Cost analysis for 24-h hauling using rack system

The costs given in this section are presented without supporting detail. They were calculated using the procedures given in [69]. They are "best estimates" given current cost parameters. All costs are given on a $/Mg DM basis for operation of a bioenergy plant consuming 23 dry ton/h (20.9 Mg DM/h). The challenge is to find a way that machine productivity (Mg/h) can be increased.

3.4.3. Total truck cost

The assumed truck cost (tractor and trailer for hauling the two racks) is $630/d for a 24-h workday, which includes ownership plus operating cost, plus labor, but excluding fuel.

Подпись: $630/d 11.5 loads/d x 12.2 dry ton/load
Подпись: $ 4.49/dry ton = $4.95/Mg DM

Truck cost, excluding fuel, is:

Truck fuel cost for the 25.4 miles (40.6 km) average haul distance is:

(25.4 mi x 2) /4 mi / gal = 12.7 gal x $3.50/ gal = $44.45/ load
$44.45/load / 12.2 dry ton / load = $3.64/dry ton ($4.01/Mg DM)

Total truck cost is ownership and operating plus fuel, that is 4.95 plus 4.01 = $8.96/Mg DM.

3.4.4. Load, unload operations

1. Handling racks at plant: $1.93 (forklift) + $1.02 (backup forklift) = $2.95/dry ton

2. SSL operation: $3.66 (telehandler) + $0.98 (extra trailers) = $4.64/dry ton

3. Rack cost: cost for 230 racks = $1.80/dry ton

4. Storage yard at processing plant: $0.13/dry ton

5. Conveyor entering plant: $0.28/dry ton

(Note: 1 dry ton = 0.907 Mg DM)

N use efficiency

After analysis of N leaf tissue content (%) by Kijeldhal method, N-use efficiency (NUE %) was calculated, as the percentage of the N uptaken by the lettuce plant respect to N supplied by the fertilizer.

In order to study the long-term effect of the alternative fertilization approaches, the soil residual N at the end of experiment was obtained after Kjeldhal digestion and titrimetric determination [20]. Then, the available N-NO3 and exchangeable N-NH4 in the soils were determined after extraction of 4 g of each soil in 40 mL of KCl 0.2 N solution and subsequent colorimetric analysis of the supernatant by Automatic Analyzer Technicon II.

1.2. Statistical analysis

Plant biometric and soil N data were evaluated by ANOVA to verify the statistical differences of the tested parameters in relation to the different fertilization treatments.

Elemental data were analysed using vector analysis, which allows the simultaneous evaluation of plant dry weight and nutrients content in an integrated graphic format

[24] ,[25]. Elemental data in relation to the different treatments in soils A and B were normalized with respect to urea al 200 kgN ha-1, taken as reference treatment.

Catalytic upgrading

Catalytic upgrading process is conducted in a number of different ways. Most commonly practiced method would be injection of bio-oil into a tubular reactor packed with a catalyst capable of deoxygenating the substrates. Due to the inefficacies associated with condensing and reheating processes, a reactor capable of accepting a direct feed of pyrolysis vapor into its catalytic chamber has become more popular. Numerous forms of Zeolites are known to be effective for deoxygenation[68] of which ZSM-5 is widely touted to be the most effective catalyst for deoxygenation (via decarboxylation, decarbonylation or dehydration pathways). ZSM-5 is a molecular sieve with 5.5 A pore channels. This pore structure is responsible for the high selectivity of ZSM-5 toward aromatic hydrocarbons. Deoxygenation reaction on ZSM-5 is believed to be catalyzed at the Bronsted acid sites and its structure is depicted in fig.6.

Partitioning of bio-mass

Among the crop cycles, plant crop recorded higher bio-mass production than succeeding ratoons. The production of crop residues were also higher (50.7 t/ha) in plant crop than 1st (45.7 t/ha and 2nd (36.6 t/ha) ratoons. The data are presented in the Table 4 below.

