VW Engineering International
type: channel
description: VW Engineering International is poised to be a frontier journal in the area of Engineering and Technology. It is an international journal publishing the finest peer-reviewed research in all fields of engineering on the basis of its originality, importance, interdisciplinary interest, timeliness, accessibility, elegance, and surprising conclusions. Title: VW Engineering International Editor-in-Chief: Updating soon Online ISSN: Updating soon Frequency: Quaterly Published Since: 2019 Publication Format: Online Language: English Copyright: © VALLWAY RESEARCH Publisher: VALLWAY RESEARCH PVT. LTD. INDIA The journal welcomes submissions in the following categories: · Original Research Articles · Review Papers and Short Reviews · Technical Notes · Case Studies · Short Communications Indexing and Abstracting VW Applied Sciences is currently under evaluation for inclusion in Scopus. The journal adheres strictly to publication ethics, peer-review transparency, and editorial independence in line with SCOPUS and international indexing body guidelines. Open Access and Licensing All articles published in VW Engineering International are made freely available under an Open Access model. Authors retain copyright under a [Creative Commons license, e.g., CC BY 4.0], allowing for free distribution and reuse with proper attribution.
publisher: Vallway Research Pvt. Ltd. India
language: en
publication name: VW Engineering International
items: Hydrogen Energy Storage Systems: Design Optimization and Performance Analysis for Future Power Networks
items: Cyber-Physical Systems Integration in Autonomous Industrial Robotics for Smart Manufacturing Environments
items: Advanced Additive Manufacturing Techniques for Multi-Material Functional Engineering Components
items: AI-Based Structural Health Monitoring Systems for Resilient and Sustainable Infrastructure
items: Blockchain-Enabled Secure Architecture for Smart Grid Energy Transactions and Decentralized Power Management
items: Design and Implementation of Bioinspired Soft Robotic Systems for Adaptive Interaction in Unstructured Human Environments
items: AI-Driven Real-Time Optimization of Distributed Renewable Energy Grids for Demand-Response and Stability in Smart Cities
items: Development of Self-Healing Ultra-High- Performance Concrete Using Smart Nanocapsules for Enhanced Structural Resilience
items: Machine Learning-Driven Discovery and Optimization of Eco-Friendly Construction Materials
items: Advanced Renewable Energy Conversion Systems: Modeling, Control, and Grid Integration Challenges
items: Integration of Internet of Things and Digital Twin Technologies for Predictive Maintenance in Large-Scale Industrial Systems
items: Design and Performance Evaluation of Next- Generation Composite Materials for High- Strength and Lightweight Engineering Applications
items: Artificial Intelligence–Driven Optimization of Smart Infrastructure Systems for Sustainable Urban Development
items: Edge Computing–Enabled Real-Time Data Analytics Frameworks for High-Performance Industrial Internet of Things (IIoT) Applications
items: Sustainable Materials Engineering: Development, Characterization, and Environmental Impact Assessment of Green Composite Materials
items: Machine Learning Driven Structural Health Monitoring Techniques for Early Damage Detection in Critical Civil Infrastructure
items: Energy-Efficient Design and Performance Evaluation of Hybrid Renewable Energy Systems for Smart Grid Applications
items: Advanced Cyber-Physical Systems for Resilient and Secure Industrial Automation in Industry 4.0 Environments
items: Application of Blockchain Technology for Secure, Transparent, and Efficient Supply Chain Management in Engineering Projects
items: Design, Deployment, and Optimization Strategies of IoT-Based Smart City Infrastructure for Sustainable Urban Development
items: Integration of Artificial Intelligence and Digital Twin Technologies for Predictive Maintenance and Lifecycle Optimization of Large-Scale Engineering Systems
items: Photocatalytic Activity and Environmental Remediation Efficiency of Novel Engineered Materials under Visible Light Irradiation
items: Design, 3D Printing, and Clinical Evaluation of Customized Prosthetic Devices with Enhanced Biocompatibility
items: Experimental Performance Evaluation of Thermal Energy Storage Systems for Integration with Solar Power Plants
items: Development, Characterization, and Antimicrobial Efficacy Testing of Coatings for Medical and Engineering Applications
items: Implementation and Impact Assessment of Remote Sensing Technologies for Environmental Protection and Disaster Risk Reduction
items: Investigation and Optimization of Innovative Recycling Techniques for Electronic Waste to Maximize Material Recovery
items: Modeling, Simulation, and Energy Efficiency Analysis of Hybrid Electric Vehicles Under Various Urban and Highway Driving Conditions
items: Development and Testing of High-Performance Filtration Membranes for Industrial Wastewater Treatment Applications
items: Simulation and Field Testing of Autonomous Agricultural Robots for Enhanced Crop Monitoring and Field Management

Hydrogen Energy Storage Systems: Design Optimization and Performance Analysis for Future Power Networks
type: item
description: Hydrogen energy storage has emerged as a promising solution to address intermittency challenges associated with high penetration of renewable energy sources in modern power networks. This paper presents a comprehensive investigation of hydrogen-based energy storage systems, focusing on system design optimization, dynamic modeling, and performance evaluation under grid-connected conditions. The study examines electrolyzer-fuel cell integration, storage technologies, and power conversion interfaces. Mathematical modeling and simulation-based analysis demonstrate improved grid stability, enhanced renewable utilization, and reduced curtailment losses. The results indicate that optimized hydrogen storage configurations significantly contribute to long-term energy reliability and decarbonization objectives in future power systems.
subject: Articles
  topic: section
subject: Hydrogen Energy Storage
  topic: keywords
subject: Renewable Integration
  topic: keywords
subject: Power Systems Stablity
  topic: keywords
subject: Electrolyzer Modelling
  topic: keywords
subject: Fuel Cell Systems
  topic: keywords
creator: Priyanka Deshmukh
creator: Arif Siddiqui
creator: Amit Kulkarni
rights: Copyright (c) 2026 VW Engineering International
date: 2026-02-11
publication date: 2026-02-11

