دانلود پایان نامه مهندسی شیلات pdf
در این صفحه دو پایان نامه در رشته مهندسی شیلات قرار داده شده است. فایل PDF این پایان نامه ها را می توانید از قسمت "فایل ها برای دانلود" در پایین همین صفحه دریافت نمایید.
مدل فیلتر دنیتریفیکاسیون چرخهای مبتنی بر گوگرد برای سیستمهای آبزی پروری در حال چرخش دریایی
Model of a Sulfur-Based Cyclic Denitrification Filter for Marine Recirculating Aquaculture Systems
Recirculating aquaculture systems (RAS) are a type of near zero-discharge fish production system that is used to treat and recirculate aquaculture wastewater and increase the biomass stocking density in the fish tank. The RAS presented in this thesis was a marine system which was operated with two temporally independent cycles, Loop 1, a continuous loop, and Loop 2, an intermittent loop. Flow in the RAS was switched between the two loops by a solenoid valve. During the operation of Loop 1, components involved in the cycle were successively the fish tank for fish production, solids filters for solids removal and moving bed bioreactor (MBBR) for nitrification. During the operation of Loop 2, the solenoid valve directed influent from the fish tank to a cyclic denitrification filter (CDF) for 10min to refresh the water held in the CDF. The time between cycles of the CDF was considered as the hydraulic residence time (HRT) (i.e. 1hr, 2hr, 4hr and 12hr). Two pilot-scale RAS were operated in the laboratory. The system was operated in two phase, a synthetic wastewater phase with varying HRT and a phase that included fish production with an HRT of 12 hrs.
Models for the RAS was developed, calibrated and used to provide a prediction of nitrogen species concentrations and nitrogen removal efficiency in the RAS and CDF. the model incorporated mass balances on particulate organic nitrogen (PON), dissolved organic nitrogen (DON), NH4+-N and NO3–-N, and was generally divided into an overall RAS process model and a CDF model. Due to the high salinity in the system, the ionic strength, 0.3M, was calculated based on the experimental data for modification of nitrogen species activity in the RAS.
The overall RAS process model included three primary components, which are the fish tank, solids filters, and the MBBR. Before calibration, the ammonification and nitrification rate constants for MBBR, kMBBR-afc and kMBBR-nfc, were determined to be 0.5 and 240 d–1 respectively based on the prior literatures. Corresponding to the 240 d-1 of kMBBR-nfc, the NH4+-N flux to biofilm was 0.27 g/m2·d, which agrees the literature value ranging from 0.14 to 0.45 g/m2·d. An Excel based matrix was operated to calibrate four parameters, including the ammonification rate constant for the fish tank, kFT-afc, the nitrification rate constant for the fish tank, kFT-nfc, the porosity of the media in the CDF, ε, and the superficial solids removal efficiency of the solids filters, fSR. It was found that kFT-afc = 0.028 d–1, kFT-nfc = 4.55 d–1, ε = 0.56, and fSR = 11.3%. The overall RAS process model was primarily used to predict the nitrogen species concentration in the fish tank. It estimated 45.5 mg/L, 0.2 mg/L, 5.8 mg/L and 1.4 mg/L for NO3–-N, NH4+-N, DON and PON concentration in the fish tank. The experimental data was observed to fluctuate in narrow neighborhoods of 45.5 ± 4.5, 0.2 ± 0.1, 5.8 ± 4.8, 1.4 ± 0.6, respectively, which proved the validity of the overall RAS process model.
The CDF model was separately developed for operation of Loop 1 and Loop 2. The CDF was treated as a batch reactor during the operation of Loop 1. The denitrification rate based on the sulfur oxidizing microorganisms was assumed to be governed by a half order reaction. The half order reaction constant, k1/2, was calibrated to 79 mg1/2/L1/2·d, and, for the typical influent concentration of 40–45 mg/L in the RAS, the minimum time required to completely remove the NO3–-N in the influent was approximately 4.45–4.72hr. During the operation of Loop 2, a hydraulic model was used to determine the effluent flow rate of the CDF over time. The equivalent diameter of the media particles was calibrated to 0.03 mm, which is much smaller than the diameter of the sulfur pellets and expanded clay particles. However, the overall RAS process model indicates a relatively high porosity, 0.56, of media in the CDF. This might be caused by biofilm that clogged the pore space in the media. Biofilm also possesses an excellent capacity to hold water, which could result in a high porosity of the media. The hydraulic model provided the variable velocity used to model the NO3–-N concentration in the CDF effluent. The dispersion coefficient was estimated to 0.0051 m2/min, and the estimate for dispersion number range from 0.39 to 1.28. The relatively high dispersion number indicates that dispersion is a significant process occurring in the CDF compared to advection.
The overall RAS process model and CDF model was then used to estimate the nitrogen fate in the RAS and compare it with a previously developed model for calculating the fate of nitrogen (CafaN). Based on the overall RAS model and CDF model, the nitrogen fate was estimated: 25% removed by fish biomass uptake, 25% by solids removal, 42.5% by denitrification in the CDF, 1% by sampling and 6.5% by microbial assimilation or other removal processes. The CafaN model indicated: 7% removed by biomass uptake, 26% by solids removal, 60% by denitrification, 1% by sampling and 6% by passive denitrification. 7% of fish biomass uptake is much lower than the literature information. During the research, the fish bred an amount of offspring, which could be a cause leading to a lower measured fish biomass assimilation rate.
Finally, results of the new developed model in the paper was used to optimize the CDF HRT and active time (i.e. the time the CDF is open during Loop 2). The original CDF design was operated at a HRT = 12hr with an active time = 10min. The CDF only provided 42.5% of total nitrogen removal. The cycle can be optimized to eight hours with a new 7 min of active time for the Loop 2 three times per day. This would enhance the CDF nitrogen removal efficiency to 70% and allow the system to support larger grow out tanks for fish production.
چگونه دانشمندان دریایی، ماهیگیران و مدیران شیلات می توانند به طور موثر برای مدیریت پایدار شیلات ایالات متحده در مواجهه با تغییرات آب و هوایی همکاری کنند
How Marine Scientists, Fishers, and Fisheries Managers Can Effectively Collaborate to Manage United States Fisheries Sustainably in the Face of Climate Change
This study investigated ways that scientists, managers, and fishers in the United States fisheries management system can better work together to manage fisheries sustainably in the face of climate change. It involves a case study on nine fisheries management actors who participated in individual interviews in which they described their positive and negative professional experiences with communication, trust, collaboration, and common goals in fisheries management. Qualitative content analysis was used to code and categorize data from the interviews to ultimately identify themes and make recommendations. The major findings were that 88% of the subjects have witnessed or experienced mistrust and poor communication between fisheries management actors; 77% have experienced common and uncommon goals, as well as aspects of knowledge sharing; and 55% have experienced trust and good communication between the actors. The major recommendations were to (1) allow fishers to become more involved in the research process, (2) assemble fishers more formally, (3) create more connections between fisheries management actors, (4) request that scientists put more effort into bridging the divide between fishers and scientists, (5) develop more tools to incorporate fishers’ knowledge into scientific information, and (6) bring more human dimensions data into fisheries-related studies.