Crop cycle

Cane weight (Tc/ha)

Tops weight (T/ha)








Tops + Trash weight T/ha

% over total








Ratoon 1







Ratoon 2







# average


Fresh: 25.6

Fresh: 18.7

Fresh: 44.3


Dry: 9.0

Dry: 16.9

Dry: 25.8

Table 4. Crop-wise partitioning of bio-mass

Results indicate that on average, 25.8 tons of dry matter (cane trash and tops) is produced from each crop cycle at harvest. In burnt cane harvesting system, all this dry matter is lost unlike in green cane harvesting. This explains the gradual decline in cane yield in such harvesting systems. The decline is presumed to be due to deteriorating organic matter and other physical and chemical properties of the soil " in [11] " .

After decomposition of cane trash, 139kg N, 59kg P2O5, 745kg K2O, 41kg Ca, 46kg Mg and 34kg S /ha were added to the soils and these added nutrients would be available to the succeeding crop at 30% of the total nutrients " in [11] " . The nutrient concentration of crop residues (trash + tops) were taken into account for computing nutrient additions to the soil and their availability to the succeeding ratoon crops and the data are presented in Table 5.

Nutrient status of crop residues (%)

Total dry matter (T/ha)

Total nutrients

Added to soil (kg/ha)

Available to crops @ 30% (kg/ha)

N :





P2O5 :










Ca :










S :





** Adopted from [11]

Table 5. Nutrient status of crop residues and their availability to the succeeding crops

Reactions involved in deoxygenation

As previously mentioned, deoxygenation involves the removal of functionality of biomass constituents associated with — OH, — COOH, and — C=O. The bond dissociation energies (BDE) for these functional groups are quite high and can be written in descending order as follows:

C-O (1076.5 kJ/mol) > C=O ( 749 kJ/mol) > C-C (610.0 kJ/mol) > O-H (429.99 kJ/mol) > C-H (338.4 kJ/mol).

Higher BDE for a particular functional group implies that the activation energy required for dissociating this bond (deoxygenation) would also be high. This would dictate rigorous reaction conditions for particularly C-O and C=O bond scissions. The presence of large amount of C=O groups in the pyrolysis products can be related to the higher activation energies required for dissociation of these bonds. However, using appropriate catalysts, these high activation energies could be overcome.

Reed canarygrass and alfalfa

In addition to switchgrass and miscanthus, two other species, reed canarygrass and alfalfa, have also been studied considerably for biofuel crops. Reed canarygrass is a C3 grass commonly used for hay and grazing in temperate agricultural ecosystems, and can yield 8-10 Mg/ha in the Midwest of USA and northern Europe [6, 12]. Similar to switchgrass, reed canarygrass is difficult to establish and normally has a low yield in the seeding year [6].

Alfalfa, one of the oldest forage crops in the world, has traditionally been used as high quality forage. However, alfalfa may also have some values for biofuel feedstock [13]. In an alfalfa biomass energy production system, the forage could be fractionated into stems and leaves. The stems could be processed to generate electricity or biofuel (ethanol), and the leaves could be sold as a supplemental protein feed for livestock. Currently, researchers in Minnesota are conducting experiments to select dual-use alfalfa varieties and developing management systems [13

English name

Scientific name

Photosynthetic Yields reported pathway Mg DM/ha/year

Crested wheatgrass

Agropyron desertorum (Fisch ex Link) Schult.




Agrostis gigantea Roth


Not available

Big bluestem

Andropogon gerardii Vitman.



Smooth bromegrass

Bromus inermis Leyss




Cynodon dactylon L.



Intermediate wheatgrass

Elytrigia intermedia [Host] Nevski. C3

Not available

Tall wheatgrass

Elytrigia pontica [Podp.] Holub.


Not available

Weeping lovegrass

Eragrostis curvula (Schrad.) Nees.



Tall Fescue

Festuca arundinacea Schreb.




Panicum virgatum L.



Western wheatgrass

Pascopyrum smithii (Rydb.) A. Love


Not available


Paspalum notatum Flugge.


Not available

Napiergrass (elephant grass)

Pennisetum purpureum Schum.



Reed canary grass

Phalaris arundinacea L.




Phleum pratense L.



Energy cane

Saccharum spp




Sorghum halepense (L.) Pers.



Eastern gammagrass

Tripsacum dactyloides (L.) L.



Table 1. The 18 perennial grass species that were screened by the US herbaceous energy crop research program [12].

