Search results for: artificial air storage reservoir

Authors: Kaining Zhang, Lang Chen, Danyang Liu, Jianying Lu, Kun Yang, Junying Wu

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In order to solve the problem of a large amount of simulation and limited simulation scale in the first-principle molecular dynamics simulation of energetic material detonation reaction, we established an artificial intelligence model for analyzing and predicting the detonation reaction mechanism of CL-20 based on the first-principle molecular dynamics simulation of the multiscale shock technique (MSST). We employed principal component analysis to identify the dominant charge features governing molecular reactions. We adopted the K-means clustering algorithm to cluster the reaction paths and screen out the key reactions. We introduced the neural network algorithm to construct the mapping relationship between the charge characteristics of the molecular structure and the key reaction characteristics so as to establish a calculation method for predicting detonation reactions based on the charge characteristics of CL-20 and realize the rapid analysis of the reaction mechanism of energetic materials.

Keywords: energetic material detonation reaction, first-principle molecular dynamics simulation of multiscale shock technique, neural network, CL-20

Procedia PDF Downloads 113

Authors: Sima Aznavi, Poria Fajri, Hanif Livani

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Smart homes are small energy systems which may be equipped with renewable energy sources, storage devices, and loads. Energy management strategy plays a main role in the efficient operation of smart homes. Effective energy scheduling of the renewable energy sources and storage devices guarantees efficient energy management in households while reducing the energy imports from the grid. Nevertheless, despite such strategies, independently day ahead energy schedules for multiple households can cause undesired effects such as high power exchange with the grid at certain times of the day. Therefore, the interactions between multiple smart home day ahead energy projections is a challenging issue in a smart grid system and if not managed appropriately, the imported energy from the power network can impose additional burden on the distribution grid. In this paper, a central energy management strategy for a network consisting of multiple households each equipped with renewable energy sources, storage devices, and Plug-in Electric Vehicles (PEV) is proposed. The decision-making strategy alongside the smart home energy management system, minimizes the energy purchase cost of the end users, while at the same time reducing the stress on the utility grid. In this approach, the smart home energy management system determines different operating scenarios based on the forecasted household daily load and the components connected to the household with the objective of minimizing the end user overall cost. Then, selected projections for each household that are within the same cost range are sent to the central decision-making system. The central controller then organizes the schedules to reduce the overall peak to average ratio of the total imported energy from the grid. To validate this approach simulations are carried out for a network of five smart homes with different load requirements and the results confirm that by applying the proposed central energy management strategy, the overall power demand from the grid can be significantly flattened. This is an effective approach to alleviate the stress on the network by distributing its energy to a network of multiple households over a 24- hour period.

Keywords: energy management, renewable energy sources, smart grid, smart home

Procedia PDF Downloads 248

Authors: H. Beisch, J. Marx, S. Garlof, R. Shvets, I. I. Grygorchak, A. Kityk, B. Fiedler

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Carbon materials, especially three-dimensional carbon foams, show very high potential in the application as electrode material for energy storage systems such as batteries and supercapacitors with unique fast charging and discharging times. Regarding their high specific surface areas (SSA) high specific capacities can be reached. Globugraphite is a newly developed carbon foam with an interconnected globular carbon morphology. Especially, this foam has a statistically distributed hierarchical pore structure resulting from the manufacturing process based on sintered ceramic templates which are synthetized during a final chemical vapor deposition (CVD) process. For morphology characterization scanning electron (SEM) and transmission electron microscopy (TEM) is used. In addition, the SSA is carried out by nitrogen adsorption combined with the Brunauer–Emmett–Teller (BET) theory. Electrochemical measurements in organic and inorganic electrolyte provide high energy densities and power densities resulting from ion absorption by forming an electrochemical double layer. All values are summarized in a Ragone Diagram. Finally, power densities up to 833 W/kg and energy densities up to 48 Wh/kg could be achieved. The corresponding SSA is between 376 m²/g and 859 m²/g. For organic electrolyte a specific capacity of 71 F/g at a density of 20 mg/cm³ was achieved.

Keywords: BET, CVD process, electron microscopy, Ragone diagram

Procedia PDF Downloads 174

Authors: Nazeef, Suleiman, Umar, Danladi Muhammad, Ja'afar Ali, Zaliha Adamu Umar

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The population dynamics of Auchenoglanis occidentalis from the Dadin-Kowa reservoir were studied. Population dynamic parameters such as growth, mortality and recruitment patterns were analyzed using length frequency data over a 12-month period employing FiSAT II software. Findings revealed that LWR (b - constant) = 2.88, K = 0.72 -yr., L∞ = 40.91 cm and Tmax = 3.57 years and Ɵ’ = 3.14. Mortality indices revealed that natural mortality (M = 1.39), fishing mortality (F = 0.22) and exploitation ratio (E = 0.14), Lc/L∞ = 0.48, Emax = 0.64, while Lopt = 26.4 cm. Uni-modal recruitment peak observed with Lm = 27.3 cm. A restocking program is suitable to ensure its continuous existence as it seems to have a low population.

