Lignocellulosic biomass feedstock transportation alternatives, logistics, equipment configurations, and modeling

Review

Correspondence to: Tony E. Grift, Energy Biosciences Institute, University of Illinois at Urbana-Champaign, 1206 West Gregory Drive, Urbana,

IL 61801-3838, USA. E-mail: [email protected]

†

Authors contributed equally to the paper.

351

Lignocellulosic biomass

feedstock transportation

alternatives, logistics,

equipment conﬁ gurations,

and modeling

Zewei Miao,

†

Yogendra Shastri,

†

Tony E. Grift, Alan C. Hansen and K.C. Ting, University of Illinois

at Urbana-Champaign, Urbana, IL, USA

Received October 17, 2011; revised November 23, 2011; accepted November 24, 2011

View online February 17, 2012 at Wiley Online Library (wileyonlinelibrary.com); DOI: 10.1002/bbb.1322;

Biofuels, Bioprod. Bioref. 6:351–362 (2012)

Abstract: Lignocellulosic biomass feedstock transportation bridges biomass production, transformation, and

conversion into a complete bioenergy system. Transportation and associated logistics account for a major portion

of the total feedstock supply cost and energy consumption, and therefore improvements in transportation can sub-

stantially improve the cost-competitiveness of the bioenergy sector as a whole. The biomass form, intended end use,

supply and demand locations, and equipment and facility availability further affect the performance of the transporta-

tion system. The sustainability of the delivery system thus requires optimized logistic chains, cost-effective transpor-

tation alternatives, standardized facility design and equipment conﬁ gurations, efﬁ cient regulations, and environmental

impact analysis. These issues have been studied rigorously in the last decade. It is therefore prudent to comprehen-

sively review the existing literature, which can then support systematic design of a feedstock transportation system.

The paper reviews the major transportation alternatives and logistics and the implementation of those for various

types of energy crops such as energy grasses, short-rotation woody coppices, and agricultural residue. It emphasizes

the importance of performance-based equipment conﬁ guration, standard regulations, and rules for calculating trans-

port cost of delivery systems. Finally, the principles, approaches, and further direction of lignocellulosic feedstock

Keywords: bioenergy; mechanical pre-processing and handling; performance-based standard and regulations;

feedstock delivery systems

Z Miao et al. Review: Lignocellulosic biomass feedstock transportation

comprehensively review the existing literature on these top-

ics, so as to conduct a systematic analysis and innovative

design of a feedstock transportation system.  is is the goal

of this review. Additionally, the review draws conclusions

and provides recommendations based on the literature.

Potential biomass transportation modes

 e transportation options of lignocellulosic feedstock

include roads, railways, waterways, pipelines, and/or a

combination of two or multiple options.

14,18,19

 e most

likely means of biomass transportation is by road using

in- eld bale-mover tractor, haulage wagon, tractor-trailer

combinations, truck-tractor-semi-trailer combinations, or

a container lorry, especially for small- and medium-scale

transportation requirements. Road transport is generally

applied for relatively short distances (<100 km) when  ex-

ibility is required and multiple (small) farm sites have to

be accessed, or when rail and waterway infrastructure is

absent.

8,9,20,21

For instance, about 80% of pulpwood deliv-

ered to US mills in 1996 arrived by truck.

22–24

In Austria,

where typical road transportation of biomass for heating and

combined heat and power (CHP) ranges from 20 to 120 km,

tractor trailers are commonly used for short distance trans-

port (about 10 km) of unchopped thinning residues, forest

wood chips, and various herbaceous feedstock.

Although

road transportation has low  xed costs, it has higher vari-

able costs such as fuel consumption, labor, tires, and wear

costs. For example, in the USA, the delivery cost of switch-

grass was 14.68 $ Mg

–1

(USD in 2000) including average

truck cost of 8.44 $ Mg

–1

and loader cost of $2.98 Mg

–1

10,24

 e energy consumption of road transport over a distance

of 100 km accounts for roughly 10% of the biomass inherent

energy content.

9,10

One must also consider the infrastructure

limitations, tra c congestion, and environmental impact

resulting in indirect costs. More than 15 truck deliveries per

hour are required for a biore nery consuming 1–2 Tg of dry

corn stover per year causing tra c congestions.

4,20,21

As the

biore nery size increases, a larger collection area and longer

transport distances are necessary to ensure year-round sup-

ply, which exacerbates these problems. Moreover, dedicated

and long-term storage facilities will be necessary since a

biore nery may typically store only up to 7–10 days of bio-

mass feedstock supply.

 erefore, an assumption of single

Introduction

he emphasis on biomass-based renewable energy,

including heat, power, and liquid fuels, has increased

in recent years owing to the depleting fossil fuel sup-

plies, increasing concerns regarding energy security, oil

price spikes, and climate change caused by greenhouse

gas (GHG) emissions from fossil fuel consumption.

1–3

Consequently, a challenging target of replacing an equivalent

of 30% of current US petroleum consumption by biomass-

based fuel has been set. Toward achieving that objective, it

has been shown that 1.4 Gt of lignocellulosic feedstock can

potentially be made available in a sustainable manner in the

USA. Agricultural feedstock is expected to satisfy a major

portion of the total biomass demand.

4–6

A sustainable, reliable, and cost-e ective biomass feedstock

delivery system is a pre-requisite for successfully achieving

the proposed targets. Such a system typically incorporates

in- eld harvest and collection, mechanical pre-processing,

handling, on-farm storage, transportation from farms to

storage facilities and from storage facilities to feedstock end-

users (e.g. a biore nery).

7–13

 us, it is a complex combina-

tion of ‘many-to-few’ and ‘one-to-one’ collection-handling-

processing-storage-delivery logistics.

14,15

Moreover, the

lignocellulosic feedstock is characterized by a low dry matter

density (64–224 kg m

–3

), low energy density (10–17 MJ kg

–1

limited  owability, irregular forms, and high moisture in

some cases, especially for agricultural residues and green

grasses.

16,17

 is increases the feedstock transportation cost

and logistic complications. Previous studies have illustrated

that the transportation costs represent between 13% and

28% of biomass production and provision costs, depending

on the biomass densi cation level and transportation mode.

