Search results for: thermal network

Authors: Roghieh A. Biroon, Zoleikha Abdollahi

Abstract:

The recent growth of renewables, e.g., solar panels, batteries, and electric vehicles (EVs) in residential and small commercial sectors, has potential impacts on the stability and operation of power grids. Considering approximately 50 percent share of the residential and the commercial sectors in the electricity demand market, the significance of these impacts, and the necessity of addressing them are more highlighted. Utilities and power system operators should manage the renewable electricity sources integration with power systems in such a way to extract the most possible advantages for the power systems. The most common effect of high penetration level of the renewables is the reverse power flow in the distribution feeders when the customers generate more power than their needs. The reverse power flow causes voltage rise and thermal issues in the power grids. To overcome the voltage rise issues in the distribution system, several techniques have been proposed including reducing transformers short circuit resistance and feeder impedance, installing autotransformers/voltage regulators along the line, absorbing the reactive power by distributed generators (DGs), and limiting the PV and battery sizes. In this study, we consider a medium-scale battery energy storage to manage the power energy and address the aforementioned issues on voltage deviation and power loss increase. We propose an optimization algorithm to find the optimum size and location for the battery. The optimization for the battery location and size is so that the battery maintains the feeder voltage deviation and power loss at a certain desired level. Moreover, the proposed optimization algorithm controls the charging/discharging profile of the battery to absorb the negative power flow from residential and commercial customers in the feeder during the peak time and sell the power back to the system during the off-peak time. The proposed battery regulates the voltage problem in the distribution system while it also can play frequency regulation role in islanded microgrids. This battery can be regulated and controlled by the utilities or a third-party ancillary service provider for the utilities to reduce the power system loss and regulate the distribution feeder voltage and frequency in standard level.

Keywords: ancillary services, battery, distribution system and optimization

Procedia PDF Downloads 131

Authors: Vitas Valinčius, Rolandas Uscila, Viktorija Grigaitienė, Žydrūnas Kavaliauskas, Romualdas Kėželis

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Determination of integral dynamical and energy characteristics of high-temperature gas flows is a very important task of gas-dynamic for hazardous substances destruction systems. They are also always necessary for the investigation of high-temperature turbulent flow dynamics, heat and mass transfer. It is well known that distribution of dynamical and thermal characteristics of high-temperature flows and jets is strongly related to heat flux variation over an imposed area of heating. As is visible from numerous experiments and theoretical considerations, the fundamental properties of an isothermal jet are well investigated. However, the establishment of regularities in high-temperature conditions meets certain specific behavior comparing with moderate-temperature jets and flows. Their structures have not been thoroughly studied yet, especially in the cases of plasma ambient. It is well known that the distribution of local plasma jet parameters in high temperature and isothermal jets and flows may significantly differ. High temperature axisymmetric air jet generated by atmospheric pressure DC arc plasma torch was investigated employing enthalpy probe 3.8∙10-3 m of diameter. Distribution of velocities and temperatures were established in different cross-sections of the plasma jet outflowing from 42∙10-3 m diameter pipe at the average mean velocity of 700 m∙s-1, and averaged temperature of 4000 K. It has been found that gas heating fractionally influences shape and values of a dimensionless profile of velocity and temperature in the main zone of plasma jet and has a significant influence in the initial zone of the plasma jet. The width of the initial zone of the plasma jet has been found to be lesser than in the case of isothermal flow. The relation between dynamical thickness and turbulent number of Prandtl has been established along jet axis. Experimental results were generalized in dimensionless form. The presence of convective heating shows that heat transfer in a moving high-temperature jet also occurs due to heat transfer by moving particles of the jet. In this case, the intensity of convective heat transfer is proportional to the instantaneous value of the flow velocity at a given point in space. Consequently, the configuration of the temperature field in moving jets and flows essentially depends on the configuration of the velocity field.

Keywords: plasma jet, plasma torch, heat transfer, enthalpy probe, turbulent number of Prandtl

Procedia PDF Downloads 182

Authors: Redouane Krini, Lutz Nuhn, Hicham El Mard Cheol Woo Ha, Yoondeok Han, Kwang-Sup Lee, Dong-Yol Yang, Jinsoo Joo, Rudolf Zentel

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To use Quantum Dots (QDs) in the two photon initiated polymerization technique (TPIP) for 3D patternings, QDs were modified on the surface with photosensitive end groups which are able to undergo a photopolymerization. We were able to fabricate fluorescent 3D lattice structures using photopatternable QDs by TPIP for photonic devices such as photonic crystals and metamaterials. The QDs in different diameter have different emission colors and through mixing of RGB QDs white light fluorescent from the polymeric structures has been created. Metamaterials are capable for unique interaction with the electrical and magnetic components of the electromagnetic radiation and for manipulating light it is crucial to have a negative refractive index. In combination with QDs via TPIP technique polymeric structures can be designed with properties which cannot be found in nature. This makes these artificial materials gaining a huge importance for real-life applications in photonic and optoelectronic. Understanding of interactions between nanoparticles and biological systems is of a huge interest in the biomedical research field. We developed a synthetic strategy of polymer functionalized nanoparticles for biomedical studies to obtain hybrid systems of QDs and copolymers with a strong binding network in an inner shell and which can be modified in the end through their poly(ethylene glycol) functionalized outer shell. These hybrid systems can be used as models for investigation of cell penetration and drug delivery by using measurements combination between CryoTEM and fluorescence studies.

