A Comprehensive Review on Pioneering Nanotechnologies in Advancing Next-Generation Biofuel Production

International Research Journal of Multidisciplinary Technovation, Journal Year: 2024, Volume and Issue: unknown, P. 110 - 133

Published: Sept. 21, 2024

Nanotechnology is transforming biofuel manufacturing by enhancing efficiency, yield, and sustainability. This review explores how nanotechnology advances next-generation production using nanomaterials like catalysts, membranes, transporters in biomass conversion, fermentation, purification. Researchers have leveraged the unique properties of nanoparticles to improve reaction kinetics, selectivity, stability pathways. Nanoscale sensors monitoring devices provide real-time process control, enabling robust scalable production. Additionally, innovative Nano biotechnology techniques, such as enzyme immobilization metabolic engineering, enhance performance biofuel-producing microorganisms. also focus on challenges feedstock diversification, energy environmental impact, suggests that advanced nanotechnologies will revolutionize production, leading a more sustainable renewable future.

Language: Английский

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Meta-Heuristic Optimization for Enhanced Sensor-Based Health Monitoring in Cloud Computing Environments

Advances in systems analysis, software engineering, and high performance computing book series, Journal Year: 2024, Volume and Issue: unknown, P. 239 - 256

Published: June 30, 2024

In this research, the integration of meta-heuristic optimization into health monitoring systems is explored for its transformative potential. The study employs a comprehensive evaluation approach, focusing on Performance Metrics, Resource Utilization, and Scalability Testing. Results indicate consistently high level accuracy (90% to 97%) swift response times (125 165 milliseconds), highlighting reliability efficiency enhanced system. Utilization demonstrates optimal memory CPU usage (110 130 MB 30% 47%, respectively), underscoring sustainable balanced operation Testing reveals system's adaptability changes in user numbers data complexity, with ranging from 150 200 milliseconds. Meta-heuristic emerges as key enabler, fine-tuning predictive capabilities, optimizing resource usage, ensuring seamless scalability.

Language: Английский

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Performance Evaluation of Simulation-Driven Metaheuristic Algorithms

Advances in systems analysis, software engineering, and high performance computing book series, Journal Year: 2024, Volume and Issue: unknown, P. 341 - 358

Published: June 30, 2024

This study investigates the application of simulation-driven metaheuristic algorithms to enhance agricultural operations, specifically focusing on their effectiveness and efficiency in addressing complexities modern systems. evaluates computational efficacy crop planning, resource allocation, decision-making using a simulation environment tailored for contexts. Efficiency parameters, such as execution time, convergence rate, scalability, offer valuable insights into algorithms' real-world Effectiveness evaluation analyze quality, resilience, variety proposed techniques, demonstrating potential react changing environmental circumstances. Statistical analysis is employed give proof observed variances performance, hence providing quantitative aspect evaluation.

Language: Английский

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Optimizing Optical Fiber Path in Wavelength Division Multiplexing Networks Using Particle Swarm Optimization

Advances in systems analysis, software engineering, and high performance computing book series, Journal Year: 2024, Volume and Issue: unknown, P. 323 - 340

Published: June 30, 2024

In this paper, we explore the application of Particle Swarm Optimization (PSO) to maximize performance Wavelength Division Multiplexing (WDM) networks by optimizing optical fiber paths. Through rigorous evaluation metrics such as Data Transmission Speed Analysis and Congestion Reduction Assessment across ten trials, our findings reveal consistent meaningful improvements. PSO effectively enhances data transfer speeds, resulting more efficient network performance. Moreover, approach reliably minimizes congestion levels, decreasing a significant challenge in WDM networks. These results highlight PSO's adaptability reliability solving challenging optimization challenges communication. The practical reveals its promise revolutionary tool for attaining higher speeds reliability, providing basis future breakthroughs communication

Language: Английский

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Experimental Investigation and Comparative Analysis of an Efficient Machine Learning Algorithm for Distribution System Reconfiguration

Advances in systems analysis, software engineering, and high performance computing book series, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 18

Published: June 30, 2024

This study studies the implementation of machine learning (ML) algorithms to improve power distribution in an industrial context, concentrating on essential issue anticipating energy consumption. Various ML models, including Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Trees (DT), and Random Forests (RF), were extensively examined compared for their usefulness demand patterns within a sector encompassing machining, forging, CNC, packaging stations. The models revealed various strengths, with SVM leading accuracy 95.6%, closely followed by ANN at 94.33%, while DT RF displayed accuracies 87.6% 85.6%, respectively. research additionally gives thorough comparison actual vs expected levels over hourly intervals, illustrating models' responsiveness dynamic use throughout day.