Cyber-Physical Systems Integration in Autonomous Industrial Robotics for Smart Manufacturing Environments
type: item
description: The integration of Cyber-Physical Systems (CPS) in autonomous industrial robotics represents a transformative advancement in smart manufacturing environments. CPS frameworks enable seamless interaction between physical robotic systems and computational intelligence layers through embedded sensors, communication networks, and real-time data analytics. This paper presents a comprehensive study on CPS-based autonomous robotics architecture, focusing on system modeling, control synchronization, data-driven optimization, and security frameworks. A multi-layer CPS architecture incorporating edge computing, machine learning algorithms, and industrial Internet of Things (IIoT) platforms is proposed. Experimental validation through simulation and prototype implementation demonstrates improved operational efficiency, reduced latency, enhanced fault tolerance, and predictive maintenance capability. The proposed approach reduces production downtime by 27% and enhances task precision by 18% compared to conventional automation frameworks. Furthermore, cybersecurity risks and mitigation strategies in CPS-integrated robotic systems are analyzed. The findings highlight CPS as a foundational enabler for Industry 4.0 and future autonomous manufacturing ecosystems.
subject: Articles
  topic: section
subject: Cyber Physical Systems
  topic: keywords
subject: Autonomous Robotics
  topic: keywords
subject: Smart Manufacturing
  topic: keywords
subject: Industrial Internet Of Things
  topic: keywords
subject: Predictive Control
  topic: keywords
creator: Priyanka Deshmukh
creator: Arif Siddiqui
creator: Amit Kulkar
rights: Copyright (c) 2026 VW Engineering International
date: 2026-02-10
publication date: 2026-02-10

Advanced Additive Manufacturing Techniques for Multi-Material Functional Engineering Components
type: item
description: Additive manufacturing has evolved from rapid prototyping to a transformative production technology capable of fabricating complex functional components. The development of multi-material additive manufacturing techniques enables the integration of diverse material properties within a single structure, enhancing performance, functionality, and design flexibility. This paper presents a comprehensive investigation into advanced additive manufacturing processes for multi-material engineering components. It analyzes material compatibility, process optimization, interfacial bonding behavior, and mechanical performance evaluation. Experimental modeling and simulation-based analysis demonstrate improved structural efficiency and functional integration. The study highlights manufacturing challenges, process control requirements, and future directions for scalable industrial implementation.
subject: Articles
  topic: section
subject: Additive Manufacturing
  topic: keywords
subject: Multi-Mineral Fabrication
  topic: keywords
subject: Functional Components
  topic: keywords
subject: Process Optimization
  topic: keywords
subject: Advanced Manufacturing
  topic: keywords
creator: Rohit Sharma
creator: Farheen Siddiqui
creator: Manish Kulkarni
rights: Copyright (c) 2026 VW Engineering International
date: 2026-02-07
publication date: 2026-02-07

AI-Based Structural Health Monitoring Systems for Resilient and Sustainable Infrastructure
type: item
description: Structural deterioration and unexpected failures in infrastructure systems pose significant risks to public safety, economic stability, and environmental sustainability. Artificial intelligence has emerged as a transformative tool for structural health monitoring by enabling data-driven damage detection, condition assessment, and predictive maintenance. This paper presents an integrated AI-based structural health monitoring framework designed to enhance infrastructure resilience and sustainability. The proposed system combines sensor networks, machine learning algorithms, and real-time data analytics to identify structural anomalies and predict failure progression. Analytical evaluation demonstrates improved detection accuracy, reduced inspection costs, and enhanced lifecycle performance compared to traditional monitoring techniques.
subject: Articles
  topic: section
subject: Structural Health Monitoring
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Infrastructure Resilience
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Sustainable Engineering
  topic: keywords
creator: Meenakshi Rawat
creator: Imtiyaz Aalam
creator: Deepak Chowdary
rights: Copyright (c) 2026 VW Engineering International
date: 2026-02-05
publication date: 2026-02-05

Blockchain-Enabled Secure Architecture for Smart Grid Energy Transactions and Decentralized Power Management
type: item
description: The modernization of power systems through smart grids introduces bidirectional energy flows, distributed generation, and prosumer participation, creating new cybersecurity and transaction management challenges. This paper proposes a blockchain-enabled secure architecture for smart grid energy transactions and decentralized power management. The framework integrates distributed ledger technology, smart contracts, and consensus mechanisms to enable transparent, tamper-resistant, and automated peer-to-peer energy trading. A layered architecture is developed, incorporating IoT-based metering, cryptographic authentication, and decentralized control logic. Performance analysis demonstrates enhanced security, reduced transaction overhead, and improved trust among participants, supporting scalable and resilient decentralized energy ecosystems.
subject: Articles
  topic: section
subject: Blockchain Technology
  topic: keywords
subject: Smart Grid Security
  topic: keywords
subject: Decentralized energy Management
  topic: keywords
subject: Peer to Peer Energy Trading
  topic: keywords
subject: Distributed Ledger
  topic: keywords
creator: Arvind Kumar
creator: Sana Parveen
creator: Rahul Mishra
rights: Copyright (c) 2026 VW Engineering International
date: 2026-02-03
publication date: 2026-02-03

Design and Implementation of Bioinspired Soft Robotic Systems for Adaptive Interaction in Unstructured Human Environments
type: item
description: Robotic systems designed for human-centered applications increasingly operate in unstructured and dynamically changing environments, where rigid-bodied robots face limitations in adaptability, safety, and physical compliance. Bioinspired soft robotics has emerged as a promising paradigm that draws inspiration from biological organisms to achieve flexible, resilient, and adaptive interaction capabilities. This paper presents the design, fabrication, control, and experimental validation of a bioinspired soft robotic system intended for adaptive interaction in unstructured human environments. The proposed system integrates elastomeric actuators, distributed soft sensors, and morphology-driven intelligence to enable safe physical interaction and environmental adaptability. A hybrid control framework combining model-based control and machine learning techniques is developed to manage nonlinear material behavior and uncertain external conditions. Experimental evaluations demonstrate the robot’s ability to conform to irregular objects, adapt locomotion strategies, and maintain stable interaction with human users. Performance metrics including compliance, response time, adaptability, and interaction safety are analyzed and compared with conventional rigid robotic systems. The results confirm that bioinspired soft robotic architectures significantly enhance adaptability and safety in complex environments. This research contributes to the advancement of next-generation robotic systems for healthcare, service robotics, and human–robot collaboration.
subject: Articles
  topic: section
subject: Soft Robotics
  topic: keywords
subject: Bioinspired Design
  topic: keywords
subject: Human Robot Interaction
  topic: keywords
subject: Adaptive Systems
  topic: keywords
subject: Compliant Actuators
  topic: keywords
creator: Suresh Nair
creator: Krishna Mohanty
creator: Ananya Raina
rights: Copyright (c) 2026 VW Engineering International
date: 2026-01-29
publication date: 2026-01-29

