1,721,278 research outputs found
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Solar photovoltaic energy technologies
No full textPhotovoltaic (PV) systems have become an essential point of clear energy, converting sunlight into electricity using semiconductor materials, with the most common being silicon-based cells. Over the past few decades, improvements in materials, such as perovskite types of materials, organic-based photovoltaics, and multijunction cells, have resulted in substantial advancement in PV module performance, stability, and cost-effectiveness, lower manufacturing costs, and greater adaptability to different environments, positioning solar energy as a more competitive and sustainable alternative to traditional power supplies. Today, PV energy is a rapidly expanding energy source, driven by ongoing advancements in materials science, system integration, and worldwide initiatives to decrease CO2 emissions and shift to renewable energy sources. In 2023, renewable energy capacity increased by 473 GW, with solar PV accounting for 347 GW of this growth. This study provides an innovative and thorough review of the integration of PV systems, their compatibility with smart grids, performance monitoring and diagnostics, large-scale installations, and the techno-economic assessment of solar PV systems. This study highlights a significant gap in the current body of research: no single, comprehensive investigation addresses every relevant aspect of this topic. While the existing literature offers valuable insights into the various dimensions of solar PV technology, it is evident that an integrated approach is necessary. This integrated study could serve as a foundational resource for scientists, policymakers, and industry professionals, facilitating informed decision-making and advancing the field as a whole
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Artificial intelligence in chemical kinetics and reactor design
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Artificial intelligence in chemical engineering and applied sciences education
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Artificial intelligence in heat and mass transfer in chemical engineering processes
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Artificial intelligence in renewable energy technologies and sustainable transition
No full textAn innovative method for maximizing energy production, consumption, and policy development is incorporating artificial intelligence (AI) in the renewable energy industry. By 2050, it is anticipated that the world's energy consumption will have increased by 50%, making AI-driven solutions crucial for improving sustainability, dependability, and efficiency. However, intermittent renewable energy sources like wind and solar present difficulties, and AI applications in natural language processing, machine learning, and predictive analytics provide encouraging answers. This research aims to increase the accuracy of energy forecasting, maximize grid dependability, and improve resource management in renewable energy systems. Large-scale datasets were analyzed, and real-world energy scenarios were simulated using AI-based defect detection systems, advanced neural networks, and anomaly detection algorithms. The findings show a 15–25% drop in energy waste, a 20–35% increase in energy forecasting accuracy, and a 30% reduction in operating expenses. Furthermore, compared with traditional techniques, AI-based grid optimization improves fault detection capabilities by 40% and energy distribution efficiency by 25%. This chapter's uniqueness is seen in its thorough AI-driven methodology that integrates real-time analytics for optimal decision-making across several renewable energy fields. Future uses include autonomous energy trading, AI-powered smart grids, and adaptive energy storage management, all of which have the potential to hasten the world's switch to sustainable energy. These results support a more robust and effective energy ecosystem by being in line with UN Sustainable Development Goals 7 (Affordable and Clean Energy) and 13 (Climate Action)
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