1,236 research outputs found

    Optimization of Bi-Directional V2G Behavior With Active Battery Anti-Aging Scheduling

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    Evaluation of Frother Types for Improved Flotation Recovery and Selectivity

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    A laboratory study was conducted to evaluate and compare the effectiveness of nine different frother types when used in a three-phase, continuously operating froth flotation system. The frothers included several that are commonly used in the industry (e.g., MIBC, 2EH, and F-1) as well as unique frother types (e.g., F-3). The tests were conducted in a 5-cm diameter laboratory flotation column that provided near plug-flow mixing conditions due to a length-to-diameter ratio of around 50:1. Test results indicate that F-1, MIBC, and MPC (in order of decreasing effectiveness) provided the weakest performance in terms of combustible recovery while F-2, MAC, and 2EH were the top three generating the highest separation efficiencies. When processing ultrafine coal, the ash content of the flotation concentrate ranged from 10% to 15% while recovering over 80% of the combustible material. F-3, F-4, and DIBC provided over 80% recovery of combustibles at the expense in the amount of hydraulic entrainment. The flotation performances were also closely examined in accordance with the fundamental properties of the nine tested frothers, and their correlations were addressed in detail

    dMAPAR-HMM: Reforming Traffic Model for Improving Performance Bound with Stochastic Network Calculus

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    A popular branch of stochastic network calculus (SNC) utilizes moment-generating functions (MGFs) to characterize arrivals and services, which enables end-to-end performance analysis. However, existing traffic models for SNC cannot effectively represent the complicated nature of real-world network traffic such as dramatic burstiness. To conquer this challenge, we propose an adaptive spatial-temporal traffic model: dMAPAR-HMM. Specifically, we model the temporal on-off switching process as a dual Markovian arrival process (dMAP) and the arrivals during the on phases as an autoregressive hidden Markov model (AR-HMM). The dMAPAR-HMM model fits in with the MGF-SNC analysis framework, unifies various state-of-the-art arrival models, and matches real-world data more closely. We perform extensive experiments with real-world traces under different network topologies and utilization levels. Experimental results show that dMAPAR-HMM significantly outperforms prevailing models in MGF-SNC
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