Abbreviations and associated units.
\r\n\tThe eye is our window to the brain. Vision is the ability to interpret and understand the information that comes in through the eyes. The visual system utilizes brain pathways to process and understand what the eyes sense. The dynamic process of vision is to identify, interpret and understand what the eyes see.
\r\n\tAn image is a sight which has been recreated. It is an appearance which has been detached from the place and time in which it first made its appearance. Sensing is not the same as seeing. The eyes and the nervous system do the sensing, while the mind does the perceiving.
\r\n\t
\r\n\tMedical imaging is the process of using technology to view the human body in the interest of diagnosing, monitoring, and treating medical problems. It is especially beneficial when it comes to detecting cancer. Such a threatening disease requires very early detection to improve the chances of survival. Medical imaging is an extremely important element in medical practice in the world of today. While medical knowledge and discernment forms the basis of diagnoses and decisions, medical imaging plays a vital role in confirming any diagnosis. With scientific advancement and a continued effective use, medical imaging will continue to help with earlier detection of health issues and provide increased preventative care.
\r\n\tThis book intends to provide readers with a comprehensive overview of the latest and most advanced findings in several aspects of ophthalmic pathology, treatment and surgical strategies, ocular imaging, vision sciences, medical images and perception that focuses on the most important developments in these critically important areas. Enough has been achieved already to make it clear that these fields have enormous possibilities for improving the human health.
\r\n\t
Commercial production of fish involves high levels of feeding. While digestive breakdown of lipids and carbohydrates yields water and carbon dioxide as waste products, digestion of proteins also yields nitrogenous compounds. In teleost (i.e., bony) fishes, these nitrogenous wastes are excreted predominately as ammonia. Total ammonia-nitrogen (TAN) consists of ionized ammonia (NH4+-N) and un-ionized ammonia (NH3-N), the latter of which can prove toxic to fish. The fraction of TAN in the unionized form is dependent upon the pH and temperature of the water (Losordo 1997, Lekang 2007) and to a lesser degree its salinity (Diaz et al. 2012). At pH values less than 7.5, most ammonia is in the ionized form, and high levels of TAN can be tolerated. At higher pH, however, levels of un-ionized ammonia become problematic. Hence, biodegradation of ammonia is critical for the success of fish culture. Nitrifying bacteria, including Nitrosomonas sp., utilize NH3-N as the energy source for growth, producing nitrite, NO2-N. While nitrite-nitrogen is not as toxic as un-ionized ammonia-nitrogen, it can prove harmful to fish. The most common mode of toxicity is anoxia, as nitrite-nitrogen crosses the gills into the circulatory system and converts hemoglobin to methemoglobin, rendering it unable to bind and transport oxygen to the tissues (Palachek and Tomasso 1984, Svobodova et al. 2005). Other nitrifying bacteria, including Nitrobacter sp., utilize nitrite as their energy source, producing nitrate, NO3-N. Nitrate-nitrogen concentrations are not generally of concern to aquaculturists, as most species can tolerate levels as high as 200 mg/L (Russo and Thurston 1991). Nitrate rarely reaches such high levels, as it is removed from the system by water exchanges and by passive denitrification in anaerobic pockets within the production or filtration systems (van Rijn 1996, Tal et al. 2006) or in denitrification reactors (Hamlin et al. 2008, Sandu et al. 2011).
Controlled degradation of nitrogenous wastes in filtration units is a major consideration in design and operation of commercial recirculating aquaculture systems. Among the technologies available (Crab et al. 2007), biological filtration is most commonly used. Biological filters are designed to provide abundant surface area for the attachment of complex microbial communities (Schreier et al. 2010) rich in Nitrosomonas and Nitrobacter species (Chen et al. 2006, Itoi et al. 2007, van Kessel et al. 2010). The nitrification capacity of the water treatment system is often the factor that limits production in a recirculating aquaculture system (Lemarie et al. 2004, Eschar et al. 2006, Diaz et al. 2012).
The production efficiency of an aquaculture system can be evaluated through analysis of the conversion of nitrogen to fish biomass and to biodegradation pathways (Thoman et al. 2001). Nitrogen dynamics can be quantified by a mass balance equation, most simply as the difference between nitrogen in the feed supply and nutrients subsequently fixed as fish biomass. A nitrogen budget can quantify nitrogen fixation in fish biomass at various fish stocking densities (Suresh and Kwei 1992; Siddiqui and Al-Harbi 1999), nutrient release into the water column as dissolved and particulate excretion of fish (Krom and Neori 1989), and deposition of nitrogen into pond sediment (Acosta-Nassar et al. 1994). By estimating total nitrogen budgets for a particular species and culture system, we can evaluate the efficacy of water treatment processes (Porter et al. 1987). Hence, a nitrogen budget provides information crucial for the design and optimization of a production system, feeding strategies, and water and effluent treatment processes.
Blue Ridge Aquaculture (BRA) in Martinsville, Virginia, USA is a large commercial facility that produces 1300 metric tons of hybrid tilapia Oreochromis sp. per year. To our knowledge, it is the largest recirculating aquaculture facility in existence. Before our study, little information existed about nitrogen budgets in commercial-scale fish production facilities, especially those using freshwater recirculating systems. By deriving a nitrogen budget, we can quantify the forms and proportions of nitrogen ingested as food as it becomes bound in tilapia biomass, excreted as metabolites, biodegraded by microorganisms, lost as gas by denitrification, or released in effluent. Knowledge of the nitrogen budget can help optimize operations, improving facility efficiency and maximizing production. Using Blue Ridge Aquaculture as our study system, our objectives were to: (1) examine nitrogen dynamics for the grow-out systems, (2) relate the nitrogen budget to water quality, (3) evaluate biofilter loading and nitrogen removal efficiency, and (4) predict maximum system carrying capacity. All abbreviations used in this chapter are shown in Table 1.
\n\t\t\t\ta = mole fraction of unionized ammonia nitrogen (decimal fraction) | \n\t\t
\n\t\t\t\tACR = areal conversion rate (mg/m2-d) | \n\t\t
\n\t\t\t\tA\n\t\t\t\tNH3-N = concentration of unionized ammonia nitrogen (mg/L) | \n\t\t
\n\t\t\t\tA\n\t\t\t\tTAN = maximum allowable concentration of total ammonia nitrogen (mg/L) | \n\t\t
BRA = Blue Ridge Aquaculture | \n\t\t
\n\t\t\t\tC\n\t\t\t\tTAN = total ammonia nitrogen concentration in fish tank (mg/L) | \n\t\t
\n\t\t\t\tC\n\t\t\t\tTANe= total ammonia nitrogen concentration in the effluent from filters (mg/L) | \n\t\t
\n\t\t\t\tC\n\t\t\t\tTANi = total ammonia nitrogen concentration in the supply water (mg/L) | \n\t\t
\n\t\t\t\tE\n\t\t\t\ta = efficiency of rotating biological contactor for removal of ammonia nitrogen (percent) | \n\t\t
\n\t\t\t\tFA = amount of feed (kg) | \n\t\t
\n\t\t\t\tFB = fish biomass (kg) | \n\t\t
\n\t\t\t\tFC = feed conversion factor (decimal fraction) | \n\t\t
\n\t\t\t\tFP = protein content of feed (decimal fraction) | \n\t\t
\n\t\t\t\tFR\n\t\t\t\tMTAN = maximum feeding rate (kg/d) | \n\t\t
\n\t\t\t\tLC50 = lethal concentration of a compound to 50% of the individuals in a population | \n\t\t
\n\t\t\t\tLN = nitrogen load (g N/kg fish produced) | \n\t\t
\n\t\t\t\tL\n\t\t\t\tTAN = ammonia loading (g/hr) | \n\t\t
\n\t\t\t\tN\n\t\t\t\tDENIT = nitrogen gas removed by denitrification (mg/L) | \n\t\t
\n\t\t\t\tN\n\t\t\t\tDIN = dissolved inorganic nitrogen (mg/L) | \n\t\t
\n\t\t\t\tN\n\t\t\t\tfeed = nitrogen fixed in feed (g/kg feed) | \n\t\t
\n\t\t\t\tN\n\t\t\t\tfish = nitrogen fixed in fish (g/kg fish produced) | \n\t\t
\n\t\t\t\tN\n\t\t\t\tmort = nitrogen fixed in dead fish (g/kg fish removed) | \n\t\t
\n\t\t\t\tN\n\t\t\t\tNO2\n\t\t\t\t- = nitrite nitrogen (mg/L) | \n\t\t
\n\t\t\t\tN\n\t\t\t\tNO3\n\t\t\t\t- = nitrate nitrogen (mg/L) | \n\t\t
\n\t\t\t\tN\n\t\t\t\tNH3vol = nitrogen removed by ammonia volatilization (mg/L) | \n\t\t
\n\t\t\t\tNO3-N\n\t\t\t\tpass = nitrate removed passively by denitrification (mg/L) | \n\t\t
\n\t\t\t\tNO3-N\n\t\t\t\texch = nitrate removed by exchange of water (mg/L) | \n\t\t
\n\t\t\t\tN\n\t\t\t\tTAN = total ammonia nitrogen (mg/L) | \n\t\t
\n\t\t\t\tN\n\t\t\t\tTON = total organic nitrogen (mg/L) | \n\t\t
\n\t\t\t\tPC = protein content of feed (decimal fraction) | \n\t\t
\n\t\t\t\tP\n\t\t\t\tNO3-N = partitioning of nitrate nitrogen (g/kg) | \n\t\t
\n\t\t\t\tP\n\t\t\t\tTAN = production rate of ammonia nitrogen (g/kg) | \n\t\t
\n\t\t\t\tQ = flow rate through system (m3/min or L/h) | \n\t\t
\n\t\t\t\tQ\n\t\t\t\tf = recirculation flow rate (m3/min or L/h) | \n\t\t
RAS = recirculating aquaculture system | \n\t\t
\n\t\t\t\tR\n\t\t\t\tTAN = ammonia removal rate (g/h) | \n\t\t
\n\t\t\t\tS = surface area (m2) | \n\t\t
\n\t\t\t\tSBM\n\t\t\t\tMTAN = maximum biomass that could be sustained by system (kg fish) | \n\t\t
\n\t\t\t\tTAN\n\t\t\t\texchange = ammonia removed by water exchange (mg/L) | \n\t\t
\n\t\t\t\tTAN\n\t\t\t\tpass+vol = ammonia removed by passive nitrification and ammonia volatilization (mg/L) | \n\t\t
\n\t\t\t\tTAN\n\t\t\t\tRBC nitrification = ammonia removed by nitrification in rotating biological contactor (mass/volume) | \n\t\t
\n\t\t\t\tt = time | \n\t\t
\n\t\t\t\tTKN = total Kjeldall nitrogen (g) | \n\t\t
\n\t\t\t\tTNI = total nitrogen input (kg/day) | \n\t\t
\n\t\t\t\tTNR = total nitrogen recovered (kg/day) | \n\t\t
\n\t\t\t\tTNUA = total nitrogen unaccounted for (kg/day) | \n\t\t
Abbreviations and associated units.
