Methanol to Propylene Process in a Moving Bed Reactor with

Subscriber access provided by NORTH DAKOTA STATE UNIV

Article

Methanol to propylene process in a moving bed reactor with byproducts recycling: kinetic study and reactor simulation Binbo Jiang, Xiang Feng, Lixia Yan, Yuntao Jiang, Zuwei Liao, Jingdai Wang, and Yongrong Yang Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/ie500250d • Publication Date (Web): 28 Feb 2014 Downloaded from http://pubs.acs.org on March 13, 2014

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Industrial & Engineering Chemistry Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Industrial & Engineering Chemistry Research

Methanol to propylene process in a moving bed reactor with byproducts recycling: kinetic study and reactor simulation Binbo Jiang, Xiang Feng, Lixia Yan, Yuntao Jiang, Zuwei Liao*, Jingdai Wang, Yongrong Yang State Key Laboratory of Chemical Engineering, Department of Chemical and Biological Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, P.R. China

Abstract A moving bed reactor concept was introduced to the methanol to propylene process. To implement this concept, a modified kinetic model of MTP reaction on a special HZSM-5 pellet catalyst was developed; a moving bed reactor model in propylene yield platform period was proposed. Applying these two models, a two-stage moving bed in series with methanol quenched between stages was investigated. Water and part of the byproducts were recycled to the first moving bed reactor. Simulation results showed that the recycle of byproducts could increase the yield of propylene to 70%. The roles of higher hydrocarbons in the two reactors were different since they acted as reactants in the first moving bed while as products in the second one. 1. Introduction Olefin and liquid fuel products are essential for satisfying the global material and energy demands. Currently, both of the products mainly rely on a single carbon source – crude oil, while their demands have increased rapidly in recent years. Since the oil price is becoming expensive, alternative routes from relatively cheap carbon sources such as coal, natural gas, and biomass are frequently considered. Among the alternative routes, the process of converting methanol to olefin and gasoline is a

ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

promising and general technology, because methanol can be produced by any gasifiable carbon sources. The typical methanol converting process is on an acid zeolite catalyst, which was firstly found by two independent Mobil research groups in 1970s.1, 2 After decades of screening the topologies, compositions and morphologies of the zeolite, two zeolites became industrialized. They are SAPO-34 and HZSM-5. According to the different characteristics of the two catalysts and the reaction conditions, the industrialized processes differ in products as shown in Table 1. During the past decades, kinetic modeling of MTP/MTO has been the research focus of both in industry and academic. A review on the catalytic materials and their behavior in methanol to hydrocarbon reactions has been given by Stöcker3 while Keil4 summarized the process technology including lumped kinetic models of this reaction. Ilias and Bhan5 reviewed the mechanism of methanol to hydrocarbons and Olsbye et al.6 described how zeolite cavity and pore size impacted on the shape selectivity. Single-event kinetic model, which describes the reaction in every elementary step on the catalyst surface, was firstly proposed by Froment7 and has been widely used in the kinetic modeling of MTO process.8-11 Park and Froment8, 9 have used carbenium ion mechanism to formulate detailed kinetic models which contained more than 30 parameters for MTO reaction over HZSM-5. Alwahabi and Froment10, 12 implemented this mechanism to MTO reaction on SAPO-34 and designed several conceptual reactors for MTO reaction on both catalysts. Kumar et al.11 developed a single-event microkinetic model based on the aromatic hydrocarbon pool mechanism on HZSM-5


Page 2 of 38

Page 3 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


catalyst firstly. However, methanol to olefin is a complex process whose products cover the range from C1 to C10+ hydrocarbons, and each component may undergo some of the following reactions like methylation, cracking, hydrogenation, dehydrogenation, oligomerization-cracking and so on.13 Because of the complex reaction network and large number of components involved, establishing a detailed kinetic model seems a little bit difficult. Low accuracy and high complexity due to the huge number of fit parameters and high degrees of freedom owing to the lack of experiment data are still the disadvantages that hinder their practical application. On the other hand, lumped kinetic models supplement these drawbacks well. Lumped kinetic models classify the reactants and products into certain groups and treat each group as a pseudo component that is named “a lump”, have been proven to be effective and useful in treating methanol to hydrocarbons reactions.14-31 Chen et al.14 firstly proposed a lumped kinetic model on the basis of autocatalytic mechanism and was modified by Chang15 who added a bimolecular reaction step. Bos et al.16 developed a model for MTO over SAPO-34, in which they treated coke as an individual component so that conversion and selectivities were all influenced by the formation of coke. Najafabadi et al.17 built a new kinetic model for MTO reaction on SAPO-34 and investigated different operation conditions on the reaction performance. A group from Bilbao at Universidad del País Vasco have widely researched the reaction and deactivation kinetics with the consideration of water in the kinetic models over HZSM-5 and SAPO-34 in MTG and MTO reactions.18-28 They have successfully designed a reaction-regeneration cycle in isothermal and adiabatic fixed



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

bed reactors, and coupled MTO process with n-butane cracking to utilize the different thermal effect of these two reactions. Light olefins like ethylene, propylene and butylene were lumped together as a single component in most of their models about MTO reaction on HZSM-5 catalyst. Researchers from Karlsruhe Institute of Technology reported their work on kinetics of methanol to olefins.30 They did the kinetic experiment in a two-stage series-flow unit. In the experiment, methanol was firstly converted to the equilibrium mixture of methanol, dimethyl-ether and water in the first DME reactor over γ-Al2O3, the mixture then entered the second reactor to convert to hydrocarbons over a structured AlPO4-bound ZSM-5 catalyst. Kaarsholm et al.31 studied the MTO reaction on a phosphorus modified HZSM-5 catalyst in a fluidized bed. Models based on hydrocarbon pool mechanism were proposed and the predicted data fitted the experimental data well. Compared with kinetic modeling researches, studies on reactor simulation are not enough. The two industrial reactors are Lurgi’s32 multi-stage adiabatic fixed bed reactor and fluidized bed of UOP and Dalian Institute of Chemical Physics6. Alwahabi and Froment12 designed several conceptual reactors for MTO reaction over HZSM-5 and SAPO-34. They pointed out that if the operation were judiciously chosen, both fluidized bed and fixed bed could lead to similar product distribution, and the type chosen might depends on economic and reliability aspects. Schoenfelder et al.33 used a kinetic model developed from fix bed reactor to predict the MTO process in a fluidized bed. The predicted and experimental data fitted satisfactorily. Soundararajan et al.34 simulated the MTO process in a circulating fluidized bed reactor and studied


