Influence of Input Biomass Conditions and Operational Parameters on

Article pubs.acs.org/EF

Influence of Input Biomass Conditions and Operational Parameters on Comminution of Short-Rotation Forestry Poplar and Corn Stover Using Neural Networks Miguel Gil,*,† Inmaculada Arauzo,*,† and Enrique Teruel*,‡ †

Centre of Research for Energy Resources and Consumptions, University of Zaragoza, Mariano Esquillor 15, E-50018 Zaragoza, Spain Departamento de Informática e Ingeniería de Sistemas, University of Zaragoza, María de Luna, E-50018 Zaragoza, Spain

‡

ABSTRACT: Two kinds of biomass, short-rotation forestry (SRF) poplar and corn stover, were milled with a hammer mill at any combination of several levels of moisture content at mill inlet, wH2O = 7−30%, feeding chip size, 20−50 mm, and under varied operational milling conditions, such as the opening sizes of the internal screen, dtarget = 2−5 mm, and the angular velocity of the hammers, 2000−3000 rpm, to determine their influence on the milling energy consumption and the physical properties of the milled product (moisture content, particle size, bulk density, and angle of repose). The use of neural networks allows one to obtain three-dimensional predictive surfaces for each output variable, which describe tendencies and behavior as a function of the milling conditions. The moisture content at the mill inlet and opening sizes of the screen were found to be the key variables in the process. Specific energy requirements per oven dry tonne of about 28 kWh for poplar and 22 kWh for corn stover were obtained with almost dry biomass (wH2O = 7%) and dtarget = 5 mm, but this energy consumption increases 10-fold under opposite conditions, in which screen blocking was promoted by the high moisture content and the small opening sizes. Under these conditions (wH2O ≈ 30% and dtarget = 2 mm), particle drying can remove half of the initial moisture because of the strong increase in residence time of the particle in the mill chamber but also barely 1% under the opposite milling conditions, with low moisture and bigger opening sizes. Additionally, a bulk density increase between 11 and 98% for poplar and between 89 and 360% for corn stover was registered in relation to the previous chipped form and as a function of the milling conditions. With regard to the angle of repose, a higher moisture content and particle size made the handling behavior worse for both biomasses.

1. INTRODUCTION

Both resources can be used as biofuels. For solid biofuels, poplar and corn stover are fired in chipped form in grate furnaces, but milling is absolutely necessary for co-firing with coal8 or for combustion on pulverized burners. Also, milling is a required pretreatment for the production of the pellets or briquettes9 for domestic or industrial boilers. Nowadays, the commercial production of poplar pellets is common, while several experiences have been carried out with corn stover.10 In relation to liquid biofuels, the most promising option is for ethanol production. Traditionally, ethanol has been produced from sugar cane or sugar beet or from starch found in the grain of cereal crops, such as wheat, barley, or corn grain. An alternative ethanol production method that is promising but still under research is to use lignocellulosic biomass from forest or agricultural residues (such as corn stover) or from energy crops (such as poplar). These kinds of biomass do not play an intrinsic role in the food chain like corn grain does and could allow for valorizing residues of low economic profit, promoting the increase in agricultural production, such as in the case of corn stover. Conversion of naturally occurring lignocellulosic materials into ethanol currently requires preprocessing to enhance the accessibility of reactive agents and to improve conversion rates and yields. According to one patent,

Poplar and corn stover were selected to study their milling behavior because of the multiple differences between them. Poplar is a short-rotation forestry (SRF) crop. This cultivation technique allows for high yields of biomass with short harvest cycles of 2−10 years for fast-growing species, such as Salicaceas spp., Eucaliptus spp., Paulownia spp., etc. Highest yields were obtained under intensive management systems, including weed control, fertilizer application, and irrigation.1 Populus spp. were selected as a realistic case of study for southern Europe because of the successful experiments carried out in Spain.2−6 In fact, poplar has a long tradition in Spain for wood manufacture. This species is of high ecological interest in terms of biodiversity and requires low fertilizer doses, with low production cost compared to other options 2 and well-adapted to the Mediterranean climate. The main disadvantage of the poplar crop is its high water requirements, which restrict the natural distribution of the poplar, traditionally cultivated in Spain on river banks. Corn stover is a herbaceous biomass residue of a traditional agricultural crop with one of the highest yields for human consumption. It is one of the most abundant agricultural residues and is a cheap, renewable, and widely available feedstock. Nowadays, only a small percentage of stover is collected for animal feeding and bedding. Some of the stover is also grazed, but farmers tend to leave all or part on the field as a cover.7 © 2013 American Chemical Society

Received: January 14, 2013 Revised: March 15, 2013 Published: March 18, 2013 2649

dx.doi.org/10.1021/ef4000787 | Energy Fuels 2013, 27, 2649−2659

Energy & Fuels

Article

Table 1. Physical Characteristics of Chipped Poplar and Corn Stover poplar biomass previous pretreatment

moisture (wH2O, %) particle size dgm (mm) Sgm d50 (mm) d10 (mm) RR parameters l (mm) m ρb (kg m−3)

H-IS (1)

h-IS (2)

corn stover

H-is (3)

h-is (4)

central point (5)

50 × 50 chipping without drying

50 × 50 chipping forced drying

20 × 20 chipping without drying

20 × 20 chipping forced drying

mixture material natural drying

25−30

6−10

25−30

6−10

15.16 3.9 21 4.61

18.42 3.07 23.38 6.57

8.64 4.24 11.94 2.48

28.21

29.92

1.24 157

1.48 143.5

H-IS (1)

h-IS (2)

central point (3)

chipping

chipping

chipping

natural drying 8−11

natural drying

17

without drying 25−30

18

14.41 3.48 17.27 4.11

11.44 4.18 15.77 2.98

16.99 3.62 22.65 4.56

16.99 3.62 22.65 4.56

16.99 3.62 22.65 4.56

16.21

22.85

21.81

30.94

30.94

30.94

1.19 145.5

1.31 134.6

1.13 142.3

1.17 52.8

1.17 44.4

1.17 47.4

hammers (but with a shorter lifetime), Bitra et al.11 determined that shear failure at high-speed impact of fibrous materials offered no advantage in energy use and more contact hitting area of 90° hammers was the influencing factor for reducing total specific energy. Other authors focused on the opening size of the rejection screen around the mill chamber.11,13−15,18,23 This variable is a key factor on output particle size and shows a positive nonlinear correlation with the energy requirements, also as a function of the evaluated size range. The angular speed of hammers was also studied by Bitra et al.;11 the hammer tip speed was studied by Bargen et al.;24 and the gravity or pneumatic discharge from the mill was studied by Himmel et al.14 This study reports and describes the results of multiple experimental tests in a semi-industrial pilot plant of biomass milling. Section 2 contains details of the chain of custody of both biomass, from the field to our facilities, and the data postprocessing used to obtain a predictive model from the experimental results, combining neural networks and statistical analysis. Section 3 reports the effects of the main influencing variables on biomass milling (the physical conditions in which the biomass is supplied and the operational variables of the mill), the energy consumption process, the drying effect of the biomass during its milling, and other physical properties of the milled product (particle size, bulk density, and angle of repose).

