Process Simulation Development of Fast Pyrolysis of Wood Using

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Process simulation development of fast pyrolysis of wood using Aspen Plus Kristin Onarheim, Yrjö Solantausta, and Jani Lehto Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/ef502023y • Publication Date (Web): 08 Dec 2014 Downloaded from http://pubs.acs.org on December 13, 2014

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Process simulation development of fast pyrolysis of wood using Aspen Plus

Kristin Onarheim*, Yrjö Solantausta, Jani Lehto VTT, P.O. Box 1000, 02044 VTT, Finland [email protected]

Abstract

A steady state Aspen Plus simulation model has been developed which provides estimated mass and energy balances for an industrial fluidizing bed fast pyrolysis process to produce bio oil. The tool can be used for assessing plant performance under varying process conditions using different feedstock. A 30 MW lower heating value (LHV) bio oil plant was modeled utilizing two different feedstock types (pine and forest residues). Fast pyrolysis product yields are functions of feedstock ash content and were calculated based on data generated by a 0.5 t/d fast pyrolysis test unit. The UNIQUAC activity coefficient method is used for the calculation of the liquid phase, and the ideal gas fugacity coefficient method is used for the vapor phase calculations. Modelling condensation of fast pyrolysis vapors is also verified against experimental data gained from the 0.5 t/d test unit. Production costs were estimated for the two concepts. Results show that the pine-based fast pyrolysis process has a better process efficiency and a lower production costs compared to the forest residue-based process. The total estimated capital investment costs including plant fixed capital investment (FCI), start-up, working capital and interest over construction period were estimated to be 24 M€ and 28 M€ for pine and forest residue-based processes, respectively. Sensitivity analyses showed that the bio oil quality and bio oil production efficiency can be improved by drying the recycle gas. Varying production cost parameters within an industrially relevant range results in a production cost of bio oil between 50 and 70 €/MWh. However, unless wood price is lower than current market price (20 €/MWh assumed here), or if excess heat may be valued higher than fuel price, production is not currently competitive compared to fossil alternatives.

ACS Paragon Plus Environment 1

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1 Introduction

Research on fast pyrolysis gained attention already after the global oil price crisis in the first half of the 1970s. As oil prices quadrupled towards the end of the crisis, both economic reasons and the need for self-sufficiency emphasised a need for alternatives to mineral oil. Production of bio oil using fast pyrolysis, initially from wastes and later especially from woody biomass, was presented as one of the alternatives. A quick and easy transition from fossil fuels to biomass-based energy systems will not happen in the near future. Instead, a diffusion of various renewable energy systems complementing fossil fuel systems is both economically more feasible and an easier way of introducing novel biomass energy concepts. One example is the introduction of fast pyrolysis bio oil to bioenergy markets and its utilization as an alternative and renewable fuel to replace fuel oils [1,2]. Fast pyrolysis is a thermal conversion process in which wood is converted into organic vapors, gases, water and char at atmospheric pressure in the absence of oxygen. Product yield composition is determined primarily by the feedstock properties. Fast pyrolysis of wood takes place at medium temperature levels (approx. 450–500°C). The main product is the organic vapor fraction, which is condensed into bio oil. Non-condensable gases, water and char are also produced. A simulation tool is needed in order to assess the performance of whole industrial scale fast pyrolysis plants, because experimental data is only available from pilot-scale plants. The majority of modelling works published earlier have been focused on computational fluid dynamics and kinetic modelling with a particular focus on the reactor [3,4]. Some studies on steady state process modelling of fast pyrolysis have been published. A model for simulating a fast pyrolysis process integrated to a power boiler was built by Yan and Zhang [5] for pyrolysis of low-rank coal. Input for their model is available from the literature, and pyrolysis oil yield was described by four single components. Ringer et al. [6] developed an Aspen Plus® model which provided mass and energy balances for a technical and economic analysis of a large-scale fast pyrolysis process. Input for this model was based partly on data from the literature, partly on assumptions and engineering judgement, and partly on lab-scale experimental data. Jones et al. [7] established a design case for the production of gasoline and diesel from biomass via fast pyrolysis. The heat and material balances were obtained by a conceptual process design made with CHEMCAD©. The input for that part of the model describing fast pyrolysis was based partly on data from the literature and partly on experience from the early stages of commercialization. Also Wright et al. [8] used an Aspen Plus fast pyrolysis model in order to simulate process mass and energy balances. The process was using corn stover and included process steps up to the distillation of bio oil into naphtha-range (gasoline blend) and diesel-range fractions. The model was mainly based on assumptions and input from the literature, but also on experimental data when available. The extent of experimental data employed input is unknown. Major assumptions were based on previous analyses of fuel production via biochemical and gasification processes. Input data largely determines the quality of the model. The models mentioned previously have been based on a range of sources accumulated into one composite compilation. In collecting data from various sources, there is a significant risk of building an incoherent model, as the data might be from various process conditions using various feedstock types. In the worst case, data could have been generated from entirely different pyrolysis processes where, for example, critical process parameters, such as residence time, temperature, and pressure, may have varied.