Keywords: fish population dynamics, auchenoglanis occidentalis, FISAT II, natural mortality

Procedia PDF Downloads 46

Authors: A. Basu, A. A. Purohit, M. M. Vaidya, M. D. Kudale

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Mumbai, being traditionally the epicenter of India's trade and commerce, the existing major ports such as Mumbai and Jawaharlal Nehru Ports (JN) situated in Thane estuary are also developing its waterfront facilities. Various developments over the passage of decades in this region have changed the tidal flux entering/leaving the estuary. The intake at Pir-Pau is facing the problem of shortage of water in view of advancement of shoreline, while jetty near Ulwe faces the problem of ship scheduling due to existence of shallower depths between JN Port and Ulwe Bunder. In order to solve these problems, it is inevitable to have information about tide levels over a long duration by field measurements. However, field measurement is a tedious and costly affair; application of artificial intelligence was used to predict water levels by training the network for the measured tide data for one lunar tidal cycle. The application of two layered feed forward Artificial Neural Network (ANN) with back-propagation training algorithms such as Gradient Descent (GD) and Levenberg-Marquardt (LM) was used to predict the yearly tide levels at waterfront structures namely at Ulwe Bunder and Pir-Pau. The tide data collected at Apollo Bunder, Ulwe, and Vashi for a period of lunar tidal cycle (2013) was used to train, validate and test the neural networks. These trained networks having high co-relation coefficients (R= 0.998) were used to predict the tide at Ulwe, and Vashi for its verification with the measured tide for the year 2000 & 2013. The results indicate that the predicted tide levels by ANN give reasonably accurate estimation of tide. Hence, the trained network is used to predict the yearly tide data (2015) for Ulwe. Subsequently, the yearly tide data (2015) at Pir-Pau was predicted by using the neural network which was trained with the help of measured tide data (2000) of Apollo and Pir-Pau. The analysis of measured data and study reveals that: The measured tidal data at Pir-Pau, Vashi and Ulwe indicate that there is maximum amplification of tide by about 10-20 cm with a phase lag of 10-20 minutes with reference to the tide at Apollo Bunder (Mumbai). LM training algorithm is faster than GD and with increase in number of neurons in hidden layer and the performance of the network increases. The predicted tide levels by ANN at Pir-Pau and Ulwe provides valuable information about the occurrence of high and low water levels to plan the operation of pumping at Pir-Pau and improve ship schedule at Ulwe.

Keywords: artificial neural network, back-propagation, tide data, training algorithm

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Authors: Fatemeh Khalili Varnamkhasti

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Numerous nations have recognized the critical ought to address natural issues, such as discuss contamination, squander transfer, worldwide warming, and common asset consumption, through the application of green innovation. The rise of cleverly advances has driven mechanical basic changes that will offer assistance accomplish carbon decrease. Manufactured insights (AI) innovation is an imperative portion of digitalization, giving unused mechanical apparatuses and bearings for the moo carbon advancement of endeavors. Quickening the brilliantly change of fabricating industry is an critical vital choice to realize the green advancement change. The reason why fabricating insights can advance the advancement of green advancement execution is that fabricating insights is conducive to the generation of "innovation advancement impact" and "fetched decrease impact" so as to advance green innovation advancement, at that point viably increment the alluring yields and essentially diminish the undesirable yields. AI improvement will boost GTI as it were when the escalated of natural direction and organization environment is over a certain edge esteem. In any case, the AI improvement spoken to by mechanical robot applications still has no self-evident impact on GTI, indeed, when the R&D venture surpasses a certain edge.

Keywords: greenhouse gas emissions, green infrastructure, artificial intelligence, environmental protection

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Authors: Tomas Berhanu Bekele

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Traffic congestion is one of the main problems related to transportation in developed as well as developing countries. Traffic control systems are based on the idea of avoiding traffic instabilities and homogenizing traffic flow in such a way that the risk of accidents is minimized and traffic flow is maximized. Lately, Intelligent Transport Systems (ITS) has become an important area of research to solve such road traffic-related issues for making smart decisions. It links people, roads and vehicles together using communication technologies to increase safety and mobility. Moreover, accurate prediction of road traffic is important to manage traffic congestion. The aim of this study is to develop an ANN model for the prediction of traffic flow and to compare the ANN model with the linear regression model of traffic flow predictions. Data extraction was carried out in intervals of 15 minutes from the video player. Video of mixed traffic flow was taken and then counted during office work in order to determine the traffic volume. Vehicles were classified into six categories, namely Car, Motorcycle, Minibus, mid-bus, Bus, and Truck vehicles. The average time taken by each vehicle type to travel the trap length was measured by time displayed on a video screen.

Keywords: intelligent transport system (ITS), traffic flow prediction, artificial neural network (ANN), linear regression

Procedia PDF Downloads 67

Authors: Yash Ambekar, Kapil Vhatkar, Prathamesh Swami, Kartikey Singh, Yashovardhan Kaware

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The rise of online courses and certifications has created new opportunities for individuals to enhance their skills. However, this digital transformation has also given rise to coun- terfeit certificates. To address this multifaceted issue, we present a comprehensive certificate management system founded on blockchain technology and strengthened by smart contracts. Our system comprises three pivotal components: certificate generation, authenticity verification, and a user-centric digital locker for certificate storage. Blockchain technology underpins the entire system, ensuring the immutability and integrity of each certificate. The inclusion of a cryptographic hash for each certificate is a fundamental aspect of our design. Any alteration in the certificate’s data will yield a distinct hash, a powerful indicator of potential tampering. Furthermore, our system includes a secure digital locker based on cloud storage that empowers users to efficiently manage and access all their certificates in one place. Moreover, our project is committed to providing features for certificate revocation and updating, thereby enhancing the system’s flexibility and security. Hence, the blockchain and smart contract-based certificate management system offers a robust and one-stop solution to the escalating problem of counterfeit certificates in the digital era.