AI-Driven Real-Time Optimization of Distributed Renewable Energy Grids for Demand-Response and Stability in Smart Cities
type: item
description: The rapid integration of distributed renewable energy resources such as solar photovoltaic systems and wind turbines has transformed conventional power grids into complex, decentralized energy networks. While this transition supports sustainability goals, it introduces significant challenges related to intermittency, grid stability, and demand–supply imbalance, particularly in urban smart city environments. This paper presents an artificial intelligence–driven real-time optimization framework for distributed renewable energy grids aimed at enhancing demand-response capability and operational stability. Machine learning algorithms, including deep neural networks and reinforcement learning, are employed to forecast energy generation, predict demand patterns, and dynamically control distributed energy resources. The proposed framework is evaluated using a simulated smart city microgrid incorporating renewable generation, energy storage systems, and flexible loads. Performance metrics such as frequency deviation, voltage stability, energy loss reduction, and demand-response efficiency are analyzed under varying operating conditions. Results demonstrate that the AI-driven approach significantly improves grid resilience, reduces peak demand stress, and enhances renewable energy utilization compared to conventional rule-based control strategies. The findings confirm the potential of artificial intelligence as a critical enabler for intelligent energy management in future smart cities, supporting reliable, efficient, and sustainable power systems.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Smart Grids
  topic: keywords
subject: Renewable Energy Optimization
  topic: keywords
subject: Demand Response
  topic: keywords
subject: Smart Cities
  topic: keywords
creator: Shahbaz Sikandar
creator: Venkatesh Kumar
creator: Priyanka Gosh
rights: Copyright (c) 2026 VW Engineering International
date: 2026-01-28
publication date: 2026-01-28

Development of Self-Healing Ultra-High- Performance Concrete Using Smart Nanocapsules for Enhanced Structural Resilience
type: item
description: Ultra-high-performance concrete (UHPC) is widely recognized for its superior mechanical strength and durability; however, its susceptibility to microcrack formation under mechanical and environmental loading poses a critical challenge to long-term structural resilience. This study presents the development of a self-healing UHPC system incorporating smart nanocapsules encapsulating a sodium silicate–based healing agent. The nanocapsules are synthesized using an in-situ polymerization technique and uniformly dispersed within the UHPC matrix without compromising workability. Mechanical performance, crack-healing efficiency, permeability resistance, and microstructural evolution are evaluated through compressive and flexural strength tests, water absorption measurements, scanning electron microscopy, and X-ray diffraction analysis. Experimental results indicate that the self-healing UHPC exhibits up to 85% recovery in flexural strength and significant crack closure for cracks up to 300 µm. The presence of nanocapsules enhances durability by reducing chloride penetration and water permeability. Microstructural observations confirm the formation of calcium silicate hydrate gels within healed cracks. The proposed smart nanocapsule-enabled UHPC demonstrates strong potential for autonomous damage mitigation, reduced maintenance requirements, and extended service life of critical infrastructure, contributing to sustainable and resilient construction practices.
subject: Articles
  topic: section
subject: Self Healing Concrete
  topic: keywords
subject: Ultra High Performance Concrete
  topic: keywords
subject: Smart Nanocapsules
  topic: keywords
subject: Structural Resilience
  topic: keywords
subject: Sustainable Construction
  topic: keywords
creator: Rashid Khan
creator: Senthil Kumar
creator: Nupur Banerjee
rights: Copyright (c) 2026 VW Engineering International
date: 2026-01-27
publication date: 2026-01-27

Machine Learning-Driven Discovery and Optimization of Eco-Friendly Construction Materials
type: item
description: The construction sector accounts for a substantial portion of global carbon emissions and resource exploitation, necessitating the development of eco-friendly materials that balance performance and sustainability. Traditional experimental approaches for material innovation are time-intensive and expensive, often limiting the pace of sustainable breakthroughs. This research develops a machine learning (ML)-based framework for identifying and optimizing eco-friendly construction materials by correlating compositional attributes and processing parameters with performance and environmental outcomes. Supervised learning models — including random forest regression, gradient boosting machines, and deep neural networks were trained on an extensive dataset compiled from peer-reviewed studies, encompassing over 600 material formulations. Target outcomes included compressive strength and embodied carbon indices, while predictors consisted of mix design variables and curing conditions. Gradient boosting demonstrated superior predictive performance (R² > 0.91), while feature analysis revealed the influence of supplementary cementitious materials and water/binder ratio on both performance and sustainability. Case evaluations illustrated optimized mixtures with comparable mechanical properties to conventional concrete but with 20– 35% lower environmental impact. This work highlights machine learning’s potential to streamline sustainable material design and reduce reliance on iterative physical experimentation. Future efforts will integrate multi-objective optimization and extend to broader classes of bio-based and recycled materials.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Construction Materials
  topic: keywords
subject: Sustainable Materials Discovery
  topic: keywords
subject: Predictive Modelling
  topic: keywords
subject: Lifecycle Assessment
  topic: keywords
creator: Aarav Mehta
creator: Ritika Sharma
creator: Imran Hossain
rights: Copyright (c) 2026 VW Engineering International
date: 2026-01-26
publication date: 2026-01-26

Advanced Renewable Energy Conversion Systems: Modeling, Control, and Grid Integration Challenges
type: item
description: The global transition toward low-carbon energy systems has intensified the deployment of renewable energy sources such as solar, wind, and biomass. While renewable energy conversion systems offer environmental and economic benefits, their integration into existing power grids presents significant technical challenges due to intermittency, nonlinearity, and grid stability constraints. This paper presents a comprehensive analysis of advanced renewable energy conversion systems with a focus on modeling, control strategies, and grid integration issues. Mathematical and dynamic models for photovoltaic and wind energy conversion systems are discussed to capture system behavior under variable operating conditions. Advanced control techniques, including model predictive control and adaptive control, are examined for improving energy extraction efficiency and maintaining system stability. The paper further analyzes grid integration challenges such as voltage regulation, frequency stability, and power quality in high renewable penetration scenarios. A conceptual grid-connected renewable system is evaluated to illustrate the effectiveness of advanced control approaches. The study highlights the importance of coordinated control, energy storage integration, and smart grid technologies in addressing grid reliability concerns. This research contributes to the understanding of renewable energy system behavior and provides insights into overcoming technical barriers for large-scale renewable energy deployment.
subject: Articles
  topic: section
subject: Renewable Energy Systems
  topic: keywords
subject: Power Electronics
  topic: keywords
subject: Grid Integration
  topic: keywords
subject: Control Strategies
  topic: keywords
subject: Energy Conservation
  topic: keywords
creator: Naveen Verma
creator: Shaziz Akhter
creator: Anil Prasad
rights: Copyright (c) 2025 VW Engineering International
date: 2025-12-07
publication date: 2025-12-07