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Reliable validation of a simulation model requires data input from large scale continuous units, as these generate more reliable data when scaling up processes to demonstration or commercial size. Using laboratory scale data as an input for modelling of commercial and industrial size plants can produce results that are challenging to validate. The purpose of this work was to develop a simulation tool for the modelling of a fast pyrolysis process based on coherent experimental data gained from a 0.5 t/d process development unit (PDU) at VTT [9]. The PDU includes a circulating fluidized bed reactor (CFB), but it has also been operated in the bubbling fluidized-bed (BFB) mode with similar yields as in the CFB-mode. Scott et al. [10] showed already much earlier that operating the fast pyrolysis reactor in the bubbling fluidized bed mode (BFB) yield similar product yields as in the CFB mode. Analyses of the bio oil produced in the PDU enables the development of a more detailed pyrolysis model. The model can be used in analysing fast pyrolysis process performance including process responses to operational changes and optimization. The PDU contains all the necessary components of a real continuous fast pyrolysis plant as illustrated in Figure 1.

Figure 1 An illustration of the VTT PDU (0.5 t/d) pyrolysis unit [9]

1.1 Fast pyrolysis of wood In fast pyrolysis, wood is thermally decomposed into condensable organic vapors, non-condensable gases, char, and water [11]. In order to achieve as high a yield of condensable vapors as possible, the reaction temperature window is limited to approximately 480–520°C. Gas residence time is short (1–2 seconds), depending on the particle size. Further information on the effect of decomposition conditions on product yields is found, for example, from Oasmaa and Peacocke [12]. After the pyrolysis reactor, char is separated from the product gas in a set of cyclones, and will be sent to a char combustor for combustion. Pyrolysis vapors are sent to the scrubber, where organic vapors and much of the water vapour are condensed. In the scrubber, condensed bio oil is used as a quenching liquid. A proportion of the non-


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condensable gases is recycled back to the reactor and used as fluidizing gas. The remaining gas is sent to the char combustor for combustion together with the char.

1.2 Fast pyrolysis bio oil Bio oil has logistic advantages over solid biomass fuels. It is a low-cost liquid biofuel [13,14], which may be used in the production of heat, and in the future, for example, in the production of power, transportation fuels or chemicals [15]. Fast pyrolysis bio oil consists primarily of highly polar oxygenated compounds such as ketones, aldehydes, organic acids, furans and anhydro sugars in addition to water-soluble and insoluble lignin fractions and water [16,17,18]. When used for heating applications instead of heavy fuel oil, CO2 emissions may be reduced more than 80% [19]. Several institutions are currently investigating the possibilities for upgrading bio oil into higher value products [20]. One disadvantage of bio oil as a fuel is its relatively high moisture content. The moisture content of forest residuebased bio oil is normally around 30 wt-% (with feedstock moisture of 8–10 wt-%) and is seldom less than 25 wt-% even for pine feedstock [9]. The high moisture content is a result of both the original feedstock moisture and water formation due to pyrolysis reactions. Also, the low pH (typically less than 3) of produced bio oil must be taken into account in the design of the end-use applications. 2 Model development The following critical issues for modelling have been addressed in the next sections: •

Estimation of process yields and bio oil product composition

•

Component properties and characteristics connected to the decomposition of wood

•

Binary interaction between components

•

Component polarity and vapor phase association reactions

•

Physical property methods

•

Heat of reaction

2.1 Feedstock specification In Aspen Plus the individual components are classified into major classes. In this work, three component classes have been used: conventional components, non-conventional components and solids. Conventional components are standard components that have a fixed molecular structure. These components are either found in the various Aspen Plus databanks or can be imported to Aspen Plus from external sources. Conventional components found in the databanks have been provided with the thermodynamic and transport properties needed for calculating physical properties and phase equilibria through selected property methods (see Chapter 2.4). Non-conventional components are not chemical components and cannot be characterized by a molecular formula. Instead, they are specified by empirical factors representing their elemental composition. These components have no thermodynamic or transport properties associated with them, and they do not participate in any phase or chemical


