Validation and application of a kinetic model for biomass gasification simulation and optimization in updraft gasifiers

Accepted Manuscript Title: Validation and application of a kinetic model for biomass gasification simulation and optimization in updraft gasifiers Authors: Jia Yu, Joseph D. Smith PII: DOI: Reference:

S0255-2701(17)31219-9 https://doi.org/10.1016/j.cep.2018.02.003 CEP 7185

To appear in:

Chemical Engineering and Processing

Received date: Revised date: Accepted date:

30-11-2017 25-1-2018 6-2-2018

Please cite this article as: Jia Yu, Joseph D.Smith, Validation and application of a kinetic model for biomass gasification simulation and optimization in updraft gasifiers, Chemical Engineering and Processing https://doi.org/10.1016/j.cep.2018.02.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Validation and application of a kinetic model for biomass gasification simulation and optimization in updraft gasifiers Jia Yu and Joseph D. Smith*

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Department of Chemical and Biochemical Engineering, Missouri University of Science and Technology, Rolla, MO 65401, United States *

Corresponding authors: [email protected] (J. D. Smith), 1101 North State Street, 110 Bertelsmeyer Hall,

Rolla, MO 65401, United States

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Tel.: +1 (573) 341-4294

Graphic Abstract (a)

wet biomass

syngas + secondary tar

(b)

gas outlet

gas outlet

drying zone

pyrolysis zone

drying

steam

pyrolysis

charcoal

reduction zone

gas inlet

volatile

primary tar

secondary tar

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combustion zone

reduction & combustion

dry biomass

light gases

reacted charcoal

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gas inlet

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biomass

unreacted charcoal

ash

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air

Highlights:

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1. A comprehensive kinetic model is introduced and validated for biomass gasification.

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2. The kinetic model is shown to be more accurate than the commonly used Gibbs free energy minimization model. 3. Effects of ER, temperature, biomass mixture and moisture content were investigated.

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4. Gasifier temperature was shown to have the dominate effect on amount of tar produced and the tar composition.

Abstract

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Biomass gasification has attracted great interest recently for its great potential as a thermal

degradation method that converts biomass or carbonaceous solids into combustible gases with a usable heating value. However, accurate simulation of biomass gasification faces significant challenges as it has a complicated dependence on reaction kinetics, reactor geometry, processing

methodology and operating condition. In this paper, a reaction model (RXN model) based on comprehensive biomass gasification kinetics is introduced and validated to predict the syngas and tar compositions. By comparing the simulating predictions with data from two updraft gasification

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experiment findings available in the literature, it is demonstrated that the RXN model is able to provide a more accurate description for updraft biomass gasification than the minimizing Gibbs free energy model (MGFE model), which can substitute for the widely used yet not accurate MGFE model. Parametric optimization studies were conducted to investigate the impacts of equivalence ratio (ER), gasifier temperature, biomass feed types on syngas and unreacted tar compositions.

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The results of this work provide vital information for large-scale gasifier design, operating

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decisions, and optimization.

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Keywords: Biomass gasification; Process simulation; Updraft gasifier; Tar prediction

1. Introduction Biomass energy has gathered attention in recent years as it has great potential to contribute to sustainable energy development and security [1-4]. Biomass gasification is one of the key technologies to convert waste biomass or carbonaceous materials to syngas efficiently and

economically. Syngas is the source for high-value chemicals (such as methanol, DME [5-7] and liquid carbohydrates [2, 8-10]). Syngas can also be used by some other devices, such as gas turbines, internal combustion engines, and fuel cells, to produce electricity or heat [11-13].

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Biomass gasification is also considered as a greener alternative as it does not produce extra carbon dioxide during the process [14].

Gasifiers are the main devices used in the gasification process, in which biomass is converted to syngas, consisting mainly of CO, H2, CO2, N2, tar, ashes and small particulates [15]. Several types of gasifiers have been developed, which includes entrained flow gasifiers [16-18], fluidized

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bed gasifiers[19-23], and fixed bed gasifiers[24, 25]. Entrained flow gasifiers produce syngas with

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a lower tar content, but about 20% more oxygen is required [26]. In fluidized bed gasifiers, high

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amounts of oxygen bypass the reactor bed due to the low level of oxygen dissemination from the

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gas bubbles, which reduces the efficiency of the gasifier. Fixed bed gasifiers can be further divided

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into downdraft [27-33] or updraft [34-38] based on the gas flow direction. In downdraft bed

calorific value [26].

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gasifiers, tar content is much lower compared to that in an updraft gasifier, but the syngas has less

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The updraft gasifier is one of the oldest types of gasifiers due to its design simplicity and

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tractability. Biomass is normally fed from the top of the reactor and moves downwards, while the gasifying agents are injected from the bottom of the gasifier and move upwards. Most chemical

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reactions take place at the bottom of the bed, where the temperature is the highest in the gasifier. The produced syngas exits from the top of the gasifier at a relatively low temperature (420K-570K) [39]. Updraft gasifiers have the ability to gasify high moisture content biomass as there is a high heat exchange rate between the combustion zone and the drying zone. Also, it could achieve relatively high carbon conversion rate due to the long residence time of biomass [40]. For all the

gasifier types, the economical tar removal process is still considered to be the main technological barrier [35], thus it is necessary for process models to predict tar residue production. Table 1 summarised the experimental and numerical studies on updraft gasifiers in past

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decades. Notice that in the experiment section, the effects of different conditions on gasifier performance have been studied, including gasifier temperature, equivalence ratio (ER), mixed gasification agents, and biomass types. Compositions of syngas were analyzed in most cases to evaluate the processing conditions, gasifying temperatures were usually around 1000K, ER was usually controlled between 0-0.4, air and steam were the popular gasifying agents, and both hard

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and soft woods were fed into the gasifiers.