Tilapias are a group of fishes of great importance to world aquaculture (Costa-Pierce and Rakocy 1997, Fitzsimmons 1997, Lim and Webster 2006). Tilapias adapt readily to a range of production systems ranging from traditional extensive pond systems to high-input intensive pond systems to super-intensive recirculating aquaculture systems. Like all fishes, tilapias are sensitive to concentrations of nitrogenous wastes. The 48-hour LC50 value for NH3 for Jordan tilapia Oreochromis aureus was 2.40 mg/L (Redner and Stickney 1979). The 48-hour LC50 value for hybrid red tilapia O. mossambicus x O.\n\t\t\t\t\tniloticus fry was 6.6 mg/L (Daud et al. 1988), although the threshold lethal concentration was 0.24 mg/L. The 24-hour LC50 value for un-ionized ammonia for O. niloticus was 1.46 mg/L (Evans et al. 2006) Sublethal effects of NH3-N include tissue damage, decreased growth, increased feed conversion ratio, acute stress response, increased disease susceptibility, and reduced reproductive capacity (Russo and Thurston 1991, Yildiz et al. 2006, El-Sherif and El-Feky 2008, Benli et al. 2008). Tilapias also exhibit sensitivity to elevated nitrite concentrations. The 96-hour LC50 for nitrite-nitrogen for O. aureus was 16.2 mg/L at pH 7.2 and 22 mg/L chloride (Palachek and Tomasso 1984). Acute nitrite toxicity for O. niloticus varied with chloride levels and with fish size, with smaller fish proving more tolerant (Atwood et al. 2001, Wang et al. 2006). Nitrite-nitrogen levels should be kept below 5 mg/L within tilapia culture vessels (Losordo 1997). Knowledge of these toxicity values is useful for setting criteria for the design or evaluating the performance of filters for biodegradation of nitrogenous wastes in aquaculture systems.
The BRA facility includes systems for broodstock holding, fish breeding, egg incubation/hatching, fingerling rearing, and food-fish production. The main building houses 42 recirculating aquaculture systems for grow-out to market size (Figure 1) that were the focus of our study. Each grow-out system (Figure 2) includes a fish production tank, a sedimentation basin for solids removal, a rotating biological contactor (RBC) for microbial biodegradation including nitrification, and an oxygenation unit. Each fish production system is rectangular in shape, built from concrete, holds 215 m3 of water, and consists of a fish-rearing tank (119 m3), a multi-tube clarifier sedimentation basin (37 m3), an air-driven rotating biological contactor (59 m3 basin volume, 13,366 m2 surface area per shaft), and an underground U-tube oxygenation system. The total volume of the grow-out unit is 9030 m3. The water surface is at the same level in the fish tank, sedimentation and RBC compartments, and water passes freely from one section into another through large pipes or apertures. A pump receives water from the rotating biological contactor compartment and pushes it through U-tubes and then to the far end of the fish production tank, driving the recirculation. The filtration rate is 3.8 m3/min, and the system turnover time is about once per hour.
Commercial-scale tilapia grow-out systems at Blue Ridge Aquaculture. The grow-out units are to the right of the catwalk and sedimentation basins to the left. Photograph courtesy of Blue Ridge Aquaculture.
A) Conceptual diagram and (B) and engineering drawing of a single recirculating tilapia grow-out system at Blue Ridge Aquaculture. Diagram courtesy of Blue Ridge Aquaculture.
BRA practices partial water exchange daily for controlling solids, dissolved organics and nutrient accumulation in fish grow-out tanks. Water is exchanged daily from the system in the interval between 2:00 p.m. and 8:00 a.m. Management practice is to completely flush the sedimentation basin after each instance that 227 kilograms of feed has been administered to a particular production unit. The exchange rate averages 22.3% per day, but the daily percentage varies among production units as a function of the size of fish, water quality requirements, and the amount of feed delivered to the system. The exchange water originates from wells, and is supplemented with municipal tap water when necessary. Exchange water replaces that used to remove settled particulate material, and thereby dilutes dissolved organic materials, dissolved nutrients, and salts.
Fish are fed commercially-prepared pelleted diets containing 36 or 40% minimum crude protein and 8-16% lipid levels, varying with the age of the fish. The feed is distributed hourly to the tanks over the 24-hour period. Fish production is managed so that 21-27 metric tons of 600-g fish reaches marketable size each week for shipment to a live market.
For the purpose of this study, the 42 recirculating aquaculture systems for grow-out were delimited as a unique system for purposes of quantifying the nitrogen budget. In certain contexts as set out below, N dynamics were quantified in greater detail in four individual systems. Broodstock holding and spawning facilities, a hatchery, and two greenhouses for fingerling production contain only a small part of the facility fish biomass, volume and exchange flow (i.e., they handle 3.0% of the fish biomass and 4.4% of the total nitrogen input). Because of their small contribution, the nitrogen budgets for these systems are not presented here, but can be found in Sandu (2004).
The nitrogen budget is expressed as a mass-balance equation of all nitrogen forms, with total inputs plus generation equal to total outputs plus consumption. We found no measurable amounts of dissolved inorganic nitrogen in the replacement water. Hence, feed provided to fish was the sole nitrogen source in the form of organic nitrogen (Nfeed). Multiplication of Nfeed by the total amount of feed provided the mass of total nitrogen input (TNI). The nitrogen budget was accounted for in five known pools:
Nitrogen fixed in fish biomass as organic nitrogen, Nfish,
Nitrogen fixed in dead fish biomass as organic nitrogen, Nmort,
Dissolved inorganic nitrogen, NDIN, which included NTAN, NNO2, and NNO3,
Total organic nitrogen in effluent, NTON, and
Nitrogen gas removed from the system by passive denitrification, Ndenit, and by ammonia volatilization, NNH3 vol.
All transformations among pools were assumed to be in a dynamic equilibrium over a defined period of time. We accounted for the mass fractions of nitrogen from Pools 1 to 4 (i.e., the measurable pools) as total nitrogen recovered (TNR), while the difference between total nitrogen input and total nitrogen recovered constituted pool 5, the mass fraction of total nitrogen unaccounted for (TNUA).
Analyses of fish and of feed for protein content followed Thiex et al. (2002), who indicated that by dry weight, 16% of protein is nitrogen. Samples were processed at the Forage Testing Laboratory, Virginia Polytechnic Institute and State University, Blacksburg, Virginia. Analyses for inorganic dissolved nitrogen forms (TAN, NO2--N, and NO3--N) were conducted on site using a Hach DR2400 spectrophotometer (Hach Company, Loveland, Colorado). Total Kjeldall nitrogen (TKN) was determined using macro-Kjeldall Standard Method 4500 – Norg B (APHA et al., 1998). Samples were acidified below pH 2 using H2SO4, refrigerated with ice, and transported to the Department of Civil and Environmental Engineering at Virginia Polytechnic Institute and State University, Blacksburg, Virginia, for analysis. Temperature and pH were measured directly on site using an Acorn Meter (Kit Model pH 6, Oakton, Vernon Hills, Illinois). Alkalinity was determined on-site using the Hach Permachem® Method. Dissolved oxygen (DO) was measured using a YSI (Model 550, Yellow Springs, Ohio) instrument. We calculated total organic nitrogen as the difference between TKN and total ammonia nitrogen (TAN).
