Pak Advances in Engineering Research is a respected, peer-reviewed academic journal published by FUTURE SCOPE RESEARCH INSTITUTE (SMC-PRIVATE) LIMITED. The journal is dedicated to publishing high-quality and innovative research across a wide range of engineering disciplines. It provides an academic platform for scholars, researchers, and industry professionals to share original research findings, technological innovations, and practical solutions that contribute to scientific and industrial advancement. The journal emphasizes both theoretical and applied research that addresses real-world engineering challenges and promotes the development of sustainable, efficient, and advanced engineering solutions. Publication Frequency: Semi-Annual
FUTURE SCOPE RESEARCH INSTITUTE (SMC-PRIVATE) LIMITED
THE ROLE OF ARTIFICIAL INTELLIGENCE IN IMPROVING INDUSTRIAL AUTOMATION AND EFFICIENCY: A CASE STUDY OF THE AUTOMOTIVE INDUSTRY
The integration of Artificial Intelligence (AI) into industrial automation represents a paradigm shift in manufacturing, with the automotive sector at the forefront of this transformation. This research examines the multifaceted role of AI technologies—including machine learning, computer vision, and collaborative robotics—in enhancing operational efficiency, production quality, and supply chain resilience. Through a quantitative, problem-based methodology, the study analyzes real-world data from a leading automotive manufacturer's implementation of AI-driven systems across assembly, quality control, and predictive maintenance. The results demonstrate significant, measurable improvements: a 27% reduction in unplanned downtime, a 15% increase in Overall Equipment Effectiveness (OEE), a 40% decrease in defect rates through visual inspection AI, and a 22% optimization in logistics and inventory costs. Furthermore, the adoption of AI-powered collaborative robots (cobots) increased task completion speed by 35% while improving worker safety. Despite these benefits, the study also identifies critical challenges, including high initial capital investment, a persistent skills gap, and data integration complexities. The findings underscore that AI is not merely an incremental upgrade but a foundational technology that redefines automotive manufacturing paradigms. Strategic implementation, coupled with workforce reskilling and robust data infrastructure, is essential for harnessing its full potential. This case study provides empirical evidence for industry stakeholders to guide investment and strategy, highlighting that the future of automotive manufacturing is inextricably linked to intelligent, adaptive, and interconnected AI systems.
Copyright (c) 2025 Usama Farid, Tahir Mehmood (Author) https://creativecommons.org/licenses/by/4.0
ENGINEERING SOLUTIONS FOR URBAN WASTE MANAGEMENT: ANALYZING THE IMPACT OF CIRCULAR ECONOMY MODELS IN CITY PLANNING
Urbanization has rendered the handling of municipal solid trash more complex and more enormous that is a huge challenge to cities nowadays in terms of environment, the economy, and the society. The paper will evaluate how engineering-based models of a circular economy affect the management of trash in urban areas within the context of municipal planning. The study applies an analytical approach based on systems in evaluating the effectiveness of circular solutions such as waste minimization, material recovery, recycling, composting and integration of waste-to-energy. The results show that urban areas implementing the concept of a circular economy achieve better waste diversion rates, reduced greenhouse gas emissions, and better resource efficiency compared to the linear systems of operation. The findings are also that circular engineering solutions bring economic benefit by saving money, developing second-market material materials, and generating employment, not to mention enhancing the quality of the urban environment. The paper also determines that intelligent technology, policy alignment and collaboration between stakeholders are also significant factors that ensure successful implementation. The study reveals that circular economy models can be a good approach to planning and waste management systems in urban areas to make them more sustainable, resilient and use their resources more effectively.