Keywords: blockchain technology, smart contracts, counterfeit certificates, authenticity verification, cryptographic hash, digital locker

Procedia PDF Downloads 46

Authors: Rakesh Namdeti

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Toxic dyes found in industrial effluent must be treated before being disposed of due to their harmful impact on human health and aquatic life. Thus, Musa acuminata (Banana Leaves) was employed in the role of a biosorbent in this work to get rid of methylene blue derived from a synthetic solution. The effects of five process parameters, such as temperature, pH, biosorbent dosage, and initial methylene blue concentration, using a central composite design (CCD), and the percentage of dye clearance were investigated. The response was modelled using a quadratic model based on the CCD. The analysis of variance revealed the most influential element on experimental design response (ANOVA). The temperature of 44.30C, pH of 7.1, biosorbent dose of 0.3 g, starting methylene blue concentration of 48.4 mg/L, and 84.26 percent dye removal were the best conditions for Musa acuminata (Banana leave powder). At these ideal conditions, the experimental percentage of biosorption was 76.93. The link between the estimated results of the developed ANN model and the experimental results defined the success of ANN modeling. As a result, the study's experimental results were found to be quite close to the model's predicted outcomes.

Keywords: Musa acuminata, central composite design, methylene blue, artificial neural network

Procedia PDF Downloads 76

Authors: Dimitrios Bakavos, Dimitrios Tsivoulas, Chaowalit Limmaneevichitr

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Aluminium cast alloys, of the Al-Si system, are widely used for shape castings. Their microstructures can be further improved on one hand, by alloying modification and on the other, by optimised artificial ageing. In this project four hypoeutectic Al-alloys, the A356, A356+ Ag, A356+Sc, and A356+Ag+Sc have been studied. The interactions of Ag and Sc during solidification and artificial ageing at 170°C to a T6 temper have been investigated in details. The evolution of the eutectic microstructure is studied by thermal analysis and interrupted solidification. The ageing kinetics of the alloys has been identified by hardness measurements. The precipitate phases, number density, and chemical composition has been analysed by means of transmission electron microscopy (TEM) and EDS analysis. Furthermore, the SHT effect onto the Si eutectic particles for the four alloys has been investigated by means of optical microscopy, image analysis, and the UTS strength has been compared with the UTS of the alloys after casting. The results suggest that the Ag additions, significantly enhance the ageing kinetics of the A356 alloy. The formation of β” precipitates were kinetically accelerated and an increase of 8% and 5% in peak hardness strength has been observed compared to the base A356 and A356-Sc alloy. The EDS analysis demonstrates that Ag is present on the β” precipitate composition. After prolonged ageing 100 hours at 170°C, the A356-Ag exhibits 17% higher hardness strength compared to the other three alloys. During solidification, Sc additions change the macroscopic eutectic growth mode to the propagation of a defined eutectic front from the mold walls opposite to the heat flux direction. In contrast, Ag has no significance effect on the solidification mode revealing a macroscopic eutectic growth similar to A356 base alloy. However, the mechanical strength of the as cast A356-Ag, A356-Sc, and A356+Ag+Sc additions has increased by 5, 30, and 35 MPa, respectively. The outcome is a tribute to the refining of the eutectic Si that takes place which it is strong in the A356-Sc alloy and more profound when silver and scandium has been combined. Moreover after SHT the Al alloy with the highest mechanical strength, is the one with Ag additions, in contrast to the as-cast condition where the Sc and Sc+Ag alloy was the strongest. The increase of strength is mainly attributed to the dissolution of grain boundary precipitates the increase of the solute content into the matrix, the spherodisation, and coarsening of the eutectic Si. Therefore, we could safely conclude for an A356 hypoeutectic alloy additions of: Ag exhibits a refining effect on the Si eutectic which is improved when is combined with Sc. In addition Ag enhance, the ageing kinetics increases the hardness and retains its strength at prolonged artificial ageing in a Al-7Si 0.3Mg hypoeutectic alloy. Finally the addition of Sc is beneficial due to the refinement of the α-Al grain and modification-refinement of the eutectic Si increasing the strength of the as-cast product.

Keywords: ageing, casting, mechanical strength, precipitates

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Authors: Mahshid TalebianKiakalayeh

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As AI technologies can be used by both civilians and soldiers, it is vital to consider the consequences emanating from AI military as well as civilian use. Indeed, many of the same technologies can have a dual-use. This paper will explore the military uses of AI and assess its compliance with international legal norms. AI developments not only have changed the capacity of the military to conduct complex operations but have also increased legal concerns. The existence of a potential legal vacuum in legal principles on the military use of AI indicates the necessity of more study on compliance with International Humanitarian Law (IHL), the branch of international law which governs the conduct of hostilities. While capabilities of new means of military AI continue to advance at incredible rates, this body of law is seeking to limit the methods of warfare protecting civilian persons who are not participating in an armed conflict. Implementing AI in the military realm would result in potential issues, including ethical and legal challenges. For instance, when intelligence can perform any warfare task without any human involvement, a range of humanitarian debates will be raised as to whether this technology might distinguish between military and civilian targets or not. This is mainly because AI in fully military systems would not seem to carry legal and ethical judgment, which can interfere with IHL principles. The paper will take, as a case study, Canada’s compliance with IHL in the area of AI and the related legal issues that are likely to arise as this country continues to develop military uses of AI.

