重油掺水乳化分散度的研究

70年代以来人们研究和开发了油掺水乳化燃烧的新技术 ,包括油水乳化方法和高效表面活性剂的研制[1～ 3] ,并在燃油设备上使用了这一技术。此外 ,国内外学者对重油掺水燃烧技术的燃烧机理进行了较多的研究[4 ,5] ,对乳化重油分散体系性质系统实验研究较少。工业炉窑燃用掺水乳化重油效果的好坏 ,除了与其粘度、稳定性有关外[6,7] ,与乳化重油的分散相(水 )微粒大小也有很大关系。水在油中分散得越均匀 ,分散的颗粒越微细 ,其在燃烧过程中的“微爆”效果则越佳 ,雾化效果越好 ;并且 ,分散体系中分散相颗粒粒径与比表面积的大小也决定着分散相…     

磁化后掺水乳化重油分散度的实验研究

乳化重油的分散相（水）微粒大小对重油掺水乳化燃烧技术的应用效果有很大影响,本人在过去实验的基础上,用光学反射显微镜法对磁场作用下掺水乳化重油中分散相（水）的分散度进行了测定,研究了乳化重油的分散度与磁场强度、温度以及流速之间的关系,实验结果表明：磁化后有利于乳化重油的微细化,且效果比较显著;温度的升高有利于分散相（水）的微细化,兼顾能耗和燃油的发泡、沸腾现象,以及磁化后温度的升高对分散度的影响减少,有一个合理的温度范围;磁化后分散度与流速的关系呈峰值关系,本实验中流速较佳值为8m/s;磁场强度的增大也有利于微细化,磁场强度太高或太低均不会取得太好的处理效果,在1000GS－1400GS范围内为宜。     

磁场作用下乳化重油稳定性的实验研究

燃油磁化燃烧技术是一种新的燃烧技术 ,目前以日本发展较快[1,2 ] ,其节能效果也已为实践所证实[3～ 5] 。我们知道 ,掺水乳化重油稳定性好 ,燃烧状况就稳定 ;稳定性差 ,油水在进入炉膛燃烧前就已分层 ,不但起不到“微爆”效果 ,而且严重时导致熄火。作者曾对未磁化乳化重油的粘度、稳定性和分散度进行了实验研究[6～ 8] ,丘纪华等学者[9～ 11] 对磁化技术在石油、化工、燃料等方面的作用机理及应用进行了一定的研究 ,但是重油掺水乳化后再经磁化处理后其稳定性发生怎样的变化 ,尚未见实验报道。因此 ,为了更好地发挥这一技术的节油和环保效…     

重油掺水乳化油稳定性的实验研究

提出了一种快速测定乳化油稳定性的实验方法－－离心分离法, 乳化油稳定性与掺水率、乳化剂量以及搅拌程度之间的关系。实验结果表明：掺水率越大,乳化油的稳定性越差,在掺水量低于１０％时稳定性较好;乳化剂量的增加和搅拌程度的增大均有利于稳定性提高。此外,还测定了两种不同乳化剂的亲水亲油平衡值（ＨＬＢ值）, ：ＨＰＥ比ＬＰＮ乳化 稳定性好;二者以一定比例混合形成的乳化剂其ＨＬＢ值在６．５￣８之间时稳定性较好     

Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN

The problems related to energy consumption and improvement of the network lifetime of WSN (wireless sensor network) have been considered. The base station (BS) location is the main concern in WSN. BSs are fixed, yet, they have the ability to move in some situations to collect the information from sensor nodes (SNs). Recently, introducing mobile sinks to WSNs has been proved to be an efficient way to extend the lifespan of the network. This paper proposes the assimilation of the fuzzy clustering approach and the Elephant Herding Optimization (EHO)‐Greedy algorithm for efficient routing in WSN. This work considers the separate sink nodes of a fixed sink and movable sink to decrease the utilization of energy. A fixed node is deployed randomly across the network, and the movable sink node moves to different locations across the network for collecting the data. Initially, the number of nodes is formed into the multiple clusters using the enhanced expectation maximization algorithm. After that, the cluster head (CH) selection done through a fuzzy approach by taking the account of three factors of residual energy, node centrality, and neighborhood overlap. A suitable collection of CH can extremely reduce the utilization of energy and also enhancing the lifespan. Finally, the routing protocol of the hybrid EHO‐Greedy algorithm is used for efficient data transmission. Simulation results display that the proposed technique is better to other existing approaches in regard to energy utilization and the system lifetime.