Slow Pyrolysis Kinetics of Two Herbaceous Feedstock: Effect of Milling

Subscriber access provided by - Access paid by the | UCSB Libraries

Article

Slow Pyrolysis Kinetics of Two Herbaceous Feedstock: Effect of Milling, Source, and Heating Rate Michael O Adenson, Jessica D Murillo, Matthew D Kelley, Joseph James Biernacki, and Clyde P. Bagley Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.7b04020 • Publication Date (Web): 01 Mar 2018 Downloaded from http://pubs.acs.org on March 3, 2018

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

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

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

Industrial & Engineering Chemistry Research

Slow Pyrolysis Kinetics of Two Herbaceous Feedstock: Effect of Milling, Source, and Heating Rate Michael O. Adenson1, Jessica D. Murillo1, 2, Matthew Kelley1, Joseph J. Biernacki*, 1, Clyde P. Bagley3 1

Department of Chemical Engineering, Tennessee Technological University, Cookeville, TN 38505 2

College of Interdisciplinary Studies, Environmental Sciences, Tennessee Technological University, Cookeville, TN 38505

3

College of Agricultural and Human Sciences, Tennessee Technological University, 38505

Abstract Kinetic models for pyrolysis of switchgrass and tall fescue were obtained using thermogravimetric analysis and a newly proposed parameter-extraction methodology. The optimization strategy demonstrates the use of the Akaike Information Criterion for the statistical identification of the number of distinct processes and a robust global search method. The effects of sample mass, heating rates, particle size, and crystallinity index on the kinetic parameters of each feedstock were considered. For whole biomass particles the kinetic parameters of the cellulose component was found to be similar to that of pure microcrystalline cellulose. Further particle size reduction through extensive milling reduced the activation energy of cellulose by 48%. X-ray diffraction indicated that the cellulose fraction of highly milled whole biomass becomes largely amorphous, and that the amorphization of the cellulose, not the particle size reduction, is likely responsible for the decrease in activation energy.

Keywords: Biomass, pyrolysis, switchgrass, tall fescue, global optimization, kinetic parameters, milling effect

1 ACS Paragon Plus Environment

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

1. INTRODUCTION Environmental sustainability, economic stability, and national energy independence are the overall goals for developing the bioenergy industry in the United States. With the Biomass Research and Development Act of 2000, the Biomass R&D Technical Advisory Committee envisioned that biomass could supply 5% of the nation’s power supply, 20% of its transportations fuels, and 25% of its chemicals by 2030, and will require about one billion tons of dry biomass annually, or five times the current feedstock use.1 Currently, the majority of renewable biofuels are produced through economically marginal processes involving food crops.2 However, to attain the nation’s ambitious near-term energy goals, additional biomass sources in the form of lignocellulosic matter will need to be explored to supplement, and largely replace, less favorable feedstock. Lignocellulosic materials are promising feedstock for the production of biofuels and bioproducts; these include agricultural wastes, residues from forestry, and purposefully grown herbaceous energy crops e.g. switchgrass and tall fescue which are projected to contribute the greater portion of biomass feedstock according to the Billion-Ton report.1

1.1 Feedstock for Biofuels Production Tall fescue is a cool-season perennial grass with a yield potential of six tons of dry matter per acre. A stand of tall fescue has a life expectancy of 50 years and is the most widely found grass in the U.S. occupying about 15 million hectares.3 It is the most important forage grass in Tennessee, and adapts to climates from the Gulf Coast, throughout most of Canada and extends west until the dry deserts. Switchgrass is a North American native warm-season perennial that is not used as food, feed or fiber, can grow almost anywhere in the U.S.,4 and is one of the most extensively studied crops for biofuels applications.


Page 2 of 44

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


1.2 Pyrolysis Kinetics of Cellulosic Biomass Slow pyrolysis of biomass remains a relevant and important industrial process.5–8 Biomass contains cellulose (most abundant), hemicellulose (second most abundant), lignin, and other small components including pectin, protein, and minerals.9 Many researchers have studied and modeled the slow pyrolysis of cellulose,10–14 lignin,15,16 and hemicellulose 9 to extract kinetic data without the additional complexities resulting from the pyrolysis of whole biomass. Consider Figure 1 which shows a summary of kinetic parameters estimated for different biomass feedstock from selected TGA-based studies including: Walkowiak et al.,17 Várhegyi et al.,18 Naranjo et al.,19 Branca et al.,20 Chen et al.,21 Tsekos et al.,22 and Mészáros et al.23 A range of reaction conditions were used in these experiments: heating rates (1 to 100 K min-1), biomass sample size in the TGA crucible (1 to 50 mg), and particle sizes (1 to 3000 µm).