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equilibrium calculations. Enthalpy and density are the only properties calculated for non-conventional components. These properties are calculated by empirical correlations. Solids are characterized either as conventional or non-conventional solids. Conventional solids are pure components that can take part in equilibrium reactions. Conventional inert solids, on the other hand, do not take part in phase equilibrium reactions, but can take part in chemical equilibrium reactions and in reactions between solids and conventional components. Non-conventional solids are heterogeneous components inert to any equilibrium, and they cannot be represented with a molecular structure [21]. Wood is modeled in the simulation as a non-conventional solid component. A set of proximate and ultimate component attributes were assigned to this component. The ultimate and proximate analyses of the feedstock types based on in-house analyses from VTT are given in Table 1. The feedstock is specified on a dry basis. The heating values are based on lower heating value (LHV) and given for both wet and dry basis. Moisture was fed into the process separately as a conventional component.

Table 1 Ultimate and proximate analysis (wt-%, dry) and heating value of feedstock ELEMENT

PINE

FOREST RESIDUE

Ash

0.2

2.6

Carbon

50.9

51.4

VM

83.9

80.0

Hydrogen

6.3

6.3

FC

15.9

17.4

Ash

0.2

2.6

Nitrogen

0.1

0.3

Oxygen

42.5

39.4

Sulfur

0

0

Chlorine

0

0

Moisture

PINE

FOREST RESIDUE

0

0

Feedstock heating value, LHV - MJ/kg, wet

8.3

8.5

- MJ/kg, dry

19.1

19.5

There is very little ash in the bio oil product, as most ash from the feedstock ends up in the char after the pyrolysis reactor. Only a small share of the ash, which is too fine to be separated with cyclones, will end up in the bio oil product. In the simulation model, all the feedstock ash was assumed to end up in the char product.

2.2 Selection of components The products from the fast pyrolysis of wood comprise several hundred components with a wide range of chemical and thermodynamic properties. The low volatility of bio oil is related to the high polarity of the compounds. The polarity is a result of highly oxygenated products combined with large molecular mass. Only around 20–40 wt-% of the components in wood fast pyrolysis product oil can be analysed directly by conventional methods such as gas chromatography. The remaining fractions are analysed as compound groups by the solvent fractionation method [12]. Another major challenge is that components present in the vapor phase and in the liquid phase may be distinct. As only little is known about the components present in the vapor phase during the fast pyrolysis of wood [22,23], it is possible that a proportion of the primary pyrolysis products in the vapor phase are present only as intermediate components. The secondary chemical reactions taking place in the vapor phase are outside the scope of this work.


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2.2.1 Organics Components used in the model simulation to represent organic vapors and liquids were selected based on analyses carried out at VTT. VTT analyses have been complemented with data from the literature. The resulting component matrix is presented in Table 2 together with a comparison of model components used in the National Renewable Energy Laboratory (NREL) [6,24] and Pacific Northwest National Laboratory (PNNL) models [7,24]. Only the main functional groups analysed from wood-based bio oil have been included in the model. Each functional group is represented by one or two major components. The reason for this is that components belonging to the same functional group will behave in a similar manner. This facilitates the modelling work and the convergence rate of the model considerably. As a consequence, a significant number of components known to be found in wood-based bio oil have been excluded from the model. However, the amount of each of these components is very small, and it was concluded that including them would not contribute to any significant improvement in the overall model.

Table 2 Model components used in simulation compared to a similar study [24] FUNCTIONAL GROUP

MODEL COMPONENTS VTT

Organic acids Alcohols Aldehydes Phenolics/lignin

NREL/PNNL

Acetic acid

Crotonic acid

Ethylene glycol, acetol

1,4-benzenediol

Glycol aldehyde

3-methoxy-4-hydroxybenzaldehyde

Guaiacol (LML*) and pyrolignin (HML*)

Isoeugenol, cellobiose, dimethoxy stilbene, dibenzofuran, oligomeric compounds with β-O-4 bond, phenylcoumaran compounds