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Even fewer numerical studies on updraft gasifier have been reported. Some researchers were

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using the equilibrium-based model to proximate the syngas compositions[41, 42]. Although equilibrium-based models were widely used, not only for updraft gasifiers but also for fluidized

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gasifiers [43-46], entrained flow gasifiers [47, 48] and downdraft gasifiers [49-53], it is known

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that equilibrium base model is not reliable, especially when the equivalence ratio lays between 0.10-0.30. A few finite-rate kinetics models were developed for updraft gasifiers, while some

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models [54, 55] approximated char conversion and tar formation was not discussed. Thus,

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developing and validation a kinetic model based on comprehensive and reliable reaction kinetics is needed. Also, a comparative study of equilibrium based model versus kinetic model is needed.

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The objective of this work is to develop and validate a kinetic model for biomass gasification

in updraft gasifiers using Aspen Plus to substitute the equilibrium-based model. The proposed kinetic model is able to correctly predict syngas composition and approximate tar formation and cracking. The paper is organized as follows. In the following section, methods and simulations

settings are presented. Then, the kinetic model (is also called RXN model in this work) was validated by comparing with the experimental data available in literature and the MGFE model. At last, the validated RXN model was applied to investigate the effects of equivalence ratio (ER),

products.

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gasification temperature, biomass mixture, biomass moisture and compositions on syngas and tar

Table 1. Experiments and simulations for the updraft gasifiers in past decades.

Experiments Authors

Gasification agent

Biomass type

Equivalence ratio or air/fuel ratio range

Aljbour and Kawamoto, 2013 [56,

Air and steam

Japanese cedar wood

0-0.3

Kayal and Chakravarty, 1994 [58]

Air

Jute-stick

Not documented

Chen et al., 2012 [34]

Air

mesquite and redberry juniper

0.25-0.37

Air and steam

Black pine pellets

Biomass feed 9g/min, gasified with 16L/min

Comments

Temperatures vary between 932K and 1223K, steam to carbon ratios vary between 0-2.0.

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Kihedu et al., 2016 [59]

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57]

Tested the influence of air inlet velocities on gas components, as well as the temperature along the bed. CO: 13-21%, H2: 1.6-3%, CH4: 1-1.5%, C2H6: 0.4-0.6%, N2: 60-64%, CO2: 1125%, and O2: 1-2%. Tested the bed height, temperature, gas and tar compositions inside the reactor

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air or 11.3L/min air+1.5g/min steam Air

Japanese Poplar woodchip

Saravanakumar et al., 2004 [60]

Air

long stick wood (leucaena

varied ERs between 0.19-0.95 in the same

species)

run

Mandl et al. 2011 [39]

Lucas, 2005 [62]

Air and steam

Air

Air/steam

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Chen et al., 2013 [64]

(prosophis species)

24.11-24.45m3/h in selected runs

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Air/steam

Gao et al., 2008 [63]

Wood varies from 45-50kg, air feed was

Peat and wood chips

Air, air/steam,

production. The gasifier was run 19 times, for 6h periods. In the top lit updraft configuration, the gasifier was a top lit updraft system, was run 27 times, and 5 h and 15 min periods. This new design produced

In the bottom lit updraft configuration, the gasifier system was run ten times under various airflow rate operating conditions, each for a period of 5 h and 15 min.

Not documented

Peat gasification: CO: 16.1%-24.2%, H2: 16.7%-18.8, CH4 2.0%-3.0%, CO2: 10.3%-14.3%. For biomass: CO: 28.9%-20.6%, H2 14.0%-17.6%, CH4: 1.6%2.5%, CO2: 6.8%-13.8%.

Softwood pellets

Air/fuel ratio 1.0-1.6 kg/kg

Wood pellets, wood chips,

20kg wood pellets feed with 50m3/h air

CO: 24.4%-26.6%, CO2: 4.7%-6.2%, CH4: 1.6%-1.7%, H2: 3.9%-6.3%, H2O: 14.6%-18.4%.

bark and charcoal pine sawdust

Batch gasification with preheated agents at different temperatures. All the gas composition data were collected along with operating time.

0, 0.05, 0.1, 0.3

The effects of temperature, ER, steam/biomass ratio, and porous ceramic reforming on the gas characteristic parameters were investigated. A steam/biomass ratio of 2.05 was found to be optimum in all runs.

Mesquite

2.7-4.3

carbon

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Presented a modified reactor design with two air inlets to decrease the tar

significant lower tar content than the conventional updraft gasifiers.

long stick wood

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Kurkela et al.,1989 [61]

Air

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Saravanakumar et al.,2007 [37]

Not documented

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Pedroso et al., 2013 [35]

Compared to air gasification, both the peak and average bed temperatures decreased for using air/steam mixtures as an oxidizing agent. Using a CO2/O2

dioxide/oxygen

mixture for gasification produced a much higher HHV syngas.

Simulations

Authors and Year

Bed type

Codes

Zone

Kayal and Chakravarty, 1994 [58]

Updraft

Matlab

Entire bed

Comments Mathematical model predicts the biomass conversion, gas production rate, gas composition and stream temperatures along the gasifier as well as the final syngas, as a function of air-input rate using jute-stick as the feedstock.

Yang et al., 2003 [65] Di Blasi, 2004 [55]

Fixed bed

Matlab

Entire bed

Effects of devolatilization rate and fuel moisture were assessed.

Updraft

Matlab

Entire bed

A one-dimensional, unsteady mathematical model was presented, energy and mass transport, drying and devolatilization, char gasification, and combustion zone were coupled.

Mandl et al., 2010 [54]

Updraft

CFD

Entire bed

The model considered one-step global pyrolysis kinetics. Drying, heat and mass transfer in the solid, gas and solid-gas phases, heat loss, particle movement and shrinkage were considered.

Ramzan et al., 2011[41]

Updraft

Matlab

Entire bed

Equilibrium method was used.

Ueki et al., 2012 [66]

Updraft

CFD

Entire bed

A Euler–Euler model was used to simulate the gasification process with superheated steam. The calculation showed there was a 150K–300 K temperature difference between gas phase and solid phase.

Chen et al., 2013 [42]

Updraft

Aspen Plus

Entire bed

Simulated two types of updraft bed based on a minimization of the Gibbs free

2. Model Development 2.1. Minimizing Gibbs Free Energy model (MGFE model)

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energy at equilibrium.

