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Influence of triton-assisted coconut shell derived graphene nanoplatelets in water-based drilling fluid lubricity and shale inhibition application

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Petroleum Research 2024年第4期9卷 620-639页

作者： muftahu N.Yahya m.N.A.m.Norddin Issham Ismail A.A.A.Rasol N.Salahudeen Jeffrey O.Oseh m.muhammad m.Shahid Shaziera b.Omar Department of Petroleum Engineering Faculty of Chemical and Energy EngineeringUniversiti Teknologi Malaysia81310Johor BahruMalaysia Department of Chemical and Petroleum Engineering Faculty of EngineeringBayero UniversityKanoPMB 3011Nigeria Malaysia Petroleum Resources Corporation Institute for Oil and Gas(MPRCeUTM) Universiti Teknologi Malaysia81310Johor BahruMalaysia Advanced Membrane Technology Research Centre(AMTEC) Nanostructured Materials Research Group(NMRG)e MD-Frontier MaterialsUniversiti Teknologi Malaysia81310Johor BahruMalaysia Department of Petroleum Engineering School of Energy and Energy EngineeringFederal University of TechnologyOwerriP.M.B.1526Imo StateNigeria

Insufficient hole cleaning,cutting suspension,clay swelling,and filtrate invasion of the formation might result from inadequate drilling mud *** effective drilling and wellbore stability,water-based mud(Wbm)rheology,lubricity,filtration,and shale inhibition must be optimized and *** react with clays and cause time-dependent borehole issues,which is their principal ***,prolonged exposure destroys certain Wbm components,resulting in minimal mud *** indicate the need for multifunctional additives to improve ***,this study developed Wbm systems employing graphene nanoplatelets(GNPs)and locally acquired discarded coconut shells to overcome severe drilling *** adding triton-X100 to coconut shell-based graphene(GN-CS),a greater dispersion of modified graphene(GN-TX)particles was ***,rheology,lubricity,inhibition,and filtration tests were performed on these GN-CS and GN-TX at concentrations of 0.125,0.25,0.375,and 0.50 wt%.Furthermore,biotoxicity,biodegradability,and heavy metal content experiments were performed to study the environmental impact of GN-CS and *** results showed that GN-TX had good thermal resistance up to 300℃ with only a 10%loss in *** EDX and FTIR tests showed that the epoxy,carboxyl,and hydroxyl groups are in the GNP-based materials'basal *** GN-CS and GN-TX had better fluid properties,including better lubricity,rheology,filtration,and inhibition over the base mud,and the optimal rheological model of the drilling muds was the Herschel buckley *** GN-TX(modified)decreased the fluid loss to 20.6e14.3 mL from 24.6 mL at 353 K,whereas the GN-CS(unmodified)reduced it to 21.3e16.7 ***-TX and GN-CS decreased the coefficient of friction of Wbm from 0.47 to 0.55 to 0.25e0.41 and 0.33e0.44,respec-tively,from 298 to 353 *** addition,0.50 wt%of GN-CS and GN-TX reduced the shale pellet swelling height to 5.4%and 5.6%,respectively,from 8.8%.moreover,the

关键词： Rheological properties Coconut shell Linear shale swelling Graphene nanoplatelets Lubricity Fluid loss

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Gaussian mixture model of Formants Transformation Technique for Dysarthric Speech Vowels

SSRN

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SSRN 2024年

作者： Sunitha, S.V. Savanth, Ashwini S. Sarala, T. Thanishka, K. Department of Electronics & Communication Engineering B.N.M Institute of Technology Karnataka Bangalore560070 India