Levoglucosan

Levoglucosan

Ketones

-

Hydroxyacetone

Furans

Furfural

Furfural

Sugar derivatives

Extractives Sulfur Nitrogen

Oleic acid

Dehydroabietic acid

Ethylthioethanol

Dibenzothiophene

2-pyrrolidone

2,4,6-trimethylpyridine

* LML = low molecular lignin, HML = high molecular lignin

The complex structure of lignin requires special attention in modelling. A fast pyrolysis lignin fraction should not be treated as one single component with a single set of given thermodynamic and transport properties. Instead, the lignin product in fast pyrolysis bio oil consists of a range of lignin fractions of varying molecular weight and varying thermodynamic and transport properties. In the model, guaiacol (C7) was chosen as the model compound representing the light molecular weight lignin fraction. Guaiacol was the most abundant phenol in the bio oil and has a moderate boiling point of 205°C. For the heavy molecular weight lignin fraction, a model component was created. The heavy molecular weight lignin model component was modeled based on analyses of a lignin polymer predicted from NMRbased lignin analysis. This compound, containing key components such as syringol and guaiacol, was simplified slightly for the simulation model. The component contains two aromatic rings and the molecular formula C24H32O4 resembles the heavy lignin fraction analysed in fast pyrolysis products. The molecular weight is 288.3 g/mol.


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During pyrolysis of cellulose above 300°C, significant amounts of anhydromonosaccharides are formed. Levoglucosan (1.6-anhydro-β-D-glucopyronose) is known to be the major cellulose product of pyrolysis [25,26], and it was, therefore, chosen as a representative model compound for these sugar derivatives. Levoglucosan was, at the time of model buildup, not part of the Aspen Plus databanks and was imported as a conventional component to the simulation from an external source.

Aerosols are formed in fast pyrolysis processes. A considerable amount of the organic components are in aerosol form after the reactor. The formation of aerosols and their behaviour have not been taken into consideration in this steady state model simulation. The formation of fast pyrolysis aerosols is a complex process, and there is no experimental data to verify the phenomenon. Further information can be found for instance from [27].

Fast pyrolysis of forest residue creates a multi-phase bio oil product in which the top phase consists mainly of extractives. Extractives are largely fatty and hydroxyl acids, fatty alcohols, resin acids and some lignin monomers. Extractives are the result of a higher share of bark and needles in the pyrolysis feedstock. Based on analyses of bio oil top phase from experimental work at VTT, oleic acid was chosen as a model component.

2.2.2 Non-condensable gases Non-condensable gas components considered are CO, CO2, H2, CH4, C2H6 and C2H4. Gases are defined as supercritical components in the simulation model. In order to correctly model the behaviour of gases dissolving in the liquid phase and the vapor-liquid equilibrium, Henry’s Law was used for the supercritical components. The basic version of Henry’s Law illustrates the constant ratio of the partial pressure of the gas in the vapor phase to its concentration in the liquid phase: p = kci

(1)

where p is the partial pressure of the gas in the vapor phase [Pa], ci is the concentration of the gas in the liquid phase [mole] at a fixed temperature and k is the Henry’s Law constant for molar solubility (ci/p) [Pa m3 mol-1]. 2.2.3 Char and sand Char generated during fast pyrolysis reaction is modeled as a non-conventional component consisting of carbon, hydrogen, oxygen and nitrogen elements. The char composition was determined by the difference in the elemental balance (element input – specified output). The resulting char component composition and heating value is comparable with analyses carried out on char from experimental samples.

Sand is used as a heating medium in the pyrolysis reactor. The heat capacity of sand varies with temperature and sand type. SiO2 was used as a model compound for sand. The temperature of the sand into the reactor is 800°C, which is determined by fluidized-bed combustion temperature. The mass flow rate was adjusted to provide sufficient heat in order to reach the fast pyrolysis reactor temperature (480°C). The temperature of the sand out of the reactor can be considered equal to the pyrolysis reaction temperature [28].


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2.3 Specification of yields Feedstock characteristics are critically important in the modelling of fast pyrolysis. Chemical composition of wood and ash content are major factors that determine product yields. In this work, product yields are estimated as a function of feedstock ash content. In the reactor, ash acts as a catalyst; the higher the ash content, the lower the organic yield, see Figure 2. Both char and non-condensable gas yields increase with increasing feedstock ash. Figure 3 shows the char yield as a function of feedstock ash content. The yields illustrated in Figures 2 and 3 were analysed from fast pyrolysis runs in the PDU. The composition of the organic liquid fraction has been taken from Oasmaa et al. [9].

Organic yield [wt-%]

70 65 60 55 50 45 40 0.1

1.0

10.0

Ash [wt-%] Figure 2 Measured yield of organic fraction as a function of feedstock ash content (dry basis) [29].