In the MGFE model, an RGIBBS operation block is used to convert biomass to syngas. Its mathematical theory is explained below. All reactions in the gasification process are assumed to

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reach chemical equilibrium before the syngas leaves the reactor. Thermodynamically, the total

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Gibbs free energy of a closed system at a constant temperature and pressure must decrease during

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an irreversible process. When the equilibrium state is reached, Gt attains its minimum value [67], ( d G t )T ,P  0

(1)

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When applying Eq. (1), each component involved in the process also follows mass balance law,

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namely, the mass of each element in the biomass and provided air equals the mass of each element

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in the syngas mixture, char, tar and ash: N



ai, j ni  A j

(2)

i0

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Based on the assumption that all reactions reach equilibrium, the MGFE cannot be used to

approximate the char and tar residues because tar is an intermediate byproduct. Thus, the

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development of reaction model is needed. 2.2. Reaction model (RXN model) In this work, an RXN model based on detailed kinetic parameters of each reaction has been developed. The RXN model is fundamentally more accurate than the MGFE model because

reaction kinetics are used instead of chemical equilibrium assumption. The kinetic reaction rate of species i of reaction r is expressed as follows: 𝑁

(3)

𝑟𝑖,𝑟 = 𝑘𝑟 ∏[𝑐𝑖,𝑟 ]𝛼𝑖,𝑟

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𝑖=1

Where kr is computed using the Arrhenius expression 𝐸𝑎

(4)

𝑘𝑟 = 𝐴𝑟 𝑇𝛽 𝑒 −𝑅𝑇

To approximate and formulate the whole gasification process, H2, O2, N2, CO, CO2, CH4, H2S,

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H2O, char, tar and ash were considered, and all the other possible components are neglected. The

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RXN model is validated by comparison with both experimental data and the MGFE model.

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2.2.1 Schematic of the kinetic model

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Figure 1(a) shows the structural diagram of a typical updraft gasifier. The simulation procedure, Figure 1(b), corresponds with the structural diagram. The bed in an updraft gasifier is

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usually divided into four zones. Biomass stream passes through four zones sequentially from the

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top to the bottom while the air stream goes upwards. Biomass is progressively decomposed when passing through each zone and reacts with air to produce syngas. The reaction path was inspired

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by previously published articles on biomass gasification stages and steps by considering more

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reactions details. Ahmed et al. [68] illustrated that gasification process consist of many steps and these steps overlap each other. Although there is no clear boundaries between each step, these

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steps were divided into several groups or zones for modeling purposes. The top layer is the drying zone in which moisture separates from the feedstock due to the heat coming from the reactor core, generating steam and dried biomass. Pyrolysis zone would decompose the biomass into gasses (CO, H2 etc.), liquids (tars and oils) and solid (char). Most Aspen Plus models only decompose biomass into basic elements and then used an RGIBBS block to optimize the final

products, which allowed no tar formation during the process This has also been a common defect in previous biomass gasification models [49, 69]. In this paper, a series of physical and chemical processes take place and dried biomass is decomposed to primary tar, volatiles, charcoal and ash

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[70]. Primary tar goes under thermal cracking to achieve light gases and secondary tar. Gasification zone (also called reduction zone) is where Water-Gas shift and Boudouard reactions are the dominant reactions taking place due to limited oxygen. The combustion zone lies at the

very bottom of the reactor, in which light gases, volatiles and charcoal are partially burnt by the incoming air and generates CO2 and H2O. Ash is considered inert for all reactions. Temperatures

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may rise to around 1200°C due to the oxidation reactions, which provides the heat for the whole

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gasification process. All processes follow mass balance and energy conservation in the RXN

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model. As the model works primarily with mixtures of hydrocarbons up to their critical points,

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Peng–Robinson equation of state was to approximate physical properties of the conventional components in the gasification process. The enthalpy and density model selected for both non-

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as pure carbon.

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conventional components biomass and ash were HCOALGEN and DCOALIGT. Char is defined

(a)

wet biomass

syngas + secondary tar

(b) biomass

gas outlet

drying zone

pyrolysis zone

drying

steam

pyrolysis

charcoal

reduction zone

gas inlet

combustion zone

gas inlet

primary tar

secondary tar

unreacted charcoal

ash

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air

volatile

light gases

reacted charcoal

reduction & combustion

dry biomass

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gas outlet

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Figure 1. (a) Structural diagram of an updraft gasifier, (b) simulator schematic for the RXN model

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2.2.2 Drying and Devolatilization

Figure 2 shows the Aspen Plus simulation flowsheet of the RXN model. Biomass was specified

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as a non-conventional component and is defined by its ultimate analysis (UA) and proximate

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analysis (PA). The temperatures of the drying, pyrolysis and combustion zones were set based on experimentally measured values, the temperature of the gasification zone was calculated by overall

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energy balance. Reed [4], Dogru et al. [28] and Lv et al. [31] reported that the temperatures in the

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drying zone were between 100°C-200°C, 70°C -200°C, 150°C-300°C, respectively. Thus, the temperature of the drying zone was set at 127°C in this work. Pyrolysis zone temperature was set

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at 330°C according to Reed [71], who estimated the pyrolysis temperatures to be 250°C-450°C, but mostly at 330°C. For drying and pyrolysis stages, see Figure 2(a) and 2(b). an RYIELD block was used to break down the biomass into basic chemical elements (carbon, hydrogen, oxygen, and sulfur etc.), ash, water and primary tar. The primary tar included acetone, toluene and phenol, and the compositions of which were defined under the assumption that tar takes 20% of the total

biomass weight [66]. The secondary tar included naphthalene and benzene only for this model. A SEP block separated the primary tar and water from the ash and basic elements. Then, an SSPLIT block was used to separate fixed carbon from rest of the volatile elements, the ratio if seperation

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equaled to fixed carbon/total remained carbon. The volatile elements contains basic elements to form volatiles, an RGIBBS block was used to convert these chemical elements to light gases (CO, H2, CO2 , N2 etc.). The mixture stream of fixed carbon, light gases, primary tar, steam and ash was considered as a pyrolysis product.