Keywords: artificial intelligence, military use, international humanitarian law, the Canadian perspective

Procedia PDF Downloads 186

Authors: Aiman Zehra, Sajad Mohd Wani

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Packaging has a vital role as it contains and protects the food that moves from the supply chain to the consumer. Chitosan (CH) has been extensively used in food packaging applications among the plentiful natural macromolecules, including all the polysaccharide class, owing to its easy film-forming capacity, biodegradability, better oxygen and water vapour barrier ability and good mechanical strength. Compared to synthetic films, the films produced from chitosan present poor barrier and mechanical properties. To overcome its deficient qualities, a number of modification procedures are required to enhance the mechanical and physical properties. Various additives such as plasticizers (e.g., glycerol and sorbitol), crosslinkers (e.g.,CaCl₂, ZnO), fillers (nanoclay), and antimicrobial agents (e.g. thyme essential oil) have been used to improve the mechanical, thermal, morphological, antimicrobial properties and emulsifying agents for the stability and elasticity of chitosan-based biodegradable films. Different novel biocomposite films based on chitosan incorporated with thyme essential oil and different additives (ZnO, CaCl₂, NC, and PEG) were successfully prepared and used as packaging material for carrot candy. The chitosan film incorporated with crosslinkers was capable of forming a protective barrier on the surface of the candy to maintain moisture content, water activity, TSS, total sugars, and titratable acidity. ZnO +PEG +NC +CaCl₂ remarkably promotes a synergistic effect on the barrier properties of the film. The combined use of ZnO +PEG +NC +CaCl₂ in CH-TO films was more effective in preventing the moisture gain in candies. The lowest a𝓌 (0.624) was also observed for the candies stored in treatment. The color values L*, a*, b* of the candies were also retained in the film containing all the additives during the 6th month of storage. The value for L*, a*, and b* observed for T was 42.72, 9.89, and 10.84, respectively. The candies packaged in film retained TSS and acidity. The packaging film significantly p≤0.05 conserved sensory qualities and inhibited microbial activity during storage. Carrot candy was found microbiologically safe for human consumption even after six months of storage in all the packaging materials.

Keywords: chitosan, biodegradable films, antimicrobial activity, thyme essential oil, crosslinkers

Procedia PDF Downloads 95

Authors: Chettou Souhaila, Soufi Omar, Roumia Mohammed Ammar

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Protecting the environment is to preserve the survival and future of humanity. Indeed, the environment is our source of food and drinking water, the air is our source of oxygen, the climate allows our survival and biodiversity are a potential drug reservoir. Preserving the environment is, therefore, a matter of survival. The objective of this project is to familiarize the general public with environmental problems not only with the theme of environmental protection, but also with the concept of biodiversity in different ecosystems. For it, the aim of our project was to create the Ecorium which is a place that preserves many species of plants of different ecosystems, schools, malls, buildings, offices, ecological transports, gardens, and many familial activities that participated in the ecosystems development, strategic biodiversity and sustainable development.

Keywords: ecological system, ecorium, environment, sustainable development

Procedia PDF Downloads 339

Authors: Yau-Luen Ng, Nathan Banka, Santosh Devasia

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In this paper, a method based on soft artificial cilia is introduced to separate particles based on size and mass. In nature, cilia are used for fluid propulsion in the mammalian circulatory system, as well as for swimming and size-selective particle entrainment for feeding in microorganisms. Inspired by biological cilia, an array of artificial cilia was fabricated using Polydimethylsiloxane (PDMS) to simulate the actual motion. A row of four individually-controlled magnetic artificial cilia in a semi-circular channel are actuated by the magnetic fields from four permanent magnets. Each cilium is a slender rectangular cantilever approximately 13mm long made from a composite of PDMS and carbonyl iron particles. A time-varying magnetic force is achieved by periodically varying the out-of-plane distance from the permanent magnets to the cilia, resulting in large-amplitude deflections of the cilia that can be used to drive fluid motion. Previous results have shown that this system of individually-controlled magnetic cilia can generate effective mixing flows; the purpose of the present work is to apply the individual cilia control to a particle separation task. Based on the observed beating patterns of cilia arrays in nature, the experimental beating patterns were selected as a metachronal wave, in which a fixed phase lead or lag is imposed between adjacent cilia. Additionally, the beating frequency was varied. For each set of experimental parameters, the channel was filled with water and polyethylene microspheres introduced at the center of the cilia array. Two types of particles were used: large red microspheres with density 0.9971 g/cm³ and 850-1000 μm avg. diameter, and small blue microspheres with density 1.06 g/cm³ and diameter 30 μm. At low beating frequencies, all particles were propelled in the mean flow direction. However, the large particles were observed to reverse directions above about 4.8 Hz, whereas reversal of the small particle transport direction did not occur until 6 Hz. Between these two transition frequencies, the large and small particles can be separated as they move in opposite directions. The experimental results show that selecting an appropriate cilia beating pattern can lead to selective transport of neutrally-buoyant particles based on their size. Importantly, the separation threshold can be chosen dynamically by adjusting the actuation frequency. However, further study is required to determine the range of particle sizes that can be effectively separated for a given system geometry.

