Dynamic modeling and optimization of a novel methanol synthesis loop with hydrogen-permselective membrane reactor

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    Engineering, Shiraz University, Shiraz 71345, Iran

    a r t i c l e i n f o

    Article history:

    Many chemical process systems consist of a reactor and

    be considered in detail because the chemical species present

    in the reactor effluent determine the separation section.

    Hence, reactor modeling, sizing, and control are considered

    before separation is addressed.

    start-up and shut-down investigations, system identification,

    the solution of a steady-state model.

    The application of membrane conversion technology in

    chemical reaction processes is now mainly focused on reac-

    tion systems containing hydrogen and oxygen, and is based

    * Corresponding author. Tel.: 98 7112303071; fax: 98 7116287294.

    Avai lab le a t www.sc iencedi rec t .com


    i n t e r n a t i o n a l j o u rn a l o f h y d r o g e n en e r g y 3 4 ( 2 0 0 9 ) 3 7 1 7 3 7 3 3E-mail address: rahimpor@shirazu.ac.ir (M.R. Rahimpour).a separation unit. These unit operations are considered to be

    the core of a chemical process. The behavior of reactor

    separatorrecycle systems is relevant for integrating concep-

    tual design and plant wide control at an early stage of

    conceptual design, when the recycle structure of the flow

    sheet is established. At this point, the reactor is the first unit to

    safety, control, optimization, and transient behavior and

    operability studies [1]. The dynamic simulation is preferred to

    steady-state simulations in operability studies since the

    former provides a realistic description of the transient states

    of the loop owing to the fact that the numerical solution

    strategies employed in dynamic models are more robust than1. Introduction The dynamic simulation of methanol synthesis processes,in particular, has a wide range applications including; theReceived 12 January 2009

    Received in revised form

    19 February 2009

    Accepted 21 February 2009

    Available online 7 April 2009


    Dynamic optimization

    Reactor loop

    PdAg membrane

    Catalyst deactivation

    Differential evolution method0360-3199/$ see front matter 2009 Interndoi:10.1016/j.ijhydene.2009.02.062a b s t r a c t

    In this paper, typical and PdAg membrane methanol loop reactors have been analyzed. In

    the proposed models all basic equipments in the methanol loop were included. Detailed

    dynamic models described by set of ordinary differential and algebraic equations were

    developed to predict the behavior of the overall processes. The conventional model was

    validated against plant data, and then the results of the hydrogen-permselective

    membrane loop are compared with the conventional model. Using this novel model,

    diffusion by membrane tubes compensates reduction of production rate due to catalyst

    deactivation. By use of the membrane model, dynamic optimization of temperatures was

    performed for improving overall methanol production. Here, differential evolution (DE)

    method was applied as powerful method for optimization of procedure. Optimal inlet

    temperatures of membrane tube, steam drum and both of them were determined. The

    optimization approaches enhanced additional yield throughout 4 years of operation as

    catalyst lifetime. Therefore, the methanol synthesis loop can be deduced to redesign based

    on this study.

    2009 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rightsreserved.Chemical Engineering Department, School of Chemical and PetroleumP. Parvasi, A. Khosravanipour Mostafazadeh, M.R. Rahimpour*Dynamic modeling and optimsynthesis loop with hydrogenreactor

    j ourna l homepage : wwational Association for Hation of a novel methanolermselective membrane

    e lsev ier . com/ loca te /heydrogen Energy. Published by Elsevier Ltd. All rights reserved.

  • i n t e r n a t i on a l j o u r n a l o f h y d r o g e n en e r g y 3 4 ( 2 0 0 9 ) 3 7 1 7 3 7 3 33718Nomenclature

    a activity of catalyst,

    Ac cross area of reactor (m),

    cpg specific heat of the gas at constant pressure

    (J kgmol1 K1),cps specific heat of the solid at constant pressure

    (J kgmol1 K1),Cj concentration of component j in the fluid phase

    (kgmol m3),Cjs concentration of component j in the solid phase

    (kgmol m3),ct Total concentration (mol m


    dp particle diameter (m),

    Di tube outside diameter (m),

    Derj diffusion coefficient of component j in the mixture

    (m2 s1),Ed activation energy used in the deactivation modelon inorganic membranes such as Pd and ceramic membranes

