Aquaculture Water Quality Research Articles

Tilapia aquaculture water quality in response to the addition of fermented herbal extract

This study aims to find a suitable Fermented Herbal Extract (FHE) dose to reduce ammonia levels in tilapia cultivation. This research was carried out for one month, from August to September 2021, at Fish Hatchery Laboratory of Marine Sciences and Fisheries Faculty, Hasanuddin University. The study was conducted using a completely randomized design with six treatments and three replications. The treatments were various doses of FHE added to the cultivation water, including 0 ppm, two ppm, four ppm, six ppm, eight ppm, and ten ppm. The juvenile tilapia measuring about 5 cm body length stocked with five fish for each aquarium. Water quality like Dissolved Oxygen, Temperature, pH, Nitrate, Nitrite, and Ammonia content was measured in this study. The results showed that adding FHE to tilapia culture media affected the ammonia concentration. In this study, six ppm of FHE treatment resulted in the lowest ammonia concentration on the 10th and 12th days of observation.

Aquaculture Prediction Model Based on Improved Water Quality Parameter Data Prediction Algorithm under the Background of Big Data

Computer science and technology under the background of big data are closely related to the development of modern agriculture. The application of information processing technology in aquaculture will promote the scientific development of aquaculture. The aquaculture water quality directly affects the effect of aquaculture. Therefore, on the basis of the dynamic monitoring model of water quality, the relevant factors affecting water quality were analyzed, and a prediction model of aquaculture water quality was constructed. Considering the complex relationship between dissolved oxygen and water quality, combined with principal component analysis, a PCA-BP (principal component analysis back propagation) water quality prediction model was proposed. The parameters of PCA-BP water quality prediction model were optimized by genetic algorithm, the threshold and weight of BP neural network were determined, and an improved PCA-BP water quality prediction model was constructed. The experimental results show that the relative error of the GPCA-BP water quality prediction model for the prediction of dissolved oxygen content is less than 0.76% in water quality prediction experiments in different times and regions, and it has the best prediction accuracy. At the same time, GPCA-BP water quality prediction model also has excellent performance in convergence accuracy, prediction accuracy, and MAE error performance test. The research content has important reference value for the application of information technology in modern aquaculture.

Modified kinetic energy feature-based graph convolutional network for fish appetite grading using time-limited data in aquaculture

Feed has the greatest impact on the carbon footprint of the aquaculture, and also determines the water quality in aquaculture to a great extent. Making appropriate feeding control strategies is one of the most effective ways to promote cleaner production as well as fish welfare in aquaculture. Reliable and accurate fish appetite grading especially based on time-limited data is a prerequisite for achieving high-precision and reasonable feeding control in practical production. To date, however, few efforts have been done on this challenge. For these, regarding Micropterus salmoides as the experimental fish, a novel and practical method, based on a modified kinetic energy feature-based graph convolutional network (GCN), was developed in this study. First, graphs were constructed based on the extracted modified kinetic energy features and their temporal correlation. Then, with the help of a series of the convolution and global pooling operations, a GCN model was customized based on the constructed graphs. Following this, the customized GCN model was enriched by the self-attention pooling mechanism and customized network structure. Results show that the proposed GCN-based approach outperforms other typical state-of-the-art methods in fish appetite grading, and the grading accuracy obtained here could be 98.60% using only the first 4.2 seconds as well as the first 8.3 seconds of input data, which is not much different from that (98.89%) using full-length (25 second-long) input data. What’s more, compared to the recurrent neural network (RNN)-based method which performance is closest to our method, the space complexity of the proposed approach here can better satisfy the requirements of real aquaculture, in which the quantity of the trainable parameters here is only 6.4% ~ 31.8% of the RNN-based method. In summary, the proposed modified kinetic energy feature-based GCN approach is favorable for the appetite grading of fish like Micropterus salmoides with time-limited data, which is a promising approach in dealing with feeding control tasks and alleviating the water environmental burden in aquaculture.

Robust control of water quality in intensive aquaculture using multi‐variable quantitative feedback theory: From an Indian context

AbstractThis paper addresses the problem of controlling turbidity, dissolved oxygen, ammonium‐ion concentration, and water‐temperature to maintain the desired water quality in an intensive aquaculture plant. The research is carried out to fit into the present scenario of intensive aquaculture in India. The plant model along with uncertain parameters are derived using physical laws and the data acquired from an aquaculture pond in Assam, India. A pre‐compensated multi‐variable quantitative feedback theory (QFT)‐based fully populated robust matrix controller is proposed to mitigate the control challenge. A novel method is used to design the pre‐compensator to enhance the diagonal dominance of plant transfer function matrix. The desired robust stability, reference tracking, and sensitivity specifications are fulfilled by the proposed QFT‐based robust controller despite the parametric uncertainty. The effectiveness of the proposed method is validated through numerical simulation. A comparative study against a controller shows that the proposed controller strategy gives a satisfactory performance compared to it.