agricultural biomass was prepared to approximately 1−6 mm for ethanol production.11 However, energy consumption of the biomass milling process can be an important drawback, making it necessary to assess these benefits through a proper characterization of the milling product. Many authors have studied the impact of grinding costs on the biomass overall energy balance for different biomass resources and milling technologies. Miu et al.12 established that hammer mills, knife mills, and disc mills were the best options for biomass processing. Several studies were performed to compare these three types of mills. A comparison between the knife and hammer mills was performed by Cadoche and López13 with hardwood, straw, and corn stover and by Himmel et al.14 with aspen, corn cob, and wheat straw, and recently, Miao et al.15 compared them to Miscanthus, switchgrass, willow, and energy cane. Currently, hammer mill is the most widespread and commercial mill used of biomass resources. On the milled product, Himmel et al.14 and Paulrud et al.16 found higher fine percentages with hammer mills than with knife mills. Some works have also studied the differences between the hammer mills and disc mills. Schell and Hardwood17 obtained lower energy consumption for hammer mills than for disk mills but with larger size particle distribution. As a conclusion of these studies, hammer mills can be considered the most appropriate option and the most widely used in the industrial process. Other studies on hammer milling are focused on comparing different biomasses or analyzing several operational or mill design variables. Esteban and Carrasco tested three forest biomasses (pine, poplar, and bark)18 and dry residues of olive kernel19 using different milling strategies to optimize energy consumption. Mani et al.20 reported experiences with herbaceous biomass (wheat and barley straw, corn stover, and switchgrass) at two different levels of moisture. Samson et al.21 studied the grinding process to obtain different particle sizes from switchgrass. Other authors focused their research on studying the operational and design variables of hammer mills, such as the shape and size of hammers. Vigneault et al.22 analyzed hammer width, and Bitra et al.11 compared 30° and 90° edge-shape hammers. Thin or sharp hammers could have an influence on shear fracture predominance by cutting action over compression failure by impact. While Vigneault et al.22 concluded that thin hammers performed better than thick

2. MATERIALS AND METHODS 2.1. Biomass Products. 2.1.1. SRF Poplar. Tested SRF poplar (Populus spp.) was planted at Fuente Vaqueros (UTM coordinates: 30N 431306 4118679), province of Granada, south of Spain, and was harvested in April 2010, after a short cultivation cycle of 5 years. The material was divided into two groups to be chipped with two different opening sizes of output screen. This way, two different particle sizes for raw material to be milled were available: higher input size (code: IS) from dscreen = 50 mm and lower input size (code: is) from dscreen = 20 mm. Subsequently, it was road-transported to Zaragoza at around wH2O = 30% over a wet basis. When received, half of the amount of each chip size was hermetically packed to avoid water evaporation (higher moisture content, code: H), and another half was dried in a rotary dryer until wH2O = 6−10% (lower moisture content, code: h). As a result, four groups of chipped poplar were obtained. The energy consumption for biomass material preparation is not included in this paper. 2650


Energy & Fuels

Article

Table 2. Experimental Data of ES (kWh ton−1), hf (wH2O, %), dgm (mm), ρb (kg m−3), and AoR (deg) at Any Combination of hi (wH2O, %), IS (mm), dtarget (mm), and rev (rpm) for Poplar hi

IS

dtarget

rev

code

ES

hf

dgm

ρb

AoR

28.25 27.90 29.70 30.20 25.02 23.81 23.86 23.24 24.04 25.88 31.03 31.80 25.55 27.97 25.03 28.09 7.93 6.58 7.31 6.50 5.71 6.52 5.89 6.36 6.46 7.21 7.17 5.87 7.50 5.11 5.17 5.51 16.64 17.19 16.75 17.83

50 50 50 50 20 20 20 20 50 50 50 50 20 20 20 20 50 50 50 50 20 20 20 20 50 50 50 50 20 20 20 20 35 35 35 35

5 5 5 5 5 5 5 5 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5 2 2 2 2 2 2 2 2 3.5 3.5 3.5 3.5

2000 2000 3000 3000 2000 2000 3000 3000 2000 2000 3000 3000 2000 2000 3000 3000 2000 2000 3000 3000 2000 2000 3000 3000 2000 2000 3000 3000 2000 2000 3000 3000 2500 2500 2500 2500

P-HIS5rpm P-HIS5rpm P-HIS5rpm P-HIS5rpm P-His5rpm P-His5rpm P-His5rpm P-His5rpm P-HIS2rpm P-HIS2rpm P-HIS2rpm P-HIS2rpm P-His2rpm P-His2rpm P-His2rpm P-His2rpm P-hIS5rpm P-hIS5rpm P-hIS5rpm P-hIS5rpm P-his5rpm P-his5rpm P-his5rpm P-his5rpm P-hIS2rpm P-hIS2rpm P-hIS2rpm P-hIS2rpm P-his2rpm P-his2rpm P-his2rpm P-his2rpm P-CP-1 P-CP-2 P-CP-3 P-CP-4

126.51 112.18 89.17 95.72 100.63 96.88 75.52 77.25 212.80 218.97 199.33 210.77 198.15 213.53 183.68 212.72 35.95 31.38 27.39 29.01 30.77 31.94 27.45 29.15 67.95 68.15 59.73 60.69 67.29 68.15 64.06 62.39 111.69 101.78 100.15 109.64

24.74 24.50 26.20 26.42 20.87 19.48 19.25 18.69 15.67 15.45 15.47 20.83 14.2 16.23 15.16 17.54 6.88 6.38 6.51 6.32 5.02 5.22 5.92 5.42 5.48 5.15 6.04 5.15 5.63 4.33 4.50 4.62 12.8 12.14 12.15 12.64

0.84 0.87 0.91 0.91 0.89 0.93 0.84 0.81 0.39 0.36 0.34 0.32 0.35 0.32 0.35 0.36 0.62 0.60 0.63 0.61 0.82 0.70 0.62 0.70 0.35 0.35 0.30 0.28 0.36 0.33 0.33 0.3 0.50 0.53 0.55 0.49

