Search results for: energy detection

Authors: Tisciane Perpetuo e Oliveira, Dante Inga Narvaez, Marcelo Gradella Villalva

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Installations of solar photovoltaic systems have increased considerably in the last decade. Therefore, it has been noticed that monitoring of meteorological data (solar irradiance, air temperature, wind velocity, etc.) is important to predict the potential of a given geographical area in solar energy production. In this sense, the present work compares two computational tools that are capable of estimating the energy generation of a photovoltaic system through correlation analyzes of solar radiation data: PVsyst software and an algorithm based on the PVlib package implemented in MATLAB. In order to achieve the objective, it was necessary to obtain solar radiation data (measured and from a solarimetric database), analyze the decomposition of global solar irradiance in direct normal and horizontal diffuse components, as well as analyze the modeling of the devices of a photovoltaic system (solar modules and inverters) for energy production calculations. Simulated results were compared with experimental data in order to evaluate the performance of the studied methods. Errors in estimation of energy production were less than 30% for the MATLAB algorithm and less than 20% for the PVsyst software.

Keywords: energy production, meteorological data, irradiance decomposition, solar photovoltaic system

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Authors: M. Wajahat, M. Shoaib, A. Waheed

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As a result of increase in world energy demand as well as the demand for heating, refrigeration and air conditioning, energy engineers are now more inclined towards the renewable energy especially solar based thermal driven refrigeration and air conditioning systems. This research is emphasized on solar assisted adsorption refrigeration system to provide comfort conditions for a building in Islamabad. The adsorption chiller can be driven by low grade heat at low temperature range (50 -80 °C) which is lower than that required for generator in absorption refrigeration system which may be furnished with the help of common flat plate solar collectors (FPC). The aim is to offset the total energy required for building’s heating and cooling demand by using FPC’s thus reducing dependency on primary energy source hence saving energy. TRNSYS is a dynamic modeling and simulation tool which can be utilized to simulate the working of a complete solar based adsorption chiller to meet the desired cooling and heating demand during summer and winter seasons, respectively. Modeling and detailed parametric analysis of the whole system is to be carried out to determine the optimal system configuration keeping in view various design constraints. Main focus of the study is on solar thermal loop of the adsorption chiller to reduce the contribution from the auxiliary devices.

Keywords: flat plate collector, energy saving, solar assisted adsorption chiller, TRNSYS

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Authors: Manpreet Kaur, Cheol-Hong Min

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The increasing advancement in the robotics equipped with machine vision sensors applied to precision agriculture is a demanding solution for various problems in the agricultural farms. An important issue related with the machine vision system concerns crop row and weed detection. This paper proposes an automatic furrow detection system based on real-time processing for identifying crop rows in maize fields in the presence of weed. This vision system is designed to be installed on the farming vehicles, that is, submitted to gyros, vibration and other undesired movements. The images are captured under image perspective, being affected by above undesired effects. The goal is to identify crop rows for vehicle navigation which includes weed removal, where weeds are identified as plants outside the crop rows. The images quality is affected by different lighting conditions and gaps along the crop rows due to lack of germination and wrong plantation. The proposed image processing method consists of four different processes. First, image segmentation based on HSV (Hue, Saturation, Value) decision tree. The proposed algorithm used HSV color space to discriminate crops, weeds and soil. The region of interest is defined by filtering each of the HSV channels between maximum and minimum threshold values. Then the noises in the images were eliminated by the means of hybrid median filter. Further, mathematical morphological processes, i.e., erosion to remove smaller objects followed by dilation to gradually enlarge the boundaries of regions of foreground pixels was applied. It enhances the image contrast. To accurately detect the position of crop rows, the region of interest is defined by creating a binary mask. The edge detection and Hough transform were applied to detect lines represented in polar coordinates and furrow directions as accumulations on the angle axis in the Hough space. The experimental results show that the method is effective.

Keywords: furrow detection, morphological, HSV, Hough transform

Procedia PDF Downloads 229

Authors: Sethu Lekshmi, Shahanas, Prettha P.

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All existing protocols in wireless networks are mainly based on Carrier Sense Multiple Access with Collision avoidance. By applying collision detection in wireless networks, the time spent on collision can be reduced and thus improves system throughput. However in a real WLAN scenario due to the use of nonlinear modulation techniques only receiver can decided whether a packet loss take place, even there are multiple transmissions. In this proposed method, the receiver or Access Point detects the collision when multiple data packets are transmitted from different wireless stations. Whenever the receiver detects a collision, it transmits a jamming signal to all the transmitting stations so that they can immediately stop their on-going transmissions. We also provide preferential access to all collided packet to reduce unfairness and to increase system throughput by reducing contention. However, this preferential access will not block the channel for the long time. Here, an in-band transmission is considered in which both the data frames and control frames are transmitted in the same channel. We also provide a simple mathematical model for the proposed protocol and give the simulation result of WLAN scenario under various capture thresholds.

