作为电力行业污染气体排放的源头,合理的电源结构对环境的影响至关重要。为减少污染气体的排放,将环境成本和需求侧管理(demand side management,DSM)项目引入目标函数中,提出了考虑环境成本和DSM项目的电源规划模型。此外,还将电力系...作为电力行业污染气体排放的源头,合理的电源结构对环境的影响至关重要。为减少污染气体的排放,将环境成本和需求侧管理(demand side management,DSM)项目引入目标函数中,提出了考虑环境成本和DSM项目的电源规划模型。此外,还将电力系统可靠性约束引入该规划模型中。采用离散细菌群体趋药性(discrete bacterial colony chemotaxis,DBCC)算法对所建模型进行求解,得到了不同可靠性约束条件下系统的投建成本、缺电成本以及DSM项目成本,算例结果验证了所建模型及算法的可行性。展开更多
根据二氧化碳减排规划的要求,并充分考虑目前二氧化碳捕集和封存(carbon capture and storage,CCS)技术发展的阶段性与不确定性,建立以阶段综合费用最小为目标函数的发电厂减排规划模型。引入技术成熟度因子,并考虑到发电厂运行参数未...根据二氧化碳减排规划的要求,并充分考虑目前二氧化碳捕集和封存(carbon capture and storage,CCS)技术发展的阶段性与不确定性,建立以阶段综合费用最小为目标函数的发电厂减排规划模型。引入技术成熟度因子,并考虑到发电厂运行参数未来的变化,将CCS技术的阶段性与不确定因素进行量化,依据技术进步率对碳捕集系统减排指标进行分解。采用离散细菌群体趋药性算法(discrete bacterial colony chemotaxis,DBCC)进行求解,通过对实际算例的方针分析,得到系统在不同减排场景下的碳捕集系统最优配置方案与碳捕集系统投资策略。最后通过灵敏度分析得到在不同减排场景下各因素对减排成本的影响。结果证明了所提模型以及优化算法的有效性和正确性。展开更多
热熔融流化床包衣过程中,颗粒的生长由颗粒团聚产生,颗粒团聚导致了颗粒粒径的非均一性。本研究采用群体平衡模型(Population Balance Model,PBM)对系统中各尺寸粒子建立守恒关系。为了求解PBM,对其进行离散化。颗粒的团聚核由基于气体...热熔融流化床包衣过程中,颗粒的生长由颗粒团聚产生,颗粒团聚导致了颗粒粒径的非均一性。本研究采用群体平衡模型(Population Balance Model,PBM)对系统中各尺寸粒子建立守恒关系。为了求解PBM,对其进行离散化。颗粒的团聚核由基于气体动力学理论的动能的等分(EKE)内核来描述。将EKE内核引入到离散的群体平衡(Discretized Population Balance,DPB)模型中,通过与基于质量的试验数据进行拟合,得出聚合速率常数β0=0.15×10^-3 kg m^-1/2 s^-1,为热熔融流化床包衣过程中颗粒的生长提供理论依据。展开更多
Due to the effectiveness, simple deployment and low cost, radio frequency identification （RFID） systems are used in a variety of applications to uniquely identify physical objects. The operation of RFID systems ofte...Due to the effectiveness, simple deployment and low cost, radio frequency identification （RFID） systems are used in a variety of applications to uniquely identify physical objects. The operation of RFID systems often involves a situation in which multiple readers physically located near one another may interfere with one another＇s operation. Such reader collision must be minimized to avoid the faulty or miss reads. Specifically, scheduling the colliding RFID readers to reduce the total system transaction time or response time is the challenging problem for large-scale RFID network deployment. Therefore, the aim of this work is to use a successful multi-swarm cooperative optimizer called pseo to minimize both the reader-to-reader interference and total system transaction time in RFID reader networks. The main idea of pS20 is to extend the single population PSO to the interacting multi-swarm model by constructing hierarchical interaction topology and enhanced dynamical update equations. As the RFID network scheduling model formulated in this work is a discrete problem, a binary version of PS20 algorithm is proposed. With seven discrete benchmark functions, PS20 is proved to have significantly better performance than the original PSO and a binary genetic algorithm, pS20 is then used for solving the real-world RFID network scheduling problem. Numerical results for four test cases with different scales, ranging from 30 to 200 readers, demonstrate the performance of the proposed methodology.展开更多
文摘热熔融流化床包衣过程中,颗粒的生长由颗粒团聚产生,颗粒团聚导致了颗粒粒径的非均一性。本研究采用群体平衡模型(Population Balance Model,PBM)对系统中各尺寸粒子建立守恒关系。为了求解PBM,对其进行离散化。颗粒的团聚核由基于气体动力学理论的动能的等分(EKE)内核来描述。将EKE内核引入到离散的群体平衡(Discretized Population Balance,DPB)模型中,通过与基于质量的试验数据进行拟合,得出聚合速率常数β0=0.15×10^-3 kg m^-1/2 s^-1,为热熔融流化床包衣过程中颗粒的生长提供理论依据。
基金Projects(61105067,61174164)supported by the National Natural Science Foundation of ChinaProjects(012BAF10B11,2012BAF10B06)supported by the National Key Technologies R&D Program of China+1 种基金Project(F11-264-1-08)supported by the Shenyang Science and Technology Project,ChinaProject(2011BY100383)supported by the Cooperation Project of Foshan and Chinese Academy of Sciences
文摘Due to the effectiveness, simple deployment and low cost, radio frequency identification （RFID） systems are used in a variety of applications to uniquely identify physical objects. The operation of RFID systems often involves a situation in which multiple readers physically located near one another may interfere with one another＇s operation. Such reader collision must be minimized to avoid the faulty or miss reads. Specifically, scheduling the colliding RFID readers to reduce the total system transaction time or response time is the challenging problem for large-scale RFID network deployment. Therefore, the aim of this work is to use a successful multi-swarm cooperative optimizer called pseo to minimize both the reader-to-reader interference and total system transaction time in RFID reader networks. The main idea of pS20 is to extend the single population PSO to the interacting multi-swarm model by constructing hierarchical interaction topology and enhanced dynamical update equations. As the RFID network scheduling model formulated in this work is a discrete problem, a binary version of PS20 algorithm is proposed. With seven discrete benchmark functions, PS20 is proved to have significantly better performance than the original PSO and a binary genetic algorithm, pS20 is then used for solving the real-world RFID network scheduling problem. Numerical results for four test cases with different scales, ranging from 30 to 200 readers, demonstrate the performance of the proposed methodology.
