Asia Pacific Journal of Energy and Environment
Not a member yet
104 research outputs found
Sort by
Innovative Additives for Rubber: Improving Performance and Reducing Carbon Footprint
Full text linkThis study investigates how to improve performance and lessen environmental effects by integrating novel compounds into rubber. The principal aims of this study are to evaluate the potential of bio-based additives, sophisticated nanomaterials, and intelligent features to enhance rubber\u27s mechanical, thermal, and chemical properties while reducing carbon emissions. The research assesses recent developments and potential future directions through an extensive secondary data analysis. Important discoveries show that carbon nanotubes and graphene considerably improve durability and tensile strength, while bio-based additives lessen reliance on fossil fuels. The automotive sector benefits significantly from these additives\u27 lightweight and increased energy efficiency. The report also emphasizes the necessity of sustainable end-of-life management and enhanced recyclability. The analysis highlights the significance of policy interventions despite the high costs and scaling issues associated with these materials. To encourage the use of sustainable additives, governments must fund R&D, set precise guidelines, and promote recycling. The rubber sector may make great strides and contribute to industrial performance and environmental sustainability by addressing these constraints through supporting legislation. This study highlights how cutting-edge additives can revolutionize rubber technology in the future
Anatomical, Histochemical, and Physiological Analysis of Abutilon Indicum (L.) Sweet, Cassia Auriculata L. and Morinda Tinctoria Roxb. Collected from Polluted and Non-Polluted Habitat
Full text linkAir pollution has become a major environmental problem facing the world today due rapid increase in industrialization & anthropogenic activity. Vast plant species are facing threats due to specific single pollutants or mixtures of pollutants. The present study analyzed the anatomical, histochemical and physiological parameters in polluted and non-polluted environmental plants such as Abutilon indica, Cassia auriculata, and Morinda tinctoria. The results of anatomical research in polluted plants revealed an increase in the layers of epidermis, hypodermis, cortex, and endodermis compared to non-polluted plants. The total chlorophyll content (sample 1-6) of the leaf in polluted plants was found to be lower (0.414±0.0) when compared to the non-polluted plant. The relative water content was high (0.823±0.0) in non-polluted plants. The highest PH value was recorded in Cassia auriculata (7.2± 0.20) growing in non-polluted habitat, and the lowest PH values were observed in polluted area plants in the range of 5-7
Data Analytics for Enhanced Business Intelligence in Energy-Saving Distributed Systems
Full text linkThis research examines how data analytics might improve Business Intelligence (BI) in energy-saving distributed systems to improve energy management and sustainability. Secondary data-based reviews synthesize literature on data analytics frameworks, data processing methods, and BI tactics in distributed energy scenarios. According to critical results, descriptive, diagnostic, predictive, and prescriptive analytics turn raw data into energy-efficient insights. Descriptive and diagnostic analytics highlight historical trends and inefficiencies, whereas predictive and prescriptive methods maximize resource allocation and real-time decision-making. Adaptive energy management requires robust BI frameworks with centralized data warehousing, visualization, and real-time analytics. However, enormous data volume, real-time processing limits, data security, and lack of standards limit these analytics\u27 usefulness. Policy guidelines should include cybersecurity safeguards, AI and edge computing integration incentives, and standardized protocols to improve data processing and system interoperability. These findings demonstrate the importance of data-driven BI in improving energy efficiency and sustainability in distributed energy systems and meeting global energy targets
Harnessing AI and IoT Technologies for Sustainable Business Operations in the Energy Sector
Full text linkThe potential for improving sustainable business operations in the energy industry through the combination of artificial intelligence (AI) and Internet of Things (IoT) technology is considerable. This research investigates the potential benefits, obstacles, and policy ramifications of utilizing AI and IoT technology for sustainable commercial activities within the energy industry. A thorough analysis of current literature, including government publications, industry reports, and peer-reviewed journal papers, is part of the methodology used. Important discoveries demonstrate how AI and IoT technology can revolutionize resource efficiency, improve grid stability, encourage the integration of renewable energy sources, and lessen environmental effects. To guarantee successful acceptance and deployment, however, obstacles must be addressed, including worries about data privacy and security, unpredictability in regulations, interoperability problems, and the need for workforce development, Clear regulatory frameworks, workforce development programs, interoperability standards, and cybersecurity measures are among the policy implications that must be addressed to enable the appropriate and successful integration of AI and IoT technologies in the energy sector. In summary, this research highlights the significance of deliberate investments, cooperation, and legislative measures when utilizing AI and IoT technology to propel sustainable business practices within the energy industry
Heat Waves in Bangladesh: Understanding the Threats and Finding Solutions
Full text linkBangladesh has seen a significant rise in the number and strength of heat waves in recent years. These heat waves are hazardous for health, farming, and the economy. This paper investigates why these heat waves are happening, how they affect different areas, and what can be done to lessen their harmful effects. By carefully studying weather data, climate models, and economic and social factors, this study aims to give valuable ideas on how to adapt and reduce the impact of heat waves. The results show that it’s essential to work together to deal with heat waves\u27 many effects and help communities become stronger. With the right actions and policies, we can lower the risks of extreme heat, protect people’s health, and ensure the economy and environment stay strong
Influence of Internal Energies on Optical Properties of Methyl Ammonium Lead Triiodide Thin Layers
