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PredictivepyrolysisprocessmodellingInAspenPlus

CONFERENCEPAPER·JANUARY2014

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JensF.Peters

KarlsruheInstituteofTechnology

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DiegoIribarren

MadridInstituteforAdvancedStudies

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JavierDufour

KingJuanCarlosUniversity

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Retrievedon:20October2015

PREDICTIVE PYROLYSIS PROCESS MODELLING IN ASPEN PLUS®

Jens F. Peters1,*, Diego Iribarren1 and Javier Dufour1,2

1 Systems Analysis Unit. Instituto IMDEA Energía. Móstoles 28935 (Spain). 2 Department of Chemical and Energy Technology. Rey Juan Carlos University. Móstoles 28933 (Spain).

* Corresponding author: Tel.: +34-91 737 11 46; E-mail address: jens.peters@imdea.org

ABSTRACT: This work presents a novel Aspen Plus® model of pyrolysis processes for lignocellulosic feedstocks. Based on kinetic reaction mechanisms, the simulation calculates product yields and composition depending on reactor conditions (temperature, residence time, flue gas flow rate) and feedstock composition (cellulose, hemicellulose and lignin fraction, atomic composition, ash and alkali metal content). The produced bio-oil is modelled with a high level of detail (33 compounds including organic acids, aldehydes, alcohols, ketenes, phenols, sugar derivatives and degraded lignin), and the char product shows realistic atomic compositions. N, S and Cl trace element release is taken into account and the corresponding emissions caused by the process can be determined. Numerous simulation runs are made in order to cross-check the simulation results with experimental data based on published literature. The results show a high correlation of the results for the most common pyrolysis processes (e.g., fast pyrolysis in bubbling or circulating fluidised beds), which is somewhat decreasing for slow pyrolysis processes. The simulation model is found to be suitable for predicting pyrolysis yields and products within the typical range of operation for pyrolysis processes. KEYWORDS: biomass, fast pyrolysis, lignocellulosic sources, process simulation, pyrolysis oil, reactor modelling

1 INTRODUCTION Second-generation biofuels are seen as a solution for further increasing the share of renewable energies in the transport sector while reducing the negative impacts associated with conventional biofuels [1]. In fact, there is an important potential of lignocellulosic biomass from forest residue, agricultural waste and energy crops which is still unused and potentially suitable for bioenergy production with low environmental impact [2]. Maximizing the use of residual biomass is necessary for fulfilling the targets regarding biofuel share and greenhouse gas emission reduction set up in the proposal for the new renewable energy directive [1]. Nevertheless, converting lignocellulosic biomass into liquid fuels is not an easy task and adequate technologies are needed. Pyrolysis, the thermal decomposition under intermediate temperature and in oxygen-free atmosphere, is one of the options for this purpose. Pyrolysis of lignocellulosic biomass yields gases, liquids (the so-called bio-oil) and a carbonaceous residue, char. The obtained yields depend on feedstock composition and pyrolysis conditions, with a typical maximum bio-oil yield of around 70 wt% under fast pyrolysis conditions around 500ºC and residence times of 1s [3,4]. Nevertheless, the obtained bio-oil is of poor quality and requires significant improvement before it can be used as transport fuel [4–6]. 2 BACKGROUND

Pyrolysis is a possible pathway for converting biomass into a liquid. Nevertheless, biomass pyrolysis is mainly in the research stage and almost no commercial pyrolysis installations exist yet [7,8]. Furthermore, as bio-oil is a very complex liquid made up of hundreds of compounds, hardly any exhaustive studies with information about the detailed compositions of bio-oils obtained from different types of feedstock are available. Technical and environmental assessments therefore mostly use a simple top-down approach to adjust the pyrolysis products of their model to existing literature data. However, pyrolysis of different types of biomass (woody, herbaceous) gives different yields and product

compositions, why a simple model is not sufficient for a detailed assessment of different processes dealing with varying types of feedstocks. As system analysis for research-stage processes is often based on process simulation, there is a need for a flexible simulation that facilitates the use of different new materials. 3 ASPEN PLUS® SIMULATION

This work presents a novel model of a pyrolysis reactor based on a kinetic reaction approach implemented in Aspen Plus®. Unlike other works, which implement the pyrolysis reactor as a black box unit giving an a priori defined pyrolysis product composition, the use of a kinetic approach permits a really predictive simulation, which estimates yield and composition of the pyrolysis products depending on reactor conditions and feedstock composition without the need of experimental data as input. It requires the fractional composition of the biomass (cellulose, hemicellulose, lignin) and its atomic composition plus its ash and alkali metal content, and calculates the pyrolysis products based on a superseded reaction model for the cellulose, hemicellulose and lignin fractions of the biomass. The modelled bio-oil is of high complexity and contains 33 compounds including organic acids, aldehydes, alcohols, ketenes, phenols, sugar derivatives and degraded lignin, thus giving a good approximation of the real bio-oil composition. Trace element release is taken into account. Hence, bio-oil, gas and char show a realistic O, N, S and Cl content, which is of importance for determining the emissions caused by the process. The pyrolysis reactor consists of three subsequent reactors, which are necessary to model all the reactions occurring during biomass pyrolysis with the required level of detail. The Aspen Plus® flowsheet of the reactor model is shown in Figure 1. Although the reactor model is integrated in a simulation of the whole plant, this work focuses explicitly on the reactor model and other components are not considered. The presented reactor yields are therefore ideal yields supposing perfect product separation.

