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Simple Summary: Soybean meal is an effective protein source in aquafeeds and is characterized by high protein content, high digestibility, reasonable price, and stable supply. The quality of soybean meal varies with factors such as seed genotype, soil type, planting area, processing conditions, storage conditions, etc. Differences in soybean meal quality may lead to differences in the production performances of animals. For this reason, we investigated the nutrient composition, anti-nutritional factors and physicochemical properties of different soybean meals produced on the same production line with soybeans from different origins, and compared the effects of different soybean meals on the physiological characteristics of Nile tilapia (Oreochromis niloticus GIFT). This study identified key indicators that could distinguish the quality of soybean meals, and established a correlation model between quality indicators of soybean meal and the growth performance of Nile tilapia. These results are of great significance for evaluating the quality of soybean meals and promoting the efficient application of soybean meal in aquafeeds. Soybean meal (SBM) is the most important plant protein source in animal feed. This study investigated the characteristics of different SBMs, produced by soybeans from America and Brazil (SBM-A and SBM-B) in 2017–2021 under the same controlled conditions. The effects of different SBMs on the growth performance of Nile tilapia (Oreochromis niloticus, GIFT) and apparent digestibility coefficients (ADCs) of nutrients and energy were studied. The results showed that protein dispersibility index (PDI), urease activity (UA), glycinin and fiber were the four primary key indicators for distinguishing the characteristics of the tested SBMs. The meta-analysis results suggested that UA, glycinin, and fiber showed a negative effect on the survival rate (SR) and weight gain rate (WGR) of the Nile tilapia, whereas β-conglycinin, PDI, and nitrogen solubility index (NSI) had a positive effect on the SR and WGR of the fish. The ADCs of dry matter, the gross energy, phosphorus, crude protein, valine (Val), lysine (Lys), histidine (His), serine (Ser), and glutamate (Glu) of the Diet-A group (SBM-A inclusion) were significantly higher than those in the Diet-B group (SBM-B inclusion) (p < 0.05). However, no significant difference was found in ADCs of macro-nutrients between the two SBMs (p > 0.05). Overall, PDI, UA, glycinin, and fiber were the main indicators reflecting the characteristics of the tested SBMs, and UA, glycinin, β-conglycinin, and PDI had the greatest impact on the growth performance of Nile tilapia in this study. PDI was a more sensitive indicator than NSI for representing the protein quality of SBM. [ABSTRACT FROM AUTHOR]

Aiming at the problem of unsatisfactory diagnosis performance of conventional fault diagnosis methods for transformer, a novel method based on maximally collapsing metric learning algorithm (MCML) and parameter optimization kernel extreme learning machine (KELM) is proposed in this study. First, a new set of dissolved gas analysis (DGA) features combination, which can reflect the transformer fault information, is used to form the input feature space. Then, the MCML is employed to reduce the feature space dimension to extract a set of optimal DGA features combination. Finally, the salp swarm algorithm (SSA) is utilized to optimize the parameters in KELM to establish an SSA‐KELM model, which is adopted to diagnose and identify transformer faults. The proposed method is applied to the International Electrotechnical Commission (IEC) TC 10 database, and the results show that the feature extraction effect of MCML is superior than that of linear discriminant analysis, neighborhood preserving embedding, and Laplacian eigenmaps. The optimal DGA feature set is more advantageous than the frequent‐used DGA data, IEC ratios, Rogers ratios, and Doernenburg Ratios. The diagnosis accuracy of SSA‐KELM is better than that of KELM, particle swarm optimization‐KELM, genetic algorithm‐KELM, and loin swarm optimization‐KELM. Furthermore, the generalization and robustness ability of the MCML and SSA‐KELM is confirmed by the China DGA samples, the obtained results verify the reliability and validity of the proposed method again. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The key to intelligent fault diagnosis for transformer is to find relevant dissolved gas characteristics with the capability of representing different types of faults. However, the existing problem is that using a single traditional ratio method as the inputs of classification module cannot obtain high classification performance. Therefore, a fault diagnosis method for transformer based on neighborhood component analysis (NCA) and capuchin algorithm (CapSA)-regularized extreme learning machine (RELM) is proposed. Firstly, 21-dimensional features of the oil-immersed transformer are extracted based on the traditional ratio methods such as IEC three-ratio and Roger's ratio. Secondly, The NCA is used to select the feature to obtain an effective 6-dimensional feature. Then, these features are utilized as the input of the RELM model for training, and the parameters of model are optimized by CapSA. Finally, the feasibility and validity of the proposed method are verified by actual data sets collected from previous publications. The experimental results show that the proposed method has higher diagnostic precision and stability. [ABSTRACT FROM AUTHOR]