Keywords: biomedical study models, lithography, photo induced polymerization, quantum dots

Procedia PDF Downloads 526

Authors: Jacques Cuenca, Claudio Colangeli, Agnieszka Mroz, Karl Janssens, Gunther Riexinger, Antonio D'Antuono, Giuseppe Pandarese, Milena Martarelli, Gian Marco Revel, Carlos Barcena Martin

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This study presents an on-site acoustic inspection methodology for quality and performance evaluation of building components. The work focuses on global and detailed sound source localisation, by successively performing acoustic beamforming and sound intensity measurements. A portable experimental setup is developed, consisting of an omnidirectional broadband acoustic source and a microphone array and sound intensity probe. Three main acoustic indicators are of interest, namely the sound pressure distribution on the surface of components such as walls, windows and junctions, the three-dimensional sound intensity field in the vicinity of junctions, and the sound transmission loss of partitions. The measurement data is post-processed and converted into a three-dimensional numerical model of the acoustic indicators with the help of the simultaneously acquired geolocation information. The three-dimensional acoustic indicators are then integrated into an augmented reality platform superimposing them onto a real-time visualisation of the spatial environment. The methodology thus enables a measurement-supported inspection process of buildings and the correction of errors during construction and refurbishment. Two experimental validation cases are shown. The first consists of a laboratory measurement on a full-scale mockup of a room, featuring a prefabricated panel. The latter is installed with controlled defects such as lack of insulation and joint sealing material. It is demonstrated that the combined acoustic and augmented reality tool is capable of identifying acoustic leakages from the building defects and assist in correcting them. The second validation case is performed on a prefabricated room at a near-completion stage in the factory. With the help of the measurements and visualisation tools, the homogeneity of the partition installation is evaluated and leakages from junctions and doors are identified. Furthermore, the integration of acoustic indicators together with thermal and geometrical indicators via the augmented reality platform is shown.

Keywords: acoustic inspection, prefabricated building components, augmented reality, sound source localization

Procedia PDF Downloads 384

Authors: M. Magrini, D. Moroni, G. Palazzese, G. Pieri, D. Azzarelli, A. Spada, L. Fanucci, O. Salvetti

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Railway crossings are complex entities whose optimal management cannot be addressed unless with the help of an intelligent transportation system integrating information both on train and vehicular flows. In this paper, we propose an integrated system named SIMPLE (Railway Safety and Infrastructure for Mobility applied at level crossings) that, while providing unparalleled safety in railway level crossings, collects data on rail and road traffic and provides value-added services to citizens and commuters. Such services include for example alerts, via variable message signs to drivers and suggestions for alternative routes, towards a more sustainable, eco-friendly and efficient urban mobility. To achieve these goals, SIMPLE is organized as a System of Systems (SoS), with a modular architecture whose components range from specially-designed radar sensors for obstacle detection to smart ETSI M2M-compliant camera networks for urban traffic monitoring. Computational unit for performing forecast according to adaptive models of train and vehicular traffic are also included. The proposed system has been tested and validated during an extensive trial held in the mid-sized Italian town of Montecatini, a paradigmatic case where the rail network is inextricably linked with the fabric of the city. Results of the tests are reported and discussed.

Keywords: Intelligent Transportation Systems (ITS), railway, railroad crossing, smart camera networks, radar obstacle detection, real-time traffic optimization, IoT, ETSI M2M, transport safety

Procedia PDF Downloads 497

Authors: Ajay Kumar Singh, Arvind Kumar, S. P. Singh

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Cellulose nanofibrils (CNF) have shown potential of their extensive use in various fields, including papermaking, due to their unique characteristics. In this study, CNF’s were prepared by fibrillating the pulp obtained from raw materials e.g. bagasse, hardwood and softwood using enzymatic pretreatment followed by mechanical refining. These nanofibrils, when examined under FE-SEM, show that partial fibrillation on fiber surface has resulted in production of nanofibers. Mixing these nanofibers with the unrefined and normally refined fibers show their reinforcing effect. This effect is manifested in observing the improvement in the physical and mechanical properties e.g. tensile index and burst index of paper. Tear index, however, was observed to decrease on blending with nanofibers. The optical properties of paper sheets made from blended fibers showed no significant change in comparison to those made from only mechanically refined pulp. Mixing of normal pulp fibers with nanofibers show increase in ºSR and consequent decrease in drainage rate. These changes observed in mechanical, optical and other physical properties of the paper sheets made from nanofibrils blended pulp have been tried to explain considering the distribution of the nanofibrils alongside microfibrils in the fibrous network. Since usually, paper/boards with higher strength are observed to have diminished optical properties which is a drawback in their quality, the present work has the potential for developing paper/boards having improved strength alongwith undiminished optical properties utilising the concepts of nanoscience and nanotechnology.

Keywords: enzymatic pretreatment, mechanical refining, nanofibrils, paper properties

Procedia PDF Downloads 353

Authors: Ruth Diego, Luis M. Romeo, Antonio Morán

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In the present work, a study of the coupling of a Bioelectrochemical System together with an oxy-combustion boiler is carried out; specifically, it proposes to connect the combustion gas outlet of a boiler with a microbial electrolysis cell (MEC) where the CO2 from the gases are transformed into methane in the cathode chamber, and the oxygen produced in the anode chamber is recirculated to the oxy-combustion boiler. The MEC mainly consists of two electrodes (anode and cathode) immersed in an aqueous electrolyte; these electrodes are separated by a proton exchange membrane (PEM). In this case, the anode is abiotic (where oxygen is produced), and it is at the cathode that an electroactive biofilm is formed with microorganisms that catalyze the CO2 reduction reactions. Real data from an oxy-combustion process in a boiler of around 20 thermal MW have been used for this study and are combined with data obtained on a smaller scale (laboratory-pilot scale) to determine the yields that could be obtained considering the system as environmentally sustainable energy storage. In this way, an attempt is made to integrate a relatively conventional energy production system (oxy-combustion) with a biological system (microbial electrolysis cell), which is a challenge to be addressed in this type of new hybrid scheme. In this way, a novel concept is presented with the basic dimensioning of the necessary equipment and the efficiency of the global process. In this work, it has been calculated that the efficiency of this power-to-gas system based on MEC cells when coupled to industrial processes is of the same order of magnitude as the most promising equivalent routes. The proposed process has two main limitations, the overpotentials in the electrodes that penalize the overall efficiency and the need for storage tanks for the process gases. The results of the calculations carried out in this work show that certain real potentials achieve an acceptable performance. Regarding the tanks, with adequate dimensioning, it is possible to achieve complete autonomy. The proposed system called OxyMES provides energy storage without energetically penalizing the process when compared to an oxy-combustion plant with conventional CO2 capture. According to the results obtained, this system can be applied as a measure to decarbonize an industry, changing the original fuel of the oxy-combustion boiler to the biogas generated in the MEC cell. It could also be used to neutralize CO2 emissions from industry by converting it to methane and then injecting it into the natural gas grid.