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2.2.3 Gasification and Combustion

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Char, secondary tar and volatiles reacted with abundant air in the combustion zone, and limited

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air in the gasification zone. In an updraft gasifier, the gas phase goes in from the bottom and flows

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to the top, while the solid phase flows in the opposite direction. Figure 2(c) shows how the streams and operation blocks corresponded to these reactor facts. The stream “Gas*” was not a recycle

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stream, but it was circulated since this gas phase stream comes out of the combustion zone and

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goes into the gasification zone. This flowsheet became complex when Aspen Plus blocks involved

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in it, see Figure 2(d). The returned stream “Gas*” was also labeled in this figure. The model divided char gasification into heterogeneous reactions and homogeneous reactions.

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Kaushal and Tyagi [46] used this thought and developed a kinetic simulation for a fluidized bed gasifier, but they did not specify any details regarding residence time. As is well-known, gas

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usually goes through the reactor much faster than the biomass: the residence time of the biomass is in the order of several hundred to several thousand times larger than that of the gas. Thus, the residence times for gas-solid phase reaction set and gas phase reaction set should be set separately. To cater this phenomenon, as shown in Figure 2(d), two RCSTR blocks were used for

gasification zone considering different residence times, the same settings were applied to the combustion zone. Since the void ratio of the bed is around 0.5, the gas phase would have back flows and diverse velocities [72]. The gasifier was considered to have the hybrid nature of both

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PFR and CSTR. To approximate this flow, one CSTR was used in each zone for each homogeneous or heterogeneous reaction set, but CSTRs in series were used to mimic the plug flow nature. Fogler [73] drew a conclusion that we can model a PFR with a large number of

CSTRs, justifying that a number of CSTRs in series could be used to mimic a PFR. Nikoo and Mahinpey [74], Abdelouahed et al.[75] also used two RCSTR blocks in series to optimize

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fluidized gasifier. Due to these reasons, the use of CSTR blocks is appropriate. Notice that

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adding sets of heterogeneous and homogeneous blocks could benefit the model accuracy, it is

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also necessary when this model is used in industry scale gasifiers.

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Heterogeneous reactions were activated in “HETERO1” and “HETERO2”, and homogeneous

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reactions were activated in blocks “HOMO1” and “HOMO2”. All the reactions and their rates

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considered in this work are shown in Table 3. In this paper, the volume of gasification zone and combustion zone were assumed to be ¼ of the total bed height. Chen et al. [34] conducted an

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experiment for updraft gasifier, in which he provided temperature profile along the bed height. The total length of the bed was 22cm, and each zone occupied approximately ¼ of the total

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length (around 6cm). Hihedu et al. [59] and Ueki et al. [38] also reported bed temperature

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profiles, indicating the combustion and gasification zones were almost the same length, and they in total took half of the reactor length. The residence time of each block was calculated by a quarter of the total residence time of gas phase and solid phase. As CSTRs were used to mimic the gasification and combustion processes, the total residence time could be calculated by the feed, void ratio and zone volume. The total residence time of solid

phase was estimated by Eq. (8), for gas phase was estimated by Eq. (9). For the approximation of heterogeneous reaction CSTR block, the simulated reactor diameters were the same as those used in experiments, the reactor length could be calculated by Eq. (10). V rt   D L / 4 2

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(5)

t h t , to ta l   b ioV r t / F b io

(6)

t h o m o , to ta l  (1   )V r t / F a ir

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(7)

(8)

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t h t , z o n e  1 / 4 t h t , to ta l

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t h m , z o n e  1 / 4 t h m , to ta l

(10)

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L h m  L h t  t h m , to ta l / t h t , to ta l

(9)

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A “COOLDOWN” block was used to simulate gas transformations and temperature change after

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it leaves the high-temperature zones and “syngas” stream was the final syngas product. Zainal et al. [33] reported that the combustion zone temperature was 1000°C. Dogru et al. [28]

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and Sheth and Babu [76] reported the combustion zone temperature for downdraft gasifiers were

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1000°C- 1200°C and 900°C-1050°C, respectively. So combustion zone temperature was set at 1000°C in this work. The gasification zone temperature was calculated by overall energy balance. The calculated temperature was around 570°C-670°C in this work, which was consistent with the values reports by Ueki [38] and Zainal [33] (approximately 450°C -800°C). The temperatures of “HOMO1” and “HETERO1” were the same, and were calculated by a

“DESIGN SPEC” block. To maintain energy conservation, the net released energy were calculated by: Q hm 1  Q ht1   Q hm 2  Q ht 2

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(11)

T hm 1  T ht1

(12)

Definitely, development and validation of a reliable model for large-scale gasifier is needed. However, the experimental data of industrial-scale gasifier that can be used for comparing with

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models is still insufficient. For example, as the gasification goes, the biomass shrinkage and

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breakup theory were not well developed yet, the void ratio along the bed length would not be as

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uniform as a pilot scale reactor. Furthermore, the heat loss to the surroundings, heat exchange

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between each zone and zone volumes would change vigorously due to the scaling up. For a large scale reactor, a large number of CSTRs will be needed, which will also make this model harder to

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converge as the number of CSTR increases.

water, primary tar and ash

(a)

wet biomass

dried biomass RYIELD

SEP

(b)

dried biomass

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water, primary tar and ash

pyrolysis products

light gases SSPLIT

RGIBBS

MIXER fixed carbon

(c)

Hetero 1

Gasification

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Gas*

Solid

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Combustion

Solid

Air

A

Hetero 2

Combustion Zone

Homo 2

Solid+Gas

Solid

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Gas

Homo 1

Solid+Gas

Solid

Solid

Gas

Gas to Cooldown

Gasification Zone

Solid

Gas

(d)

syngas

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COOLDOWN

SPLIT

HOMO1

HETERO1

RCSTR

RCSTR

SPLIT HOMO2

HETERO2

RCSTR

RCSTR

ash

carbon

Reduction Zone

air Combustion Zone

Gas*

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pyrolysis products

RCSTR

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Figure 2. (a) Flowsheet for drying and breaking down; (b) flowsheet for pyrolysis; (c) streams and operation blocks corresponded to reactor facts; and (d) Aspen flowsheet for gasification and oxidation zones Table 3. Reactions involved in biomass gasification and their kinetic parameters Reactions