Long‐Term Changes and Influencing Factors of Water Quality in Aquaculture Dominated Lakes Unveiled by Sediment Records and Time Series Remote Sensing Images

AbstractHydraulic connectivity has great effects on water quality. Enclosure aquaculture can largely alter lake flow regime and thus deteriorate water quality. Understanding the dynamics and influencing factors of water quality in enclosure aquaculture lakes is of great significance to ecosystem restoration of degraded lakes. However, it remains challenging due to the lack of long time series data. In this study, the dynamics of water quality in aquaculture dominated lakes were captured through 210Pb and 137Cs dating based on sediment records. Meanwhile, the effects of landscape pattern within aquaculture lakes on water quality were revealed by long time series of remote sensing images. Results showed the 210Pb and 137Cs sediment dating could provide an effective way to obtain long‐term series of lake water quality data in the unmonitored area. The contents of lake sediment nutrients showed an upward trend from 1960 to 2018. The transformation of lakes from agriculture dominated to aquaculture dominated had increased sediment nutrients level. The long‐term changes of lake sediment nutrients could be well explained by the landscape pattern metrics within aquaculture lakes, with an average explain power of 87.6%. The configuration metrics at class level had the most contribution (73.6%) to the changes of lake sediment nutrients, followed by the composition metrics (48.8%) and the configuration metrics at landscape level (33.4%). This study can provide a promising method to understand water quality changes in lakes with no historical monitoring data available and is of great benefit to water quality management in aquaculture dominated lakes.

Design and Realization of a Novel Hybrid-Drive Robotic Fish for Aquaculture Water Quality Monitoring

Design and Realization of a Novel Hybrid-Drive Robotic Fish for Aquaculture Water Quality Monitoring

Microbubble Aeration in A Recirculating Aquaculture System (RAS) Increased Dissolved Oxygen, Fish Culture Performance, and Stress Resistance of Red Tilapia (Oreochromis sp.)

Microbubble aeration has been recognized as a tool to improve dissolved oxygen and fish culture performance in limited water aquaculture. This research aims to investigate the effect of microbubble aeration on increasing dissolved oxygen, fish culture performance, and stress resistance of red tilapia. The study used 3 treatments of aerations namely microbubble, blower, and without aeration with 3 replicates each. Red tilapia weighing 115.27±3.93 g were stocked in a plastic tank of 800 L water volume at density 50 individual/tank and cultured for 50 days in a recirculating aquaculture system (RAS). Dissolved oxygen, fish culture performance, and stress resistance were evaluated. Dissolved oxygen was monitored daily. In the middle of the research period, a stress test was performed using different salinity at 12 and 24 ppt. Statistical analysis was subjected to water quality, fish culture performance, and stressed resistance. The microbubble aeration was able to stabilize DO level on 4.28 mg/L until the end of the experiment and suppressed the CO2 and ammonia content. Fish biomass was higher in microbubble treatment, with a lower FCR value, lower stress level indicating fish in good health and promoting good culture performance.
 HIGHLIGHTS
 
 The critical parameter of water quality in aquaculture is dissolved oxygen (DO) and to increase its content is by aeration
 A nobel microbubble aeration was able to increase DO level higher than blower aeration and stabilize DO at requirement level until the end of the fish culture
 The strong relationship of DO with fish hematology parameters those more stable that indicate a lower stress level
 Stable physiological conditions and low stress of fish implied that the cultivated fish is in good health and good performance by microbubble aeration
 
 GRAPHICAL ABSTRACT

PROFIL DARAH IKAN NILA (Oreochromis niloticus) YANG DIBERI PAKAN DENGAN PENAMBAHAN EKSTRAK DAUN MANGROVE API-API PUTIH (Avicennia marina)