180 190 180.0 174.0 169.3 170.7 178.7 177.3 222 224 238.7 208 226 222 226 204 222 222 208 213.3 210.7 211.3 182 188 257.3 258.7 256 246.7 265.3 242.7 232 248 178.7 188 180 173.3

51.27 50.07 49.37 52.05 47.52 47.90 51.12 48.24 45.95 45.95 45.27 47.16 51.65 48.63 45.86 45.61 45.68 42.66 46.41 44.54 45.72 47.5 51.18 51.18 40.03 42.26 37.46 37.56 38.76 42.43 40.78 41.31 46.55 51.92 49.58 51.94

In Table 1, the particle size characterization for these four groups of chipped poplar is shown. They show a log-normal particle size distribution; therefore, the geometric mean diameter has been considered as the representative mean size. Poplar size differences between types 1 and 3 (dgm = 15.16−8.64 mm) are due to the different opening sizes of the screen during previous chipping. In types 2 and 4, the increase of the mean size after drying is due to the partial fine particle removal during forced drying. Also shown is a decrease in geometric deviation and an increase of m (Rosin−Rammler parameter), indicating a less disperse size distribution because of fine removal. This effect was more noticeable when poplar had been chipped with dscreen = 20 mm as a result of higher fines present prior to the forced drying. The highest poplar bulk density (157 kg m−3) is linked with the highest moisture content and chip size; furthermore, the lowest poplar bulk density (134.6 kg m−3) was registered on the opposite physical conditions (dscreen = 20 mm and dried material). These values are in accordance with 148.75 kg m−3 registered by Esteban and Carrasco18 for chipped poplar at wH2O = 11.89% and darithm = 9.2 mm. 2.1.2. Corn Stover. Corn plant (Zea mays L.) was cultivated in Sariñena (UTM coordinates: 30N, 738030 4629314) province of Zaragoza, northeast of Spain. After harvesting and corn grain extraction, corn stover (stalk, leafs, and cobs) was chipped and divided into two parts. Half was hermetically packed to avoid water

evaporation at wH2O = 30% (code: H), and the second half was dried naturally until wH2O = 6−10% (code: h). With regard to physical characteristics, dgm and Sgm present generally higher values than poplar (Table 1). The bulk density was around 44.4−52.8 kg m−3, 3 times lower than poplar. 2.2. Biomass Analysis. Samples from chipped and milled materials were collected under standard specifications (CEN/TS 14778-1:2005 EX25) and analyzed in the laboratory. The moisture content was based on the loss in weight after drying for 24 h in an oven at 105 °C (standard CEN/TS 14774-1:200426). The bulk density was calculated by the mass material contained in a standard container of 5 L volume for milled material and 50 L for chipped material (CEN/TS 15103:200527). The particle size analysis for chipped biomass was performed by oscillating the screen method (standard CEN/TS 15149-1:200628) with standard sieves of aperture sizes of 63, 45, 25, 16, and 5 mm (ISO 3310-1:200029). For milled biomass materials (CEN/TS 151492:200630), a vibrating screen machine with standard sieves (ISO 33101:200029) with apertures of 3.15, 2, 1, 0.8, 0.5, 0.355, 0.25, 0.15, 0.1, and 0.045 mm was used. For both methods, mass percentages of each particle size were calculated by the mass retained on each sieve. Average particle sizes, diameters, and moments from particle size distributions were calculated according to ISO 9276-2:2001.31 2651


Energy & Fuels

Article

Table 3. Experimental Data of ES (kWh ton−1), hf (wH2O, %), dgm (mm), ρb (kg m−3), and AoR (deg) at Any Combination of hi (wH2O, %), IS (mm), dtarget (mm), and rev (rpm) for Corn Stover hi

IS

dtarget

rev

code

ES

hf

dgm

ρb

AoR

25.48 23.20 27.47 30.41 24.22 22.36 36.43 26.51 9.69 9.88 9.73 9.49 8.24 8.19 10.78 10.53 15.27 15.31 15.33

50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50

5 5 5 5 2 2 2 2 5 5 5 5 2 2 2 2 3.5 3.5 3.5

2000 2000 3000 3000 2000 2000 3000 3000 2000 2000 3000 3000 2000 2000 3000 3000 2500 2500 2500

CS-HIS5rpm CS-HIS5rpm CS-HIS5rpm CS-HIS5rpm CS-HIS2rpm CS-HIS2rpm CS-HIS2rpm CS-HIS2rpm CS-hIS5rpm CS-hIS5rpm CS-hIS5rpm CS-hIS5rpm CS-hIS2rpm CS-hIS2rpm CS-hIS2rpm CS-hIS2rpm CS-CP-1 CS-CP-2 CS-CP-3

113.62 106.81 117.87 108.85 190.8 164.23 199.68 218.23 22.46 24.38 23.89 25.72 60.37 57.66 61.52 62.75 74.21 76.78 75.92

23.36 21.75 25.28 22.02 16.33 12.89 20.31 17.99 10.20 9.82 9.83 9.78 7.09 7.19 9.30 9.10 12.63 12.09 12.08

0.77 0.65 0.74 0.59 0.35 0.38 0.33 0.26 0.80 0.65 0.73 0.70 0.37 0.36 0.26 0.26 0.47 0.5 0.45

100 102 100 100 198.7 204.0 198.7 204 112 133.3 108 108 186.7 185.3 198.7 204 148 146.7 150.7

53.15 50.00 52.85 51.40 44.15 43.70 43.30 44.42 42.64 42.48 41.92 41.49 32.11 35.39 38.53 32.23 43.33 42.49 41.98

Bayesian regularization.35 We opted to train from experimental data two different ANNs for each biomass product, one having the specific energy requirement (R2 ≥ 0.97), final moisture content (R2 ≥ 0.97), and geometric mean diameter (R2 ≥ 0.98) as outputs and another one having the bulk density (R2 ≥ 0.87), and angle of repose (R2 ≥ 0.85) as outputs. Plotting one output of the predictive model against two of the inputs (keeping the other two constant) serves to partially illustrate the influences. This can be readily performed from ANN models (see, for instance, Figure 1 showing ES as a function of hi and dtarget), where the experimental points can be superimposed. However, it does not provide a systematic influence analysis of every input and their interactions on output variables. For such a purpose, output results from the ANN models were analyzed statistically by means of standard response surface methodology (RSM) by central composite design (CCD) to determine their influence with a confidence level of 95%. CCD analysis consists of 2n full factorial runs, with 2n axial runs and n center runs. Notice that CCD could not be applied directly to experimental data, mainly because of the high moisture content variability in the samples. However, a set of input variables with the desired moisture levels can be generated from the ANN trained with the experimental data, with these levels being easily identified (−1, 0, and +1). 2.5. Test Planning. Two main groups of variables can affect the biomass milling process: the physical conditions of the biomass and the operational variables of the facility. Raw chipped biomass was supplied with two different levels of moisture contents and particle sizes. With regard to operational parameters, we aimed to study the role of two different physical aspects. The first one is the impact energy between the particle against the hammer to observe the relevance on particle fracture and energy consumption. The impact energy is determined by relative kinetic energy and, thus, the angular velocity of the hammer. The angular velocity was set at two levels: 2000 and 3000 rpm, which means a hammer tip speed of 56.43 and 84.73 m s−1, respectively, according to the speed range of a commercial hammer mill,11 48−87 m s−1. The second operational variable was the opening size of the screen surrounding the milling chamber (5 and 2 mm). The opening size is the main factor in particle classification. Particles remain in the milling chamber until they are small enough to pass through the screen. In summary, four factors were analyzed: (1) biomass moisture content (h), wH2O = 5−10 and 25−30%; (2) biomass input particle size