    [2]. In many hydrogen-related reaction systems, Pdalloy

    membranes on a stainless steel support were used as the

    hydrogen permeable membrane [3]. It is also well known that

    the use of pure palladium membranes is hindered by the fact

    that palladium shows a transition from the a-phase

    (hydrogen-poor) to the b-phase (hydrogen-rich) at tempera-

    tures below 300 C and pressures below 2 MPa, depending onthe hydrogen concentration in the metal. Since the lattice

    constant of the b-phase is 3% larger than that of the a-phase,

    this transition leads to lattice strain and, consequently, after

    a few cycles, to a distortion of the metal lattice [4]. Alloying the

    palladium, especially with silver, reduces the critical

    temperature for this embitterment and leads to an increase in

    the hydrogen permeability. The highest hydrogen perme-

    ability was observed at an alloy composition of 23 wt% silver

    [5]. Palladium-based membranes have been used for decades

    in hydrogen extraction because of their high permeability and

    good surface properties and because palladium, is 100%

    selective for hydrogen transport [6]. These membranes

    (J kgmol1),fj partial fugacity of component j (bar),

    Ft Total molar flow rate per tube (mol s1),

    hf gassolid heat transfer coefficient (W m2 K1),

    DH298i enthalpy of reaction i at 298 K,

    k1 reaction rate constant for the 1st rate equation

    (mol kg1 s1 bar1/2),

    k2 reaction rate constant for the 2nd rate equation

    (mol kg1 s1 bar1/2),k3 reaction rate constant for the 3rd rate equation

    (mol kg1 s1 bar1/2),kjg mass transfer coefficient for component j (m s

    1),keff conductivity of fluid phase (W m

    1 K1),Kd deactivation model parameter constant (s

    1),Kj adsorption equilibrium constant for component j

    (bar1),Kpi equilibrium constant based on partial pressure for

    component i,

    M number of reactions,

    N number of components,P Total pressure (bar),

    r radial coordinate (m),

    r1 rate of reaction for hydrogenation of CO

    (kgmol m3 s1),

    r2 rate of reaction for hydrogenation of CO2(kgmol m3 s1),

    r3 reaction rate constant for the 3rd rate equation

    (kgmol m3 s1),ri reaction rate of component i (kgmol m

    3 s1),R universal gas constant (J kgmol1 K1),Ri inner diameter of reactor (m),

    Ro outer diameter of reactor (m),

    t time (s),

    T bulk gas phase temperature (K),

    TR reference temperature used in the deactivation

    model (K),

    Ts temperature of solid phase (K),combine excellent hydrogen transport and discrimination

    properties with resistance to high temperatures, corrosion,

    and solvents. Key requirements for the successful develop-

    ment of palladium-based membranes are low costs as well as

    permselectivity combined with good mechanical, thermal and

    long-term stability [7]. These properties make palladium-

    based membranes such as PdAg membranes very attractive

    for use with petrochemical gases. A thin palladium or palla-

    dium-based alloy layer is prepared on the surface or inside the

    pores of porous supports. Many researchers have developed

    supporting structures for palladium or palladium-based alloy

    membranes. The materials in commercial use for porous

    supports are: ceramics, stainless steel and glass. The

    membrane support should be porous, smooth-faced, highly

    permeable, thermally stable and metal adhesive [8].

    Like in the world of modeling, the field of dynamic opti-

    mization has its own jargon to address specific characteristics

    of the problem. Most optimization problems in process

    industry can be characterized as non-convex, non-linear, and

    constrained optimization problems. For plant optimization,

    Tshell temperature of coolant stream (K),

    ushell overall heat transfer coefficient between coolant

    and process streams (W m1 s1),ur radial velocity of fluid phase (m s

    1),V total volume of reactor (m3),

    z axial reactor coordinate.

    Greek letters

    3 void fraction of catalytic bed,

    3s void fraction of catalyst,

    v stoichiometric coefficient,

    h catalyst effectiveness factor,

    r density of catalytic bed (kg m3),rs density of catalyst (kg m


    Superscripts and subscripts

    0 inlet conditions,

    i reaction number index (1, 2 or 3),

    j number of components,

    s at catalyst surface,

    ss initial conditions (i.e., steady-state condition).

  • Ag (23 wt% Ag) wall to hydrogen has been used. This work

    showed how FBMR can be useful for catalytic naphtha

    i n t e r n a t i o n a l j o u rn a l o f h y d r o g e n en e r g y 3 4 ( 2 0 0 9 ) 3 7 1 7 3 7 3 3 3719typical optimization parameters are equipment size, recycle

    flows and operating conditions like temperature, pressure and

    concentration. An optimum design is based on the best or

    most favorable conditions. In almost every case, these

    optimum conditions can ultimately be reduced to a consider-

    ation of costs or profits. Thus an optimum economic design

    could be based on conditions giving the least cost per unit of

    time or the maximum profit per unit of production. When one

    design variable is changed, it is often found that some costs

    increase and others decrease. Under these conditions, the

    total cost may go through a minimum at one value of the

    particular design variable, and this value would be considered

    as an optimum. A number of search algorithms methods for

    dealing with optimization problems have been proposed in

    the last few years in the fields of evolutionary programming

    (EP) [9], evolution strategies (ES) [10], genetic algorithms (GA)

    [11] and particle swarm optimization (PSO) [12].