35 30

Char yield [wt-%]

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25 20 15 10 5 0.1

1 Ash [wt-%]

10

Figure 3 Measured yields of char as a function of feedstock ash content (dry basis)

Yields for non-condensable gases and water were also calculated with similar correlations derived from PDU data. The char yield is calculated by difference and can be compared to results from the PDU (Figure 3). Typical yields for fluidized bed reactors when the reaction temperature is 480°C are presented in Table 3. The reactor model does not predict the effects of varying process conditions such as reaction temperature, particle residence time, or particle size distribution. The results are, therefore, applicable at a reactor temperature of 480–520˚C, a gas residence time of 1 second, and an average particle size distribution of D50 = < 5 mm.


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Table 3 Average fast pyrolysis yields in fluidized bed reactor (wt-%, dry) PRODUCT YIELD

PINE

FOREST RESIDUE

Organic compounds

59.3

49.2

Non-condensable gases

10.2

12.6

Char

19.9

25.4

Pyrolysis water

10.6

12.8

2.4 Physical property method Modelling processes require an appropriate selection of thermodynamic property methods in order to achieve results that are as correct and realistic as possible. As a consequence, the selection of a property method was one of the most critical issues in this study. The physical property methods in a simulation engine are sets of routines and equations for calculating thermodynamic and transport properties of material flows. Physical property methods enable the simulation to interpret component behaviour under various given conditions. Property methods are chosen based on the conditions simulated and the components involved. Pyrolysis vapor products originate from the cellulose, hemicellulose and lignin in wood. These components are thermally broken at the pyrolysis reaction temperature, and they decompose through a set of different mechanisms into several hundreds of different components. This vast number of products in both vapor, liquid and solid phases means that there is a wide variety of thermodynamic properties affecting the process. No thermodynamic property method in Aspen Plus has been developed to represent accurately all these potential conditions and mixtures simultaneously. Physical property methods are either based on the activity coefficient method or the equation of state method. Due to the highly non-ideal organic component matrix and the possible component interactions during fast pyrolysis processes, the activity coefficient method was chosen to simulate the pyrolysis reactor and the condensation of the pyrolysis vapors. The activity coefficient method represents the deviation of the mixture from an ideal mixture (yi > 1), which means that it has a higher fugacity than an ideal mixture. Activity coefficient property methods are also able to deal with supercritical components such as the gases involved in the process by adapting Henry’s Law. UNIQUAC was chosen as the physical property method for the fast pyrolysis simulation. UNIQUAC was the only property method that was able to correctly estimate the required thermodynamic and transport property parameters for the selected model compounds in parameter estimation and binary parameter runs. This property method applies the UNIQUAC activity coefficient method in the liquid phase, and for the vapor phase it applies the ideal gas fugacity coefficient method. One weakness in the UNIQUAC property method when applied to fast pyrolysis is its inadequacy to properly simulate the gas phase association of acetic acid at high temperatures. Dimerization of acetic acid affects vaporliquid equilibrium (VLE) properties such as enthalpy and density in addition to the VLE liquid phase enthalpy. Aspen Plus offers correction methods for the vapor phase where the degree of dimerization is evaluated from the ratio of the compressibility factor, Z. However, applying a property method that accounts for the vapor phase dimerization of acetic acid (for instance UNIQ-NTH) turned out to require an input specification of several property parameters that could not be retrieved or determined. Thus it was decided to use UNIQUAC without a correction method for vapor phase association.


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2.5 Binary interaction parameters Some of the selected compounds are known to interact with each other under the specific fast pyrolysis conditions. Few binary interaction parameters exist for the proposed model compounds in the current literature. After choosing the model compounds, thorough property and binary parameter analyses were performed with the Aspen Plus property estimation and binary run functions. The UNIFAC-DMD property estimation method for VLE was used, as the Dortmund databank is the most advanced and recently updated databank in the software. The method was applied in the temperature range 25–500°C. Binary parameters were estimated between all conventional components in the simulation, including the imported levoglucosan and the user defined pyrolignin.

2.6 Unit operations Figure 4 shows a simplified block diagram of the fast pyrolysis process as modeled in Aspen plus. Following is a description of how the simulation operational blocks were defined.

Low temperature heat Recycle gas Feedstock Air

Pretreatment

Steam

Combustion air

Pyrolysis reactor

Sand

Char

Scrubber

Bio oil

Gas

Char combustor Ash Flue gas Evaporate Air

Figure 4 Block diagram of the modeled fast pyrolysis process

2.6.1 Feedstock pre-treatment (drying and grinding) Initial feedstock moisture content is responsible for the major part of the moisture in the bio oil. Bio oil with moisture content above 30 wt-% will cause phase separation, which one would typically try to avoid since the handling of a twophased product is more difficult than that of one-phased oil products. Feedstock is dried to achieve a moisture content of 8 wt-%. The dryer is modeled based on the principle of a belt dryer. A variety of energy sources can be used for this type of dryer, including steam, waste heat and electricity [30]. Heat sources for the modeled dryer are heat from the


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condensation of fast pyrolysis vapors and energy produced in the char combustor (see Chapter 2.5.4). Steam is used in the dryer at 127°C and 3 bar.

