information referred to the general kinetic pattern, reaction mechanism and the reactor model [13,14]. Aboudehir et al. (2006) [14] has reported a fixed bed reactor simulation for the crude ethanol steam reforming, demonstrating that, when the kinetic and operational conditions are well controlled the radial dispersion factor is unimportant and the plug flow approach (PFR) can be assumed. Also the axial dispersion factor must be taken into consideration. No optimization has been reported regarding the ethanol steam reforming. The mathematical complexity of the kinetic expressions does affect the complexity of the design procedure. Due to this, detailed kinetic models must be developed joined to the reactor design by means of mathematical techniques to obtain a robust reformer design that allows obtaining the optimal process conditions and reactor configuration. In the present paper a Lagmuir-Hishelwood kinetic pattern reported previously [15] is used to obtain the optimum process conditions regarding total annualized cost. 2. STEAM REFORMER DESIGN. 2.1. Reaction Scheme and Kinetic Behavior The kinetic data offered in a previous paper [15] is used in the present communication to obtain the optimal design of a bioethanol steam reformer, working with a Ni/Al2O3 catalyst. In that paper [15], a complex reaction scheme composed by six reactions (Eq. 1 to 6) was proposed to describe the ethanol steam reforming:
CH 3CH 2 OH ⎯ ⎯→ CH 4 + CO + H 2
(1)
CO + H 2 O ⎯ ⎯→ CO2 + H 2
(2)
CH 4 + H 2 O ⎯ ⎯→ CO + 3H 2
(3)
CH 4 + 2H 2 O ⎯ ⎯→ CO2 + 4H 2
(4)
2CO ⎯ ⎯→ C + CO2
(5)
C + H 2O ⎯ ⎯→ CO + H 2
(6)
A kinetic analysis of the system was carried out and a LagmuirHishelwood model was proposed to explain the ethanol steam reforming pattern.
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Arteaga et al.: Design, Modeling and Simulation of an Ethanol Steam Reformer
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All these equations are used in the present investigation to design, optimize and simulate the steam reforming of bioethanol. 2.2. Design - Optimization Model The design of the reaction device is carried out establishing an analogy between a multitube fixed bed reactor and a plug flow ideal pattern (PFR), by means of the pseudo-homogeneous model highlighted by Smith [16]. Where: for a flow of gases that follows a PFR pattern, the following expressions of the mass, energy and pressure balances can be written at the steady state: Mass Balance
dn(z, j)
dz
= At (1 - ε )
∑α(j,i) ⋅ r(z,i)
Energy Balance Tube side: dTm (z)
dz
(7)
i
= A t (G m ⋅ Cp m )
−1
⎡4 ⋅ U t ⋅ (D to ) −1 ⋅ (Ttw (z) − Tm (z)) −⎤ ⎥ ⋅⎢ ⎢− (1 − ε )∑ α(j, i) ⋅ r(i) ⋅ ΔHr(i) ⎥ i ⎣ ⎦
Shell side:
dTg (z)
dz
[
(
2
= As ⋅ G g ⋅ Cp g D s − D to
2
)]
−1
(8)
⎡4U t ⋅ N tub ⋅ D to ⋅ (Tg (z) − Tm (z)) + ⎤ ⋅⎢ ⎥ (9) ⎢⎣+ 4U s ⋅ D s ⋅ (Tg (z) − Ta ) ⎥⎦
Pressure drop: Modified Ergun equation [17]. dP(z) = -G 2 ⋅ ρ ⋅ A 2 ⋅ D −1 ⋅ (1 - ε ) ⋅ ε −3 ⋅ 1.75 + 4.2 ⋅ Re -0.167 ⋅ (1 - ε ) m m t p dz
[
] [
][
]
(10)
The optimization is developed using a total annualized cost function (COTA), constrained by equality and inequality vectors (Gk and HI) and solved coupled to the conservation laws represented by Eq. 7 to 10. In this way a tradeoff between cost and energy savings could be established:
Pop
min
[COTA] : COTA is a complex function of the following vectors:
•
f j ( X , X, Z, p) = 0 : Gk (X, Z, p) = 0 : H l (X, Z, p) < 0
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Vol. 7 [2009], Article A59
Where: X = f (ni,T,Tg): Conversion. •
X = ∂X/∂z : is the conversion change along the axial position. Z: is the length variable. ni is the component molar flow. Z=[cpi , ki , μi, ρg, Ut, Keff, Re, Pr, …], are the independent variables p=[Dt, Dp, θ, Tg0, P0, RAE, ε, Kcat, At, Atc, Ntub…], is the design - optimization vector. [Dt, Dp, θ, fto, RAE]opt : is the parameters optimization vector. All the optimization restrictions are summarized in the Table 1. I 1 2 3 4 5 6
Table 1. Constraints used in the optimization. Constraints I Constraints (rP0 - 0.3)