Copyright (c) 2025 Mahnoor Aslam, Rizwan Akhtar (Author) https://creativecommons.org/licenses/by/4.0
OPTIMIZING WATER RESOURCE MANAGEMENT USING SMART SENSORS AND DATA ANALYTICS IN URBAN ENGINEERING PROJECTS
The rapid urbanization and climate change coupled with the old infrastructure place more pressure on the urban water systems. That is why new and evidence-based solutions of management are needed. The presented paper discusses the efficacy of using smart sensors together with advanced data analytics to enhance the management of water resources in urban engineering development based on a mixed approach through the experimental design. We deployed flow, pressure, and water quality smart sensors network in some metropolitan areas of water distribution. These coprocessors reacted with high-resolution real time data. With the application of time-series analysis and predictive forecasting and optimization analysis, through an analytical modeling, we could observe the level of the system that was being operated before implementation and after the implementation of the system. These results show that there is a major decrease in the quantity of non-revenue water, greater stability of pressure, enhanced reliability in anticipating the demand and provable savings of energy in the pumping processes. Real-time anomaly detection allowed the leakage and operational problems to be spotted in time minimizing the response time and service failures. Qualitative evaluation also helped in the enhancement of the efficiency and transparency of the decision-making process and the operation between the stakeholders of water utilities. Overall, the results suggest that smart sensor-based data analytics may have a significant impact on urban water management systems to improve the efficiency, resilience, and sustainability of the systems. This provides us with a scaling, and practical method of the urban infrastructure preparation in the coming generation.
Copyright (c) 2025 Aqsa Rehman, Saqlain Haider (Author) https://creativecommons.org/licenses/by/4.0
ADVANCEMENTS IN SUSTAINABLE BUILDING MATERIALS FOR CLIMATE-RESILIENT INFRASTRUCTURE IN COASTAL REGIONS
Coastal regions are increasingly vulnerable to climate related stressors such as increases in sea level, salt exposure and intense weather patterns. This implies that we must create infrastructure materials that are long-lasting and environmental friendly. This paper experimentally evaluated the innovations in sustainable building materials of climate-resistant infrastructure within coastal environments using a mixed-methods approach. Mechanical performance, survival under simulated marine conditions, resistance to chloride penetration and corrosion, and life-cycle testing measured embodied carbon and energy effects, and the response of quantitative laboratory and pilot-scale tests to mechanical testing. The findings indicate that sustainable material systems are more effective in preventing moisture, chloride, and equivalent damage of concrete materials over time compared to ordinary materials, such as geopolymer concretes, recycled aggregates composite, and bioreinforced material. This does not impact on strength of the structure. Environmental research indicated that embodied carbon reduced significantly and therefore, climate adaptation and mitigation are both beneficial. Huge construction capability and adherence to the coastal engineering criteria were also indicated by qualitative evaluations. In general, the findings indicate that sustainable building materials are one of the significant steps towards constructing robust, low impact coastal infrastructure. They will provide a scientifically tested substitute of conventional construction materials in the light of increasing risks of climate change.
Copyright (c) 2025 Sadia Karim, Bilal Rafiq (Author) https://creativecommons.org/licenses/by/4.0
DESIGN AND DEVELOPMENT OF AUTONOMOUS CONSTRUCTION ROBOTS FOR LARGE-SCALE BUILDING PROJECTS
The growing scope of large-scale construction work has predetermined the growing urgency of the demand in new technologies that contribute to the efficiency, safety, and sustainability. The paper will examine the design and development of autonomous construction robot and establish their roles in construction of large buildings. Those findings indicate that with the help of autonomous robotic systems of artificial intelligence, machine vision and sensor-based navigation, the accuracy of tasks, their efficiency, and the safety of the location can be increased significantly in comparison to the traditional ways of building. It was also demonstrated that time needed to construct, the workforce needed and the quantity of waste in terms of material all lesser and the precision of the structure as well as its uniformity stayed at a high stage. The robots proved that the adaptation of the varying conditions of the site was easy with the assistance of the real-time path planning system and the obstacle avoidance system. Besides, the collaboration with robots increased the procedure of the project running and minimized the risks in the working place. Economic analysis showed that the system is a good offer in the long run although the initial cost of adopting the system is high because in the long-term, the system will save on the labor cost, delays and poor quality control. This paper summarizes that autonomous construction robots represent an acceptable and scalable answer to large scale building projects of the current day. They may change the construction process and contribute to the transition to smart and eco-friendly development of infrastructure.
