Pot trial on lettuce (plant biometric survey and elemental analysis)

In Figure 4, lettuce plants differently fertilized in the two soils at the end of the experiment (60 days) are shown.

The effect of the different treatments and soils is evident when comparing the lettuce growth. The phytotoxic effect of urea at highest dose was dramatically shown in Soil A, while at the same urea dose, in soil B the lettuce was able to increase its development although the excess of N mineral supply: this result attested clearly the role of soil in influencing the actual availability of nutrients, and in particular of N, for plant uptake. It was promising the good performances of both the not-digested and the digested solid fraction of livestock manure at the highest N dose, particularly in Soil A: even if the 400 kgN*ha-1 supplied by urea gave the worst result on lettuce, apparently the excess of N added with the organic biomasses did not determine any decrease of lettuce growth, but on the contrary, a very good development of lettuce foliage (Figure 5).

This aspect is a positive point in order to propose the increase of the limit rate of 170 kgN*ha-1 year-1 for digestate application to soil, especially because these results were obtained in a soil with a sandy texture, low organic C content and, consequently, particularly vulnerable for nitrates.

Подпись: SOIL A Подпись: 0200
Подпись: UREA 200
Подпись: □200
Подпись: ND200
Подпись: ND400
Подпись: SOIL В


(ND = not-digested solid fraction of livestock manure; D = digested solid fraction of livestock manure; 200 = 200

kgNxha-1; 400 = 400 kgNxha-1).

Figure 4. Lettuce grown in relation to the different fertilization treatments in soil A and B.

(400 = 400 kgNxha-1).

Подпись: Figure 5. Example of lettuce leaves development in relation to the different fertilization treatments in Soil A.

The quantitative results related to biomass production and quality of plants are reported in Table 2.

Soil A

Dry weight (g plant’1)

Dry matter (%)

Total leaf area (cm-2)

Number of leaves

Not fertilized

1,4 b

5,2 b

450 b

15 a

Urea 200 Kg ha1

2,9 d

4,8 b

1450 d

24 c

Urea 400 Kg ha-1

0,4 a

5,4 c

180 a

13 a

Not digested 200 Kg ha-1

1,2 b

4,4 a

730 c

17 b

Not digested 400 Kg ha-1

2,5 c

7,3 d

750 c

18 b

Digested 200 Kg ha-1

1,9 c

5,0 b

760 c

17 b

Digested 400 Kg ha-1

2,4 c

5,0 b

850 c

18 b

Soil B

Dry weight (g plant’1)

Dry matter (%)

Total leaf area (cm’2)

Number of leaves

Not fertilized

0,7 a

1,9 a

900 b

18 ab

Urea 200 Kg ha-1

3,5 c

3,7 c

2000 c

23 c

Urea 400 Kg ha-1

0,8 a

5,2 cd

1300 bc

22 c

Not digested 200 Kg ha-1

1,7 b

3,7 c

850 b

18 ab

Not digested 400 Kg ha-1

1,6 b

3,2 b

1250 bc

20 b

Digested 200 Kg ha-1

0,7 a

2,1 a

700 a

17 a

Digested 400 Kg ha-1

1,6 b

3,5 c

1000 b

20 b

Table 2. Lettuce dry weight, dry matter, total leaf area and number of leaves obtained after fertilization treatments in A and B soils (average value; different letters means significant differences at P — level<0.05).

Firstly, again a strong "soil effect" was recorded in relation to plant growth parameters for all the treatments, due to the different chemical-physical characteristics and biological fertility of the two soils. Not fertilized plants showed a limited vegetative development
while, as expected, lowest urea dose (200 kgN*ha-1) gave the best plant growth (6.7 g plant-1), as confirmed by recorded parameters as shoot dry weight, percentage of dry matter, leaf area and leaf number, especially in B Soil. On the contrary, urea at 400 kgN*ha-1 dramatically depressed plant growth in Soil A (0.8 g plant-1), due to evident toxicity phenomena.

Digested and not digested biomasses gave best results when applied at the higher dose respect to the lowest one; actually, in treatments with both the biomasses at 400 kgN*ha-1, plant parameters were closer to those obtained with urea at 200 kgN*ha-1. It is relevant that in Soil A the application of both the digested and the not-digested solid fractions of livestock manure at 400 kgN*ha-1 gave weight parameters higher than those observed at 200 kgN*ha-1. Otherwise, in Soil B, only fertilization with digested solid fraction of livestock manure at 400 kgN*ha-1 gave an increase of all the tested parameters respect to the 200 kgN*ha-1 dose, while the not digested biomass did not show any differences among the N rates.

No toxicity phenomena were detected also at the highest doses of added biomasses and this is an important and positive result in the scope of utilizing these materials in substitution of mineral fertilizer also with high doses of N supply without collateral effect on plant.