The method for measuring the angle of repose was defined by Ileleji and Zhou32 as piling AoR fixed base. The milled biomass falls by gravity from a hopper to a circular aluminum plate. Because of bridging caused by interlocking particles, mechanical agitation is often required to enable a uniform flow. A non-ideal cone shape pile is formed by the discharged material. The angle of repose is determined as a function of the height of the pile and the radius of the base. 2.3. Biomass Milling Facility. The main equipment is a horizontal axis hammer mill, which combines three fixed cutting blades in a side disc and three radial lines of six floating hammers each, driven by an electric motor of 11 kW and 3000 rpm nominal rotation speed. The feed rate is regulated and supplied by a belt feeder. Biomass feed rate control was designed to keep the electric current of the hammer mill motor at about 18 A. This is a safety margin below the drive nominal value (21 A) to allow the reaction to mill clogging. The control loop consists of a PI controller to regulate the linear velocity of the belt conveyor and, therefore, the feed rate of the material. This linear velocity is regulated as a function of the current consumption of the mill. This control loop stabilizes the energy consumption of the hammer mill, smoothing its variability because of the heterogeneity of biomass physical conditions and to avoid mill clogging. The electric consumption was sampled every second using network analyzers. The energy required to run the hammer mill empty was not subtracted because it is considered a necessary and, hence, a computable cost. 2.4. Predictive Models from Experimental Data and Statistical Analysis of Influences. Even with a large and costly number of experiments, it is not possible to try all of the possible combinations of the variables of interest; therefore, it is necessary to obtain a predictive model from the relatively scarce experimental data. Artificial neural networks (ANNs)33,34 provide a suitable model, because they can be regarded as a generalization of regression; they are able to “learn” complex multivariate nonlinear relationships even from noisy and incomplete data, and at the same time, they are conceptually simple, computationally efficient, and well-supported by professional technical computing software environments. The inputs of ANN models are all of the relevant independent variables of experimental tests (section 2.5), while the energy consumption and physical characteristics are the outputs. The selected ANN architecture was, in all cases, a small conventional feed-forward network, with (five) sigmoid neurons in the hidden layer and one linear neuron in the output layer. ANNs were trained using backpropagation (Levenberg−Marquardt) algorithms with automated 2652


Energy & Fuels

Article

milling process, the output moisture content, the geometric mean diameter, the bulk density, and the angle of repose of milled biomass were measured and calculated. Tables 2 and 3 show the results obtained in the experimental tests. The code of each test identifies, first, the tested biomass (P, poplar; CS, corn stover) and, second, a combination of a capital or a small letter corresponding to the four variables under analysis: moisture content (H and h), input particle size (IS and is), target particle size (5 and 2), and angular velocity (RPM and rpm). Capital and small letters represent the highest or lowest level of each variable. Finally, central point tests are identified as CP code.

3. RESULTS This paper presents two main goals: obtain a predictive model from experimental data using neural networks and determine the effects of the physical conditions of biomass (moisture and chip size) and milling operational parameters (screen opening size and angular speed of the hammers) on ES, hf, dgm, ρb, and AoR. In this section, examples of three-dimensional (3D) figures of predictive surfaces and superimposed experimental data at 2000 rpm and high input chip size are represented, varying dtarget and hi. Influences of main parameters are determined by statistical analysis. 3.1. Specific Energy Requirement. Predictive neural network surfaces show similar tendencies for both biomass. Experimental specific energy requirement values (ES, related to the dry matter weight, db) for poplar presented a wide range from 28 to 215 kWh ton−1 and for corn stover from 22.5 to 218.2 kWh ton−1 as a function of input biomass conditions and milling operational variables. For both kinds of biomass, the most relevant variable influence on specific energy requirement was moisture (hi), followed by target particle size (dtarget). In Figure 1, predictive surfaces of the neural network show visually this effect for poplar and corn stover. Even not only the variables themselves but also interactions between them are more relevant than other individual variables, such as IS or rev. In the case of angular velocity rev, there is a slightly higher consumption at lower rev for poplar, whereas for corn stover, rev is not a relevant variable at least in the range studied. The slope of the surfaces is steeper when projected on planes of constant values of dtarget, which means that moisture is the most determinant variable but also varies when projected on planes of constant values of moisture content, indicating that this variable is also important. Finally, we can see that the slope of these projections varies depending upon the cutting plane; it reflects the joint interaction of both variables. As seen in Table 4, when hi is increased from 7 to 27%, ES increases almost 3-fold, regardless of the input variable combination and kind of biomass; even ΔES is higher for corn stover at dtarget = 5 mm. If the dtarget influence is analyzed, one observes that the increase is about 2-fold, and therefore, a lower repercussion than for hi can be concluded. Also noticed is

Figure 1. Predictive surface of the neural network and experimental data points for the specific energy requirement (ES) for (a) poplar and (b) corn stover at 2000 rpm, varying hi and dtarget. (is), as a function of chipping pretreatment; (3) process target diameter (dtarget), 5 and 2 mm; and (4) angular speed on the hammer mill rotor (rev), 2000 and 3000 rpm. The combinations of these four variables resulted in 16 experimental tests. A repeat for each test was performed. The first biomass tested was SRF poplar. As will be shown in later sections, biomass input particle size had a very small influence on poplar results. Therefore, we assumed that this effect is also negligible for corn stover to decrease the experimental costs of the test campaign, reducing from 32 (24) to 16 (23) tests. The central point of the range considered was characterized with three additional tests (wH2O ≈ 18%, mixture input particle size at 50% on a mass basis, dtarget = 3.5 mm, and rev = 2500 rpm) to achieve a more detailed analysis. A total of 54 experimental tests (32 + 3 for SRF poplar and 16 + 3 for corn stover) were performed. The tests consisted of mostly stationary continuous grinding in the open circuit of around 15−20 kg of biomass. As output variables, the specific energy requirement of the