Copyright (c) 2025 Ahsan Jamil, Farooq Ahmed (Author) https://creativecommons.org/licenses/by/4.0
ADVANCED MACHINE LEARNING TECHNIQUES FOR REAL-TIME OPTIMIZATION OF POWER GRID STABILITY IN THE PRESENCE OF DISTRIBUTED ENERGY RESOURCES
This study explores the application of advanced machine learning techniques for real-time optimization of power grid stability in the presence of Distributed Energy Resources (DERs). As renewable energy sources such as solar and wind become more integrated into power grids, their intermittent nature poses significant challenges for grid stability. The research employs deep learning and reinforcement learning algorithms to manage power generation fluctuations, optimize grid performance, and enhance energy efficiency. The results demonstrate that machine learning models can successfully forecast power generation from DERs and optimize grid stability in real-time. Studied data indicates that solar power operates better alongside wind electricity generation and artificial intelligence algorithms to provide continuous enhancement of precision allowing increased operational output for stabilizing power grids. Machine learning operates efficiently by detecting particular power loss positions in gearboxes during dynamic optimization testing. DERs controlling the power flow stability function most intensely when solar power generates maximum output. The study explains modern management systems powered by AI which boost operation of power grids that rely on renewable energy resources. Energy transition in modern times depends on machine learning systems to create flexible electric grids that provide reliable service according to scientific studies.
Copyright (c) 2025 Ahmed Ali , Muhammad Danial Ahmad Qureshi (Author) https://creativecommons.org/licenses/by/4.0
DESIGN AND SCALE-UP OF GREEN HYDROGEN PRODUCTION THROUGH ELECTROCHEMICAL WATER SPLITTING: CHALLENGES IN EFFICIENCY AND SUSTAINABILITY
This study investigates the design and scale-up of green hydrogen production through electrochemical water splitting, focusing on the challenges of efficiency, sustainability, and scalability. The research evaluates the performance of various catalysts, including platinum-based and non-precious metal alloys, under different operating conditions. Our findings demonstrate that while platinum catalysts provide superior efficiency, non-precious metal catalysts, such as iron-nickel alloys, offer a more cost-effective and sustainable solution for large-scale applications. The developed numerical model demonstrated that temperature and production rate established a clear connection but higher temperatures led to reduced hydrogen output. Data from the techno-economic analysis demonstrates that PEM electrolysers have the shortest payback duration thus making them the best choice for commercial implementation due to their financial feasibility. PEM electrolysers begin with increased initial environmental impact but their efficient operations demonstrate enhanced sustainability during operation. Hydrogen production sustainability increases substantially when solar and wind energy sources become integrated into the electrolysis process to reduce overall environmental effects. The presented framework delivers a competent synthesis of performance outcomes with cost efficiency and environmental advantages to maximize electrochemical hydrogen production methods. Upcoming research should work to extend the lifespan of non-precious metal catalysts together with optimizing electrolyser operation protocols and exploring new materials for producing affordable and effective green hydrogen.
Copyright (c) 2025 Wajid Raza, Nida Hafeez (Author) https://creativecommons.org/licenses/by/4.0
SMART CONCRETE: SELF-HEALING MATERIALS FOR STRUCTURAL INTEGRITY IN EXTREME WEATHER CONDITIONS
This study investigates the performance of self-healing concrete under extreme weather conditions, focusing on its crack repair efficiency, compressive strength, water permeability, and durability in freeze-thaw cycles. The results demonstrate that self-healing concrete exhibits superior properties compared to traditional concrete, particularly in terms of crack reduction, water absorption, and compressive strength retention. After exposure to simulated freeze-thaw cycles, self-healing concrete showed a 75% reduction in crack width, significantly outperforming traditional concrete, which displayed only a 43% reduction. Furthermore, self-healing concrete exhibited a 37.6% decrease in water absorption, compared to a 23.3% decrease in traditional concrete. The total durability decreased by 6.4% in self-healing concrete compared to traditional concrete which decreased by 15.7%. Tests under freeze-thaw conditions displayed superior performance by self-healing concrete since it retained 87% durability after three cycles whereas traditional concrete dropped to 70%. The experimental results demonstrate that self-healing concrete has capacity to increase durability and sustainability of weather-exposed infrastructure because it outperforms standard concrete alternatives at reducing costs while fostering structural reliability. Self-healing technologies in civil engineering demonstrate significant promise according to this research so future projects can implement these solutions in actual infrastructure applications.