A cost-competitive and reliable feedstock transporta-

tion system requires not only the optimization of delivery

logistics, transport modes and pathways (or route), but also

the con guration of processing, handling, and transporta-

tion equipment and facilities in terms of biore nery plant

size and conversion technology.

 e design and operation

of the transportation system signi cantly depends on the

intended use of biomass, feedstock type and productiv-

ity, geographical location, and natural resource availabil-

ity.  ese issues have been discussed in the literature, but

o en independently.  erefore, it becomes necessary to

Review: Lignocellulosic biomass feedstock transportation Z Miao et al.

operating costs, the large demand and supply rates for

lignocellulosic feedstock may justify the development of a

pipeline network. It has been shown that by using a slurry of

wood chips, pipelines would be economical in comparison

to delivery by trucks only at large capacity (greater than 0.5

million dry tons per year for a one-way pipeline, and 1.25

million dry tons per year for a two-way pipeline that returns

the carrier  uid to the pipeline inlet), and at medium to long

distances (greater than 75 km for one-way and 470 km for

two-way at a capacity of 2 million dry tons per year). For

corn stover at 20% of solids concentration or higher, pipe-

line transport is more economical than truck transport at

capacities greater than 1.4 million dry tons per year when

compared to a mid-range of truck transport costs. In addi-

tion to taking advantage of the economy of scale of the plant,

transportation using pipeline also o ers the opportunity

to implement innovative logistics such as simultaneous

transportation and sacchari cation of biomass.

22,23

 e

challenges include maintaining the feedstock quality and

stability as it mixes with the carrier  uid, and providing a

large amount of water resource. Maintaining pipeline tem-

peratures and prevent them from freezing will also be criti-

cal and may restrict their use to limited regions.

Intermodal transportation combining multiple transpor-

tation types may be a solution for a large-scale biore nery.

Here, two or more modes of transportation are combined

without changing the containment, and may require the

development of facility or infrastructure such as distribution

centers (e.g. centralized storage or depot facilities).

4,18,19,20,22,23

One likely example is the combination of road transport

with rail or waterway transport, as done in Australia for

transporting sugarcane to the mills.

20,25

Trucks or trailors

can be used for on-farm collection, short distance hauling to

a local storage, processing or depot facility alongside a rail

track or waterways.  e feedstock can then be loaded onto

rail cars or ships (a er possible short-term storage) and trans-

ported directly to the mill.  e  nal leg of transportation

for local distribution can again be carried out by road.  us,

intermodal transportation typically takes advantage of the

low variable costs for rail or waterborne transportation and

high  exibility of road transportation.

 e mills can have

dedicated railway tracks or waterways to ensure that the feed-

stock supply is reliable and meets the biore nery demands. A

similar arrangement using pipeline transportation can also be

biomass transportation mode could be overly simplistic and

not really optimum.

Rail transportation usually requires a large  xed invest-

ment to develop infrastructure and o ers lower  exibility.

However, it becomes cost-e ective for medium to long

overland transport distances (>100 km) involving stable and

constant  ow of goods.  is is owing to its low variable cost,

especially for logs, bales, bundles and industrial densi ed

biomass (e.g. pellet, briquette, bagged powder, wood saw, or

sorghum chip modules).

For example, in Alberta, Canada,

the distance variable cost for rail transport of straw and

wood chips was 0.0277 and 0.0306 $ dry Mg

–1

(USD

in 2004), respectively.  ese were signi cantly lower than

0.1309 and 0.1114 $ dry Mg

–1

(USD in 2004) for road

transportation.

 e  xed costs for rail transport, however,

were 17.01 and 9.97 $ dry Mg

–1

(USD in 2004), respec-

tively, which were signi cantly higher than 4.76 and 4.98 $

dry Mg

–1

(USD in 2004) for road transport.  e railcar

equipment cost is a function of biomass type, form, quantity

and distance to be transported.

21,24

 e cost bene ts of rail

transport for long-distance and large-scale feedstock deliv-

ery also depend on the availability of return freights, trans-

fer terminal policies and route infrastructure.

Waterborne transportation is applied for long distances,

especially in international transport. It has a cost struc-

ture similar to rail transportation, requiring high capital

investment in ships and freighters, but incurring low vari-

able costs and low energy use per Mg-km.

It is especially

relevant for the transportation of pellets or briquettes, which

are becoming an internationally traded feedstock form. In

Scandinavia, for instance, the transport of pellets by water,

within Scandinavia as well as from Canada, has become greatly

relevant for combustion and co- ring.

23,24

In Europe, long-

distance transport of pellets costs between 0.020–0.022 € dry

–1

(euro in 2003) (0.021–0.023 $, USD in 2003) by train

and only between 0.001–0.012 € dry Mg

–1

(euro in 2003)

(0.001–0.0126 $, USD in 2003) by ship.

In addition to pellets,

woodchips and bales (or bundles) can also be transported by

ship. Inland use of this mode of transport though is limited by

the availability of waterways such as rivers and lakes.

Pipeline transportation o ers another alternative to deliver

the low energy density biomass feedstock to a large scale

bioenergy plant.

21,22

Although pipeline transportation is

associated with high capital investment and low per-unit

Z Miao et al. Review: Lignocellulosic biomass feedstock transportation

Lignocellulosic biomass feedstock delivery

logistics

Lignocellulosic feedstock delivery logistics depend signi -

cantly on the feedstock type.

Delivery logistics of major

feedstock types such as dry energy grasses, green energy

crops, short-rotation woody biomass and agricultural resi-

dues are thus synthesized as follows (Fig. 1).

Dry energy grasses

As a major lignocellulosic feedstock source, the common

annual and perennial dry energy grasses include Miscanthus

(Miscanthus × giganteus), switchgrass (Panicum virgatum),

envisioned.

 e coordination of intermodal transportation

is normally more complex than that of unimodal transporta-

tion, because it requires more handling (trans-loading) of

feedstock as well as interactions among several stakeholders.

In summary, an appropriate feedstock transportation

mode depends on the intended biomass use, biore nery

plant capacity, facility and infrastructure, biomass form and

quality variables, and environmental impacts. A perform-

ance-based evaluation and analysis of alternative modes

that incorporates these attributes within the transportation

logistics framework along with equipment con gurations is

therefore required.

……

Farm n

Road

transport for

short distance

Two- or multi-pass

harvest: cutting-

windrow-baling (or

bundling)

Bioenergy

production:

Combustion for

heating and

power;

hydrolysis-

fermentation or

hydrothermal-

pyrolysis for

liquid biofuel

Handling: bulk

augering, belt

conveying, compression

in trailer or container

Farm 1

Farm 2

“Many-to-few” short distance transport

Dry grass energy crop yield: 7-

25 Mg ha

-1

; MC: 10-20%.

SRW yield: 7-30 Mg ha

-1

;

MC: 10-20%;

Crop residue: 1-10 Mg ha

-1

;

MC: 20-70%.