For better clarify the effect of the alternative fertilization treatments on lettuce plant parameters in relation of the two soils, radar graphs are reported in Figure 6.


Figure 6. Radar graphs of plant parameters related to the fertilization treatments in soil A and B.

Lettuce number of leaves was little affected by soil characteristics, while dry weight, dry matter and total leaf area were evidently influenced by both the soil and the fertilization. On general terms, in the Soil A the percentages of dry matter of lettuce were higher respect to the corresponding values recorded in Soil B; on the contrary, the total leaf areas were lower in Soil A than in Soil B. The water uptake seems to have had a great importance in the two considered systems: probably, it was strongly affected by physical characteristics of the two soils. In Soil A, with about 50% of sand content, the water was less available to plant because of its lower water retention capacity respect to Soil B, so to determine the tendency to reduce the lettuce total leaf area and increase leaves dry matter. On the contrary, in the loamy Soil B, the higher water availability determined a decrease in percentages of lettuce dry matter and the increase of leaf areas, so to give an indication of the dependence of lettuce quality mainly from soil characteristics rather than the fertilization treatments. Anyway, taking into account 200 kgN*ha-1 of urea as the reference dose for lettuce production, it is relevant that the not-digested solid fraction of livestock manure at 400 kgN*ha-1 gave the highest value of lettuce dry matter among all the treatments.

For evaluating macro (N, P, K, Mg) and micronutrients (Cu, B, Fe, Mn) use efficiency, biomass dry weight and elemental concentrations were plotted, including curved content isoclines [32],[33]. Each point on the bidimensional plot represent a vectors, taken as control equal to 100% both the concentration and the related dry weight obtained after addition of 200 kgN*ha-1 urea (intersection point) in graphs (Figure 7 and Figure 8).

Plant tissue composition was significantly affected by the different treatments, depending on the added materials and rate. In relation to both the macro and the micronutrients, it should be remarked that all results were shifted to the limiting vector space (left side of the plot, under 100% lettuce dry matter, corresponding to urea at 200 kgN*ha-1), representing the reducing growth treatments.

In relation to N (Figure 7), the phytotoxic effect of mineral fertilizer at 400 kgN*ha-1 is particularly evident in Soil A, where the increase of concentration of N corresponded to the greatest decrease of lettuce dry matter respect to the control; the same severe phytotoxicity was not recovered after treatments with organic biomasses. The most promising results were obtained after addition of not-digested and digested solid fraction of livestock manure at highest dose, giving a dry matter similar to those obtained with the control mineral fertilization, but with a net decrease in N uptake: this finding attests that the N use efficiency was particularly high when 400 kgN*ha-1 of both organic materials were added to Soil A, since the lack in N prompt availability was not so heavy in limiting plant growth. Such a positive result was not so evident in Soil B, because of the clear reduction of lettuce dry matter (about -50%) after treatments with digested and not-digested materials respect to urea at 200 kgN*ha-1, even if the 400 kgN*ha-1 urea application gave a tendency to an excess of N consumption by lettuce, which did not correspond to an increase of dry

Similar results were obtained also for Mg (Figure 7), since again this nutrient concentration seemed to be another limiting factor for lettuce growth, in both the soils.

Different behaviour was recorded for P and K (Figure 7): their related uptakes were strongly affected by both the soils characteristics and the fertilization treatments: while in Soil A the limiting factor for lettuce growth was clearly the soil P and K availability (left space of the graph, under 100% of nutrient concentration for the urea control), in Soil B the effect was opposite (especially for K), since the excess of nutrients appeared to be the main cause of plant growth decrease (left space of the graph, above 100% of nutrient concentration for control 200 kgNxha-1), determining a typically defined "nutrient luxury consumption".

In relation to micronutrients Cu, B, Fe and Mn (Figure 8), sometimes their deficiency represented the main limiting factor for lettuce growth (as Fe in Soil B for all the treatments), sometimes the excess of their concentration could have again determined a luxury consumption (as Cu and B in Soil A, after addition of 200 kgN*ha-1 of digested livestock manure). It is interesting the effect played by soils on Mn lettuce uptake: in Soil A, after addition of 200 kgN*ha-1 of digested livestock manure, both the Mn uptake and the lettuce dry weight were reduced only of about 30% respect to the values posed to 100% for urea control; on the contrary, in Soil B, the same parameters strongly decreased of about 80%, so confirming the role of soil chemical and physical characteristics on nutrient availability in relation to the different treatments.