Copyright (c) 2025 Muhammad Ammad, Imran Ali (Author) https://creativecommons.org/licenses/by/4.0
EDGE AI FOR AUTONOMOUS SYSTEMS: REAL-TIME DEEP LEARNING ALGORITHMS FOR SENSOR FUSION AND DECISION-MAKING IN DRONE NAVIGATION
This study explores the integration of real-time deep learning algorithms and sensor fusion techniques for autonomous drone navigation, focusing on the use of edge AI to optimize system performance under constrained computational resources. The research demonstrates the critical role of multi-sensor fusion—incorporating camera, LiDAR, IMU, and GPS—in improving the accuracy and reliability of drone navigation systems. The hybrid fusion model achieved an impressive 95% accuracy in environmental perception, significantly outperforming individual sensor modalities. Convolutional neural networks (CNNs) in object identification applications possess 50 ms of inference time enabling real-time operation. The reinforcement learning (RL) models exhibited superior decision-making capabilities through their slower operation speed (150 ms). The hybrid RL model cut down 100-meter route travel time to achieve 25 seconds which optimized overall route planning effectiveness. According to measurements the CNN model operated with 3.5 W as the most economical solution while the hybrid approach provided reasonable power-management compared to performance output. A sensor fusion model decreased its response time to 90 milliseconds which secured quick reactions needed for changing environments. Edge artificial intelligence demonstrates its ability to enhance autonomous drones through better navigation precision and decision capabilities and efficiency which makes them suitable for real-time energy-efficient applications such as environmental observation and search and rescue operations. Results demonstrate that autonomous navigation requires both deep learning and sensor fusion to build a stronger dependable autonomous navigation system.
Copyright (c) 2025 Armaghan Umer , Muhammad Amir Rafique (Author) https://creativecommons.org/licenses/by/4.0
DEVELOPMENT OF HIGH-PERFORMANCE HYBRID NANOMATERIALS FOR NEXT-GENERATION HEAT EXCHANGER SYSTEMS IN RENEWABLE ENERGY APPLICATIONS
The fundamental objective of this research involves manufacturing advanced hybrid nanomaterials intended for next-generation renewable energy heat exchanger systems. This work examines the synthesis and characterization and evaluation of hybrid nanomaterials made from graphene and carbon nanotubes base nanomaterials together with copper and silver metallic particles and ceramic materials because they show better thermal conductivity and mechanical strength alongside corrosion resistance. Hybrid composites obtained through the integration of chemical vapor deposition, sol-gel synthesis, high-energy ball milling and sonication method resulted in well-dispersed distributions. Several characterization techniques including transmission electron microscopy and scanning electron microscopy, X-ray diffraction and Fourier-transform infrared spectroscopy proved that composites contained nanoparticles at specific locations while ensuring uniform material distribution. Nanocomposites made from hybrid materials achieved thermal conductivity that rose by 45% above standard material values and this improvement was confirmed through thermal conductivity tests. Tests performed under mechanical stress revealed the composites maintained solid structural integrity because of their maximum tensile strength improvement reaching 30%. The hybrid materials performed well when compared to other materials through demonstrating outstanding resistance to corrosion under renewable energy system conditions and extreme temperature cycles. Computational fluid dynamics models validated by experimental testing on heat exchanger prototypes showed that pressure drop decreased while maximum thermal recovery times shortened substantially thus leading to elevated heat transfer rates and improved fluid flow dynamics. The hybrid nanomaterials display satisfactory environmental friendliness throughout their life cycle while offering both sustainability and affordability for major industrial applications. These sophisticated hybrid nanomaterials show potential for heat exchanger system improvements in renewable energy technologies by enhancing durability alongside performance and efficiency together with reduced environmental impact according to experimental results.