Centralized storage with

container, bag, silo, silage

tubes, large vertical

structure

“Many-to-few” or “One-to-one” long distance transport

System self-adaption in equipment, facility and infrastructure

configurations based on real-time weather, traffic and market demand

Single-pass

harvest: cutting-

chopping

Farm buffer or local-

distributed intermediate

storage with container,

silo or silage tubes

Handling with

trailer dumping,

augering or belt

conveying

Road, rail or

pipeline

transport for

long distance

Farm buffer or local-

distributed intermediate

bale (or bundle) storage

and preprocessing

Centralized storage,

processing and depot

facility with grinding,

pelleting or torrefaction

Feedstock supply interface

Handling: Bale lifting,

stacking, turning or

tarpping or wrapping

Road, rail or

pipeline

transport for

long distance

Road

transport for

short distance

Road and rail

transport for

long distance

Road, rail

transport for

long distance

Handling: Bale lifting,

stacking, turning or

tarpping or wrapping

(a)

Essential

Optional

Legend

……

Farm n

Single or two-pass

harvest: cutting-drying-

baling (bundling or

moduling) or ratooning

for low sugar species

Bioenergy

production:

Combustion for

heating and

power;

hydrolysis-

fermentation or

hydrothermal-

pyrolysis for

liquid biofuel

Farm 1

Farm 2

Green energy crop:

yield: 14-35 Mg ha

-1

; MC: 15-80%.

Single-pass harvest:

cutting-loading for high-

sugar species

Local-distributed

intermediate bale

drying or wet storage

Centralized storage, pre-

processing facility drying or

wet storage, preprocessing or

torrefaction

Feedstock supply interface

Shredding

and juicing

Conversion to

liquid bioethanol

Juice

Bagasse

Drying, grinding or

pelleting

(b)

Essential

Optional

Legend

Figure 1. Schematic chart of lignocellulosic feedstock delivery logistics of (a) dry grass energy crops, short rotation woody coppice, crop residues

and (b) green energy crops. Feedstock drying or wet storage and preprocessing are the major differences in supply logistics between dry and

green energy crops. Note: MC – moisture content (%).

Review: Lignocellulosic biomass feedstock transportation Z Miao et al.

and transport  owable feedstock to end-users with standard

equipment and management procedure.

We recommend a

hybrid delivery scenario combining the advantages of baling

at the farm-gate followed by intermediate pre-processing to a

standardized uniform particle supply, where the uniformity

refers to the physical and chemical properties of the feed-

stock.

In this scenario, bales are transported from a farm

to a centralized facility for storage and comminution. Here,

a horizontal or tub grinder (chopper or shredder) and large-

scale grinding facility (e.g. pilot demonstration unit (PDU)

developed by Idaho National Lab of the US Department

of Energy) is used to produce  ne particles based on the

demands of the biore nery.  e uniform particles are trans-

ported immediately to the biore nery at a constant supply

rate with standard handling and transport equipment and

procedure.

 e bioenergy plant capacity signi cantly a ects the

logistics and e ciency of dry grass feedstock delivery. For

a small Miscanthus-burning power plant with less than 20

km transport distance, the total transportation cost of 3-cm

Miscanthus chips was 35% of that of bales and much lower

than that of pellets.

Bales or pellets (briquettes or cubes)

are widely considered as an e cient form for a medium or

large combustion-power plant. For a large-scale bioenergy

plant, multiple storage (e.g. storage and processing depots)

units and intermodal transportation can be employed.  e

optimization of satellite storage locations or centralized stor-

age and processing facility becomes important for a local-

distributed depot processing-delivery system.

7,35

Short-rotation woody (SRW) feedstocks

In North America, the SRW energy crops mainly include

black willow (Salix nigra M.), hybrid poplar (Populus

hybrids), cottonwood (Populus deltoides), American syca-

more (Platanus occidentalis L.), slash pine (Pinus elliottii),

loblolly pine (Pinus taeda L.), sweetgum (Liquidambar

styraci ua L.), leucaena (Leucaena leucocephala (Lam.)), and

castor bean (Rininus communis).

28,39

SRW plantations are

featured with high moisture content, high yield, multiple-

stem plantations, and spatial harvest rotations of tree shoots

over 2–5 years for at least 30 years. For example, average

UK commercial willow feedstock yield is 7–18 Mg DM ha

–1

which are within the yield range of spring harvested

prairie cordgrass (Spartina pectinata), reed canary grass

(Phalaris arundinacea L.), elephant grass (Pennisetum

purpureum Schumach.), big bluestem (Andropogon gerardii),

and eastern gamagrass (Tripsacum dactyloides). Dry energy

grass feedstock is characterized by high biomass yield, sea-

sonal availability, low moisture content, low packing bulk

density, and signi cant biomass loss during harvesting,

processing and delivery.  e average dry matter yield of

Miscanthus × giganteus ranges from 7 to 25 Mg ha

–1

27,29

 e harvest-to-delivery logistics of dry herbaceous energy

crops require large equipment for harvest, preprocessing

and handling, high volumetric capacity of transportation

vehicles, and large storage facilities. Low moisture of the dry

grass feedstock makes storage relatively easy. For instance,

the moisture content of Miscanthus and switchgrass ranges

from 10 to 20% when harvesting in late fall and early spring,

respectively, at the Energy Farm of the University of Illinois

at Urbana-Champaign.  rough in- eld windrowing, the

moisture content of Miscanthus and switchgrass reached

as low as 10–15%, the baling moisture level.

Dry grass

feedstock could then be stored in bales or in bulk form at

intermediate facilities such as open air shield, on-farm bu er

(with or without tarp or wrapping), silage pits, bunkers, or

existing farm buildings.

9,30–36

 e seasonal availability and

yield uncertainty caused by weather may increase the cost of

obtaining these resources, while leading to suboptimal uti-

lization of the harvesting and on-farm handling equipment,

workforce as well as storage space.

20,25

Mechanical densi cation, size reduction, and torrefac-

tion take a crucial role in converting grass biomass from

highly variable resources into a reliable commodity for

bio-based industries. However, mechanical comminution

and compression of dry grasses o en represents a major

portion of the supply costs, with some estimates placing

it at 20 to 40% of the total biomass-to-biofuel cost.

With

commercial-scale hammer mills, for instance, energy con-

sumption for grinding switchgrass through 0.8 mm and

hardwood through 1.6 mm mill screens are between 1–3%

of their inherent heating value.