Kinetic Parameters

Heterogeneous Reactions

ArTβ (mol(1-α)m(3α-3)s-1)

Ea (kJ/kmol)

Boudouard [77]

C+CO2→2CO

589T

222829

Carbon shift [78]

C+H2O→CO+H2

5.714T

129706

Combustion I [48]

C+0.5O2→ CO

0.002

79000

Combustion II [79]

C+O2→CO2

7.96×10-7

27118

Methanation [80]

C+2H2→CH4

0.0342T

129706

ArTβ (mol(1-α)m(3α-3)s-1)

Ea (kJ/kmol)

1.3×1011CH2O0.5

125600

Fr

2.78

12560

Rr

104.830

78364

Fr

312

30000

Rr

6.09×1014

257000

Homogeneous Reactions Combustion III [81]

CO+0.5O2→CO2

Water-gas shift [77]

CO+H2O↔CO2+H2

Steam methane reform [82]

CO+3H2↔CH4+H2O H2+0.5O2→H2O

3.53×108.4

30514

Acetone cracking [66]

C3H6O2→0.5C6H6O+1.5H2O

104

136000

Phenol cracking [66]

C6H6O→0.5C10H8+CO+H2

107

100000

Toluene cracking [66]

C7H8+H2→C6H6+CH4

3.3×1010

247000

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Combustion IV [77]

3. Model Validation

To validate the RXN model, the predicted syngas compositions at different equivalence ratios (ERs) were compared with the corresponding experimental data. ER is an essential parameter

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provided oxygen to oxygen required for full combustion: th e a c tu a l a ir/fu e l ra tio

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ER=

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influencing gasification temperatures and syngas compositions. It is defined as the ratio of

(13)

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a ir/fu e l ra tio fo r s to ic h io m e tric c o m b u s tio n

3.1. Simulation objects

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Two representative experiments were selected to test and validate the RXN-model. Test Case I

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was conducted by Yasuaki Ueki et al [38]. The gasifier had a height of 1m and an inner diameter

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of 0.102m, and the biomass bed height was 0.6m. The steady state biomass feeding rate was 0.75kg/h with 20min internals, air flow rate was set at 20L/min. Test Case II was reported by

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Seggiani et al. [83], who adopted a gasifier of 2m height and 0.165m internal diameter with a bed height 1m. Experimental tests were conducted at different mixing ratios of blending sewage sludge

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(SS) and wood pellets (WP) mixtures to analyze the effects on gasification behaviors. ER ratios at 0.15, 0.20, and 0.25 were operated for all mixtures. Table 2 shows the results of the proximate analysis and ultimate analysis of biomass species used in these cases.

Table 2. Proximate and ultimate analysis of biomass in the two experiments Case I Wood pellets

Case II Wood pellets

Sewage sludge

74.1 17.2 0.7 8.0

44.0 5.1 30.9 20.0

49.3 6.2 44.5 <0.1 <0.1

51.2 8.2 31.8 7.1 1.7

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Proximate Analysis (wt.%) Volatile matter 79.70 Fixed carbon 15.09 Ash 0.57 Moisture 4.64 Ultimate Analysis (wt.% dry, ash-free basis) Carbon 49.58 Hydrogen 6.65 Oxygen 43.59 Nitrogen 0.19 Sulfur

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3.2. Validation

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In Figure 3, the RXN simulation results and the MGFE model predictions were compared with

M

experimental data at air/fuel ratio equals to 1.6m3/kg for Case I. It can be seen that the predictions

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of the RXN model corresponded closely to the experimental data, while MGFE model results

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failed to correctly predict the syngas compositions. For CO, the prediction error of the RXN model was about 11%, while the error of MGFE model was approximately 36%. For CO2 and H2, the

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values predicted by the RXN model were very close to experimental data, while the MGFE model gave a value twice bigger than the experimental data. Both the RXN and the MGFE models

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overpredicted CH4. As is described in Case I, the woody biomass pellets were pre-dried for 24

A

hours prior to each run, yet the original analysis was used in the simulations. This also resulted in bigger errors.

0.30 Laboratory data RXN model MGFE model

0.20

0.15

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Volume Fraction

0.25

0.10

0.05

0.00 CO2

H2

CH4

U

CO

Figure 3. Case I syngas compositions for CO, CO2, H2 and CH4

N

Figure 4 shows the comparison of the RXN and the MGFE models predictions with

A

experimental data at ER 0.10 to 0.30 for Case II. In Figure 4a, the volume fractions of CO at ER=

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0.15, 0.20, 0.25 obtained from the experiment were about 0.3. The RXN model reasonably

D

underpredicted the CO mole fraction, while the MGFE model significantly misestimated it. In

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Figure 4(b), the experimental measurement of CO2 volume fraction slightly increases with the increasing ER from 0.15 to 0.25. The RXN model prediction gives a similar trend with the

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experimental data, while MGFE model gives an opposite trend. For CH4 and H2, the RXN model

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correctly predicted their value, while MGFE model gave more than 100% errors in CO2 predictions and more than 200% in CH4 predictions, both of which are not acceptable.

A

According to the comparisons above, the RXN model is more accurate than the MGFE model.

The RXN model benefits from the theoretical fundamentals of using reaction kinetics as it takes the time scale into account, it. The application of different residence times for heterogeneous and homogeneous reactions are also considered the main parameter for a more precise model.