Figure 1. Summary of kinetic parameters of biopolymers for different whole biomass feedstock from literature, refer to Table S1 in the Supporting Information for details and sources.17–23

Just as the experimental conditions are different, the mathematical analysis, including objective function formulation, reaction order, and optimization technique are not the same from study-to-study. The summary of extracted kinetic parameters for up to four components 3 ACS Paragon Plus Environment


or processes identified in these previous works shows an interesting linear pattern when the activation energy is plotted against the frequency factor, as shown in Figure 1. Reported values for cellulose activation energy vary from as low as 56 to as high as 241 kJ mol-1. Recent works, however, find consensus and suggest that the apparent activation energy for crystalline cellulose pyrolysis under conditions that limit (reduce) transport artifacts is in the neighborhood of 200 kJ mol-1, e.g. Bradbury et al.,24 Lin et al.,13 and Adenson et al.25 estimated 198.9, 198.0, and 198.8 kJ mol-1 respectively. A variation of 25 to 173 kJ mol-1 for lignin and 25 to 196 kJ mol-1 for hemicellulose have been reported without general agreement. It might be expected that the activation energy for cellulose in a whole biomass should have a similar value unless its interaction with hemicellulose, lignin, and inorganic or catalytically active matter has impact on the kinetics in its unprocessed raw state. There is no consensus on the effect of component interaction on biomass pyrolysis, Hosoya et al.,26 and Wu et al.27 argued that interactions have a significant impact on product distribution, while Yang et al.28 and Zhang et al.29 showed that they have negligible effect on woody biomass. In this work, the effect of biopolymer interactions on kinetic parameters will be assessed by comparing the previously estimated kinetic parameters of pure microcrystalline cellulose with cellulose biopolymer present in a whole biomass of different feedstock. Recent works in biomass pyrolysis modeling 30–32 adopted the distributed activation energy models (DAEM) and isoconversional models of Flynn-Wall-Ozawa and Kissinger-AkahiraSunose.33,34 Nonetheless, this work adopts the traditional parallel multi-reaction models because DAEM is a multi-parallel Gaussian distribution model which can be best approximated as a first-order kinetics while isoconversional approach estimates several activation energies at different conversions. The approach in this work easily allows for


Page 4 of 44

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


components such as lignin whose kinetics can best be described as third order to be incorporated without assumptions of unity order and can provide a global kinetic parameter, even for several heating rate experimental data.

1.3 Composition of Biomass Feedstock The components of herbaceous feedstock (hemicellulose, cellulose, and lignin) have different chemical structure and reactivity, thereby making the proportions of these three biopolymers an important process design consideration for operation and optimization of pyrolysis units.35 Biomass of the same specie also have composition that vary with location, weather, and harvest time; this implies that determination of feedstock composition could become a repetitive process.36,37 Estimating the fraction of biopolymer components is usually performed experimentally through a wet analysis, which requires analytical equipment that are expensive, and/or by procedures that are time-consuming. Thus, obtaining an inexpensive and reliable estimate using TGA data can be a very useful outcome. The authors’ previous work illustrates that it is difficult to identify the optimal kinetic parameters of cellulose pyrolysis, and that even a factor such as choice of objective function can bias parameter estimation.25 Since mathematical analysis methodology could easily obfuscate the kinetic parameters of cellulose, it is conceivable that it would have similar effect on apparent kinetic parameters of the three or more biopolymers when extracted from whole biomass experiments. Thus, this work demonstrates the application of a global optimization strategy and a carefully chosen objective function to estimate the kinetic parameters and component compositions (weight fractions) for two herbaceous feedstock (switchgrass and tall fescue). In addition, the number of components or processes identifiable from thermogravimetric analysis (TGA) data will be identified using statistical



analysis in contrast to various subjective criteria that have either been used or suggested in the past.21–23,38 Furthermore, after minimizing the likelihood of obtaining false kinetic parameters, the actual effect of sample mass (i.e. mass of TGA crucible charge), particle size, structure of biomass (amorphous vs. crystalline as determined by extent of comminution process), biomass type, and heating rate will be studied to isolate variations due to each factor and reduce the likelihood of optimization-error-induced variation. Other factors including the inorganic content of the whole biomass are also relevant. Extraction, of catalytically active inorganic components, however, may also affect the structure of the residual biomass and thus the extracted matter may or may not reflect the native thermal response when pyrolyzed. Thus, in this study, we have limited the investigation to the effects of milling (particle size), heating rate, and biomass source on kinetic parameters. Finally, the use of TGA as an alternative to expensive and laborious chemical analysis methods for estimating the biopolymer composition of whole biomass is discussed. 2. MATERIALS AND METHODS 2.1 Sample Preparation Switchgrass (Panicum virgatum L.) used in this study was donated by the Center for Renewable Carbon, the University of Tennessee, Knoxville (UTK). The biopolymer composition of this sample was estimated using a wet analysis method.39 Additional switchgrass and tall fescue straw (Festuca arundinacea) samples were grown and collected from Shipley Farm in Tennessee Technological University (TTU), Cookeville, TN. Raw materials obtained from Shipley Farm were grown in the summer and harvested in the fall. All the samples were mildly milled for about 20 minutes using a Braun KSM2 coffee mill, while TTU samples were further milled using an SPEX SamplePrep 8000M Mixer/Mill for an additional 1, 2, 4, and 8 hours in order to study the impact of milling on biomass pyrolysis. The mildly milled samples were sieved to obtain particles in the range 50 to 1000 µm using 6 ACS Paragon Plus Environment