Keywords: microbial electrolysis cells, oxy-combustion, co2, power-to-gas

Procedia PDF Downloads 108

Authors: Md. Mahadi Hasan Sany, Mumenunnessa Keya, Sharun Khushbu, Sheikh Abujar

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Since the beginning of China's economic reform, trade between the U.S. and China has grown rapidly, and has increased since China's accession to the World Trade Organization in 2001. The US imports more than it exports from China, reducing the trade war between China and the U.S. for the 2019 trade deficit, but in 2020, the opposite happens. In international and U.S. trade, Washington launched a full-scale trade war against China in March 2016, which occurred a catastrophic epidemic. The main goal of our study is to measure and predict trade relations between China and the U.S., before and after the arrival of the COVID epidemic. The ML model uses different data as input but has no time dimension that is present in the time series models and is only able to predict the future from previously observed data. The LSTM (a well-known Recurrent Neural Network) model is applied as the best time series model for trading forecasting. We have been able to create a sustainable forecasting system in trade between China and the US by closely monitoring a dataset published by the State Website NZ Tatauranga Aotearoa from January 1, 2015, to April 30, 2021. Throughout the survey, we provided a 180-day forecast that outlined what would happen to trade between China and the US during COVID-19. In addition, we have illustrated that the LSTM model provides outstanding outcome in time series data analysis rather than RFR and SVR (e.g., both ML models). The study looks at how the current Covid outbreak affects China-US trade. As a comparative study, RMSE transmission rate is calculated for LSTM, RFR and SVR. From our time series analysis, it can be said that the LSTM model has given very favorable thoughts in terms of China-US trade on the future export situation.

Keywords: RFR, China-U.S. trade war, SVR, LSTM, deep learning, Covid-19, export value, forecasting, time series analysis

Procedia PDF Downloads 198

Authors: ChunYi Peng, Wei Hsuan CHeng, Shyh Kuang Ueng

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This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

Procedia PDF Downloads 57

Authors: Qinglin Liu, Zhiyong Lin, Zongheng Wei, Jianfeng Wen, Congming Yi, Hai Liu

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The multi-channel blind rendezvous problem in distributed cognitive radio networks (DCRNs) refers to how users in the network can hop to the same channel at the same time slot without any prior knowledge (i.e., each user is unaware of other users' information). The channel hopping (CH) technique is a typical solution to this blind rendezvous problem. In this paper, we propose a quinary coding and matrix structure-based CH algorithm called QCMS-CH. The QCMS-CH algorithm can guarantee the rendezvous of users using only one cognitive radio in the scenario of the asynchronous clock (i.e., arbitrary time drift between the users), heterogeneous channels (i.e., the available channel sets of users are distinct), and symmetric role (i.e., all users play a same role). The QCMS-CH algorithm first represents a randomly selected channel (denoted by R) as a fixed-length quaternary number. Then it encodes the quaternary number into a quinary bootstrapping sequence according to a carefully designed quaternary-quinary coding table with the prefix "R00". Finally, it builds a CH matrix column by column according to the bootstrapping sequence and six different types of elaborately generated subsequences. The user can access the CH matrix row by row and accordingly perform its channel, hoping to attempt rendezvous with other users. We prove the correctness of QCMS-CH and derive an upper bound on its Maximum Time-to-Rendezvous (MTTR). Simulation results show that the QCMS-CH algorithm outperforms the state-of-the-art in terms of the MTTR and the Expected Time-to-Rendezvous (ETTR).

Keywords: channel hopping, blind rendezvous, cognitive radio networks, quaternary-quinary coding

Procedia PDF Downloads 91

Authors: Haidy S. Ghali, Engy Serag, A. Samer Ezeldin

Abstract:

Copper is an essential raw material used in the construction industry. During the year 2021 and the first half of 2022, the global market suffered from a significant fluctuation in copper raw material prices due to the aftermath of both the COVID-19 pandemic and the Russia-Ukraine war, which exposed its consumers to an unexpected financial risk. Thereto, this paper aims to develop two ANN-LSTM price prediction models, using Python, that can forecast the average monthly copper prices traded in the London Metal Exchange; the first model is a multivariate model that forecasts the copper price of the next 1-month and the second is a univariate model that predicts the copper prices of the upcoming three months. Historical data of average monthly London Metal Exchange copper prices are collected from January 2009 till July 2022, and potential external factors are identified and employed in the multivariate model. These factors lie under three main categories: energy prices and economic indicators of the three major exporting countries of copper, depending on the data availability. Before developing the LSTM models, the collected external parameters are analyzed with respect to the copper prices using correlation and multicollinearity tests in R software; then, the parameters are further screened to select the parameters that influence the copper prices. Then, the two LSTM models are developed, and the dataset is divided into training, validation, and testing sets. The results show that the performance of the 3-Month prediction model is better than the 1-Month prediction model, but still, both models can act as predicting tools for diverse economic situations.

Keywords: copper prices, prediction model, neural network, time series forecasting

Procedia PDF Downloads 113

Authors: Hedi Dghim, Ahmed El-Naggar, Istvan Erlich

Abstract:

Power electronic converters are being introduced in low voltage (LV) grids at an increasingly rapid rate due to the growing adoption of power electronic-based home appliances in residential grid. Photovoltaic (PV) systems are considered one of the potential installed renewable energy sources in distribution power systems. This trend has led to high distortion in the supply voltage which consequently produces harmonic currents in the network and causes an inherent voltage unbalance. In order to investigate the effect of harmonic distortions, a case study of a typical LV grid configuration with high penetration of 3-phase and 1-phase rooftop mounted PV from southern Germany was first considered. Electromagnetic transient (EMT) simulations were then carried out under the MATLAB/Simulink environment which contain detailed models for power electronic-based loads, ohmic-based loads as well as 1- and 3-phase PV. Note that, the switching patterns of the power electronic circuits were considered in this study. Measurements were eventually performed to analyze the distortion levels when PV operating under different solar irradiance. The characteristics of the load-side harmonic impedances were analyzed, and their harmonic contributions were evaluated for different distortion levels. The effect of the high penetration of PV on the harmonic distortion of both positive and negative sequences was also investigated. The simulation results are presented based on case studies. The current distortion levels are in agreement with relevant standards, otherwise the Total Harmonic Distortion (THD) increases under low PV power generation due to its inverse relation with the fundamental current.