Keywords: 802.11, WLAN, capture effect, collision detection, collision resolution, receiver initiated

Procedia PDF Downloads 359

Authors: Olomon Wu, Hung Lu, Nick Wilkins, Daniel Kerr, Zekeriya Aliyazicioglu, H. K. Hwang

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Multiple Input Multiple Output (MIMO) radar has received increasing attention in recent years. MIMO radar has many advantages over conventional phased array radar such as target detection, resolution enhancement, and interference suppression. In this paper, the results are presented from a simulation study of MIMO Uniformly-Spaced Linear Array (ULA) antennas. The performance is investigated under varied parameters, including varied array size, Pseudo Random (PN) sequence length, number of snapshots, and Signal to Noise Ratio (SNR). The results of MIMO are compared to a traditional array antenna.

Keywords: MIMO radar, phased array antenna, target detection, radar signal processing

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Authors: Shuen-Tai Wang, Ying-Chuan Chen, Yu-Ching Lin

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There has been renewal of interest in the relation between Green IT and cloud computing in recent years. Cloud computing has to be a highly elastic environment which provides stable services to users. The growing use of cloud computing facilities has caused marked energy consumption, putting negative pressure on electricity cost of computing center or data center. Each year more and more network devices, storages and computers are purchased and put to use, but it is not just the number of computers that is driving energy consumption upward. We could foresee that the power consumption of cloud computing facilities will double, triple, or even more in the next decade. This paper aims at resource allocation and scheduling technologies that are short of or have not well developed yet to reduce energy utilization in cloud computing platform. In particular, our approach relies on recalling services dynamically onto appropriate amount of the machines according to user’s requirement and temporarily shutting down the machines after finish in order to conserve energy. We present initial work on integration of resource and power management system that focuses on reducing power consumption such that they suffice for meeting the minimizing quality of service required by the cloud computing platform.

Keywords: cloud computing, energy utilization, power consumption, resource allocation

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Authors: Khalil H. Al-Bayati, G. Nasma, Hussein Ban H. Adel

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The purpose of the present work is to calculate the expectation value of potential energy for different spin states (ααα ≡ βββ, αβα ≡ βαβ) and compare it with spin states (αββ, ααβ ) for lithium excited state (1s2s3s) and Li-like ions (Be+, B+2) using Hartree-Fock wave function by partitioning technique. The result of inter particle expectation value shows linear behaviour with atomic number and for each atom and ion the shows the trend ααα < ααβ < αββ < αβα.

Keywords: lithium excited state, potential energy, 1s2s3s, mathematical physics

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Authors: Amir Abbas Fatemi, Zahra Tabrizian, Kabir Sadeghi

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To find the location and severity of damage that occurs in a structure, characteristics changes in dynamic and static can be used. The non-destructive techniques are more common, economic, and reliable to detect the global or local damages in structures. This paper presents a non-destructive method in structural damage detection and assessment using GA and static data. Thus, a set of static forces is applied to some of degrees of freedom and the static responses (displacements) are measured at another set of DOFs. An analytical model of the truss structure is developed based on the available specification and the properties derived from static data. The damages in structure produce changes to its stiffness so this method used to determine damage based on change in the structural stiffness parameter. Changes in the static response which structural damage caused choose to produce some simultaneous equations. Genetic Algorithms are powerful tools for solving large optimization problems. Optimization is considered to minimize objective function involve difference between the static load vector of damaged and healthy structure. Several scenarios defined for damage detection (single scenario and multiple scenarios). The static damage identification methods have many advantages, but some difficulties still exist. So it is important to achieve the best damage identification and if the best result is obtained it means that the method is Reliable. This strategy is applied to a plane truss. This method is used for a plane truss. Numerical results demonstrate the ability of this method in detecting damage in given structures. Also figures show damage detections in multiple damage scenarios have really efficient answer. Even existence of noise in the measurements doesn’t reduce the accuracy of damage detections method in these structures.

Keywords: damage detection, finite element method, static data, non-destructive, genetic algorithm

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Authors: Vimala Rani P, Narasimha Malikarjunan, Mercy Shalinie S

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Data Networking was brought forth as an instantiation of information-centric networking. The attackers can send a colossal number of spoofs to take hold of the Pending Interest Table (PIT) named an Interest Flooding attack (IFA) since the in- interests are recorded in the PITs of the intermediate routers until they receive corresponding Data Packets are go beyond the time limit. These attacks can be detrimental to network performance. PIT expiration rate or the Interest satisfaction rate, which cannot differentiate the IFA from attacks, is the criterion Traditional IFA detection techniques are concerned with. Threshold values can casually affect Threshold-based traditional methods. This article proposes an accurate IFA detection mechanism based on a Multiple Feature-based Extreme Learning Machine (MF-ELM). Accuracy of the attack detection can be increased by presenting the entropy of Internet names, Interest satisfaction rate and PIT usage as features extracted in the MF-ELM classifier. Furthermore, we deploy a queue-based hostile Interest prefix mitigation mechanism. The inference of this real-time test bed is that the mechanism can help the network to resist IFA with higher accuracy and efficiency.

