External Water Subcooling To Improve The Performance Of A CO2 Heat Pump For Water Heating That Uses Greywater As Heat Source

The use of CO2 heat pumps for water heating combined with energy recovery from greywater is a promising technology that can help to improve the efficiency of both domestic hot water (DHW) generation and space heating. A key aspect to keep in mind during the design of any CO2 refrigeration or heat pump system is the fact that the optimal gas cooler pressure in a transcritical CO2 cycle is mainly dependent on the refrigerant temperature at the gas cooler outlet. In space heating applications as well as DHW generation, operating conditions require high temperatures of refrigerant at the gas cooler outlet. That can lead to very high optimal pressures, in some cases, even higher than the maximum pressure of the system. The use of a subcooler fed by the same greywater used in the evaporator can help to reduce the optimal pressure and improve the efficiency of the system. When the greywater passes first through the subcooler, the evaporation temperature can be increased while the optimal pressure is reduced. When the greywater passes first through the evaporator, the evaporation temperature remains constant, but the refrigerant temperature at the gas cooler outlet can be reduced to a lower value. So, the order in which the water flows through subcooler and evaporator can affect the system’s efficiency and the best control strategy will depend on the operating conditions. First, a numerical model is used to model an experimental facility and model results are compared to some preliminary experimental results. Finally, this contribution analyses the influence that the greywater conditions (temperature, mass flow rate and flow order), as well as the subcooler efficiency have in the system’s efficiency depending on the operating conditions (DHW generation or space heating) in order to stablish the control strategy that optimize the system’s performance

Heterogeneous Photocatalytic Degradation of Ibuprofen Over TiO2–Ag Supported on Activated Carbon from Waste Tire Rubber

In recent years it has been discovered that some common use medicines, such as ibuprofen and other nonsteroidal anti-inflammatory drugs, are found in water sources in concentrations that have the potential to affect aquatic organisms. On the other hand, waste used tires are a massive problem for the environment due to the leaching of toxic compounds to soils and water. Also, the exposition to environmental conditions can make them sources of vectors like mosquitoes. In this work, three activated carbon (AC) catalysts derived from waste tire rubber, titanium dioxide and silver were synthesized using the sol–gel method. Morphological characterizations such as SEM and TEM were performed in which, the agglomeration of titanium particles and silver crystals on the surface of the AC is evident. In the XRD analysis, the presence of elemental silver nanoparticles was detected. In the diffuse reflectance spectroscopy analysis, the decrease in the titanium band gap, as well as activity in the visible spectrum, was observed. The photocatalytic tests were performed at pH 3 and 7 in the presence of UV/Vis radiation. These tests show that there are differences between the catalyst in both, UV and visible regions. Adsorption is a major phenomenon for the removal of ibuprofen, followed by photolytic decomposition. In visible spectra, the catalysts show a good performance for the removal of ibuprofen

Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.MCIU - Nvidia(UMA18-FEDERJA-084

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Computational approaches to Explainable Artificial Intelligence:Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.</p