Page 4 of 38

Page 5 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


the effect of coke content and exit geometry on olefin yield. An optimum coke content of 5 wt% was found to be best for the production of ethylene with a yield of 27.2 wt%. Guo et al.35 compared the performance between monolithic and randomly packed reactors for MTP process, and found out that monolithic catalyst could intensify the methanol conversion efficiency and propylene selectivity significantly. As a new tool of reactor simulation, CFD has been implemented more and more to get the concentration and temperature profiles in a certain reactor. Luo et al.36-39 researched the MTO process in a fixed bed reactor and the intraparticle mass and heat transfer in a single SAPO-34 particle. Zhao et al.40 used CFD to design and optimize operation conditions in a large scale MTO fluidized bed. The profitability of the so called MTO, MTP and MTG processes might be threatened by the price fluctuation of methanol. Therefore, before expanding these processes, great efforts are required to improve their product yield and operating efficiency. The improvement can be down by optimizing reactor and reaction conditions. In fact, there do have great space to improve the efficiencies of the processes. For example, HZSM-5 catalysts usually choose fixed bed reactor for the slower deactivation profile. However, to slow down the deactivation procedure, the reaction condition is strict to the feed quality of methanol. Only purified methanol with concentration more than 99.85%41 is permitted. Such quality requirement will push up the methanol cost. From the economical aspect, it is rational to lower the methanol quality requirement with the cost of deactivation speeding up. But this is not operable



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

as the fixed bed reactors switch frequently. On the other hand, this is operable in the moving bed reactor in which the catalyst is continuously moved and regenerated. Another advantage of moving bed reactor is that they ensure the catalysts are always in their best performance in the reactor by the well-designed catalyst moving. For another example, the product of HZSM-5 catalysts ranges from C1 to C10. Byproducts such as C4~C6 can be recycled to enhance the yield of propylene. However, both the reaction feature and the reactor design under recycle conditions are not considered yet. Based on these points, it is necessary to develop moving bed reactor process for the HZSM-5 catalyst in both MTP and MTG processes. This paper focuses on developing moving bed concept for the MTP process under byproduct recycling. 2. Reaction kinetics 2.1 Experiments Accurate experiment data and optimal data processing play a significant role in the acquisition of a kinetic model. Comparing with common extruded catalyst used in fix-bed reactor, catalyst pellets used in moving bed should have higher mechanical strength and much smoother surface. To meet these requirements, there might be some catalytic activity loss in the preparation and shaping process. The industrial catalyst was provided by RIPP (SINOPEC) with a Si/Al atom ratio of 200. Catalysts used in the experiment have been sieved to a particle diameter between 0.6 and 0.9mm. Kinetic experiment was carried out in a laboratory scale fix-bed tubular reactor (Φ20×2 mm, L 600 mm) with a continuous plug flow, which was electrically


Page 6 of 38

Page 7 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


heated with 3 thermocouples located on the top, middle and bottom in axial direction in the bed to measure the temperature. Another thermocouple was located in the middle of catalyst bed to measure the temperature and the temperature difference should be controlled less than 1K to maintain the reaction operated under isothermal condition. The mixture of products and reactants were analyzed by on-line gas chromatograph (Kexiao GC-1690A with FID detection, 30 m×0.32 mm HP PLOT-Q capillary column, Agilent).The initial conditions applied in the kinetic experiments are as follows: temperature ranging from 673K to 773K, space time from 16 gcatalyst·min·molCH2-1 to 384 gcatalyst·min·molCH2-1 and time on stream of 2h. Before doing kinetic experiments, we had a life time test of this catalyst and found that there was a yield platform period of propylene in which the yield of propylene kept almost constant with time on stream (seen in Figure 1). If the moving bed is operated under this platform period, coking of catalyst would have little influence on the products distribution and could be negligible. Therefore, catalysts used in our kinetic were all pre-reacted for about 50h and the catalytic activity attenuation was not considered in our kinetic models. Figure 2 shows the sketch of experiment equipment and operation process. 2.2 Reaction scheme When adopting a lumped kinetic model, the number of pseudo components is a key factor which determines the accuracy and complexness of the whole model. The more lumps considered, the higher accuracy and complexity the model would be. On the other hand, few pseudo components usually mean little information which could be



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

obtained in reactor simulation. Hence, adequate number of lumps and rational designed reaction network are the key to a successful kinetic model. The common practice is to keep the important pathway individually and separate the others into several lumps. Based on the modeling requirements of the moving bed reactor, we built up a modified lumped kinetic model in this paper. The model consists of 8 individual lumps: MDOH (methanol/dimethyl ether), methane, ethylene, ethane, propylene, propane, C4 and C5+ hydrocarbons. The reaction scheme is shown in Figure 3. Methanol and DME are treated as a single species, named as MDOH in this paper. According to the “hydrocarbon pool theory”,42-44 ethylene, propylene, C4 and C5+ hydrocarbons are the primary products formed from MDOH independently. Different from most published kinetic models of MTO/MTP, the conversion of MDOH to light paraffins such as methane, ethane and propane are all taken into consideration and treated individually. Since the formation of paraffins is strong exothermic reactions which have big impact on the temperature rise, it is necessary to take them into consideration for the reactor design. For the reaction pathway between hydrocarbons, we adopt the “dual-cycle theory”,45-48 which has been widely recognized as the predominant reaction pathway over HZSM-5 catalyst. This theory found that there are two mechanistic cycles running simultaneously during the methanol to hydrocarbons reaction. In this paper, we take the olefin cycle into consideration. Therefore, the methylation of propylene to C4 hydrocarbons together with the cracking of C5+ hydrocarbons is in our reaction scheme. Ethylene formation is mechanistically