Hydrodeoxygenation (HDO)

Due to the hydrogen deficient nature of biomass (C:H<1 ), catalytic upgrading of bio-oil often leads to deoxygenation via decarboxylation and/or decarbonylation routes leading to losing of precious carbon assimilated during photosynthesis. Analogously, dehydration, in a hydrogen-lean environment leads to formation of large unsaturated compounds commonly known as coke. In order to circumvent these issues, extra hydrogen is supplied to the reactor — and this process is called hydrodeoxygenation.

So far the most reliable and extensively studied method for deep deoxygenation is hydrodeoxygenation which involves gaseous hydrogen and heterogeneous catalyst such as sulfided NiMo, CoMo supported on alumina[69] [70] .The idea of using hydrogen to upgrade bio-oil originates from the use of hydrogen in the petrochemical industry. Hydroprocessing is a crucial step in petroleum refining process that basically involves five types of reaction classes: hydrodenitrogenation (HDN), hydrodesulfurization (HDS), hydrodeoxygenation (HDO), hydrodemthylation (HDM) and hydrogenation (HYD)[70-72].

The process where oxygen in the feed is removed via dehydration using gaseous hydrogen is called hydrodeoxygenation (HDO). In a typical hydro processing process, the order of these reaction classes are HDS>HDO>HDN. This is because in a conventional petroleum feed, the sulfur and nitrogen content is significantly higher compared to oxygen. Therefore, HDO chemistry has received only little attention in petrochemical refining [27]. Hydrotreatment of crude petroleum is challenging for the catalyst due to the presence of sulfur and nitrogen in the feed in significant amounts. The products of hydrotreatment such as water, ammonia, hydrogen sulfide has been reported to poison hydrotreating catalysts [73]. Nevertheless, since bio-oil contains less sulfur and nitrogen, HDO would be a better fit for bio-oil upgrading. The presence of significant amounts of oxygen and C=C compounds in bio-oil increases chances of simultaneous occurrence of HDO as well as the hydrogenation reactions. More negative Gibbs free energies of deoxygenation reactions compared to hydrogenation reactions implies that deoxygenation is more favorable. However, saturation of aromatic rings is not desirable as it consumes large amounts of hydrogen and reduces the octane number of the fuel reducing the fuel quality.

Much of the studies on HDO are based on catalysts such as Co-Mo, Ni-Mo, Ni-W, Ni, Co, and Pd. A catalyst, to be effective for HDO, should ideally perform two tasks, i. e., activating the dihydrogen molecule as well as activating the oxygen group of the compound. The oxygen group activation usually occurs on the transition metal oxides such as Mo, W, Co, Mn, Zr, Ce, Y, Sr and La while the activation of hydrogen is known to happen on noble metals such as Pt, Pd, and Rh [74].

Studies on HDO chemistry has mostly been done using model compound such as phenol, cresole, guaiacol, napthol etc. [75, 76] which are abundantly present in bio-oil. Some of these model compounds and their proposed reaction pathways are shown in Table (2).

Most of the earlier work in HDO used sulfided forms of Mo as the active element and Co or Ni as promoters on y. AhO3 [27, 70, 79]. However, sulfide catalyst would not work for bio-oil since the feed does not contain significant amounts of sulfur. In the HDO process, a sulfide catalyst would soon be deactivated if an external sulfur source is not provided [80]. Nevertheless, the oxygen content of the feed is said to have a negative effect on the sulphide structure resulting in losses such as the catalyst deactivation and changes in product distribution. In-depth studies on Co (or Ni)-promoted MoS2 catalysts have revealed that the edges on MoS2 is also important in terms of catalyst activity since they are dominated by promoter atoms in the so-called Co-Mo-S structures [81]. The studies have revealed that poly-condensation products formed have shortened the life by deactivation. Alumina in this regard is quite susceptible to deactivation by coke formation. Therefore, investigations on new catalysts that do not require sulfidation and supports such as activated carbon that are tolerant to deactivation are needed[82].


Table 2. Common reaction pathways proposed for HDO reactions. ( Reaction schemes were extracted from Senol et al [77]and Laurent et al.[78] )

The suitability of Ni as a catalyst to activate the dihydrogen molecule in HDO has been reported by several groups[79, 83]. In comparison to noble metals, the use of Ni is extremely economical especially for large scale applications. For example, the gas-phase hydrodeoxygenation of a series of aromatic alcohols that include aldehydes and acids has been reported with Ni/SiO2. This study analyzed kinetic effects of the gas phase hydrogenolysis of — CH2OH, — CHO and — COOH groups attached on to aromatic ring structures in the presence of Ni/SiO2 [79]. They concluded that the oxygenated aromatics get weakly adsorbed on the catalyst and the surface mobility facilitates reaction with adsorbed hydrogen atoms. Further, the adsorption reactions of H2 and aromatic species were considered to be a competitive adsorption.