16,17

 e location and time of

size reduction, densi cation and/or torrefaction also in u-

ences the e ciency of the whole supply logistics.  ere is

an argument to place size reduction, densi cation, and tor-

refactions before transportation and to uniformly handle

Z Miao et al. Review: Lignocellulosic biomass feedstock transportation

14–35 Mg DM ha

–1

.  e harvesting and transportation logis-

tics of forage sorghum are expected to be similar to those for

dry grass energy crops. If sorghum moisture is in the bal-

ing range, forage chopping is a convenient mean of harvest.

 e harvested sweet sorghum must be shredded and juiced

within 16–48 h due to high sugar fraction.  e logistics and

equipment of shredding (or chopping) and extracting sugar

from sugarcane could be a paradigm for sweet sorghum. For

some regions, energy sorghum management practices (e.g.

ratooning) included multiple harvests in a single season and

‘just-in-time’ harvest systems, thereby requiring minimal

storage.

With proper management practices, sorghum

moisture can reduce to the 15–20% range required for

baling.

 e harvest and transportation operations of energy

cane are governed by the composition of the energy cane.

Varieties with high sugar content need to be processes

quickly, while those with high  ber and low sugar contents

can be processed and handled similar to grass or woody bio-

mass.  e current harvesting, handling, and pre-processing

technologies and equipment for energy cane are similar to

those for sugarcane due to the similar physical characteris-

tics.

 e energy cane systems comprise soldier harvesters

or combines that chop the cane into billets. Preliminary

studies have shown that the total transportation cost for

energy cane is in the range of 4–5 $ Mg

–1

, which is about

14% of the total production cost.

 e storage and com-

minution of energy cane billet and bagasse are challenging

because of its high moisture, low storability and grindability.

Similar to sweet sorghum, harvest management and ratoon-

ing of energy cane can reduce the storage requirements.

Agricultural residues

Agricultural residues mainly include arable crop residues,

and stalk and branch residues from orchard and horticul-

tural plants. Hereina er, we mainly discuss crop residue

delivery logistics.

Crop residues including crop straw or stover, cotton- and

sun ower-stalk, are characterized by seasonal availability,

low bulk density, and uncertain moisture content. Biomass

yields of crop residues range from 1–10 Mg DM ha

–1

which is signi cantly lower than that of energy crops.

43–46

 e moisture content of corn stover, soybean stems and

Miscanthus × giganteus of 7–25 Mg DM ha

–1

.  e mois-

ture content of winter-harvested willow generally is in the

range of 40–55% at harvest.  us, drying of SRW feedstock

from about 40–50% (dry basis) to less than 15% (dry basis) is

challenging.

 e SRW harvest-to-delivery logistics and equipment

requirement is usually composed either of single-pass

cutting (or slashing)-bundling, cutting-baling or cutting-

chipping, or two-pass cutting-baling or cutting-bundling

systems. In North America, cutting (or slashing)-baling or

cutting-chipping systems are more popular for SRW cop-

pice, while cutting-bundling harvest equipment is widely

used in Europe.  e SRW harvest-to-delivery can use the

equipment for harvesting and transporting understory for-

est biomass feedstock. A comparative study of the single-

pass Biobaler and a two-pass Fecon mulcher cutting head

combined with a Claas baling system showed that by using

the single-pass Biobaler system, biomass loss (57%) is 9%

higher than that of the two-pass Fecon mulcher/Claas baler

system (48%). However, the cost of the Biobaler system per

unit area (320.91 $ ha

–1

) was lower than that of the mulcher/

Claas baler two-pass system (336.62–596.77 $ ha

–1

 e

cutting, baling and handling systems for SRW coppice usu-

ally consume more energy than that for the energy grasses.

For example, for baling SRW crops, the Biobaler MT565B

and WB55 required a minimum PTO power of 108–135 kW,

which is higher than the 75–90 kW of the New Holland 9000

series balers for grass energy crops.

SRW bales and chips

are suitable to be transported by road for short-distances. In

some cases, the SRW coppices are also densi ed to pellets on

farm or at satellite and centralized pellet mills. Pellets from

SRW coppices can be transported over long-distances for

regional or international trade by rail or ship.

Green energy crops

Green energy crops mainly include di erent varieties of sor-

ghum and energy cane. High moisture content, high yield,

and the associated quality issues o en lead to collection

and logistics that are di erent from those for the dry energy

grasses.

 e sorghum varieties include grain sorghum, forage

sorghum, sweet sorghum, and photoperiod-sensitive

sorghum.

 e average yield of energy sorghum is between

Review: Lignocellulosic biomass feedstock transportation Z Miao et al.

Performance-based standardizations and

conﬁ gurations of feedstock preprocessing,

handling and transport equipment, and

regulations

Non-standardized and diversi ed equipment, vehicles,

and management procedures create barriers to simplify

feedstock delivery logistics and streamline supply manage-

ment.

50–52

Presently, there is a lack of specialized equipment,

facility, and management regulations for harvesting, pre-

processing, handling, transporting, and storing dedicated

energy crops.  e majority of existing equipment, facility,

management procedures and regulations used for energy

crops were designed for agricultural crop, forage, or forest

residues rather than dedicated energy crops. For instance,

14.6-m trailers are commonly used to deliver forage bale, but

15.8-m and 16.2-m trailers or truck beds are also employed

in the USA. For a self-loading-self-unloading bale-hauling

truck, a hydraulic bale pick-up arm can place about 36–40

bales on the bed, but a 15.8-m  atbed truck can only haul

about 25 bales with a bale size of 0.9×1.2×2.1 m. Walking

 oor trucks are able to transport 10, 000 kg (~100m

)

of Miscanthus chips.

50–52

 erefore, performance-based

evaluation and standardization of the transport vehicles,

handling and processing equipment, storage facilities, and

management procedures are needed to improve delivery

e ciency and enforce regulations and policies of feedstock

transportation.

Specialized delivery equipment and facili-

ties should be developed for dedicated energy crops.

 e standardization of transport equipment and manage-

ment regulations has to consider the biomass form, properties,

and biofuel conversion technology.

Hess et al. reported that

the standardized uniform or advanced uniform supply logis-

tic and equipment can increase e ciencies by comminuting

biomass feedstock to small sizes and improving bulk-handling

e ciency and bulk density.

Miao et al. suggested that the

volumetric  ow e ciencies of Miscanthus and switchgrass

particles ground through a 12.7-mm screen by tub grinder are

2.0 and 2.8 times higher than the counterparts of the particles

through the 25.4-mm screen, respectively.