Keywords: information-centric network, pending interest table, interest flooding attack, MF-ELM classifier, queue-based mitigation strategy

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Authors: Swapnil Kumar Singh

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Gamma-ray bursts (GRB's), short and intense pulses of low-energy γ rays, have fascinated astronomers and astrophysicists since their unexpected discovery in the late sixties. GRB'sare accompanied by long-lasting afterglows, and they are associated with core-collapse supernovae. The detection of delayed emission in X-ray, optical, and radio wavelength, or "afterglow," following a γ-ray burst can be described as the emission of a relativistic shell decelerating upon collision with the interstellar medium. While it is fair to say that there is strong diversity amongst the afterglow population, probably reflecting diversity in the energy, luminosity, shock efficiency, baryon loading, progenitor properties, circumstellar medium, and more, the afterglows of GRBs do appear more similar than the bursts themselves, and it is possible to identify common features within afterglows that lead to some canonical expectations. After an initial flash of gamma rays, a longer-lived "afterglow" is usually emitted at longer wavelengths (X-ray, ultraviolet, optical, infrared, microwave, and radio). It is a slowly fading emission at longer wavelengths created by collisions between the burst ejecta and interstellar gas. In X-ray wavelengths, the GRB afterglow fades quickly at first, then transitions to a less-steep drop-off (it does other stuff after that, but we'll ignore that for now). During these early phases, the X-ray afterglow has a spectrum that looks like a power law: flux F∝ E^β, where E is energy and beta is some number called the spectral index. This kind of spectrum is characteristic of synchrotron emission, which is produced when charged particles spiral around magnetic field lines at close to the speed of light. In addition to the outgoing forward shock that ploughs into the interstellar medium, there is also a so-called reverse shock, which propagates backward through the ejecta. In many ways," reverse" shock can be misleading; this shock is still moving outward from the restframe of the star at relativistic velocity but is ploughing backward through the ejecta in their frame and is slowing the expansion. This reverse shock can be dynamically important, as it can carry comparable energy to the forward shock. The early phases of the GRB afterglow still provide a good description even if the GRB is highly collimated since the individual emitting regions of the outflow are not in causal contact at large angles and so behave as though they are expanding isotropically. The majority of afterglows, at times typically observed, fall in the slow cooling regime, and the cooling break lies between the optical and the X-ray. Numerous observations support this broad picture for afterglows in the spectral energy distribution of the afterglow of the very bright GRB. The bluer light (optical and X-ray) appears to follow a typical synchrotron forward shock expectation (note that the apparent features in the X-ray and optical spectrum are due to the presence of dust within the host galaxy). We need more research in GRB and Particle Physics in order to unfold the mysteries of afterglow.

Keywords: GRB, synchrotron, X-ray, isotropic energy

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Authors: Abdulsallam A. Bakdash, Aiyshah M. Alshehri, Hind M. Alenzi

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Benzatropine (benztropine) is used to treat symptoms of Parkinson's disease or involuntary movements due to the side effects of certain psychiatric drugs. We report in this study, results of a procedure for the determination of benzatropine in hair using LLE, once with methanol and second with phosphate buffer (pH 6.0), followed by filtration and then re-extraction with dichloromethane. A GC/MS method was developed and validated for this determination using selected ion monitoring (SIM) detection without derivatization. Linearity established over the concentration range 0.1-20.0 ng/mg hair, and the correlation coefficients were greater than 0.99. Recoveries were 52.2% and 21.1% using methanol and phosphate buffer extraction, respectively. Detection limits of benzatropine in hair were between 0.65 and 3.0 ng/mg hair, while the accuracy were 10.4% and 18.5% (RSD), respectively. We also applied this method to the analysis of soaked hair samples and demonstrated that the LLE using methanol meets the requirement for the analysis of benzatropine in hair.

Keywords: hair analysis, benzatropine, liquid-liquid extraction, GC/MS

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Authors: Muhammad Solikhudin Nafi, Wahyu Afif Mufida, Mita Erna Wati, Fitri Setyani Rokim, M. Al-Rizqi Dharma Fauzi

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High dose activity without any pre-exercise will impact Delayed Onset Muscle Soreness (DOMS). DOMS known as delayed pain post-exercise and induce skeletal injury which will decrease athletes’ performances. From now on, post-exercise muscle damage can be detected by measuring skeletal troponin I (sTnI) concentration in serum using ELISA but this method needs more time and cost. To prevent decreased athletes performances, screening need to be done rapidly. We want to introduce our new prototype to detect DOMS acutely. Rapid detection tests are based on immunological reaction between skeletal troponin I antibodies and sTnI in human serum or whole blood. Chemical methods that are used in the manufacture of diagnostic test is lateral flow immunoassay. The material used is rat monoclonal antibody sTnI, colloidal gold, anti-mouse IgG, nitrocellulose membrane, conjugate pad, sample pad, wick and backing card. The procedure are made conjugate (colloidal gold and mAb sTnI) and insert into the conjugate pad, gives spray sTnI mAb and anti-mouse IgG into nitrocellulose membrane, and assemble RDT. RDT had been evaluated by measuring the sensitivity of positive human serum (n = 30) and negative human serum (n = 30). Overall sensitivity value was 93% and specificity value was 90%. ADCOR as the first rapid detection test qualitatively showed antigen-antibody reaction and showed good overall performances for screening of muscle damage. Furthermore, these finding still need more improvements to get best results.