In a separate study, HDO of model compound anisole using Ni-Cu on АЮз, CeO2 and ZrO2 supports found that Ni-Cu supported on CeO2 and AhO3 was the most active catalyst in comparison to pure Ni catalyst[74]. The significance of this study was that the catalysts tested were not in the sulfided form and therefore would be highly suitable for bio-oil-type application(s).

The need for hydrogen in HDO process has always been a point of concern due to high expenses associated with hydrogenations. A study in this regard attempted using hydrogen generated in situ for performing HDO. A study conducted using Pt on TiO2, CeO2, and ZrO2 supports showed that the oxygenates undergo dehydrogenation and subsequently HDO using the produced H2. The catalysts tested were Pt/CeO2, Pt/CeZrO2, Pt/TiO2, Pt/ZrO2, Pt/SiO2-AleO3, and Pt/AhO3. Of all catalysts tested, Pt on AhO3 showed the highest activity with a reduction of oxygen content from 41.4 wt% to 2.8 wt% after upgrading [84].

It has also been shown that Pd on supports such as carbon, AhO3, ZSM-5, MCM 41 [76, 85, 86] are active for HDO. A study conducted with benzophenone with 5% Pd on active carbon and on ZSM-5 supports proved to be very active toward hydrogenation as opposed to supports such as AhO3, and MCM 41. However, the HDO of benzophenone was significantly higher with Pd on supports like active carbon and acid zeolytes. Furthermore, it was concluded that the acidity of the zeolite support affects the HDO reaction [76].

Co processing of bio-oil with straight run gas oil (SRGO) can be considered to have more practical significance. The concept behind this method is the simultaneous use of HDS process with the HDO of biocrude [69, 87-89]. The model compound guaiacol (5000 ppm) representing oxygenates in bio-oil has been used together with SRGO (containing13,500 ppm of S) in a trickle bed reactor. At low temperature and low space velocities, a decrease in the HDS reaction has been observed with CoMo/Al2O3. The possible explanation is the competition of intermediate phenol with the sulfur containing molecules for adsorption on hydrogenation/ hydrogenolysis sites. At higher temperatures (above 3800C), the HDO of guaiacol was observed along with HDS taken place without further inhibition [88] .

Although the role of sulfur on HDS is well understood, the effect of sulfur on HDO is not yet well explained. Certain studies indicate that the presence of H2S has an inhibitory effect on the HDO while other studies support maintaining the sulfidasation level of the catalyst [90-92]. For example, the effect of using a sulfiding agent, H2S has been studied with model compounds like phenol and anisole during hydrotreatment. The results conclude that the presence of H2S decreases the HDO activity of the sulfided CoMo/y-AhO3 catalyst and the product yield depends on the concentration of H2S [80]. A similar study conducted using H2S for HDO of phenol on Co-Mo and Ni-Mo arrived at the same conclusion. However, quite interestingly, the presence of H2S during the HDO of aliphatic oxygenates has shown a promoting effect. The reason for this observation is that the sulfiding agent, H2S, enhances the acid catalyzed reactions of aliphatic oxygenates. However, direct hydrogenolysis reaction of phenol is suppressed due to competitive adsorption of both phenol and H2S [77, 93].

Renewable energy potential

3.1.2. Ethanol productivity

Sugarcane, given its energy balance advantage, is likely to be beneficial if promoted as bio-fuel feedstock as this is likely to increase sugarcane prices to the benefit of the small scale farmer.

Promoting sugarcane as a feedstock for ethanol is likely to improve rural livelihood and also minimize on forest encroachment since energy output per unit land area is very high for sugarcane.

In Brazil for example the production of sugar cane for ethanol only uses 1% of the available land and the recent increase in sugar cane production for bio-fuels is not large enough to explain the displacement of small farmers or soy production into deforested zones " in [13] ".

To minimize competition over land, it is advisable to grow sugarcane that has high yields with higher energy output compared to other biofuel crops. High yielding bio-fuels are preferable as they are less likely to compete over land "in [16]".


The increase in biomass related research and applications is driven by overall higher interest in sustainable energy and food sources, by increased awareness of potentials and pitfalls of using biomass for energy, by the concerns for food supply and by multitude of potential biomass uses as a source material in organic chemistry, bringing in the concept of bio-refinery. The present, two volume, Biomass book reflects that trend in broadening of biomass related research. Its total of 40 chapters spans over diverse areas of biomass research, grouped into 9 themes.