Keywords: harmonic distortion analysis, power quality, PV systems, residential distribution system

Procedia PDF Downloads 267

Authors: Petr Slobodian, Pavel Riha, Jiri Matyas, Robert Olejnik, Nuri Karakurt

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Thermoelectric devices generate an electrical current when there is a temperature gradient between the hot and cold junctions of two dissimilar conductive materials typically n-type and p-type semiconductors. Consequently, also the polymeric semiconductors composed of polymeric matrix filled by different forms of carbon nanotubes with proper structural hierarchy can have thermoelectric properties which temperature difference transfer into electricity. In spite of lower thermoelectric efficiency of polymeric thermoelectrics in terms of the figure of merit, the properties as stretchability, flexibility, lightweight, low thermal conductivity, easy processing, and low manufacturing cost are advantages in many technological and ecological applications. Polyethylene-octene copolymer based highly elastic composites filled with multi-walled carbon nanotubes (MWCTs) were prepared by sonication of nanotube dispersion in a copolymer solution followed by their precipitation pouring into non-solvent. The electronic properties of MWCNTs were moderated by different treatment techniques such as chemical oxidation, decoration by Ag clusters or addition of low molecular dopants. In this concept, for example, the amounts of oxygenated functional groups attached on MWCNT surface by HNO₃ oxidation increase p-type charge carriers. p-type of charge carriers can be further increased by doping with molecules of triphenylphosphine. For partial altering p-type MWCNTs into less p-type ones, Ag nanoparticles were deposited on MWCNT surface and then doped with 7,7,8,8-tetracyanoquino-dimethane. Both types of MWCNTs with the highest difference in generated thermoelectric power were combined to manufacture polymeric based thermoelectric module generating thermoelectric voltage when the temperature difference is applied between hot and cold ends of the module. Moreover, it was found that the generated voltage by the thermoelectric module at constant temperature gradient was significantly affected when exposed to vapors of different volatile organic compounds representing then a self-powered thermoelectric sensor for chemical vapor detection.

Keywords: carbon nanotubes, polymer composites, thermoelectric materials, self-powered gas sensor

Procedia PDF Downloads 153

Authors: Mayank, Navneet Kaur, Narinder Singh

Abstract:

The V599 mutations in the BRAF protein are extremely oncogenic, responsible for countless of malignant conditions. Along with wild type, V599E, V599D, and V599R are the important mutated variants of the BRAF proteins. The BRAF inhibitory anticancer agents are continuously developing, and sorafenib is a BRAF inhibitor that is under clinical use. The crystal structure of sorafenib bounded to wild type, and V599 is known, showing a similar interaction pattern in both the case. The mutated 599th residue, in both the case, is also found not interacting directly with the co-crystallized sorafenib molecule. However, the IC50 value of sorafenib was found extremely different in both the case, i.e., 22 nmol/L for wild and 38 nmol/L for V599E protein. Molecular docking study and MMGBSA binding energy results also revealed a significant difference in the binding pattern of sorafenib in both the case. Therefore, to explore the role of distinctively situated 599th residue, we have further conducted comprehensive computational studies. The molecular dynamics simulation, residue interaction network (RIN) analysis, and residue correlation study results revealed the importance of the 599th residue on the therapeutic outcome and overall dynamic of the BRAF protein. Therefore, although the position of 599th residue is very much distinctive from the ligand-binding cavity of BRAF, still it has exceptional control over the overall functional outcome of the protein. The insight obtained here may seem extremely important and guide us while designing ideal BRAF inhibitory anticancer molecules.

Keywords: BRAF, oncogenic, sorafenib, computational studies

Procedia PDF Downloads 115

Authors: Sophia Shi

Abstract:

Acute kidney injury (AKI) is the sudden onset of kidney damage in which the kidneys cannot filter waste from the blood, requiring emergency hospitalization. AKI patient mortality rate is high in the ICU and is virtually impossible for doctors to predict because it is so unexpected. Currently, there is no hybrid model predicting AKI that takes advantage of two types of data. De-identified patient data from the MIMIC-III database and de-identified kidney images and corresponding patient records from the Beijing Hospital of the Ministry of Health were collected. Using data features including serum creatinine among others, two numeric models using MIMIC and Beijing Hospital data were built, and with the hospital ultrasounds, an image-only model was built. Convolutional neural networks (CNN) were used, VGG and Resnet for numeric data and Resnet for image data, and they were combined into a hybrid model by concatenating feature maps of both types of models to create a new input. This input enters another CNN block and then two fully connected layers, ending in a binary output after running through Softmax and additional code. The hybrid model successfully predicted AKI and the highest AUROC of the model was 0.953, achieving an accuracy of 90% and F1-score of 0.91. This model can be implemented into urgent clinical settings such as the ICU and aid doctors by assessing the risk of AKI shortly after the patient’s admission to the ICU, so that doctors can take preventative measures and diminish mortality risks and severe kidney damage.