Vocalization is a predominant type of communication. However, for individuals having communication disorders, communicating could prove challenge. A disorder which is called dysarthria impairs articulation and makes it difficult to pronounce some phonemes correctly. This makes it more difficult to comprehend communication. Accordingly, the primary objective of this initiative is to make disarticulated speech more comprehensible, which will enable people to engage in conversation without any difficulty. The importance of vowels is evident in the comprehension of speech. However, the vowels in speech that are dysarthric are elongated and their formants are constrained resulting in an aberrant voice. The vowel portion from dysarthric and normal pronunciation are extracted by using PRAAT software. 60% of the dysarthric and normal pronunciation are given off for training, while 40% are applied in testing. Using burg's algorithm, the retrieved vowel component’s formants are determined as F1, F2, and F3. The dysarthric speech frequencies’ formants are more dimensional than those of normal formants caused by the length of the vowels. To match the dimension of dysarthric formants, normal formants are expanded using Dynamic Time Warping (DTW). Vowel identities are decided by the formant trajectories. Nevertheless, dysarthric vowel space (formant frequencies) is often limited. The analysis of new formants for dysarthric formant variations involves examining if they are segregated against or not by using normal formants. There is a discussion of the three distinct formant transformation methods. © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

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XGboost based Synthetic battery Parameter Generation to Overcome Limited battery Dataset Challenges

XGBoost Based Synthetic Battery Parameter Generation to Over...

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2022 IEEE International Conference on "Power Electronics, Smart Grid, and Renewable Energy", PESGRE 2022

作者： Naaz, Nida Channegowda, Janamejaya Department of Electrical and Electronics Engineering B.M.S. College of Engineering Karnataka Bengaluru India Department of Electrical and Electronics Engineering M S Ramaiah Institute of Technology MSR Nagar Karnataka Bengaluru560054 India

ISBN: (纸本)9781665448376

Global decarbonization efforts have led to rapid growth in Electric Vehicles (EVs) as they aid to curb rising pollution levels. Energy Storage Systems such as Lithium-ion batteries (LIbs) play a vital role in powering this electric transportation revolution. Estimation of State-of-charge and State-of-health of the EV battery pack is crucial to determine available range in the vehicle. One of the key challenges faced by researchers in the energy storage domain is access to high quality battery parameter data chiefly due to confidentiality constraints from battery and EV manufacturers. This paper aims to alleviate the limited dataset challenge by employing an extreme gradient boosting methodology called XGboost. The technique used in this paper is effective for sparse dataset scenarios as XGboost adds up all the predictions of a sample in individual trees. The introduced method was also compared with Random forest algorithm for different horizons to evaluate the effectiveness of the technique. © 2022 IEEE

关键词： Energy storage

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Cognitive Fire Alert Framework for Realizing Fire in Unfriendly Environment using Wireless Sensor Network 7

Cognitive Fire Alert Framework for Realizing Fire in Unfrien...

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7th International Conference on Computing methodologies and Communication, ICCmC 2023

作者： Sathiya, A. Velmurugan, A.K. Santhuja, P. Indra, G. Department of Artificial Intelligence and Data Science Sri Sairam Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India Department of Computer Science Engineering B V Raju Institute of Technology Telangana Hyderabad India Department of Computer Science Engineering R.M.K College of Engineering and Technology Tamil Nadu Chennai India

ISBN: (纸本)9781665464086

Nowadays, Wireless Sensor Networks (WSNs) are extensively distributed in environmental monitoring, operational health monitoring, and industrial monitoring. It is an inexpensive application. It contains small, less energy-consuming, lower-cost devices combined with restricted computation, observation, and radio transmission capacities. People nowadays use fire alarms with sensors to detect fires. Fire sensors are unreliable. With the fast growth of WSNs, people can now remotely watch their surroundings to ascertain the current status. A standard fire alarm system is only able to detect one of the environmental parameters at a time. It is possible that there is a fire if the detection value is greater than the threshold;this would indicate that there is a fire. Copper, insulated, or cables are used to make the connections between detectors and alarms in a traditional fire alarm system. Due to the presence of a large number of elements at the site of the fire, the smoke alarm system will be unable to make an accurate determination as to the location of the flames. This article proposes a cognitive fire alert framework (CFAF) for realizing fire in unfriendly environments using WSN. It has a phase for wireless transmission, a phase for cognitive fire recognition, and a phase for the Random Forest method (RFm). This approach utilizes the RFm to classify the fire by data classification. The results of the experiments show that the suggested technique minimizes the number of false alarms by evaluating the delay value by utilizing data samples and comparing the temperature, humidity, pressure, and dust level. © 2023 IEEE.

