The intelligent buildings provided various incentives to get highly inefficient energy-saving caused by the non-stationary building environments. In the presence of such dynamic excitation with higher levels of nonlinearity and coupling effect of temperature and humidity, the HVAC system transitions from underdamped to overdamped indoor conditions. This led to the promotion of highly inefficient energy use and fluctuating indoor thermal comfort. To address these concerns, this study develops a novel framework based on deep clustering of lagrangian trajectories for multi-task learning (DCLTML) and adding a pre-cooling coil in the air handling unit (AHU) to alleviate a coupling issue. The proposed DCLTML exhibits great overall control and is suitable for multi-objective optimisation based on cooperative multi-agent systems (CMAS). The framework of DCLTML is used greedy iterative training to get an optimal set of weights and tabulated as a layer for each clustering structure. Such layers can deal with the challenges of large space and its massive data. Then the layer weights of each cluster are tuned by the Quasi-Newton (QN) algorithm to make the action sequence of CMAS optimal. Such a policy of CMAS effectively manipulates the inputs of the AHU, where the agents of the AHU activate the natural ventilation and set chillers into an idle state when the outdoor temperature crosses the recommended value. So, it is reasonable to assess the impact potential of thermal mass and hybrid ventilation strategy in reducing cooling energy; accordingly, the assigning results of the proposed DCLTML show that its main cooling coil saves >40% compared to the conventional benchmarks. Besides significant energy savings and improving environmental comfort, the DCLTML exhibits superior high-speed response and robustness performance and eliminates fatigue and wear due to shuttering valves. The results show that the DCLTML algorithm is a promising new approach for controlling HVAC systems. It is more robust to environmental variations than traditional controllers, and it can learn to control the HVAC system in a way that minimises energy consumption. The DCLTML algorithm is still under development, but it can potentially revolutionise how HVAC systems are controlled.
For many years, the construction industry damages have been overlooked such as unreasonable consumption of resources in addition to producing a lot of construction waste but with global awareness growth towards the sustainable development issues, the sustainable construction practices have been adopted, taking into account the environment and human safety. The research aims to propose a management system for construction practices which could be adopted during constructing different types of sustainable buildings besides formulating flowcharts which clarify the required whole phases of sustainable buildings life cycle. The research includes two parts: theoretical part which generally ,handles the sustainability concepts at construction i
... Show MoreChilled ceilings systems offer potential for overall capital savings. The main aim of the present research is to investigate the thermal performance of the indirect contact closed circuit cooling tower, ICCCCT used with chilled ceiling, to gain a deeper knowledge in this important field of engineering which has been traditionally used in various industrial & HVAC systems. To achieve this study, experimental work were implemented for the ICCCCT use with chilled ceiling. In this study the thermal performances of closed wet cooling tower use with chilled ceiling is experimentally and theoretically investigated. Different experimental tests were conducted by varying the controlling parameters to investigate their effects
... Show MoreAs cities across the world grow and the mobility of populations increases, there has also been a corresponding increase in the number of vehicles on roads. The result of this has been a proliferation of challenges for authorities with regard to road traffic management. A consequence of this has been congestion of traffic, more accidents, and pollution. Accidents are a still major cause of death, despite the development of sophisticated systems for traffic management and other technologies linked with vehicles. Hence, it is necessary that a common system for accident management is developed. For instance, traffic congestion in most urban areas can be alleviated by the real-time planning of routes. However, the designing of an efficie
... Show MoreEnergy is one of the components of the national security of countries and is of particular importance to the industrialized countries, including Germany. Energy policy includes many areas and has an impact on various sectors such as the environment, climate, agriculture and others. During the past few years, Germany has witnessed many transformations, the most important of which is the energy transition towards renewable energy, and it was strengthened in the strategy that was It was developed in 2010, which aims to achieve a long-term energy transformation, and sales of the German energy technology sector have evolved from 2010 to 2020, and this issue is related on the other hand to the concept of energy security and because of its strateg
... Show MoreSorting and grading agricultural crops using manual sorting is a cumbersome and arduous process, in addition to the high costs and increased labor, as well as the low quality of sorting and grading compared to automatic sorting. the importance of deep learning, which includes the artificial neural network in prediction, also shows the importance of automated sorting in terms of efficiency, quality, and accuracy of sorting and grading. artificial neural network in predicting values and choosing what is good and suitable for agricultural crops, especially local lemons.
After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings
... Show MoreA procedure, depending on the derivatization and determination of aniline was depicted andvalidated in this study. 8-hydroxyquinoline (8-HQ) was used as the derivatizing agent for thedetermination of aniline. An optimization study was performed for the derivatization reaction, i.e.,the diazonium coupling reaction, the optimum parameters were as follows: 22 mM of hydrochloricacid, 54mM of sodium hydroxide, and 1.8mM of sodium nitrate. The optimization study of themethod of cloud point extraction (CPE) revealed that the extraction solvent was 0.5 ml of Triton X-100, the optimum temperature was 90 °C, and the incubation time was 25 min. The linearity,correlation coefficients, molar absorptivities, and limits of detection were improved using t
... Show MoreMicrobial antibiotics resistance is considered a serious health issue in the Middle East and developing countries. In this study, the Fe2O3 nanoparticles was prepared chemically, and the particles size and shape were analyzed by using Scan electron microscope (SEM) and X-Ray diffraction (XRD). Different concentration of Fe2O3 nanoparticles were used and examined on E.coli and S. aureus. Using liquid dilution and in vitro cytotoxicity assay by microplate toxicity test (MTT). The microbial cell metabolic activity was measured on gram-negative, gram-positive bacteria and fungi after treating with different concentrations of Fe2O3 nanoparticl
... Show MoreThe use of deep learning.