The grinding process of fast pyrolysis feedstock was not modeled in Aspen Plus. Only the power requirement for the grinding was modeled according to Figure 5, which is based on work by Rensfelt et al. [31] and Solantausta et al. [32]. The x-axis shows the maximum particle size (Dp) of the product and the y-axis shows the power consumption per tonne dry matter (DM). Wood chips are assumed to be ground from an initial size of 25 mm to a reactor feed size of about 5 mm, resulting in a power consumption of 71.2 kWh/t DM.

250 200 kWh/t DM

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150 100 50 0 0.1

1

10

100

Particle size [mm] Figure 5 Energy consumption for grinding of wood

2.6.2 Pyrolysis reactor The fast pyrolysis reactor was modeled using an RYield reactor block. In an RYield block the reactor products are specified. The yield reactor was preferred over equilibrium or kinetic reactors, as the kinetics and reaction mechanisms for fast pyrolysis of wood are poorly understood and would be accompanied by a certain degree of unreliability. The reactor products were specified according to the method described in Chapter 2.1. Part of the non-condensable gas is used as fluidizing medium in the pyrolysis reactor. The amount of recycle gas used is a function of the reactor design, reactor specifications and reaction conditions such as reactor size, gas velocity and heat density in addition to gas and vapor component thermodynamic and transport properties. Currently, there is only a limited amount of data concerning reactions between recycle gas and pyrolysis reactor feed, or primary pyrolysis products in or immediately after the reactor. As a consequence, component interactions in the gas phase have been taken into account exclusively based on binary interaction parameters under the prevailing conditions in the model.

2.6.3 Scrubber A RadFrac column was chosen to simulate the vapor quenching process. The quenching process is a counter-current wet scrubber where part of the condensed pyrolysis vapors is recycled to the column as spraying liquid. The RadFrac unit block is a rigorous column model that is able to simulate most types of multistage vapor-liquid fractionation operations. It also fulfils the requirements of simulating a two-phase system, including a strongly non-ideal liquid phase with a wide window of boiling points and azeotropic mixtures. The condensed bio oil is cooled to 50°C or


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less, preferably to 35°C, in order to avoid polymerization and secondary reactions, especially of cellulose derivatives in the liquid phase. The proportion of the condensed pyrolysis vapors that is not recycled in the scrubber as quench liquid is withdrawn as a bio oil product. The oil flow returning to the RadFrac column is a recycle loop controlled by the temperature of the vapor phase of the column.

2.6.4 Char combustor Char separated from the fast pyrolysis vapors after the reactor is combusted in a char combustor together with the noncondensable off-gases. The char combustor is modeled with an RStoic reactor that generates the combustion reactions. Heat generated in the char combustor is used in the dryer for drying the fast pyrolysis feedstock and for re-heating the sand to the fast pyrolysis reactor.

2.7 Non-conventional component specific heat capacity The specific property methods for enthalpy and density for wood were chosen based on the general enthalpy model for coal. The wood enthalpy was calculated with the HCOALGEN method, which is based on ultimate analyses and proximate analyses. The heat capacity for wood is calculated with the Kirov correlation (3) taking into consideration the mass fraction of the proximate analysis (moisture, fixed carbon (FC), primary volatile matter (VM), secondary volatile matter and ash): ncn

C p, j = ∑ w j C p,ij

(2)

j −1

where Cp is the heat capacity, i is the component index, j is the constituent index and wj is the mass fraction of the jth constituent on dry basis. The temperature dependent heat capacity of each proximate analysis is taken into account according to the formula below: , = + + +

(3)

where a is a parameter or element, i is the component index, and n is the constituent index (1 = moisture, 2 = FC, 3 = primary VM, 4 = secondary VM, 5 = ash). The standard heat of formation is based on heat of combustion. The char enthalpy was calculated with the same enthalpy and density methods as for wood, with the exception that the heat of combustion is calculated by the Boie correlation (4) instead of by a manual specification: ∆ ℎ = , + 2 , + 3 , + 4 , + 5 !, "10 + %

(4)

where a is a parameter or element, w is the mass fraction of elements C (carbon), H (hydrogen), S (sulfur), O (oxygen) and N (nitrogen). Default values were used for the parameters 1 – 6 and can be found from the Aspen Plus user manuals.