Keywords: Acute kidney injury, Convolutional neural network, Hybrid deep learning, Patient record data, ResNet, Ultrasound kidney images, VGG

Procedia PDF Downloads 131

Authors: Peng Chun-Yi, Chen Wei-Hsuan, Ueng Shyh-Kuang

Abstract:

This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

Procedia PDF Downloads 59

Authors: I. Belyamani, M. K. Hassan, J. U. Otaigbe, W. R. Fielding, K. A. Mauritz, J. S. Wiggins, W. L. Jarrett

Abstract:

We have investigated the flame-retardant efficiency of special new phosphate glass (P-glass) compositions having different glass transition temperatures (Tg) on the processing conditions of polyamide 6,6 (PA6,6) and the final hybrid flame retardancy (FR). We have showed that the low Tg P glass composition (i.e., ILT 1) is a promising flame retardant for PA6,6 at a concentration of up to 15 wt. % compared to intermediate (IIT 3) and high (IHT 1) Tg P glasses. Cone calorimetry data showed that the ILT 1 decreased both the peak heat release rate and the total heat amount released from the PA6,6/ILT 1 hybrids, resulting in an efficient formation of a glassy char layer. These intriguing findings prompted to address several questions concerning the mechanism of action of the different P glasses studied. The general mechanism of action of phosphorous based FR additives occurs during the combustion stage by enhancing the morphology of the char and the thermal shielding effect. However, the present work shows that P glass based FR additives act during melt processing of PA6,6/P glass hybrids. Dynamic mechanical analysis (DMA) revealed that the Tg of PA6,6/ILT 1 was significantly shifted to a lower Tg (~65 oC) and another transition appeared at high temperature (~ 166 oC), thus indicating a strong interaction between PA6,6 and ILT 1. This was supported by a drop in the melting point and crystallinity of the PA6,6/ILT 1 hybrid material as detected by differential scanning calorimetry (DSC). The dielectric spectroscopic investigation of the networks’ molecular level structural variations (i.e. hybrids chain motion, Tg and sub-Tg relaxations) agreed very well with the DMA and DSC findings; it was found that the three different P glass compositions did not show any effect on the PA6,6 sub-Tg relaxations (related to the NH2 and OH chain end groups motions). Nevertheless, contrary to IIT 3 and IHT 1 based hybrids, the PA6,6/ILT 1 hybrid material showed an evidence of splitting the PA6,6 Tg relaxations into two peaks. Finally, the CPMAS 31P-NMR data confirmed the miscibility between ILT 1 and PA6,6 at the molecular level, as a much larger enhancement in cross-polarization for the PA6,6/15%ILT 1 hybrids was observed. It can be concluded that compounding low Tg P-glass (i.e. ILT 1) with PA6,6 facilitates hydrolytic chain scission of the PA6,6 macromolecules through a potential chemical interaction between phosphate and the alpha-Carbon of the amide bonds of the PA6,6, leading to better flame retardant properties.

Keywords: broadband dielectric spectroscopy, composites, flame retardant, polyamide, phosphate glass, sustainable

Procedia PDF Downloads 237

Authors: V. Morenov, E. Leusheva

Abstract:

Power supplying of well drilling on oil and gas fields at ambient air low temperatures is characterized by increased requirements of electric and heat energy. Power costs for heating of production facilities, technological and living objects may several times exceed drilling equipment electric power consumption. Power supplying of prospecting and exploitation drilling objects is usually done by means of local electric power structures based on diesel power stations. In the meantime, exploitation of oil fields is accompanied by vast quantities of extracted associated petroleum gas, and while developing gas fields there are considerable amounts of natural gas and gas condensate. In this regard implementation of gas-powered self-sufficient power units functioning on produced crude products for power supplying is seen as most potential. For these purposes gas turbines (GT) or gas reciprocating engines (GRE) may be used. In addition gas-powered units are most efficiently used in cogeneration mode - combined heat and power production. Conducted research revealed that GT generate more heat than GRE while producing electricity. One of the latest GT design are microturbines (MT) - devices that may be efficiently exploited in combined heat and power mode. In conditions of ambient air low temperatures and high velocity wind sufficient heat supplying is required for both technological process, specifically for drilling mud heating, and for maintaining comfortable working conditions at the rig. One of the main heat regime parameters are the heat losses. Due to structural peculiarities of the rig most of the heat losses occur at cold air infiltration through the technological apertures and hatchways and heat transition of isolation constructions. Also significant amount of heat is required for working temperature sustaining of the drilling mud. Violation of circulation thermal regime may lead to ice build-up on well surfaces and ice blockages in armature elements. That is why it is important to ensure heating of the drilling mud chamber according to ambient air temperature. Needed heat power will be defined by heat losses of the chamber. Noting heat power required for drilling structure functioning, it is possible to create combined heat and power complex based on MT for satisfying consumer power needs and at the same time lowering power generation costs. As a result, combined power supplying scheme for multiple well drilling utilizing heat of MT flue gases was developed.

Keywords: combined heat, combined power, drilling, electric supply, gas-powered units, heat supply

Procedia PDF Downloads 577

Authors: Fakhreldin O. Suliman, Alia H. Al-Battashi

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This work explored the interaction of three different imidazoline drugs, naphazoline nitrate (NPH), oxymetazoline hydrochloride (OXY) and xylometazoline hydrochloride (XYL) with two different synthesized cucurbit[n]urils CB[n], cucurbit[7]uril (CB[7]) and cucuribit[8]uril (CB[8]). Three binary inclusion complexes have been investigated in solution and in the solid state. The solid complexes were obtained by lyophilization, whereas the physical mixtures of guests and hosts at a stoichiometric ratio of 1:1 were obtained for each drug. 1HNMR, electrospray ionization mass spectrometry (ESI-MS), and matrix-assisted laser desorption-ionization time-of-flight (MALDI-TOF) mass spectrometry was used to study the complexes prepared in aqueous media. The lyophilized solid complexes were characterized by Fourier transform-infrared spectroscopy (FT-IR), powder X-ray diffractometry (PXRD), thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC). MS, FT-IR and PXRD experimental results established in this work reveal that NPH, OXY and XYL molecules form stable inclusion complexes with the two hosts. The TGA and DSC confirmed the enhancement of the thermal stability of each drug and the production of a thermally stable solid complex. The 1HNMR has shown that the protons of the guests faced shifting in ppm and broadening of their peaks upon the formation of inclusion complexes with the selected CB[n]. The aromatic protons of the guest exhibited the highest changes in the chemical shifts and shape of the NMR peaks, suggesting their inclusion into the cavity of the CB[n]. The diffusion coefficients (D), developed from the diffusion-controlled NMR Spectroscopy (DOSY) measurements, for the complexation of the selected imidazoline drugs with CB[7] and CB[8], were decreased in the presence of hosts compared to the free guests indicating the formation of the guest-host adduct. Furthermore, we conducted molecular dynamic simulations and quantum mechanics calculations on these complexes. The results of the theoretical study corroborate the experimental findings and have also shed light on the mechanism of inclusion of the guests into the two hosts. This study generates initial data for potential drug delivery or drug formulation systems for these three selected imidazoline drug compounds based on their inclusion into the CB[n] cavities.