(b) 0.40

0.40 RXN model MGFE model Laboratory data

0.30 0.25 0.20 0.15 0.10

0.30 0.25 0.20 0.15 0.10 0.05

0.05 0.00 0.05

0.10

0.15

0.20

0.25

0.30

0.00 0.05

0.35

RXN model MGFE model Laboratory data

0.15

0.20

0.25

0.30

0.35

0.25 0.20 0.15

0.25 0.20 0.15 0.10

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0.10

0.30

RXN model MGFE model Laboratory data

U

0.30

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.00 0.05

0.10

0.15

0.20

0.25

0.30

0.35

ER

M

ER

A

0.05 0.00 0.05

0.40 0.35

Volume Fraction of H 2

Volume Fraction of CH4

(d)

0.40 0.35

0.10

ER

ER

(c)

RXN model MGFE model Laboratory data

0.35

Volume Fraction of CO2

Volume Fraction of CO

0.35

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(a)

Figure 4. Case II syngas compositions for (a) CO; (b) CO2; (C)CH4 (d)H2

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4. Model Application and Optimization

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4.1 Equivalence ratio influences

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In updraft gasifiers, the ERs are usually controlled between 0.10-0.30 depending on diverse types of feed. Generally, a higher value of ER means more oxygen is provided, resulting in better

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combustion as well as a higher reactor temperature. For RXN simulation of Case II, it can be seen that CO volume fraction first increases as ER increases to peak values and then decreases. This

A

trend is not obvious in Case II experimental data, but it can be clearly seen in the experiment of Chen et al. [34] and Blasi et al. [84] in the updraft gasifiers. Chen et al. reported that the CO volume fraction peak is at ER=0.3 for juniper gasification and 0.27-0.37 for Mesquite gasification. This difference of ER peaks may due several reasons, such as differences in bed void ratios, gasifier sizes, and chemical and physical properties of the biomass. For example, beds filled up by biomass

of difference shapes have different void ratios, which influence gas phase flow pattern significantly. Picks usually suffer from a worse bridging phenomena than pellets [85], bridging and the resulted flow pattern would cause more air bypassing the bed, and therefore requires a higher optimized

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ER. Blasi et al. found that the CO volume fraction increases with the increasing ER (air/fuel ratio 0.80-1.50kg/kg), which matches the trendline before reaching the CO peak. This phenomenon is also observed in some downdraft gasification experiments [21, 36, 86].

A slight discrepancy can be observed between the RXN prediction and the experiment. For example, the RXN model gives CO values lower than the experimental data. Possible reasons are

U

as follows. First, the gasification reaction kinetics are very complex and highly coupled as reported

N

frequently in the literature. Therefore, the final predictions would divert from the experimental

A

data, even if one of the reaction kinetics is not accurate. Moreover, as Aspen Plus restrains the

M

RXN model to consider the detailed temperature and component concentration distributions inside

D

the gasifier, uniform distributions were assumed for each zone. This also explains why there is a

in simulation cases.

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1% oxygen in the experimental data, while oxygen concentrations are always calculated to be zero

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To better understand the quality and calorific value of the syngas product, the lower heating

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value (LHV) of syngas is introduced and defined as follows: LHV=CO×12.636+H2×10.798+CH4×35.818+C2H4×59.036+C2H6×63.772

(14)

A

where, CO, H2, CH4, C2H4 and C2H6 are the molar percentages of components in the syngas. The η, LHV per kg biomass, represents the energy extracts from 1kg of biomass, which is calculated by η=LHV×dgp/Fbio

(15)

Figure 5(a) and 5(b) show the LHV and η as functions of ER, respectively. It can be seen that LHV increases from ER=0.10 to ER=0.125, and then decreases, with the maximum value (7.0MJ/m3) occurring at ER=0.125. It has the same pattern with CO volume fraction as the function

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of ER as shown Figure 4(a). The maximum value of η exists at ER=0.15, which means the maximum energy of 15.5MJ can be obtained from 1kg of biomass. The η peak and CO volume fraction peak exist at the same ER, both are close to the LHV peak at ER=0.125. From the aspect of energy conversion, ER=0.15 is the optimum operation condition for gasification of the biomass we used in our simulation (wood pellets). The optimal ER may be adjusted according to the

U

following chemical processes. For example, if the syngas will be fed into a Fischer–Tropsch

N

reactor to produce hydrocarbons, the ER should be set to 0.30 in order to cater the ideal feeding

(a)

M

A

ratio H2/CO=2 [87, 88].

10

(b)

6

4

EP

2

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0 0.05

η (MJ/kg)

D

16

TE

3

LHV (MJ/m )

8

18

0.10

0.15

0.20

14

12

10

0.25

ER

0.30

0.35

8 0.05

0.10

0.15

0.20

0.25

0.30

0.35

ER

Figure 5. The RXN simulations for Case II (a) LHV and (b) η at different ERs

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4.2 Gasification temperature influence The biomass gasifiers are preferred to be energy self-sustainable, as the heat produced from the

combustion zone transfers to other areas of the bed and fully support all reactions. Additional heat can be provided by adding water steam as a co-gasification agent or applying hot wire to raise the bed temperature. The syngas compositions would be reformed at a higher gasification temperature

and thus preserve more energy. Figure 6(a) gives the syngas compositions at ER=0.25, +0°C represents the calculated temperature with no extra heat funneled, gas compositions are also given for raised temperatures to evaluate the effect of gasification temperatures. The figure clarifies the

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CO and H2 mole fractions increase with the increase of temperature, while the H2O mole fraction decreases. This prediction matches the experiment facts in the review paper[89]. The mole fraction of syngas compositions after the water condensed are given in Figure 6(b). CO compositions are comparable at low temperatures (+0°C-+90°C) due to increased steam composition, but it increases to 0.24 at +120°C, H2 increases with increasing temperature. CH4 mole fraction remains

U

fairly steady, CO2 fraction increases to a peak value at +60°C and then decrease on both wet and

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dry basis. This same trend can be found in experiments conducted by Lapuerta et al. [90] and Peng

A

et al. [91]. The extra energy needed and produced are shown in Figure 6(c), this illustrates that the

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syngas has bigger heating values if the gasifier is operated at higher temperatures, but this needs a lot more energy to be put into the system. According to this prediction, it is not wise to use

D

commercial energy resources (such as electricity or natural gas) to increase the bed temperature,

A

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EP

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but the hybrid of gasification and exothermic chemical processes could be a better solution.