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Energy & Fuels

2.8 Heat of reaction Heat required for fast pyrolysis reactions is conveyed to the reactor by hot sand from the char combustor. In the RYield reactor, the heat of reaction is calculated as the difference in reactor feed stream enthalpy and product stream enthalpy, and iterated for the specified reaction temperature. In the simulation, the non-conventional component wood is involved in chemical reactions and the enthalpy is calculated based on the heat of formation as:

T

H = ∆h f + ∫ C p dT Tref

(5)

where Tref = 25°C, 1 atm, hf is heat of formation. As the heat of formation is not known and cannot be obtained due to the absence of the non-conventional component molecular structure, the heat of formation is calculated based on the heat of combustion. As mentioned above, the heat of combustion for wood was manually specified in the simulation and, based on this, the heat of formation was calculated according to the following equation:

∆h f = ∆hc + ∆h f , Cp

(6)

where hc is heat of combustion. Equation 6 is based on the sum of the heats of formation of the reaction products from the pyrolysis reactor iterated with the specific heat capacity of the individual components.

3 Results and discussion The main process inputs and outputs are set out in Table 4 below. The heat for pyrolysis includes both heating of feedstock and recycle gas to the reaction temperature and heat from chemical decomposition of feedstock to pyrolysis vapors. Mass and energy balances, based on the pine fast pyrolysis case, are illustrated in Figures 6 and 7. Note that lower heating values (LHV) are used in Figure 7 and that the figure gives a simplified overall process energy balance.


Energy & Fuels

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Table 4 Main input and results for the bio oil production plant UNIT

PINE

FOREST RESIDUE

- Capacity, LHV

MW

40.0

49.8

- Mass flowa

kg/s

4.8

5.8

b

12.6

Feedstock (50 wt-% moist)

MW

10.3

Additional fuel to char boiler

MW

1.3

kg/s

0.17

Enthalpy to pyrolysis

MW

4.6

Dryer duty

6.3

Bio oil - Mass flowa

kg/s

1.9

2.1

- Moisture

wt-%

24.2

29.9

- Capacity, LHV

MW

30.0

30.0

Char - Mass flow

kg/s

0.5

0.7

- Capacity, LHV

MW

12.2

21.9

- Mass flowa

kg/s

0.3

0.4

- Moisture

wt-%

3.7

3.8

- Capacity, LHV

MW

2.4

3.6

Recycle gasa

kg/s

1.7

1.2

Sand flow

kg/s

15.9

21.9

Electricity inputc

MW

1.3

1.6

%

69.3

55.8

Purge gas

Bio oil production efficiency

MW

Excess heat, LHV a

b

6.7

c

On wet basis Feedstock moisture after dryer is 8 wt-% Includes power in put for pumps, compressors, feedstock grinding and belt dryer

As shown in Table 4, the fast forest residue-based pyrolysis processes produces an excess energy of 6.7 MW. The excess heat has been estimated ensuring a temperature in the flue gas stack above 140°C. The reason for the significant excess heat available from this process is due to the higher char yield generated in forest residue-based fast pyrolysis. The pinebased fast pyrolysis process, on the other hand, will need an additional input of feedstock in order to maintain a flue gas temperature above 140°C. The efficiency (η) of the bio oil production was calculated according to Equation 7, where Q is the energy in the feedstock and products based on lower heating values and P is the electricity input. In this calculation the power input was converted to fuel equivalent assuming an efficiency of 40%.

& = '(

()*+ +*,

-../01+23 4.67

8 ∗ 100

(7)


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Energy & Fuels

Table 5 Ultimate analyses of bio oil and char (wt-%, dry) BIO OIL ELEMENT

PINE

CHAR

FOREST RESIDUE

PINE

FOREST RESIDUE

0

0

1.0

10.2

Carbon

54.5

54.4

73.4

77.4

Hydrogen

7.2

7.3

2.9

3.9

Nitrogen

0.1

0.5

0.1

0.2

Oxygen

38.2

37.7

22.6

8.2

Sulfur

0

0.1

0

0.03

Ash

Table 6 Carbon analysis of bio oil (wt-%, dry) PRODUCT

PINE

FOREST RESDIUE

Bio oil

63.4

52.1

Gas

7.9

9.7

Char

28.7

38.2

Figure 6 Mass balance for the overall fast pyrolysis bio oil production using pine


Energy & Fuels

Figure 7 A simplified energy balance (based on LHV) for the overall fast pyrolysis bio oil production using pine (grinding not included) The moisture content of bio-oil is an important indicator of its quality, as high moisture content increases for the likelihood of forming more than one phase during storage. Therefore, moisture content should be below about 30 wt-% in order to prevent phase separation. High moisture content in bio oil also significantly reduces the heating value. Increasing feedstock moisture to the reactor will increase bio oil moisture content, as shown in Figure 8. In this sensitivity simulation, the scrubber gas phase temperature was kept constant at 40°C.