Keywords: magnetic cilia, particle separation, tunable separation, soft actutors

Procedia PDF Downloads 199

Authors: Mira Ezora Zainal Abidin, Siti Fauzuna Othman, Zalina Harun, M. Hafiz M. Pikri

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In a fire incident involving a Nitrogen storage tank that over-pressured and exploded, resulting in a fire in one of the units in a refinery, lack of data and evidence hampered the investigation to determine the root cause. Instrumentation and fittings were destroyed in the fire. To make it worst, this incident occurred during the COVID-19 pandemic, making collecting and testing evidence delayed. In addition to that, the storage tank belonged to a third-party company which requires legal agreement prior to the refinery getting approval to test the remains. Despite all that, the investigation had to be carried out with stakeholders demanding answers. The investigation team had to devise alternative means to support whatever little evidence came out as the most probable root cause. International standards, practices, and previous incidents on similar tanks were referred. To narrow down to just one root cause from 8 possible causes, transient simulations were conducted to simulate the overpressure scenarios to prove and eliminate the other causes, leaving one root cause. This paper shares the methodology used and details how transient simulations were applied to help solve this. The experience and lessons learned gained from the event investigation and from numerous case studies via transient analysis in finding the root cause of the accident leads to the formulation of future mitigations and design modifications aiming at preventing such incidents or at least minimize the consequences from the fire incident.

Keywords: fire, transient, simulation, relief

Procedia PDF Downloads 95

Authors: Yating Yang, Xue Zhang, Chengli Zhao

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Technology capability refers to the comprehensive ability that influences all factors of technological development. Among them, researchers’ resources serve as the foundation and driving force for technology capability, representing a significant manifestation of a country/region's technological capability. Therefore, the cross-border transfer behavior of researchers to some extent reflects changes in technological capability between countries/regions, providing a unique research perspective for technological capability assessment. This paper proposes a technological capability assessment model based on personnel transfer networks, which consists of a researchers' transfer network model and a country/region role evolution model. It evaluates the changes in a country/region's technological capability roles from the perspective of researcher transfers and conducts an analysis using artificial intelligence as a case study based on literature data. The study reveals that the United States, China, and the European Union are core nodes, and identifies the role evolution characteristics of several major countries/regions.

Keywords: transfer network, technological capability assessment, central-peripheral structure, role evolution

Procedia PDF Downloads 93

Authors: Hubert Beisch, Janik Marx, Svenja Garlof, Roman Shvets, Ivan Grygorchak, Andriy Kityk, Bodo Fiedler

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Three-dimensional carbon materials can be used as electrode materials for energy storage systems such as batteries and supercapacitors. Fast charging and discharging times are realizable without reducing the performance due to aging processes. Furthermore high specific surface area (SSA) of three-dimensional carbon structures leads to high specific capacities. One newly developed carbon foam is Globugraphite. This interconnected globular carbon morphology with statistically distributed hierarchical pores is manufactured by a chemical vapor deposition (CVD) process from ceramic templates resulting from a sintering process. Via scanning electron (SEM) and transmission electron microscopy (TEM), the morphology is characterized. Moreover, the SSA was measured by the Brunauer–Emmett–Teller (BET) theory. Measurements of Globugraphite in an organic and inorganic electrolyte show high energy densities and power densities resulting from ion absorption by forming an electrochemical double layer. A comparison of the specific values is summarized in a Ragone diagram. Energy densities up to 48 Wh/kg and power densities to 833 W/kg could be achieved for an SSA from 376 m²/g to 859 m²/g. For organic electrolyte, a specific capacity of 100 F/g at a density of 20 mg/cm³ was achieved.

Keywords: BET, carbon foam, CVD process, electrochemical cell, Ragone diagram, SEM, TEM

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Authors: Abdullah Mohammad, Abdullah Alshemali, Esmaeil Alsaleh

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The chemical composition of desalinated water is modified to make it more acceptable to the end-user. Sometimes, this modification is approached by mixing with brackish water that is known to contain a variety of minerals. Expectedly, besides minerals, brackish water indigenous bacterial communities access the final mixture hence reaching the end consumer. The current project examined the safety of using brackish water as an ingredient in potable water. Pseudomonas aeruginosa strains were detected in potable and brackish water samples collected from storage facilities in residential areas as well as from main water distribution and storage tanks. The application of molecular and biochemical fingerprinting methods, including phylogeny, RFLP (restriction fragment length polymorphism), MLST (multilocus sequence typing) and substrate specificity testing, suggested that the potable water P. aeruginosa strains were most probably originated from brackish water. Additionally, all the sixty-four isolates showed multi-drug resistance (MDR) phenotype and harboured the three genes responsible for biofilm formation. These virulence factors represent serious health hazards compelling the scientific community to revise the WHO (World Health Organization) and USEP (US Environmental Protection Agency) A potable water quality guidelines, particularly those related to the types of bacterial genera that evade the current water quality guidelines.