Under steady-state conditions, fish biomass does not fluctuate significantly over time (i.e., harvest equals growth), and the daily rations of feed are constant. Under these assumptions, we derived the nitrogen budget by determining the nitrogen input with feed and the output of nitrogenous compounds in known pools. We quantified daily amounts of nitrogen in feed, fish, and mortalities using information on feed consumption, fish production, and mortalities provided by BRA management. We measured the components of dissolved inorganic nitrogen and total organic nitrogen pools directly. We extrapolated mean values to the entire exchange volume from a day to determine the mass of nitrogen recovered in these forms. We assumed that the amount of nitrogen missing from the balance was lost by passive denitrification and by ammonia volatilization.
We considered both types of feed used in the system (with 36% or 40% standard protein content) to determine nitrogen fixed in feed, Nfeed. We collected samples from three different points in storage silos for nitrogen content determination. We calculated Nfeed as a composite using the equation:
where FA = amount of feed, PC = protein content of the feed, and 0.16 = concentration of nitrogen in protein (Thiex et al. 2002). We determined PC by laboratory analyses because protein content may differ from that claimed by the feed producer. We obtained the total mass of nitrogen originating from the feed input, TNI, by multiplying Nfeed by the amount fed, FA.
To determine fixation of nitrogen in fish, Nfish, we analyzed protein content in triplicate samples of muscle tissue from fish from three size-classes. We estimated the proportions of fish in each size-class as 5% juveniles (i.e., newly introduced to the system from the hatchery), 60% intermediate, and 35% marketable size. With data on protein content of each fish size-class, we determined Nfish as a composite using the equation:
where FB = biomass of fish, and FP = protein content of the fish.
About 3.5% of the fish production (by number) was lost as mortalities. We assumed that nitrogen fixed in dead fish, Nmort, had the same nitrogen content as Nfish. In order to determine the biomass of Nmort, we collected mortalities daily from the production system for a two-week period, sorted them by size, and weighed them. We used these data to determine Nmort using equation 2.
Nitrogen load, LN, entered the water column as ammonia and as organic nitrogen bound in feces. We quantified LN as all nitrogen from feed that was not accounted for as living or dead fish as using the equation:
Hence, LN quantified the amount of nitrogen that sustained the nitrogen cycle throughout the system, supplying all effluent nitrogen pools.
We quantified total organic nitrogen as the difference between TKN and TAN from the effluent. We obtained values for TKN, TAN, NO2--N, and NO3--N by analyzing seven samples collected from the effluent discharge pipe at 3-hour intervals between 2:00 p.m. and 8:00 a.m. because effluent originated from the production system only during that interval. We repeated the tests twice (on different days) and averaged the results. We estimated daily production of these nitrogen forms by multiplying the average concentration (mg/L) by the volume of wastewater released from the system during a one-day period.
All nitrogen in feed that was not recovered as living or dead fish or as total organic nitrogen represented the dissolved inorganic fraction that entered the water as TAN. Hence, we determined ammonia production as:
The sum of TAN, NO2—N, and NO3--N found in the effluent represented the fraction of nitrogen recovered as dissolved inorganic nitrogen, NDIN. The summation of NDIN, Nfish, Nmort, and NTON provided the value for total nitrogen recovered, TNR. We determined total nitrogen unaccounted for, TNUA, by subtracting total nitrogen recovered, TNR, from total nitrogen input, TNI.
We used a simplified version of a model proposed by Losordo and Westers (1994) to determine the carrying capacity of the production system; that is, we considered only the parts of the model concerning maximum system carrying capacity with respect to TAN. Modeling of the flow rate through biofilters was unnecessary because the flow rate was fixed among all recirculating aquaculture systems at 3.78 m3/min.
Four recirculating aquaculture systems chosen for intensive study held different age-groups of fish from juvenile to marketable size in order to represent the overall population in the facility. We knew total fish biomass, fish size, feeding rate, crude protein content of feed, daily percent body weight fed, flow rate through the system, and daily rate of exchange for each selected system. We measured other parameters, such as TAN, NO3--N, NO2--N, pH, temperature, and dissolved oxygen, using standard methods (APHA et al., 1998). We performed these analyses on composite samples collected from the fish-rearing tanks or from the rotating biological contactor’s influent and effluent at four-hour intervals. By sampling from appropriate locations, we determined the effects of fish tanks, biofilters or sedimentation basins on each parameter. The experiments extended between consecutive water exchanges. We scaled the data to 24-hour intervals and determined mean and variance for each water quality parameter.
We determined the maximum system carrying capacity with respect to TAN as follows. We calculated the maximum allowable TAN concentration, ATAN, as:
where a = the mole fraction of unionized ammonia nitrogen as determined by pH and temperature (Huguenin and Colt, 1989). We calculated maximum feed rate, FRmTAN, by assuming that the TAN concentration of a fish tank equals ATAN, as:
where Qf = the recirculating flow rate, or flow rate to the RBC, known to be 227,100 L/hr, and 0.092 = model constant coefficient. We determined the efficiency of the rotating biological contactor for removal of ammonia nitrogen, Ea as:
We estimated the maximum biomass that could be sustained within the system, SBMmTAN as:
where %BW = the feeding rate, expressed as a percent of body weight per day.
PTAN is the rate of production of TAN in the system by metabolism of fish and microbial degradation of uneaten feed. We estimated PTAN as a function of the feed rate and the percentage of protein in feed:
where t = the period of time from the onset of feeding to the next feeding.
This equation is based on the following assumptions and empirical estimates:
16% of feed protein is nitrogen,
80% of the nitrogen is assimilated,
unassimilated nitrogen in fecal matter is removed rapidly from the tank,
80% of assimilated nitrogen is excreted, and
all of the TAN is excreted during t hours.
The coefficient 0.102 represents the product of values suggested by assumptions a through d (i.e., 0.16 x 0.8 x 0.8 = 0.102).
We determined the mass flow rate of TAN to a rotating biological contactor, or ammonia loading, LTAN, from known (Qf) and experimentally determined (CTANf) parameters as:
We determined the ammonia removal rate, RTAN, as:
The fraction (RTAN x 100) / PTAN represents the percentage of TAN that was removed by means other than the rotating biological contactor.
We estimated the nitrification performance of a rotating biological contactor as areal conversion rate, ACR, representing the amount of TAN oxidized by a unit of surface area in 24 hours:
where S, the surface area of an RBC, was 13,336 m2.
The mass balance quantifying the partitioning of PTAN removal was:
We used a similar approach to determine NO3--N partitioning using the equation:
We used linear regressions to determine the relationship between daily TAN production (PTAN) and TAN removal efficiency per pass (Ea), and between fish biomass and percent PTAN transformed by passive denitrification in the four systems tested.
We derived the nitrogen budget for the entire production system for mean conditions of 28.4ºC, pH 7.14, and alkalinity 119.0 mg/L as CaCO3. For annual production of 1300 metric tons of fish biomass, BRA administers 2210 metric tons of feeds. These amounts correspond to 6054.8 kg feed consumed per day and 3561.6 kg fish weight gain per day. Of the feed utilized, 95% (5752.0 kg) was nominally 36% protein and 5% (302.8 kg) 40% protein content. However, laboratory analyses showed that the actual protein contents of the two feeds were somewhat lower, 35.0±0.2% and 39.8±0.2%, respectively. The estimated percentages of feed types and the laboratory-determined protein concentrations were used to determine the nitrogen fixed in feed, Nfeed = 56.38 g/kg feed. By extrapolating Nfeed, we determined a total nitrogen input of TNI = 341.381 kg/day.
Laboratory analyses showed that the three size-classes of fish from small to large had 18.04±0.16, 20.75±0.02 and 22.26±0.74% protein content, respectively. From these data, we determined that the nitrogen fixed in fish was Nfish = 33.83 g/kg produced. Extrapolating to the daily biomass of fish produced, the total nitrogen assimilated in fish was 120.49 kg/day.
Loss of fish represented 3.5% of the total production by number, with weighing of dead fish indicating losses of 2, 1, and 0.5% from the respective size-classes. This was the equivalent of 30.6 kg fish/day or 1.03 kg total Nmort/day, representing 0.86% of the total nitrogen assimilated. Hence, 35.3% of nitrogen from feed was assimilated in fish flesh (34.4% harvested and 0.86% removed with mortalities), and 64.7% was unassimilated or excreted in different forms. This latter term included nitrogen in uneaten feed that we accounted for in the overall budget as NTON. The nitrogen excreted, LN, was 62.0 g/kg fish produced. Subsequently, the cumulative daily nitrogen loading for the entire system, LN, was 221.3 kg.