Table 4. Change in Specific Energy Consumption for the Studied Range of Moisture and Target Particle Size poplar −1

corn stover

ES (kWh ton )

2 mm

5 mm

ΔES by Δdtarget (%)

2 mm

5 mm

ΔES by Δdtarget (%)

hi = 7% code hi = 27% code ΔES by Δhi (%)

65.7 (hIS2rpm) 196.1 (HIS2rpm) 298

31.4 (hIS5rpm) 86.42 (HIS5rpm) 275

209

55.6 (hIS2rpm) 187.8 (HIS2rpm) 338

19.2 (hIS5rpm) 110.8 (HIS5rpm) 577

289

227

2653

169


Energy & Fuels

Article

the significance of the hi−dtarget interaction because it presents a different behavior as a function of which combination of hi and dtarget is evaluated. In addition, it is clearly observed that the input variables have a greater influence on corn stover than poplar. These results are in good agreement with those by Esteban and Carrasco18 in hammer milling of poplar at wH2O = 11.89%. They obtained values for specific energy of 27.5, 37.7, and 82.6 kWh ton−1, decreasing the opening size of mesh at 6, 4, and 1.5 mm, respectively. With regard to corn stover, Mani et al. performed hammer milling tests varying the opening size of the screen between 3.2 and 0.8 mm. They obtained specific energy consumptions of 11 and 34.3 kWh ton−1 at wH2O = 12%. At drier stover (wH2O = 6.2%), energy requirements decreased until 7 and 22 kWh ton−1. Results on corn stover apparently disagree, but it must be taken into account that Mani et al. subtracted the unloaded power consumption of the mill from the overall energy consumption. Similar effects were found by Miao et al.15 with Miscanthus, switchgrass, and willow. They obtained a power relationship between ES and dtarget (ES = adbtarget, where a and b are coefficients) as well as the impact of the higher the moisture content (wH2O = 15%), the higher the ES for finer particle comminution (dtarget = 1 and 2 mm). For a coarse milling screen (6 mm), however, moisture did not significantly affect ES. This fact is not in accordance with our results, in which moisture also has a strong effect at dtarget = 5 mm, probably because the moisture content is twice as large (wH2O = 30 versus 15%). If a ES comparison between both biomasses is analyzed, poplar presents a higher energy requirement than corn stover at low angular velocity, 2000 rpm, but both are very similar at 3000 rpm. This different behavior of the biomass is not easy to explain, but a possible cause is that poplar has a higher particle resistance under fracture than corn stover. The amount of impact energy at 2000 rpm is not effective enough to cause particle breakage around the limit of the required energy for poplar fracture, whereas the same amount of impact energy is very effective on stover particles, decreasing the overall energy consumption of the process. However, this hypothesis must be confirmed by further studies on material breakage functions. 3.2. Output Moisture Content. This section shows the drying effect of biomass during the milling process, because moisture at the mill input was always higher than moisture at the mill output. This drying effect was also reported before.18,23 Surfaces based on neural networks predict very similar behavior between both biomasses (Figure 2). As for ES, the most significant variable is hi, followed by dtarget, whereas IS has no repercussion. A high moisture at the mill inlet implies a higher drying effect. As an example, consider the case of poplar for dtarget = 2 mm and high angular velocity (code: IS2RMP). With low moisture at input, wH2O = 7.17%, output moisture was found to be wH2O = 6.04%. At high moisture at input, wH2O = 31.04%, the drying effect is much more relevant and output moisture was found to be wH2O = 15.47%. These results are in accordance with those registered by Esteban and Carrasco18 for hammer milling of poplar from 11.89 to 8.1%. Under the same experimental conditions (code: IS2RMP), similar results were registered for corn stover: a slighter decrease in moisture, from

Figure 2. Predictive surface of neural network and experimental data points for the output moisture content (hf) for (a) poplar and (b) corn stover at 2000 rpm, varying hi and dtarget.

10.52 to 9.1% at a low input moisture content and a stronger decrease from 26.5 to 17.99% at high moisture. This behavior is in agreement with drying kinetics of biomass. Moisture in biomass can remain as no bound water in the lumen (nuclei of vegetal cell) or as bound water in the cell walls. In drying processes, no bound water from lumen is released in the first place. When this moisture has evaporated, bound water from the external surface is lost and from inside the cell goes to the particle surface by means of capillarity and diffusion; this drying is slower than the first stage at high moisture, and particle shrinkage is also observed.36,37 For woody biomass, bound water can reach values of about 23%.38 During the milling process, biomass particles break several times, increasing very quickly the particle area in contact with the drying agent (the conveying air) and promoting better moisture evaporation inside the mill chamber. This is in agreement with the fact that the second most relevant variable is dtarget, the most significant variable in size reduction. A lower value of dtarget increases the drying effect because of the exponential increase of the contact area, and therefore, the final moisture of milled biomass (hf) decreases strongly. As a representative example, in Table 5, the drying effect for high and low levels of moisture content and particle size target are shown. The higher exposed area is probably one of the most 2654


Energy & Fuels

Article

Table 5. Drying Effect in Biomass within the Analyzed Range of Moisture and Target Particle Size Δh (wH2O, %) poplar corn stover