However, biomass

form and properties such as size, weight, and bulk density vary

with farm and species. For instance, among the 1.1×0.8×

1.1-m, 1.2×1.2×2.4-m or 0.9×0.9×2.1-m rectangular

leaves, rice straw and sun ower stalk varies between 30%

and 70%, while the moisture contents of wheat, oat and bar-

ley straw range from 10% to 20%. Natural windrowing or

arti cial drying is the critical step for some green residues,

for example, early harvest crop residues.

 e collection and delivery of crop residues have o en

been integrated with harvest and processing of the primary

products (e.g. grain, oil seed, or fruit).  ere are two types

of harvest-to-delivery systems commonly used for crop resi-

dues: (i) delivery of the whole crop (e.g. single-pass one- or

two-stream systems) including feedstock and grain harvest

altogether, and (ii) delivery of the primary product (grain or

fruit) and agriculture residues separately (by-product) (e.g.

conventional two-pass harvesting system).

43,44

One-pass

systems are usually regarded as an e cient way to decrease

biomass loss and collect more feedstock than that of the

conventional two-pass systems.  e residue obtained from

the two-pass system may be contaminated with soil in the

process of in- eld windrowing.

47–49

By setting the com-

bine mower header at ground level, the one-pass combine

machine collected approximately 2.5 Mg ha

–1

more wheat-

straw than swathing and baling following windrowing.

47–49

Richey et al. reported that the collection of corn stover by

baling or stacking following windrowing recovered only

about 50% of the windrowed material.

 e harvest-to-delivery technology and equipment for

crop residues are more complicated and more seasonal than

those for forage and energy grass because the crop harvest,

processing, and delivery operations have to manage the

grain as well as the residues. For the single-pass harvester,

the two-stream harvest combine is more popular for grain

crops, while the one-stream equipment is o en adapted for

prairie grass.  e capacities of the in- eld bale pick-up, haul-

age trailer and the intermediate storage facility of crop resi-

dues are smaller than those for the energy crops. Similar to

grass energy crops, bulk densities of large bales and modules

of corn stover are only 200 and 110 kg DM m

–3

, respectively,

and densi cation of crop residues is required to reduce

the transportation and storage costs. Local delivery from

 eld to an intermediate storage facility takes an important

role because of the lower feedstock yield of crop residues,

and is mainly by road transport with a tractor-wagon or

truck-trailer.

Z Miao et al. Review: Lignocellulosic biomass feedstock transportation

links) to analyze storage and transportation options. Shastri

et al.

34,35,58

developed a system-level MILP model called

BioFeed that optimizes annual transportation  eet sizing

and scheduling. Other MILP model applications in feedstock

industry include Mapemba et al.,

56,59

Milan et al.,

Grunow

et al.,

and Cundi et al.

Discrete-event simulation and system dynamics

 e IBSAL (Integrated Biomass Supply and Logistics) model

adopted the DES approach to simulate biomass feedstock supply

chains.

9,10

Iannoni and Morabito

applied the DES approach

to sugarcane logistics from farms to the mill. Mukunda et al.

applied DES to corn-stover logistics for a biore nery, while

Ravula et al.

11,12,64

used DES to compare various policy strate-

gies for scheduling trucks in cotton-gin logistics systems and

biomass transportation for ethanol production. Similar to

discrete event simulation, the system dynamic approach has

been used to investigate the impacts of biomass feedstock price,

transportation costs, and government regulations/incentives on

the growth of the US corn ethanol industry.

Queuing theory

Applications of queuing theory for a vehicle routing problem

can be found in Van Woensel et al.,

Vandaele et al.,

Jain

and MacGregor Smith,

and Kang et al.

Heuristic and agent-based models

A multi-objective heuristic approach has been employed

to optimize forest biomass supply chains.

70–72

Ramstedt

developed a multi-agent-based simulator (TAPAS) to explore

the in uence of transport policies on decision-making in

transport chains.  e Argonne National Laboratory of the

US Department of Energy developed an agent-based model

to analyze alternative combinations of energy production

and delivery systems and determine the best transportation

in terms of cost, safety, and energy e ciency. Sche ran and

BenDor developed a spatial-agent dynamic model to investi-

gate the in uence of decision rules, demands, prices, subsides,

carbon credits, the location of ethanol plants and transporta-

tion patterns on energy crop production in Illinois (USA).

Artiﬁ cial Neural Networks

Celik

used the supervised learning method of neural net-

works to simulate freight distribution. NREL (US National

Miscanthus and switchgrass bales, the weights range from 570

to 720 kg DM and from 280 to 350 kg DM, respectively.

Bulk

densities of corn stover and switchgrass pellets and briquettes

range from about 352 to 609 kg m

–3

Standardization of end-

users’ feedstock demand and biore nery technology is a pre-

requisite to standardize feedstock preprocessing and handling

equipment, delivery vehicles, and storage facilities.

Standard delivery vehicles should be multipurpose and

infrastructure compatible, and be able to transport not only

high weight load, but also high volume load.  e specialized

vehicles and facilities must remain within certain parameters

such as axle mass limits and comply with local tra c laws

and regulations.

36,53–55

In South Africa, for instance, a  eet

of vehicles for sugarcane transport must comply with a set of

regulations, which specify limits on length, power-to-weight-

ratio, axle loadings, and gross mass.

53–55

According to US road

tra c rules for bale trailers, the steering axle weight should

not exceed 5440 kg and the second and third axle weights

should not exceed 15 400 kg per axle.  e total weight of truck

and biomass should not exceed 36 000 kg. Fixed trucks, which

are less than 12.2-m in length, allow more maneuverability in

tra c-tight areas.

 ese factors must be considered while

designing the performance-standardized equipment.

Biomass feedstock transportation logistic

modeling

 e complexity and interdependency of challenges high-

lighted in the preceding sections motivate the use of system-

level mathematical modeling approaches for simulating and

optimizing biomass transportation logistics.  ese models

usually include biomass production, feedstock transforma-

tion, and supply and demand components, which are viewed

from economic, environmental, and even social perspec-

tives.

In the past, two major types of models have been

developed for feedstock delivery: optimization models, and

event- (or process) based simulation models.

In recent

times, heuristic, agent-based, and arti cial intelligent self-

learning and self-adaptive models have also gained popular-

ity and acceptance.  ese are brie y described below:

Mathematical programming

De Mol et al.

proposed an MILP (mixed integer linear

programming) model using a network map (nodes and

Review: Lignocellulosic biomass feedstock transportation Z Miao et al.

combination with biore nery capacity, feedstock type and

form, intended use, storage and pretreatment technology,

handling and processing equipment, infrastructure speci-

 cations, and geo-spatial features.  e optimal selection

should be aided by performance-based standardization of

feedstock forms, delivery equipment, facility and regula-

tions.  ere is an argument to place mechanical (and tor-

refaction) pre-processing before transportation and storage,

and incorporate storage with pretreatment to unify the

lignocellulosic feedstock transportations.