Page 8 of 38

Page 9 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


separated from the formation of higher alkenes.45-47 Consequently, we ignore the reaction of C5+ cracking to ethylene. In addition, the methylation rate of ethylene is at least one order of magnitude slower than the methylation of propylene and butylene. Therefore, the methylation of ethylene is not considered. There are a dozen of reactions between C4 and C5+, which include both oligomerization and methylation. Methylation is occurred between C4 and MDOH, however oligomerization only includes C4. Since the methylation is limited by MDOH concentration while oligomerization not, it is hard to use only methylation to represent reactions from C4 to C5+. In order to simplify the kinetic model and indicate the changing trends as well, only one reactant is considered in the reaction of C4 to C5+, as shown in eq 3. This simplified model fits the experiment data well, as illustrated in the latter section. Each reaction step of the kinetic scheme is assumed to be elemental and proportional to each reactant with a reaction order of one. While expressing the concentration of components as the carbon basis molar fraction, xj, the rate equations can be written as:  E  1 1  ri  ki exp  i     xMDOH  R  T 773  

i  1,...,7

(1)

  E  1 1  r8  k8 exp  8     xMDOH  xpropylene  R  T 773  

(2)

 E  1 1  r9  k9 exp  9     xC4  R  T 773  

(3)

 E  1 1  r10  k10 exp  10     xC5+  R  T 773  

(4)

where ri, ki and Ei are the reaction rate, pre-exponential factor and activation energy of



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

the ith reaction step respectively. 2.3 Results and discussion The estimation of kinetic parameters was carried out by minimizing the sum of the squares of the errors between experiment and calculated concentration with an initial guess of the unknown pre-exponential factor and activation energy using the MATLAB (2009a, The Mathworks) large-scale algorithm function lsqnonlin, meanwhile an ode45 function was also used to solve the ordinary differential equations of eq 1 to eq 4 to calculate the concentration of each component respectively. To calculate the confidence intervals of the parameters, the MATLAB function nlparci was employed with a confidence level of 95%. The values of kinetic parameters are all shown in Table 2. By comparing the experimental and calculated data of each certain component at three different temperatures: 400, 450 and 500℃(Figure 4a, b and c), we can find that the predicated data and experimental data fits well. With the increase of temperature, the differences between predicted and experimental data decrease. From the kinetic result, the products have different profiles. The production of ethylene and paraffins are almost unaffected by the temperature changes, propylene selectivity increases while selectivities of C4 and C5+ decrease apparently. One reasonable explanation to this phenomenon is that the reduced C4 and C5+ have converted to propylene. The selectivities of C4 and C5+ have a constant and stable growth with the increase of space time at 400 ℃ . Conditions have changed slightly when the temperature increases to 450℃, where their selectivities reach to a maximum point and then


Page 10 of 38

Page 11 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


decrease slightly. At the highest temperature of 500℃, more distinct selectivities reduction of C4 and C5+ can be observed with the increase of space time from 128 to 384 gcatalyst·min·molCH2-1. This is in accord with the explanation mentioned before. Therefore, considering the consuming reactions of C4 and C5+ in our kinetic scheme is necessary and realistic. 3. Reactor model As shown in Figure 5, the reactor consists of two perforated coaxial cylinders between which the catalyst pellets slowly move downwards as a result of gravity. The feed gas flow is a mixture of methanol/dimethyl-ether (DME), water, ethylene, C4 and C5+ hydrocarbons. The feed gas enters the outside of the catalyst bed, while the product gas leaves from the inner side. This radial flow pattern leads to low pressure drop. Since the catalyst is gradually coked in a reaction pass, after leaving the reactor, coked catalyst will be sent to a continuous regeneration unit to burn the coke and recover reaction activity. In order to describe the reaction process in the moving bed, a simplified flow model was used which includes the assumptions as follows: since the catalyst pellets move slowly, the axial flow of feed gas can be ignored; the radial flow of feed gas is recognized as a plug flow pattern; the diffusion of mass and heat fluxes in radial direction are negligible due to the high gas velocity; specific heat capacities of gas phase and catalyst pellets remain constant; the whole reactor is operated in adiabatic type. Consequently, the mass and energy balance equation of each circular differential element can be written as:



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

(  Rj )  x j  2 rL  C  F0  r   T =   CpC  2 r  L T    2 r  L  (ri )(H i ) C  r  Fj  Cpj z  Fj  Cpj 

Page 12 of 38

（5）

The boundary conditions are: at r=rout, xi= xi,0, T=T0; z=0, xi= xi,0, T=T0. where xj is the carbon basis molar fraction of the jth component; (-Rj) is the reaction rate of the jth component, molCH2·min-1·kgcatalyst-1. F0 and Fj are the total carbon basis molar flow rate of all inlet organic compounds and the jth component respectively, molCH2·min-1; (-ri) and (-ΔHi) are reaction rate and heat of the ith reaction step respectively; ρC is the density of catalyst bed, kg·m-3; L is the length of reactor, m; φ is the moving rate of the catalyst, kg·m-2·min-1; Cpc is the heat capacity of catalyst, J·kg-1·K-1; Cpj is the heat capacity of the jth component, J·mol-1·K-1. From the mass balance of eq 5, there is concentration distribution in the radial direction. According to the energy balance of eq 5, there is a temperature gradient in the axial direction. This is caused by the moving of catalyst. The temperature difference will influence the reaction rates in the axial direction, thus result in concentration distribution of products in axial directions. The reactor model includes a number of partial differential equations (PDEs) that are difficult to have analytical solutions. Hence, to solve these equations, numerical method which splits the reactor into a number of grid points and converts those PDEs to some easily-solved ordinary differential equations (ODEs) should be used. In this work, considering the actual situation, orthogonal collocation method was employed