moisture decrease 5 2 5 2

mm mm mm mm

from from from from

25 to 20.9% 25.6 to 14.2% 25.5 to 23.4% 24.2 to 16.3%

test code His5rpm His2rpm His5rpm His2rpm

important facts, but other effects can also be important. At lower values of dtarget, the following can also be observed: (1) higher residence time of the particle inside the mill chamber, (2) an increase in the number of impacts (at each impact, part of the energy is released as heat39,40), and (3) smaller particles having a shorter distance from internal points to the external surface (as a result, evaporation of the internal moisture content by diffusion and capillarity processes is faster). The interaction of the two main factors hi−dtarget is also relevant. As seen in Figure 2, the decrease of moisture is higher when dtarget decreases at high moisture ratios rather than at lower moisture ratios. In addition, for the comparison of the drying effect for both biomasses, a widespread higher moisture decrease and, therefore, a lower hf in poplar than in corn stover at the same milling conditions was registered. 3.3. Geometric Mean Diameter of Milled Biomass. Size and cumulative size distributions represent the mass percentage or the cumulative undersize mass percentage for each size. As seen in Figure 3, cumulative particle size distribution of comminuted biomass shows a log-normal distribution behavior. For both resources, the final particle size depends statistically upon the four input variables under study. The restriction of the opening size of the screen (dtarget) that blocks the particle from leaving the mill chamber can be observed clearly by the left displacement of the cumulative size distributions (Figure 3) when a lower dtarget is imposed. The geometric mean diameter (dgm) of the size distribution was taken as the representative mean size of the distribution. Evidently, analysis of variation (ANOVA) confirms the great and meaningful influence of dtarget on dgm; the lower the dtarget, the lower the dgm. However, dgm always presents much lower values than dtarget (between 6 and 8 times lower at dtarget = 5 mm and between 5 and 7 times lower at dtarget = 2 mm). Mani et al.20 reported the same differences for corn stover at wH2O = 6.22%, obtaining dgm = 0.41, 0.26, and 0.19 mm for dtarget = 3.2, 1.6, and 0.8 mm, respectively. Consequently, dtarget must be considered as a reference size of the process, but it does not represent the overall particle size of the milled product. The influences of the other three input variables are much smaller in comparison to dtarget. In general, for both biomass, with decreasing values of hi and IS and increasing rev, the geometric mean diameter decreases. The differences between biomass was mainly due to the higher repercussion of hi on poplar than corn stover, in which the repercussion of dtarget is overall. The aforementioned rev trend is also in agreement with the value reported by Bitra et al.11 that registered the lower the revolutions, the lower the dgm. Also to be noted (Figure 4) is the slight influence of the interaction between hi and dtarget; although there is almost no repercussion on hi at low dtarget (2 mm), it is remarkable at high dtarget (5 mm). 3.4. Bulk Density. Poplar always presented higher bulk density than corn stover for milled material, but the bulk density increase related to chipped form was noticeable higher on corn stover because of its low chipped ρb. The bulk density

Figure 3. Cumulative particle size distribution for (a) poplar and (b) corn stover at three levels of dtarget (5, 3.5, and 2 mm).

(ρb) of poplar increased from 134 to 157 kg m−3 in the chipped form (Table 1) and from 170 to 265 kg m−3 after its milling. This means an increase of between 11 and 97% depending upon the input variables, and it is in agreement with the effect reported by Esteban and Carrasco18 for poplar at wH2O = 11.89%. They registered an average bulk density increase of about 50% from 150 to 225 kg m−3. For corn stover, the registered Δρb was between 89 and 360%, from 44.4 to 52.8 kg m−3 at the chipped form and from 100 to 204 kg m−3 in the milled form. The final values of ρb for corn stover are also in agreement with the data registered by Mani et al.,20 at 131, 136, and 158 kg m−3 for dtarget = 3.2, 1.6, and 0.8 mm, respectively. Miao et al.15 also reported a similar ρb range for other woody biomass than poplar (willow, around 165−240 kg m−3) and for other herbaceous biomass than corn stover (Miscanthus and switchgrass, 125−210 kg m−3). The bulk density increase of the biomass resources after milling is due to three reasons: (1) the strong particle size reduction, (2) the higher homogenization of the particle shape, and (3) the typical characteristics of the particle size distribution of milled biomass by hammer mill: wide and well-graded (e.g., see Figure 3 and also refs 11, 18, 20, and 23). The three causes allow for a better rearrangement of the 2655


Energy & Fuels

Article

Figure 5. Predictive surface of neural network and experimental data points for the bulk density for (a) poplar and (b) corn stover at 2000 rpm, varying hi and dtarget.

the corn stover. The surface response of corn stover presents mainly linear behavior related to dtarget, whereas a strong increase can be observed at the lowest moisture content and lowest particle size for poplar. Indirectly, it can also be associated with the influence of the particle size itself because it was previously also influenced by hi (section 3.3). However, for corn stover, the influence of hi on dgm was negligible, and therefore, it is not important for ρb. Because bulk density depends strongly upon the particle size, part of the hi influence on poplar results from its influence on its particle size. 3.5. Angle of Repose (AoR). The AoR measures the angle of inclination of the free surface to the horizontal of a bulk solid. AoR indicates the interparticulate friction and has been used to characterize the flow behavior of powders and granular materials with respect to flowability, avalanching, stratification, and segregation.32 Biomass flowability has scarcely been studied.32,41−43 AoR is one of the most widely used32,44 among several flow static parameters.45 In our case, poplar presented an AoR range between 37° and 52°, similar to corn stover (between 32° and 53°). However, as observed in Figure 6, poplar presents more homogeneity at AoR values of about 45−50° and only decreases strongly for low hi and low dtarget. However, a continuum graphical slope is more noticeable in all of the analyzed range for corn stover. In addition, AoR always presents higher values for poplar than corn stover; only in conditions of high moisture and particle size does the AoR become similar for both biomasses. In other words, the variation of the input variables has higher repercussion on the AoR for corn stover than for poplar. AoR is a property of the material and also depends upon its particle size and moisture content, and therefore, just as for

Figure 4. Predictive surface of neural network and experimental data points for the geometric mean diameter (dgm) for (a) poplar and (b) corn stover at 2000 rpm, varying hi and dtarget.

particles because the finer particles can move more easily to the spaces between the bigger particles. The bulk density of a material depends mainly upon the physical properties of their particles and the interaction between them. Particle density (e.g., kind of biomass), size and shape, and moisture content play a key role on ρb. In this study, poplar always presented higher values than corn stover, probably because of the higher particle density. With regard to the influence of the particle size (dgm; section 3.3) and moisture content (hf; section 3.2) of the milled material, it is reasonable to expect that those input variables with a strong influence on these physical properties will also play a relevant role on ρb. This can be observed in Figure 5, as well as the statistical ANOVA that confirms the highest influence of dtarget on ρb. As explained in the previous section, dtarget was also the key factor in the particle size (dgm) and, therefore, in determining its bulk density. The second variable influencing ρb is the input moisture content (hi). The moisture content of the milled product (hf; section 3.2) is determined mainly by the input moisture content (hi), and this is the origin of the influence of hi on ρb. However, the tendency with hi is not the same for both biomasses. The 3D shape of the ρb response surfaces (Figure 5) was different under different conditions mainly because of the higher influence of the moisture content on the poplar than on 2656


Energy & Fuels

Article

biomasses present worse handling behavior. In agreement with this strong effect, Gil et al.43 obtained also the influences of hi and dtarget and particle shape (obtained by image processing46) on other important flow parameters, such as angle of internal friction, effective angle of friction, cohesion, unconfined yield strength, and major consolidation stress. With regard to the other two input variables under analysis, rev presents a slight secondary influence in comparison to hi and dtarget and IS does not show any repercussion on the final AoR.