Keywords: cucurbit[n]urils, imidazoline, inclusion complexes, molecular dynamics, DFT calculations, mass spectrometry

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Authors: Aina Barcelona, Carolyn Oldham, Jordi Colomer, Teresa Serra

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The loss of seagrass meadows worldwide is being tackled by formulating coastal restoration strategies. Seagrass loss results in a network of vegetated patches which are barely interconnected, and consequently, the ecological services they provide may be highly compromised. Hence, there is a need to optimize coastal management efforts in order to implement successful restoration strategies, not only through modifying the architecture of the canopies but also by gathering together information on the hydrodynamic conditions of the seabeds. To obtain information on the hydrodynamics within the patches of vegetation, this study deals with the scale analysis of the minimum lengths of patch management strategies that can be effectively used on. To this aim, a set of laboratory experiments were conducted in a laboratory flume where the plant densities, patch lengths, and hydrodynamic conditions were varied to discern the vegetated patch lengths that can provide optimal ecosystem services for canopy development. Two possible patch behaviours based on the turbulent kinetic energy (TKE) production were determined: one where plants do not interact with the flow and the other where plants interact with waves and produce TKE. Furthermore, this study determines the minimum patch lengths that can provide successful management restoration. A canopy will produce TKE, depending on its density, the length of the vegetated patch, and the wave velocities. Therefore, a vegetated patch will produce plant-wave interaction under high wave velocities when it presents large lengths and high canopy densities.

Keywords: seagrass, minimum patch size, turbulent kinetic energy, oscillatory flow

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Authors: Samuel O. Alamu, Seong W. Lee, Blaise Kalmia, Marc J. Louise Caballes, Xuejun Qian

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The application of combustion technologies for thermal conversion of biomass and solid wastes to energy has been a major solution to the effective handling of wastes over a long period of time. Lab-scale biomass combustion systems have been observed to be economically viable and socially acceptable, but major concerns are the environmental impacts of the process and deviation of temperature distribution within the combustion chamber. Both high and low combustion chamber temperature may affect the overall combustion efficiency and gaseous emissions. Therefore, there is an urgent need to develop a control system which measures the deviations of chamber temperature from set target values, sends these deviations (which generates disturbances in the system) in the form of feedback signal (as input), and control operating conditions for correcting the errors. In this research study, major components of the feedback control system were determined, assembled, and tested. In addition, control algorithms were developed to actuate operating conditions (e.g., air velocity, fuel feeding rate) using ladder logic functions embedded in the Programmable Logic Controller (PLC). The developed control algorithm having chamber temperature as a feedback signal is integrated into the lab-scale swirling fluidized bed combustor (SFBC) to investigate the temperature distribution at different heights of the combustion chamber based on various operating conditions. The air blower rates and the fuel feeding rates obtained from automatic control operations were correlated with manual inputs. There was no observable difference in the correlated results, thus indicating that the written PLC program functions were adequate in designing the experimental study of the lab-scale SFBC. The experimental results were analyzed to study the effect of air velocity operating at 222-273 ft/min and fuel feeding rate of 60-90 rpm on the chamber temperature. The developed temperature-based feedback control system was shown to be adequate in controlling the airflow and the fuel feeding rate for the overall biomass combustion process as it helps to minimize the steady-state error.

Keywords: air flow, biomass combustion, feedback control signal, fuel feeding, ladder logic, programmable logic controller, temperature

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Authors: Elham Taghizadeh, Mostafa Abedzadeh, Mostafa Setak

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Logistics network is expected that opened facilities work continuously for a long time horizon without any failure; but in real world problems, facilities may face disruptions. This paper studies a reliable joint inventory location problem to optimize cost of facility locations, customers’ assignment, and inventory management decisions when facilities face failure risks and doesn’t work. In our model we assume when a facility is out of work, its customers may be reassigned to other operational facilities otherwise they must endure high penalty costs associated with losing service. For defining the model closer to real world problems, the model is proposed based on p-median problem and the facilities are considered to have limited capacities. We define a new binary variable (Z_is) for showing that customers are not assigned to any facilities. Our problem involve a bi-objective model; the first one minimizes the sum of facility construction costs and expected inventory holding costs, the second one function that mention for the first one is minimizes maximum expected customer costs under normal and failure scenarios. For solving this model we use NSGAII and MOSS algorithms have been applied to find the pareto- archive solution. Also Response Surface Methodology (RSM) is applied for optimizing the NSGAII Algorithm Parameters. We compare performance of two algorithms with three metrics and the results show NSGAII is more suitable for our model.

Keywords: joint inventory-location problem, facility location, NSGAII, MOSS

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Authors: Prashansa Singh, Rohit Bajaj, Manjot Kaur

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In the modern agricultural landscape, precision farming has emerged as a pivotal strategy for enhancing crop yield and optimizing resource utilization. This paper introduces an innovative Crop Recommendation System (CRS) that leverages the Internet of Things (IoT) technology and the Message Queuing Telemetry Transport (MQTT) protocol to collect critical environmental and soil data via sensors deployed across agricultural fields. The system is designed to address the challenges of real-time data acquisition, efficient data transmission, and dynamic crop recommendation through the application of advanced Artificial Intelligence (AI) and Machine Learning (ML) models. The CRS architecture encompasses a network of sensors that continuously monitor environmental parameters such as temperature, humidity, soil moisture, and nutrient levels. This sensor data is then transmitted to a central MQTT server, ensuring reliable and low-latency communication even in bandwidth-constrained scenarios typical of rural agricultural settings. Upon reaching the server, the data is processed and analyzed by AI/ML models trained to correlate specific environmental conditions with optimal crop choices and cultivation practices. These models consider historical crop performance data, current agricultural research, and real-time field conditions to generate tailored crop recommendations. This implementation gets 99% accuracy.