Clostridium autoethanogenum protein (CAP) is a novel single-cell protein source derived from the inactivated bacterial biomass produced by CO syngas biological fermentation. There has been limited research on the effect of process parameters on the physical quality of aquatic extruded feed containing CAP. A low-starch formula containing 180 g/kg CAP was used to study the effects of preconditioning moisture content (22–30 %), screw speed (240–300 rpm), die temperature (120–150 ℃) and feed rate (56.5–94.5 kg/h) on the physical quality of extruded feed and specific mechanical energy (SME) of the extruder. Preconditioning moisture content had significant effects on the bulk density (P < 0.01), hardness (P < 0.05) and oil absorption capacity (P < 0.01). Screw speed had significant effects on the bulk density (P < 0.05). Die temperature did not have a significant impact on the physical quality of the feed and SME (P > 0.05). Feed rate had significant effects on the bulk density and SME (P < 0.01). The interactions between moisture content and feed rate had significant effects on the bulk density, expansion ratio, hardness, oil absorption capacity and SME (P < 0.01). The quadratic term of moisture content had significant effects on the bulk density, expansion ratio, hardness, oil absorption capacity (P < 0.01) and SME (P < 0.05). The quadratic term of screw speed had significant effects on the oil absorption capacity (P < 0.05). The quadratic term of feed rate had significant effects on the SME (P < 0.01). The water solubility values (WSI; 3.84–5.28 %) and water absorption index (WAI) values (2.61–3.71) of the extruded feeds were distributed within a narrow range. The WSI values decreased with an increase in the feed rate, while the hydration time increased with the increase in moisture content. Based on the Response Surface Methodology results and imposed constraints, the operation window of process parameters for the test diet was obtained. • Extrusion conditions affect the physical quality of low-starch floating feed containing 180 g/kg Clostridium autoethanogenum protein. • Response Surface Methodology was used to analyze the impacts of process parameters on the physical quality of extruded feed. • Searching for optimal process parameters for processing a low-starch extruded floating fish feed. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the extrusion cooking behaviours of two wheat flour samples (W1DK, W2CH) in two different twin-screw extrusion systems, where the wheat flour samples were characterised by rapid visco analyser (RVA) measurements. A new method is proposed to model and compare the wheat flour extrusion behaviours in the two systems through the RVA measurements, an extrudate bulk density model and a melt viscosity calculation. A new method is suggested to calculate the melt viscosity in the extrusion process. The average viscosity value of the W1DK sample was 64% higher than that of the W2CH sample in the RVA measurements. The comparison results show that the high viscosity for W1DK obtained from the RVA corresponds to a high reduced melt viscosity (ηr, (1/s)γ) for W1DK, i.e., ηr = 0.25-2.61 (1/s)γ (ηr = 0.042-0.082 (1/s)γ for W2CH), a high specific mechanical energy (SME) for W1DK, i.e., SME = 8.16-11.28 kJ/kg (SME = 5.57-9.99 kJ/kg for W2CH) and a low extrudate bulk density (ρB) for W1DK, i.e., ρB = 185-488 g/L (1 g/L = 1 kg/m3) (ρB = 406-510 g/L for W2CH) in the two extrusion systems. The RVA viscosity and the calculated melt viscosity can be used to identify a recipe characteristic in terms of torque and extrudate bulk density in the two extrusion systems. A comparison has also been made for the bulk density model and response surface methodology in the extrudate bulk density prediction. [ABSTRACT FROM AUTHOR]

• The ratio of amylose to amylopectin could be used to adjust feed pellet quality in low starch feed recipe extrusion. • High moisture content and high die temperature were more conducive to the production of low-starch extruded floating feed. • The Bulk Density Model can replace the Response Surface Methodology to predict the bulk density of low starch extruded feed and provide a physical explanation for the interactions between pellet bulk density and process parameters. The purpose of the investigation was to study the effects of the ratio of wheat flour and cassava and extrusion process parameters on the physical qualities of low-starch content floating feed in a pilot extrusion plant in order to transfer the production characteristics to scale manufacture. The results show that with an increase of cassava content in formulations, the feed pellet bulk density decreases. When the cassava content is ≥ 54.3 g/kg (wt), the floatability of feed pellet reaches 100 %. The feed pellet hardness depends significantly on the starch sources (P < 0.05). The ratio of wheat flour and cassava in a recipe has significant but irregular impacts on the feed pellet water stability. A wheat flour/cassava ratio (54.3/54.3) was selected as an optimal formulation. For the optimal recipe, a pilot extrusion trial shows that an increase of moisture content and die temperature gives a decrease in the feed pellet bulk density, then an increase in pellet floating possibility. Based on Response Surface Methodology, the optimal parameters for processing the selected recipe are moisture content 32 % (wt), screw speed 247 rpm and die temperature 135 ℃. A theoretical model was also applied to cross-check the results from Response Surface Methodology. [ABSTRACT FROM AUTHOR]

This study aimed to estimate the effects of extrusion variables on extruded aquafeed water solubility using a starch gelatinization model. A new equation was derived from a classical starch gelatinization degree model to calculate aquafeed water solubility. After considering the data of five formulations, the average absolute deviation (AAD) of the new equation was 7.97% for the extrudate water solubility and 2.24% for the water solubility index. In the most cases, water solubility and water solubility index increased with an increase in the process screw speed and temperature but decreased with a decrease in water content. On the other hand, when the moisture content increased from 0.2 to 0.3 kg/kg, the water solubility increased from 4.91 to 5.22% (wt) for a typical recipe in the low moisture content extrusion process. The new equation established a way to quantitatively elaborate the relationship between the extrudate water solubility and extrusion process variables. [ABSTRACT FROM AUTHOR]