Analyses of the effluent wastewater (estimated at 2017 m3/day) indicated, on average, 2.88 mg/L TAN, 1.09 mg/L NO2--N, 49.3 mg/L NO3--N, and 32.05 mg/L TON. Extrapolated to the entire effluent volume, the overall flows were 5.8 kg NTAN/day (representing 1.70% of total nitrogen input, TNI), 2.2 kg NNO2/day (0.64% TNI), 99.4 kg NNO3/day (29.1% TNI), and 64.6 kg NTON/day (18.9% TNI). Determination of total organic nitrogen, NTON, allowed estimation of PTAN = 25.81 g/kg feed. The recovered fraction of dissolved inorganic nitrogen, NDIN, resulted from the summation:
Total nitrogen recovered, TNR, was determined as a percentage of TNI as:
We then estimated total nitrogen unaccounted for, TNUA, as 14.3% of TNI. Hence, the subsequent nitrogen mass balance for the production system was:
Table 2 summarizes the daily nitrogen budget for the production system. The relatively low value of total nitrogen unaccounted for, TNUA, was presumably due to nitrogen lost as nitrogen gas produced by denitrification and as ammonia lost to volatilization. Passive denitrification was likely the primary pathway because recirculated fish culture water passed through the sedimentation basin numerous times. As discussed below, the sediment blanket and associated thick biofilm in the multi-tube clarifier created anoxic conditions favorable for microbially mediated denitrification.
\n\t\t\t\tUnits\n\t\t\t | \n\t\t\t\n\t\t\t\tNitrogen pool\n\t\t\t | \n\t\t|||||||
\n\t\t\t\t\n\t\t\t\t\tTNI\n\t\t\t\t\n\t\t\t | \n\t\t\t\n\t\t\t\t\n\t\t\t\t\tHfish\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t | \n\t\t\t\n\t\t\t\t\n\t\t\t\t\tNmort\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t | \n\t\t\t\n\t\t\t\t\n\t\t\t\t\tNTAN\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t | \n\t\t\t\n\t\t\t\t\n\t\t\t\t\tNNO2\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t | \n\t\t\t\n\t\t\t\t\n\t\t\t\t\tNNO3\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t | \n\t\t\t\n\t\t\t\t\n\t\t\t\t\tNTON\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t | \n\t\t\t\n\t\t\t\t\n\t\t\t\t\tTNUA\n\t\t\t\t\n\t\t\t | \n\t\t|
Kg | \n\t\t\t341.38 | \n\t\t\t119.45 | \n\t\t\t1.03 | \n\t\t\t5.81 | \n\t\t\t2.20 | \n\t\t\t99.44 | \n\t\t\t64.65 | \n\t\t\t48.85 | \n\t\t
% | \n\t\t\t100.00 | \n\t\t\t34.99 | \n\t\t\t0.30 | \n\t\t\t1.70 | \n\t\t\t0.64 | \n\t\t\t29.12 | \n\t\t\t18.94 | \n\t\t\t14.31 | \n\t\t
Daily nitrogen budget for the grow-out system at Blue Ridge Aquaculture.
The carrying capacity model indicated that recirculating aquaculture systems at Blue Ridge Aquaculture could support biodegradation of up to 3.15 mg TAN/L. This value corresponds to 0.025 mg/L maximum allowable unionized ammonia (ATAN) at conditions of pH 7.0 and temperature of 30ºC (Huguenin and Colt 1989); our average values of these parameters for the four recirculating aquaculture systems monitored in greater detail were pH 7.09 and 27.8ºC. At 0.025 mg/L TAN, a recirculating system should be able to receive a maximum feeding rate of FRmax TAN = 269.8 kg feed/day, which would support a fish biomass of SBMmax TAN = 10,287.4 kg fish/system. Estimates of these parameters for each selected RAS are presented in Table 3. Comparison with actual feeding rates at the time of experiment (Table 4) showed that system loadings were 56.7 - 91.5% of the maxima estimated (Table 3, Figure 3). Over the four tanks examined in detail, TAN removal efficiency per pass, Ea, averaged 54.4%. We determined the rate of TAN production (PTAN, Table 3). We determined PTAN per kg of feed consumed by dividing these values by the daily amount of feed introduced into a system: i.e., 40.6 g/kg feed for feed with 40% crude protein content, and 36.7 g/kg feed for feed with 36% crude protein content. We found a positive, linear relationship between PTAN (which also was proportional to the feeding rate) and Ea (slope = 0.0013, r2 = 0.72), thereby showing that the rotating biological contactors efficiently removed various loadings of ammonia. None of the RBCs tested were working at maximum capacity.
\n\t\t\t\tParameter\n\t\t\t | \n\t\t\t\n\t\t\t\tUnits\n\t\t\t | \n\t\t\t\n\t\t\t\tRAS Tested\n\t\t\t | \n\t\t||||
\n\t\t\t\tA12\n\t\t\t | \n\t\t\t\n\t\t\t\tA11\n\t\t\t | \n\t\t\t\n\t\t\t\tB16\n\t\t\t | \n\t\t\t\n\t\t\t\tA18\n\t\t\t | \n\t\t\t\n\t\t\t\tAverage\n\t\t\t | \n\t\t||
Maximum feed rate (FR\n\t\t\t\tmaxTAN) | \n\t\t\tkg/day | \n\t\t\t240.4 | \n\t\t\t286.1 | \n\t\t\t261.6 | \n\t\t\t290.9 | \n\t\t\t269.8 | \n\t\t
Maximum system biomass (SBM\n\t\t\t\tmaxTAN) | \n\t\t\tkg | \n\t\t\t4202.5 | \n\t\t\t11443.0 | \n\t\t\t9871.0 | \n\t\t\t15633.0 | \n\t\t\t10287.4 | \n\t\t
Actual BW as % of SBM\n\t\t\t\tmaxTAN\n\t\t\t | \n\t\t\t% | \n\t\t\t56.66 | \n\t\t\t91.52 | \n\t\t\t66.54 | \n\t\t\t76.77 | \n\t\t\t72.87 | \n\t\t
TAN tank concentration | \n\t\t\tmg/L | \n\t\t\t1.77 | \n\t\t\t2.32 | \n\t\t\t2.04 | \n\t\t\t2.10 | \n\t\t\t2.06 | \n\t\t
TAN conc. in RBC influent (CTAN\n\t\t\t\t\tf\n\t\t\t\t) | \n\t\t\tmg/L | \n\t\t\t1.77 | \n\t\t\t2.32 | \n\t\t\t2.04 | \n\t\t\t2.10 | \n\t\t\t2.06 | \n\t\t
TAN conc. in RBC effluent (CTAN\n\t\t\t\t\te\n\t\t\t\t) | \n\t\t\tmg/L | \n\t\t\t0.84 | \n\t\t\t1.01 | \n\t\t\t0.99 | \n\t\t\t0.90 | \n\t\t\t0.94 | \n\t\t
TAN removal efficiency per pass (Ea\n\t\t\t\t) | \n\t\t\t% | \n\t\t\t52.39 | \n\t\t\t56.47 | \n\t\t\t51.47 | \n\t\t\t57.28 | \n\t\t\t54.40 | \n\t\t
\n\t\t\t\tP\n\t\t\t\tTAN/kg feed | \n\t\t\tg | \n\t\t\t40.6 | \n\t\t\t36.7 | \n\t\t\t36.7 | \n\t\t\t36.7 | \n\t\t\t37.7 | \n\t\t
Daily TAN production (P\n\t\t\t\tTAN) | \n\t\t\tg/day | \n\t\t\t5522.4 | \n\t\t\t9626.4 | \n\t\t\t6397.9 | \n\t\t\t8161.9 | \n\t\t\t7427.1 | \n\t\t
Ammonia loading (L\n\t\t\t\tTAN) | \n\t\t\tg/hr | \n\t\t\t402.19 | \n\t\t\t526.87 | \n\t\t\t463.28 | \n\t\t\t478.50 | \n\t\t\t467.71 | \n\t\t
Ammonia removal rate (R\n\t\t\t\tTAN) | \n\t\t\tg/hr | \n\t\t\t210.75 | \n\t\t\t297.50 | \n\t\t\t238.46 | \n\t\t\t274.11 | \n\t\t\t255.20 | \n\t\t
Areal conversion rate (ACR) | \n\t\t\tmg TAN/m2-d | \n\t\t\t378.4 | \n\t\t\t534.2 | \n\t\t\t428.2 | \n\t\t\t429.2 | \n\t\t\t442.5 | \n\t\t
\n\t\t\t\tACR at SBW\n\t\t\t\tmaxTAN\n\t\t\t | \n\t\t\tmg TAN/m2-d | \n\t\t\t667.8 | \n\t\t\t583.7 | \n\t\t\t643.4 | \n\t\t\t641.1 | \n\t\t\t634 | \n\t\t
Mass TAN introduced by exchange | \n\t\t\tg/day | \n\t\t\t39.47 | \n\t\t\t73.10 | \n\t\t\t50.79 | \n\t\t\t90.93 | \n\t\t\t63.57 | \n\t\t
\n\t\t\t\tP\n\t\t\t\tTAN introduced with water exchange | \n\t\t\t%/day | \n\t\t\t0.71 | \n\t\t\t0.76 | \n\t\t\t0.79 | \n\t\t\t1.11 | \n\t\t\t0.84 | \n\t\t
*Total TAN removed by water exchange | \n\t\t\t%/day | \n\t\t\t0.08 | \n\t\t\t0.34 | \n\t\t\t0.22 | \n\t\t\t0.35 | \n\t\t\t0.25 | \n\t\t
Experimentally determined and predicted parameters for estimation of maximum system carrying capacity with regard to TAN for tested units.