关键词： Wireless sensor networks

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Storage Virtualization mechanism for Securing Electronic Health Records in Cloud 4

Storage Virtualization Mechanism for Securing Electronic Hea...

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4th International Conference on Cognitive Computing and Information Processing, CCIP 2022

作者： Ramana Reddy, b. Indiramma, m. Chaitanya Bharathi Institute of Technology Department of CSE Hyderbad India B.M.S. College of Engineering Department of CSE Bangalore India

ISBN: (数字)9781665456487

ISBN: (纸本)9781665456487

In the present situation, storing digital health records in the cloud for the immediate usage of patients and treatment providers is the most convenient and economical way for patients. Cloud based Electronic Health Records contain information about the patients and also provide updates to the treatment providers. From the treatment providers' perspective, it is easy for them to see the previous health records of their patients. As a result, the duplication of health records is eliminated. However, the major issue in this system is storing health records and protecting the privacy of patient's details in the cloud. Currently, there are many research scholars who are working constantly to maintain and update the existing electronic health records in the cloud. The aim of this paper is to create virtual storage to secure electronic health records and to provide privacy and backups to customers. © 2022 IEEE.

关键词： Patient treatment

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Towards Efficient Energy management of Smart buildings: A LSTm-AE based model 1

Towards Efficient Energy Management of Smart Buildings: A LS...

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1st IEEE International Conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： madhukar, Sonar Rahulkumar Singh, Kavita Kanniyappan, S.P. Krishnan, Thulasirajan Sarode, Ghanasham C. Suganthi, D. SNJB's LS K. B. Jain COE Department of Mechanical Engineering Maharashtra Chandwad India Institute of Aeronautical Engineering Department of Civil Engineering Telangana Hyderabad India R.M.K. Engineering College Department of Civil Engineering Tamilnadu Kavaraipettai India Annamacharya Institute of Technology and Sciences Department of Civil Engineering Tirupati India Dr. D. Y. Patil Institute of Technology Department of Civil Engineering Pune Pimpri India Saveetha College of Liberal Arts and Sciences SIMATS Department of Computational Intelligence Tamil Nadu Thandalam India

ISBN: (纸本)9798350371802

Optimal home energy utilization and the development of residential segment renewable energy systems are two problems that might be addressed by interacting energy systems in smart homes. Reduced grid electricity usage, particularly during peak hours, increased economic efficiency, user satisfaction (via power seller/purchaser choice), and consumer engagement in energy management are the primary goals of these energy interactions. Performing preprocessing, feature selection, and model training in the proper order is of the utmost importance. Using these measures for data preparation can lead to erroneous model interpretations or lower model performance. Principal component analysis (PCA) is a well-liked method for feature selection that uses linear transformation to reduce the dimension of data in a high-dimensional feature space. When contrasted with the cutting-edge LSTm and AE, this method seems to be quite innovative. A considerable improvement in accuracy, reaching 97.50%, was demonstrated by the results. © 2024 IEEE.

关键词： Energy utilization

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Combined Torrefaction and Densification of Rice Husk: Effect of Process Parameters 7th

Combined Torrefaction and Densification of Rice Husk: Effect...