Keywords: Iot, MQTT protocol, machine learning, sensor, publish, subscriber, agriculture, humidity

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Authors: K. Hamdi, Z. Aboura, W. Harizi, K. Khellil

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Improving the electrical properties of organic matrix composites has been investigated in several studies. Thus, to extend the use of composites in more varied application, one of the actual barrier is their poor electrical conductivities. In the case of carbon fiber composites, organic matrix are in charge of the insulating properties of the resulting composite. However, studying the properties of continuous carbon fiber nano-filled composites is less investigated. This work tends to characterize the effect of carbon black nano-fillers on the properties of the woven carbon fiber composites. First of all, SEM observations were performed to localize the nano-particles. It showed that particles penetrated on the fiber zone (figure1). In fact, by reaching the fiber zone, the carbon black nano-fillers created network connectivity between fibers which means an easy pathway for the current. It explains the noticed improvement of the electrical conductivity of the composites by adding carbon black. This test was performed with the four points electrical circuit. It shows that electrical conductivity of 'neat' matrix composite passed from 80S/cm to 150S/cm by adding 9wt% of carbon black and to 250S/cm by adding 17wt% of the same nano-filler. Thanks to these results, the use of this composite as a strain gauge might be possible. By the way, the study of the influence of a mechanical excitation (flexion, tensile) on the electrical properties of the composite by recording the variance of an electrical current passing through the material during the mechanical testing is possible. Three different configuration were performed depending on the rate of carbon black used as nano-filler. These investigation could lead to develop an auto-instrumented material.

Keywords: carbon fibers composites, nano-fillers, strain-sensors, auto-instrumented

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Authors: Anand Kumar Yadav

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Purpose – The purpose of this paper is to investigate the fluctuation of amplitude ratios of various transmitted and reflected waves. Design/methodology/approach – The reflection and transmission of plane waves on the interface between an orthotropic hygro-thermo-elastic half-space (OHTHS) and a viscous-fluid half-space (VFHS) were investigated in this study with reference to coupled hygro-thermo-elasticity. Findings – The interface, where y = 0, is struck by the principal (P) plane waves as they travel through the VFHS. Two waves are reflected in VFHS, and four waves are transmitted in OHTHS as a result namely longitudinal displacement, Pwave − , thermal diffusion TDwave − and moisture diffusion mDwave − and shear vertical SV wave. Expressions for the reflection and transmitted coefficient are developed for the incidence of a hygrothermal plane wave. It is noted that these ratios are graphically displayed and are observed under the influence of coupled hygro-thermo-elasticity. Research limitations/implications – There isn't much study on the model under consideration, which combines OHTHS and VFHS with coupled hygro-thermo-elasticity, according to the existing literature Practical implications – The current model can be applied in many different areas, such as soil dynamics, nuclear reactors, high particle accelerators, earthquake engineering, and other areas where linked hygrothermo-elasticity is important. In a range of technical and geophysical settings, wave propagation in a viscous fluid-thermoelastic medium with various characteristics, such as initial stress, magnetic field, porosity, temperature, etc., gives essential information regarding the presence of new and modified waves. This model may prove useful in modifying earthquake estimates for experimental seismologists, new material designers, and researchers. Social implications – Researchers may use coupled hygro-thermo-elasticity to categories the material, where the parameter is a new indication of its ability to conduct heat in interaction with diverse materials. Originality/value – The submitted text is the sole creation of the team of writers, and all authors equally contributed to its creation.

Keywords: hygro-thermo-elasticity, viscous fluid, reflection coefficient, transmission coefficient, moisture concentration

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Authors: Mauricio A. Sarabia-Vallejos, Carmen M. Gonzalez-Henriquez, Adolfo Del Campo-Garcia, Aitzibier L. Cortajarena, Juan Rodriguez-Hernandez

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In this study, it was explored the formation and the morphological differences between wrinkled hydrogel patterns obtained via generation of surface instabilities. The slight variations in the polymerization conditions produce important changes in the material composition and pattern structuration. The compounds were synthesized using three main components, i.e. an amphiphilic monomer, hydroxyethyl methacrylate (HEMA), a hydrophobic monomer, trifluoroethyl methacrylate (TFMA), and a hydrophilic crosslinking agent, poly(ethylene glycol) diacrylate (PEGDA). The first part of this study was related to the formation of wrinkled surfaces using only HEMA and PEGDA and varying the amount of water added in the reaction. The second part of this study involves the gradual insertion of TFMA into the hydrophilic reaction mixture. Interestingly, the manipulation of the chemical composition of this hydrogel affects both surface morphology and physicochemical characteristics of the patterns, inducing transitions from one particular type of structure (wrinkles or ripples) to different ones (creases, folds, and crumples). Contact angle measurements show that the insertion of TFMA produces a slight decrease in surface wettability of the samples, remaining however highly hydrophilic (contact angle below 45°). More interestingly, by using confocal Raman spectroscopy, important information about the wrinkle formation mechanism is obtained. The procedure involving two consecutive thermal and photopolymerization steps lead to a “pseudo” two-layer system. Thus, upon photopolymerization, the surface is crosslinked to a higher extent than the bulk and water evaporation drives the formation of wrinkled surfaces. Finally, cellular, and bacterial proliferation studies were performed to the samples, showing that the amount of TFMA included in each sample slightly affects the proliferation of both (bacteria and cells), but in the case of bacteria, the morphology of the sample also plays an important role, importantly reducing the bacterial proliferation.