*Daily TAN percentage removal by water exchange, assuming that exchange water is treated using the treatment train tested by Sandu (2004) with 1.60 mg/L TAN.
\n\t\t\t\tParameter\n\t\t\t | \n\t\t\t\n\t\t\t\tUnits\n\t\t\t | \n\t\t\t\n\t\t\t\tRAS Tested\n\t\t\t | \n\t\t||||
\n\t\t\t\tA12\n\t\t\t | \n\t\t\t\n\t\t\t\tA11\n\t\t\t | \n\t\t\t\n\t\t\t\tB16\n\t\t\t | \n\t\t\t\n\t\t\t\tA18\n\t\t\t | \n\t\t\t\n\t\t\t\tAverage\n\t\t\t | \n\t\t||
Water exchange rate | \n\t\t\t% volume/day | \n\t\t\t11.5 | \n\t\t\t21.3 | \n\t\t\t14.8 | \n\t\t\t18.4 | \n\t\t\t16.5 | \n\t\t
Flow rate through system (Q) | \n\t\t\tL/hr | \n\t\t\t1028.0 | \n\t\t\t1903.7 | \n\t\t\t1322.7 | \n\t\t\t1645.8 | \n\t\t\t1475.1 | \n\t\t
Fish size | \n\t\t\tg/fish | \n\t\t\t43 | \n\t\t\t192 | \n\t\t\t245 | \n\t\t\t424 | \n\t\t\t226 | \n\t\t
Fish biomass | \n\t\t\tkg | \n\t\t\t2381.0 | \n\t\t\t10473.0 | \n\t\t\t6568.5 | \n\t\t\t12002.0 | \n\t\t\t7856.1 | \n\t\t
Feeding rate (FR) | \n\t\t\tkg/day | \n\t\t\t136.0 | \n\t\t\t262.0 | \n\t\t\t174.0 | \n\t\t\t222.3 | \n\t\t\t198.6 | \n\t\t
Feed protein content (FP) | \n\t\t\t% | \n\t\t\t40 | \n\t\t\t36 | \n\t\t\t36 | \n\t\t\t36 | \n\t\t\t37 | \n\t\t
Percent body weight fed | \n\t\t\tkg feed/kg fish-d | \n\t\t\t5.72 | \n\t\t\t2.50 | \n\t\t\t2.65 | \n\t\t\t1.85 | \n\t\t\t3.18 | \n\t\t
Characteristics of the recirculating aquaculture systems selected for evaluation.
Towards the end of a tilapia production cycle, stocking densities approach system carrying capacity. Photograph courtesy of Blue Ridge Aquaculture.
The mass flow-rate of TAN to a rotating biological contactor, LTAN, averaged 467.7 g/hr, which was removed at an average rate of RTAN= 255.2 g/hr. Per-system values are presented in Table 3. The ratio of RTAN to PTAN showed that rotating biological contactors removed an average of 84.0% of total ammonia nitrogen from the selected systems. From the difference, 1.1% of TAN was recovered from exchanged water and 15.0% remained unaccounted for, probably transformed to NO2--N and NO3--N by passive nitrification or lost by volatilization of ammonia. Data in Table 3 show that fish biomass in the system was positively correlated with the percentage of total ammonia nitrogen transformed by passive nitrification (slope = 0.0015, r2 = 0.69); although the correlation was not strong, it shows that systems with higher biomass had lower water quality and larger microbial populations, including nitrifiers that promoted in-situ biodegradation of ammonia.
The rotating biological contactors removed between 378.4 and 534.2 mg TAN/m2/day (442.5 mg TAN/m2/day on average, Table 3). The areal conversion rate, ACR, increased with the loading of total ammonia nitrogen. Average ACR under conditions of maximum system biomass was estimated at 634.0 mg TAN/m2/day. We note that the difference between existing ACR and predicted maximum ACR is consistent with that between the existing fish biomass and predicted maximum fish biomass.
We derived a daily nitrogen budget partitioning the total ammonia nitrogen removal from each RAS (Table 5). On average among systems, 84.0% of TAN was removed by rotating biological contactors, 14.9% by passive nitrification and ammonia volatilization, and only 1.1% was removed by periodic water exchange.
\n\t\t\t\tSystem\n\t\t\t | \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t1PTAN\n\t\t\t\t\n\t\t\t | \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t2TANpass + vol\n\t\t\t\t\n\t\t\t | \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t3TANRBC nitrification\n\t\t\t\t\n\t\t\t | \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t4TANexchange\n\t\t\t\t\n\t\t\t | \n\t\t||||
\n\t\t\t\tg\n\t\t\t | \n\t\t\t\n\t\t\t\t%\n\t\t\t | \n\t\t\t\n\t\t\t\tg\n\t\t\t | \n\t\t\t\n\t\t\t\t%\n\t\t\t | \n\t\t\t\n\t\t\t\tg\n\t\t\t | \n\t\t\t\n\t\t\t\t%\n\t\t\t | \n\t\t\t\n\t\t\t\tg\n\t\t\t | \n\t\t\t\n\t\t\t\t%\n\t\t\t | \n\t\t|
A12 | \n\t\t\t5522.4 | \n\t\t\t100 | \n\t\t\t421.30 | \n\t\t\t7.63 | \n\t\t\t5057.41 | \n\t\t\t91.58 | \n\t\t\t43.69 | \n\t\t\t0.79 | \n\t\t
A11 | \n\t\t\t9626.4 | \n\t\t\t100 | \n\t\t\t2380.50 | \n\t\t\t24.73 | \n\t\t\t7139.90 | \n\t\t\t74.17 | \n\t\t\t106.00 | \n\t\t\t1.10 | \n\t\t
B16 | \n\t\t\t6397.9 | \n\t\t\t100 | \n\t\t\t610.22 | \n\t\t\t9.54 | \n\t\t\t5722.92 | \n\t\t\t89.45 | \n\t\t\t64.76 | \n\t\t\t1.01 | \n\t\t
A18 | \n\t\t\t8161.9 | \n\t\t\t100 | \n\t\t\t1463.66 | \n\t\t\t17.93 | \n\t\t\t6578.49 | \n\t\t\t80.60 | \n\t\t\t119.75 | \n\t\t\t1.47 | \n\t\t
Average | \n\t\t\t7427.2 | \n\t\t\t100 | \n\t\t\t1108.51 | \n\t\t\t14.93 | \n\t\t\t6235.09 | \n\t\t\t83.95 | \n\t\t\t83.55 | \n\t\t\t1.12 | \n\t\t
Partitioning of total ammonia nitrogen removal for each recirculating aquaculture system studied.
1 TAN production over a 24-hour period.
2 TAN removed by passive nitrification and by ammonia volatilization.
3 TAN removed by nitrification in RBC.
4 TAN removed with exchanged water.
We conducted tests on the same recirculating aquaculture systems to determine the fate of NO3--N following its production by nitrification. We regarded PNO3--N as approximately equal to PTAN by assuming that TAN lost from the systems by water exchange and volatilization was negligible. Data on PNO3--N, water exchange rates, and NO3--N concentrations before and after water exchange allowed us to determine the total mass of NO3--N in the systems at these times and the amounts of NO3--N lost by water exchange and passive denitrification. That is, we derived a daily mass balance quantifying PNO3--N removal pathways from each RAS (Table 6). Results indicated that NO3--N accumulation was in the range of 9.1 – 17.2 mg/L in each RAS over a 24-hour period. On average, 44.1% of NO3--N was removed by water exchange, and the difference of 55.9% was removed by passive denitrification. NO3--N in effluent could be subject to microbial denitrification if water reuse is implemented (Sandu et al. 2008).