The first volume starts with the Biomass Sustainability and Biomass Systems sections, dealing with broader issues of biomass availability, methods for biomass assessment and potentials for its sustainable use. The increased tendency to take a second look at how much biomass is really and sustainably available is reflected in these sections, mainly applied to biomass for energy use. Similarly, Biomass for Energy section specifically groups chapters that deal with the application of biomass in the energy field. Notably, the chapters in this section are focused to those applications that deal with waste and second generation biofuels, minimizing the conflict between biomass as feedstock and biomass for energy. Next is the Biomass Processing section which covers various aspects of the second-generation bio-fuel generation, focusing on more sustainable processing practices. The section on Biomass Production covers short — rotation (terrestrial) energy crops and aquatic feedstock crops.

The second volume continues the theme of production with the Biomass Cultivation section, further expanding on cultivation methods for energy, the feedstock crops and microbial biomass production. It is followed by the Bio-reactors section dealing with various aspects of bio-digestion and overall bio-reactor processes. Two more chapters dealing with aquatic microbial and phytoplankton growth technologies are grouped into the Aquatic Biomass section, followed by the Novel Biomass Utilization section which concludes the second volume.

I sincerely hope that the wide variety of topics covered in this two-volume edition will readily find the audience among researchers, students, policy makers and all others with interest in biomass as a renewable and (if we are careful) sustainable source of organic material for ever wider spectrum of its potential uses. I also hope that further exploration of second-generation energy sources from biomass will help in resolving the conflict of biomass for food and biomass for energy.

Miodrag Darko Matovic

Department of Mechanical and Materials Engineering, Queen’s University, Kingston, Canada


Compared to the above four widely studied species, many other species for potential biofuel crops are more regional specific and related to local climatic conditions. In the southern region of the U. S., subtropical and tropical grasses such as bermudagrass and napiergrass have been evaluated as biomass crops [6]. In southwestern Quebec, Canada, a short growing season environment, Madakadze et al. (1998) [23] evaluated 22 warm-season grasses in 5 species (sandreed, switchgrass, big bluestem, Indian grass and cordgrass). They found that the most productive entries were cordgrass and several entries of switchgrass. Switchgrass from high latitude tended to produce less biomass. The sandreed showed little potential for forage or biomass production. This study was conducted using space-planted nursery conditions and these data represent individual plant potential. Thereafter, their studies were only focused on switchgrass under solid sward conditions [23-25].

English name

Scientific name



Yields reported Mg DM/ha/year

Meadow Foxtail

Alopecurus pratensis L.



Big Bluestem

Andropogon gerardii Vitman



Giant Reed

Arundo donax L.



Cypergras, Galingale

Cyperus longus L.



Cocksfoot grass

Dactylis glomerata L.



Tall Fescue

Festuca arundinacea Schreb.




Lolium ssp.




Miscanthus spp.




Panicum virgatum L.



Napier Grass

Pennisetum purpureum Schum



Reed canary grass

Phalaris arundinacea L.




Phleum pratense L.



Common Reed

Phragmites communis Trin.



Energy cane

Saccharum officinarum L.



Giant Cordgrass/

Spartina cynosuroides L.



Salt Reedgrass


Prairie Cordgrass

Spartina pectinata Bosc.



Table 2. Perennial grasses grown or tested as energy crops in Europe [12].

Study of Satellite Storage Locations (SSLs)

An SSL should be established where sufficient feedstock density will ensure the investment is justified. Location affects the costs associated with transporting the biomass from the production field to the SSLs and from the SSLs to the biorefinery. An additional factor that needs to be considered is how often a SSL is filled and unloaded. If a SSL is emptied only once a year, then the total SSL storage area would be doubled as compared to a twice-per- year unload schedule. Note, multiple fillings of a SSL is possible if harvest can be extended over several months and is properly matched with the SSL unload sequence.

Specialized equipment, with high productivity (ton/h) will be utilized to empty each SSL. This equipment can be permanent equipment for each SSL, or the equipment may be mobile and move from one SSL to the next. Questions that need to be answered to implement the mobile option are: 1) how many sets of equipment should be used to service the entire area? 2) What is the sequence of SSLs that each equipment set should unload? 3) Is an SSL ready to be unloaded? This last question directly affects the hauling company’s contract. These questions are best considered in a virtual environment where scenarios can be compared and contrasted.

A case study to characterize feedstock resources and establish the SSL’s was analyzed by Resop et al. [57] for a 48-km radius around Gretna, VA, USA. The GIS analysis identified potential production fields based on current land utilization determined using aerial photography and landuse classification (Figure 5). The analysis selected fields such that the production area was 6% of the total land area within the radius. The biomass produced is sufficient to supply the demand for a 1,944 Mg/d biorefinery, assuming 47 operating weeks per year. SSLs were established at 199 locations, and the existing road network (GIS database) was used to determine the travel distance from each SSL to the proposed plant location in Gretna. A weighted Mg-km parameter for transportation from the SSLs to the plant was computed to be 44.8 km, which implies that, averaged across all 199 SSLs, each Mg traveled an average of 44.8 km to the biorefinery.

