Conference Proceedings

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Browsing Conference Proceedings by Subject "Blockchains"

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    Agricultural waste to biofuel: Transforming crop residue into next-generation aviation fuel.
    (IEEE, 2025-12-29) Maury, Shyam; Mangaiyarkarasi, V.; Madaminjonugli, Bakhriddinov Makhamadali; MuhamedAle, Hasssan; Arunkumar, E.; Ali, Guma; Rakhimov, Navruzbek; Shetty, Chinmai
    The rapid expansion of international aviation has significantly contributed to greenhouse gas emissions. The existing type of jet fuel consumes a considerable amount of crude oil, which has compounded the demand. As a result, the industry accounts for a carbon footprint that reduces the pressure on alternative energy sources with lower carbon emissions. Agricultural waste comprises straw, husks, and stalks, which denotes one such available lignocellulosic feedstock that is not exhaustively utilised to offer a solution to environmental and economic dilemmas in the aviation energy production. The given research paper proposes an integrated solution to transform agricultural waste materials into high-energy-density biofuels, which can be utilised in the aviation industry through a two-stage biochemical and thermochemical treatment, followed by subsequent fuel upgrading to produce a high-quality product. Pre-treatment options, which were attempted on a lab scale, included steam explosion, dilute acid hydrolysis, enzyme saccharification, fermentation, pyrolysis, and Fischer-Tropsch catalytic upgrading. The parameters of the process conditions were optimised to achieve a high yield and minimise energy consumption. Results were statistically analysed to ensure reproducibility, and fuel properties were compared to ASTM D7566 standards to verify that they conformed to conventional jet fuel specifications. The results show that biofuels produced from agricultural waste have an energy density similar to that of Jet A fuel, with notable reductions in carbon and particulate emissions, making them a viable option for mitigating greenhouse gas emissions caused by aviation. The techno-economic analysis also demonstrates the viability of large-scale implementation, based on the availability of feedstock, process effectiveness, and compliance with regulations. Twith regulations. The practice is also compatible with the principles of the circular economy , which emphasises the value of agricultural residues, agrarian eco nomies, and sustainable waste management. Moreover, it is possible to optimise it with AI, emulate the use of blockchains to track feedstock, and adopt the concept of hybrid biofuel electric to make the future of biofuel work more efficiently and easily tr acked. Comprehensively, the paper demonstrates that agricultural waste can be a feasible and sustainable aviation biofeedstock of the next generation, as it can help minimise carbon footprints, make biofuels economically viable, and promote the current trend of carbon neutral aircraft in the global community.
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    AIoT-driven smart agri-grid (ASAG) for sustainable precision agriculture
    (IEEE, 2025-12-29) Sundaram, N. Kalyana; Rajendran, Megala; Ehssan, Muhamed; Soy, Aakansha; Anandhi, K.; Begum, T Ummal Sariba; Ali, Guma; Dhananjaya, B
    By advising and teaching farmers on how to apply modern farm practices that embrace Artificial Intelligence (AI) and the Internet of Things (IoT), precision agriculture is revolutionising sustainable farming by optimising for usages that are as much as possible and waste as little as can be afforded. In this research, we propose an AIoT-driven Smart Agri Grid (ASAG) framework that integrates real-time nanosensor networks, an AI-operational control microclimate, an autonomous decision-support system, and secure data sharing via a blockchain using encrypted statistical data. To achieve real-time analytics, edge computing is used in the framework for real-time data analytics, predictive algorithms for dynamic irrigation & nutrient management, and federated learning for distributed AI training, which maintains privacy and scalability. In addition, the system uses AI-based waste-minimisation techniques, such as predictive harvest timing and the conversion of bio-waste into organic fertilisers, thereby reducing post-harvest losses. Experimental results show that ASAG can improve crop yield by 20 to 30%, reduce water waste by up to 50%, and reduce chemical overuse by up to 30%, with its economic and environmental benefits. The feasibility of such deployment on a large scale in precision agriculture is further confirmed by a cost-benefit analysis. The results reinforce the power of AI and IoT in transforming contemporary farming into a self-optimising, climate-resilient system. For long-term sustainability in global agriculture, quantum AI will be used to predict soil health, monitor AI-assisted carbon sequestration, and enable genomic AI for climate-resistant crops.