Copyright (c) 2025 Muzammil Ali , Ahmed Ali (Author) https://creativecommons.org/licenses/by/4.0
DEVELOPING ADVANCED COATINGS TO OVERCOME CHALLENGES IN CORROSION RESISTANCE FOR MARINE AND INDUSTRIAL APPLICATIONS
Corrosion is a major challenge in both marine and industrial applications, leading to substantial economic losses and safety concerns. This study investigates the development of advanced corrosion-resistant coatings that incorporate nanomaterials and self-healing agents to enhance protection against environmental degradation. Coatings containing graphene and titanium dioxide nanoparticles were synthesized and subjected to electrochemical impedance spectroscopy (EIS), salt spray testing, and mechanical property evaluations. The results demonstrated superior corrosion resistance of nanocoating since their impedance values performed better at 10 kHz and 100 kHz. Traditional coatings demonstrate corrosion rates at 0.12 mm/year but nanocoating present lower rates at 0.02 mm/year. The laboratory tests verified that nanocoating possess durable qualities due to their increased hardness level and improved scratch resistance alongside increased adhesive strength. The lifetime of coatings increased through self-healing agents that restored corrosion resistance but graphene-based coatings reached 85.6% in recovery capabilities. Research into environmental sustainability demonstrates that nanocoating have better replacement potential over traditional coatings since their biodegradation characteristics improve while toxicity reduces. Updated coatings provided a double benefit which paid back initial investment costs due to longer product lifespan and reduced maintenance requirements. Nanocoating used together with self-healing systems demonstrate practical industry and marine applications because of their extended lifespan and sustainable advantages and cost-effective characteristics. Future anti-corrosive coatings built from innovative concepts appear in this work to address persistent corrosion issues during the operational lifetime of important infrastructure
Copyright (c) 2024 Ahmed Ali (Author) https://creativecommons.org/licenses/by/4.0
OVERCOMING EFFICIENCY BARRIERS IN THE OPTIMIZATION OF HEAT EXCHANGERS FOR ENHANCED ENERGY RECOVERY IN INDUSTRIAL SYSTEMS
This study investigates the optimization of heat exchangers for enhanced energy recovery in industrial systems, addressing key efficiency barriers such as fouling, material limitations, and operational design inefficiencies. The research employs a multifaceted approach, incorporating experimental testing, computational simulations, and machine learning-based optimization models to evaluate the performance of various heat exchanger configurations. The results reveal that optimized flow arrangements, along with the use of advanced corrosion-resistant materials, significantly improve heat transfer rates and energy recovery efficiency. Specifically, heat exchangers with optimized designs exhibited up to a 30% increase in heat transfer performance and a 15% reduction in energy losses compared to conventional systems. The system achieves better efficiency and utilizes waste heat recovery specifically that maintains peak operation under diverse energy input conditions. Results demonstrate machine learning models show strong forecasting abilities to predict optimal system conditions that simultaneously reduce operation expenses while enhancing energy recovery. Flexible heat exchangers must be developed with scalable characteristics that will match the changing industrial requirements. The research introduces an entire system to improve heat exchanger efficiency through advanced materials integration with innovative flow structures coupled with predictive data models running in real-time to achieve industrial energy efficiency goals and sustainability advancements. The experimental results help future industrial efforts by providing essential insights to achieve higher efficiency and reduce environmental impact of their sustainable energy systems
Copyright (c) 2024 Olumhense Benedict Adoghe, Ghulam Mujtaba Noor (Author) https://creativecommons.org/licenses/by/4.0
ADVANCED MANUFACTURING TECHNIQUES FOR ADDITIVE MANUFACTURING OF TITANIUM ALLOYS: OPTIMIZING MECHANICAL PROPERTIES FOR AEROSPACE APPLICATIONS