Page 13 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


to split the equation in axial direction and Runge-Kutta method was applied to solve the ODEs. All the solving process mentioned were achieved through MATLAB. 4 Modeling results and discussion In order to simulate the moving bed reactor, there are many variables such as feed temperature, composition, pressure and number of beds that need to be considered. For ease of illustration, we choose a simple case of two beds sequential reactors with a DME pre-reactor. As shown in Figure 7, methanol feed enters the DME reactor and then convert to an equilibrium mixture of methanol, dimethyl-ether and water. The equilibrium mixture (so-called MDOH stream) is then divided into two individual feeding streams entering the following MTP moving bed reactors with a molar ratio of 1:1.2. The smaller one of the two MDOH streams is mixed with methane, ethylene, ethane, propane, C4, C5+ hydrocarbons and water and then enters the first MTP reactor. The above hydrocarbons are recycled byproducts, because they are assumed to be separated from the products. The recycle concept is adopted to improve the yield of the main product: propylene. Here, the ratio between each recycled byproduct and MDOH is obtained under two principles: the amount of each recycled byproduct should be larger than its inlet flow at the outlet of the second MTP reactor and whole reactor temperature is maintained below 500℃. According to our recycle concept, the amount of each recycled byproducts in the outlet of the second MTP reactor should be larger than that in the inlet of the first one. Only in this case, the amount of each recycled byproducts can remain constant in the operation period without any supplement after separation process, and we regard the process like this as a stable



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 14 of 38

one. Efforts have been taken before the reactor simulation to find a proper feed composition just like the one in this paper to meet this requirement. Products stream from the first reactor is quenched to 470℃ by the other MDOH stream and then the mixture of these two streams enter the second MTP reactor. Feed temperature is 470℃. Table 3 illustrates the detailed specifications of the reactors, feed, operation conditions and catalyst properties. Reactant conversion, Xi, and product selectivity, Si, are calculated respectively through the following two equations: Xj 

Yj 

x0j  x j

(6)

x0j

x j  x0j

(7)

x0, MDOH

where x0j and xj are the carbon basis molar fraction of the jth component in the feed and product stream along each reactor in radical direction. Figure 8a to 8d illustrates the profiles of MDOH conversion, propylene concentration, C5+ concentration and temperature at different axial positions in the first MTP moving bed reactor. Temperature increases along both radial and axial directions, the same trend can be observed both in the propylene concentration and the MDOH conversion profiles. The radial temperature increase is mainly caused by the increase of reaction time, while the axial temperature increase is the result of catalyst moving. Since temperature increasing accelerates the conversion of MDOH, the conversion of MDOH increases along both radial and axial directions. It should be noted that the axial outlet temperature increase (less than 2K) is much smaller than the radial temperature increase (about 20K), as shown in Figure 8b. Similar condition lies


Page 15 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


in Figure 8c and 8d: the axial distribution of propylene and C5+ products is much smaller than the radial case. Therefore, we will only discuss the distributions in radial direction while use the average value for axial direction in the following sections. We can also find inflection points in the temperature, propylene and C5+ profiles of Figure 8b to 8d: after a sharp rising section, these profiles increase slowly or decrease at the remaining radial sections. This is due to the changing of reactant concentration and reaction rates. We will discuss these phenomena in the following section. Figure 9 represents the radial flow profiles in the two moving bed reactors. From the figure, we can see that the component changing rules in both of the two moving bed reactors are similar. In each of the reactor, the generating rates of olefins are much faster than that of the parrafins. For olefins, the amounts of every products rise quickly at the front section of the reactor. This is because the olefin generating rates of reaction 2, 5, and 7 are relatively fast due to the high concentration of MDOH. However, when MDOH concentration decreases to certain extent, the changing rules of olefins in the remaining section become apparently different. The ethylene profile climbs slowly in the remaining section, since ethylene is only produced by MDOH. On the other hand, C4 and C5+ profiles decrease in the remaining section. This is because reaction 8 and 9 become the main reactions in the remaining section, and the consuming rates of these species start to exceed their generating rates. As we all know, the cracking of C5+ hydrocarbons is an endothermal reaction which favors high temperature. In addition, the heat absorption capacity of this reaction is smaller than the heat release capacity of other reactions. Consequently, temperature continue rising



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

in this section while the rising speed slowed down. This result explains the inflection points of temperature profile in Figure 8b well. From Figure 9, we can see a continuous and stable increasing of the propylene product in the whole reactor. The stable rising is a result of two independent propylene generating pathways: from MDOH and from C5+ cracking. By recycling hydrocarbons like C5+, total propylene yield of the two reactors can reach to 70%. Meanwhile, the yields of propylene obtained from Figure 1 and 4 in the single pass without byproducts recycling are less than 40% and much lower than that with byproducts recycling. Although the product profiles in the two reactors are similar, one can find the mass balance of C4 and C5+ components are different: the outlet amounts of C4 and C5+ are larger than their inlet amounts in the second MTP reactor, while the condition in the first one is absolutely opposite. Therefore, it is obvious that the reaction rates of C4 and C5+ are different in these two MTP reactors. In the first reactor, high reaction rates lead to the amount decrease of C4 and C5+ in the outlet flow compared with their feed flow. In this case, they have been consumed as a whole, resulting in more propylene been generated. Conditions are totally opposite in the second one. Consequently, it is the different reaction rates that make them act as reactants in the first reactor while products in the second one as a whole. The outlet increase of C4 and C5+ olefins in the second reactor ensures the concept of hydrocarbon recycling. There is another important difference between the two reactors: the propylene yield. The two lines in Figure 10 are the yields of propylene in each reactor respectively, just calculated with eq. 7, while the total yield of the both reactors could be obtained by