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7th International Conference on mechanical, manufacturing and Plant engineering, ICmmPE 2021

作者： Ibitoye, Segun E. mahamood, Rasheedat m. Jen, Tien-Chien Akinlabi, Esther T. Department of Mechanical Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg P. O. Box 524 Auckland Park2006 South Africa Department of Mechanical Engineering Faculty of Engineering and Technology University of Ilorin P. M. B. 1515 Ilorin Nigeria Department of Materials and Metallurgical Engineering Faculty of Engineering and Technology University of Ilorin P. M. B. 1515 Ilorin Nigeria Pan African University for Life and Earth Sciences Institute Ibadan Nigeria

ISBN: (纸本)9789811933066

Torrefaction and densification technologies have been reported to enhance biomass properties for efficient and effective energy production. The enormous quantity of agricultural residues generated every year globally has threatened the people and the environment due to indiscriminate dumping and burning of the residues. Therefore, this study aims to convert agricultural residues (rice husk) into usable energy through the combined application of torrefaction and densification technology. The collected and sorted rice husk was torrefied at different temperatures (220, 240, 260, and 280 °C) and 60 min as residence time. The densification process was carried out on the torrefied samples at two different pressures (5 and 10 mPa), while the dwelling time was set at 4 min. The densification products’ water resistance, shatter index, density, and drop to fracture properties were investigated following the established standards. The results showed that the outcomes of the torrefaction process were influenced by temperature and residence time, though the effect of temperature was more pronounced. Torrefaction biogas, biochar, and bio-oil yields varied between 19.23–26.28, 35.90–53.85, and 26.92–37.82%, respectively. The shatter index and drop to fracture properties increase with densification pressure and decrease with torrefaction temperature. However, the water resistance property increases with torrefaction temperature. The highest and the lowest shatter index and drop to fracture were obtained from the sample produced at 220 °C (10 mPa) and 280 °C (5 mPa), respectively. The briquettes made in this study displayed good fuel properties and can be utilized for different energy purposes. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fracture

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Precision mango Farming: Using Compact Convolutional Transformer for Disease Detection 13th

Precision Mango Farming: Using Compact Convolutional Transfo...

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13th International Conference on Innovations in bio-Inspired Computing and Applications, IbICA 2022, and 12th World Congress on Information and Communication Technologies, WICT 2022

作者： Shereesha, m. Hemavathy, C. Teja, Hasthi Reddy, G. madhusudhan Kumar, bura Vijay Sunitha, Gurram Department of CSE B V Raju Institute of Technology Telangana Narsapur India Department of CSE Annamacharya Institute of Technology and Sciences A.P. Tirupati India Department of CSE K M M Institute of Technology and Science A.P. Tirupati India School of Computer Science and Artificial Intelligence SR University Warangal India Department of CSE Sree Vidyanikethan Engineering College A.P. Tirupati India

ISBN: (纸本)9783031274985

With every forward step that humanity takes, it is our responsibility to contribute to the development of agricultural ecosystem. Precision agriculture effectually leverages science and technology for practicing agronomic principles towards the goal of sustainable agriculture. Deep learning supports the identification of plant disease and enables early and timely disease diagnosis for effective disease detection and management. To investigate the application of Compact Convolutional Transformer (CCT) for classification of mango leaf diseases is the prime objective of the work undertaken in this paper. Two diseases - anthracnose and powdery mildew, that most commonly affect the mango leaves in Andhra Pradesh region, India were considered for our research. An CCT-based prediction model is proposed to use for developing the automated system for detection of anthracnose and powdery mildew diseases from mango leaf images. Two CCT models CCT-7/8 × 2 and CCT-7/4 × 2 were experimented with. The proposed methodology has been evaluated against the VGG16. The CCT-7/8 × 2 model demonstrated an increased performance in terms of - accuracy of 3%–5%, F1-score of 3%–4%, precision of 2%–3% over the VGG16 model. The experimental results demonstrated that the proposed CCT-based model performed automated detection of anthracnose and powdery mildew diseases effectually and eases to treat the affected area of plant properly and it aids in increasing mango output and supplying the world market. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Design of a Low-Cost Air Quality monitoring System Using Arduino and ThingSpeak

