Evaluation of full-scale carbon dioxide stripping columns in a coldwater recirculating system

The objective of this research was to evaluate the dissolved carbon dioxide stripping efficiency of two types of 1-m tall structured plastic packing (tubular NORPAC and structured block CF-3000 Accu-Pac media) that were placed separately in two full-scale forced-ventilation cascade columns that were located within a coldwater recirculating aquaculture system at the Freshwater Institute. These two structured packing types were selected because they both provide large 4–5 cm void spaces that are either vertically-continuous (e.g. the tubular NORPAC) or an open structure with zigzagging but continuous void spaces (e.g. the blocks of cross-corrugated sheet media), which should reduce the likelihood of plugging with biosolids. Water flow rates were adjusted so that each cascade column was loaded with either 87, 136 and 187 m³/h water flow per m² of cascade column plan area (i.e. 35, 56 and 76 gpm/ft²). Air:water loading rates of 2.2:1 to 3.4:1, 5.1:1 to 5.6:1, and 9.5:1 to 9.9:1 were produced by setting the water flow rates through each column at 1.62, 2.54 and 3.48 m³/min, respectively, and then measuring the resulting air flow rate through the column under these conditions. As expected, the dissolved carbon dioxide removal efficiencies of both structured packing tested were found to depend on the volumetric air:water loading rate applied. The lowest volumetric air:water loading rate (i.e. 2.2:1 to 3.4:1) resulted in only 21–24% dissolved carbon dioxide removal. However, the dissolved carbon dioxide removal efficiencies rose to 32.4–33.6 and 35.8–37.2% for the medium and high air:water loading rates, i.e. 5.1:1 to 5.6:1 and 9.5:1 to 9.9:1, respectively. A second objective of this research was to determine if either packing would plug with biosolids after long-term operation. At the end of approximately 1 year of operation, both of the plastic packing materials were examined from the top of the packing to determine if potential fouling or plugging problems were apparent. A thin layer of brown biofilm covered both packings, but the biofilm did not appear to threaten water or airflow through the packing. In addition, no large mats of biosolids were visible from the top of either column. However, flooding at the interface of the support screen and the tubular NORPAC was suspected to have reduced air flows measured at the highest hydraulic loading rate tested (i.e. at 187 m³/h per m²), which coincided with the lowest air:water loading rates tested.     

光照强度和营养盐对海带幼苗释放溶解有机碳的影响

为了探究光照与营养盐对海带(Saccharina japonica)幼苗释放溶解有机碳(DOC)的影响,测定了不同光强[0、83、165和250 μmol photons/(m2·s)]和不同营养条件下(氮加富、磷加富、氮磷加富和自然海水)海带幼苗DOC的释放速率。结果显示,自然海水条件下,海带幼苗DOC释放速率与光照呈正相关性(P<0.05),光照250 μmol photons/(m2·s)时达到最高,为(24.31±5.84) μmol/(g·h),约为黑暗条件的4倍左右。单一氮加富、磷加富对海带幼苗释放DOC无显著影响,其释放速率分别为(23.04±4.23)和(18.18±4.59) μmol/(g·h)。氮磷共同加富显著提升了海带幼苗DOC释放速率,其释放速率为(37.15±6.77) μmol/(g·h),约为自然海水条件下的3倍。海带幼苗释放DOC很可能同时受“溢出”与“扩散”两种调节机制影响。在寡营养条件下,“溢出”机制占主导,表现为与光照正相关,释放物质相对分子量较高;在富营养条件下,“扩散”机制占主导,表现为受氮、磷营养盐整体水平调节,释放物质相对分子量较低。     

Use of avoidance response by rainbow trout to carbon dioxide for fish self-transfer between tanks