Medicinal plants can be used as alternative prevention and treatment of fish diseases. Its use can be mixed in the feed, but must be by the dose so as not to interfere with fish health. Fish health can be evaluated by measuring the blood profile. The blood profile of this study includes hemoglobin, hematocrit, blood glucose, and total erythrocytes. The purpose of this research was to determine the effect of adding Avicennia marina leaf extract as seen from the total erythrocytes, hemoglobin levels, hematocrit values, and blood glucose levels. The research method used was CRD consisting of 4 treatments and 5 individual replicates of tilapia. The addition of Avicennia marina extract to fish feed (1 g/kg; 1,5 g/kg; and 2 g/kg) had a total erythrocytes range of 0,995x106–2,658x106 cell/mm3, hemoglobin ranged from 8,3–11,6 g/dL, hematocrit ranged from 23.1–38,4 g/dL, and blood glucose ranged from 46–209 mg/dL. The results showed that the addition of Avicennia marina leaf extract had no significant effect (P>0,05) on the blood profile of fish as measured by total erythrocytes, hemoglobin, hematocrit​​, and glucose. Aquaculture water quality in the form of temperature and pH has the optimum value for tilapia cultivation.

Predicting Aquaculture Water Quality Using Machine Learning Approaches

Good water quality is important for normal production processes in industrial aquaculture. However, in situ or real-time monitoring is generally not available for many aquacultural systems due to relatively high monitoring costs. Therefore, it is necessary to predict water quality parameters in industrial aquaculture systems to obtain useful information for managing production activities. This study used back propagation neural network (BPNN), radial basis function neural network (RBFNN), support vector machine (SVM), and least squares support vector machine (LSSVM) to simulate and predict water quality parameters including dissolved oxygen (DO), pH, ammonium-nitrogen (NH3-N), nitrate nitrogen (NO3-N), and nitrite-nitrogen (NO2-N). Published data were used to compare the prediction accuracy of different methods. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM for predicting DO were 0.60, 0.99, 0.99, and 0.99, respectively. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM for predicting pH were 0.56, 0.84, 0.99, and 0.57. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM for predicting NH3-N were 0.28, 0.88, 0.99, and 0.25, respectively. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM for predicting NO3-N were 0.96, 0.87, 0.99, and 0.87, respectively. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM predicted NO2-N with correlation coefficients of 0.87, 0.08, 0.99, and 0.75, respectively. SVM obtained the most accurate and stable prediction results, and SVM was used for predicting the water quality parameters of industrial aquaculture systems with groundwater as the source water. The results showed that the SVM achieved the best prediction effect with accuracy of 99% for both published data and measured data from a typical industrial aquaculture system. The SVM model is recommended for simulating and predicting the water quality in industrial aquaculture systems.

EVALUATION OF THE VALUE OF AMMONIA, NITRATE, AND NITRITE ON CULTIVATION MEDIA OF CATFISH FED MAGGOT

Water quality is a limiting factor of life for aquatic biota, especially biota that lives in a pond. Therefore, water quality management is very necessary so that cultivation activities, especially ponds can continue and do not cause negative impacts on the surrounding environment. Various factors can decrease the water quality of aquaculture ponds one of which is from the remnants of feed and fish manure. Feed is one of the contributors to the dissolved organic matter in the cultivation pond. Maggot or black shoulder fly larvae are one of the alternatives that can be used as fish feed. Maggot has a high animal protein content of 30-45%, so it is very effective to be used as an alternative to additional feed in fish farming. The purpose of this research is to find out the difference in pond water quality between ponds by using maggot and pond feed by using pellet feed. The data taken in this study is primary data using the Complete Randomized Design (RAL) method with 3 treatments and 3 replays in each treatment. These treatments include Full Maggot Control (MU), 50% pellet, and 50% maggot (PU). Data obtained include; ammonia. Nitrites, and nitrates. The results showed that the average final value of ammonia, nitrite, and nitrates respectively treatment is KU ( 0.22 mg / L, 0.44 mg/L, 3.667 mg/L), PU (0.022 mg/L, 0.042 mg/L, 3.433 mg/L), MU (0.046 mg/L, 0.028 mg/L, 2.653 mg/L). The results illustrate the conclusion that the provision of maggots as an alternative and additional feed can improve the quality of aquaculture water.