Keywords: DOMS, sTnI, rapid detection test, ELISA

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Authors: Sadegh Torfi

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Realization of anticipated energy efficiency from recuperative run-around energy recovery (RER) systems requires identification of the system components influential parameters. Because simulation modeling is considered as an integral part of the design and economic evaluation of RER systems, it is essential to calibrate the developed models and validate the performance predictions by means of comparison with data from experimental measurements. Several theoretical and numerical analyses on RER systems by researchers have been done, but generally the effect of distance between hot and cold flow is ignored. The objective of this study is to develop a thermohydroulic model for a typical RER system that accounts for energy loss from the interconnecting piping and effects of interconnecting pipes length performance of run-around energy recovery systems. Numerical simulation shows that energy loss from the interconnecting piping is change linear with pipes length and if pipes are properly isolated, maximum reduction of effectiveness of RER systems is 2% in typical piping systems.

Keywords: combined heat and power, heat recovery, effectiveness, CGS

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Authors: Tami A. Ulhiza, Noor I. M. Puad, Azlin S. Azmi, Mohd. I. A. Malek

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High amount of chemical oxygen demand (COD) in starchy waste can be harmful to the environment. In common practice, starch processing wastewater is discharged to the river without proper treatment. However, starchy waste still contains complex sugars and organic acids. By the right pretreatment method, the complex sugar can be hydrolyzed into more readily digestible sugars which can be utilized to be converted into more valuable products. At the same time, the global demand of energy is inevitable. The continuous usage of fossil fuel as the main source of energy can lead to energy scarcity. Hydrogen is a renewable form of energy which can be an alternative energy in the future. Moreover, hydrogen is clean and carries the highest energy compared to other fuels. Biohydrogen produced from waste has significant advantages over chemical methods. One of the major problems in biohydrogen production is the raw material cost. The carbohydrate-rich starchy wastes such as tapioca, maize, wheat, potato, and sago wastes is a promising candidate to be used as a substrate in producing biohydrogen. The utilization of those wastes for biohydrogen production can provide cheap energy generation with simultaneous waste treatment. Therefore this paper aims to review variety source of starchy wastes that has been widely used to synthesize biohydrogen. The scope includes the source of waste, the performance in yielding hydrogen, the pretreatment method and the type of culture that is suitable for starchy waste.

Keywords: biohydrogen, dark fermentation, renewable energy, starchy waste

Procedia PDF Downloads 223

Authors: Badr Eddine Lebrouhi, Melissa Lopez Viveros, Silvia De Los Santos, Kolthoum Missaoui, Pamela Ramirez Vidal

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Since the Paris Climate Agreement, numerous countries, including France, have committed to achieving carbon neutrality by 2050 by enhancing renewable energy capacity and decarbonizing various sectors, including aviation. In this way, the Occitanie region aspires to become a renewable energy pioneer and has focused on Toulouse's Blagnac airport—a prominent hub characterized by high-energy demands. As part of a holistic strategy to reduce the airport's energy dependency, green hydrogen has emerged as a promising alternative fuel, offering the potential to significantly enhance aviation's environmental sustainability. This study assesses the technical and economic aspects of green hydrogen production, particularly its potential to replace fossil kerosene in aviation at Toulouse-Blagnac airport. It analyzes future liquid hydrogen fuel demand, calculates energy requirements for electrolysis and liquefaction, considers diverse renewable energy scenarios, and assesses the Levelized Cost of Hydrogen (LCOH) for economic viability. The research also projects LCOH evolution from 2023 to 2050, offering a comprehensive view of green hydrogen's feasibility as a sustainable aviation fuel, aligning with the region's renewable energy and sustainable aviation objectives.

Keywords: Toulouse-Blagnac Airport, green hydrogen, aviation decarbonization, electrolysis, renewable energy, technical-economic feasibility

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Authors: Charmaine Lamiel, Van Hoa Nguyen, Jae-Jin Shim

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Supercapacitors are energy storage devices capable of storing more energy than conventional capacitors and have higher power density than batteries. The advantages of this method include the non-use of reducing agents and acidic medium, and no further use of a post-heat treatment unlike the conventional processes, in which calcination is generally employed after obtaining the initial product. Furthermore, it also offers a shorter reaction time at low temperatures and low power requirements, which allows low fabrication and energy cost. In this study, microwave irradiation was used for the facile and rapid synthesis of mesoporous RGO@(Co,Mn)3O4 nanosheets as an electrode material. The as-prepared electrode exhibited a high capacitance of 953 F•g^−1 at 1 A•g^−1 in a 6 M KOH electrolyte solution. Moreover, the electrode exhibited a high energy density of 76.2 Wh•kg^−1 at a power density of 720 W•kg^−1, and a high power density of 7200 W•kg^−1 at an energy density of 38 Wh•kg^−1. The successful methodology was considered to be efficient and cost-effective, thereby providing an active electrode material with very promising electrochemical performance.