Convenient, economical, and reduced labor fish harvest and transfer systems are required to realize operating cost savings that can be achieved with the use of much larger and deeper circular culture tanks. To achieve these goals, we developed a new technology for transferring fish based on their avoidance behavior to elevated concentrations of dissolved carbon dioxide (CO₂). We observed this behavioral response during controlled, replicated experiments that showed dissolved CO₂ concentrations of 60–120 mg/L induced rainbow trout (Oncorhynchus mykiss) to swim out of their 11 m³ “growout” tank, through a transfer pipe carrying a flow with ≤23 mg/L dissolved CO₂, into a second 11 m³ “harvest” tank. The research was conducted using separate groups of rainbow trout held at commercially relevant densities (40–60 kg/m³). The average weight of fish ranged from 0.15 to 1.3 kg during the various trials. In all trials that used a constant flow of low CO₂ water (≤23 mg/L) entering the growout tank from the harvest tank, approximately 80–90% of the fish swam from the growout tank, through the transfer pipe, and into the harvest tank after the CO₂ concentration in the growout tank had exceeded 60 mg/L. The fish that remained in the growout tank stayed within the area of relatively low CO₂ water at the entrance of the transfer pipe. However, the rate of fish transfer from the growout tank to the harvest tank was more than doubled when the diameter of the transfer pipe was increased from 203 to 406 mm. To consistently achieve fish transfer efficiencies of 99%, water flow rate through the fish transfer pipe had to be reduced to 10–20% of the original flow just before the conclusion of each trial. Reducing the flow of relatively low CO₂ water near the end of each fish transfer event, restricted the zone of relatively low CO₂ water about the entrance of the fish transfer pipe, and provided the stimulus for all but a few remaining fish to swim out of the growout tank. Results indicate that the CO₂ avoidance technique can provide a convenient, efficient, more economical, and reduced labor approach for fish transfer, especially in applications using large and well mixed circular culture tanks.     

Impact of high water carbon dioxide levels on Atlantic salmon smolts (Salmo salar L.): Effects on fish performance, vertebrae composition and structure

The role of high carbon dioxide (CO₂) levels on fish performance, bone structure/composition and as a potential cause of spinal deformities was studied. Two groups of fish were exposed to a low (control) and a high level of CO₂ for 135 days during the freshwater period. After smoltification, the fish were transferred to seawater and followed up for 517 days until they reached harvest weight (3.1 kg BW).Differences in body weight between the control and high CO₂ groups were observed. At the end of the freshwater period, average weight in the group exposed to high CO₂ levels was 20.9% lower than in the control group. Specific growth rates (SGR) from the start of the experiment (10 g BW) to smolt stage were 1.63 ± 0.04 and 1.36 ± 0.01 for the control group and the high CO₂ group, respectively. Differences in body weight were maintained during the initial stages of the seawater period, but were not observed at harvest weight.Nephrocalcinosis was not observed in any of the experimental groups at the end of the freshwater period and no external signs of spinal deformities were observed either at smolt stage or at harvest weight. X-rays revealed mild abnormalities in some vertebrae bodies, which could not be related to any experimental group. Despite the lack of signs of pathological bone alterations, the histological examination suggested that the exposure to high CO₂levels resulted in an increase in trabeculae volume and a higher rate of bone remodeling at the end of the freshwater period. Furthermore, fish exposed to a high CO₂ level presented a higher bone ash content at the end of the freshwater period. These differences could not be observed at the end of the grow-out period.     

溶解无机氮加富对海带养殖水体无机碳体系的影响

通过室内模拟实验,研究了在海带养殖水体中添加不同浓度的无机氮(NO-3-N和NH+4-N)对海水无机碳体系的影响。结果表明,无机碳体系各组分的变化趋势与无机氮添加浓度和无机氮形态有关。当NO-3-N和NH+4-N浓度范围分别在(4.73~52.78)μmol/L和(2.56~34.66)μmol/L时,DIC、HCO-3和pCO2均随着营养盐浓度的增加呈下降趋势,其中以NO-3-3和NH+4-3组变化最为明显,均达到最低值,分别为2 054、2 112μmol/L,1 776、1 869μmol/L,86、114μatm;而当NO-3-N和NH+4-N浓度范围分别为(52.78~427.29)μmol/L、(34.66~268.33)μmol/L时,DIC、HCO-3和pCO2随着营养盐浓度的增加,其下降幅度逐渐减弱,但实验结束时DIC、HCO-3和pCO2仍低于对照组。NO-3-N对海带养殖水体无机碳体系的影响较NH+4-N明显,加NO-3-N组对水体的固碳能力显著高于加NH+4-N组。当NO-3-N和NH+4-N浓度分别为52.78μmol/L、34.66μmol/L时,海带的光合固碳能力达到最大,过高或者过低均会降低海带对水体无机碳的吸收固定。     

Research of dissolved oxygen prediction in recirculating aquaculture systems based on deep belief network

