首页|Investigators from China University of Petroleum Zero in on Machine Learning (Th e Design of Hydrogen Saline Aquifer Storage Processes Using a Machine-learning A ssisted Multiobjective Optimization Protocol)
Investigators from China University of Petroleum Zero in on Machine Learning (Th e Design of Hydrogen Saline Aquifer Storage Processes Using a Machine-learning A ssisted Multiobjective Optimization Protocol)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “The global effort toward dec arbonization has intensified the drive for low- carbon fuels. Green hydrogen, ha rnessed from renewable sources such as solar, wind, and hydropower, is emerging as a clean substitute.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), CNPC Innovation Fund, China University of Petroleum, Beijing , Science and Technology Leading Talents Team Funds for the Central Universities for the Frontiers Science Center for Deep-time Digital Earth, China University of Geosciences (Beijing) (Fundamental Research Funds for the Central Universitie s).Our news journalists obtained a quote from the research from the China Universit y of Petroleum, “Challenges due to the variable needs and instable green hydroge n production highlight the necessity for secure and large - scale storage soluti ons. Among the geological formations, deep saline aquifers are noteworthy due to their abundant capacity and ease of access. Addressing technical hurdles relate d to low working gas recovery rates and excessive water production requires well - designed structures and optimized cushion gas volume. A notable contribution o f this study is the development of a multiobjective optimization (MOO) protocol using a Kalman filter - based approach for early stopping. This method maintains solution accuracy while employing the MOO protocol to design the horizontal wel lbore length and cushion gas volume in an aquifer hydrogen storage project and a ccounting for multiple techno- economic goals. Optimization outcomes indicate th at the proposed multiobjective particle swarm (MOPSO) protocol effectively ident ifies the Pareto optimal sets (POSs) in both two- and three- objective scenarios , requiring fewer iterations. Results from the two- objective optimization study , considering working gas recovery efficacy and project cost, highlight that ext ending the horizontal wellbore improves hydrogen productivity but may lead to un expected fluid extraction. The three- objective optimized hydrogen storage desig n achieves a remarkable 94.36% working gas recovery efficacy and a 59.59% reduction in water extraction.”
BeijingPeople’s Republic of ChinaAsi aCyborgsElementsEmerging TechnologiesGasesHydrogenInorganic Chemical sMachine LearningChina University of Petroleum
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