Transportation logistics and equipment con guration are

substantially dependent upon feedstock features. For prairie

grass energy crops and agricultural residues, densi ca-

tion (including torrefaction), and size reduction are crucial

logistical steps to improve feedstock delivery e ciency. For

short-rotation woody biomass and green biomass stock, nat-

ural or forced drying may be necessary to control biomass

degradation during transportation and storage.  e review

identi ed that there is a lack of literature on performance-

based evaluation and design of feedstock supply procedures,

equipment, facility, transportation regulation and policy. An

integrated framework that addresses these challenges will

be useful to develop a biomass transportation model and

management system at an operational level. To standardize

the feedstock delivery systems, the e ciency modeling of

feedstock delivery systems should be based on the currency

purchase power parity or the ratio of energy consumption of

the systems to inherent heating value of the feedstock.

Acknowledgements

 e work was funded by the Energy Biosciences Institute

of the University of Illinois, through a program titled

‘Engineering solutions for biomass feedstock production’.

 e authors appreciate the constructive comments of Dr

Heather Youngs in the writing of this paper.

References

1. Koonin SE, Getting serious about biofuels. Science 311:435–435 (2006).

2. DOE, Biomass: Multi-year program plan, in Energy Do. USDOE, Ofﬁ ce of

the Biomass Program, Washinton DC, USA (2008).

3. Somerville C, The billion-ton biofuels vision. Science 312:1277–1277

(2006).

4. Perlack R, Wright LL, Turhollow AF, Graham RL, Stokes BJ and Erbach

DC, Biomass as feedstock for a bioenergy and bioproducts industry: The

technical feasibility of a billion ton annual supply. DOE/GO-102005-2135,

Renewable Energy Laboratory) DENNIS

(Distributed

Energy Neural Network Integration System) simulated

energy spatial interactive delivery systems.

Among these modeling approaches, the mathematic pro-

gramming, heuristic, agent-based, and neural network model

are more applicable to strategic planning and management

analysis. Queuing theory, discrete event simulation and

system dynamic could be used to develop event- or process-

based model at feedstock delivery tactical and operational

levels by combining with real-time geo-referenced informa-

tion and technology.  e operational level models should

preferably include spatially explicit real time weather and traf-

 c conditions, route optimization,  eet tracking, and facility

and infrastructure con gurations.  ese models should also

be calibrated and validated using a real system, which is o en

challenging. It is also important to ensure uniformity in the

evaluation criteria across multiple models.  is is especially

true for non-economic performance measures such as energy

consumption. Here, we recommend the use of percentage of

inherent heating value (PIHV) to quantify energy consump-

tion and compare alternatives.  e PIHV of an operation

gives the percentage of the inherent heating value of the

biomass that is used up for that operation.

29,55

For economic

analysis, currency purchase power parity rather than simple

US dollar or euro currencies should be adopted as the rules of

thumb.

Conclusion and recommendations

Transportation is a crucial component of the biomass pro-

duction system. Di erent modes of transportation, includ-

ing roads, railways, waterways, pipelines, and combinations

of these, have been extensively studied in the literature.

Currently, road transport is the most preferred alternative,

primarily due to the high  exibility o ered at relatively

short collection distances. As transportation distance and

feedstock demands grow, intermodal transportation incor-

porating rail, waterways or pipeline will need to be con-

sidered. However, the design of a cost-e ective and reliable

transportation system goes beyond simple mode selection

and includes the consideration of farm, storage, and biore-

 nery sites, transportation mode availability, equipment

and facility con gurations, regulation, policy and environ-

mental impact analysis.  ese issues must be considered in

Z Miao et al. Review: Lignocellulosic biomass feedstock transportation

26. Ileleji KE, Sokhansanji S and Cundiff JS, Farm-gate to plant-gate delivery

of lignocellulosic feedstocks from plant biomass for biofueld production,

in Biofuels from Agricultural Wastes and Byproducts, ed by Blaschek H,

Ezeji HC, Scheffran J. Blackwell Publishing, Oxford, UK (2010).

27. Clifton-Brown J, Long SP and Jørgensen U, Miscanthus productivity, in

Miscanthus for Energy and Fibre, ed by Walsh M and Jones MB. James &

James (Science Publisher) Ltd, London, UK (2001).

28. Wilkinson JM, Evans EJ, Bilsborrow PE, Wright C, Hewison WO and

Pilbeam DJ, Yield of willow cultivars at different planting densities in

a commercial short rotation coppice in the north of England. Biomass

Bioenerg 31:469–474 (2007).

29. Miao Z, Grift TE, Hansen AC and Ting KC, Energy requirment for com-

minution of biomass in relation to particle physical properties. Ind Crop

Prod 33:504–513 (2011).

30. Khanna M, Dhungana B and Clifton-Brown J, Cost of producing

Miscanthus and switchgrass for bioenergy in Illinois. Biomass Bioenerg

32:482–493 (2008).

31. Huisman W, Venturi G and Molenaar J, Cost of supply chain for

Miscanthus x Giganteus. Ind Crop Prod 6:353–366 (1997).

32. Igathinathane C, Womac AR, Sokhansanj S and Narayan S, Knife grid

size reduction to preprocess packed beds of high- and low-moisture

switchgrass. Bioresource Technol 99:2254–2264 (2008).

33. Igathinathane C, Womac AR, Sokhansanj S and Narayan S, Size reduc-

tion of high- and low-moisture corn stalks by linear knife grid system.

Biomass Bioenerg 33:547–557 (2009).

34. Shastri YN, Hansen AC, Rodríguez LF and Ting KC, Optimization of

miscanthus harvesting and handling as an energy crop: BioFeed model

application. Biol Engng 3:37–69 (2010).

35. Shastri Y, Rodríguez L, Hansen A and Ting KC, Impact of distributed stor-

age and pre-processing on Miscanthus production and provision sys-

tems. Biofuels Bioprod Bioref 6:21–31 (2012).

36. Hess JR, Wright CT and Kenney KL, Cellulosic biomass feedstocks

and logistics for ethanol production. Biofuels Bioprod Bioref 1:181–190

(2007).

37. Volk TA, Verwijst T, Tharakan PJ, Abrahamson LP and White EH, Growing

fuel: A sustainability assessment of willow biomass crops. Front Ecol

Environ 2:411–418 (2004).