Recirculating aquaculture has received more and more attention because of its high efficiency of treatment and recycling of aquaculture wastewater. The content of dissolved oxygen is an important indicator of control in recirculating aquaculture, its content and dynamic changes have great impact on the healthy growth of fish. However, changes of dissolved oxygen content are affected by many factors, and there is an obvious time lag between control regulation and effects of dissolved oxygen. To ensure the aquaculture production safety, it is necessary to predict the dissolved oxygen content in advance. The prediction model based on deep belief network has been proposed in this paper to realize the dissolved oxygen content prediction. A variational mode decomposition (VMD) data processing method has been adopted to evaluate the original data space, it takes the data which has been decomposed by the VMD as the input of deep belief network (DBN) to realize the prediction. The VMD method can effectively separate and denoise the raw data, highlight the relations among data features, and effectively improve the quality of the neural network input. The proposed model can quickly and accurately predict the dissolved oxygen content in time series, and the prediction performance meets the needs of actual production. When compared with bagging, AdaBoost, decision tree and convolutional neural network, the VMD-DBN model produces higher prediction accuracy and stability.     

A combined model of dissolved oxygen prediction in the pond based on multiple-factor analysis and multi-scale feature extraction

As dissolved oxygen (DO) is an important indicator of water quality in aquaculture, an accurate prediction for DO can effectively improve quantity and quality of product. Accordingly, a novel hybrid dissolved oxygen prediction model, which combines the multiple-factor analysis and the multi-scale feature extraction, is proposed. Firstly, considering that dissolved oxygen is affected by complex factors, water temperature and pH are chosen as the most relevant environmental factors for dissolved oxygen, using grey relational degree method. Secondly, the ensemble empirical mode decomposition (EEMD) is adopted to decompose the dissolved oxygen, water temperature and pH data into several sub-sequences, respectively. Then, the sample entropy (SE) algorithm reconstructs the sub-sequences to obtain the trend component, random component and detail component. Lastly, regularized extreme learning machine (RELM), a currently effective and stable artificial intelligent (AI) tool, is applied to predict three components independently. The prediction models of random component, detail component and trend component are RELM1, RELM2 and RELM3 respectively. The dissolved oxygen, water temperature and pH of the random component forms the input layer of RELM1, and predicted value of dissolved oxygen in the random component is the output layer of RELM1. The input and output of RELM2 and RELM3 are similar to that of RELM1. Final prediction results are obtained by superimposing three components predicted values. One of the main features of the proposed approach is that it integrates the multiple-factor analysis and the multi-scale feature extraction using grey correlation analysis and EEMD. Its performance is compared with several outstanding algorithms. Results for experiment show that the proposed model has satisfactory performance and high precision.     

Prediction of dissolved oxygen in pond culture water based on K-means clustering and gated recurrent unit neural network

Dissolved oxygen in water is an important ecological factor in ensuring the healthy growth of aquatic products, as hypoxic stress is known to restrict the growth of aquatic products. The accurate monitoring and prediction of dissolved oxygen is the key to precise regulation and control of pond aquaculture water quality. The current dissolved oxygen prediction model has some limitations, such as a short prediction period and inadequate prediction accuracy for actual production demands. Therefore, a prediction model of dissolved oxygen in pond culture was proposed based on K-means clustering and Gated Recurrent Unit (GRU) neural network. Firstly, the key factors affecting the changes in dissolved oxygen were selected by principal component analysis (PCA). The dissolved oxygen time series was then subjected to K-means clustering, and the dissolved oxygen prediction model was constructed using GRU. To improve the clustering effect, we enhanced the similarity calculation for the time series based on the variation of dissolved oxygen. This process combined the Euclidean distance with the dynamic time-warping distance. The proposed method can predict the dissolved oxygen content of aquaculture water over different time intervals according to the demands of real-world scenarios. The average absolute error of the 30-min interval model was 0.264, and the mean absolute percentage error was 3.5 %. Experimental results indicated that the proposed method achieves higher prediction accuracy and flexibility than the conventional approach.     

Simulation of future dissolved oxygen distribution in pond culture based on sliding window-temporal convolutional network and trend surface analysis

