Search
516 articles for “drying optimization”
-
AI-Powered Solutions for Sustainable Waste Management in Construction Projects
Abstract: The construction industry is a significant contributor to global waste, posing challenges to sustainability and environmental health. This research explores AI-powered solutions for sustainable waste management in construction projects, focusing on optimizing waste reduction, recycling, and resource efficiency. By integrating machine learning algorithms and IoT-enabled sensors, real-time monitoring of waste generation and segregation can be achieved. Predictive analytics and AI-driven decision-making tools are employed to enhance material reuse and minimize …
Published in Recent Trends in Civil Engineering & Technology · Vol. 15, Issue 2, 2025 · pp. 1–5 Read article
-
AI and Big Data for Optimized Water Resource Management in Arid Regions
Abstract: Water scarcity in arid regions is an escalating global challenge, driven by climate change, population growth, and increasing demands from urban, industrial, and agricultural sectors. Effective water resource management (WRM) is crucial for sustaining livelihoods, economic stability, and infrastructure resilience. Emerging technologies such as artificial intelligence (AI), machine learning (ML), and big data offer innovative solutions for optimizing water use, enhancing efficiency, and improving sustainability in water-scarce environments. This paper …
Published in Trends in Transport Engineering and Applications · Vol. 12, Issue 1, 2025 · pp. 1–5 Read article
-
Role of Fluid Engineering in Biomedical and Healthcare Systems: A Comprehensive Review
Abstract: Fluid engineering — the study and application of fluid behavior, transport, and interaction — has become a cornerstone of modern biomedical and healthcare systems. This review synthesizes the multifaceted roles fluid engineering plays across diagnostics, therapeutics, biomedical devices, and physiological modeling. Micro-fluidics enables precise manipulation of microliter and nanoliter volumes, facilitating rapid point-of-care diagnostics, high-throughput screening, and the fabrication of uniform nano particles for targeted drug delivery. In cardiovascular medicine, …
Published in Trends in Mechanical Engineering & Technology · Vol. 16, Issue 1, 2026 · pp. 1–5 Read article
-
Bayesian Optimization–Driven Operating Parameter Tuning for Maximizing Methane Yield in Anaerobic Digestion
Abstract: To achieve maximum methane production in an anaerobic digestion (AD) process, a combination of various operational parameters must be tuned nonlinearly in the digestion ecosystem. The conventional trial and error optimization methods are slow, resource consuming, and in most instances, cannot model the intricate parameter interaction in biogas production. The current work introduces a Bayesian Optimization-based model to optimize the set of conditions to maximize the level of methane produced …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 1–8 Read article
-
Improving Supply Chain Resilience through Predictive Analytics and Real-Time Data Integration
Abstract: Demand forecasting has under gone major changes because to the incorporation of automated analytics into supply chain management (SCM), which has improved company productivity, accuracy, and responsiveness. Central to this transformation is the application of machine learning (ML), which enables the analysis of large and complex datasets to identify patterns, detect trends, and generate precise forecasts. Conventional methods for predicting frequently rely on linear models and historical sales data, which …
Published in International Journal of Industrial and Product Design Engineering · Vol. 3, Issue 2, 2025 · pp. 8–17 Read article
-
Advanced Digital Twin and AI Integration for Real-Time Optimization in Polymer Production
Abstract: The integration of Internet of Things (IoT) with Artificial Intelligence (AI) technologies opens up considerable avenues for reshaping polymer manufacturing by improving operational effectiveness, securing exceptional product standards, and advancing sustainability in the environment. This academic manuscript delineates an advanced framework that integrates IoT and AI with synergistic technologies, including blockchain, edge computing, and digital twin methodologies, to revolutionize polymer manufacturing processes. The proposed architecture utilizes IoT sensors for the …
Published in Journal of Polymer & Composites · Vol. 13, Issue 3, 2025 · pp. 81–89 Read article
-
A Data-Driven Analysis of Machine Learning Classification Models for Reliable Crop Yield Prediction
Abstract: The adoption of ML technologies in agriculture is reshaping farming practices, empowering producers to make informed, data-oriented decisions that improve yields, sustainability, and long-term resilience. In mango cultivation, ML analyzes data from weather, soil, and pests to optimize irrigation, fertilization, and pest control. Predictive analytics help forecast ideal farming practices, minimizing resource wastage and improving yield. Real-time monitoring and image-based disease detection allow timely interventions to maintain plant health and …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 15, Issue 1, 2026 · pp. 12–17 Read article