Judd et al. studied three equipment options for the operations performed at a SSL [58]. Two options utilize the rack concept [59]. Ravula et al. analyzed a round bale logistics system utilizing this rack system; the rack size emulate a 20-ft (6.1-m) ISO container providing two levels of 8 bales, total of 16 bales, to be handled as a single unit [40]. A tractor-trailer truck can haul two racks (32 bales, 14.4 Mg). The results showed the rack hauling had higher transportation costs due to not loading the truck to maximum allowable weight.


Figure 5. Example of Satellite Storage Locations (SSLs) located over a 30-mi (48-km) radius around a chosen bioenergy location. The refinery is located in the center of the circle. Each cross represents an SSL location with access to the public road system. The smallest SSL was a storage that can store biomass from 60 ac (24 ha) of production fields and the largest stores biomass from 1200 ac (486 ha) of production fields.

At a SSL, the round bales could be handled in one of two ways. The first option loads bales into the rack from the rear and is referred to as the "rear-loading." The second option, referred to as the "side-loading," loads the bales into a rack from the side. These two options were compared to grinding the bales at the SSL and compacting the ground material into a briquette (maximizes over-the-road load) for delivery [50]. Judd et al. [58] found that it was more cost effective to use the "side-load" option and haul the round bales than to form briquettes at the SSL, if the haul distance was less than 50 miles (81 km).

The large biorefinery concept calls for delivery of feedstock from a large geographic area. The results [58] suggest that some type of intermediate processing step, referred to as an Advanced Regional Biomass Processing Depot (ARBPD) by Eranki et al. [60], may be needed. These depots will convert the raw biomass to a more energy dense (higher value) product, and this product will then be delivered to a large biorefinery for final processing.

Why was the expense of size reduction and densification into briquettes at the SSL considered? Independent studies [61-63] reported that the tradeoff between the additional cost for densification and the reduction in transportation cost is significant for the hauling of logging residues. For additional work in the area of densification, see [64-65].

Influencing factors of dominant microflora biological stability

During the IBAC process, the biological stability of the dominant microflora is the key factor to ensure an effective operation. However, the factors including the property of the activated carbon, the dosage of ozone, hydraulic retention time and the condition of backwash all have impacts on the dominant biocommunity at different levels. When all combined influences considered, the main factors of the activated carbon effects on the stability of dominant biocommunity are the distribution of pores in activated carbon, the physical and mechanical properties as well as its chemical property, among which molasses value is the primary controlling index [44-46]. Although ozonation may improve the biodegradability of water, provide an oxygen-rich condition and reduce the incursion of undesirable bacteria, as well as enhance the biological activity of dominant biocommunity, however, relevant research shows that when the residual ozone reaches even over 0.1 mg/L in the inflow of IBAC system, a restrain on dominant biocommunity will be shown[47]. By increasing the contact time of the dominant microflora with organics, the adsorption and mass transfer of organics can be enhanced, thus the biological activity of the dominant biocommunity is improved. Generally, the most appropriate retention time is about 20 min[48]. The backwash step has an impact on the biological activity of the dominant biocommunity, which makes biological activity of the community in each layer rapidly decreases from the normal level to the minimum, but still remains in a certain range. After the sharp cutoff, the biological activity of the community will recover at normal level rapidly. Meanwhile, the intensity of backwash and its time also have a great impact on biological stability[49].

Hybrid bioreactor mathematical modeling

The mathematical modeling is a very useful method of process simulation, because there is no need time and costs consuming experimental methods using. However, there are many problems in adequate mathematical description of complex biochemical processes and parameters’ estimation.

For hybrid bioreactor modeling the ASMH1 model [11] can be applied. It is based on the ASM1 model, but significantly modified: nitrogen removal processes are more completely treated by implementation of two stages nitrification and denitrification (figure 14). The intermittent aeration and oxygen accessibility to the second phase of nitrification was


The model calibration using laboratory data allowed to identify the kinetic and stechiometric parameters values. The model was implemented in to the POLYMATH program and relatively good agreement with experimental results was achieved (st. dev. 10%).

4. Conclusions

The basic technological parameters related to removal efficiency of pollutants from septic tank in hybrid MBBR, at intermittent aeration, continuous/sequencing flow and elevated pH were presented in table 3. The parameters’ values concerning the purified sewage fulfill the Polish law requirements for 2000 p. e. WWTPs.

