Asia Pacific Journal of Energy and Environment
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104 research outputs found
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Advances in Autonomous Robotics for Environmental Cleanup and Hazardous Waste Management
Full text linkThis research investigates the progress made in autonomous robots for environmental cleaning and hazardous waste management. The objective is to evaluate their efficacy, adaptability, and prospective influence on existing methods. The study used a secondary data review process to combine information from several case studies, technical breakthroughs, and upcoming robot trends. Autonomous robots improve cleaning efficiency and safety in soil cleanup, oil spill response, garbage sorting, and disaster recovery. AI, sensors, and multi-modal robots boost performance. However, sensor accuracy, navigation, and energy management issues persist. The paper emphasizes the policy implications, such as the need for uniform rules, more investment in research and development, and the significance of addressing ethical and social concerns. By focusing on these specific areas, the incorporation of autonomous robots may be enhanced, resulting in more efficient and environmentally friendly solutions for handling environmental risks and waste
Code Refactoring for Energy-Saving Distributed Systems: A Data Analytics Approach
Full text linkThis research uses data analytics and code refactoring to improve distributed system energy usage. The goal is to provide a framework for energy profiling, performance monitoring, and predictive analytics to discover inefficiencies and save energy. Secondary data analysis is used to analyze research and case studies on energy-aware refactoring and distributed computing data analytics. Energy profiling is essential for discovering inefficiencies, while algorithm improvement, intelligent job allocation, and redundancy reduction considerably cut energy use. Predictive analytics allows dynamic energy optimization, and real-time feedback loops optimize energy-saving measures. The report also notes data accuracy, computational overhead, and energy efficiency-system performance balance issues. The policy implications include industry standards, clear guidelines, and government incentives required to disseminate energy-efficient code. By promoting energy-aware refactoring, these rules might create more sustainable and cost-effective distributed systems. This study emphasizes data-driven energy efficiency in distributed systems and advances sustainable computing expertise
The Role of Artificial Intelligence in Optimizing Rubber Manufacturing Processes
Full text linkThis review article examines how Artificial Intelligence (AI) can be used to optimize rubber production processes. The main goals are to list rubber manufacturers\u27 difficulties, investigate AI applications, highlight significant discoveries, and discuss the policy ramifications for effective AI integration. Using a secondary data-based methodology, the study gathers information about AI applications unique to the rubber manufacturing business by reviewing a large body of literature from conferences, peer-reviewed journals, and industry reports. The results show that artificial intelligence (AI) technologies in rubber manufacturing facilitate improved process optimization, predictive maintenance, quality control, and adaptive process control. Artificial intelligence (AI)-powered technologies enhance compounded formulations, automate shaping procedures, forecast equipment breakdowns, and maximize resource efficiency. The policy consequences encompass resolving data privacy issues, allocating resources toward workforce training, instituting moral AI governance structures, and offering monetary incentives to encourage the deployment of AI. In summary, artificial intelligence has revolutionary prospects for rubber producers to improve productivity, excellence, and environmental friendliness. Rubber manufacturing processes can be made more innovative and continuously enhanced by embracing AI-driven solutions and strategic plans
Enhancing Energy Efficiency in Distributed Systems through Code Refactoring and Data Analytics
Full text linkThis research examines code restructuring and data analytics to improve distributed system energy efficiency. The main goal is to optimize software design and use data-driven insights to decrease energy usage without compromising performance. The secondary data-based assessment examines code refactoring methods like algorithm optimization and memory management and data analytics tools like predictive models and real-time monitoring. Key findings show that code refactoring streamlines algorithms, reduces redundant processes, and improves task distribution. At the same time, data analytics enables adaptive energy management through predictive forecasting, anomaly detection, and dynamic resource allocation. Combining these methods yields a scalable distributed energy efficiency solution. However, ongoing data processing energy costs and integration complexity persist. The report emphasizes the need for incentives for technology investments, training, and established best practices to promote energy-efficient distributed systems. These results indicate that a balanced strategy combining code optimization and powerful data analytics may maintain and improve energy efficiency in the continually changing distributed computing ecosystem
AI-Driven Robotics in Solar and Wind Energy Maintenance: A Path toward Sustainability
Full text linkAccording to this research, AI-driven robots may improve operational efficiency, save costs, and promote sustainability objectives in solar and wind energy system maintenance. The paper examines AI and robotics technology, analyzes their applications in renewable energy maintenance, and identifies difficulties and prospects for maximizing their utilization. Using secondary data, the research synthesizes significant findings and trends from peer-reviewed publications, case studies, and industry reports. Primary results show that AI-driven robots may transform maintenance processes by boosting inspection accuracy, safety, and downtime while meeting sustainability objectives via resource efficiency and waste reduction. High initial costs, technological constraints in severe settings, and regulatory complexity still prevent broad implementation. Policy implications involve focused research and development, consistent rules, and financial incentives to make these technologies more accessible to smaller operators to solve these difficulties. Governments, business leaders, and academics must work together to overcome these challenges and maximize AI-driven robots in renewable energy. This research stresses robots\u27 crucial role in expediting sustainable energy infrastructure transformation