Page 16 of 38

Page 17 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


dividing the propylene flow in the outlet of the second MTP reactor by the sum of MDOH added in both two MTP reactors. As shown in Figure 10, the yield of propylene increases along the radial direction of each reactor while decreases with increase of reactor numbers. At the entrance of catalyst bed layer, the initial MDOH concentration is high, and hence the initial yield line has a relative sharp increase. As MDOH concentration decreases along the radial direction, the yield increasing rate slows down. Since the inlet propylene concentration of the second reactor is larger than the first reactor, due to the methylation of propylene with MDOH, we may come to the conclusion that the yield in the second MTP reactor is lower than that of the first reactor at the same radial position. Meanwhile, the decline of propylene yield usually means more MDOH has been transformed to other products. Therefore, from the propylene yield aspect, less number of reactors means higher propylene yield. However, less number of reactors will result in more energy consumption. Less number of reactors means larger MDOH consuming capacity of each single reactor. Larger MDOH capacity needs higher amount of recycled byproducts to control the reaction temperature. Finally, more energy will be consumed to separate and transport these byproducts. At these points, trade-offs between propylene yield and energy consumption should be carried out when choosing the number of reactors. Figure 11 illustrates the temperature distribution through the two reactors where the reaction temperature is constrained within the range of [470℃, 500℃]. From the figure we can see that although the endothermic reactions of C5+ cracking increases in the latter half along radial direction, temperature increases irreversibly in the radial



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

direction for both of the two reactors. Furthermore, we can find that temperature increase in the second reactor is higher than that of the first one. It is the different speed of C5+ cracking together with the reaction of propylene and C4 that cause the different temperature rise in these two reactors. 5 Conclusions Based on the propylene yield “platform period” observed in catalyst long time evaluation, a new possible approach of converting methanol to propylene through moving bed reactor on HZSM-5 has been introduced in this paper. A new kinetic model consisting of 8 lumps of MDOH, methane, ethylene, ethane, propylene, propane, C4 and C5+ hydrocarbons and 10 individual reactions among these components has been presented. On the basis of the kinetic model, we have proposed a mathematical model for the simulation of MTP reaction in the moving bed reactor. From the simulation results, we can conclude that the recycling of higher hydrocarbons can increase the yield of propylene to 70%. The roles of higher hydrocarbons in the two reactors are different since they act as reactants in the first moving bed while products in the second reactor. Kinetic model developed in this paper can be further developed by separating the C5+ hydrocarbons lump into several more detailed lumps such as sum C5, C6+ and aromatics. In this way, more information about the products distribution can be obtained in reactor simulation. Though, the reaction is operated under “platform period”, deactivation kinetics of the HZSM-5 stills seems to be necessary for the calculation of coke distribution in the reactor. Therefore more detailed and


Page 18 of 38

Page 19 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


well-designed kinetic study is required for the moving bed catalyst, and this is under construction now. Acknowledgement This work is supported by the National High-tech R&D Program of China (863 Program) (Grant 2012AA030304). Nomenclature Cpc = heat capacity of catalyst, J·mol-1·K-1 Cpj = heat capacity of jth component, J·mol-1·K-1 Ei = activation energy of the ith reaction step, kJ·mol-1 F0 = total carbon basis molar flow rate of all inlet organic compounds, molCH2·min-1 Fj = carbon basis molar flow rate of the jth component, molCH2·min-1 ki = pre-exponential factor of the ith reaction step, molCH2·min-1·kg catalyst-1 L = length of reactor, m r = radial coordinate, m rout = outer diameter of the reactor, m ri = reaction rate of the ith reaction step, molCH2·min-1·kg catalyst-1 Si = selectivity of the ith component T = temperature, K T0 = temperature at the entrance of reactor, K x0i, x0j = carbon basis molar fraction of the ith and jth component in the feed xi, xj = carbon basis molar fraction of the ith and jth component in the product stream Xi = conversion of the ith component



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

z = axial coordinate, m Greek symbols -ΔHi = reaction heat of the ith reaction step, J·mol CH2-1 ρC = density of catalyst bed, kg·m-3 φ = moving rate of the catalyst, kg·m-2·min-1 References 1.

Chang, C. D.; Silvestri, A. J. The conversion of methanol and other O-compounds to

hydrocarbons over zeolite catalysts. J. Catal. 1977, 47, 249-259. 2.

Chang, C. D. Methanol conversion to light olefins. Catal. Rev.Sci. Eng. 1984, 26, 323-345.

3.

Stocker, M. Methanol-to-hydrocarbons: catalytic materials and their behavior. Microporous

Mesoporous Mater. 1999, 29, 3-48. 4.

Keil, F. J. Methanol-to-hydrocarbons: process technology. Microporous Mesoporous Mater. 1999,

29, 49-66. 5.

Ilias, S.; Bhan, A. Mechanism of the catalytic conversion of methanol to hydrocarbons. ACS Catal.

2013, 3, 18-31. 6.

Olsbye, U.; Svelle, S.; Bjorgen, M.; Beato, P.; Janssens, T. V. W.; Joensen, F.; Bordiga, S.;

Lillerud, K. P. Conversion of methanol to hydrocarbons: How zeolite cavity and pore size zontrols product selectivity. Angew. Chem. Int. Ed. 2012, 51, 5810-5831. 7.

Froment, G. F. Single event kinetic modeling of complex catalytic processes. Catal. Rev. Sci. Eng.

2005, 47, 83-124. 8.