Full text linkIn this study, various forms of energies affecting optoelectronic properties of CH3NH3PbI3 thin films are presented and explained experimentally and using theoretical models. Different concentrations of CH3NH3PbI3 solution were prepared, and thin films were deposited using spin-coating at a speed of 1000 rpm for 90 seconds and annealed at 100o C for about 60 minutes. Optical measurements were obtained, and the films were analyzed. The results showed that some properties, like absorption coefficients, ranged between 4.68073 - 22.19402×102 cm-1, dielectric constant between 4.10497 - 4.96329, and band gap between 1.6121 – 2.1642 eV. Various energies were determined, including transition energies, obtained as 1.742 eV, VE losses as 1.732 eV, average band gap at 1.723 eV, and SE losses at 1.714 eV. These values of internal energy had a significant direct influence on the optoelectronic properties of CH3NH3PbI3 and thus concluded that they could be used to provide initial helpful information in designing and modeling hybrid perovskite optical devices
Driving the Shift to Sustainable Industry 5.0 with Green Manufacturing Innovations
Full text linkWith an emphasis on the factors driving the shift towards sustainability in industrial sectors—such as drivers, obstacles, impacts, constraints, and policy implications—this study examines the shift towards Sustainable Industry 5.0 with Green Manufacturing Innovations. The research aims to investigate the effects of sustainable industrial development on the economy, environment, and society, identify important forces and obstacles, and evaluate the significance of the findings for policymakers and regulatory agencies. The study\u27s methodology entails a thorough analysis of the body of research on green manufacturing techniques, the transition to a sustainable industrial sector, and legislative frameworks. The main conclusions emphasize Sustainable Industry 5.0\u27s enormous economic potential, favorable environmental effects, and social ramifications. The study also points out restrictions on the generalizability and availability of data, and it emphasizes how crucial it is to fortify regulatory frameworks, fund R&D, increase stakeholder engagement, and support capacity building to propel the industrial sectors\u27 transition to sustainability. These findings\u27 policy implications are significant in propelling sustainable industrial development and expediting the shift towards Sustainable Industry 5.0
Data Analytics for Energy-Efficient Code Refactoring in Large-Scale Distributed Systems
Full text linkIt examines how data analytics improves energy efficiency in large-scale distributed systems via code reworking. The primary goal is to study how data-driven techniques maximize resource allocation, energy usage, and system performance. Secondary data-based reviews of energy-efficient data analytics case studies from Google, Facebook, AWS, and Microsoft are used in the process. Significant results show that performance profiling, real-time monitoring, predictive modeling, and energy-aware resource management reduce energy use and ensure system scalability and performance. Energy savings were realized utilizing dynamic resource allocation, job scheduling, load balancing, and predictive analytics using machine learning. Energy consumption is also reduced by managing network traffic and data storage. However, integrating contemporary analytics tools into older systems and handling their massive data sets remain substantial obstacles. The paper recommends uniform legislation to promote energy-efficient practices, incentives for sustainable computing research, and industry best practices. This work emphasizes energy efficiency in large-scale distributed systems and advances sustainable computing research
Assessment of Water Quality Index of Groundwater Resources in Iwo Local Government Area, Osun State, Southwestern Nigeria
Full text linkThis study assessed the groundwater quality of 30 selected wells and boreholes in Iwo Local Government Area, Osun State, Nigeria. Groundwater sources were randomly stratified and identified according to the 15 political wards using hand-held GPS equipment. The sources were sampled during the rainy season (October) and dry season (January) to determine water quality. The physico-chemical and microbiological parameters of the water samples such as temperature, turbidity, total suspended solids, pH, electrical conductivity, total dissolved solids, total alkalinity, total hardness, chloride, sulphate, nitrate, phosphate, magnesium, calcium, iron, zinc, lead, manganese, cadmium, chromium, and total coliform were determined using standard methods. The results showed that total hardness, calcium, cadmium, sulphate, and phosphate had mean values above the acceptable values for rainy and dry seasons; their mean values in mg/l for the rainy season were 252.933, 98.267, 0.018, 305.119, and 1.762, respectively, while their values for the dry season were 299.633, 115.831, 0.020, 285.695 and 1.705, respectively. The Water Quality Index (WQI) values showed that 30% of the selected groundwater sources were fit for consumption while 60% were poor and 10% were unfit for drinking during the rainy season. During the dry season, 50% of the groundwater sources were fit for consumption, 40% were poor, and 10% were unfit for consumption
Real-Time Scheduling for Energy Optimization: Smart Grid Integration with Renewable Energy
Full text linkThis research investigates the scheduling of tasks in real-time to optimize energy use in the context of integrating renewable energy sources into the smart grid. The primary goals are to analyze the influence of fluctuations in renewable energy on grid synchronization, evaluate the efficiency of different optimization methods, and identify significant obstacles and corresponding remedies. Secondary data studies advanced forecasting methods, energy storage systems, and optimization techniques, including Linear Programming (LP), Dynamic Programming (DP), and metaheuristics. The significant findings show that renewable energy fluctuations affect power system stability. Advanced prediction methods and energy storage are essential in reducing these impacts. Optimization approaches enhance the scheduling efficiency, but the computational complexity and practical application constraints limit their effectiveness. Challenges such as frequency regulation, voltage management, and integrating Distributed Energy Resources (DERs) need specific solutions such as dynamic voltage support and grid modernization. The policy implications include supporting advanced technologies, encouraging real-time scheduling system research, and enhancing grid infrastructure to increase resilience. These measures are essential for integrating renewable energy, ensuring a reliable smart grid, and achieving a sustainable future