Keywords: antibiofouling properties, hydrophobic/hydrophilic balance, morphologic characterization, wrinkled hydrogel patterns

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Authors: Dan Akesson, Gauthaman Kuzhanthaivelu, Martin Bohlen, Sunil K. Ramamoorthy

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Bioplastics can be defined as polymers derived partly or completely from biomass. Bioplastics can be biodegradable such as polylactic acid (PLA) and polyhydroxyalkonoates (PHA); or non-biodegradable (biobased polyethylene (bio-PE), polypropylene (bio-PP), polyethylene terephthalate (bio-PET)). The usage of such bioplastics is expected to increase in the future due to new found interest in sustainable materials. At the same time, these plastics become a new type of waste in the recycling stream. Most countries do not have separate bioplastics collection for it to be recycled or composted. After a brief introduction of bioplastics such as PLA in the UK, these plastics are once again replaced by conventional plastics by many establishments due to lack of commercial composting. Recycling companies fear the contamination of conventional plastic in the recycling stream and they said they would have to invest in expensive new equipment to separate bioplastics and recycle it separately. This project studies what happens when bioplastics contaminate conventional plastics. Three commonly used conventional plastics were selected for this study: polyethylene (PE), polypropylene (PP) and polyethylene terephthalate (PET). In order to simulate contamination, two biopolymers, either polyhydroxyalkanoate (PHA) or thermoplastic starch (TPS) were blended with the conventional polymers. The amount of bioplastics in conventional plastics was either 1% or 5%. The blended plastics were processed again to see the effect of degradation. The results from contamination showed that the tensile strength and the modulus of PE was almost unaffected whereas the elongation is clearly reduced indicating the increase in brittleness of the plastic. Generally, it can be said that PP is slightly more sensitive to the contamination than PE. This can be explained by the fact that the melting point of PP is higher than for PE and as a consequence, the biopolymer will degrade more quickly. However, the reduction of the tensile properties for PP is relatively modest. Impact strength is generally a more sensitive test method towards contamination. Again, PE is relatively unaffected by the contamination but for PP there is a relatively large reduction of the impact properties already at 1% contamination. PET is polyester, and it is, by its very nature, more sensitive to degradation than PE and PP. PET also has a much higher melting point than PE and PP, and as a consequence, the biopolymer will quickly degrade at the processing temperature of PET. As for the tensile strength, PET can tolerate 1% contamination without any reduction of the tensile strength. However, when the impact strength is examined, it is clear that already at 1% contamination, there is a strong reduction of the properties. The thermal properties show the change in the crystallinity. The blends were also characterized by SEM. Biphasic morphology can be seen as the two polymers are not truly blendable which also contributes to reduced mechanical properties. The study shows that PE is relatively robust against contamination, while polypropylene (PP) is sensitive and polyethylene terephthalate (PET) can be quite sensitive towards contamination.

Keywords: bioplastics, contamination, recycling, waste management

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Authors: Ernest Gyan Bediako, Petra Dancova, Tomas Vit

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Flow boiling heat transfer remains an important area of research due to its relevance in thermal management systems and other applications. Despite the enormous work done in the field of flow boiling heat transfer over the years to understand how flow parameters such as mass flux, heat flux, saturation conditions and tube geometries influence the characteristics of flow boiling heat transfer, there are still many contradictions and lack of agreement on the actual mechanisms controlling heat transfer and how flow parameters impact the heat transfer. This work thus seeks to experimentally investigate the heat transfer characteristics and flow patterns at low mass fluxes, low heat fluxes and low saturation pressure conditions which are of less attention in literature but prevalent in refrigeration, air-conditioning and heat pump applications. In this study, flow boiling experiment was conducted for R134a working fluid in a 5 mm internal diameter stainless steel horizontal smooth tube with mass flux ranging from 80- 100 kg/m2 s, heat fluxes ranging from 3.55kW/m2 - 25.23 kW/m2 and saturation pressure of 460 kPa. Vapor quality ranged from 0 to 1. A well-known flow pattern map created by Wojtan et al. was used to predict the flow patterns noticed during the study. The experimental results were correlated with well-known flow boiling heat transfer correlations in literature. The findings show that, heat transfer coefficient was influenced by both mass flux and heat fluxes. However, for an increasing heat flux, nucleate boiling was observed to be the dominant mechanism controlling the heat transfer especially at low vapor quality region. For an increasing mass flux, convective boiling was the dominant mechanism controlling the heat transfer especially in the high vapor quality region. Also, the study observed an unusual high heat transfer coefficient at low vapor qualities which could be due to periodic wetting of the walls of the tube due to slug flow pattern and stratified wavy flow patterns. The flow patterns predicted by Wojtan et al. flow pattern map were mixture of slug and stratified wavy, purely stratified wavy and dry out. Statistical assessment of the experimental data with various well-known correlations from literature showed that, none of the correlations reported in literature could predicted the experimental data with enough accuracy.

Keywords: flow boiling, heat transfer coefficient, mass flux, heat flux.

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Authors: Parthiban Srinivasan

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With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

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Authors: Shabadini Sampath, Jinglang Feng

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With huge kinetic energy, space debris poses a major threat to astronauts’ space activities and spacecraft in orbit if a collision happens. The active removal of space debris is required in order to avoid frequent collisions that would occur. In addition, the amount of space debris will increase uncontrollably, posing a threat to the safety of the entire space system. But the safe and reliable removal of large-scale space debris has been a huge challenge to date. While capturing and deorbiting space debris, the space manipulator has to achieve high control precision. However, due to uncertainties and unknown disturbances, there is difficulty in coordinating the control of the space manipulator. To address this challenge, this paper focuses on developing a robust and intelligent control algorithm that controls joint movement and restricts it on the sliding manifold by reducing uncertainties. A neural network adaptive sliding mode controller (NNASMC) is applied with the objective of finding the control law such that the joint motions of the space manipulator follow the given trajectory. A computed torque control (CTC) is an effective motion control strategy that is used in this paper for computing space manipulator arm torque to generate the required motion. Based on the Lyapunov stability theorem, the proposed intelligent controller NNASMC and CTC guarantees the robustness and global asymptotic stability of the closed-loop control system. Finally, the controllers used in the paper are modeled and simulated using MATLAB Simulink. The results are presented to prove the effectiveness of the proposed controller approach.

Keywords: GNC, active removal of space debris, AI controllers, MatLabSimulink

Procedia PDF Downloads 132