Keywords: cobalt-manganese oxide, electrochemical, graphene, microwave synthesis, supercapacitor

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Authors: Zofia Romanowska

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Recently there have been significant changes in the Polish energy market. Due to the government's decision to strengthen energy security as well as to strengthen the implementation of the European Union common energy policy, the Polish energy market has been undergoing significant changes. In the face of these, it is necessary to answer the question about the direction the Polish energy rationing sector is going, how wide apart the powers of the state are and also whether the real regulator of energy projects in Poland is not in fact the European Union itself. In order to determine the role of the state as a regulator of the energy market, the study analyses the basic instruments of regulation, i.e. the licenses, permits and permissions to conduct various activities related to the energy market, such as the production and sale of liquid fuels or concessions for trade in natural gas. Bearing in mind that Polish law is part of the widely interpreted European Union energy policy, the legal solutions in neighbouring countries are also being researched, including those made in Germany, a country which plays a key role in the shaping of EU policies. The correct interpretation of the new legislation modifying the current wording of the Energy Law Act, such as obliging the entities engaged in the production and trade of liquid fuels (including abroad) to meet a number of additional requirements for the licensing and providing information to the state about conducted business, plays a key role in the study. Going beyond the legal framework for energy rationing, the study also includes a legal and economic analysis of public and private goods within the energy sector and delves into the subject of effective remedies. The research caused the relationships between progressive rationing introduced by the legislator and the rearrangement rules prevailing on the Polish energy market to be taken note of, which led to the introduction of greater transparency in the sector. The studies refer to the initial conclusion that currently, despite the proclaimed idea of liberalization of the oil and gas market and the opening of market to a bigger number of entities as a result of the newly implanted changes, the process of issuing and controlling the conduction of the concessions will be tightened, guaranteeing to entities greater security of energy supply. In the long term, the effect of the introduced legislative solutions will be the reduction of the amount of entities on the energy market. The companies that meet the requirements imposed on them by the new regulation to cope with the profitability of the business will in turn increase prices for their services, which will be have an impact on consumers' budgets.

Keywords: license, energy law, energy market, public goods, regulator

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Authors: Mi-Ju Kim, Hae-Yeong Kim

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Product mislabeling and adulteration have been increasing the concerns in processed meat products. Relatively inexpensive pork meat compared to meat such as beef was adulterated for economic benefit. These food fraud incidents related to pork were concerned due to economic, religious and health reasons. In this study, a rapid on-site detection method using loop-mediated isothermal amplification (LAMP) was developed for the simultaneous identification of beef and pork. Each specific LAMP primer for beef and pork was designed targeting on mitochondrial D-loop region. The LAMP assay reaction was performed at 65 ℃ for 40 min. The specificity of each primer for beef and pork was evaluated using DNAs extracted from 13 animal species including beef and pork. The sensitivity of duplex LAMP assay was examined by serial dilution of beef and pork DNAs, and reference binary mixtures. This assay was applied to processed meat products including beef and pork meat for monitoring. Each set of primers amplified only the targeted species with no cross-reactivity with animal species. The limit of detection of duplex real-time LAMP was 1 pg for each DNA of beef and pork and 1% pork in a beef-meat mixture. Commercial meat products that declared the presence of beef and/or pork meat on the label showed positive results for those species. This method was successfully applied to detect simultaneous beef and pork meats in processed meat products. The optimized duplex LAMP assay can identify simultaneously beef and pork meat within less than 40 min. A portable real-time fluorescence device used in this study is applicable for on-site detection of beef and pork in processed meat products. Thus, this developed assay was considered to be an efficient tool for monitoring meat products.

Keywords: beef, duplex real-time LAMP, meat identification, pork

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Authors: Alireza Toloei, Ahmad R. Saffary, Reza Ghasemi

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Due to the growing need for energy and limited fossil resources, the use of renewable energy, particularly wind is strongly favored. We all wind energy can’t be saved. Betz law, 59% of the total kinetic energy of the wind turbine is extracting. Therefore turbine control to achieve maximum performance and maintain stable conditions seem necessary. In this article, we plan for a horizontal axis wind turbine variable-speed variable-pitch nonlinear controller to obtain maximum output power. The model presented in this article, including a wide range of wind turbines are horizontal axis. However, the parameters used in this model is from Vestas V29 225 kW wind turbine. We designed second order sliding mode controller, which was robust in the face of changes in wind speed and it eliminated chattering by using of super twisting algorithm. Finally, using MATLAB software to simulate the results we considered the accuracy of the simulation results.

Keywords: non linear controller, robust, sliding mode, kinetic energy

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Authors: Amir Hossein Hejazi, Nima Amjady

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In recent years, due to environmental issues traditional energy sources had been replaced by renewable ones. Wind energy as the fastest growing renewable energy shares a considerable percent of energy in power electricity markets. With this fast growth of wind energy worldwide, owners and operators of wind farms, transmission system operators, and energy traders need reliable and secure forecasts of wind energy production. In this paper, a new forecasting strategy is proposed for short-term wind power prediction based on Echo State Networks (ESN). The forecast engine utilizes state-of-the-art training process including dynamical reservoir with high capability to learn complex dynamics of wind power or wind vector signals. The study becomes more interesting by incorporating prediction of wind direction into forecast strategy. The Big Bang-Big Crunch (BB-BC) evolutionary optimization algorithm is adopted for adjusting free parameters of ESN-based forecaster. The proposed method is tested by real-world hourly data to show the efficiency of the forecasting engine for prediction of both wind vector and wind power output of aggregated wind power production.