Page 6 of 44

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


Tyler screens. The milling operation was performed intermittently in order to prevent excessive heat buildup in the unit such that the temperature of biomass sample never exceeds 55 oC. 2.2 Particle Size Analysis (PSA) Biomass samples weighing between 200 and 300 mg were analyzed using a Beckman Coulter LS 13 320 Laser Diffraction Particle Size Analyzer equipped with a Dry Powder System (Tornado) module sample holder. 2.3 Room-Temperature X-Ray Diffraction (RT-XRD) A Rigaku (UltimaIV) X-ray diffractometer was used to determine biomass crystallinity. Parallel beam optics and a Cu Kα (λ = 1.54 ˚A) radiation source operated at a voltage and current of 40 kV and 44 mA respectively, with an Ultra IV goniometer was used. The sample was scanned at a speed of 4° min-1 from 10 to 65o 2θ range. A glass slide was used to level the milled samples placed on a zero background holder. The XRD peak height method was used to quantify crystallinity. This method is recommended for estimating relative crystallinity and should not be considered the absolute crystallinity.40,41 Segal et al.42 defined crystallinity index (CI) as follows:

=

× 100

(1)

The 002 plane peak (

) occurs at a 2θ of about 22.5o for crystalline cellulose while amorphous scattering ( ) occurs at a 2θ of about 18.3o. Rigaku PDXL software version 1.8.0.3 was used to smoothen the raw XRD data using Gaussian convolution. Roomtemperature data was normalized by dividing the intensities of each dataset by the maximum intensity (at a 2θ of about 22.5o).



Page 8 of 44

2.4 TGA Pyrolysis Pyrolysis experiments were performed using a TA Instruments SDT Q600 Simultaneous DSC-TGA. Samples of approximately 5, 13, and 20 mg of the milled biomass samples were placed in an open-top alumina crucible. Each feedstock was analyzed at linear heating rates of 5, 10, 30, and 50 K min-1 from room temperature to 1073 K at atmospheric pressure using ultra-high-purity grade nitrogen purge flow set at 50 mL min-1. A replicate of six runs was made at a heating rate of 10 K min-1 to ensure data. Other selected experiments were run in triplicate to establish variability. 2.5 Kinetic Model By assuming homogenous decomposition of biomass,43,44 the rate of mass loss can be modeled as:

= −

(2.1)

= 0) =

(2.2)

where, i is the index of the “ith” component, m = residual mass at any given time, t = time, n = reaction order, and the rate constant, k, is defined as:

= e

!$ "# %

(3)

where, A = pre-exponential (frequency) factor, Ea = activation energy, R = universal gas constant, and T = absolute temperature in Kelvin. Typically, kinetic analysis is more convenient when mass loss is expressed in a dimensionless extent of reaction term, αexp, which can be calculated from the thermogravimetric data as:

&'() = 1 −

*

(4)

*


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


where, m∞ is the measured mass at the end of the TGA experiment. The mass loss rate, Equation (2), can be transformed to an extent rate given by: +

= 1 − & )

(5)

where, αi is the indexed extent of reaction of specie i. Equation (6) shows the relationship between mass loss and the extent rate; the derivation of Equations (5) and (6) is provided in the Supporting Information. , ,

= −

,+

(6)

,

To extract kinetic parameters from dynamic TGA data, a linear heating rate (- ) is typically used where:

-=

#

(7)

Equation (5) can be rearranged and combined with Equations (3) and (7) to give a temperature-dependent rate equation for the decomposition of biomass: + #

=

. !$ % "# 1 e /

− & )

(8.1)

The initial conditions are given by:

α 12 ) = 0

(8.2)

The calculated extent, αi, for each component i was obtained by solving the ordinary differential equations (ODEs) (Equation (8) for hemicellulose, cellulose, and lignin) using a stiff-ODE integrator. And by assuming there is no interaction between these biopolymers, the overall extent rate was modeled as the weighted sum of the rate extents of the main biomass components as shown below: 9 ACS Paragon Plus Environment


+345 #

= ∑8 29 7

+

(i =cellulose, hemicellulose, lignin)

#

Page 10 of 44

(9)

For a three-component model, there are eight decision variables (variables whose values provide the smallest objective function value given by Equation (10)): Ai, Eai and two of the fi (the third is fixed by a unity summative constraint). The optimal decision variables are simulated by minimizing the objective function given as:

: = ∑P I29 ;

Slow Pyrolysis Kinetics of Two Herbaceous Feedstock: Effect of Milling

Recommend Documents