This study investigates the optimization of mechanical properties of titanium alloys for aerospace applications using advanced additive manufacturing (AM) techniques. Specifically, Selective Laser Melting (SLM), Electron Beam Melting (EBM), and Direct Energy Deposition (DED) were employed to fabricate Ti6Al4V titanium alloy samples, and their mechanical properties, including tensile strength, fatigue resistance, hardness, and microstructure, were evaluated. The results revealed that SLM-produced samples exhibited superior tensile strength (801 MPa), fatigue life (1,200,000 cycles), and hardness (400 HV) compared to those produced by EBM and DED. Additionally, microstructural analysis showed that SLM resulted in finer and more uniform grain structures, contributing to its enhanced mechanical performance. Applications of post-processing heat treatments on samples achieved important improvements to tensile strength and fatigue resistance particularly for SLM-produced titanium alloy components. The results prove SLM stands as the leading AM technology for manufacturing titanium components destined for aerospace applications because it enables production of superior material properties that heat treatment can then enhance. The study brings essential knowledge about AM parameter optimization and post-treatment enhancements which supports aerospace component development of high-performance titanium parts
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-nd/4.0
ADVANCED DRUG DELIVERY SYSTEMS: NANOPARTICLE-BASED PLATFORMS FOR TARGETED THERAPY AND CONTROLLED RELEASE
This study investigates the development and evaluation of nanoparticle-based drug delivery systems designed to enhance targeted therapy and controlled drug release, with a focus on cancer treatment. The synthesized nanoparticles exhibited optimal characteristics, including sizes ranging from 150 to 200 nm, which are ideal for enhanced tumor penetration and retention. The zeta potential values between -20 mV and -30 mV contributed to the stability and prolonged circulation time of the nanoparticles. Notably, the encapsulation efficiency was greater than 80%, ensuring sustained release and minimizing premature drug leakage.The drug delivery system reveals pH-dependent release features that demonstrate acidic pH (pH 5.5) achieves the highest drug release rate while reproducing conditions related to tumor environments to enhance targeted drug delivery for cancer treatment. The most successful nanoparticle formulation named NP5 exhibited superior tumor growth suppression (80%) along with great cytotoxicity due to its IC50 value of 3.0 µg/mL. Effective anticancer action emerges from the studies while the targeted delivery effects become strengthened by surface functionalization which results in NP5 achieving both optimal cellular intake and noticeable tumor concentration. The biodistribution studies revealed that NP5 delivered most of its content to tumors where it minimized distribution to non-target organs thus improving treatment results while reducing systemic adverse effects. Our results point to an upcoming clinical use of nanoparticle-based systems which offer better cancer treatment through targeted delivery and controlled drugs. The study enables scientists to make nanoparticle systems dedicated for treating various illnesses while directing drugs precisely and minimizing toxicities
Copyright (c) 2024 Moaz Israr , Saad Abdullah (Author) https://creativecommons.org/licenses/by/4.0
NANOTECHNOLOGY FOR WATER PURIFICATION: DEVELOPMENT OF SUSTAINABLE FILTRATION SYSTEMS TO REMOVE HEAVY METALS AND MICROPLASTICS
The increasing contamination of water sources by heavy metals and microplastics has become a significant environmental concern, necessitating the development of effective water purification technologies. This study investigates the use of nanomaterials, particularly hybrid composites, for the removal of heavy metals (lead, arsenic, cadmium) and microplastics (polystyrene, polyethylene) from contaminated water. Various nanomaterials, including carbon nanotubes, graphene oxide, titanium dioxide, zinc oxide, and a hybrid composite, were synthesized and evaluated for their removal efficiencies. Experimental results demonstrate that hybrid composites outperform single nanomaterials through their superior performance which achieves a 96% removal rate for admixed heavy metal and microplastic contamination during water purification. The hybrid composite achieved 95% removal of polystyrene and 94% removal of polyethylene together with 95% removal of lead and 90% removal of arsenic and 92% removal of cadmium. The nanomaterials used in the hybrid composite exhibited excellent 95% regeneration efficiency for multiple filtering cycles. Data showed that the hybrid composite exhibited 280 m²/g surface area and 0.55 cm³/g pore volume through physical property analysis which contributed to its improved adsorption capacity. Testing under environmental impact evaluation showed the hybrid composite exhibits the lowest leaching rate at 0.5% together with minimal toxicity to aquatic life which supports its potential application as sustainable material. The study demonstrates the effectiveness of hybrid nanomaterials as solutions for dual heavy metal and microplastic water pollution thereby creating possibilities for large-scale water purification systems. Further study must conduct to analyze the long-term environmental effects and scalability of these materials when put into practical usage.