-
Industry 6.0: Building a Sustainable Future
Abstract: The next stage of industrialization, known as Industry 6.0, is centered on developing intelligent, fully integrated manufacturing systems that require little human involvement. India is in a perfect position to become a major player in the sector 6.0 ecosystem, thanks to its robust technological infrastructure, fast expanding economy, and large talent pool. It blends big data, cloud computing energy, human-robot interaction, artificial intelligence, and quantum computing. The main aim of …
Published in Journal of Microcontroller Engineering and Applications · Vol. 12, Issue 3, 2025 · pp. 25–32 Read article
-
Machine Learning Optimization for VARTM Carbon Polymer Laminates
Abstract: Vacuum-assisted resin transfer moulding (VARTM) is a key low-cost, out-of-autoclave process for manufacturing large-scale carbon-fibre reinforced polymer (CFRP) laminates crucial to aerospace wings, wind-turbine blades, marine hulls, and automotive structures. Unpredictable resin flow often leads to voids, dry spots, and race-tracking defects, resulting in 27.9% scrap rates and lengthy, costly trial-and-error design cycles. Although surrogate models provide rapid impregnation predictions for simple flat-plate geometries, vision-based monitoring is limited to idealized …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 229–245 Read article
-
Optimizing Green Composites for Automotive Applications Using Taguchi-Based Grey Relational Analysis
Abstract: The increasing demand for sustainable and lightweight materials in the automotive industry has driven the exploration of green composites as an alternative to conventional synthetic composites. This study focuses on optimizing the mechanical properties of green composites using Taguchi-based Grey Relational Analysis. Four natural fibers—pineapple, sisal, coir, and Nava—were reinforced with cashew resin and synthetic resin matrices, incorporating silica and granite fillers of varying sizes. Key properties, including hardness, impact …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 743–754 Read article
-
Codal Validation and Optimization of Gantry Girders Under Variable Wheelbase and Impact Loads: A Review of Analytical, Numerical, and Codal Approaches
Abstract: Gantry girders serve as critical structural elements in industrial facilities such as steel plants, workshops, and heavy manufacturing units, where electric overhead traveling (EOT) cranes operate. The design of these girders is governed by stringent codal provisions to ensure safety under bending, shear, and deflection. However, discrepancies between codal predictions, analytical formulations, and finite element analysis (FEA) results, particularly under variable wheelbase and dynamic impact loads, have been widely reported. …
Published in Journal of Offshore Structure and Technology · Vol. 12, Issue 3, 2025 · pp. 23–29 Read article
-
Geotechnical Influence Factors in Empirical Design of Highway Pavement: The A123 Ilorin – Lokoja Highway, Nigeria Case Studies
Abstract: Site investigation and application of geological and geotechnical influence factors on the highway were investigated. Method involved field sampling, geotechnical laboratory and x-ray diffraction analysis and use of SRTM extracted slope and drainage parameters; and their integration in a GIS based multi-criteria decision analysis to derive a subgrade suitability map as a design tool. The suitability criteria classified the highway into four (4) design sections. Section one for very low …
Published in Journal of Geotechnical Engineering · Vol. 11, Issue 3, 2024 · pp. 53–70 Read article
-
Enhancing Production Line Efficiency: Simulating and Optimizing Single and Parallel Line Processes
Abstract: During a time of fast-paced industrial growth, increasing production line effectiveness is a core issue for manufacturers looking to maximize output, reduce waste, and stay competitive. This study explores the use of simulation-based optimization methods to enhance single and parallel production line designs. Stepping beyond traditional trial-and-error methods, the research utilizes Siemens Tecnomatix Plant Simulation to simulate actual manufacturing scenarios, considering intricacies like buffer capacities, machine sequencing, and event-driven scheduling. …
Published in Journal of Production Research & Management · Vol. 15, Issue 1, 2025 · pp. 22–32 Read article
-
A Review of Solar-Powered Electric Vehicle Models Handling Unpredictable Changes in Modern Power Grids