Evaluation of extruded full-fat soybean as the substitution for fish meal in diets for juvenile Scophthalmus maximus based on growth performance, intestinal microbiota, and aquaculture water quality

Extrusion technology is applied to improve the utilization of soy products, providing an alternative to fish meal (FM) as a component of feed. In this study, a 60-day feeding of turbot (Scophthalmus maximus) was conducted to evaluate the effects of the partial substitution of FM with extruded full-fat soybean (EFS) on growth performance, digestion capacities, intestinal microbiota, and aquaculture water quality. Four isonitrogenous (427.95 ± 0.38 g/kg crude protein) and isolipidic (9.80 ± 1.13 g/kg crude lipid) diets were formulated to contain FM with EFS substitution of 0% (EFS 0%), 10% (EFS 10%), 20% (EFS 20%), and 30% (EFS 30%). Each diet was fed twice a day in triplicate to juvenile turbot (initial body weight, 5.35 ± 0.07 g). The results showed that the turbot fed with EFS 30% had significantly lower body weight, specific growth rate, and protein efficiency ratio than turbot with other diets. In addition, the EFS 30% significantly decreased total serum cholesterol, low-density lipoprotein contents, intestinal folds height, and apparent digestibility coefficients of dry matter and crude protein. It also increased pathogenic Cyanobacteria at the phylum level, decreased probiotics, Rhodobacterales, and Pseudomonadales at the order level, and decreased leuconostoc and Psychrobacter at the genus level. Compared with other treatments, the EFS 30% displayed the lowest proportion of nitrogen for growth and the highest fecal nitrogen loss. Furthermore, the water quality indicators such as ammonia nitrogen discharge, total suspended solids, and fine solid accumulation (< 15 μm) reached the peak in the EFS 30% group. In conclusion, EFS is a feasible turbot feed that can substitute up to 20% of FM with no adverse effects on growth performance, digestive function, intestinal microbiota, and aquaculture water quality.

Optimizing crude protein production from minute duckweed (Lemna perpusilla Torr) grown in varied NPK based medium

A suitable growth medium has to be developed to implement the potential use of minute duckweed (L. perpusilla Torr.). This tiny floating aquatic plant has been widely promoted as a source of protein. This research aims to find the optimal NPK based media for enhancing plant protein productivity. The duckweed was grown in twelve various NPK based media formulations (respectively named as Box-A to box-L, each has a surface area of 0.0945 m2), and the crude protein productivity was examined. Determination of crude protein through the total biomass nitrogen (TN) analysis was performed using the modified Salicylic acid method, which combined alkaline persulphate digestion before the absorbance measurement with a UV-Vis spectrophotometer. The highest crude protein productivity in dry weight was 18.02 mg/m2.day or estimated equal to 65.76 kg/ha/year found on the Box D (consisted of blending Terminalia catappa leaf extract as bioalgacide agent and trace elements in NPK of 100 mg/L). Meanwhile, the highest Total Nitrogen removal (98.6%) was observed on box F (mixed of NPK 100 mg/L, Terminalia catappa leaf extract, trace elements, and molasses solutions). The alternative formulation was blending NPK 300 mg/L with trace element (box L) that enable to yield of crude protein as much as 30.97 mg/m2.day or estimated equal to 113.04 kg/ha.year. These findings proved that duckweed biomass could be potentially used as a protein source material and improve water quality in aquaculture. Further, the use of three-dimensional models to demonstrate the effect of the remaining concentration of TN and TP in medium against crude protein content in the biomass was also discussed.

PERTUMBUHAN IKAN NILA NIRWANA (Oreochromis niloticus) PADA SISTEM BIOFLOK DENGAN SUMBER KARBON EKSTERNAL DARI TEPUNG SORGUM MANIS (Sorghum bicolor)

Biofloc technology (BFT) is an alternative that can be used to provide additional feed source in addition to control and improve water quality for aquaculture. BFT can be done by adding an organic carbon source to increase the C/N ratio in the culture media to stimulate the growth of heterotrophic bacteria. The results of the observation were that the highest specific growth rate was obtained in treatment B (5ml floc) with an average value of 2.80 grams/day. The highest average length value in treatment B (5ml floc) reached 10.26 cm. The highest survival rate was in treatment B (5ml floc) with an average value of 86.78%. The highest efficiency value was found in treatment B (5ml floc) with an average of 95.90%. Based on Duncan's further test (p&lt;0.05) the application of biofloc with an external carbon source from sweet sorghum had a significant effect on the specific growth rate, absolute growth, survival rate and feed efficiency of tilapia fry. Keywords : Biofloc; Sweet Sorghum; Tilapia Nirwana ; Aquacukture

Water quality prediction in sea cucumber farming based on a GRU neural network optimized by an improved whale optimization algorithm.