Keywords: wind power forecasting, echo state network, big bang-big crunch, evolutionary optimization algorithm

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Authors: Angesom Gebrezgabiher Tesfay, Afafaw Hailesilasie Tesfay, Muyiwa Samuel Adaramola

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The energy demand boost in Ethiopia urges sustainable fuel options while it is mainly supplemented by traditional biomass and imported conventional fuels. To satisfy the deficiency it has to be sourced from all renewables. Thus identifying resources and estimating potential is vital to the sector. This study aims at an in-depth assessment to quantify, prioritize, and analyze agricultural residue biomass energy and related characteristic forms. Biomass use management and modernization seeks successive information and a clue about the resource quantity and characteristic. Five years of crop yield data for thirteen crops were collected. Conversion factors for their 20 residues are surveyed from the literature. Then residues amount potentially available for energy and their energy is estimated regional, crop-wise, residue-wise, and shares compared. Their potential value for energy is analyzed from two perspectives and prioritized. The gross potential is estimated to be 495PJ, equivalent to 12/17 million tons of oil/coal. At 30% collection efficiency, it is the same as conventional fuel import in 2018. Maize and sorghum potential and spatial availability are preeminent. Cotton and maize presented the highest potential values for energy from application and resource perspectives. Oromia and Amhara regions' contributions are the highest. The resource collection and application trends are required for future management that implicates a prospective study.

Keywords: crop residue, biomass potential, biomass resource, Ethiopian energy

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Authors: Aditya Karade, Sharada Falane, Dhananjay Deshmukh, Vijaykumar Mantri

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Brain tumors pose a significant challenge in healthcare due to their complex nature and impact on patient outcomes. The application of deep learning (DL) algorithms in medical imaging have shown promise in accurate and efficient brain tumour detection. This paper explores the performance of various pre-trained DL models ResNet50, Xception, InceptionV3, EfficientNetB0, DenseNet121, NASNetMobile, VGG19, VGG16, and MobileNet on a brain tumour dataset sourced from Figshare. The dataset consists of MRI scans categorizing different types of brain tumours, including meningioma, pituitary, glioma, and no tumour. The study involves a comprehensive evaluation of these models’ accuracy and effectiveness in classifying brain tumour images. Data preprocessing, augmentation, and finetuning techniques are employed to optimize model performance. Among the evaluated deep learning models for brain tumour detection, ResNet50 emerges as the top performer with an accuracy of 98.86%. Following closely is Xception, exhibiting a strong accuracy of 97.33%. These models showcase robust capabilities in accurately classifying brain tumour images. On the other end of the spectrum, VGG16 trails with the lowest accuracy at 89.02%.

Keywords: brain tumour, MRI image, detecting and classifying tumour, pre-trained models, transfer learning, image segmentation, data augmentation

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Authors: Christos Chaschatzis, Chrysoula Karaiskou, Pantelis Angelidis, Sotirios K. Goudos, Igor Kotsiuba, Panagiotis Sarigiannidis

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Over the past few decades, the rapid growth of the global population has led to the need to increase agricultural production and improve the quality of agricultural goods. There is a growing focus on environmentally eco-friendly solutions, sustainable production, and biologically minimally fertilized products in contemporary society. Precision agriculture has the potential to incorporate a wide range of innovative solutions with the development of machine learning algorithms. YOLOv5 and YOLOv8 are two of the most advanced object detection algorithms capable of accurately recognizing objects in real time. Detecting tree diseases is crucial for improving the food production rate and ensuring sustainability. This research aims to evaluate the efficacy of YOLOv5 and YOLOv8 in detecting the symptoms of Armillaria spp. in sweet cherry trees and determining their health status, with the goal of enhancing the robustness of precision agriculture. Additionally, this study will explore Computer Vision (CV) techniques with machine learning algorithms to improve the detection process’s efficiency.

Keywords: Armillaria spp., machine learning, precision agriculture, smart farming, sweet cherries trees, YOLOv5, YOLOv8

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Authors: Amirhossein Eisapour, Mohammad Emamjome Kashan, Alan S. Fung

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This research addresses the pressing global challenges of clean energy and water supplies, emphasizing the need for sustainable solutions for the building sector. The research centers on integrating Reverse Osmosis (RO) systems with building energy systems, incorporating Solar Thermal Collectors (STC)/Photovoltaic Thermal (PVT), water-to-water heat pumps, and an Insulated Concrete Form (ICF) based building foundation wall thermal energy storage. The study explores an innovative configuration’s effectiveness in addressing water and heating demands through clean energy sources while addressing ICF-based thermal storage challenges, which could overheat in the cooling season. Analyzing four configurations—STC-ICF, STC-ICF-RO, PVT-ICF, and PVT-ICF-RO, the study conducts a sensitivity analysis on collector area (25% and 50% increase) and weather data (evaluating five Canadian cities, Winnipeg, Toronto, Edmonton, Halifax and Vancouver). Key outcomes highlight the benefits of integrated RO scenarios, showcasing reduced ICF wall temperature, diminished unwanted heat in the cooling season, reduced RO pump consumption and enhanced solar energy production. The STC-ICF-RO and PVT-ICF-RO systems achieved energy savings of 653 kWh and 131 kWh, respectively, in comparison to their non-integrated RO counterparts. Additionally, both systems successfully contributed to lowering the CO2 production level of the energy system. The calculated payback period of STC-ICF-RO (2 years) affirms the proposed systems’ economic viability. Compared to the base system, which does not benefit from the ICF and RO integration with the building energy system, the STC-ICF-RO and PVT-ICF-RO demonstrate a dramatic energy consumption reduction of 20% and 32%, respectively. The sensitivity analysis suggests potential system improvements under specific conditions, especially when implementing the introduced energy system in communities of buildings.