(a)

CO CO2

0.35

H2

0.30

CH4 H2 O

0.25 0.20 0.15 0.10 0.05 0.00

+0

+30

+90

+60

Reduction Zone Off-balance Temperatrue (ºC)

(b)

CO CO2

U

0.35

H2

0.30

N

CH4

0.25

A

0.20

M

0.15 0.10 0.05 0.00

+0

D

Mole Fraction of Syngas (dry basis)

0.40

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Mole Fraction of Syngas (wet basis)

0.40

+30

+60

+90

TE

Reduction Zone Off-balance Temperatrue (ºC)

(c)

55000 50000 45000

A

Energy (Btu/hour)

CC

EP

40000

Extra Energy Needed Extra Energy Produced Fitting Curve for Needed Energy Fitting Curve for Produced Energy

35000 30000

2

E= -1.55+573T-1.42T 2 R =0.999

25000 20000 15000 10000

2

E=267+263T-1.46T 2 R =0.950

5000 0

-30

+0

+30

+60

+90

+120

Reduction Zone Temperature (ºC)

Figure 6. Reduction zone temperature influence on (a) syngas composition (wet basis); (b) syngas composition (dry basis); (c) extra energy needed and extra energy produced.

4.3 Biomass mixture influence Several authors have investigated the co-gasification of biomass-coal [92-97] as well as cogasification of biomass mixtures [91, 98, 99]. It is very important for gasification plants to have

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different biomass feeds at the different times of the year because of the particular biomass harvest times.

Feed type with a higher heating value (i.e., lower moisture, lower ash, and higher carbon content) will produce syngas with more combustible components, which means higher syngas quality. This phenomenon could be found in the experiments by Ong et al. [98]and Seggiani etc. [83], Ong et

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al. gasified sewage sludge and wood chips mixture, while Seggiani et al. used sewage sludge with

N

wood pellets to test the compositions in syngas products. The wood pellets/chips have higher

A

heating values than the sewage sludge, higher CO compositions are also found as a higher ratio of

M

wood chip/pellets were mixed. Figure 7 shows the gasification products and their LHVs using

D

different mixtures of sewage sludge and wood pellets, their UA and PA are provided by Seggiani

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et al. [83]. As it can be seen, the RXN simulation gave a similar prediction as experiments mentioned above. CO decreases significantly due to higher mixed sewage sludge, both H2 and CO2

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composition first increase to a maximum and then decrease, while CH4 stays fairly constant. LHV decreases significantly as more sewage sludge was fed, this verifies the fact that it is favorable to

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add higher heating value biomass into the lower heating value biomass to reform the syngas

A

composition. This operation also stabilizes the bed temperature and prevents the bed from a shutdown.

0.25

CO CH4 H2

0.20

10

8

0.10

4

0.05

2

3

6

LHV (MJ/m )

Tar LHV

0.15

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Mole Fraction

CO2

0.00

0

100% wood pellets 0% sewage sludge

70% wood pellets 30% sewage sludge

50% wood pellets 50% sewage sludge

30% wood pellets 70% sewage sludge

0% wood pellets 100% sewage sludge

Figure 7. Syngas compositions and lower heating value for different ratio of wood pellets and sewage sludge mixtures Because of degradation on heating values, coal is a better fuel than hardwood, while softwood

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is the worst among these three. Traditionally, hardwood has been the preferred fuel in wood stoves

N

and fireplaces due to its low moisture content. Softwood is easy to ignite so it is convenient for

A

startups. However, if both hardwood and softwood are dried and compressed to the same density,

M

their BTUs are very similar, which means they will have the almost the same performance on

D

gasification.

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4.4 Moisture content influence

Moisture content has a significant impact on the outcome of the gasification process. Some type

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of biomass needs to be preheated and control the moisture content less than 15% for most gasifiers [100], yet some updraft reactors may process biomass with a moisture content as high as 50%[101].

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Moisture content above 30% usually results in ignition difficulty and is more likely to cause bed failures. Higher moisture content means more energy is required for water evaporation in the

A

drying zone, which lowers the bed temperature. Decreased bed temperature results in incomplete tar cracking and degrade syngas formation. Figure 8 gives the syngas compositions with different moisture contents at ER=0.15. The biomass UA and PA are exactly the same as wood pellets[83], except for the moisture contents are set at 4%, 8%, 12% and 16%. Figure 8(a) CO and CH4

compositions illustrate syngas compositions are almost the same, yet the H2 composition decreases significantly as moisture content increases, thus resulting in a lower LHV value, as shown in Figure 8(b). At the same time, the dry gas production also decreases, so the energy produced per kilogram

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of biomass (η) also decreases as moisture content gets higher. This verifies the increasing moisture impairs the syngas quality.

0.40 0.35

H2

0.30

20

LHV Dry Gas Production η Fitting Curve for LHV Fitting Curve for Dry Gas Production Fitting Curve η

2

η=14.14+39.73MC-326.0MC 2 R =0.931

3

CH4

25

0.25 0.20 0.15

15

10

2

DGP=7.706+11.91MC-99.44MC 2 R =0.981

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Mole Fraction

(b)

Tar CO CO2

U

0.45

LHV (MJ/m ) 3 Dry Gas Production (m /h) η (MJ/kg)

(a)

0.05 0.04

0.08

0.12

0.20

0 0.00

0.04

2

LHV=6.160+8.904MC-69.13MC 2 R =0.810

0.08

0.12

0.16

0.20

Moisture Content

TE

D

Moisture Content

0.16

M

0.00 0.00

5

A

0.10

Figure 8. Moisture content influence on (a) syngas compositions of wood pellets gasification;

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(b) LHV, dry gas production and η

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4.5 Gasification conditions influences on tar yield In the downstream of the gasification process, high molecular weight compounds are condensed

A

to form tar if the temperature drops below 450ºC. Most tar is deposited in the pipe line and the rest remains as an aerosol in the syngas. Two strategies are developed to remove the tar: (1) apply removal apparatus to clean the syngas [102], and (2) improve the gasification technology to reduce the tar formation[103]. Mechanical/physical methods for tar removal face serious problems such as high operation cost and environmental discomfort [104], thus it became essential to enhance tar

cracking (i.e. thermal and catalyst cracking) in the gasification process. Brandt et al. [105] claimed that it is necessary for the gas to stay at 1250 ºC for more than 0.5 seconds in order to achieve efficient tar thermal cracking. Updraft gasifiers produce at least 10 times more tar than the

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downdraft gasifiers [26] due to the limited residence time. Since tar formation will cause harm to the engine system, it is beneficial to predict how much tar will be in the final syngas product.