4. CONCLUSION The effects of the physical condition of biomass (moisture content and inlet chip size) and the operational variables (opening size of the screen and hammer angular velocity) on the specific energy requirement of the milling process and physical properties of the milled product (moisture, particle size, bulk density, and angle of repose) were analyzed for two types of biomass: SRF poplar and corn stover. A set of 54 experimental tests at semi-industrial scale were carried out, and gathered data were analyzed using neural networks and statistical analysis. It allowed us to determine the influences of each input variable and also to obtain a semiempirical tool to predict the milling stage behavior to regulate and optimize operational parameters as a function of the conditions in which the biomass is supplied. The results obtained not only helped establish the milling energy consumption and characterize the milled product for both types of biomass but also identified several common qualitative tendencies for both resources. The moisture content of the biomass at the mill inlet (hi) and the opening size of the screen (dtarget) were found to be the key variables on the milling process. The first one can involve a milling energy requirement increase of about 3−5 times when hi increases from wH2O = 7 to 27%, but it also implies a drying effect of the biomass, which can reach about 50% at high moisture values (≈30%). These great increases are partially due to the screen blocking (effective mesh outlet surface decrease), promoting a higher probability of blocking the openings at lower screen size, as well as the material caking by the liquid bridges between particles over the screen at a high moisture content. As a result, the particle remains longer inside the mill chamber, partially evaporating the moisture to break the liquid bridge but also increasing the milling energy consumption. On the other hand, this moisture content reduction must also be taken into account in the global biomass pretreatment strategy, mainly at high levels of moisture content (wH2O ≈ 30%), when another forced drying stage is required. Poplar particles present generally higher drying than corn stover. To obtain the desired particle size of the final milled biomass, the opening size of the screen (dtarget) is the key factor. However dtarget must be considered only as a reference size, with the geometric mean size (dgm) as the representative size of the whole size distribution. The design of the screen characteristics plays a key role because an opening size decrease from 5 to 2 mm can involve a very different decrease on dgm, from around 0.8 to 0.33 mm, meaning an approximately 2-fold milling energy consumption increase. The effect of the angular speed of hammers (impact velocity against the biomass particles) is much smaller in comparison to dtarget but also generates finer particles at 3000 rpm than at 2000 rpm. The particle size and the moisture content of the milled biomass presented a strong effect on the bulk density and the

Figure 6. Predictive surface of neural network and experimental data points for the AoR for (a) poplar and (b) corn stover at 2000 rpm, varying hi and dtarget.

bulk density, these properties are influenced by the input variables. For poplar, dtarget is the main input variable for influences, followed by hi. For corn stover, it is in the opposite order but always with a stronger repercussion than for poplar. As was explained for bulk density, the influences of these input variables occur indirectly because of their influences on the particle size (dgm) and moisture content (hf) of the milled product. Registered data of AoR for poplar and corn stover are in agreement with the scarce literature about handling properties of milled biomass. For corn stover, Ileleji and Zhou32 determined an AoR range between 40° and 43° for dry particles (wH2O < 10%), significantly lower (AoR between 46.7° and 49.8°) than wet particles (>20%). In addition, larger particles had a higher AoR than smaller particles, 6.4, 3.2, and 1.6 mm in size. Also for corn stover and if low (wH2O = 8−10%) and high (wH2O = 25−30%) levels of moisture content are taken into account, the reported AoR in this study is between 32° and between 42.6° and 43.3° and 53.1°, respectively. Data are within a wider value range than reported by Ileleji and Zhou32 but are in accordance with the trend established: when hi and dtarget are increased, the AoR also increases, and therefore, both 2657


Energy & Fuels

■

angle of repose and, therefore, its handling behavior. In general, the lower the particle size and the moisture content, the higher the bulk density and the lower the angle of repose and, therefore, improved downstream handling labors. In addition, the milling process produces a bulk density increase evaluated between 11 and 97% for poplar and between 89 and 360% for corn stover. The effects of input chip size and the angular speed of the hammers (revolutions) play a secondary role in the performance of the milling stage, when they move into the usual values of commercial mills. The effect of the revolution shows slight peculiarities as a function of the kind of biomass, and the input chip size (in the analyzed range) has no relevant effect on the milling process. With regard to the flexible previous chipping stage, the recommendation is to focus on obtaining a product with better handling behavior to reduce the usual problems with feeding systems or road transport instead of the secondary repercussions on the milling process.