Figure 1: Aspen Plus® flowsheet of the reactor model 3.1. Decomposition reactor

The decomposition reactor (RDECOMP in Figure 1) is responsible for the decomposition of the biomass (modelled as a non-conventional component) into its fractional components: cellulose, hemicellulose, and lignin. Hemicellulose and cellulose are represented in the simulation by their monomers: C5H8O4 (xylan) and C6H10O5 (xylose-like cellulose monomer), respectively. Lignin is represented by 6 different monomers with different O/C and H/C ratios, which permits adjusting the atomic composition of the decomposition products to the biomass composition by varying the amounts of the different lignin components [9]. The nitrogen content of the biomass is taken into account by including two representative N-containing species in the decomposition products, glutamic acid and pyrrole, again with different O/C and H/C ratios to adapt to different biomass compositions. Both are frequent in biomass [10], the amino acid represents proteins while pyrrole is the basic compound of more complex molecules like chlorophyll or porphyrins. The decomposition reactor is simulated using an RYield reactor, for which a calculation block provides the yield data for the decomposition fractions. The fractions are calculated by an iterative algorithm that, for a given initial atomic and fractional composition of the biomass, adjusts the lignin components to fit the atomic composition. 3.2. Primary pyrolysis reactor

The primary pyrolysis reactor (RCTPRIM in Figure 1) is alternatively an RBatch or RCStir reactor which calculates the pyrolysis products based on a kinetic reaction mechanism. The reaction mechanism is based primarily on the works of Faravelli et al. [9], Dupont et al. [11], Calonaci et al. [12], Ranzi et al. [13], van de Velden et al. [14], and Graham et al. [15]. It implements over 150 individual reactions, including primary decomposition, secondary decomposition and radical substitution. The reactor type can be chosen according to the pyrolysis reactor which wants to be modelled. For fast pyrolysis, an RCStir reactor is used, while an RBatch reactor is used for slow pyrolysis. For modelling different reactor types, the operating temperature and the bed and vapour residence times for the simulated reactor are required.

The char fraction is modelled by a series of fractional char species, which allows including H, O, N, S and Cl in the char fraction, accounting for the real pyrolysis char composition which contains an important share of oxygen (influencing the heating value of the char). Nevertheless, as these fractional species pose problems in subsequent Aspen Plus® blocks due to missing properties and/or wrongly estimated properties, these are all converted in non-conventional char in the following secondary pyrolysis reactor (RCTSECND in Figure 1). 3.3. Secondary pyrolysis reactor

Apart from the homogeneous vapour-phase reactions, heterogeneous tar condensation reactions occur in the hot vapour phase. These are partially catalysed by the alkali components in the char fraction [5,16,17] and give rise to increasing char and gas yields at the expense of the organic liquid fraction. Especially for biomass with high alkali ash content and at longer residence times play these effects an important role in the overall reaction mechanism. As these condensation reactions are very complex and not yet fully understood, no chemical models for their detailed description can be found in literature. To account for them nevertheless, a linear regression algorithm based on works which give quantitative data about the change in fractional yields [16] has been included in the reactor model. For this purpose, an RYield reactor (RCTSECND in Figure 1) is implemented that modifies the fractional yields according to a linear regression depending on vapour residence time, temperature and char alkali metal content. Furthermore, this reactor condenses all the different char fractions into one non-conventional component (NC-Char) with the corresponding elemental composition. The char shows an atomic composition similar to the values found in literature. Radicals and other non-ordinary fractional components still contained in the oil are condensed into conventional model components. This significantly simplifies the simulation as the resulting bio-oil then consists only of standard components with well-defined properties.

3.4. Product gas recycle loop

For modelling bubbling fluidised bed reactors and other types that require a fluidising gas, an optional product gas recycle loop (RFLUEGAS in Figure 1) is implemented. This is important as a high amount of fluidising gas changes the concentrations of the components in the reactors, which might affect the results.

3.5. Hot sand loop

The pyrolysis reactor requires a certain amount of heat. This heat is provided by a combustor which burns the pyrolysis gases and part of the char (combustion reactor not included in Figure 1). Hot sand is circulated between the combustor and the pyrolysis reactor (SANDCYCL in Figure 1) and acts as a heat carrier. It is heated (HXCCOMB) and then recycled to the pyrolysis reactor where it heats the biomass particles by direct contact in the reactor bed (HXCREACT). For simplicity purposes, the sand loop is separated in the simulation and the heat is transferred by a heat exchanger to the reactor bed, although, especially in circulating fluidised bed reactors, it is part of the reactor product flow and not separated until the cyclone.

4. SIMULATION RESULTS

In order to validate the reactor model, numerous simulation runs were performed and the results were compared with literature data.