AI-Driven Solutions for Energy Optimization and Environmental Conservation in Digital Business Environments
Full text linkThe potential of AI-driven solutions for environmental preservation and energy optimization in digital business settings is examined in this paper. The main goals were to investigate how AI technologies may support sustainability, identify major obstacles and opportunities, and evaluate the policy implications for implementation. The approach thoroughly examined the literature, including research articles and case studies, to assess AI\u27s uses in energy optimization and environmental preservation. The main conclusions show how AI technologies can revolutionize energy optimization by enabling intelligent control systems, integrating renewable energy sources, and enabling precision energy optimization. To guarantee successful implementation, constraints, including data quality problems, technological complexity, and ethical issues, need to be resolved. To encourage the ethical and responsible usage of AI-driven solutions for sustainability in digital business environments, regulators and enterprises must work together and establish clear legislative frameworks and incentives for technology adoption. This work generally advances knowledge of the potential and difficulties of utilizing AI technology for energy optimization and environmental preservation in the digital age
Integrating Blockchain and AI to Enhance Supply Chain Transparency in Energy Sectors
Full text linkThis research examines how Blockchain and AI might improve energy supply chain transparency. These technologies are discussed to solve energy supply chain inefficiencies, fraud, and transparency while encouraging sustainability and operational optimization. The paper evaluates Blockchain and AI applications in energy systems using secondary data from literature, case studies, and industry sources. In summary, Blockchain provides an immutable and decentralized ledger for transparency and data integrity, while AI improves operational efficiency via predictive analytics, demand forecasting, and asset management. These technologies provide real-time tracking, cost reduction, and renewable energy integration. Scalability, data integrity, and regulatory ambiguity remain issues for Blockchain. The paper also stresses the need for clear legislative frameworks to govern energy industry blockchain and AI deployment. Policymakers should stimulate innovation, invest in digital infrastructure, and set safe and efficient technology integration standards. The study shows that Blockchain and AI can transform energy supply chains by improving transparency, efficiency, and sustainability and solving sector issues
Optimizing Home Energy Usage: HEMS-IoT Integration with Big Data and Machine Learning
Full text linkThe goal of this project is to optimize household energy consumption by combining machine learning (ML), big data analytics, and the Internet of Things (IoT) with household Energy Management Systems (HEMS). The primary goals are to assess how well HEMS-IoT integration contributes to cost savings, environmental sustainability, and energy efficiency in residential contexts. The methodology includes a thorough analysis of current literature, real-world case studies, and experimental results to examine the advantages, restrictions, and policy implications of HEMS-IoT integration. Among the key findings are personalized energy management, cost savings, increased energy efficiency, and home behavioral changes. Policy implications emphasize how crucial it is to address issues with fairness, data privacy, accessibility, and interoperability through proactive regulatory frameworks and policy interventions. The study highlights how HEMS-IoT integration can revolutionize residential energy efficiency and move us closer to a more robust and sustainable energy ecosystem
Power Electronics Innovations: Improving Efficiency and Sustainability in Energy Systems
Full text linkThis study examines how power electronics advancements might alter energy system efficiency and sustainability. The main goals were to study wide bandgap (WBG) semiconductor materials, control algorithms, renewable energy integration, and future trends and problems. Synthesizing current knowledge and trends from peer-reviewed literature, conference papers, and industry reports was done using secondary data. Significant discoveries show that WBG semiconductors like SiC and GaN have superior electrical characteristics, improving power electronic device efficiency and reliability. Model predictive control (MPC) and AI-based control algorithms optimize system performance and handle renewable energy source variability. Modern inverters and converters help integrate renewable energy into the grid, improving energy efficiency and the environment. Policy implications underline the need for supporting regulatory frameworks, research funding, and industry collaboration to reduce cost barriers, ensure interoperability, and optimize power electronics breakthroughs in global energy transitions
Bioprocess Automation with Robotics: Streamlining Microbiology for Biotech Industry
Full text linkOne key tactic for optimizing microbiology in the biotech sector is the combination of robotics and bioprocess automation. This research aims to improve scalability, accuracy, and efficiency in microbial bioprocessing by investigating the effects of automated technologies. The study uses a secondary data-based review methodology to look at present trends, technological developments, and prospects in bioprocess automation with robotics. Important discoveries demonstrate notable scalability, accuracy, and efficiency gains fueled by higher throughput and sophisticated AI algorithms. However, obstacles to widespread adoption include expensive initial investment costs and the requirement for specialized knowledge. The policy implications emphasize the significance of focused investments, incentives, and teamwork in removing obstacles and realizing the full potential of robotics-assisted bioprocess automation in the biotech sector, spurring innovation and advancing sustainability