The hybrid MBBR has occurred an effective system for carbon and nitrogen compounds removal from septic tank effluent. The carbon and nitrogen compounds can be removed

with at least 80% and 50% removal efficiency respectively. It can be achieved even at loading of 2 g COD/gdmd and 12 hours HRT. Similar results were reported by [53] for partial nitrification-denitrification process in combination of aerobic and anoxic reactors with Kaldnes carriers. The system needed internal recirculation. Thanks to attached biomass the nitrification process in the hybrid MBBR was effective at low and high loaded reactors. The remaining of ammonium nitrogen in treated wastewater appeared at high loadings only. The intermittent aeration and dissolved oxygen limitation enabled simultaneous nitrification — denitrification process (SND) in one reactor. The inhibition of second phase of nitrification by free ammonia has intensified nitrogen removal and resulted in energy savings and internal source of carbon using as a sole source. The shortened process of carbon compounds removal was confirmed by medium products appearance. The long time aeration cycles and long time operating cycles can result in denitrification disturbances due to the organic substances oxidation and limitation of that energy source for denitrifying bacteria.


Continuous flow reactor CFR

Batch reactor

CFR with increased pH

Pollution load

-organic compounds, g COD/d — nitrogen compounds, g Ntot/d

19.29 — 29.25 5.93 — 6.42

15.56 — 31.57 2.77 — 7.87

26.51 — 27.89 7.30 — 8.25

Hydraulic load, dm3/dm3d

1.53 — 1.90

1.65 — 3.05

1.85 — 2.04

Concentration in purified sewage

-organic compounds, g COD/m3 — nitrogen compounds, g Ntot/m3

26.91 — 56.22 21.57 — 31.07

31.31 — 59.46 27.49 — 34.94

23.00 — 37.00 17.10 — 25.28


-activated sludge density, g/dm3 — biofilm mass, g/m2

0.62 — 1.18 2.58 — 5.46

0.48 — 4.03 0.62 — 3.70

5.58 — 6.52 1.55 — 3.58

Biomass loading of — organic compounds, g COD/gdmd — nitrogen compounds,

g Ntot/gdmd

0.247 — 0.945 0.067 — 0.201

0.081 — 1.080 0.011 — 0.155

0.075 — 0.082 0.011 — 0.021

Efficiency of removal,% — organic compounds — nitrogen compounds

75 — 84 37 — 54

69 — 82 20 — 35

80 — 87 51 — 68

Pollution removal rate — organic compounds, g COD/gdmd — nitrogen compounds,

g Ntot/gdmd

0.206 — 0.663 0.030 — 0.113

0.110 — 0.620 0.004 — 0.032

0.064 — 0.124 0.011 — 0.021

Yield coefficient, gdm/gCODrem

0.31 — 0.43

0.42 — 0.50

0.34 — 0.47

Table 3. Technological parameters and treatment efficiency in hybrid reactors

The pH elevation brought about higher treatment efficiency. The higher volumetric fraction of moving media (carriers) — the better performance. The continuous flow reactor was more effective in treatment and more stable than the sequencing batch reactor.

It was stated and statistically confirmed that: aeration regime, biomass loading and media volume fraction have an impact on the pollutants (especially organic compounds) removal efficiency.

Advantages of hybrid MBBR reactors operating in the modified conditions are as follows: lower energy consumption (up to 40%) related to the shorter aeration time, possibility of specific wastewater treatment (low N/C ratio), simultaneous processes maintaining in one reactor (internal recirculation elimination), overloading resistance (stable performance) and reduction in smaller reactors’ volume. The mathematical model ASMH1 allows to simulate the reactor performance at the specific conditions.

Phytoplankton Biomass Impact on the Lake Water Quality

Ozden Fakioglu

Additional information is available at the end of the chapter http://dx. doi. org/10.5772/55361

1. Introduction

Pyhtoplankton is a plant plankton which cannot move actively and changes location depending on the movement of water. Phytoplankton communities are widely spreaded from aquatic to terresial lands. Plankton form the first ring of food chain in aquatic environment effecting the efficiency of this environment. Daily, seasonally and yearly changes are important for calculating efficiency in aquatic fields. Phytoplankton composition is a trophic indication of the water mass. In addition, phytoplankton species are used as an indicator for determining the nutrient level which is the basis for preparing and monitoring the strategies of the lake management in the lakes. Using phytoplankton communities or other aquatic organisms for evaluating water quality is based on very ancient times. Saprobik and trophic inducator types are used in many researches [1-3]. In addition, various numerical indices have been developed [1-2]. However, none of them have been accepted extensively. This is caused by several reasons. Those reasons are:

a. Differences in phytoplankton groups and group concept

b. Dynamic properties of phytoplankton groups

c. Habitat diversity of freshwater ecosystems

d. Phytogeographical differences [4].

Phytoplankton communities are influenced by significant changes every year. The competitive environment known as seasonal succession has been changing [5]. If the conditions don’t change, this process results in the choice of communities dominated by one or more species. Phytoplankton responds rapidly to the condition chages. Conditional changes will result the formation of high compositional diversity [6].