Abstract: The transition to Electric Vehicles (EVs) is a critical strategy for mitigating global warming and reducing dependence on diminishing fossil fuel reserves. However, the environmental benefits of EVs are significantly diminished if the charging power is sourced from carbon-intensive electrical grids. To achieve true sustainability, it is vital to integrate Renewable Energy Sources (RES), particularly solar energy, into the charging infrastructure. Beyond transportation, EVs offer a unique opportunity to act …
Published in Trends in Electrical Engineering · Vol. 16, Issue 1, 2025 Read article
-
Deep Learning-Based Thermal Prediction Models for Solid-State Electronic Devices
Abstract: The rapid advancement of solid-state electronic devices in high-performance computing, communication systems, automotive electronics, and renewable energy applications has significantly increased concerns related to thermal management and device reliability. Excessive heat generation in semiconductor devices adversely affects operational efficiency, switching performance, lifespan, and overall system stability. Traditional thermal prediction methods often require complex numerical computations and extensive simulation time, making them less suitable for real-time monitoring and adaptive control applications. …
Published in International Journal of Solid State Innovations & Research · Vol. 4, Issue 1, 2026 Read article
-
Entropy, Symmetry, and Data Fusion: Emerging Methods in Multi-Objective Decision- Making and Smart Systems
Abstract: In the era of intelligent technologies and data-driven systems, multi-objective decision-making (MODM) has become an essential aspect of managing complex environments such as smart cities, autonomous systems, and cyber-physical networks. As decision-making scenarios become increasingly dynamic and uncertain, there is a growing need for advanced methodologies that can handle diverse objectives, conflicting constraints, and incomplete information. This review highlights the emerging role of entropy, symmetry, and data fusion as foundational …
Published in Emerging Trends in Symmetry · Vol. 1, Issue 2, 2025 · pp. 44–49 Read article
-
Designing Self-Optimizing Operating Systems: Information-Theoretic Approaches to Thread Scheduler Implementation
Abstract: Thread Level Scheduling (TLS) in multi-core and many-core processor environments represents a critical frontier in next-generation operating system design. As computing systems grow increasingly heterogeneous and concurrent, traditional scheduling strategies often rely on heuristics or localized resource metrics, frequently overlooking the deeper, quantifiable relationships and uncertainties inherent in complex concurrent workloads. This study explores the application of information-theoretic approaches, specifically entropy-based task allocation, mutual information-driven dependency analysis, and channel capacity-inspired …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 3, 2025 · pp. 31–39 Read article
-
ML-Based Predictive Modeling of Mechanical Properties in 3D-Printed Polymer Composites for IoT Applications
Abstract: This study aims to develop an interpretable and high-accuracy machine learning framework for predicting the mechanical properties of 3D-printed fiber-reinforced polymer composites, with a focus on structure–property correlations relevant to polymer processing and functional performance. Composite specimens based on PLA and ABS matrices were fabricated using FDM with varying weight fractions (5–20 wt%) of carbon and glass fibers. Standardized mechanical testing (ASTM D638, D256, D790) was performed to evaluate tensile …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 61–78 Read article
-
Machine-Learning-Assisted Development of Polymer-Biochar Composite Adsorbents for the Removal of Heavy Metals from Gomti River Water
Abstract: Rapid urbanization, industrial discharge, and agricultural runoff pose a significant threat to freshwater sustainability and public health. Within these ecosystems, polymer pollutants—such as microplastics, nanoplastics, synthetic fibres, and additive residues—have emerged as persistent vectors capable of adsorbing and transporting toxic heavy metals. Because these polymeric contaminants dynamically interact with conventional aquatic parameters to alter pollutant mobility and ecological risk profiles, there is an urgent need to transition from passive environmental …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 72–95 Read article
-
Evaluating Chassis Designs for Gesture-Driven Robots: A Study of Iron, Acrylic, and 3D Printed Solutions
Abstract: Gesture-driven robotic systems are becoming increasingly relevant in applications such as assistive technology, industrial automation, and interactive systems. The choice of chassis material plays a crucial role in determining the robot's mechanical strength, weight, and ease of fabrication. This study systematically evaluates three chassis materials—iron, acrylic, and 3D printed polymers—to determine their suitability for gesture-controlled robots. A standardized robotic configuration, including two rear motors, two front dummy axles, an L298N …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 686–692 Read article