scholarly journals Development of a Real-Time Controlled Bio-Liquor Circulation System for Swine Farms: A Lab-Scale Study

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 311
Author(s):  
Seungsoo Kim ◽  
Arif Reza ◽  
Soomin Shim ◽  
Seunggun Won ◽  
Changsix Ra
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Swine Manure ◽  
Circulation Type ◽  
Time Profile ◽  
Circulation System ◽  
Efficient Operation ◽  
Swine Farms ◽  
Efficient Detection ◽  
Odor Emission

In this study, an attempt was made to develop a real-time control strategy using oxidation–reduction potential (ORP) and pH (mV) time profiles for the efficient operation of bio-liquor circulation system (BCS) in swine farms and its effectiveness in reducing odor emission through improving manure properties in the slurry pit was evaluated. The lab-scale BCS used in this study comprised a bioreactor and a slurry pit. The bioreactor was operated in a sequence of inflow of swine manure → anoxic phase → aerobic phase → circulation to the slurry pit. The improvement in swine manure properties was elucidated by comparing the results of the BCS slurry pit (circulation type, CT) and conventional slurry pit (non-circulation type, NCT). The results revealed that the ORP time profile successfully detected the nitrate knee point (NKP) in the anoxic phase. However, it was less stable in detecting the nitrogen break point (NBP) in the aerobic phase. The pH (mV) time profile showed a more efficient detection of NBP. Compared to the NCT slurry pit, concentrations of ammonium nitrogen (NH4-N) and soluble total organic carbon (STOC) and other analyzed swine manure properties were much lower in the CT slurry pit. In the aspect of odor reduction, around 98.3% of NH3 was removed in the CT slurry pit. The real-time controlled BCS can overcome the drawbacks of fixed time-based BCS operation and therefore can be considered as a useful tool to reduce odor emission from intensive swine farming operations. However, further studies and refinement in control algorithms might be required prior to its large-scale application.

Methods for the Quantification of Salivary Cortisol and of a-amylase in Biosensors and Portable Devices

2018 ◽  
Vol 68 (12) ◽  
pp. 2857-2859
Author(s):  
Cristina Mihaela Ghiciuc ◽  
Andreea Silvana Szalontay ◽  
Luminita Radulescu ◽  
Sebastian Cozma ◽  
Catalina Elena Lupusoru ◽  
...  
Keyword(s):  
Real Time ◽  
Medical Practice ◽  
Salivary Cortisol ◽  
Clinical Studies ◽  
Large Scale ◽  
Psychological Research ◽  
Portable Devices ◽  
Salivary Amylase ◽  
Specificity And Sensitivity

There is an increasing interest in the analysis of salivary biomarkers for medical practice. The objective of this article was to identify the specificity and sensitivity of quantification methods used in biosensors or portable devices for the determination of salivary cortisol and salivary a-amylase. There are no biosensors and portable devices for salivary amylase and cortisol that are used on a large scale in clinical studies. These devices would be useful in assessing more real-time psychological research in the future.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

scholarly journals Mantle-derived helium released through the Japan trench bend-faults

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jin-Oh Park ◽  
Naoto Takahata ◽  
Ehsan Jamali Hondori ◽  
Asuka Yamaguchi ◽  
Takanori Kagoshima ◽  
...  
Keyword(s):  
Upper Mantle ◽  
Large Scale ◽  
Japan Trench ◽  
Helium Isotope ◽  
Normal Faults ◽  
Circulation System ◽  
Deep Penetration ◽  
Oceanic Plate ◽  
Seismic Reflection Data ◽  
Reflection Data

AbstractPlate bending-related normal faults (i.e. bend-faults) develop at the outer trench-slope of the oceanic plate incoming into the subduction zone. Numerous geophysical studies and numerical simulations suggest that bend-faults play a key role by providing pathways for seawater to flow into the oceanic crust and the upper mantle, thereby promoting hydration of the oceanic plate. However, deep penetration of seawater along bend-faults remains controversial because fluids that have percolated down into the mantle are difficult to detect. This report presents anomalously high helium isotope (3He/4He) ratios in sediment pore water and seismic reflection data which suggest fluid infiltration into the upper mantle and subsequent outflow through bend-faults across the outer slope of the Japan trench. The 3He/4He and 4He/20Ne ratios at sites near-trench bend-faults, which are close to the isotopic ratios of bottom seawater, are almost constant with depth, supporting local seawater inflow. Our findings provide the first reported evidence for a potentially large-scale active hydrothermal circulation system through bend-faults across the Moho (crust-mantle boundary) in and out of the oceanic lithospheric mantle.

scholarly journals Improving the Anaerobic Digestion of Swine Manure through an Optimized Ammonia Treatment: Process Performance, Digestate and Techno-Economic Aspects

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 787
Author(s):  
Anna Lymperatou ◽  
Niels B. Rasmussen ◽  
Hariklia N. Gavala ◽  
Ioannis V. Skiadas
Keyword(s):  
Anaerobic Digestion ◽  
Large Scale ◽  
Treatment Process ◽  
Aqueous Ammonia ◽  
Swine Manure ◽  
Batch Experiments ◽  
Ammonia Treatment ◽  
Aqueous Ammonia Soaking ◽  
High Ammonia ◽  
Reduction Efficiency

Swine manure mono-digestion results in relatively low methane productivity due to the low degradation rate of its solid fraction (manure fibers), and due to the high ammonia and water content. The aqueous ammonia soaking (AAS) pretreatment of manure fibers has been proposed for overcoming these limitations. In this study, continuous anaerobic digestion (AD) of manure mixed with optimally AAS-treated manure fibers was compared to the AD of manure mixed with untreated manure fibers. Due to lab-scale pumping restrictions, the ratio of AAS-optimally treated manure fibers to manure was only 1/3 on a total solids (TS) basis. However, the biogas productivity and methane yield were improved by 17% and 38%, respectively, also confirming the predictions from a simplified 1st order hydrolysis model based on batch experiments. Furthermore, an improved reduction efficiency of major organic components was observed for the digester processing AAS-treated manure fibers compared to the non-treated one (e.g., 42% increased reduction for cellulose fraction). A preliminary techno-economic analysis of the proposed process showed that mixing raw manure with AAS manure fibers in large-scale digesters could result in a 72% increase of revenue compared to the AD of manure mixed with untreated fibers and 135% increase compared to that of solely manure.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

scholarly journals A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

2021 ◽  
Vol 77 (2) ◽  
pp. 98-108
Author(s):  
R. M. Churchill ◽  
C. S. Chang ◽  
J. Choi ◽  
J. Wong ◽  
S. Klasky ◽  
...  
Keyword(s):  
Real Time ◽  
International Collaboration ◽  
Large Scale ◽  
Data Transfer ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Scale Data
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