Modeling of biomass delivery systems

Linear programming models have been used to analyze system interactions in biomass delivery systems. Dunnett et al. [53] proposed a program to optimize scheduling of a biomass supply system for direct combustion. This model simulated storage on farms and delivery to one location with a variable demand for heat. They suggest that costs of biomass handling can be improved 5 to 25% with the model recommendations. Bruglieri and Liberti proposed a "branch and bound" nonlinear model to determine biorefinery locations as well as the optimum transport method [54]. Their model focused on multiple feedstocks but did not use actual equipment performance data.

A model comprised for multiple purposes can bring attributes of benchmarking, simulation, and linear programming together to solve for the best solution. Leduc et al. a system of wood gasification plants optimized [55]. Their model focused on establishing a biorefinery plant in a location that is suitable for distribution of the product being manufactured (in this case, methanol). Other similar models have focused on silage handling operations [56].

A number of models have proven that a single chain of handling procedures can be optimized, but fail to adequately address the "disconnect" caused by storage, specifically satellite storage. Unfortunately, few models consider different harvest systems (or feedstock) supplying a single biorefinery. For example, one equipment system delivers chopped material directly from the field to the plant (probably from fields close to the plant), and a second set of equipment bales material and places it in storage which will be delivered during months when direct delivery of field chopped material is not possible.

As described earlier, a satellite storage location (SSL) is a pre-designated location that is used as a storage location for the biomass collected by the producers within a defined geographic region. SSLs are a logical transition point between "agricultural" and "industrial" operations and thus are critical elements in a logistics system design.

Growth characteristics of dominant microflora on the surface of activated carbon[43]

In the early period of IBAC operation, the results for the biomass and biological activity on the surface of activated carbon monitored continuously are shown in Fig. 11. and Fig. 12.. At initiating stage, there is a rapidly dropping period for the dominant micoflora biomass on activated carbon. As time goes by, dominant biocommunity becomes steady gradually. At initial stage of BAC process, the biomass on activated carbon surface is in little, different from IBAC process, therefore, the microbial action can be ignored basically. During this period, variation of the biological activity for dominant biocommunity is similar to the change of biomass.


Figure 11. Variations of biomass on activated carbon


Operation time (d)

Figure 12. Variations of biological activity in two activated carbon processes

The variations of biomass and biological activity of microbe on activated carbon in long­term operation process are shown in Fig. 13. As shown, the biomass on carbon bed remains constant which mainly because of a gradual adaptation process of dominant biocommunity to the water, during which the adhesive ability of the dominant biocommunity is relatively low. Therefore microbe on the upper layer will be washed into the lower layer of the activated carbon, which has a beneficial effect on the reasonable distribution of the dominant biocommunity. With the extension of operating time, the dominant biocommunity will be adapted to the environment gradually. Meanwhile, due to the higher concentration of nutrient media in the upper layer, the biomass on the upper layer will be greatly increased and remains constant for a long period. Being different from the constancy of biomass on activated carbon, the biological activities in the upper and the lower layers of IBAC bed have a decreasing tendency as operation time goes. Shown from the result of PCR-DGGE, this reduction is mainly caused by the continuous incursion of the native bacteria with low capacity.


Figure 13. Variation of biomass and biological activity on activated carbon

Hybrid bioreactors biomass characteristic

In this research relatively wide (but typical for activated sludge reactors) range of activated sludge flocs size was observed: 150-500 pm in all reactors.

The biomass attached to the moving bed carriers surfaces was poorly developed. Attached biomass did not cover the outside carriers surface and existed only on the inert surface not as continuous film but as separate small mushroom shape colonies. Small colonies of stalked ciliates (figure 11) were observed on the inner surface of carriers. There were observed some differences between biofilm and activated sludge flocks groups of organisms, in both biomass forms. Stalked, creeping and free-swimming ciliates, filamentous microorganisms, rotifers and nematodes were observed.

Epistylis and Vorticella were dominating genera of ciliates in both suspended and attached biomass. Stalked ciliates were observed in relatively high number both in attached and suspended biomass. The domination of this form of Ciliata was probably related to good pollutant removal efficiency, what was reported by other authors [1, 54].

The observed number of rotifers in the attached biomass was much higher than in suspended biomass (t-Student statistics; t calculated: 2.83, critical value: 2.78, a: 0.05, replications no.: 5, df: 4), what was the most evident in period 3 in SBRs, and was probably related to the long time of growth of rotifers. Also the differences in concentrations of filamentous microorganisms were observed — in continuous flow reactors the number of filamentous microorganisms was often lower in attached biomass than in suspended biomass (figure 12). Filamentous microorganisms concentration was the highest in the


highest COD loaded reactor: R3 (t-Student statistics; t calculated: 8.98, critical value: 2.2, a: 0.05, replications no.: 13, df: 11).