It is impossible for MGFE model to achieve tar compositions, due to the fact that tar is an intermediate product. Some researchers have developed models shows tar formation. Gagliano et al. [106] developed a numerical model for downdraft gasifiers. In Gagliano’s model to

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approximate downdraft gasification process, tar yield was fixed to 4.5% w/w independent of the

N

operating conditions, and the chemical formula C6H6O0.2 was used to represent the tar, and the

A

thermochemical properties were assumed to be the same as benzene. An obvious disadvantage

M

arises due to the tar yield ratio assumption that the tar yields and compositions do not change due

D

to different operating conditions (such as ERs and bed temperatures). While in this model, the

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RXN model defined specific chemicals for primary and secondary tar, the amount of each chemical could be viewed in the final “syngas” stream.

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Downdraft gasifiers usually produce 4.5% w/w tar [26, 40]. Since the updraft gasifiers produce

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more tar than downdraft gasifiers [107], the calculated tar yield 6-8% w/w could be trusted. This model predicted a complete char conversion, means that all the char reacts inside of the gasifier to

A

support the gasification process. Experimentalists regularly report syngas compositions as they consider syngas as the mean product. Some researchers consider tar, the mean contaminant, but its composition is hard to analyze. Char is neither important nor hazardous so it seldom draws any attention. Although this model predicts char amount, the predictions are not justified due to the lack of char information in the experimental findings.

Figure 9(a) shows the simulated tar composition of wood pellets gasification by the RXN model, it can be seen that tar composition decreases drastically with increasing ER due to increasing gasification temperature. Figure 9(b) also gives the same trend that tar composition decreases with

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increasing gasification zone temperatures at ER=0.2. It can be concluded that higher gasification temperature is helpful for tar cracking to increase the combustible compositions. Figure 9(c) illustrates there is very little benzene presence in tar, toluene and acetone occupy more than 50% mole fraction of tar and they escalate a little, while the naphthalene and phenol vary as ER

0.08 RXN Simulations Fitting Curve

0.07

0.040

(b)

0.035

Mole Fraction of Tar

0.030

Tar = 0.1755exp(-ER/0.05442)+0.02610 2 R =0.980

0.03 0.02 0.01

0.10

0.15

0.20

0.25

TE

0.00 0.05

0.020

Tar = 0.01424exp(-T/92.27)+0.01493 2 R =0.985

M

0.04

0.025

A

0.05

D

Mole Fraction of Tar

0.06

RXN simulations Fitting Curve

N

(a)

U

increases.

0.30

0.015 0.010 0.005 0.000

0.35

ER

1.3 1.2 1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0.075

Naphthalene

+30

+60

+90

+120

Reduction Zone Off-balance Temperatrue (ºC) Benzene

Toluene

Acetone

Phenol

A

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Mole Fration

EP

(c)

+0

-30

0.100

0.125

0.150

0.175

0.200

0.225

0.250

0.275

0.300

0.325

ER

Figure 9. Impact on tar amounts due to: (a) ER value, (b) reduction zone temperatures, (c) tar compositions

5. Conclusions A kinetic model of biomass gasification was developed to simulate and optimize reactor performance in updraft gasifiers. The kinetic model was validated by comparison with two

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experimental findings available in the literature. It is found that the kinetic model could correctly predict the syngas and tar compositions, which is more accurate than the equilibrium model. Systematic parametric studies were conducted to investigate the effect of ER, gasification temperature, biomass mixture and moisture content on syngas and tar compositions. This study reveals that: 1)The optimal ER depends on the specific application of syngas, 2) Recovering waste

U

heat to achieve a higher gasification temperature is favorable, 3) Lower heating value biomass

N

could be mixed with higher heating value biomass to improve syngas quality, 4) The gasification

M

A

temperature has a significant effect on tar production amount and compositions.

Acknowledgements

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The authors gratefully acknowledge financial support from the Wayne and Gayle Laufer

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Endowment.

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Notation

number for atoms

A

total number of atoms entering the reactor

Ar

frequency factor (mol(1-α)m(3α-3)s-1)

CC

𝑎

A

C

concentration (mol/m3)

D

diameter of the gasifier (m)

dgp

dry gas production (m3/h)

Ea

activation energy (kJ/kmol)

ER

equivalence ratio

Fbio

feed rate of biomass (kg/h)

feed rate of air (m3/h)

Gt

total Gibbs energy of system (kJ/mol)

kr

reaction rate coefficient (mol(1-α)m(3α-3)s-1)

L

total length of combustion and gasification zones (m)

Lhm

reactor length of homogeneous reactor (m)

Lht

reactor length of homogeneous reactor (m)

LHV

lower heating value (MJ/m3)

n

moles (mol)

Qhm1

heat generated by block “HOMO1” (J/sec)

Qht1

heat generated by block “HETERO1” (J/sec)

Qhm2

heat generated by block “HOMO2” (J/sec)

Qht2

heat generated by block “HETERO2” (J/sec)

R

gas constant (kJ/kmol K)

r

rate of reaction (mol/sec•m3)

thm,total

total residence time of combustion and gasification zones for homogeneous reactor (h)

thm,zone

total residence time of combustion or gasification zone for homogeneous reactor (h)

tht,total

total residence time of combustion and gasification zones for heterogeneous reactor (h)

tht,zone

total residence time of combustion or gasification zone for heterogeneous reactor (h)

T

reaction temperature

Thm1

temperature (K)

Tht1

temperature (K)

Vrt

volume (m3)

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EP

TE

D

M

A

N

U

SC RI PT

Fair

Greek letters

A

α

ρbio

reaction order bulk density of biomass (kg/m3)

β

temperature exponent

η

lower heating value of syngas out of 1kg biomass (MJ/kg)

φ

void ratio

i

species index

j

element index

r

reaction index

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Subscripts

References

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