Park, T. Y.; Froment, G. F. Kinetic modeling of the methanol to olefins process. 1. Model

formulation. Ind. Eng. Chem. Res. 2001, 40, 4172-4186. 9.

Park, T. Y.; Froment, G. F. Kinetic modeling of the methanol to olefins process. 2. Experimental

results, model discrimination, and parameter estimation. Ind. Eng. Chem. Res. 2001, 40, 4187-4196. 10. Alwahabi, S. M.; Froment, G. F. Single event kinetic modeling of the methanol-to-olefins process on SAPO-34. Ind. Eng. Chem. Res. 2004, 43, 5098-5111. 11. Kumar, P.; Thybaut, J. W.; Svelle, S.; Olsbye, U.; Marin, G. B. Single-event microkinetics for methanol to olefins on H-ZSM-5. Ind. Eng. Chem. Res. 2013, 52, 1491-1507. 12. Alwahabi, S. M.; Froment, G. F. Conceptual reactor design for the methanol-to-olefins process on SAPO-34. Ind. Eng. Chem. Res. 2004, 43, 5112-5122. 13. Wu, W. Z.; Guo, W. Y.; Xiao, W. D.; Luo, M. Dominant reaction pathway for methanol conversion to propene over high silicon H-ZSM-5. Chem. Eng. Sci. 2011, 66, 4722-4732. 14. Chen, N. Y.; Reagan, W. J. Evidence of auto-catalysis in methanol to hydrocarbon reactions over zeolite catalysts. J. Catal. 1979, 59, 123-129. 15. Chang, C. D. Kinetic-model for methanol conversion to hydrocarbons. Chem. Eng. Sci. 1980, 35, 619-622. 16. Bos, A. N. R.; Tromp, P. J. J.; Akse, H. N. Conversion of methanol to lower olefins: Kinetic modelling, reactor simulation and selection. Ind. Eng. Chem. Res. 1995, 34, 3808-3816.


Page 20 of 38

Page 21 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


17. Najafabadi, A. T.; Fatemi, S.; Sohrabi, M.; Salmasi, M. Kinetic modeling and optimization of the operating condition of MTO process on SAPO-34 catalyst. J. Ind. Eng. Chem. 2012, 18, 29-37. 18. Gayubo, A. G.; Benito, P. L.; Aguayo, A. T.; Castilla, M.; Bilbao, J. Kinetic model of the MTG process taking into account the catalyst deactivation. Reactor simulation. Chem. Eng. Sci. 1996, 51, 3001-3006. 19. Aguayo, A. T.; del Campo, A. E. S.; Gayubo, A. G.; Tarrio, A.; Bilbao, J. Deactivation by coke of a catalyst based on a SAPO-34 in the transformation of methanol into olefins. J. Chem. Technol. Biotechnol. 1999, 74, 315-321. 20. Aguayo, A. T.; Gayubo, A. G.; Atutxa, A.; Olazar, M.; Bilbao, J. Regeneration of a catalyst based on a SAPO-34 used in the transformation of methanol into olefins. J. Chem. Technol. Biotechnol. 1999, 74, 1082-1088. 21. Aguayo, A. T.; Gayubo, A. G.; Olazar, M.; Ortega, J. M.; Moran, A. L.; Bilbao, J. Coke combustion and reactivation kinetics of a ZSM-5 zeolite based catalyst used for the transformation of methanol into hydrocarbons. Chem. Eng. Commun. 1999, 176, 43-63. 22. Gayubo, A. G.; Aguayo, A. T.; Castilla, M.; Olazar, M.; Bilbao, J. Catalyst reactivation kinetics for methanol transformation into hydrocarbons. Expressions for designing reaction-regeneration cycles in isothermal and adiabatic fixed bed reactor. Chem. Eng. Sci. 2001, 56, 5059-5071. 23. Gayubo, A. G.; Aguayo, A. T.; Moran, A. L.; Olazar, M.; Bilbao, J. Role of water in the kinetic modeling of catalyst deactivation in the MTG process. AIChE J. 2002, 48, 1561-1571. 24. Gayubo, A. G.; Aguayo, A. T.; Castilla, M.; Moran, A. L.; Bilbao, J. Role of water in the kinetic modeling of methanol transformation into hydrocarbons on HZSM-5 zeolite. Chem. Eng. Commun. 2004, 191, 944-967. 25. Aguayo, A. T.; Mier, D.; Gayubo, A. G.; Gamero, M.; Bilbao, J. Kinetics of methanol transformation into hydrocarbons on a HZSM-5 zeolite catalyst at high temperature (400-550 degrees C). Ind. Eng. Chem. Res. 2010, 49, 12371-12378. 26. Mier, D.; Gayubo, A. G.; Aguayo, A. T.; Olazar, M.; Bilbao, J. Olefin production by cofeeding methanol and n-Butane: Kinetic modeling considering the deactivation of HZSM-5 zeolite. AIChE J. 2011, 57, 2841-2853. 27. Aguayo, A. T.; Castano, P.; Mier, D.; Gayubo, A. G.; Olazar, M.; Bilbao, J. Effect of cofeeding butane with methanol on the deactivation by coke of a HZSM-5 zeolite catalyst. Ind. Eng. Chem. Res. 2011, 50, 9980-9988. 28. Aguayo, A. T.; Gayubo, A. G.; Ateka, A.; Gamero, M.; Olazar, M.; Bilbao, J. Joint transformation of methanol and n-butane into olefins on an HZSM-5 zeolite catalyst in reaction-regeneration cycles. Ind. Eng. Chem. Res. 2012, 51, 13073-13084. 29. Chen, D.; Grlnvold, A.; Moljord, K.; Holmen, A. Methanol conversion to light olefins over SAPO-34: Reaction network and deactivation kinetics. Ind. Eng. Chem. Res. 2007, 46, 4116-4123. 30. Menges, M.; Kraushaar-Czarnetzki, B. Kinetics of methanol to olefins over AlPO4-bound ZSM-5 extrudates in a two-stage unit with dimethyl ether pre-reactor. Microporous Mesoporous Mater. 2012, 164, 172-181. 31. Kaarsholm, M.; Rafii, B.; Joensen, F.; Cenni, R.; Chaouki, J.; Patience, G. S. Kinetic modeling of methanol-to-olefin reaction over ZSM-5 in fluid bed. Ind. Eng. Chem. Res. 2010, 49, 29-38. 32. Koempel, H.; Liebner, W. Lurgi's Methanol To Propylene (MTP): Report on a successful commercialisation. In Natural Gas Conversion Viii, Proceedings of the 8th Natural Gas Conversion Symposium; Noronha, F. B.; Schmal, M.; SousaAguiar, E. F. Eds. Elsevier Science Bv: Amsterdam,