Copyright (c) 2024 Fahad Ali , Abdul Jabbar (Author) https://creativecommons.org/licenses/by/4.0
ADVANCING IMAGE PROCESSING SYSTEMS THROUGH EMBEDDED FPGA TECHNOLOGY: IMPROVING PERFORMANCE, EFFICIENCY, AND REAL-TIME APPLICATIONS
Digital image processing (DIP) continues to expand as it enables multiple applications which include security monitoring through cameras and diagnosis in healthcare. The need for high-speed real-time intelligent systems has become essential because massive data collected from medical equipment and satellites requires analysis before running complex DIP algorithms. The deployment of image processing algorithms to hardware systems has proven to be the best method for improving image processing system speed in recent times. This article evaluates the beneficial characteristics of FPGAs for implementing DIP applications against software programming and DSP technology platforms. Our research investigates preceding FPGA work and presents current approaches for simplifying FPGA application development to application programmers. Due to their outstanding features such as low price and increased computational density and optimization capabilities FPGAs have become the preferred choice for contemporary image processing professionals. It has been concluded that Annual FPGA revenue surpasses billions while their performance exceeds or matches the projections of Moore's Law. Researchers in this sector find FPGAs highly attractive based on this research which proves their excellent performance capabilities. FPGAs demonstrate outstanding capabilities in multiple video and image processing applications that include broadcast infrastructure together with medical imaging and high-definition video conferencing and video surveillance and military imaging. On-chip integration of FPGAs with the main processor represents the best possible future direction because it removes communication limitations from using a coprocessor and provides general-purpose acceleration benefits. Programs written directly in VHDL remain tough to develop but offer enhanced efficiency while compilers continue to develop which expects to bridge this performance gap. The implementation effectiveness of FPGAs requires that their current limitations should be compensated to maximize their efficiency and usefulness
Copyright (c) 2024 Salma Ali, Olumhense Benedict Adoghe (Author) https://creativecommons.org/licenses/by/4.0
OPTIMIZATION OF WAREHOUSE OPERATIONS THROUGH THE INTEGRATION OF ROBOTICS AND ARTIFICIAL INTELLIGENCE: ENHANCING EFFICIENCY, ACCURACY AND AUTOMATION IN MODERN SUPPLY CHAINS
Organizations need to adapt consistently changing environmental factors for providing service to current market clients throughout all industries. The logistics sector falls under this application. Companies operating international warehouses need to provide fast order fulfillment for products moved throughout every part of the world. This research seeks to explore process optimization methods that fulfill the modern market requirements. The goal is to maximize operations with artificial intelligence and automated process optimization. The presentation includes illustrations of operational problems within a benchmark firm that could benefit from combining artificial intelligence (AI) with automated systems. This article reveals the discovery of an automated warehouse management system which improves picking efficiency. Integrating automation for storage and retrieval into the system effectively reduces waste and raises accuracy for picking and transportation processes and storage operations. The report lists down the beneficial aspects of artificial intelligence. The process of inventory planning becomes more precise while route planning at the picking stage experiences improved efficiency. The study establishes theoretical evidence to support warehouse managers making automation and AI part of their operations. The reference company will benefit from insights into its operations because the research findings supply primary advantages and remedies which match up against its current processes and demand further AI implementation
Copyright (c) 2024 Ramzan Umer, Muhammad Danial Ahmad Qureshi (Author) https://creativecommons.org/licenses/by/4.0
DESIGN AND OPTIMIZATION OF HIGH-FREQUENCY POWER CONVERTERS FOR ENHANCED EFFICIENCY AND PERFORMANCE IN ELECTRIC VEHICLE APPLICATIONS
The paper reviews high-frequency converter innovations for electric vehicles and renewable power systems with a focus on sustainability and energy system improvement. Using the PRISMA 2020 methodology, 73 excellent research spanning 2014 to 2024 was evaluated for new converter models and advanced materials and control methods and future trends. The research field produced vital findings showing how converter topologies advanced with the invention of LLC resonant designs and dual active bridges enabling soft switching and enhanced scalability and efficiency. Silicon carbide and gallium nitride function as wide-bandgap semiconductors which enable developments in power density while improving compactness along with thermal control capabilities. Efficient operation and stability enhancement in microgrids together with vehicle-to-grid systems result from implementing adaptive and AI-based control techniques. Power conversion along with system reliability improvements become possible through converter applications in wind and solar power systems. The upcoming opportunities align hybrid and multipurpose systems by connecting smart control techniques featuring artificial intelligence platforms with digital twin systems to renewable energy and electric vehicles