Dissolved oxygen (DO) is a key ecological factor to measure the quality of water in the aquaculture. As the pond water body is affected by the breeding environment, the spatial distribution of DO shows a certain law in the entire pond. Therefore, to simulate the distribution of DO in aquaculture waters and grasp the temporal and spatial variation of DO is the key to achieving precise regulation of DO. For this purpose, this paper proposed a method for simulating the temporal and spatial distribution of DO in pond culture based on a sliding window-temporal convolutional network together with trend surface analysis (SW-TCN-TSA). This paper first utilized SW to construct DO data sets with different prediction durations, and then used the improved TCN model to realize one-dimensional time series prediction for DO at single monitoring point. Based on the prediction results of DO, a TSA method was performed on the predicted values of DO at the extreme moments of all discrete monitoring points, so as to realize the simulation of the temporal and spatial distribution of DO in the pond. Experimental results show that the SW-TCN model has better prediction performance for one-dimensional time series prediction of DO. Compared with traditional deep networks, such as CNN, GRU, LSTM, CNN-GRU and CNN-LSTM, the values of evaluation indicators (MSE, MAE and RMSE) have been greatly improved. In the process of trend surface fitting, all fitting R² of DO at different water depths are higher than 0.9, indicating that the TSA can accurately reflect the temporal and spatial distribution of DO. This method can provide a basis for the prediction and early warning of DO in the three-dimensional space of the pond and has high practicability in aquaculture.     

Effects of dissolved carbon dioxide on energy metabolism and stress responses in European seabass (Dicentrarchus labrax)

Elevated carbon dioxide concentrations reduce feed intake and growth in several fish species and induce stress responses. In this study, the effects of moderately elevated CO₂ levels on performance, energy partitioning, swimming activity and stress response in European seabass were assessed. European seabass (140.0 g) were reared under two levels of CO₂ (1.6 and 7.0 mg L^?1) and two feeding levels (FLs) (maintenance and satiation) for 60 days, and fish swimming speed was recorded. At the end of the experiment, fish were subjected to an acute stress test. Blood cortisol, glucose and lactate were analysed. Energy and nitrogen balances were quantified based on measurements of body composition and digestibility coefficients. Moderately elevated chronic CO₂ level did neither affect energy requirements for maintenance nor the utilization of digestible energy for growth. However, swimming activity data suggests that FL dependent alterations in energy partitioning took place. Blood cortisol values after the acute stress were affected by additional CO₂ exposure and this effect was also dependent on FL. The elevated CO₂ exposure of 7.0 mg L^?1 appears to act as a chronic stressor as adaptive responses took place, however, this CO₂ exposure seems to be still within the allostatic load of the fish.     

高密度CO2与热处理对凡纳滨对虾肉品质的影响

高密度CO₂(DPCD)是未来非常具有前景的非热加工技术之一,可以实现对食品的杀菌和钝酶,并对其品质产生影响。为了探讨高密度CO₂对凡纳滨对虾肉品质的影响,实验以凡纳滨对虾为研究对象,采用DPCD和热处理,测定虾肉营养成分、质量损失、pH值、持水力、质构、蛋白质组成和呈味成分等的变化。结果显示:与鲜虾相比,DPCD处理会造成虾肉水分和粗脂肪含量显著降低(P<0.05),但粗蛋白含量无显著变化(P>0.05),而经热处理会使虾肉粗蛋白和粗脂肪含量都显著减少(P<0.05);DPCD处理造成虾肉质量损失达16.02%±1.90%,但对虾肉pH值无显著影响(P>0.05);DPCD和热处理都能使虾肉蛋白质发生变性,造成持水力显著下降(P<0.05),从(84.79±5.25) g/100 g分别下降至(65.18±2.06)和(65.58±2.08) g/100 g;DPCD处理对虾肉硬度无显著影响(P>0.05),而热处理则造成虾肉硬度显著升高(P<0.05),从(3.48±0.49) N上升到(7.37±0.76) N,DPCD处理和热处理都显著降低了虾肉弹性(P<0.05),从0.88±0.08分别下降到0.71±0.03和0.78±0.03;除甜菜碱、PO^3-₄、Cl^-,DPCD处理对虾肉主要呈味成分(游离氨基酸、ATP及关联化合物、有机酸、糖原等)无显著影响(P>0.05),而热处理则会造成大部分呈味成分的损失。实验表明,DPCD处理对虾肉品质的影响要小于热处理对虾肉品质的影响。     

A multivariate study on the effects of dietary vitamin K, vitamin D3 and calcium, and dissolved carbon dioxide on growth, bone minerals, vitamin status and health performance in smolting Atlantic salmon Salmo salar L.