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

2007; Vol. 167, pp 261-267. 33. Schoenfelder, H.; Hinderer, J.; Werther, J.; Keil, F. J. Methanol to olefins-prediction of the performance of a circulating fluidized-bed reactor on the basis of kinetic experiments in a fixed-bed reactor. Chem. Eng. Sci. 1994, 49, 5377-5390. 34. Soundararajan, S.; Dalai, A. K.; Berruti, F. Modeling of methanol to olefins (MTO) process in a circulating fluidized bed reactor. Fuel 2001, 80, 1187-1197. 35. Guo, W.; Xiao, W.; Luo, M. Comparison among monolithic and randomly packed reactors for the methanol-to-propylene process. Chem. Eng. J. 2012, 207, 734-745. 36. Zhuang, Y. Q.; Gao, X.; Zhu, Y. P.; Luo, Z. H. CFD modeling of methanol to olefins process in a fixed-bed reactor. Powder Technol. 2012, 221, 419-430. 37. Chen, X. M.; Xiao, J.; Zhu, Y. P.; Luo, Z. H. Intraparticle mass and heat transfer modeling of methanol to olefins process on SAPO-34: A Single Particle Model. Ind. Eng. Chem. Res. 2013, 52, 3693-3707. 38. Chen, X. M.; Luo, Z. H.; Zhu, Y. P.; Xiao, J.; Chen, X. D. Direct concurrent multi-scale CFD modeling: The effect of intraparticle transfer on the flow field in a MTO FBR. Chem. Eng. Sci. 2013, 104, 690-700. 39. Zhuang, Y. Q.; Chen, X. M.; Luo, Z. H.; Xiao, J. CFD-DEM modeling of gas-solid flow and catalytic MTO reaction in a fluidized bed reactor. Comput. Chem. Eng. 2014, 60, 1-16. 40. Zhao, Y. F.; Li, H.; Ye, M.; Liu, Z. M. 3D numerical simulation of a large scale MTO fluidized bed reactor. Ind. Eng. Chem. Res. 2013, 52, 11354-11364. 41. Liang, J. B. Brief of MTP device in Shenhua Ningxia Coal Group. Guangzhou Chem. Ind. 2013, 41, 190-192 (in Chinese). 42. Dahl, I. M.; Kolboe, S. On the reaction mechanism for propene formation in the MTO reaction over SAPO-34. Catal. Lett. 1993, 20, 329-336. 43. Dahl, I. M.; Kolboe, S. On the reaction mechanism for hydrocarbon formation from methanol over SAPO-34 .1. Isotropic labeling studies of the co-reaction of ethene and methanol. J. Catal. 1994, 149, 458-464. 44. Dahl, I. M.; Kolboe, S. On the reaction mechanism for hydrocarbon formation from methanol over SAPO-34 .2. Isotopic labeling studies of the co-reaction of propene and methanol. J. Catal. 1996, 161, 304-309. 45. Svelle, S.; Ronning, P. A.; Kolboe, S. Kinetic studies of zeolite-catalyzed methylation reactions 1. Coreaction of C-12 ethene and C-13 methanol. J. Catal. 2004, 224, 115-123. 46. Svelle, S.; Ronning, P. O.; Olsbye, U.; Kolboe, S. Kinetic studies of zeolite-catalyzed methylation reactions. Part 2. Co-reaction of C-12 propene or C-12 n-butene and C-13 methanol. J. Catal. 2005, 234, 385-400. 47. Svelle, S.; Joensen, F.; Nerlov, J.; Olsbye, U.; Lillerud, K. P.; Kolboe, S.; Bjørgen, M. Conversion of methanol into hydrocarbons over zeolite H-ZSM-5: Ethene formation is mechanistically separated from the formation of higher alkenes. J. Am. Chem. Soc. 2006, 128, 14770-14771. 48. Bjørgen, M.; Svelle, S.; Joensen, F.; Nerlov, J.; Kolboe, S.; Bonino, F.; Palumbo, L.; Bordiga, S.; Olsbye, U. Conversion of methanol to hydrocarbons over zeolite H-ZSM-5: On the origin of the olefinic species. J. Catal. 2007, 249, 195-207.


Page 22 of 38

Page 23 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


List of Figures: Figure 1. Conversion of MDOH and “platform period” of the yield of propylene with time on stream (470℃, 1atm, WHSV=1.0h-1). Figure 2. Sketch of the experiment equipment. Figure 3. Reaction scheme proposed for HZSM-5 with an atom ratio Si/Al=200. Figure 4. Comparison of experimental (symbols) and calculated carbon basis molar fraction as a function of space time at temperature of a) 400℃, b)450℃ and c) 500℃. Figure 5. Scheme of a radial cross flow moving bed reactor. Figure 6. Scheme of a circular differential element in the moving bed reactor. Figure 7. Sketch of the designed reaction process. Figure 8. Profiles of a) conversion of MDOH, b) temperature, c) propylene concentration and d) C5+ concentration Figure 9. Products flow profiles in the two moving bed reactors. Figure 10. Propylene yield profiles in the both two rectors. Figure 11. Average temperature profiles in the two reactors.



