Rheological Properties of Sand-Laden Dairy Manure: Modeling by Concentration and Temperature

Liquid dairy manure is applied with irrigation water or injected into the soil, as well as being used for the production of biogas. One of the problems facing dairy operations is the slurry manure transportation and pumping through pipelines to distant locations of the farm, especially when the sand content (used as bedding material) increases. In this study, rheological properties of sand-laden dairy manure (SLDM) including total solids (TS%), density and apparent viscosity were determined at four levels of manure solids (7, 10, 13 and 16 %TS) as well as the liquid manure taken from a manure separator tested at shear rates of 1.76 to 225.28  using a concentric cylindrical rheometer. Effect of temperature on the apparent viscosity at five levels (10, 21, 30, 40 and 50 °C) and various shear rates was investigated. Fresh manure collected with a scraper contained 36% sand. Results of the study showed that sand-laden manure is a non-Newtonian fluid, and behaves as a shear thinning material (pseudoplastic), but approaches Newtonian fluid when concentration decreases. Increasing the sand content, will increase density and reduce the viscosity of the slurry manure. Apparent viscosity at a shear rate of 112.64  and ambient temperature of 21°C, for 7,10,13,16 TS% and effluent of separator was 37.1, 101.5, 352.9, 773.4 and 147.4 mPa.s, respectively. The relationship between temperature, concentration (TS%) and shear rate with apparent viscosity was represented  by an exponential model

Analysis of the combinative effect of ultrasound and microwave power on Saccharomyces cerevisiae in orange juice processing

High temperature in conventional method for juice pasteurization causes adverse effects on nutrients and nutritional value of food. The objective of this study was to examine the effect of microwave output power, temperature, ultrasound power, and ultrasonic exposure time on Saccharomyces cerevisiae in orange juice. Based on our findings, microwave output power, ultrasound power, ultrasonic exposure time orange juice temperature were the most effective factors to reduce S. cerevisiae. The results showed that the quadratic model included was the best model for account. The model showed that regarding decrease of S. cerevisiae account microwave-induced temperature was more effective than microwave output power. Also, compared to microwave power, the ultrasound power was more effective on S. cerevisiae reduction. The optimum processing condition was 350 W microwave power, 35 °C temperature, 778.2 W ultrasonic power, and 11 min of exposure. Based on our result, the consumption energy was 142.77 J/mL with no remaining of S. cerevisiae. The results showed that the given scores by panelists to the combinative and conventional methods for color and flavor indices were significant (P b 0.05). Industrial Relevance: In order to reduce the adverse effects (loss of vitamins, flavor, and non-enzymatic browning) of the thermal pasteurization method, other methods capable of inactivation of microorganisms can be applied. In doing so, non-thermal methods are of interest, including pasteurization using high hydrostatic pressure processing (HPP), electric fields, and ultrasound waves. The ultrasound technology has been the main focus of studies in recent years. However, the main challenge facing the non-thermal technologies in food processing is the inactivation of pathogenic microorganisms and food spoilage agents, which can be achieved by various methods. The aim of present research was examined simultaneous effect of ultrasonic and microwave to remove microorganism. This research introduces new, innovative, and combined method for fruit juice pasteurization, and this method can benefit the food industry

Emulating the Human Mind: A Neural-symbolic Link Prediction Model with Fast and Slow Reasoning and Filtered Rules

Link prediction is an important task in addressing the incompleteness problem of knowledge graphs (KG). Previous link prediction models suffer from issues related to either performance or explanatory capability. Furthermore, models that are capable of generating explanations, often struggle with erroneous paths or reasoning leading to the correct answer. To address these challenges, we introduce a novel Neural-Symbolic model named FaSt-FLiP (stands for Fast and Slow Thinking with Filtered rules for Link Prediction task), inspired by two distinct aspects of human cognition: "commonsense reasoning" and "thinking, fast and slow." Our objective is to combine a logical and neural model for enhanced link prediction. To tackle the challenge of dealing with incorrect paths or rules generated by the logical model, we propose a semi-supervised method to convert rules into sentences. These sentences are then subjected to assessment and removal of incorrect rules using an NLI (Natural Language Inference) model. Our approach to combining logical and neural models involves first obtaining answers from both the logical and neural models. These answers are subsequently unified using an Inference Engine module, which has been realized through both algorithmic implementation and a novel neural model architecture. To validate the efficacy of our model, we conducted a series of experiments. The results demonstrate the superior performance of our model in both link prediction metrics and the generation of more reliable explanations