storage units. These innovative developments establish high-frequency converters as revolutionary energy solutions to address global power issues while advancing environmentally sustainable power generation and locomotion systems. This paper includes essential details about present research along with prospective paths for science advancement. Advanced control methods such as sliding mode control and predictive control together with AI-driven adaptive strategies remain essential for this research because they guarantee stable operation of intricate networks. These approaches deliver three substantial benefits for renewable microgrids including dynamic load control and harmonic reduction with maximum power flow enhancement for V2G systems. The technology behind digital twins positions itself as a major growth opportunity because it delivers live system tracking alongside predictive equipment care and works to spot issues early
Copyright (c) 2024 Areeba Shabbir, Zeeshan Khan (Author) https://creativecommons.org/licenses/by/4.0
OPTIMIZING BIOFUEL PRODUCTION PROCESSES USING ALGAE AND AGRICULTURAL WASTE: INNOVATIVE TECHNIQUES, SUSTAINABILITY IMPACTS, AND PROCESS EFFICIENCY
Production of biofuel serves as an effective pathway toward environmental sustainability because it has established itself as one of the main renewable energy alternatives. The research analyzes present-day biofuel creation methods while assessing their potential as fossil fuel replacement options. This report focuses on technological developments in biofuel manufacturing while examining all available raw material options from plant sources through algae and agricultural waste. The study evaluates the superior green qualities of biofuels and their strong potential to reduce emissions and contrasts them with fossil fuels. Biofuels contribute to energy security by decreasing the dependency on scarce fossil fuels resulting in reduced price instabilities as well as geopolitical disturbances. The assessment describes economic benefits of biofuel production which include employment growth and added income for people who grow biofuel plants. The analysis considers biofuel development obstacles together with the main influencing factors alongside its potential capabilities. Researchers scrutinize existing problems to determine resource allocation availability and disputes regarding land territory along with sustainability effects. Biofuel sustainability in the long term requires careful implementation through proper resource stewardship along with strategic land use strategies plus adherence to sustainable guidelines. The paper dedicates special attention to research and development work to improve both feedstock cultivation methods and biofuel conversion methods and end product efficiency. The success of biofuel production depends on technological evolution which will decrease operating expenses and boost the quantity of biofuel obtained from each batch. The document presents a distinctive review regarding biofuel manufacturing operations which covers selection through applications. Progress in new technologies allows researchers to investigate sustainability indicators and financial viability of various biofuel paths through complete analysis. This paper delivers an extensive model for renewable energy research guidance that evaluates biofuel development relationships between technological innovation and environmental effects and socioeconomic factors from an integrated perspective beyond past studies.
Copyright (c) 2024 Muhammad Amir Rafique, Hammad Ur Rehman (Author) https://creativecommons.org/licenses/by/4.0
ADVANCED SMART GRID TECHNOLOGIES FOR SEAMLESS INTEGRATION OF DISTRIBUTED ENERGY RESOURCES IN URBAN ENVIRONMENTS: CHALLENGES, OPPORTUNITIES AND FUTURE DIRECTIONS
Sustainable energy advancements in the global market increasingly depend on urban areas due to environmental needs and growing urban population. The research analyzes the mutual advantages between smart cities and smart grids to develop resilient sustainable urban energy systems. The article uses case studies of cities to show the transformative impacts achieved through smart grids together with energy-saving initiatives on sustainability and urban strength. The upcoming analysis explains how financial and legal and technical barriers prevent urban areas from implementing broad smart energy solutions across the board. The final section recommends different strategic solutions for the identified challenges through the development of stakeholder engagement procedures and digital infrastructure investments and innovative legislative frameworks. Research shows that smart urban areas need an interdisciplinary approach to fulfill their potential because this method allows cities to take charge of worldwide energy transition objectives. It has been concluded that The world experiences increasing cooling demands because of rising global temperature trends. Current space cooling equipment capacity of 850 GW is projected to achieve approximately double growth by 2030 and will subsequently double again by 2050. The increased need for cooling creates power grid operational problems that drives up electricity requirements thus impacting consumer access levels and increasing power costs. The prediction suggests that power consumption across ASEAN territories will increase from 10% to 30% throughout the period 2040 by metropolitan regions because of cooling demands. Studies indicate that an increase in world temperature levels up power demand by more than 4% during peak usage times
Copyright (c) 2024 Abdul Jabbar, Uran Abazi (Author) https://creativecommons.org/licenses/by/4.0