■

Article

REFERENCES

(1) Vande Walle, I.; Van Camp, N.; Van de Casteele, L.; Verheyen, K.; Lemeur, R. Biomass Bioenergy 2007, 31, 267−275. (2) Gasol, C. M.; Gabarrell, X.; Anton, A.; Rigola, M.; Carrasco, J.; Ciria, P.; Rieradevall, J. Biomass Bioenergy 2009, 33, 119−129. (3) González-García, S.; Gasol, C. M.; Gabarrell, X.; Rieradevall, J.; Moreira, M. T.; Feijoo, G. Renewable Energy 2010, 35, 1014−1023. (4) Butnar, I.; Rodrigo, J.; Gasol, C. M.; Castells, F. Biomass Bioenergy 2010, 34, 1780−1788. (5) Tolosana, E.; Laina, R.; Martínez-Ferrari, R.; Ambrosio, Y. Biomass Bioenergy 2011, 35, 2570−2580. (6) Barrio-Anta, M.; Sixto-Blanco, H.; Viñas, I. C. n.-R. D.; CastedoDorado, F. For. Ecol. Manage. 2008, 255, 1167−1178. (7) Sokhansanj, S.; Turhollow, A.; Cushman, J.; Cundiff, J. Biomass Bioenergy 2002, 23, 347−355. (8) Savolainen, K. Appl. Energy 2003, 74, 369−381. (9) Naimi, L. J.; Sokhansanj, S.; Mani, S.; Hoque, M.; Bi, T.; Womac, A. R.; Narayan, S. Cost and performance of woody biomass size reduction for energy production. Proceedings of the 2006 CSBE/SCGAB Annual Conference; Edmonton, Alberta, Canada, 2006; Paper 06-107. (10) Mani, S.; Tabil, L. G.; Sokhansanj, S. Biomass Bioenergy 2006, 30, 648−654. (11) Bitra, V. S. P.; Womac, A. R.; Chevanan, N.; Miu, P. I.; Igathinathane, C.; Sokhansanj, S.; Smith, D. R. Powder Technol. 2009, 193, 32−45. (12) Miu, P. I.; Womac, A. R.; Igathinathanae, C.; Sokhansanj, S. Analysis of biomass comminution and separation processes in rotary equipmentA review. Proceedings of the ASABE Annual International Meeting; St. Joseph, MI, 2006; Paper 066169. (13) Cadoche, L.; López, G. D. Biol. Wastes 1989, 30, 153−157. (14) Himmel, M.; Tucker, M.; Baker, J.; Rivard, C.; Oh, K.; Grohmann, K. Biotechnol. Bioeng. Symp. 1986, 39−58. (15) Miao, Z.; Grift, T.; Hansen, a. C.; Ting, K. Ind. Crops Prod. 2011, 33, 504−513. (16) Paulrud, S.; Mattsson, J. E.; Nilsson, C. Fuel Process. Technol. 2002, 76, 23−39. (17) Schell, D. J.; Hardwood, C. Fuel Process. Technol. 1994, 45, 159− 168. (18) Esteban, L. S.; Carrasco, J. E. Powder Technol. 2006, 166, 139− 151. (19) Esteban, L. S.; Carvajal, A.; Velasco, M.; Carrasco, J. E. Optimization of the grinding of dry residues from olive kernel oil extraction for its utilization in a pulverized biomass power plant. Proceedings of the 12th European Conference on Biomass for Energy, Industry and Climate Protection; Amsterdam, The Netherlands, 2002; pp 295−298. (20) Mani, S.; Tabil, L. G.; Sokhansanj, S. Biomass Bioenergy 2004, 27, 339−352. (21) Samson, P.; Duxbury, P.; Drisdelle, M.; Lapointe, C. Assessment of Pelletized Biofuels; http://www.reap-canada.com/online_library/ feedstock_biomass/15%20Assessment%20of.PDF (accessed Feb 2011). (22) Vigneault, C.; Rothwell, T. M.; Bourgeois, G. Can. Agric. Eng. 1992, 34, 203−207. (23) Gil, M.; Arauzo, I.; Teruel, E.; Bartolome, C. Biomass Bioenergy 2012, 41, 145−156. (24) Bargen, K. V.; Lamb, M.; Neels, D. E. Energy requirement of particle size reduction of crop residue. Proceedings of the ASAE Annual International Meeting; St. Joseph, MI, 1986. (25) European Committee for Standardization (CEN). CEN/TS 14778-1. Solid Biofuels. Sampling. Methods for Sampling; CEN: Brussels, Belgium, 2005. (26) European Committee for Standardization (CEN). CEN/TS 14774-1. Solid Biofuels. Methods for the Determination of Moisture Content. Oven Dry Method. Part 1: Total Moisture. Reference Method; CEN: Brussels, Belgium, 2004. (27) European Committee for Standardization (CEN). CEN/TS 15103. Solid Biofuels. Methods for the Determination of Bulk Density; CEN: Brussels, Belgium, 2005.

AUTHOR INFORMATION

Corresponding Author

*Telephone: +34-976-761863. Fax: +34-976-732078. E-mail: [email protected] (M.G.); [email protected] (I.A.); eteruel@ unizar.es (E.T.). Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS This work was partially supported by Project ENE2008-03358/ ALT, funded by the Ministry of Education in Spain. The authors are grateful to Ó scar Puyó for his extensive support in the laboratory work and to the agricultural cooperative of Sariñena for the corn stover supply.

■

NOMENCLATURE ANN = artificial neural network AoR = angle of repose CCD = central composite design darithm = arithmetic mean diameter (mm) dgm = geometric mean diameter (mm) dp = particle diameter (mm) dscreen = opening size of the installed screen on the chipper (mm) dtarget = maximum particle size of the obtained product from the milling process (mm) d50 = median diameter (mm) d10 = effective size (mm) ES = specific energy requirement of the milling process (kWh ton−1, where 1 ton = 1 Mg) hi = input moisture content hf = output moisture content IS = input particle size l = Rosin−Rammler size parameter (mm) m = Rosin−Rammler distribution parameter rev = experimental variable under analysis: angular speed RSM = response surface methodology RR = Rosin−Rammler distribution Sgm = geometric standard deviation SRF = short-rotation forestry ρb = bulk density (kg m−3) 2658


Energy & Fuels

Article

(28) European Committee for Standardization (CEN). CEN/TS 15149-1. Solid Biofuels. Methods for the Determination of Particle Size Distribution. Oscillating Screen Method Using Sieve Apertures of 3.15 mm and Above; CEN: Brussels, Belgium, 2006. (29) International Organization for Standardization (ISO). ISO33101. Technical Requirements and TestingPart 1: Test Sieves of Metal Wire Cloth; ISO. Geneva, Switzerland, 2000. (30) European Committee for Standardization (CEN). CEN/TS 15149-2. Solid Biofuels. Methods for the Determination of Particle Size Distribution. Part 2: Vibrating Screen Method Using Sieve Apertures of 3.15 mm and Below; CEN: Brussels, Belgium, 2006. (31) International Organization for Standardization (ISO). ISO92762. Representation of Results of Particle Size AnalysisPart 2: Calculation of Average Particle Sizes/Diameters and Moments from Particle Size Distributions; ISO. Geneva, Switzerland, 2001. (32) Ileleji, K. I.; Zhou, B. Powder Technol. 2008, 187, 110−118. (33) Bishop, C. M. Neural Networks for Pattern Recognition; Oxford University Press: New York, 1995. (34) Hagan, M.; Demuth, H.; Beale, M. Neural Network Design; PWS Publishing: Boston, MA, 1996. (35) Demuth, H.; Beale, M.; Hagan, M. Neural Network Toolbox. For Use with MATLAB; MathWorks: Natick, MA, 2001. (36) Mujumdar, A. S. Handbook of Industrial Drying; CRC Press: Boca Raton, FL, 1995. (37) McCabe, W.; Smith, J.; Harriot, P. Unit Operations of Chemical Engineering; McGraw-Hill: New York, 1991. (38) Kubler, H. For. Prod. Abstr. 1987, 10, 300−327. (39) Austin, L. Powder Technol. 2002, 126, 85−90. (40) Austin, L. Powder Technol. 2004, 144, 240−252. (41) Chevanan, N.; Womac, A. R.; Bitra, V. S. P.; Yoder, D. C.; Sokhansanj, S. Powder Technol. 2009, 193, 79−86. (42) Wu, M. R.; Schott, D. L.; Lodewijks, G. Biomass Bioenergy 2011, 35, 2093−2015. (43) Gil, M.; Schott, D.; Arauzo, I.; Teruel, E. Fuel Process. Technol. 2013, 112, 76−85. (44) Geldart, D.; Abdullah, E. C.; Verlinden, A. Powder Technol. 2009, 190, 70−74. (45) Krantz, M.; Zhang, H.; Zhu, J. Powder Technol. 2009, 194, 239− 245. (46) Gil, M.; Teruel, E.; Arauzo, I. Manuscript submitted Oct 2012.

2659


Influence of Input Biomass Conditions and Operational Parameters on

Recommend Documents