Conversion of MDOH Yield of propylene

100

Conversion & Yield, %

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 38

80

60

40

20

0 0

20

40

60

80

100

120

140

160

180

Time on stream, h

Figure 1. Conversion of MDOH and “platform period” of the yield of propylene with time on stream (470℃, 1atm, WHSV=1.0h-1).


Page 25 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 2. Sketch of the experiment equipment.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 3. Reaction scheme proposed for HZSM-5 with an atom ratio Si/Al=200.


Page 26 of 38

0.30

a

Carbon basis molar fraction

0.25

0.20

0.15

0.10

0.05

0.00 0

50

CH4

C2H4

100

150

200

250

W/F gcatalyst·min/mol CH2 · C2H6 C3H6 C3H8

300

350

sum C4

400

sum C5+

0.35

b 0.30

Carbon basis molar of fraction

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0.25

0.20

0.15

0.10

0.05

0.00 0

50

CH4

C2H4

100

150

200

250


300

350

sum C4

400

sum C5+

0.40

c 0.35

Carbon basis molar of fraction

Page 27 of 38

0.30 0.25 0.20 0.15 0.10 0.05 0.00 0

CH4

50

C2H4

100

150

200

250


300

350

sum C4

400

sum C5+

Figure 4. Comparison of experimental (symbols) and calculated carbon basis molar fraction as a function of space time at temperature of a) 400℃, b)450℃ and c) 500℃.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 5. Scheme of a radial cross flow moving bed reactor.


Page 28 of 38

Page 29 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 6. Scheme of a circular differential element in the moving bed reactor.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 7. Sketch of the designed reaction process.


Page 30 of 38

100

a

Conversion of MDOH

80

60

40

z=0 z=0.25 z=0.50 z=0.75 z=1.0

20

0 0.0

0.2

0.4

0.6

0.8

1.0

Dimensionless radial length

495

b

Temperature, ℃

490

485

480

z=0 z=0.25 z=0.50 z=0.75 z=1.0

475

470 0.0

0.2

0.4

0.6

0.8

1.0


0.25

Carbon basis molar fraction of propylene

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


c 0.20

0.15

0.10

z=0 z=0.25 z=0.50 z=0.75 z=1.0

0.05

0.00 0.0

0.2

0.4

0.6

0.8

1.0


Carbon basis molar flow of C5+, mol/min

Page 31 of 38

0.35

d

0.30

z=0 z=0.25 z=0.50 z=0.75 z=1.0

0.25

0.20 0.0

0.2

0.4

0.6

0.8

1.0


Figure 8. Profiles of a) conversion of MDOH, b) temperature, c) propylene concentration and d) C5+ concentration.



1500 propylene

Carbon basis molar flow, mol/h

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 32 of 38

C5+

1000

C4 ethylene

500

propane ethane methane MDOH

0 0.0

0.2

0.4

0.6

0.8

1.0

1.2


Figure 9. Products flow profiles in the two moving bed reactors.


Page 33 of 38

1.0

0.8

Yield of propylene

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0.6

0.4

0.2

1st MTP reactor 2nd MTP reactor

0.0 0.0

0.2

0.4

0.6

0.8


Figure 10. Propylene yield profiles in the both two rectors.


1.0


500

495

490

Temperature, ℃

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 34 of 38

485

480

475

The first reactor The second reactor

470 0.0

0.2

0.4

0.6

0.8

Mass of catalyst (dimensionless)

Figure 11. Average temperature profiles in the two reactors.


1.0

Page 35 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


List of Tables: Table 1. Current commercial processes on methanol to hydrocarbons Table 2. Value of kinetic parameters Table 3. Specifications of reactors, feed, operation conditions and catalyst properties



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 36 of 38

Table 1. Current commercial processes on methanol to hydrocarbons Process name

Catalyst

Reactor type

Reaction Temperature

Main product

Methanol to Olefin (MTO) Methanol to Propylene (MTP) Methanol to Gasoline (MTG)

SAPO-34 HZSM-5 HZSM-5

Fluidized bed Fixed bed Fixed bed

450℃ 470℃ 400℃

Propylene, ethylene Propylene, gasoline Gasoline


Page 37 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 2. Value of kinetic parameters Kinetic parameters

value

k1/molCH2·min-1·kg catalyst-1

0.44 ± 0.17


3.89 ± 0.21


0.15±0.14


9.80 ± 0.73


1.76 ± 0.18


8.91 ± 0.76


8.73 ± 0.76


0.05 ±0.01


0.14 ± 0.08


0.66 ± 0.15

E1/kJ·mol-1

64.01 ± 38.44

E2/kJ·mol-1

40.25 ± 4.03

E3/kJ·mol-1

83.03 ±37.11

E4/kJ·mol-1

52.16 ± 3.51

E5/kJ·mol-1

43.86± 7.87

E6/kJ·mol-1

42.4 ± 4.82

E7/kJ·mol-1

39.46 ± 4.58

E8/kJ·mol-1

75.00± 10.27

E9/kJ·mol-1

78.36 ± 32.50

E10/kJ·mol-1

29.67 ± 21.08



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 38 of 38

Table 3. Specifications of reactors, feed, operation conditions and catalyst properties parameter

value

unit

Inlet temperature

470

℃

Methanol feed flow

3571

kg·h-1

Feed composition (molCH2 %) Methanol

22

Methane

2

Ethylene

15

Ethane

3

Propane

3

C4

25

C5+

30 800

kg·m-3

0.0011

m·min-1

Reactor 1

1623

kg

Reactor 2

1948

kg

Catalyst density Catalyst flow rate Catalyst loading


Methanol to Propylene Process in a Moving Bed Reactor with

Recommend Documents