\n\t\t\t\tParameter\n\t\t\t | \n\t\t\t\n\t\t\t\tUnits\n\t\t\t | \n\t\t\t\n\t\t\t\tRAS Tested\n\t\t\t | \n\t\t||||
\n\t\t\t\tA12\n\t\t\t | \n\t\t\t\n\t\t\t\tA11\n\t\t\t | \n\t\t\t\n\t\t\t\tB16\n\t\t\t | \n\t\t\t\n\t\t\t\tA18\n\t\t\t | \n\t\t\t\n\t\t\t\tAverage\n\t\t\t | \n\t\t||
Daily NO3\n\t\t\t\t--N production (P\n\t\t\t\tNO3\n\t\t\t\t—\n\t\t\t\tN) | \n\t\t\tg | \n\t\t\t5522.4 | \n\t\t\t9626.4 | \n\t\t\t6397.9 | \n\t\t\t8161.9 | \n\t\t\t7427.9 | \n\t\t
NO3\n\t\t\t\t--N conc. before exchange | \n\t\t\tmg/L | \n\t\t\t57.3 | \n\t\t\t57.3 | \n\t\t\t50.9 | \n\t\t\t49.1 | \n\t\t\t53.6 | \n\t\t
System mass NO3\n\t\t\t\t--N before exchange | \n\t\t\tg | \n\t\t\t12290.85 | \n\t\t\t12290.85 | \n\t\t\t10918.05 | \n\t\t\t10531.95 | \n\t\t\t11507.92 | \n\t\t
NO3\n\t\t\t\t--N conc. after exchange | \n\t\t\tmg/L | \n\t\t\t40.5 | \n\t\t\t40.1 | \n\t\t\t38.9 | \n\t\t\t40.0 | \n\t\t\t39.9 | \n\t\t
System mass NO3\n\t\t\t\t--N after exchange | \n\t\t\tg | \n\t\t\t8687.25 | \n\t\t\t8601.45 | \n\t\t\t8344.05 | \n\t\t\t7872.15 | \n\t\t\t8376.22 | \n\t\t
NO3\n\t\t\t\t--N and removed by exchange | \n\t\t\tg/day | \n\t\t\t3603.6 | \n\t\t\t3689.4 | \n\t\t\t2574.0 | \n\t\t\t2659.8 | \n\t\t\t3132.45 | \n\t\t
\n\t\t\t\tP\n\t\t\t\tNO3\n\t\t\t\t-\n\t\t\t\t-N removed by exchange | \n\t\t\t%/day | \n\t\t\t65.25 | \n\t\t\t38.33 | \n\t\t\t40.23 | \n\t\t\t32.58 | \n\t\t\t44.10 | \n\t\t
NO3\n\t\t\t\t--N lost by passive denitrification | \n\t\t\tg/day | \n\t\t\t1918.8 | \n\t\t\t5937.0 | \n\t\t\t3823.9 | \n\t\t\t5502.1 | \n\t\t\t4295.45 | \n\t\t
\n\t\t\t\tP\n\t\t\t\tNO3\n\t\t\t\t-\n\t\t\t\t-N lost by passive denitrification | \n\t\t\t%/day | \n\t\t\t34.75 | \n\t\t\t61.67 | \n\t\t\t59.77 | \n\t\t\t67.42 | \n\t\t\t55.90 | \n\t\t
Dynamics and partitioning of PNO3--N removal for each recirculating aquaculture system studied.
NO2--N always remained at concentrations lower than 0.3 mg/L in the fish tanks. Its concentration increased slightly as water passed through the sedimentation basin, but decreased again to concentrations lower that those in fish tanks after contact with the RBC, creating an equilibrium concentration. Because NO2--N concentrations were generally stable and below levels considered a threat to fish, we pursued no further determination of NO2--N dynamics.
We quantified nitrogen fixation and biodegradation through the recirculating tilapia production system at Blue Ridge Aquaculture, a large commercial production facility. The 34.4% of total nitrogen input assimilated by the fish indicated excellent nitrogen utilization relative to other production systems. For example, Suresh and Kwei (1992) found that less than 20% of nitrogen was assimilated by tilapia using feed with 22% crude protein content and much lower fish stocking densities than those at BRA. Using feed with 34% crude protein content, Siddiqui and Al-Harbi (1999) reported 21.4% nitrogen assimilation by red tilapia. Although Suresh and Kwei (1992) found decreasing nitrogen assimilation with increasing fish density, Refstie (1977), Rakcocy and Allison (1981), and Vijayan and Leatherland (1988) reported the opposite finding. We attribute the high nitrogen assimilation in our study to higher protein content in feeds used at BRA, well-managed water quality, and to production of selectively bred fish (Hallerman 2000). Also, most earlier studies reported higher mortality rates, diminishing total nitrogen accumulated in fish.
The small amounts of nitrogen recovered as TAN and NO2--N likely were due to biodegradation in rotating biological contactors, which oxidized them effectively to NO3--N. Most of the nitrogen recovered as total organic nitrogen (18.93%) was probably due to feces, noting that feed was consumed by fish almost instantly at distribution, and that only fine particulates could escape as wasted feed. Assuming that some organic nitrogen in feces dissolved upon contact with water, our results with tilapia, which accounted for nitrogen from the entire organic pool, broadly agree with those of Porter et al. (1987, who found 10% fecal nitrogen) and Thoman et al. (2001, who recovered 14% nitrogen from suspended solids) for other species.
For total nitrogen unaccounted for (14.31%), removal of N2 gas through passive denitrification is the most reasonable explanation. Although denitrification may seem surprising given the relatively high dissolved oxygen in the recirculating systems, development of anoxic microsites in sediment provides likely sites for denitrification (Brandes and Devol 1997). Anoxic microsites could arise in fish tanks where particles accumulated, or more likely, in the sedimentation basin, where a blanket of sediments developed for 19 – 36 hours before removal. We observed that large amounts of gases rapidly collected beneath the water surface in the sedimentation basin; however, samples we collected were contaminated with oxygen, precluding evaluation of biologically-generated nitrogen production. A thick biofilm on the tanks’ walls also could have provided anoxic microsites, contributing to NO3--N removal. This explanation was supported by our results for Tank A12, where fish were harvested and the biofilm removed from the walls less than two weeks before our monitoring began. The time for regrowth of the biofilm to a thickness that could allow denitrification was limited. Subsequently, less than 35% of NO3--N production was removed by passive denitrification from this particular system, considerably less than in the other three systems monitored. In-situ denitrification has been reported by other authors. For example, Bovendeur et al. (1987) found that 40 – 80% of TAN oxidized by nitrification then was reduced by denitrification. Thoman et al. (2001) attributed 9 – 21% losses of systems’ nitrogen to denitrification. The 56% removal of NO3--N by passive denitrification in our study represented an important, positive outcome, because it could reduce by more than half the investment necessary for nitrogen removal should the effluent be treated and reused as suggested by Sandu et al. (2008, 2011).
Our results indicated that despite high fish densities maintained at BRA, the systems are not being operated at their maximum carrying capacity. Our results showed that an average of 73% of the recirculating systems’ productive potential was utilized, although utilization approached 92% in systems holding fish close to harvest size. In particular, much productive potential can be realized in systems holding smaller fish for long periods. By better distributing fish biomass among systems via more frequent grading, net production could be increased within existing space. Our suggestion for increased production is supported by the excellent average removal efficiency for rotating biological contactors (54.4%) at a recirculation rate of almost one pass per hour, and by an average areal conversion rate of 442.5 mg TAN/m2/day, which maintained an average TAN of 2.06 mg/L in fish tanks. Up to 2830 mg TAN/m2/day can be removed by a rotating biological contactor (Rogers and Klemeston 1985), suggesting that the biofilters could function successfully under the maximum conditions of 3.15 mg/L TAN and 634 mg TAN/m2/day areal conversion rate that we predicted. Additionally, reusing water using a treatment train such as that described by Sandu (2004) and Sandu et al. (2008, 2011) with 1.6 mg/L TAN, only 0.84% of total ammonia nitrogen produced would be reintroduced to the recirculating systems. This additional loading would be removed easily by the rotating biological contactors, without significant increase of TAN throughout the systems.
Routine aquaculture production generates waste products for which controlled biodegradation in treatment units is a major consideration in design and operation of recirculating aquaculture systems. Biodegradation of nitrogenous wastes is critical, especially for un-ionized ammonia and nitrite, which are toxic to fish. We quantified the dynamics of nitrogen through a large commercial recirculating aquaculture facility producing hybrid tilapia Oreochromis sp. Our nitrogen budget evaluated total ammonia nitrogen (TAN) production and removal in biofilters, quantifying the fate of nitrate-nitrogen (NO3--N) and determining the systems’ maximum carrying capacity under steady-state conditions. Most of the recovered nitrogen was in fish, nitrate-nitrogen, and total organic nitrogen pools, with relatively small proportions as total ammonia nitrogen, mortalities, and nitrite-nitrogen, totaling 86%. The remaining 14% of the nitrogen budget unaccounted for likely was lost by passive denitrification to nitrogen gas and by volatilization of ammonia. Our nitrogen biodegradation model predicts that the systems could operate safely at up to 3.15 mg/L total ammonia nitrogen. Under current production conditions, system loading was 57-92% of the maximum fish biomass that could be supported. The biofilters’ areal conversion rate could be increased by half under conditions of maximum biomass loading. NO2--N was not a parameter of concern, always remaining below 0.3 mg /L. Our results showed that microbial biodegradation of fish wastes was more than adequate and that fish production could be increased within the existing farm infrastructure, especially by more frequent grading of fish in order to stock production systems at densities approaching carrying capacity. With denitrification, discharged culture water could be reused to realize savings in operating costs. Beyond the narrow interest in our study system, our approach can be applied more broadly to other fish culture systems.
Gate-all-around (GAA) is a widely-using structure such as logic field-effect transistor (FET) due to its excellent short channel characteristics [1, 2, 3, 4, 5, 6] or its high surface-to-volume ratio [7, 8], 3-D NAND flash memory for bit-cost scalability [9, 10], photodiode due to its waveguide effect [11, 12], and gas sensor due to its high physical fill factor or surface-to-volume ratio [13, 14]. Especially for logic applications, GAAFETs have been introduced by attaining good gate electronics and increasing current drivability under the same active area.
Currently, fin-shaped FETs (FinFETs) have been scaled down to 10-nm node [15] and further to 5-nm node [16] by forming ultra-sharp fin for high current drivability while maintaining gate-to-channel controllability. GAAFETs are possibly showing great potential to substitute FinFETs in the following technology node, and the performance comparisons between FinFETs and GAAFETs have been investigated [3, 4, 5, 6, 17]. But more detailed analysis between FinFETs and GAAFETs is needed to set the device guideline by considering fine TCAD calibration and middle-of-line levels.