4.1. Temperature and time dependency The dependency of the fractional yields on temperature and residence time is tested with two different types of feedstock: pine wood and wheat straw. The two types of feedstock are chosen due to their different properties, with wheat straw being a rather low- lignin biomass with a high content of ash and trace elements, while pine wood is a rather “pure” biomass with very low ash and trace element content. The results of the simulation runs are presented in Figures 2-7 (temperature and time axes are not linear).

Figure 2: Gas yield with pine wood as feedstock

Figure 3: Oil yield with pine wood as feedstock

Figure 4: Char yield with pine wood as feedstock

As can be observed, the yield curves show the typical shape for pyrolysis reactions, with a maximum liquid yield at around 500ºC and residence times of 1 s [18–20]. At low temperatures and short residence times, the char yield increases sharply, indicating incomplete decomposition.

The effect of the feedstock composition can be observed when comparing the yield curves for the two feedstock types. Higher liquid yields of above 75% are achieved by the pine wood feedstock, while wheat straw hardly exceeds 65% liquid yield. This is mainly due to the higher ash content of the straw, which catalyses secondary decomposition reactions and increases gas and char yields at the expense of the liquid fraction. Char gasification reactions are not included in the model, so char yields tend to be somewhat high for high temperatures and long residence times, and the results for these operation conditions should be taken with care. Nevertheless, these are rather extreme conditions which do not represent the typical operation range of pyrolysis reactors for lignocellulosic biomass.

Figure 5: Gas yield with wheat straw as feedstock

Figure 6: Oil yield with wheat straw as feedstock

Figure 7: Char yield with wheat straw as feedstock 4.2. Product composition

In order to assess the impact of the residence time not only on the fractional yields, but on the pyrolysis product composition, four different types of feedstock are simulated under fast (500ºC, 1 s residence time) and slow pyrolysis conditions (425ºC, 30 min residence time, heating rate 0.5ºC/s). The results of the simulation runs are shown in Figures 8 and 9.

The influence of the catalytic effect of the alkaline metal contained in the ashes can be observed for wheat straw, whose char yield is higher than that of lower ash-containing woody feedstocks, especially under slow pyrolysis conditions.

Furthermore, the effect of the feedstock type on the bio-oil fractions can be seen. The higher cellulose and hemicellulose content of, for example, wheat straw in comparison to pine wood gives rise to a lower share of degraded lignin compounds and more organic acids contained in the bio-oil.

In the slow pyrolysis oils the water content is significantly higher than in the fast pyrolysis oils, as the secondary condensation reactions increase water and char yields. A decrease in the degraded lignin and the sugar-derived fraction can also be observed, along with an increase in the water fraction, indicating the decomposition / polymerisation of these fractions which is more severe in the ash-rich wheat straw oil [16,21].

Figure 8: Product yields for fast pyrolysis of four different types of feedstock

Figure 9: Product yields for slow pyrolysis of four different types of feedstock 4.3. Verification with literature data

Verification with literature data is difficult, as works providing all the required data (details about feedstock composition regarding fractional and atomic composition, and a detailed analysis of the fractional composition of the bio-oil) are hard to find. Nevertheless, verification with works that do not give all required information [22–25] is possible using standard values regarding biomass composition from the Phyllis database [26]. Figures 10-13 show the comparison of the simulation-derived composition of the bio-oils from fast pyrolysis of two types of feedstock with the composition given in literature [23]. Pine wood and eucalyptus are used as only for these all required information is given.

As can be observed, the correlation between the product compositions given by the simulation and the literature data is high. The proposed simulation gives somewhat lower water content, but on the other hand it accounts for organic acids which are not explicitly analysed in the used literature data.

Figure 10: Composition of pine wood fast pyrolysis oil - Simulation results

Figure 11: Composition of pine wood fast pyrolysis oil - Literature data [23]

Figure 12: Composition of eucalyptus wood fast pyrolysis oil - Simulation results

Figure 13: Composition of eucalyptus wood fast pyrolysis oil - Literature data [23]

5 CONCLUSIONS

Overall, the results obtained from the simulation for the different (woody and herbaceous) feedstocks are found to be in line with existing studies in the temperature range typical for pyrolysis. The model gives temperature-yield curves comparable to the ones published in literature and the dependency of the fraction yields on residence time and feedstock composition correspond. Data for rigorous verification providing all required input data and at the same time a detailed product analysis is scarce but could be done for two types of feedstock under fast pyrolysis conditions. Results also show good correlation between experimental and simulation data under this regard. The simulation gives a bio-oil of slightly lower water content, but on the other hand accounts for organic acids which are not considered in the available literature data. In conclusion, the model is found to be suitable for predicting fast and slow pyrolysis reactions for lignocellulosic biomass feedstock and can be used for calculating pyrolysis products without the need for experimental data.

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7 ACKNOWLEDGEMENTS This research has been partly supported by the Regional Government of Madrid (S2009/ENE-1743) and the Spanish Ministry of Economy and Competitiveness (ENE2011-29643-C02-01 and IPT-2012-0219-120000).