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Computers, materials & Continua 2022年第1期70卷 151-169页

作者： Anabi Hilary Kelechi mohammed H.Alsharif Chidumebi Agbaetuo Osichinaka UbadikeAlex Aligbe Peerapong Uthansakul Raju Kannadasan Ayman A.Aly Department of Aerospace Engineering College of EngineeringAirForce Institute of TechnologyKadunaNigeria Department of Electrical Engineering College of Electronics and Information EngineeringSejong UniversitySeoul05006Korea Department of Electrical Engineering and Information Engineering College of EngineeringCovenant UniversityCanaanlandOtaP.M.B 1023OgunNigeria School of Telecommunication Engineering Suranaree University of TechnologyNakhon RatchasimaThailand Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringChennai602117India Department of Mechanical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia

The impact of daily emissions of gaseous and particulate pollutants of machines and industries on human health and the environment has attracted increasing *** impact has significantly led to a notable increase in mortality in the highly industrialized ***,monitoring air quality and creating public awareness are important for a safer future,which led the governments globally to investmulti-billion in policymaking and solution stratification to address the *** study aims to design a realtime Internet of Things low-cost air quality monitoring *** system utilizes air quality and carbon monoxide sensors for monitoring gaseous ***,the system utilizes an Arduino Nano development board equipped with a WiFi module to effectively send readings to a ThingSpeak online channel platformfor instantaneous and real-time display of air *** ThingSpeak uses HTTP protocols to send emails in raising awareness of poor air *** level of concentration is monitored graphically through channels with the help of ThingSpeak to aid remote *** value is ***,when pollutants have become unhealthy and harmful,the system trips off an alarm,and e-mail notifications are sent to the *** results have shown that the work was successfully implemented a design of a low-cost air quality monitoring system using Arduino and ThingSpeak,showing that an air quality system can be implemented using a low-cost technology,Arduino and ThingSpeak.

关键词： IoT air quality mQ 135 sensor mQ-7 sensor air pollution

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AI-Driven Fraud Detection in Financial Transactions with Graph Neural Networks and Anomaly Detection

AI-Driven Fraud Detection in Financial Transactions with Gra...

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2024 International Conference on Science, technology, engineering and management, ICSTEm 2024

作者： Thilagavathi, m. Saranyadevi, R. Vijayakumar, N. Selvi, K. Anitha, L. Sudharson, K. Sir Theagaraya College Chennai India Department of Commerce A.M.Jain College Chennai India Department of Commerce B.S.Abdur Rahman Crescent Institute of Science & Technology Chennai India Department of MBA S.A. Engineering College Chennai India Department of AIML R.M.D. Engineering College Chennai India

ISBN: (纸本)9798350376913

Detecting fraudulent financial transactions is crucial for upholding the integrity of economic systems. Traditional methods often lag behind evolving fraud tactics, prompting the need for innovative approaches. We propose a pioneering framework combining Graph Neural Networks (GNNs) with anomaly detection techniques to enhance fraud detection. Transactions are represented as graphs, allowing GNNs to capture intricate fraud patterns. Anomaly detection methods flag suspicious transactions. Experimentation on the widely used Credit Card Fraud Detection dataset, comprising transactions made by European cardholders, showcases substantial advancements over baseline methods. The dataset is highly unbalanced, with fraudulent transactions accounting for only 0.172% of all transactions. Our approach achieves a detection rate of 95% with a false positive rate of 2%, surpassing the performance of the current state-of-the-art Gradient boosting Classifier by 10%. It exhibits resilience against various fraud schemes, including account takeover and identity theft. Ablation studies underscore the significance of graph-based representations and anomaly detection mechanisms. Our research underscores the efficacy of GNNs and anomaly detection in bolstering financial fraud detection, presenting a promising solution against sophisticated fraudulent activities. © 2024 IEEE.

关键词： Anomaly detection

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