Keywords: potable water, brackish water, pseudomonas aeroginosa, multidrug resistance

Procedia PDF Downloads 122

Authors: Sean Goltz, Michael Mayo

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The purpose of this research is to use Global-Regulation.com database of the world laws, focusing on tax treaties between countries, in order to create an AI-driven tax simulator that will run an AI agent through potential tax scenarios across countries. The AI agent goal is to identify the scenario that will result in minimum tax liability based on tax treaties between countries. The results will be visualized by a three dimensional matrix. This will be an online web application. Multinational corporations are running their business through multiple countries. These countries, in turn, have a tax treaty with many other countries to regulate the payment of taxes on income that is transferred between these countries. As a result, planning the best tax scenario across multiple countries and numerous tax treaties is almost impossible. This research propose to use Global-Regulation.com database of word laws in English (machine translated by Google and Microsoft API’s) in order to create a simulator that will include the information in the tax treaties. Once ready, an AI agent will be sent through the simulator to identify the scenario that will result in minimum tax liability. Identifying the best tax scenario across countries may save multinational corporations, like Google, billions of dollars annually. Given the nature of the raw data and the domain of taxes (i.e., numbers), this is a promising ground to employ artificial intelligence towards a practical and beneficial purpose.

Keywords: taxation, law, multinational, corporation

Procedia PDF Downloads 199

Authors: Samira Melki, Moncef Gueddari

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In Tunisia, solid wastes storage continue to be uncontrolled. It is eliminated by land raising without any protection measurement against water table and soil contamination. Several industries are located in Sfax area, especially those of the Tunisian Chemical Group (TCG) for the enrichment and transformation of phosphate. The activity of the TCG focuses primarily on the production of chemical fertilizers and phosphoric acid, by transforming natural phosphates. This production generates gaseous emissions, liquid discharges and huge amounts of phosphogypsum (PG) stored directly on the soil surface. Groundwater samples were collected from Tunisian Chemical Group (TCG) site, to assess the effects of phosphogypsum leatchate on groundwater quality. The measurements of various physicochemical parameters including heavy metals (Al, Fe, Zn and F) and stable isotopes of the water molecule (¹⁸O, ²H) were determined in groundwater samples and are reported. The moderately high concentrations of SO₄⁼, Ortho-P, NH₄⁺ Al and F⁻ in groundwater particularly near to the phosphogypsum storage site, likely indicate that groundwater quality is being significantly affected by leachate percolation. The effect of distance of the piezometers from the pollution source was also investigated. The isotopic data of water molecule, showed that the waters of the Sfax-Agreb aquifer amount to recent-evaporation induced rainfall.

Keywords: phosphogypsum leatchate, groundwater quality, pollution, stable isotopes, Sfax-Agareb, Tunisia

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Authors: K. S. Ghosh, Sagnik Bose, Kapil Tripati

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Potentiodynamic polarization studies carried out on AA2024 and AA2014 Al-Cu-Mg alloys of various tempers in 3.5 wt. % NaCl and in 3.5 wt. % NaCl + 1.0 % H2O2 solution characteristic E-i curves. Corrosion potential (Ecorr) value has shifted towards more negative potential with the increase of artificial aging time. The Ecorr value for the alloy tempers has also shifted anodically in presence of H2O2 in 3.5 % NaCl solution. Further, passivity phenomenon has been observed in all the alloy tempers when tested in 3.5 wt. % NaCl solution at pH 12. Stress corrosion cracking (SCC) behaviour of friction stir weld (FSW) joint of AA2014 alloy has been studied bu slow strain rate test (SSRT) in 3.5 wt. % NaCl solution. Optical micrographs of the corroded surfaces of polarised samples showed general corrosion, extensive pitting and intergranular corrosion as well. Further, potentiodynamic cyclic polarization curves displayed wide hysteresis loop indicating that the alloy tempers are susceptible to pit growth damage. Attempts have been made to explain the variation of observed electrochemical and SCC behaviour of the alloy tempers and the electrolyte conditions with the help of microstructural features.

Keywords: AA 2014 and AA 2024 Al-C-Mg alloy, artificial ageing, potentiodynamic polarization, TEM micrographs, stress corrosion cracking (SCC)

Procedia PDF Downloads 334

Authors: Rudolph Carl Barrientos, Juwaln Diego Descallar, Rainer James Palmiano

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Household Appliance Scheduling Systems (HASS) coupled with a Time-of-Use (TOU) pricing scheme, a form of Demand Side Management (DSM), is not widely utilized in the Philippines’ residential electricity sector. This paper’s goal is to encourage distribution utilities (DUs) to adopt HASS and TOU by analyzing the effect of household schedulers on the electricity price and load profile in a residential environment. To establish this, a city based on an implemented survey is generated using Monte Carlo Analysis (MCA). Then, a Binary Particle Swarm Optimization (BPSO) algorithm-based HASS is developed considering user satisfaction, electricity budget, appliance prioritization, energy storage systems, solar power, and electric vehicles. The simulations were assessed under varying levels of user compliance. Results showed that the average electricity cost, peak demand, and peak-to-average ratio (PAR) of the city load profile were all reduced. Therefore, the deployment of the HASS and TOU pricing scheme is beneficial for both stakeholders.