    DE algorithm is a stochastic optimization method mini-

    mizing an objective function that can model the problems

    objectives while incorporating constraints. The algorithm

    mainly has three advantages; finding the true global minimum

    regardless of the initial parameter values, fast convergence, and

    using a few control parameters. Being simple, fast, easy to use,

    veryeasilyadaptable for integerand discreteoptimization,quite

    effective in non-linear constraint optimization including

    penalty functions and useful for optimizing multi-modal search

    spaces are the other important features of DE [13].

    Several works have been performed on application of PdAg

    membrane reactors. Rahimpour and Ghader [14] investigated

    PdAg membrane reactor performance for methanol

    synthesis. They considered steady-state homogeneous model

    for methanol reactor. Rahimpour and Lotfinejad [15] presented

    dynamic model for studying PdAg dual-type membrane

    reactor for methanol production. They showed methanol

    production can be increased in membrane dual-type reactor.

    Rahimpour and Lotfinejad [16] compared co-current and

    counter-current modes of operation for a membrane dual-type

    methanol reactor. Khosravanipour Mostafazadeh and Rahim-

    pour [17] proposed a PdAg membrane catalytic bed for

    naphtha reforming. Rahimpour et al. [18] suggested a new

    approach to improve the methanol production in an industrial

    single methanol synthesis reactor by applying selective

    permeation of hydrogen from synthesis gas and adding it to the

    reaction side. They considered quasi-steady-state model for

    simulation of membrane methanol reactor and also they

    modeled single reactor without considering the loop. Rahim-

    pour and Alizadehhesari [19] developed fluidized-bed

    membrane reactor for methanol synthesis. Recently, Rahim-

    pour and Alizadehhesari [20] developed a model of membrane

    methanol reactor for increasing carbon dioxide removal.

    Rahimpour and Elekaei Behjati presented a novel fluidized-bed

    hydrogen-permselective membrane reactor [21]. Iulianelli

    et al. [22] investigated CO-free hydrogen production by steam

    reforming of acetic acid in a PdAg membrane-assisted reactor.

    The goal of this study was to perform the AASR reaction in a Pd

    Ag MR in order to study the acts for hydrogen selectivity,

    hydrogen yield and CO-free hydrogen recovery by varying the

    mode of operation, the reaction pressure and the sweep factor.Tostia et al. [23] performed a study on design and process of Pd

    membrane reactors. In that research, the permeator tube wasreforming by enhancement of aromatic production, increase of

    catalyst activity and hydrogen production. Gallucci et al. [25]

    presented co-current and counter-current modes for ethanol

    steam reforming in a dense PdAg membrane reactor. In their

    work a conventional and a palladium membrane reactor

    packed with a CO-based catalyst was modeled and the results

    for both co-current and counter-current modes of operation

    are showed in terms of ethanol conversion and molar fraction

    versus temperature, pressure, the molar feed flow rate ratio

    and axial coordinate. Molaei Dehkordi and Memari [26] did

    a compartment model for methane steam reforming in

    a membrane bubbling fluidized-bed reactor. A compartment

    model for methane steam reforming was performed to illus-

    trate the flow pattern of gas contained by the dense region of

    a membrane fluidized-bed reactor, in the bubbling configura-

    tion both with (adiabatic) and without (isothermal) inflowing


    Also several researches were performed on reactor

    modeling and optimization of methanol synthesis. Rahim-

    pour and Elekaei Behjati [27] simulated and optimized

    membrane dual-type methanol reactor. Parvasi et al. [28]

    simulated a dynamic methanol loop in the presence of cata-

    lyst deactivation. Askari et al. [29] optimized dual-type

    methanol reactor using genetic algorithm.

    The previous studies focused on optimization and appli-

    cation of membrane reactor for methanol production without

    considering the role of the loop of methanol synthesis. The

    purpose of this work is to study the typical and membrane

    synthesis loop with exothermic, high-pressure gas phase

    chemical reactor systems for methanol production and finally

    optimization of membrane loop parameters using DE method

    as a strong method of optimization.

    2. Description of methanol synthesis loops

    2.1. Conventional process

    Fig. 1 shows a typical real methanol synthesis loop in Shiraz

    Petrochemical Complex. Methanol synthesis is generally

    performed by passing synthesis gas comprising hydrogen,

    carbon oxides and any inert gasses like nitrogen at an elevated

    temperature and pressure through one or more beds of

    a methanol synthesis catalyst, which is often a copper-con-planned that permits the free elongation and contraction of the

    palladium alloy tube keeping away from any mechanical

    stress. The different patterns of Pd membrane reactors applied

    for separating pure hydrogen are explained and a membrane

    process for producing highly pure hydrogen from ethanol

    reforming is also implemented. Rahimpour [24] studied on

    hydrogen production in a fluidized-bed membrane reactor for

    naphtha reforming. In aforesaid work, a novel fluidized-bed

    membrane reactor (FBMR) for naphtha reforming in the pres-

    ence of catalyst deactivati...


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