The first approaches to classify algal communities don’t have wide range of application in determining water quality. Pankin’s approaches to classify algal communities in 1941 and 1945 weren’t generally accepted [4]. Reynold’s [7-8] applied a classic phytosociological

approach to phytoplankton data obtained from the lakes in northeast england and classified them into various communities. Sommer [5] found high similarities among the compositions of species and seasonal succession of Alpine lakes. Mason [9] reported that oligotrophic and eutrophic lakes have the communities of characteristic phytoplankton.

There are qualitative differences among phytoplankton communities in oligotrophic and eutrophic lakes. The compositon and the amoung of phytoplankton communities are affected by environmental conditions. For example, a numerical decrease is observed in Anabeana and Aphanizomenon species from heterosis blue-green algae found in mesotrophic lake layers with a decrease of nitrogen saturation in the lakes. In addition, there are differences among the environmental conditions preferred by Diatoms, Dinoflagellate or Cosmarium, Pandorina and Gemellicystis species, even though they can be found in the lakes with same level of nutrients [10].

In the works related with phytoplankton communities used for predicting the ecological structure of aquatic systems, it has been tried to develop functional groups by improving the systematic investigation. Furthermore, some indices was developed according to numerical and biovolume values of phytoplankton (Palmer Index (1969), Descy Index (1979), TDI Index (1995) etc.). HPLC pigment analysis is used for the diagnosis of phytoplankton species in recent years [11].

The methods we will mention for examination of plankton in aquatic environments are summarized by compiling researchers’ methods and techniques. The main target of these suggested methods is to obtain values close to the actual volume and weight of plankton in freshwater and to calculate the volume and weight (biomass) of these organisms by this method.

Besides, the methods and techniques in this subject are sensitive, determining ecological parameters which are characterizing the aquatic environment are important for the researches. Sampling error in this review may cause errors far beyond the susceptibility of calculations. In addition, vertically and horizontally distributions of plankton may show big changes against the effects of wind and light. For this reason, evaluating various samples collected as verticaly and horizontaly causes to get more reliable results from each sample.

Many techniques have been developed depending on the number, volume and cell structures of fresh water phytoplankton. In this section, studies conducted by calculating biovolume of phytoplankton used for estimating ecological characteristics of freshwater ecosystems will be summarized.

. Effect of carbon sources on the induction of multienzyme complex in P. curdlanolyticus B-6

The effect of polymeric substances such as cellulose, xylan, corn hull, and sugarcane bagasse, and of soluble sugars such as L-arabinose, D-galactose, D-glucose, D-xylose, and cellobiose on the induction of multienzyme complexes in a facultatively anaerobic bacterium, P. curdlanolyticus B-6, was investigated under aerobic conditions (Waeonukul et al., 2008; 2009b). Cells grown on each carbon source adhered to cellulose. Hence strain B-6 cells from all carbon sources must have an essential component responsible for anchoring the cells to the substrate surfaces. Native-PAGE, SDS-PAGE, zymograms analysis, and enzymatic assays revealed that many proteins having xylanolytic and cellulolytic activities from P. curdlanolyticus B-6 grown on each carbon source were produced as multienzyme complex into the culture supernatants. These results indicated that strain B-6 produced multienzyme complexes when grown on both polymeric substances and soluble sugars. However, the subunits expressed in the multienzyme complex of strain B-6 depended on the carbon sources. These observations are consistent with previous reports that the enzymatic activities and enzyme compositions of the cellulosomes of C. thermocellum (Bayer et al., 1985; Bhat et al., 1993; Nochur et al., 1993), C. cellulolyticum (Mohand-Oussaid et al., 1999), and C. cellulovorans (Kosugi et al., 2001; Han et al., 2004; 2005) and the xylanosome of S. olivaceoviridis E-86 (Jiang et al., 2004) were affected by carbon sources in the media.

Many investigators have reported that the synthesis of cellulosome assemblies requires the presence of crystalline cellulose under anaerobic conditions, and that synthesis hardly occurs in growth on glucose or other soluble carbohydrates (Nochur et al., 1992; Blair & Anderson; 1999a; Bayer 2004; Doi & Kosugi, 2004). Some strains of C. thermocellum (Bayer et al., 1985; Bhat et al., 1993), however, can induce cellulosome synthesis when grown on cellobiose. P. curdlanolyticus B-6 differs from most cellulosome-producing microorganisms in that it produces multienzyme complex when grown on both polymeric substances and soluble sugars under aerobic conditions. Therefore, the mechanism of multienzyme complex formation by strain B-6 must be different from that of other microorganisms.