摘要
由一名新闻记者兼机器人与机器学习的新闻编辑每日新闻-调查人员发布了关于马学习的新报告。根据NewsRx记者从中华人民共和国北京发回的消息，研究表明，“全球去碳化的努力加强了低碳燃料的动力。从太阳能、风能和水电等可再生资源中提取的绿色氢正在成为一种清洁的替代品。”本研究的资金支持单位包括国家自然科学基金(NSFC)、中国石油天然气集团公司创新基金、北京中国石油大学、中央大学深时数字地球前沿科学中心科技骨干人才队伍基金、中国地质大学(北京)(中央大学基础研究基金)新闻记者从中国石油大学的研究中获得一句话:“需求变化和绿色水文生产不稳定带来的挑战突出了安全和大规模储存方案的必要性。在地质构造中,有一个地质构造是安全和大规模储存方案。”深部含水层因其丰富的容量和容易进入而受到关注。解决与低工作气回收率和过量产水有关的技术障碍需要精心设计的结构和优化的垫层气体积。本研究的一个显著贡献是开发了一个多目标优化(MOO)协议，该协议使用基于卡尔曼滤波的方法进行早期停止。该方法保持了解的精度。采用MOO协议对某含水层贮氢工程的水平钻孔长度和缓冲气体量进行了设计，并对多个技术经济目标进行了计算，优化结果表明，所提出的多目标粒子群(MOPSO)协议在两目标和三目标情况下都能有效识别Pareto最优集(POSs)，所需迭代次数较少。综合考虑工作气回收效率和工程造价，强调水平井延伸提高了氢气产能，但可能导致不期望的流体抽采，三目标优化储氢设计取得了显著的工作气回收效率94.36%，水抽采率降低59.59%。
Abstract
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.”
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