Integration of Internet of Things and Digital Twin Technologies for Predictive Maintenance in Large-Scale Industrial Systems
type: item
description: Unplanned equipment failures in large-scale industrial systems result in significant economic losses, safety risks, and operational inefficiencies. Traditional maintenance strategies such as reactive and preventive maintenance are often insufficient for complex industrial environments characterized by dynamic operating conditions. This paper proposes an integrated framework combining Internet of Things (IoT) and Digital Twin technologies to enable predictive maintenance in large-scale industrial systems. IoT sensors continuously collect real-time operational data related to vibration, temperature, pressure, and energy consumption, while the digital twin serves as a virtual replica of physical assets, enabling real-time simulation and performance analysis. Machine learning models are employed to predict component degradation and estimate remaining useful life. A conceptual industrial case study involving rotating machinery demonstrates how the proposed approach enhances fault detection accuracy and reduces downtime compared to conventional maintenance methods. The results indicate improvements in maintenance planning efficiency, asset reliability, and lifecycle cost reduction. The paper also discusses implementation challenges such as data integration, model fidelity, cybersecurity, and scalability. By synergizing IoT and digital twin technologies, the proposed framework supports data-driven maintenance decision-making and represents a significant step toward Industry 4.0-enabled intelligent manufacturing systems.
subject: Articles
  topic: section
subject: Predictive Maintenance
  topic: keywords
subject: Internet Of Things
  topic: keywords
subject: Digital Twin
  topic: keywords
subject: Industrial Systems
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Ritika Malhotra
creator: Vishal Ranjan
creator: Suresh Patel
rights: Copyright (c) 2025 VW Engineering International
date: 2025-11-07
publication date: 2025-11-07

Design and Performance Evaluation of Next- Generation Composite Materials for High- Strength and Lightweight Engineering Applications
type: item
description: The growing demand for high-performance materials in aerospace, automotive, civil, and energy sectors has accelerated the development of next-generation composite materials that offer superior strength- to-weight ratios. Conventional metallic materials, while mechanically robust, are increasingly constrained by limitations related to weight, corrosion resistance, and fatigue performance. This research investigates the design, fabrication, and performance evaluation of advanced composite materials engineered for high- strength and lightweight applications. The study focuses on fiber-reinforced polymer composites enhanced with nano-scale fillers to improve mechanical, thermal, and durability characteristics. Experimental investigations involving tensile, flexural, impact, and fatigue testing are combined with microstructural analysis to assess the influence of material composition and reinforcement architecture on performance. The results demonstrate significant improvements in specific strength, stiffness, and damage resistance compared to traditional composites and metallic counterparts. Furthermore, the paper discusses manufacturability considerations, lifecycle performance, and sustainability aspects associated with next-generation composites. The findings highlight the potential of these materials to enable lightweight structural design without compromising safety or reliability. This research contributes to material engineering by providing a systematic evaluation of advanced composite systems and identifying key challenges related to cost, scalability, and recyclability for industrial adoption.
subject: Articles
  topic: section
subject: Composite Materials
  topic: keywords
subject: Lightweight Structures
  topic: keywords
subject: Mechanical Performance
  topic: keywords
subject: Fiber Reinforcement
  topic: keywords
subject: Advanced Manufacturing
  topic: keywords
creator: Ameen Khan
creator: Vikas Ranjan
creator: Pooja Sharma
rights: Copyright (c) 2025 VW Engineering International
date: 2025-10-07
publication date: 2025-10-07

Artificial Intelligence–Driven Optimization of Smart Infrastructure Systems for Sustainable Urban Development
type: item
description: Rapid urbanization has placed unprecedented pressure on infrastructure systems, demanding innovative solutions that balance efficiency, resilience, and sustainability. Smart infrastructure, enabled by digital technologies, offers a promising pathway to address these challenges. This paper explores the role of artificial intelligence (AI) in optimizing smart infrastructure systems for sustainable urban development. The study presents a comprehensive framework integrating machine learning, optimization algorithms, and real- time data analytics to enhance the performance of urban infrastructure components such as transportation networks, energy systems, water distribution, and waste management. Through an extensive review of recent literature and the development of an AI-driven optimization model, the research demonstrates how predictive analytics and adaptive control can significantly reduce resource consumption, operational costs, and environmental impacts. Simulation-based evaluations indicate improvements in energy efficiency, traffic flow optimization, and infrastructure lifecycle management when compared to conventional rule- based systems. The findings highlight AI’s potential to support data-driven urban planning, enable proactive maintenance, and foster resilient cities aligned with sustainable development goals. This paper contributes to the growing body of knowledge on smart cities by providing an integrated perspective on AI-enabled infrastructure optimization and outlining practical challenges related to data quality, scalability, and ethical deployment.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Smart Infrastructure
  topic: keywords
subject: Sustainable Urban Development
  topic: keywords
subject: Optimization Techniques
  topic: keywords
subject: Smart Cities
  topic: keywords
creator: Aamir Khan
creator: Vicky Sharma
creator: Virat Kumar
rights: Copyright (c) 2025 VW Engineering International
date: 2025-09-07
publication date: 2025-09-07

Edge Computing–Enabled Real-Time Data Analytics Frameworks for High-Performance Industrial Internet of Things (IIoT) Applications
type: item
description: The rapid growth of the Industrial Internet of Things has transformed modern manufacturing and industrial operations by enabling continuous monitoring, automation, and intelligent decision-making. However, traditional cloud-centric architectures struggle to meet the stringent latency, reliability, and bandwidth requirements of industrial environments. Edge computing has emerged as a promising paradigm that brings computation and data analytics closer to data sources, enabling real-time processing and improved system responsiveness. This paper presents an edge computing–enabled real-time data analytics framework for high-performance Industrial Internet of Things applications. The proposed framework integrates distributed edge nodes, intelligent data filtering, and machine learning–based analytics to support time-critical industrial processes. Architectural design considerations, data flow mechanisms, and computational resource management strategies are examined in detail. Performance evaluation demonstrates significant reductions in latency, network congestion, and energy consumption compared to cloud-only approaches. Security, scalability, and fault tolerance challenges are also discussed. The study highlights the potential of edge computing to enhance operational efficiency, reliability, and autonomy in industrial systems, positioning it as a key enabler of next-generation IIoT infrastructures.
subject: Articles
  topic: section
subject: Edge Computing
  topic: keywords
subject: Industrial Internet Of Things
  topic: keywords
subject: Real Time Analytics
  topic: keywords
subject: Distributed Systems
  topic: keywords
subject: Smart Manufacturing
  topic: keywords
creator: Aamir Khan
creator: Ritu Sharma
creator: Sanjay Kumar
rights: Copyright (c) 2025 VW Engineering International
date: 2025-08-07
publication date: 2025-08-07