Bone deformities represent an increasing, unsolved problem for intensive farming of Atlantic salmon, Salmo salar L. To increase insight into nutritional and environmental impacts on bone mineralization, 16 groups of Atlantic salmon parr were fed diets with different levels of menadione sodium bisulphite (MSB), vitamin D₃, and calcium (Ca), and exposed to different levels of dissolved carbon dioxide (CO₂) based on a multivariate factorial design (2⁴). The composition of the experimental diets was either a high or low level of each of the design variables. In addition, four groups (centre points) were fed a diet with intermediate levels of all dietary parameters and exposed to an intermediate level of dissolved CO₂ to record the variation among tanks. After 6 weeks in fresh water, fish were marked according to group, transferred to a common net pen in sea water and fed commercial feed for another 11 weeks. High levels of CO₂ decreased fish growth both in fresh water and sea water. No visible bone deformities were recorded at any time. Dry matter, protein and ash compositions of bone and concentrations of Ca, phosphorus (P) and magnesium (Mg) in bone were not significantly affected by any of the design variables. Minor effects were observed in clinical parameters recording health performance, stress and plasma Ca homeostasis. The mean level of vitamin D₃ measured as cholecalciferol in fish organs increased significantly during the freshwater period and reflected dietary vitamin D₃, whereas the level decreased when fish were fed commercial feed in sea water. The level of menaquinone in liver increased significantly in response to high dietary levels of MSB, indicating that dietary MSB was converted to menaquinone in salmon.     

二氧化碳麻醉对鲢生理生化指标及神经调控相关基因表达的影响

为研究CO₂麻醉对鲢(Hypophthalmichthys molitrix)的生理效应及其作用机制,设置充CO₂、充O₂实验组,并以持续充空气作为对照组,分别测定鲢的生理生化指标、神经递质含量和神经调控相关基因表达变化。结果显示:与对照组比较,CO₂麻醉处理后,鲢呼吸频率降低37%,血清K⁺浓度显著升高4倍,肝糖原含量降低86.96%,血清中乳酸、尿素氮浓度分别升高1.08和1.57倍。以上指标在充O₂处理下变化趋势与充CO₂处理下相似,但是测定值高于充CO₂处理组。CO₂处理组血清皮质醇水平无显著变化,但充O₂处理组皮质醇显著升高2.41倍;CO₂处理组的脑组织多巴胺和5-羟色胺水平下降。qPCR结果显示,CO₂麻醉后脑组织双孔K⁺通道蛋白kcnk3a、kcnk9和缺氧相关hif-1α和epo基因表达...     

基于灰色–马尔科夫模型评估石雀滩海洋牧场岩礁鱼类碳储量

本研究以石雀滩海洋牧场许氏平鲉(Sebastes schlegelii)、大泷六线鱼(Hexagrammos otakii)、褐菖鲉(Sebasticus marmoratus)、铠平鲉(Sebastes hubbsi)、厚头平鲉(Sebastes pachycephalus)等岩礁鱼类为研究对象,基于2017—2020年地笼网采捕的渔获量数据,采用元素分析法和灰色−马尔科夫模型,统计并预测了岩礁鱼类现存生物量的碳储量(以下简称岩礁鱼类碳储量)。结果显示,岩礁鱼类干样碳含量分布范围为42.95%~50.19%,平均值为(46.11±2.34)%;鲜样碳含量分布范围为11.05%~13.25%,平均值为12.30%。2017年春季、2018年冬季、2019年春季和2020年冬季鱼礁区岩礁鱼类碳储量分别为293.46、104.49、119.40和48.48 t,呈波动式下降趋势;对照区分别为21.64、59.07、6.73和0 t,呈先上升后下降的趋势。灰色−马尔科夫模型验证数据平均相对误差为8%,较GM(1,1)模型预测精度提升了12%。经预测,鱼礁区岩礁鱼类碳储量呈下降趋势,2021年春季、2022年冬季、2023年春季和2024年冬季分别为64.84 、49.84、25.28和19.43 t。研究结果可为评估岩礁鱼类碳汇潜力提供依据,为建立渔业碳汇基础上的海洋牧场岩礁鱼类资源开发策略提供科学参考。     

渔具模型空间形状的计测方法

为便于对渔具作业形状的数值计算结果的精度评价提供更详细的实验验证数据,提出了利用2台数码摄像机同时从实验水槽的下方和侧面立体拍摄渔具模型展开形状的实验方法和通过图像解析装置及软件读取各节点的位置坐标并进行误差修正的解析方法。利用本方法得到的一系列节点位置坐标和自行编制的计算机绘图程序,不仅可以方便地进行水流中渔具模型的二维或三维展开形状的虚拟呈现,而且还可以任意地将图形进行旋转、放大和缩小,以对渔具展开形状的各个局部进行比实际模型试验更为详细的观察和比较。通过对实验例的比较和分析,表明本方法具有较好的精度和实用性。