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Hongxiang Chai,Liang Li,Yinghua Wei,Jian Zhou,Wei Kang,Zhiyu Shao,Qiang He 대한환경공학회 2016 Environmental Engineering Research Vol.21 No.2
Membrane bioreactor (MBR) technology has previously been used by water industry to treat high salinity wastewater. In this study, an anoxic-oxic biofilm-membrane bioreactor (AOB-MBR) system has been developed to treat mustard tuber wastewater of 10% salinity (calculated as NaCl). To figure out the effects of operating conditions of the AOB-MBR on membrane fouling rate (KV), response surface methodology was used to evaluate the interaction effect of the three key operational parameters, namely time interval for pump (t), aeration intensity (UGr) and transmembrane pressure (TMP). The optimal condition for lowest membrane fouling rate (KV) was obtained: time interval was 4.0 min, aeration intensity was 14.6 m3/(m2￥h) and transmembrane pressure was 19.0 kPa. And under this condition, the treatment efficiency with different influent loads, i.e. 1.0, 1.9 and 3.3 kg COD m-3d-1 was researched. When the reactor influent load was less than 1.9 kg COD m-3d-1, the effluent could meet the third discharge standard of “Integrated Wastewater Discharge Standard”. This study suggests that the model fitted by response surface methodology can predict accurately membrane fouling rate within the specified design space. And it is feasible to apply the AOB-MBR in the pickled mustard tuber factory, achieving satisfying effluent quality.
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( Youming Sun ),( Hongxiang Shao ),( Xin Liu ),( Jian Zhang ),( Junfei Qiu ),( Yuhua Xu ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.11
This paper investigates the traffic offloading over unlicensed bands for two-tier multi-mode small cell networks. We formulate this problem as a Stackelberg game and apply a hierarchical learning framework to jointly maximize the utilities of both macro base station (MBS) and small base stations (SBSs). During the learning process, the MBS behaves as a leader and the SBSs are followers. A pricing mechanism is adopt by MBS and the price information is broadcasted to all SBSs by MBS firstly, then each SBS competes with other SBSs and takes its best response strategies to appropriately allocate the traffic load in licensed and unlicensed band in the sequel, taking the traffic flow payment charged by MBS into consideration. Then, we present a hierarchical Q-learning algorithm (HQL) to discover the Stackelberg equilibrium. Additionally, if some extra information can be obtained via feedback, we propose an improved hierarchical Q-learning algorithm (IHQL) to speed up the SBSs` learning process. Last but not the least, the convergence performance of the proposed two algorithms is analyzed. Numerical experiments are presented to validate the proposed schemes and show the effectiveness.
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