38. Volk TA, Abrahamson LP, Nowak CA, Smart LB, Tharakan PJ and White

EH, The development of short-rotation willow in the northeastern United

States for bioenergy and bioproducts, agroforestry and phytoremedia-

tion. Biomass Bioenerg 30:715–727 (2006).

39. Do Canto JL, Klepac J, Rummer B, Savoie P and Seixas F, Evaluation of

two round baling systems for harvesting understory biomass. Biomass

Bioenerg 35:2163–2170 (2011).

40. Rooney WL, Blumenthal J, Bean B, and Mullet JE, Designing sorghum as

a dedicated bioenergy feedstock. Biofuels Biop Bioref 1:147–157 (2007).

41. Linton JA, Miller JC, Little RD, Petrolia DR and Coble KH, Economic

feasibility of production sweet sorghum as and ethanol feedstock in the

southeastern United States. Biomass Bioenerg 35:3050–3057 (2011).

42. Richard Jr. EP, Tew TL, Cobill RM, and Hale AL, Sugar/Energy Canes

as Feedstocks for the Biofuels Industry. Proc. Short Rotational Crops

International Conference, August 19–21, Bloomington, MN, USA (2008).

43. Sokhansanj S and Turhollow AF, Baseline cost for corn stover collection.

Appl Eng Agric

18:525–530 (2002).

ORNL/TM-2005/66. Oak Ridge National Laboratory, Oak Ridge, TN, USA

(2005).

5. McLaughlin S, De La Torre Ugarte D, Garten Jr C, Lynd L, Sanderson M,

Tolbert V and Wolf D, High-value renewable energy from prairie grasses.

Environ Sci Technol 36:2122–2129 (2002).

6. Somerville C, Young H, Taylor C, Davis S and Long S, Feedstocks for

lignocellulosics. Science 329:790–792 (2010).

7. Petrolia DR. The economics of harvesting and transporting corn stover

for conversion to fuel ethanol: A case study for Minnesota. Biomass

Bioenerg 32:603–612 (2008).

8. Sokhansanj S and Fenton J, Cost beneﬁ t of biomass supply and preproc-

essing. BIOCAP, Kinston, ON, Canada, pp.1–31 (2006).

9. Sokhansanj S, Kumar A and Turhollow AF, Development and implementa-

tion of integrated biomass supply analysis and logistics model (IBSAL).

Biomass Bioenerg 30:838–847 (2006).

10. Sokhansanj S and Kumar A, Switchgrass (Panicum vigratum, L.) delivery

to a bioreﬁ nery using integrated biomass supply analysis and logistics

(IBSAL) model. Bioresource Technol 98:1033–1044 (2007).

11. Ravula PP, Design, simulation, analysis and optimization of transportation

system for a biomass to ethanol conversion plant. Virginia Polytechnic

Institute, Blacksburg, VA, USA, pp. 1 (2007).

12. Ravula PP, Grisso RD, Cundiff JS, Cotton logistics as a model for a bio-

mass transportation system. Biomass Bioenerg 32:314–325 (2008).

13. Mani S, Tabil LG and Sokhansanj S, Grinding performance and physi-

cal properties of wheat and barley straws, corn stover and switchgrass.

Biomass Bioenerg 27:339–352 (2004).

14. Rentizelas AA, Tatsiopoulos IP and Tolis A, An optimization model for multi-

biomass tri-generation energy supply. Biomass Bioenerg 33:22–33 (2009).

15. Ramstedt L, Transportation policy analysis using multi-agent-based sim-

ulation. Blekinge Institute of Technology, Printfabriken, Sweden (2008).

16. Mani S, Tabil LG and Sokhansanj S, Effects of compressive force, particle

size and moisture content on mechanical properties of biomass pellets

from grasses. Biomass Bioenerg 30:648–654 (2006).

17. Leboreiro J and Hilaly AK, Biomass transportation model and optimum

plant size for the production of ethanol. Bioresource Technol 102:2712–

2723 (2011).

18. Hamelinck CN, Suurs RAA and Faaij APC, International bioenergy trans-

port costs and energy balance. Biomass Bioenerg 29:114–134 (2005).

19. Van Loo S and Koppejan J, The Handbook of Biomass Combustion &

Co-Firing. Earthscan, Sterling, VA, USA (2008).

20. Souček J, Kocánová V and Novák M, Parametres of energy crop biomass

handling. Res Agr Eng 53:161–165 (2007).

21. Kumar A, Cameron JB and Flynn PC, Pipeline transport of biomass. Appl

Biochem Biotech 113 –116:27–39 (2004).

22. Kumar A, Cameron JB and Flynn PC, Pipeline transport and simultane-

ous sacchariﬁ cation of corn stover. Bioresource Technol 96:819–829

(2005).

23. Searcy E, Flynn P, Ghafoori E and Kumar A, The relative cost of biomass

energy transport. Appl Biochem Biotech 136–140:639–652 (2007).

24. Mahmudi H and Flynn PC, Rail vs truck transport of biomass. Appl

Biochem Biotech 129–132:88–103 (2006).

25. John S and Watson A, Establishing a Grass Energy Crop Market in the

Decatur Area. Report of the Upper Sangamon Watershed Farm Power

Project. The Agricultural Watershed Institute, Decatur, IL, USA (2007).

Review: Lignocellulosic biomass feedstock transportation Z Miao et al.

44. Mukunda A, Ileleji KE and Wan H, Simulation of corn stover logistics from

on-farm storage to an ethanol plant, 2006 ASABE Annual International

Meeting, Portland, OR, July 9–12 (2006).

45. Jenkins BM and Sumner HR, Harvesting and handling agricultural residue

for energy. Trans ASAE 29:824–36 (1986).

46. Sokhansanj S, Turhollow AF, Cushman J and Cundiff J, Engineering

aspects of collecting corn stover for bioenergy. Biomass Bioenerg

23:347–355 (2002).

47. Shinners KJ, Binversie BN, Muck RE and Weimer PJ, Comparison of wet

and dry corn stover harvest and storage. Biomass Bioenerg 31:211–221

(2007).

48. Richey CB, Liljedahl JB and Lechtenberg VL, Corn stover harvest for

energy production. Trans ASAE 25:834–839,844 (1982).

49. Allen J, Browne M, Hunter A, Boyd J and Palmer H, Logistics manage-

ment and costs of biomass fuel supply. Int J Phys Distrib Logist Manage

28:463–477 (1998).

50. Moore Recycling Associates Inc., Guidelines for proper handling, load-

ing, safety & bale speciﬁ cations.[Online]. Available at: http://www.

plasticsmarkets.org/plastics/guidelines.html [February 12, 2011].