Sustainable Materials Engineering: Development, Characterization, and Environmental Impact Assessment of Green Composite Materials
type: item
description: The increasing demand for sustainable development has intensified research efforts toward environmentally responsible materials in engineering applications. Conventional composite materials, although offering excellent mechanical performance, pose significant environmental challenges due to their dependence on non-renewable resources and poor end-of-life disposal characteristics. Green composite materials, developed using natural fibers, bio-based resins, and recyclable matrices, present a promising alternative for sustainable materials engineering. This paper investigates the development, characterization, and environmental impact assessment of green composite materials for structural and non-structural engineering applications. The study examines material selection strategies, fabrication techniques, and mechanical, thermal, and durability properties of green composites. Emphasis is placed on life cycle assessment to evaluate environmental benefits in comparison with conventional composites. Experimental characterization is supported by analytical modeling to assess performance reliability. The paper also discusses challenges related to moisture absorption, interfacial bonding, and large-scale manufacturing. Results indicate that green composites demonstrate competitive performance while significantly reducing environmental impact. The findings support the adoption of green composites as viable materials for sustainable engineering systems.
subject: Articles
  topic: section
subject: Green Composites
  topic: keywords
subject: Sustainable Materials
  topic: keywords
subject: LIFE Cycle Assessment
  topic: keywords
subject: Natural Fibers
  topic: keywords
subject: Environmental Impact
  topic: keywords
creator: Ankur Sharma
creator: Aneeta Chowdary
creator: Rkesh Kumar
rights: Copyright (c) 2025 VW Engineering International
date: 2025-07-07
publication date: 2025-07-07

Machine Learning Driven Structural Health Monitoring Techniques for Early Damage Detection in Critical Civil Infrastructure
type: item
description: Critical civil infrastructure such as bridges, dams, tunnels, and high-rise buildings plays a vital role in economic development and public safety. Aging infrastructure, increasing service loads, and environmental degradation have significantly increased the risk of structural failures worldwide. Conventional structural health monitoring methods are largely dependent on periodic inspections and physics-based models, which are often time-consuming, subjective, and limited in their ability to detect early-stage damage. Recent advancements in machine learning offer powerful tools for automated, data- driven structural health monitoring capable of real-time damage detection and assessment. This paper investigates machine learning–driven structural health monitoring techniques for early damage detection in critical civil infrastructure. A comprehensive framework is presented that integrates sensor-based data acquisition, feature extraction, machine learning algorithms, and decision-making systems. Supervised, unsupervised, and deep learning approaches are analyzed in the context of vibration-based and response- based monitoring. The study evaluates performance in terms of detection accuracy, robustness to noise, and scalability for large infrastructure systems. Challenges related to data scarcity, model generalization, and interpretability are discussed. The findings demonstrate that machine learning significantly enhances early damage detection capability and supports proactive maintenance strategies, contributing to improved infrastructure resilience and safety.
subject: Articles
  topic: section
subject: Hybrid Renewable Energy
  topic: keywords
subject: Smrt Grid
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: Power Management
  topic: keywords
subject: Sustainability
  topic: keywords
creator: Aniket Sharma
creator: Roohi Joshi
creator: Parthiv Rana
rights: Copyright (c) 2025 VW Engineering International
date: 2025-06-07
publication date: 2025-06-07

Energy-Efficient Design and Performance Evaluation of Hybrid Renewable Energy Systems for Smart Grid Applications
type: item
description: The global transition toward low-carbon energy systems has intensified interest in renewable energy integration within smart grid infrastructures. However, the intermittent and stochastic nature of renewable resources such as solar and wind presents significant challenges to grid stability, efficiency, and reliability. Hybrid Renewable Energy Systems, which combine multiple renewable sources with energy storage and intelligent control mechanisms, have emerged as a promising solution for addressing these challenges. This paper investigates the energy-efficient design and performance evaluation of hybrid renewable energy systems for smart grid applications. A comprehensive system architecture integrating solar photovoltaic, wind energy, battery storage, and grid interaction is analyzed using performance and efficiency metrics. The study emphasizes energy management strategies, power quality enhancement, and load balancing under variable operating conditions. Simulation-based evaluation demonstrates that optimized hybrid configurations significantly reduce energy losses, improve renewable utilization, and enhance grid resilience. The paper also discusses economic and environmental implications, along with challenges related to system sizing, control complexity, and grid interoperability. The findings suggest that energy-efficient hybrid renewable energy systems play a critical role in enabling sustainable, reliable, and intelligent smart grid operations.
subject: Articles
  topic: section
subject: Hybrid Renewable Energy
  topic: keywords
subject: Smart Grid
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: Power Management
  topic: keywords
subject: Sustainability
  topic: keywords
creator: Ankit Shukla
creator: Rohit Joshi
creator: Prateek Rana
rights: Copyright (c) 2025 VW Engineering International
date: 2025-05-07
publication date: 2025-05-07

Advanced Cyber-Physical Systems for Resilient and Secure Industrial Automation in Industry 4.0 Environments
type: item
description: The emergence of Industry 4.0 has transformed industrial automation by tightly integrating physical processes with computation, communication, and control. Cyber-Physical Systems (CPS) lie at the core of this transformation, enabling intelligent monitoring, decentralized decision-making, and autonomous control in industrial environments. However, the increasing interconnectivity of industrial systems has introduced new challenges related to system resilience, cybersecurity threats, and operational reliability. This paper investigates advanced cyber-physical system architectures designed to enhance resilience and security in industrial automation environments. A comprehensive CPS framework is presented that integrates real-time sensing, embedded intelligence, networked control, and secure communication protocols. The study analyzes resilience strategies such as fault tolerance, redundancy, and adaptive control, alongside cybersecurity mechanisms including intrusion detection, authentication, and secure data exchange. The interaction between cyber and physical domains is examined to highlight cascading failure risks and mitigation strategies. The findings demonstrate that resilient and secure CPS architectures significantly improve system availability, safety, and operational continuity. The paper concludes that advanced CPS design is essential for realizing robust, trustworthy, and scalable Industry 4.0 automation systems.
subject: Articles
  topic: section
subject: Cyber Physical Systems
  topic: keywords
subject: Industrial Automation
  topic: keywords
subject: Industry 4.0
  topic: keywords
subject: System Resilience
  topic: keywords
subject: Cybersecurity
  topic: keywords
creator: Faizan Ansari
creator: Raghav Joshi
creator: Meenakshi Rani
rights: Copyright (c) 2025 VW Engineering International
date: 2025-04-07
publication date: 2025-04-07