Keywords: appliance scheduling, DSM, TOU, BPSO, city-wide simulation, electric vehicle, appliance prioritization, energy storage system, solar power

Procedia PDF Downloads 98

Authors: Ahmed Al-Adaileh

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Renewable energy sources, domestic storage systems, controllable loads and machine learning technologies will be key components of future smart homes management systems. An energy management scheme that uses a Deep Learning (DL) approach to support the smart home management systems, which consist of a standalone photovoltaic system, storage unit, heating ventilation air-conditioning system and a set of conventional and smart appliances, is presented. The objective of the proposed scheme is to apply DL-based machine learning to predict various running parameters within a smart home's environment to achieve maximum comfort levels for occupants, reduced electricity bills, and less dependency on the public grid. The problem is using Reinforcement learning, where decisions are taken based on applying the Continuous-time Markov Decision Process. The main contribution of this research is the proposed framework that applies DL to enhance the system's supervised dataset to offer unlimited chances to effectively support smart home systems. A case study involving a set of conventional and smart appliances with dedicated processing units in an inhabited building can demonstrate the validity of the proposed framework. A visualization graph can show "before" and "after" results.

Keywords: smart homes systems, machine learning, deep learning, Markov Decision Process

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Authors: Sirus Mohammadi, Rohala Moghimi

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The main advantages of microgrids are high energy efficiency through the application of Combined Heat and Power (CHP), high quality and reliability of the delivered electric energy and environmental and economic advantages. This study presents an energy management system (EMS) to optimize the operation of the microgrid (MG). In this paper an Adaptive Modified Firefly Algorithm (AMFA) is presented for optimal operation of a typical MG with renewable energy sources (RESs) accompanied by a back-up Micro-Turbine/Fuel Cell/Battery hybrid power source to level the power mismatch or to store the energy surplus when it’s needed. The problem is formulated as a nonlinear constraint problem to minimize the total operating cost. The management of Energy storage system (ESS), economic load dispatch and operation optimization of distributed generation (DG) are simplified into a single-object optimization problem in the EMS. The proposed algorithm is tested on a typical grid-connected MG including WT/PV/Micro Turbine/Fuel Cell and Energy Storage Devices (ESDs) then its superior performance is compared with those from other evolutionary algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Fuzzy Self Adaptive PSO (FSAPSO), Chaotic Particle PSO (CPSO), Adaptive Modified PSO (AMPSO), and Firefly Algorithm (FA).

Keywords: microgrid, operation management, optimization, firefly algorithm (AMFA)

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Authors: Marjan Golmaryami, Marzieh Behzadi

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Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.

Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange

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Authors: Wu You, Burra Venkata Durga Kumar

Abstract:

This study examines the promising potential of integrating Blockchain and Artificial Intelligence (AI) technologies to scalability in Distributed Systems within the field of supply chain finance. The finance industry is continually confronted with scalability challenges in its Distributed Systems, particularly within the supply chain finance sector, impacting efficiency and security. Blockchain, with its inherent attributes of high scalability and secure distributed ledger system, coupled with AI's strengths in optimizing data processing and decision-making, holds the key to innovating the industry's approach to these issues. This study elucidates the synergistic interplay between Blockchain and AI, detailing how their fusion can drive a significant transformation in the supply chain finance sector's Distributed Systems. It offers specific use-cases within this field to illustrate the practical implications and potential benefits of this technological convergence. The study also discusses future possibilities and current challenges in implementing this groundbreaking approach within the context of supply chain finance. It concludes that the intersection of Blockchain and AI could ignite a new epoch of enhanced efficiency, security, and transparency in the Distributed Systems of supply chain finance within the financial industry.

Keywords: blockchain, artificial intelligence (AI), scaled distributed systems, supply chain finance, efficiency and security

Procedia PDF Downloads 93

Authors: Grace O. Babarinde, Adegoke O.Gabriel, Rahman Akinoso, Adekanye Bosede R.

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Health risks associated with the use of synthetic chemicals to control post-harvest losses in fruit calls for use of natural biodegradable compounds. The potential of Piper guineense as postharvest preservative for Roma tomato (Solanum lycopersicum L.) was investigated. Freshly harvested red tomato (200 g) was dipped into five concentrations (1, 2, 3, 4 and 5% w/v) of P. guineense aqueous extract, while untreated fruits served as control. The samples were stored under refrigeration and analysed at 5-day interval for physico-chemical properties. P. guineense essential oil (EO) was characterised using GC-MS and its tomato preservative potential was evaluated. Percentage weight loss (PWL) in extract-treated tomato ranged from 0.0-0.68% compared to control (0.3-19.97%) during storage. Values obtained for firmness ranged from 8.23-16.88 N and 8.4 N in extract-treated and control. pH reduced from 5.4 to 4.5 and 3.7 in extract-treated and untreated samples, respectively. Highest value of Total Soluble Solid (1.8 °Brix) and maximum retention of Ascorbic acid (13.0 mg/100 g) were observed in 4% P. guineense-treated samples. Predominant P. guineense EO components were zingiberene (9.9%), linalool (10.7%), β-caryophyllene (12.6%), 1, 5-Heptadiene, 6-methyl-2-(4-methyl-3-cyclohexene-l-yl) (16.4%) and β-sesquiphellandrene (23.7%). Tomatoes treated with EO had lower PWL (5.2%) and higher firmness (14.2 N) than controls (15.3% and 11.9 N) respectively. The result indicates that P. guineense can be incorporated in to post harvest technology of Roma tomato fruit.