Sea cucumber farming is an important part of China’s aquaculture industry, and sea cucumbers have higher requirements for aquaculture water quality. This article proposes a sea cucumber aquaculture water quality prediction model that uses an improved whale optimization algorithm to optimize the gated recurrent unit neural network(IWOA-GRU), which provides a reference for the water quality control in the sea cucumber growth environment. This model first applies variational mode decomposition (VMD) and the wavelet threshold joint denoising method to remove mixed noise in water quality time series. Then, by optimizing the convergence factor, the convergence speed and global optimization ability of the whale optimization algorithm are strengthened. Finally, the improved whale optimization algorithm is used to construct a GRU prediction model based on optimal network weights and thresholds to predict sea cucumber farming water quality. The model was trained and tested using three water quality indices (dissolved oxygen, temperature and salinity) of sea cucumber culture waters in Shandong Peninsula, China, and compared with prediction models such as support vector regression (SVR), random forest (RF), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory neural network (LSTM). Experimental results show that the prediction accuracy and generalization performance of this model are better than those of the other compared models.

A Low-Cost AI Buoy System for Monitoring Water Quality at Offshore Aquaculture Cages

The ocean resources have been rapidly depleted in the recent decade, and the complementary role of aquaculture to food security has become more critical than ever before. Water quality is one of the key factors in determining the success of aquaculture and real-time water quality monitoring is an important process for aquaculture. This paper proposes a low-cost and easy-to-build artificial intelligence (AI) buoy system that autonomously measures the related water quality data and instantly forwards them via wireless channels to the shore server. Furthermore, the data provide aquaculture staff with real-time water quality information and also assists server-side AI programs in implementing machine learning techniques to further provide short-term water quality predictions. In particular, we aim to provide a low-cost design by combining simple electronic devices and server-side AI programs for the proposed buoy system to measure water velocity. As a result, the cost for the practical implementation is approximately USD 2015 only to facilitate the proposed AI buoy system to measure the real-time data of dissolved oxygen, salinity, water temperature, and velocity. In addition, the AI buoy system also offers short-term estimations of water temperature and velocity, with mean square errors of 0.021 °C and 0.92 cm/s, respectively. Furthermore, we replaced the use of expensive current meters with a flow sensor tube of only USD 100 to measure water velocity.

Evaluation of Salipiger thiooxidans and Exiguobacterium aestuarii from the Saemangeum Reservoir as Potential Probiotics for Pacific White Shrimp (Litopenaeus vannamei).

In this study, two bacterial species, Salipiger thiooxidans and Exiguobacterium aestuarii, were extracted and screened from the Saemangeum Reservoir. This study examined these species’ suitability as a probiotic by confirming the effects of S. thiooxidans and E. aestuarii added to rearing water for L. vannamei. Three experimental groups were evaluated for 6 weeks: (1) a control group reared in natural (i.e., untreated) water (CON), (2) an experimental group in which S. thiooxidans was added to natural water (SMG-A), and (3) natural water inoculated with E. aestuarii (SMG-B). The SMG-B group inoculated with E. aestuarii showed significantly higher final body weight, weight gain, specific growth rates, and feed efficiency than the control group. The SMG-B group inoculated with E. aestuarii exhibited significantly higher levels of serum lysozyme, and ACP and ALP activity than the control and SMG-A groups. The SMG-A and SMG-B groups inoculated with probiotics showed significantly lower total ammonia nitrogen and nitrite than the control group. Our findings suggest that S. thiooxidans and E. aestuarii extracted from the Saemangeum Reservoir can improve the water quality of aquaculture water, and, in particular, E. aestuarii is a potential probiotic for L. vannamei.

Development of portable colorimeter for on-site determination of water quality in aquaculture

PurposeThis study aims to describe the development of a battery-operated portable colorimeter for on-site determination of water quality in aquaculture.Design/methodology/approachA simple and economical colorimeter is built using light sources of different wavelength and a light-dependent resistor combined with an electrical circuit. The whole system was fabricated as to fit into the pocket or palm for easy handling. The developed portable colorimeter was calibrated for estimation of nitrite. Further, the performance of developed portable colorimeters was compared with the commercially available colorimeter.FindingsThree colorimeters with different light sources were developed and calibrated for determination of nitrite in water. Among them, colorimeter with yellow light source exhibited higher potential for determination of nitrite in the range of 0.5–3.5 ppm. Further, the results of the developed colorimeter are comparable with the commercial colorimeter.Originality/valueThe portable colorimeter developed in this study exhibits potential for determination of nitrite in aquaculture. Determination of nitrite at low concentrations is important for assessing the quality of culture as well as wastewater in aquaculture industry. The accuracy, portable nature, economy and simple operation of these portable colorimeters offer opportunity for on-site determination of water quality parameters in aquaculture.