51. Badger PC and Fransham P, Use of mobile fast pyrolysis plants to densify

biomass and reduce biomass handling costs—A preliminary assessment.

Biomass Bioenerg 30:321–325 (2006).

52. Badger PC, Processing Cost Analysis for Biomass Feedstocks. Oak

Ridge National Laboratory, Environmental Sciences Division, Bioenergy

Systems Group, Oak Ridge, TN, USA (2002).

53. Le Gal PY, Lyne PWL, Meyer E and Soler LG, Impact of sugarcane sup-

ply scheduling on mill sugar production: A South African case study. Agr

Syst 96:64–74 (2008).

54. Nordengen PA, Prem H and Lyne PWL, Performance-Based Standards

(PBS) Vehicles for Transport in the Agricultural Sector. Proc S Afr Sug

Technol Ass 81:445–53 (2008).

55. Lyne P, The latest in transport management and technologies. SAIAE

News Letter April 2003:3–5 (2010).

56. Kang S, Önal H, Ouyang Y, Scheffran J and Tursun ÜD, Optimizing

the Biofuels Infrastructure: Transportation Networks and Bioreﬁ nery

Locations in Illinois, in Handbook of Bioenergy Economics and Policy,

ed by Khanna M, Scheffran J, Zilberman D. Springer, Berlin, Germany,

pp. 151–173 (2010).

57. De Mol RM, Jogems MAH, Van Beek P and Gigler J, Simulation and opti-

mization of the logistics of biomass fuel collection. Netherlands J Agri Sci

45:219–228 (1997).

58. Shastri Y, Hansen AC, Rodríguez LF and Ting KC, Development and

application of BioFeed model for optimization of herbaceous biomass

feedstock production. Biomass Bioenerg 35(7):2961–2974 (2011).

59. Mapemba LD, Epplin FM, Huhnke RL and Taliaferro CM, Herbaceous

plant biomass harvest and delivery cost with harvest segmented by

month and number of harvest machines endogenously determined.

Biomass Bioenerg 31:1016–1027 (2008).

60. Milan EL, Fernandez SM and Aragones LMP, Sugar cane transportation

in Cuba, a case study. Eur J Operat Res 174

:374–386 (2006).

61. Grunow M, Gunther HO and Westinner R, Supply optimization for the

production of raw sugar. Int J Prod Econ 110:224–239 (2007).

62. Cundiff J, Dias N and Sherali HD, A linear programming approach for

designing a herbaceous biomass delivery system. Bioresource Technol

59:47–55 (1997).

63. Iannoni AP and Morabito R, A discrete simulation analysis of a logistics

supply system. Trans Res Part E 42:191–210 (2006).

64. Ravula PP, Grisso RD and Cundiff JS, Comparison between two policy

strategies for scheduling trucks in a biomass logistic system. Bioresource

Technol 99:5710–5721 (2008).

65. Kibira D, Shao G and Nowak S, System dynamics modeling of corn ehta-

nol as a bio-fuel in the United States. National Institute of Standards and

Technology, Lincoln, NE, USA (2010).

66. Van Woensel T, Kerbache L, Peremans H and Vandaele N, Vehicle routing

with dynamics travel times. Eur J Oper Res 186:990–1007 (2008).

67. Vandaele N, Van Woensel T and Verbruggen A, A queueing based trafﬁ c

ﬂ ow model. Trans Res 5:121–135 (2000).

68. Jain R and MacGregor Smith J, Modeling vehicular trafﬁ c ﬂ ow using

M/G/C/C state dependent queueing models. Trans Sci 31:324–336

(1997).

69. Kang S, Medina JC and Ouyang Y, Optimal operations of transportation

ﬂ eet for unloading activities at container ports. Trans Res Part B 42:970–

984 (2008).

70. Hamann JD, Optimizing the primary forest products supply chain: A

multi-objective heuristic approach. A doctoral dissertation. Oregon State

University, Corvallis, OR, USA (2009).

71. Gunnarsson H, Supply Chain Optimization in the Forest Industry.

Dissertations No. 1105. Linköpings University, Linköping, Sweden

(2007).

72. Gunnarsson C, Vågström L and Hansson P, Logistics for forage harvest

to biogas production—Timeliness, capacities and costs in a Swedish

case study. Biomass Bioenerg 32:1263–1273 (2008).

73. Scheffran J and BenDor T, Bioenergy and land use: a spatial-agent

dynamic model of energy crop production in Illinois. Int J Environ Pollut

39:4–27 (2009).

74. Murat Celik H, Modeling freight distribution using artiﬁ cial neural net-

works. J Transp Geogr 12:141–148 (2004).

75. Regan T, Sinnock H and Davis A, Distributed Energy Neural Network

Integration System: Year One Final Report. Co. NREL/SR-560-34216.

National Renewable Energy Laboratory, Golden, CO, USA (2003).

Zewei Miao

Zewei Miao, PhD, is a Research Assistant

Professor in biomass feedstock preprocess-

ing and transportation at Energy Biosciences

Institute, University of Illinois. He has worked

in ecological and environmental modeling at

the Chinese Academy of Sciences, Catholic

University of Italy, Canadian Forest Services,

McGill University, and Rutgers University.

Z Miao et al. Review: Lignocellulosic biomass feedstock transportation

Yogendra Shastri

Yogendra Shastri, PhD, is a Research Assist-

ant Professor at Energy Biosciences Institute,

University of Illinois at Urbana-Champaign.

He is a chemical engineer with a PhD in

Bioengineering from the University of Illinois.

His expertise is in developing and applying

systems-theory-based approaches in the field

of bioenergy and sustainability.

Tony E. Grift

Tony E. Grift, PhD, is an Associate Professor

of the Department of Agricultural and Biologi-

cal Engineering, University of Illinois. As a

principal investigator, he is leading the Bio-

mass Transportation Task within a program

titled ‘Engineering Solutions for Biomass

Feedstock Production’, which is part of the

BP-funded Energy Biosciences Institute.

Alan C. Hansen

Alan C. Hansen received his PhD from the

University of KwaZulu-Natal in South Africa,

where he joined the Department of Agricul-

tural Engineering in 1979 as faculty before

transferring to the University of Illinois in

1999. His research interests include biofuels

and biomass feedstock production.

K.C. Ting

K.C. Ting, PhD, PE, is Professor and Head

of the Agricultural and Biological Engineer-

ing Department, University of Illinois. He

specializes in agricultural systems informatics

and analysis. He currently leads a BP Energy

Biosciences Institute program on ‘Engineering

Solutions for Biomass Feedstock Production’.

He is Fellow of ASABE and ASME.