Keywords: insulated concrete form, thermal energy storage, reverse osmosis, building energy systems, solar thermal collector, photovoltaic thermal, heat pump

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Authors: Muhammad Asghar, Aamer Iqbal Bhatti, Qadeer Ahmed, Tahir Izhar

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Three wheeler auto Rickshaw, often termed as ‘auto rickshaw’ is very common in Pakistan and is considered as the most affordable means of transportation to the local people. Problems caused by the gasoline engine on the environment and people, the researchers and the automotive industry have turned to the hybrid electric vehicles and electrical powered vehicle. The research in this paper explains the design of energy efficient Electric auto Rickshaw. An electric auto rickshaw is being developed at Center for Energy Research and Development, (Lahore), which is running on the roads of Lahore city. Energy storage capacity of batteries is at least 25 times heavier than fossil fuel and having volume 10 times in comparison to fuel, resulting an increase of the Rickshaw weight. A set of specifications is derived according to the mobility requirements of the electric auto rickshaw. The design choices considering the power-train and component selection are explained in detail. It was concluded that electric auto rickshaw has many advantages and benefits over the conventional auto rickshaw. It is cleaner and much more energy efficient but limited to the distance it can travel before recharging of battery. In addition, a brief future view of the battery technology is given.

Keywords: conventional auto rickshaw, energy efficiency, electric auto rickshaw, internal combustion engine, environment

Procedia PDF Downloads 286

Authors: M. Paraipan, V. M. Javadova, S. I. Tyutyunnikov

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The concept of energy amplifier or accelerator driven system (ADS) involves the use of a particle accelerator coupled with a nuclear reactor. The accelerated particle beam generates a supplementary source of neutrons, which allows the subcritical functioning of the reactor, and consequently a safe exploitation. The harder neutron spectrum realized ensures a better incineration of the actinides. The almost generalized opinion is that the optimal beam for ADS is represented by protons with energy around 1 GeV (gigaelectronvolt). In the present work, a systematic analysis of the energy gain for proton beams with energy from 0.5 to 3 GeV and ion beams from deuteron to neon with energies between 0.25 and 2 AGeV is performed. The target is an assembly of metallic U-Pu-Zr fuel rods in a bath of lead-bismuth eutectic coolant. The rods length is 150 cm. A beryllium converter with length 110 cm is used in order to maximize the energy released in the target. The case of a linear accelerator is considered, with a beam intensity of 1.25‧10¹⁶ p/s, and a total accelerator efficiency of 0.18 for proton beam. These values are planned to be achieved in the European Spallation Source project. The energy gain G is calculated as the ratio between the energy released in the target to the energy spent to accelerate the beam. The energy released is obtained through simulation with the code Geant4. The energy spent is calculating by scaling from the data about the accelerator efficiency for the reference particle (proton). The analysis concerns the G values, the net power produce, the accelerator length, and the period between refueling. The optimal energy for proton is 1.5 GeV. At this energy, G reaches a plateau around a value of 8 and a net power production of 120 MW (megawatt). Starting with alpha, ion beams have a higher G than 1.5 GeV protons. A beam of 0.25 AGeV(gigaelectronvolt per nucleon) ⁷Li realizes the same net power production as 1.5 GeV protons, has a G of 15, and needs an accelerator length 2.6 times lower than for protons, representing the best solution for ADS. Beams of ¹⁶O or ²⁰Ne with energy 0.75 AGeV, accelerated in an accelerator with the same length as 1.5 GeV protons produce approximately 900 MW net power, with a gain of 23-25. The study of the evolution of the isotopes composition during irradiation shows that the increase in power production diminishes the period between refueling. For a net power produced of 120 MW, the target can be irradiated approximately 5000 days without refueling, but only 600 days when the net power reaches 1 GW (gigawatt).

Keywords: accelerator driven system, ion beam, electrical power, energy gain

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Authors: Punit Gandhi, J. C. Brezet, Tim Gorter, Uchechi Obinna

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This paper introduces how weather forecasting could help in more efficient energy management for smart homes with the use of Home Energy Management Systems (HEMS). The paper also focuses on educating consumers and helping them make more informed decisions while using the HEMS. A combined approach of technical and user perspective has been selected to develop a novel HEMS-product-service combination in a more comprehensive manner. The current HEMS switches on/off the energy intensive appliances based on the fluctuating electricity tariffs, but with weather forecasting, it is possible to shift the time of use of energy intensive appliances to maximum electricity production from the renewable energy system installed in the house. Also, it is possible to estimate the heating/cooling load of the house for the day ahead demand. Hence, relevant insight is gained in the expected energy production and consumption load for the next day, facilitating better (more efficient, peak shaved, cheaper, etc.) energy management practices for smart homes. In literature, on the user perspective, it has been observed that consumers lose interest in using HEMS after three to four months. Therefore, to further help in better energy management practices, the new system had to be designed in a way that consumers would sustain their interaction with the system on a structural basis. It is hypothesized that, if consumers feel more comfortable with using such system, it would lead to a prolonged usage, including more energy savings and hence financial savings. To test the hypothesis, a survey for the HEMS is conducted, to which 59 valid responses were recorded. Analysis of the survey helped in designing a system which imparts better information about the energy production and consumption to the consumers. It is also found from the survey that, consumers like a variety of options and they do not like a constant reminder of what they should do. Hence, the final system is designed to encourage consumers to make an informed decision about their energy usage with a wide variety of behavioral options available. It is envisaged that the new system will be tested in several pioneering smart energy grid projects in both the Netherlands and India, with a continued ‘design thinking’ approach, combining the technical and user perspective, as the basis for further improvements.