Improvement of aquaculture water quality by mixed Bacillus and its effects on microbial community structure.

Microbial remediation, especially the application of probiotics, has recently gained popularity in improving water quality and maintaining aquatic animal health. The efficacy and mechanism of mixed Bacillus for improvement of water quality and its effects on aquatic microbial community structure remain unknown. To elucidate these issues, we applied two groups of mixed Bacillus (Bacillus megaterium and Bacillus subtilis (A0 + BS) and Bacillus megaterium and Bacillus coagulans (A0 + BC)) to the aquaculture system of Crucian carp. Our results showed that the improvement effect of mixed Bacillus A0 + BS on water quality was better than that of A0 + BC, and the NH4+-N, NO2--N, NO3--N, and total phosphorus (TP) concentrations were reduced by 46.3%, 76.3%, 35.6%, and 80.3%, respectively. In addition, both groups of mixed Bacillus increased the diversity of the bacterial community and decreased the diversity of the fungal community. Microbial community analysis showed that mixed Bacillus A0 + BS increased the relative abundance of bacteria related with nitrogen and phosphorus removal, such as Proteobacteria, Actinobacteria, Comamonas, and Stenotrophomonas, but decreased the relative abundance of pathogenic bacteria (Acinetobacter and Pseudomonas) and fungi (Epicoccum and Fusarium). Redundancy analysis showed that NH4+-N, NO2--N, and TP were the primary environmental factors affecting the microbial community in aquaculture water. PICRUST analysis indicated that all functional pathways in the A0 + BS group were richer than those in other groups. These results indicated that mixed Bacillus A0 + BS addition produced good results in reducing nitrogenous and phosphorus compounds and shaped a favorable microbial community structure to further improve water quality.

Effects of low-light stress on aquacultural water quality and disease resistance in Nile tilapia.

Light intensity has an important environmental influence on the quality and yield of aquatic products. It is essential to understand the effects of light intensity on water quality and fish metabolism before large-scale aquaculture is implemented. In this study, two low-intensity light levels, 0 lx and 100 lx, were used to stress Nile tilapia (Oreochromis niloticus), with a natural light level (500 lx) used as control. The pH, dissolved oxygen and ammonia contents were significantly lower in the water used in the 0 lx and 100 lx groups than in controls, while the levels of nitrite and total phosphorus were apparently higher. Moreover, the numbers of heterotrophic bacteria, Vibrio and total coliforms in aquaculture water were 157.1%, 314.2% and 502.4% higher, respectively, after 0 lx light stress for 15 days. The survival rate of Nile tilapia decreased significantly to 90.6% under 0 lx light on the 15th day. Of the immune-related genes, the expressions of IFN-γ, IL-12 and IL-4 were 390.3%, 757.8% and 387.5% higher under 0 lx light and 303.3%, 471.2% and 289.7% higher under 100 lx light, respectively. These results indicate that low-intensity light changes the physicochemical parameters of aquaculture water and increases the number of bacteria it hosts while decreasing the survival rate and increasing the disease resistance of Nile tilapia.

Experimental characterization of an oxygen transfer model of a fine pore diffuser aerator

Dissolved oxygen (DO) is an important aspect of water quality in aquaculture. To prevent oxygen deficiency, aeration is used to improve DO and increase yields in aquaculture farms. However, the experimental quantification of standard oxygen transfer rate (SOTR) of aerators is highly case dependent and varies under different aerator operational conditions. Hence, using SOTR for modeling, simulation, and aerator design purposes tends to be unreliable. This work validates an existing theoretical oxygen transfer model of an economical fine-pore diffuser using a bench-scale aeration system. Modifications were made to the model to account for physical conditions and to relate the model to SOTR. Aeration tests were conducted using various airflow rates and diffuser depths for three water volumes (137 L, 160 L, 192 L). As a parameter to the model, bubble diameters were determined for each experiment using an automated image processing tool developed for this study. The percentage error between the theoretical and experimental oxygen transfer coefficients range from 12.87% to 19.99% and are well within the 95% confidence interval of both respective results. The oxygen transfer coefficient and rate increased with diffuser submergence and airflow rate. However, standard aeration efficiency decreased exponentially with increasing airflow rates. The use of this model provides an approximate estimate of oxygen transfer rates in different conditions, which can be used to optimally design aerators and strategically create cost-saving practices for both intensive and extensive aquaculture applications.