Bio oil moisture [wt-%]

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40 35 30

Forest residue

25

Pine

20 8

10

12

14

16

Feedstock moisture [wt-%]

Figure 8 Bio oil moisture content as a function of feedstock moisture content A recent patent [33] describes a process that enables a reduction of water in biomass-derived pyrolysis oil. When water is removed from recycle gas by condensing, the bio oil produced has a lower moisture content. This can in turn improve process efficiency. However, a proportion of the organic components will be lost in the waste water produced. The decrease in bio oil heating value caused by losing some of the organic fraction is offset by the decreased duty needed in


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the feedstock dryer. The energy lost as a result of recycle gas condensation at 20°C is shown in Figure 9. In this sensitivity simulation, the bio oil moisture content was kept constant. 0.8 0.7 Energy loss [MW]

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Energy & Fuels

0.6 0.5 0.4

Pine

0.3

Forest residue

0.2 0.1 0 8

10

12

14

16

Feedstock moisture [wt-%]

Figure 9 Energy lost in recycle gas condensation at 20°C The effects of feedstock moisture on the scrubber temperature needed to reach constant bio oil moisture levels are shown in Figure 10. At higher temperatures (above ~75°C) there is a risk of increased polymerization of the organics components. The PDU scrubber has been operated up to 65°C. For scrubbers in continuous operations larger than laboratory scale operating above this temperature, there is not much data available. What is known is that viscosity increases significantly, and the related equipment (pumps, heat exchangers, nozzles) has to be designed accordingly.

Figure 10 Required scrubber temperature as a function of feedstock moisture content at constant bio oil moisture. Constant bio oil moisture for these sensitivities is reported in Table 4.


Energy & Fuels

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A sensitivity analysis of the scrubber performance was carried out by varying the gas phase temperature from 40°C to 90°C. The sensitivity test was carried out with recycle gas condensation. As expected and as illustrated in Figure 11, the higher the temperature in the scrubber gas phase, the less water is condensed. As a result, bio oil moisture drops as a function of gas phase temperature. The results have been compared with corresponding measurements from the PDU.

Figure 11 Bio oil moisture content as a function of scrubber temperature A similar sensitivity addressing the effect of scrubber gas phase temperature on the behaviour of bio oil model components is shown in Figures 12, 13a and 13b. Note the large difference in mass flow units of Figures 13a and 13b. The loss rate of levoglucosan and pyrolignin are negligible due to their high boiling points.

Figure 12 Loss of organics as a function of scrubber temperature


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Energy & Fuels

Figures 13a and 13b Loss of individual organic components as a function of scrubber gas phase temperature 4 Techno-economic assessment Production costs for fast pyrolysis bio oil are estimated for both pine and forest residue cases. Production costs are estimated based on the previously mentioned test unit performance data and on estimated investment and operating costs. Investment costs in this case are derived from the news release by Fortum [34], and operating cost estimates are derived using generic correlations (Table 8). Input parameters for the assessment are listed in Table 5. It is assumed that pine and forest residue chips have different prices, 22 and 20 €/MWh, respectively. Table 5 Base input parameters. Energy prices are based on Statistics Finland 2014 [35]. PARAMETER

UNIT

VALUE

- Pine

€/MWh

22.0

- Forest residue

€/MWh

20.0

Excess heat

€/MWh

20.0

Electricity

¢/kWh

4.0

Labor rate

M€/a

0.03

Interest rate

%

5.0

Service life

a

20

h/a

6 500

Feedstock cost

Capital recovery factor

0.08

Annual operating hours*

* Annual operating hours are based on the need for district heating Estimated capital investment costs for the plants are shown in Table 7. Production costs using these base values are shown in Table 8. Annual production cost is evaluated by adding operating and capital costs, and deducting by-product value. Operating cost includes fixed and variable terms. The capital recovery factor is estimated using Equation 8; : ;

Process Simulation Development of Fast Pyrolysis of Wood Using

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