Fuzzy logic based classification of faults in mechanical differential

Mechanical differentials are widely used in automotive, agricultural machineries and heavy industry applications due to their large transmission ratio, strong load-bearing capacity and high transmission efficiency. The tough operation conditions of heavy duty and intensive impact load may cause damage, hence condition monitoring of these machines is very important. This paper proposes a data driven model-based condition monitoring scheme that is applied to differential. The scheme is based upon a fuzzy inference system (FIS) in combination with decision trees. To achieve this objective, the acoustic signals from a microphone were captured for the following conditions: Health, bearing fault, worn pinion, broken pinion, worn cranwheel and broken cranwheel for tow working levels of differential (1500 and 3000 r/min). Taken signals were in time domain and for extraction more information was converted from time domain to time-frequency domains using wavelet transformation. Subsequently, statistical features were extracted from signals using descriptive statistic parameters, better features were selected by J48 algorithm and used for developing decision trees. In the next stage, fuzzy logic rules were written using the decision tree and fuzzy inference engines were produced. In order to evaluate the proposed J48-FIS model, the data sets obtained from acoustic signals of the differential were used. The total classification accuracy for 1500 and 3000 r/min conditions were 92.5 % and 95 %, respectively, so the work conducted has demonstrated the potential of used method to classify the fault conditions which are represent in differential

Energy consumption, thermal utilization efficiency and hypericin content in drying leaves of St John’s Wort (Hypericum Perforatum)

Massive consumption of energy in the drying industry has prompted extensive research regarding various aspects of drying energy and requirements. Thermal utilization efficiency, specific energy requirement, total energy consumption and hypericin content in drying of St John’s Wort were determined using a hot-air dryer. Experiments were conducted at four air temperature levels (40, 50, 60, and 70°C), three air velocities (0.3, 0.7, and 1 m/s) and three sample thicknesses (1, 2 and 3 cm). Based on the results of data analysis, minimum and maximum levels of energy consumption were 0.56 and 3.24 kWh, respectively. The required specific energy decreased with increasing sample thickness. The minimum and maximum required specific energies were 4.41 and 17.53 for 2 cm thick and 4.28 and 13.67 for 3 cm thick layers (kWh/kg), respectively. The maximum and minimum values of thermal utilization efficiency in different treatments were found to be 14% and 72%, respectively. Hypericin content decreased with increasing temperature and increased with air velocity and product sample thickness, so that the minimum and maximum hypericin amounts were 67 and 355 ppm, respectively

Modelling Rupture Force based on Physical Properties – a Case Study for Roma Tomato (Solanum lycopersicum) Fruits

Biophysical properties of agricultural materials are important in designing of processing machines. In this study, some physical properties of tomato (Solanum lycopersicum) fruits were determined and their mutual relationships were studied. Dimensions (major diameter, minor diameter, and length), mass, volume, fresh and dry matter weight, as well as rupture point under uniaxial loading were measured. Other properties; including Poisson's ratio, modulus of elasticity, energy for rupture, density, arithmetic mean diameter, geometric mean diameter, diameter of equivalent volume sphere, and sphericity were calculated accordingly. Statistical analysis of the data indicated significant correlations between the rupture force and fresh weight, volume, dry weight, major diameter, minor diameter, arithmetic mean diameter, geometric mean diameter, and diameter of equivalent volume sphere. Fruit volume has significant correlations with fresh weight and average diameter. Correlations between major and minor diameters are very significant in this variety. Finally, regression equations were developed to model tomato biophysical properties

Germination and respiration of cotton seeds as affected by oxygen and carbon dioxide

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Effects of microwave pretreatment on the energy and exergy utilization in thin-layer drying of sour pomegranate arils

Energy and exergy analyses may be considered as important tools for design, analysis and optimization of thermal systems. This paper reports on energy and exergy analyses of thin-layer drying of sour pomegranate arils with microwave pretreatment. There were two microwave pretreatments (100W for 20 min and 200 W for 10 min) along with a control treatment (convection drying with no microwave pretreatment). Experiments were carried out at three air temperatures (50, 60 and 70ºC) and three air velocities (0.5, 1 and 1.5 m/s). Results showed that energy utilization and energy utilization ratio increased with time, while exergy efficiency decreased. Energy utilization and drying time decreased considerably with microwave pretreatment of pomegranate arils. The minimum values of exergy loss and exergy efficiency were associated with the 200W microwave pretreatment, while they were maximum for control treatment

Development of a smart machine vision based system to detect water stress in greenhouse tomato plants

Timely detection of water stress in agricultural crops is important. In this paper, a smart classification algorithm was developed to detect water stress in tomato plants that were grown in the greenhouse. During the growth period, thermal and visible light images were acquired from the canopy tops in two states: (1) plants in normal conditions; and (2) plants under water stress. Images were obtained using a camera that recorded simultaneous frames of thermal and visible (red, green, and blue (RGB)) features. Based on these features, 22 parameters were defined and applied to classify the image frames. In order to develop an efficient algorithm, principal component analysis (PCA) was applied to optimize the classifying of parameters. For normalizing the data in PCA, 6 normalization methods were applied and assessed. Among them, peak normalization was the best as its PC1 and PC2 described 94% and 5% of total variation, respectively. Based on the PCA results, 9 parameters were found with most loadings as the most effective indexes that all obtained from the visible features. In other words, the thermal features were not as useful for detecting plant water stress. These parameters were used in multilayer perceptron neural networks (MLPNN) to develop the classification algorithm. The resulting mean-square error and r values for the MLPNN with ten hidden layer were 6.05×10-3 and 0.9905, respectively which shows the robustness of the classification algorithm. This algorithm accuracy was 83.3%