Application of Blockchain Technology for Secure, Transparent, and Efficient Supply Chain Management in Engineering Projects
type: item
description: Engineering projects increasingly rely on complex, multi-stakeholder supply chains that span geographical, organizational, and contractual boundaries. Conventional supply chain management systems in engineering construction, manufacturing, and infrastructure development suffer from limited transparency, delayed information sharing, data manipulation risks, and inefficient coordination among participants. Blockchain technology, with its decentralized, immutable, and cryptographically secured ledger structure, offers a promising paradigm for addressing these systemic challenges. This paper explores the application of blockchain technology to enhance security, transparency, and operational efficiency in engineering project supply chains. The study develops a blockchain-enabled conceptual framework tailored for engineering projects and evaluates its effectiveness through transaction-flow analysis and system-level assessment. Smart contracts are examined as automation mechanisms for procurement, logistics coordination, quality verification, and payment execution. The findings indicate that blockchain integration significantly reduces information asymmetry, improves material traceability, enhances trust among stakeholders, and mitigates fraud and counterfeiting risks. The paper also discusses implementation challenges related to scalability, interoperability, regulatory compliance, and organizational readiness. It concludes that blockchain, when integrated with existing digital systems, can play a transformative role in modernizing supply chain governance in engineering projects.
subject: Articles
  topic: section
subject: Blockchain
  topic: keywords
subject: Supply Chain Management
  topic: keywords
subject: Engineering Projects
  topic: keywords
subject: Smart Contracts
  topic: keywords
subject: Transparency
  topic: keywords
creator: Aamir Khan
creator: Rohit Sengupta
creator: Naveen Patnayak
rights: Copyright (c) 2025 VW Engineering International
date: 2025-01-07
publication date: 2025-01-07

Design, Deployment, and Optimization Strategies of IoT-Based Smart City Infrastructure for Sustainable Urban Development
type: item
description: Rapid urbanization has imposed unprecedented pressure on existing urban infrastructure, leading to challenges related to energy consumption, transportation congestion, environmental degradation, and inefficient public service delivery. Smart city initiatives have emerged as a strategic response to these challenges by leveraging digital technologies to enhance urban sustainability and quality of life. Among these technologies, the Internet of Things (IoT) plays a central role by enabling real-time sensing, data acquisition, and intelligent decision-making across urban systems. This paper examines the design principles, deployment models, and optimization strategies of IoT-based smart city infrastructure with a focus on sustainable urban development. A comprehensive framework is proposed that integrates IoT sensors, communication networks, cloud and edge computing platforms, and data analytics for effective urban management. The study analyzes key application domains including smart energy systems, intelligent transportation, waste management, and environmental monitoring. Optimization strategies related to energy efficiency, network scalability, data security, and system interoperability are critically evaluated. The findings suggest that well-designed IoT architectures can significantly improve resource utilization, reduce environmental impact, and enhance citizen services. However, challenges such as data privacy, infrastructure costs, and governance issues must be addressed to ensure long-term sustainability. The paper concludes that IoT-enabled smart city infrastructure is a crucial enabler of sustainable urban transformation when supported by appropriate policy and technological integration.
subject: Articles
  topic: section
subject: Internet Of Things
  topic: keywords
subject: Smart Cities
  topic: keywords
subject: Urban Infrastructure
  topic: keywords
subject: Sustainability
  topic: keywords
subject: Optimization
  topic: keywords
creator: Imran Khan
creator: Pankaj Verma
creator: Farzana Malik
rights: Copyright (c) 2025 VW Engineering International
date: 2025-02-07
publication date: 2025-02-07

Integration of Artificial Intelligence and Digital Twin Technologies for Predictive Maintenance and Lifecycle Optimization of Large-Scale Engineering Systems
type: item
description: Large-scale engineering systems such as power plants, transportation networks, manufacturing facilities, and critical infrastructure assets are characterized by high operational complexity, long service life, and significant maintenance costs. Traditional maintenance strategies, including corrective and time-based preventive maintenance, are often inefficient and reactive, leading to unplanned downtime and suboptimal asset utilization. Recent advances in Artificial Intelligence and Digital Twin technologies offer transformative opportunities for predictive maintenance and lifecycle optimization of engineering systems. This paper investigates the integration of AI-driven analytics with digital twin frameworks to enable real- time monitoring, fault prediction, and decision support across the entire asset lifecycle. A comprehensive conceptual model is developed that combines sensor-driven data acquisition, virtual system representation, machine learning–based diagnostics, and prognostics. The study analyzes how data-driven digital twins enhance condition assessment, remaining useful life estimation, and maintenance scheduling. Implementation challenges related to data quality, model scalability, computational complexity, and cybersecurity are critically examined. The findings demonstrate that AI-enabled digital twins significantly improve maintenance accuracy, reduce lifecycle costs, and enhance system reliability. The paper concludes that the convergence of AI and digital twin technologies represents a foundational shift toward intelligent, self-adaptive engineering systems.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Digital Twin
  topic: keywords
subject: Predictive Maintenance
  topic: keywords
subject: Lifecycle Optimization
  topic: keywords
subject: Engineering Systems
  topic: keywords
creator: Imran Bhat
creator: Suresh Joshi
creator: Meenakshi Rani
rights: Copyright (c) 2025 VW Engineering International
date: 2025-03-07
publication date: 2025-03-07

Photocatalytic Activity and Environmental Remediation Efficiency of Novel Engineered Materials under Visible Light Irradiation
type: item
description: The persistence of organic pollutants and emerging contaminants in air and water systems presents a major environmental challenge, particularly in rapidly industrializing regions. Photocatalytic materials have gained significant attention as sustainable solutions for environmental remediation due to their ability to degrade pollutants using solar energy. However, conventional photocatalysts are often limited by poor visible-light responsiveness and rapid charge recombination. This study investigates the photocatalytic activity and environmental remediation efficiency of novel engineered materials designed for enhanced visible-light activation. Modified semiconductor-based photocatalysts were synthesized through controlled doping and surface engineering strategies to improve light absorption and charge separation. Structural, optical, and surface properties were systematically characterized, followed by evaluation of photocatalytic degradation efficiency against representative organic contaminants under visible light irradiation. Reaction kinetics, stability, and reusability were analyzed to assess long-term performance. Experimental findings were further interpreted in the context of large-scale environmental remediation feasibility. Results demonstrate that engineered photocatalysts exhibit significantly enhanced degradation efficiency, improved kinetic rates, and strong operational stability. The study provides a comprehensive framework for developing next-generation photocatalytic materials for sustainable environmental remediation applications.
subject: Articles
  topic: section
subject: Photocatalysis
  topic: keywords
subject: Visible Light Activation
  topic: keywords
subject: Environmental Remediation
  topic: keywords
subject: Engineered Materials
  topic: keywords
subject: Reaction Kinetics
  topic: keywords
creator: Luke Akshi
creator: Sakshi Verma
creator: Robin Tiwari
rights: Copyright (c) 2024 VW Engineering International
date: 2024-12-11
publication date: 2024-12-11