Therefore, in this work, DC/AC performances of 3-nm-node GAAFETs were investigated using fully-calibrated TCAD platform. By changing the GAA geometries, we found optimal GAA structure to minimize the RC delay for three different applications such as low power (LP), standard performance (SP), and high performance (HP) applications.
All the simulation works were performed using Sentaurus TCAD [18]. Drift diffusion transport equations were calculated self-consistently with Poisson and electron/hole continuity equations. Density-gradient model was adopted for the quantum confinement of carriers within the channel. Slotboom bandgap narrowing model was used to consider the doping-dependent energy bandgap. Mobility models include Lombardi for the mobility degradation at the channel/oxide interface, inversion and accumulation layer model for impurity, phonon, and surface roughness scatterings, and low-field ballistic model for quasi-ballistic effects in ultra-short gate length (Lg). Shockley-Read-Hall, Auger, and Hurkx band-to-band tunneling recombination models were adopted. Deformation potential model was used to consider the stress-induced energy bandgap, effective mass, and effective density-of-states. All these physical models were used equivalently in [19, 20].
Figure 1 shows the schematic diagrams of FinFETs and three-stacked GAAFETs. FinFETs have highly-doped punch-through-stopper (PTS) at 2 × 1018 and 4 × 1018 cm−3 for NFETs and PFETs, respectively, in order to prevent the sub-fin leakage currents at off state [21, 22]. GAAFETs, on the other hand, have buried oxide (BOX) layer beneath the source/drain (S/D) regions without PTS so that the bottom leakage currents are completely blocked [1, 23]. Bulk FinFETs can adopt the BOX layer according to [24], but the conventional device structure was considered in this work. S/D doping concentrations of the n-type and p-type devices are 2 × 1020 and 4 × 1020 cm−3, respectively. Interfacial layer (IL), HfO2, and low-k spacer regions have the dielectric constants of 3.9, 22.0, and 5.0, respectively. Contact resistivity at S/D and silicide interface is fixed to 10−9 Ω·cm2 [25]. Equivalent oxide thickness (EOT) is 1.0 nm, which consists of 0.7-nm-thick IL and 1.7-nm-thick HfO2.
Schematic diagrams of FinFETs and GAAFETs. 2-D cross-sections of nanosheet and nanowire channels were also specified to the right.
Table 1 shows the geometrical parameters and values of 3-nm-node FinFETs and GAAFETs. Contacted poly pitch (CPP) and fin pitch (FP) are 42 and 21 nm, following 3-nm technology node [5]. There are two types of GAAFETs: nanowire FETs (NWFETs) having the same width and thickness as WNW, and nanosheet FETs (NSFETs) having thin NS thickness (TNS) of 5 nm but wide NS width (WNS) as 10, 20, 30, 40, and 50 nm. The number of NW or NS channels (Nch) is varied as 1, 2, 3, 4, and 5.
Geometrical parameters | Values | |
---|---|---|
CPP | Contacted poly pitch | 42 nm |
FP | Fin pitch | 21 nm |
NP | Nanowire/sheet pitch | WNW or WNS + 16 nm |
Lg | Gate length | 12 nm |
Lsp | Spacer length | 5 nm |
Wfin | Fin width | 5 nm |
Hfin | Fin height | 46 nm |
WNW | Nanowire width | 5, 6, 7, 8, 9, 10 nm |
WNS | Nanosheet width | 10, 20, 30, 40, 50 nm |
TNS | Nanosheet thickness | 5 nm |
TSP | Nanowire/sheet spacing | 10 nm |
Nch | The number of channels | 1, 2, 3, 4, 5 |
Geometrical parameters and values of FinFETs and GAAFETs.
Figure 2 shows the schematic process flows of GAAFETs. The detailed gate-las process flows are described in [1]. After depositing Si0.7Ge0.3/Si multi-layer and etching like fin structure, poly-Si gate and low-k regions are formed. Inner-spacer is formed by etching sidewalls of Si0.7Ge0.3 regions selectively and depositing low-k regions. Followed by depositing BOX layer, selective epitaxial growth of S/D regions is performed. After removing poly-Si gate, channel release process is performed by etching Si0.7Ge0.3 regions selectively. Replacement metal gate, silicidation, and metal contact formations are done afterwards.
Process flows of GAAFETs. Key process schemes of GAAFETs are Si0.7Ge0.3/Si multi-layer stacking, inner-spacer formation, and channel release by etching Si0.7Ge0.3 regions selectively.
All the TCAD results were calibrated to Intel 10-nm node FinFETs [15]. Detailed calibration flows are as follows. Geometrical parameters such as Lg, fin width (Wfin), fin height (Hfin), CPP, and FP were referred from [15]. Subthreshold characteristics such as subthreshold swing (SS) and drain-induced barrier lowering (DIBL) were fitted by changing annealing temperature and time for proper S/D doping profiles. Saturation velocity was tuned to fit the drain current (Ids) in the saturation region, whereas minimum low-field mobility and ballistic coefficient were varied to fit the Ids in the linear region. Some parameters related to surface roughness scatterings were also modified to fit the Ids in the strong inversion region accordingly. These calibration flows were equivalent as in [26]. After calibration, FinFETs were scaled down to the 3-nm node for comparison with GAAFETs.
Figure 3 shows the Ids of all the GAAFETs having different WNW or WNS at the fixed Nch of 3 at the drain voltages (Vds) of 0.70 V. It is not shown in this figure, but the Ids increases generally as the WNW or WNS increases irrespective of Nch. As the WNW increases, the Ids shifts leftward and the gate-induced drain leakage (GIDL) increases by losing the gate-to-channel controllability [27]. P-type NWFETs have larger GIDL than n-type NWFETs due to larger S/D doping penetrations into the channel for p-type devices. On the other hand, NSFETs have small GIDL and Ids shifts as thin TNS of 5 nm forms 1-D structural confinement and maintains good short channel characteristics. To the following, there are three applications at different off-state currents (Ioff): LP at the Ioff of 100 pA/μm, SP at the Ioff of 10 nA/μm, and HP at the Ioff of 100 nA/μm [28]. These values were normalized to NP.
Ids of n-type (top) and p-type (bottom) NWFETs and NSFETs having different WNW or WNS at the fixed Nch of 3 at the drain voltages (Vds) of 0.70 V. it is not shown in this figure, but the GAAFETs have the same Ids trends irrespective of Nch (Ids increases as the WNW or WNS increases).
Figure 4 shows SS and DIBL of all the devices. Threshold voltages (Vth) and SS are extracted at the constant current of Weff/Lg × 108 A, where Weff is the effective width equal to 2 × Hfin + Wfin for FinFETs, 4 × WNW × Nch for NWFETs, and (2 × WNS + 2 × TNS) × Nch for NSFETs. DIBL is calculated as the difference of the Vth at two different Vds of 0.05 and 0.70 V for n-type (−0.05 and − 0.70 V for p-type) devices [29]. NWFETs degrade the short channel characteristics much than FinFETs as the WNW is 9 and 10 nm. NSFETs, on the other hand, have smaller SS and DIBL than FinFETs even as the WNS increases up to 50 nm because the gate-to-channel controllability is maintained by GAA structure and thin TNS of 5 nm. But when the NWFETs have ultra-small WNS of 5 or 6 nm, 2-D structural confinement decreases the SS and DIBL greatly, which would be preferable for LP applications. It is not shown in this figure, but the SS and DIBL are independent of Nch.
SS (left) and DIBL (right) of FinFETs, NWFETs, and NSFETs having fixed Nch of 3. It is not shown in this figure, but the GAAFETs have the same SS and DIBL irrespective of Nch.
Figure 5 summarizes the effective currents (Ieff) of n-type (top) and p-type (bottom) GAAFETs having different WNW (or WNS) and Nch. Ieff was calculated using two Ids at different Vds and gate voltages (Vgs) as
Ieff of n-type (top) and p-type (bottom) GAAFETs having different WNW (or WNS) and Nch. Ieff of n-type and p-type FinFETs are also specified as yellow symbols. Blue regions indicate that the GAAFETs have superior Ieff than the FinFETs.
where IH = Ids (Vgs = VDD, Vds = VDD/2) and IL = Ids (Vgs = VDD/2, Vds = VDD) [30], and VDD is the operation voltage fixed to 0.7 V. All the Ieff were normalized to the NP, and the Ioff were fixed to 10 nA/μm for SP applications. GAAFETs need to have at least the Nch of 3 to outperform the FinFETs. As the WNW is 9 nm, both n-type and p-type NWFETs suffer from short channel effects (SCEs) and thus have smaller Ieff than the devices having smaller WNW in spite of larger Weff. NSFETs, on the other hand, have larger Ieff as the WNS is larger as the SCEs are reduced by thin TNS of 5 nm. But even though small same SS and DIBL are maintained for all the Nch, the increasing rate of Ieff as a function of Nch decreases as Nch increases.