Figure 11. The inner surface of a carrier covered by a small stalked ciliates colony [16]



Figure 12. Filamentous organisms in reactor R2 during stage II [16]



Figure 13. Biofilm on the MBBR carrier


Conclusions and perspectives of BFT

Biosecurity is a priority in aquaculture industry. For example, in shrimp farming, considerable impact of disease outbreaks during the past two decades greatly affected the operational management of shrimp farms worldwide [10]. Infected PLs and incoming water seem to be the main pathway for pathogen introduction. This scenario forced farmers to look for more biosecure culture practices to minimize the risk associated with exposure to pathogens [2]. Biofloc technology brings an obvious advantage of minimizing consumption and release of water, recycling in situ nutrients and organic matter. Furthermore, pathogens introduction is reduced, improving the farm biosecurity.

Biofloc technology will enable aquaculture grow towards an environmental friendly approach. Consumption of microorganisms in BFT reduces FCR and consequently costs in feed. Also, microbial community is able to rapidly utilize dissolved nitrogen leached from shrimp faeces and uneaten food and convert it into microbial protein. These qualities make minimal-exchange BFT system an alternative to extensive aquaculture. Microorganisms in biofloc might partially replace protein content in diets or decrease its dependence of fishmeal.

Related to biofloc meal and its perspectives, the study [17] detected initial estimates of cost for producing a metric ton of biofloc meal is approximately $400 to $1000. The same authors cited that global soymeal market varied approximately from $375 to $550/metric ton from January 2008 through May 2009. During the same time period, fishmeal varied approximately from $1000 to $1225, suggesting feasibility on replacement of either soybean and/or fish meal by biofloc meal. Moreover, generated from a process that cleans aquaculture effluents [17, 39] biofloc meal production avoids discharge of waste water and excessive damage to natural habitats [4]. This ingredient seems to be free of deleterious levels of mycotoxins, antinutritional factors and other constituents that limit its use in aquafeeds [79]. Large-scale production of biofloc meal for use in aquaculture could result in environmental benefits to marine and coastal ecosystems, as the need for wild fish as an aquafeed ingredient is reduced [79, 92].

Sensorial quality of BFT products is also an important issue. BFT may bring higher profit if fresh non-frozen shrimp/fish is sold to near-by market, mainly at inland locations. These advantages certainly should be more explored and niche markets achieved, contributing to social sustainability.

Author details

Mauricio Emerenciano

Posgrado en Ciencias del Mar y Limnologia, Universidad Nacional Autonoma de Mexico (UNAM), Unidad Multidisciplinaria de Docencia e Investigation (UMDI), UNAM, Sisal, Yucatan, Mexico Santa Catarina State University, Centro de Educagao Superior da Regiao Sul (CERES), Laguna, Santa Catarina, Brazil

Gabriela Gaxiola

UMDI, Facultad de Ciencias, UNAM, Sisal, Yucatan, Mexico Gerard Cuzon

Ifremer (Institut Frangais de Recherche pour l’Exploitation de la Mer) Taravao, Tahiti, French Polynesia


The authors would like to thank CONCYTEY (Consejo de Ciencia y Tecnologia del Estado de Yucatan), Coordenagao de Aperfeigoamento de Pessoal de Nivel Superior-CAPES,

Brazilian Ministry of Education (PhD grant number 4814061 provided to the primary author) and Consejo Nacional de Ciencia y Tecnologfa-CONACyT, Mexico (grant 60824) for research support. The authors also would like to thank Wilson Wasielesky, Yoram Avnimelech and Manuel Valenzuela for photos courtesy and Miguel Arevalo, Maite Mascara, Elsa Norena, Santiago Capella, Adriana Paredes, Gabriela Palomino, Korynthia Aguiar, Moises Cab, Nancy Aranda Cirerol, Concepcion Burgos, Manuel Valenzuela and all staff of Programa Camaron-UMDI for their contribution towards researches performed at UMDI-UNAM cited in this chapter.