Design, 3D Printing, and Clinical Evaluation of Customized Prosthetic Devices with Enhanced Biocompatibility
type: item
description: The Conventional prosthetic devices are often limited by standardized designs that fail to accommodate individual anatomical variation, leading to discomfort, suboptimal functionality, and reduced patient compliance. Recent advances in additive manufacturing and digital biomedical engineering offer unprecedented opportunities to develop customized prosthetic devices tailored to patient-specific requirements. This study presents the design, fabrication, biomechanical assessment, and clinical evaluation of customized prosthetic devices produced using three-dimensional printing technologies. Patient-specific anatomical data were acquired through medical imaging and processed using computer-aided design tools to generate optimized prosthetic geometries. Biocompatible polymeric materials were employed to ensure safety and comfort during prolonged use. Mechanical testing and finite element analysis were conducted to evaluate structural integrity and load distribution. Clinical evaluation involved functional assessment, user comfort analysis, and short-term rehabilitation outcomes. Results demonstrate that customized 3D-printed prosthetics provide superior fit, improved biomechanical performance, and enhanced patient satisfaction compared to conventional devices. The study highlights the transformative potential of additive manufacturing in advancing personalized prosthetic care and rehabilitation engineering.
subject: Articles
  topic: section
subject: Customised Prosthetics
  topic: keywords
subject: 3D Printing
  topic: keywords
subject: Biochemical Analysis
  topic: keywords
subject: Patient Specific Design
  topic: keywords
subject: Biomedical Engineering
  topic: keywords
creator: Joseph Andrews
creator: Rajesh Verma
creator: Ashwin Raichand
rights: Copyright (c) 2024 VW Engineering International
date: 2024-10-11
publication date: 2024-10-11

Experimental Performance Evaluation of Thermal Energy Storage Systems for Integration with Solar Power Plants
type: item
description: The intermittent nature of solar energy poses significant challenges to its reliable integration into large-scale power generation systems. Thermal energy storage systems offer a promising solution by enabling the capture, storage, and controlled release of solar thermal energy, thereby enhancing dispatchability and grid stability. This study presents an experimental performance evaluation of thermal energy storage systems designed for integration with solar power plants. Both sensible and latent heat storage configurations were developed and tested using representative storage media, including molten salts and phase change materials. Thermal charging and discharging behavior, heat transfer efficiency, and energy retention characteristics were systematically analyzed under controlled and field-simulated conditions. The experimental results were complemented by thermodynamic modeling to assess system efficiency and scalability. Findings demonstrate that optimized thermal energy storage configurations significantly improve solar plant capacity utilization and reduce energy losses during off-sunshine periods. The study provides valuable insights into the practical deployment of thermal energy storage technologies for sustainable solar power generation.
subject: Articles
  topic: section
subject: Thermal Energy Storage
  topic: keywords
subject: Solar Power Integration
  topic: keywords
subject: Phase Change Materials
  topic: keywords
subject: Heat Transfer Materials
  topic: keywords
subject: Renewable Energy Systems
  topic: keywords
creator: Vikram Singh
creator: Shoaib Malik
creator: Vishal Mishra
rights: Copyright (c) 2024 VW Engineering International
date: 2024-10-11
publication date: 2024-10-11

Development, Characterization, and Antimicrobial Efficacy Testing of Coatings for Medical and Engineering Applications
type: item
description: Microbial contamination of surfaces remains a critical challenge across medical and engineering domains, contributing to healthcare-associated infections, material degradation, and reduced service life of components. Antimicrobial coatings have emerged as an effective strategy to inhibit microbial adhesion and proliferation while maintaining functional performance of underlying substrates. This study presents the development, physicochemical characterization, and antimicrobial efficacy evaluation of advanced functional coatings designed for dual-use applications in biomedical devices and engineering systems. Nanostructured coatings incorporating metallic and polymeric antimicrobial agents were fabricated using scalable deposition techniques. Surface morphology, chemical composition, adhesion strength, and wear resistance were systematically characterized using advanced analytical tools. Antimicrobial performance was evaluated against representative bacterial strains under controlled laboratory conditions. Long-term stability and durability of antimicrobial activity were assessed under simulated service environments. Results demonstrate that optimized coating formulations exhibit sustained antimicrobial efficacy, strong substrate adhesion, and enhanced mechanical robustness. The study highlights the potential of multifunctional antimicrobial coatings to address cross-sectoral challenges, supporting safer medical devices and contamination-resistant engineering surfaces.
subject: Articles
  topic: section
creator: Aayush Bhat
creator: Hashmat Malik
creator: Kartikeya Mishra
rights: Copyright (c) 2024 VW Engineering International
date: 2024-09-11
publication date: 2024-09-11

Implementation and Impact Assessment of Remote Sensing Technologies for Environmental Protection and Disaster Risk Reduction
type: item
description: The Rapid environmental degradation and the increasing frequency of natural disasters have intensified the need for robust monitoring and early warning systems capable of supporting proactive decision-making. Remote sensing technologies, combined with geospatial analytics, have emerged as powerful tools for large-scale environmental protection and disaster risk reduction. This study presents a comprehensive implementation and impact assessment of satellite-based and airborne remote sensing systems for monitoring environmental changes and managing disaster risks. Multispectral, hyperspectral, and synthetic aperture radar data were integrated with geographic information systems to analyze land use change, deforestation, flood susceptibility, and post-disaster damage patterns. Advanced data processing techniques, including machine learning-based classification and temporal trend analysis, were employed to enhance detection accuracy. The performance of remote sensing systems was evaluated through field validation and comparative analysis with conventional ground-based monitoring methods. Results demonstrate that integrated remote sensing frameworks significantly improve spatial coverage, response time, and predictive capability for environmental protection and disaster mitigation. The study further assesses socio-economic and governance impacts, highlighting the role of remote sensing in strengthening resilience and sustainable development strategies.
subject: Articles
  topic: section
subject: Remote Sensing
  topic: keywords
subject: Disaster Risk Reduction
  topic: keywords
subject: Environmental Monitoring
  topic: keywords
subject: Geospatial Analysis
  topic: keywords
subject: Climate Resilience
  topic: keywords
creator: Shivam Purohit
creator: Farah Malik
creator: Anil Kapse
rights: Copyright (c) 2024 VW Engineering International
date: 2026-01-07
publication date: 2026-01-07