Figure 6 shows the S/D parasitic resistance (Rsd) of the GAAFETs having the WNW or 7 nm and the WNS of 30 nm as a function of Nch. Other WNW and WNS have the same Rsd trends and thus are not shown in this work. Rsd was possibly extracted using Y-function method due to the linearity of Y-function at high Vgs [31]. As the Nch increases, Rsd of the GAAFETs decrease but at decreasing rate. Furthermore, Rsd becomes saturated as the Nch is 3 or 4. This phenomena can be explained by 2-D schematic diagrams shown in the right of Figure 6. Since the S/D contacts reside at the top of the S/D epi, current paths start from the top toward the channels at the bottom. As the Nch increases, longer current paths are needed to flow the bottom-side channels, facing more Rsd components at the S/D epi. Thus, increasing the Nch beyond 3 or 4 does not help DC performance improvements greatly.
Rsd of n-type and p-type GAAFETs having the WNW of 7 nm and the WNS of 30 nm as a function of Nch (left) and the 2-D schematic diagram of half of the GAAFETs showing the current paths and Rsd components (right).
Figure 7 summarizes the gate capacitances (Cgg) of all the GAAFETs. The Cgg is extracted at the Vgs and the Vds of VDD. Generally, Cgg increases as the WNW (or WNS) or Nch increases due to the increased Weff. PFETs have larger Cgg than NFETs due to larger S/D doping concentrations and penetrations into the channels. Different from the Ieff trends, the GAAFETs have Nch smaller than 3 to outperform the FinFETs, thus there are performance trade-offs between Ieff and Cgg as a function of Nch. Furthermore, the increasing rate of Cgg as a function of Nch is constant while the increasing rate of Ieff as a function of Nch decreases, which would degrade the RC delay (= IeffVDD/Cgg) as the Nch increases.
Cgg of n-type (top) and p-type (bottom) GAAFETs having different WNW (or WNS) and Nch. Cgg of n-type and p-type FinFETs are also specified as yellow symbols. Blue regions indicate that the GAAFETs have smaller Cgg than the FinFETs.
Figure 8 shows the Cgg and parasitic capacitances (Cpara) of the GAAFETs varying Nch and WNW (or WNS). Cpara is extracted at off-state for SP applications. For all the cases, PFETs have larger Cpara than NFETs due to larger S/D doping and penetrations into the channels [20]. At the fixed Nch of 3, larger WNW or WNS, except for p-type NWFETs, decreases the Cpara/Cgg because the proportion of the channels out of the metal gate increases. For the same reason, larger Nch decreases the Cpara/Cgg. Large Cpara/Cgg at the WNW of 9 nm for NFETs is because large SS forms on state before reaching strong inversion region.
Cgg and Cpara of NWFETs (left) and NSFETs (right) having different WNW (or WNS) at the fixed Nch of 3 and having different Nch at the fixed WNW of 7 nm (or WNS of 30 nm). Percentages represent the Cpara/Cgg.
Figure 9 shows the S/D doping profiles of NFETs (top) and PFETs (bottom) having different WNW at the fixed Nch of 3. In general, NFETs have larger doping concentrations in the middle of channels than PFETs because the Ge intermixing within multi-stacked Si/Si0.7Ge0.3 layers increases the Ge concentration at the channels and assists more phosphorus dopants diffusing into the channels while it segregates boron dopants [32, 33, 34]. Both NFETs and PFETs increase the doping concentrations in the middle of channels as the WNW increases because the dopant segregations near the low-k spacer regions decrease [35]. But PFETs increase the doping concentrations in the middle of channels much due to smaller Ge intermixing for larger WNW. This great increase of the doping concentrations in the middle of channels increases the Cpara/Cgg for p-type NWFETs (as shown in Figure 8).
S/D doping profiles of NFETs (top) and PFETs (bottom) having different WNW at the fixed Nch of 3. Doping concentrations in the middle of top-side channels are also specified.
Figure 10 finalizes the RC delay of all the GAAFETs for LP, SP, and HP applications. N-type FinFETs have smaller RC delay than p-type FinFETs for all the applications due to better short channel characteristics, greater Ieff (as shown in Figure 5) and smaller Cgg (as shown in Figure 8). For LP applications, n-type GAAFETs having small WNW equal to 5 or 6 nm can outperform n-type FinFETs by decreasing SS and DIBL critically. But as the Nch is 1 (or 5), the Ieff decreases greatly (or the Cgg increases greatly), thus degrading the RC delay. On the other hand, p-type GAAFETs have more WNW or WNS options to outperform p-type FinFETs because boron dopants of the GAAFETs are segregated by Si/Si0.7Ge0.3 intermixing and have more abrupt S/D doping profile than p-type FinFETs. For LP applications, both n- and p-type GAAFETs have the minimum RC delay at the WNW of 5 nm and the Nch of 4. For both SP and HP applications, both n- and p-type GAAFETs have the minimum RC delay at the WNS of 50 nm and the Nch of 3. As the WNS increases beyond 50 nm, RC delay decrease but a little (as shown in Appendix). All these RC delay are achieved by enhancing the Ieff rather than the Cgg. To outperform the FinFETs, therefore, GAAFETs should be NWFETs, showing outstanding short channel characteristics, for LP applications and NSFETs, showing superior DC performance, for SP and HP applications.
RC delay of all the GAAFETs for (a) LP, (b) SP, and (c) HP applications. RC delay of FinFETs for three different applications are also specified. The devices having the RC delay smaller than FinFETs are marked as yellow.
3-nm-node GAAFETs have been analyzed by changing WNW (or WNS) and Nch using fully-calibrated TCAD. Compared to FinFETs, GAAFETs have smaller and SS and DIBL as the WNW is smaller than 9 nm but irrespective of the WNS. Both Ieff and Cgg of the GAAFETs increase as the Nch increases, but the increasing rate of Ieff decreases due to the increase of Rsd at the longer S/D epi. The increasing rate of Cgg, on the other hand, is almost constant. Because of these phenomena, Minimum RC delay are formed at the middle Nch of 3 or 4. The NWFETs having the WNW of 5 or 6 nm achieve smaller RC delay than the FinFETs by achieving better gate electronics for LP applications, whereas the NSFETs having the WNS of 40 or 50 nm increase the Ieff greatly and thus decrease the RC delay for SP and HP applications. Overall, GAAFETs are possible candidates to substitute FinFETs in the 3-nm technology node for all the applications by adopting different WNW or WNS.
The authors declare no conflict of interests.
Figure A1 shows the DC/AC performances of the NSFETs as the WNS increases from 40 to 100 nm. Minimum RC delay are formed at the WNS of 50 nm and the Nch of 3 as shown in Figure 10, but much smaller RC delay can be attained as the WNS increases to 100 nm by increasing the Ieff rather than the Cgg even though larger WNS extends the device area. For the most, RC delay decrease by 5.4% for PFETs as the WNS increases from 40 to 100 nm.
Ieff, Cgg, and RC delay of the NSFETs having the WNS of 40, 50, 60, 70, 80, 90, and 100 nm at the fixed Nch of 3 for SP and HP applications.
Supporting women in scientific research and encouraging more women to pursue careers in STEM fields has been an issue on the global agenda for many years. But there is still much to be done. And IntechOpen wants to help.
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\\n\\nWe aim to publish 100 books in our Women in Science program over the next three years. We are looking for books written, edited, or co-edited by women. Contributing chapters by men are welcome. As always, the quality of the research we publish is paramount.
\\n\\nAll project proposals go through a two-stage peer review process and are selected based on the following criteria:
\\n\\nPlus, we want this project to have an impact beyond scientific circles. We will publicize the research in the Women in Science program for a wider general audience through:
\\n\\nInterested? If you have an idea for an edited volume or a monograph, we’d love to hear from you! Contact Ana Pantar at book.idea@intechopen.com.
\\n\\n“My scientific path has given me the opportunity to work with colleagues all over Europe, including Germany, France, and Norway. Editing the book Graph Theory: Advanced Algorithms and Applications with IntechOpen emphasized for me the importance of providing valuable, Open Access literature to our scientific colleagues around the world. So I am highly enthusiastic about the Women in Science book collection, which will highlight the outstanding accomplishments of women scientists and encourage others to walk the challenging path to becoming a recognized scientist." Beril Sirmacek, TU Delft, The Netherlands
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\n\nWe aim to publish 100 books in our Women in Science program over the next three years. We are looking for books written, edited, or co-edited by women. Contributing chapters by men are welcome. As always, the quality of the research we publish is paramount.
\n\nAll project proposals go through a two-stage peer review process and are selected based on the following criteria:
\n\nPlus, we want this project to have an impact beyond scientific circles. We will publicize the research in the Women in Science program for a wider general audience through:
\n\nInterested? If you have an idea for an edited volume or a monograph, we’d love to hear from you! Contact Ana Pantar at book.idea@intechopen.com.
\n\n“My scientific path has given me the opportunity to work with colleagues all over Europe, including Germany, France, and Norway. Editing the book Graph Theory: Advanced Algorithms and Applications with IntechOpen emphasized for me the importance of providing valuable, Open Access literature to our scientific colleagues around the world. So I am highly enthusiastic about the Women in Science book collection, which will highlight the outstanding accomplishments of women scientists and encourage others to walk the challenging path to becoming a recognized scientist." Beril Sirmacek, TU Delft, The Netherlands
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