Keywords: weather forecasting, smart grid, renewable energy forecasting, user defined HEMS

Procedia PDF Downloads 229

Authors: M. O. Oke, T. S. Workneh

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Drying behaviour of blanched sweet potato in a cabinet dryer using different five air temperatures (40-80oC) and ten sweet potato varieties sliced to 5 mm thickness were investigated. The drying data were fitted to eight models. The Modified Henderson and Pabis model gave the best fit to the experimental moisture ratio data obtained during the drying of all the varieties while Newton (Lewis) and Wang and Singh models gave the least fit. The values of Deff obtained for Bophelo variety (1.27 x 10-9 to 1.77 x 10-9 m2/s) was the least while that of S191 (1.93 x 10-9 to 2.47 x 10-9 m2/s) was the highest which indicates that moisture diffusivity in sweet potato is affected by the genetic factor. Activation energy values ranged from 0.27-6.54 kJ/mol. The lower activation energy indicates that drying of sweet potato slices requires less energy and is hence a cost and energy saving method. The drying behavior of blanched sweet potato was investigated in a cabinet dryer. Drying time decreased considerably with increase in hot air temperature. Out of the eight models fitted, the Modified Henderson and Pabis model gave the best fit to the experimental moisture ratio data on all the varieties while Newton, Wang and Singh models gave the least. The lower activation energy (0.27-6.54 kJ/mol) obtained indicates that drying of sweet potato slices requires less energy and is hence a cost and energy saving method.

Keywords: sweet potato slice, drying models, moisture ratio, moisture diffusivity, activation energy

Procedia PDF Downloads 515

Authors: Mohamad R. Moshtagh, Ahmad Bagheri

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Accurate fault detection in rotating machinery is of utmost importance to ensure optimal performance and prevent costly downtime in industrial applications. This study presents a robust fault detection system based on vibration data collected from rotating gears under various operating conditions. The considered scenarios include: (1) both gears being healthy, (2) one healthy gear and one faulty gear, and (3) introducing an imbalanced condition to a healthy gear. Vibration data was acquired using a Hentek 1008 device and stored in a CSV file. Python code implemented in the Spider environment was used for data preprocessing and analysis. Winner features were extracted using the Wiener feature selection method. These features were then employed in multiple machine learning algorithms, including Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), and Random Forest, to evaluate their performance in detecting and classifying faults in both the training and validation datasets. The comparative analysis of the methods revealed the superior performance of the Wiener-CNN approach. The Wiener-CNN method achieved a remarkable accuracy of 100% for both the two-class (healthy gear and faulty gear) and three-class (healthy gear, faulty gear, and imbalanced) scenarios in the training and validation datasets. In contrast, the other methods exhibited varying levels of accuracy. The Wiener-MLP method attained 100% accuracy for the two-class training dataset and 100% for the validation dataset. For the three-class scenario, the Wiener-MLP method demonstrated 100% accuracy in the training dataset and 95.3% accuracy in the validation dataset. The Wiener-KNN method yielded 96.3% accuracy for the two-class training dataset and 94.5% for the validation dataset. In the three-class scenario, it achieved 85.3% accuracy in the training dataset and 77.2% in the validation dataset. The Wiener-Random Forest method achieved 100% accuracy for the two-class training dataset and 85% for the validation dataset, while in the three-class training dataset, it attained 100% accuracy and 90.8% accuracy for the validation dataset. The exceptional accuracy demonstrated by the Wiener-CNN method underscores its effectiveness in accurately identifying and classifying fault conditions in rotating machinery. The proposed fault detection system utilizes vibration data analysis and advanced machine learning techniques to improve operational reliability and productivity. By adopting the Wiener-CNN method, industrial systems can benefit from enhanced fault detection capabilities, facilitating proactive maintenance and reducing equipment downtime.

Keywords: fault detection, gearbox, machine learning, wiener method

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Authors: Ayse Arslan

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One of the important elements underpinning a conscious design is the degree of toxicity in communication. This study explores the mechanisms and strategies for identifying toxic content by avoiding dark patterns. Given the breadth of hate and harassment attacks, this study explores a threat model and taxonomy to assist in reasoning about strategies for detection, prevention, mitigation, and recovery. In addition to identifying some relevant techniques such as nudges, automatic detection, or human-ranking, the study suggests the use of major metrics such as the overhead and friction of solutions on platforms and users or balancing false positives (e.g., incorrectly penalizing legitimate users) against false negatives (e.g., users exposed to hate and harassment) to maintain a conscious design towards fairness.

Keywords: AI, ML, algorithms, policy, system design

Procedia PDF Downloads 119