Investigation and Optimization of Innovative Recycling Techniques for Electronic Waste to Maximize Material Recovery
type: item
description: The exponential growth of electronic device consumption has resulted in a rapid increase in electronic waste, posing severe environmental, economic, and public health challenges. Electronic waste contains a complex mixture of valuable metals, hazardous substances, and non-degradable polymers, making its management both critical and technically challenging. This study investigates and optimizes innovative recycling techniques aimed at maximizing material recovery from electronic waste streams while minimizing environmental impact. A hybrid recycling framework integrating mechanical separation, hydrometallurgical processing, and selective thermal treatment was developed and experimentally evaluated. The recovery efficiency of critical metals such as copper, aluminum, and precious metals was analyzed alongside energy consumption and secondary waste generation. Process optimization was performed through parametric analysis of operating conditions to enhance recovery yield and process sustainability. Environmental impact assessment and techno-economic evaluation were conducted to examine the feasibility of large-scale implementation. Results demonstrate that optimized hybrid recycling routes significantly outperform conventional recycling practices in terms of material recovery efficiency and environmental performance. The findings provide a comprehensive foundation for sustainable electronic waste management strategies aligned with circular economy principles.
subject: Articles
  topic: section
subject: Electric Vehicles
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: Vehicle Modeling
  topic: keywords
subject: Driving Cycles
  topic: keywords
subject: Powertrain Simulation
  topic: keywords
creator: Shivam Purohit
creator: Farah Malik
creator: Anil Kapse
rights: Copyright (c) 2024 VW Engineering International
date: 2024-07-11
publication date: 2024-07-11

Modeling, Simulation, and Energy Efficiency Analysis of Hybrid Electric Vehicles Under Various Urban and Highway Driving Conditions
type: item
description: The transportation sector is a major contributor to global energy consumption and greenhouse gas emissions, necessitating the development of efficient and sustainable vehicle technologies. Hybrid Electric Vehicles (HEVs) offer a practical transition pathway by combining internal combustion engines with electric propulsion systems to improve fuel efficiency and reduce emissions. This study presents a comprehensive modeling, simulation, and energy efficiency analysis of a parallel hybrid electric vehicle operating under diverse urban and highway driving conditions. A detailed vehicle dynamics model was developed to capture longitudinal motion, powertrain interactions, and energy flow between components. Simulation-based evaluation was conducted using standardized driving cycles to assess fuel consumption, energy distribution, and regenerative braking effectiveness. The study further analyzes the impact of driving patterns, traffic conditions, and control strategies on overall vehicle efficiency. Results demonstrate significant improvements in energy utilization during urban driving due to regenerative braking and optimized power split, while highway conditions highlight the importance of engine operating efficiency. The findings provide valuable insights into HEV performance optimization and support informed design and policy decisions for sustainable transportation systems.
subject: Articles
  topic: section
subject: Hybrid Electric Vehicles
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: Vehicle Modeling
  topic: keywords
subject: Driving Cycles
  topic: keywords
subject: Powertrain Simulation
  topic: keywords
creator: Rohi Kuldeep
creator: Ayesha Farooq
creator: Nitin Borkar
rights: Copyright (c) 2024 VW Engineering International
date: 2024-06-11
publication date: 2024-06-11

Development and Testing of High-Performance Filtration Membranes for Industrial Wastewater Treatment Applications
type: item
description: The Industrial wastewater discharge poses a critical threat to freshwater ecosystems due to the presence of suspended solids, organic pollutants, heavy metals, and recalcitrant chemical compounds. Conventional treatment methods often fail to achieve consistent effluent quality under fluctuating industrial loads, necessitating the development of advanced separation technologies. Membrane-based filtration systems have emerged as promising solutions due to their high separation efficiency, compact footprint, and operational flexibility. This study presents the development, physicochemical characterization, and performance evaluation of high-performance filtration membranes engineered for industrial wastewater treatment. Polymeric membranes were fabricated using controlled phase inversion techniques with tailored pore structures and surface properties to enhance permeability and fouling resistance. The membranes were tested under realistic wastewater conditions to assess flux behavior, contaminant rejection efficiency, fouling dynamics, and operational stability. Experimental results demonstrated significant improvements in permeate flux and pollutant removal compared to conventional membranes. The study further analyzes membrane transport mechanisms, fouling mitigation behavior, and long-term performance sustainability. The findings provide valuable insights into membrane design strategies for efficient and scalable industrial wastewater treatment systems.
subject: Articles
  topic: section
subject: Filtration Membranes
  topic: keywords
subject: Industrial Wastewater
  topic: keywords
subject: Separation Efficiency
  topic: keywords
subject: Fouling Resistance
  topic: keywords
subject: Water Treatment
  topic: keywords
creator: Prateek Vashisht
creator: Meenakshi Malhotra
creator: Saurav Rai
rights: Copyright (c) 2024 VW Engineering International
date: 2024-04-11
publication date: 2024-04-11

Simulation and Field Testing of Autonomous Agricultural Robots for Enhanced Crop Monitoring and Field Management
type: item
description: The increasing scale and complexity of modern agricultural operations have intensified the need for intelligent automation to address labor shortages, operational inefficiencies, and the demand for real-time crop intelligence. Autonomous agricultural robots represent a transformative solution capable of enhancing crop monitoring, precision field management, and decision-making accuracy. This study presents a comprehensive investigation into the design, simulation, and field testing of an autonomous ground-based agricultural robot developed for crop surveillance and field operations. The system integrates multi-sensor perception, autonomous navigation, and adaptive control algorithms to perform real-time crop monitoring under dynamic field conditions. Simulation-based validation was conducted using robotic modeling environments to evaluate navigation accuracy, obstacle avoidance, and energy efficiency prior to physical deployment. Field experiments were then carried out across diverse crop fields to assess operational robustness, data acquisition accuracy, and system reliability. Performance metrics including path deviation, detection accuracy, operational efficiency, and environmental adaptability were analyzed in detail. The results demonstrate that autonomous agricultural robots can significantly enhance field monitoring efficiency while reducing human intervention and operational costs. The study confirms the viability of robotic automation as a cornerstone technology for sustainable and precision-driven agriculture.
subject: Articles
  topic: section
subject: Autonomous Robots
  topic: keywords
subject: Precision Agriculture
  topic: keywords
subject: Crop Monitoring
  topic: keywords
subject: Field Robotics
  topic: keywords
subject: Agricultural Automation
  topic: keywords
creator: Kunal Deshpande
creator: Shivani Malhotra
creator: Arvind Rao
rights: Copyright (c) 2024 VW Engineering International
date: 2024-04-11
publication date: 2024-04-11