Keywords: aqueous extract, essential oil, piper guineense, Roma tomato, storage condition

Procedia PDF Downloads 476

Authors: Hussam Ashwi, Magbool Oraiby, Ali Muyidi, Hamad Al-Oufi, Mohammed Al-Oufi, Adel Al-Juhani, Salman Al-Zemaa, Saeed Al-Shahrani, Amal Abuallah, Wedad Sherwani, Mohammed Alattas, Ibraheem Attafi

Abstract:

Ethanol must be accurately identified and quantified to establish their use and contribution in criminal cases and forensic medicine. In some situations, it may be necessary to reanalyze an old specimen; therefore, it is essential to comprehend the effect of storage conditions and how long the result of a reanalyzed specimen can be reliable and reproducible. Additionally, ethanol can be produced via multiple in vivo and in vitro processes, particularly in diabetic patients, and the results can be affected by storage conditions and time. In order to distinguish between in vivo and in vitro alcohol generation in diabetes patient urine samples, various factors should be considered. This study identifies and quantifies ethanol and EtG in diabetic patients' urine samples stored in two different settings over time. Ethanol levels were determined using gas chromatography-headspace (GC-HS), and ethyl glucuronide (EtG) levels were determined using the immunoassay (RANDOX) technique. Ten urine specimens were collected and placed in a standard container. Each specimen was separated into two containers. The specimens were divided into two groups: those kept at room temperature (25 °C) and those kept cold (2-8 °C). Ethanol and EtG levels were determined serially over a two-week period. Initial results showed that none of the specimens tested positive for ethanol or EtG. At room temperature (15-25 °C), 7 and 14 days after the sample was taken, the average concentration of ethanol increased from 1.7 mg/dL to 2 mg/dL, and the average concentration of EtG increased from 108 ng/mL to 186 ng/mL. At 2–8 °C, the average ethanol concentration was 0.4 and 0.5 mg/dL, and the average EtG concentration was 138 and 124 ng/mL seven and fourteen days after the sample was collected, respectively. When ethanol and EtG levels were determined 14 days post collection, they were considerably lower than when stored at room temperature. A considerable increase in EtG concentrations (14-day range 0–186 ng/mL) is produced during room-temperature storage, although negative initial results for all specimens. Because EtG might be produced after a sampling collection, it is not a reliable indicator of recent alcohol consumption. Given the possibility of misleading EtG results due to in vitro EtG production in the urine of diabetic patients.

Keywords: ethyl glucuronide, ethanol, forensic toxicology, diabetic

Procedia PDF Downloads 123

Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Reza Ehsani, Hawa Ze Jaffar, Ishak Aris

Abstract:

Ripeness estimation of oil palm fresh fruit is important processes that affect the profitableness and salability of oil palm fruits. The adulthood or ripeness of the oil palm fruits influences the quality of oil palm. Conventional procedure includes physical grading of Fresh Fruit Bunches (FFB) maturity by calculating the number of loose fruits per bunch. This physical classification of oil palm FFB is costly, time consuming and the results may have human error. Hence, many researchers try to develop the methods for ascertaining the maturity of oil palm fruits and thereby, deviously the oil content of distinct palm fruits without the need for exhausting oil extraction and analysis. This research investigates the potential of infrared images (Thermal Images) as a predictor to classify the oil palm FFB ripeness. A total of 270 oil palm fresh fruit bunches from most common cultivar of oil palm bunches Nigresens according to three maturity categories: under ripe, ripe and over ripe were collected. Each sample was scanned by the thermal imaging cameras FLIR E60 and FLIR T440. The average temperature of each bunches were calculated by using image processing in FLIR Tools and FLIR ThermaCAM researcher pro 2.10 environment software. The results show that temperature content decreased from immature to over mature oil palm FFBs. An overall analysis-of-variance (ANOVA) test was proved that this predictor gave significant difference between underripe, ripe and overripe maturity categories. This shows that the temperature as predictors can be good indicators to classify oil palm FFB. Classification analysis was performed by using the temperature of the FFB as predictors through Linear Discriminant Analysis (LDA), Mahalanobis Discriminant Analysis (MDA), Artificial Neural Network (ANN) and K- Nearest Neighbor (KNN) methods. The highest overall classification accuracy was 88.2% by using Artificial Neural Network. This research proves that thermal imaging and neural network method can be used as predictors of oil palm maturity classification.

Keywords: artificial neural network, maturity classification, oil palm FFB, thermal imaging

Procedia PDF Downloads 360

Authors: Ş. Karabulut, A. Güllü, A. Güldaş, R. Gürbüz

Abstract:

This study investigates the effects of the lead angle and chip thickness variation on surface roughness during the machining of compacted graphite iron using ceramic cutting tools under dry cutting conditions. Analytical models were developed for predicting the surface roughness values of the specimens after the face milling process. Experimental data was collected and imported to the artificial neural network model. A multilayer perceptron model was used with the back propagation algorithm employing the input parameters of lead angle, cutting speed and feed rate in connection with chip thickness. Furthermore, analysis of variance was employed to determine the effects of the cutting parameters on surface roughness. Artificial neural network and regression analysis were used to predict surface roughness. The values thus predicted were compared with the collected experimental data, and the corresponding percentage error was computed. Analysis results revealed that the lead angle is the dominant factor affecting surface roughness. Experimental results indicated an improvement in the surface roughness value with decreasing lead angle value from 88° to 45°.

Keywords: CGI, milling, surface roughness, ANN, regression, modeling, analysis

Procedia PDF Downloads 448