Language: Английский

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Experimental investigation on mechanical properties of Jute nano SiC and Nano carbon Hybrid polymer composite

E3S Web of Conferences, Journal Year: 2024, Volume and Issue: 556, P. 01027 - 01027

Published: Jan. 1, 2024

The present research aims to study the synergistic influence of nanoparticle reinforcement on mechanical properties and water absorption outcome jute nano SiC carbon hybrid polymer composites. results obtained in this confirmed that there were substantial improvements across tensile, impact, hardness results, following an increase concentration particle production five composites total. material response tensile stress, impact loading, deformation indicated option is a feasible strategy improve resistance imposed load deformation. test, other end, considerable reduction after introduction increased composite formulation, suggesting robust superior dimensional changes. These findings support use capacity jute-based seek explore possible applications automotive, construction, aerospace industries. approach utilized herein, therefore, assists materials’ industries providing means optimize their formulation enhance environmental properties.

Language: Английский

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Optimizing Aluminum Metal Matrix Composites with SiC Nanoparticles using Taguchi-ANN Approach for Enhanced Mechanical Performance

E3S Web of Conferences, Journal Year: 2024, Volume and Issue: 556, P. 01019 - 01019

Published: Jan. 1, 2024

The current research explores the optimization of Silicon Carbide particle-reinforced aluminum metal matrix composites to improve mechanical properties. An integrated method based on Taguchi Design Experiment and Artificial Neural Network has been adopted, with novel approach explore optimal combination parameters. obtained best set includes minimum load 30 N, speed 100 rpm, larger composition 9% SiC particle. designed L9 orthogonal experimental plan was used conduct experiments, findings explicitly indicated significant impacts reduction specific wear rate friction force . Furthermore, trained through backpropagation algorithm estimated all percentages correctly ideal combination, equivalent 100% in predicting target responses. Moreover, confirmation experience validated as it approaches 0.0019, 10.5. These results highlight role for assessing parameters MMCs required Consequently, study highlights importance integration predictive modeling optimizing materials, applies various engineering fields where resistance performance are critical.

Language: Английский

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Estimation of Machining Performance in Wire EDM of Aluminum Silicon Nitride Composite an Experimental Analysis and ANN Modeling

E3S Web of Conferences, Journal Year: 2024, Volume and Issue: 556, P. 01022 - 01022

Published: Jan. 1, 2024

The primary objective of the current research is to optimize machining performance in Al 7010 alloyreinforced with silicon nitride nanoparticles. This has been accomplished through a combination ofexperimental analysis and predictive modeling methodologies. Initially, composite materials were createdusing stir casting, varied percentages incorporated into material supplementits mechanical properties. Wire Electrical Discharge Machining was performed using different parameters suchas Pulse On Time , Off Current range these defined according tolevels . Material Removal Rate Surface Roughness chosen as responses indicatedhigh sensitivity variations parameters. Each response thoroughly investigated detectedusing before establishing optimized levels. Taguchi design experiments signal-tonoiseratio two common techniques used investigate parameter interactions, they also todetermine optimum combinations for both optimizing MRR minimizing SR.Moreover, an Artificial Neural Network (ANN) model established foresee readingswith great precision predict effect enhance further capabilities inmachining. present optimization results indicated that maximum obtained at OnTime levels, while minimum SR OffTime These findings provide promising avenues field aerospace,indicating possibility components superior machinability strength.Furthermore, predicting ability ANN helps obtaining insights engineers optimizetheir process by gaining information about response.

Language: Английский

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Study On The Process Parameters Of Delamination On Novel Madar/Ramie Fibers Reinforced Mgo Nanoparticles Blended Epoxy Matrix Composite

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103738 - 103738

Published: Dec. 1, 2024

Language: Английский

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Machine Learning in Industrial IoT Applications for Safety, Security, Asset Localization, Quality Assurance, and Sustainability in Smart Production

Advances in systems analysis, software engineering, and high performance computing book series, Journal Year: 2024, Volume and Issue: unknown, P. 49 - 66

Published: June 30, 2024

This study explores the integration of machine learning techniques, notably Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), with industrial production processes for quality assurance. The emphasis is on examining performance SVM CNN through a rigorous assessment precision, recall, F1 score in Performance Metrics Evaluation. Additionally, tests algorithms against existing baseline approaches, evaluating their accuracy efficiency fault identification. results reveal consistent strong CNN, highlighting revolutionary potential revolutionizing control systems. findings provide essential insights into properties each algorithm, demonstrating ability to outperform methods contribute more versatile efficient approach assurance settings.

Language: Английский

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