Simplifying the powder metallurgy manufacturing process using soft computing tools

The tools of soft computing will aid the knowledge mining in predicting and classifying the properties of various parameters while designing the composite preforms in the manufacturing of Powder Metallurgy (P/M) Lab. In this paper, an integrated PRNET (PCA-Radial basis functional neural NET) model is proposed in different versions to select the relevant parameters for preparing composite preforms and to predict the deformation and strain hardening properties of Al–Fe composites. It reveals that the predictability of this model has been increased by 67.89% relatively from the conventional models. A new PR-filter is proposed by slightly modifying the conventional filters of RBFNN, which improves the power of PRNET even though raw data are highly non-linear, interrelated and noisy. Moreover, fixing the range of input parameters for classifying the properties of composite preforms can be automated by the Fuzzy logic. These types of models will avoid expensive experimentation and risky environment while preparing sintered composite preforms. Thus the manufacturing process of composites in P/M Lab will be simplified with minimum energy by the support of these soft-computing tools.     

Bayesian network construction from event log for lateness analysis in port logistics

The handling of containers in port logistics consists of several activities, such as discharging, loading, gate-in and gate-out, among others. These activities are carried out using various equipment including quay cranes, yard cranes, trucks, and other related machinery. The high inter-dependency among activities and equipment on various factors often puts successive activities off schedule in real-time, leading to undesirable activity down time and the delay of activities. A late container process, in other words, can negatively affect the scheduling of the following ones. The purpose of the study is to analyze the lateness probability using a Bayesian network by considering various factors in container handling. We propose a method to generate a Bayesian network from a process model which can be discovered from event logs in port information systems. In the network, we can infer the activities’ lateness probabilities and, sequentially, provide to port managers recommendations for improving existing activities.     

矿用可移动式救生舱的构成及安放

该文对矿用可移动式救生舱进行了分类,对比了各自的优缺点,指出国内厂家和高校均以硬体式救生舱作为主要研究对象;随后着重对此种救生舱的总体构成进行了说明,介绍了过渡舱、生活舱和设备舱的具体功能和内部设备;最后论述了几种固定和安放方式的利弊,指出在实际应用中,应根据所在巷道的地质特点合理选择安置方案。     

块体理论在坚硬特厚煤层综放开采中的应用探索

坚硬特厚煤层采用综放开采时,顶煤受到节理裂隙面的截割,将形成相互耦合的块体,当顶煤下方形成临空面时,部分顶煤块体将首先出现垮落。针对此情况,结合现场钻孔窥视运用块体理论研究方法,分析顶煤体受到的截割状况。从块体沿着单一节理面的垮落和双节理面的垮落类型出发,分析其运动关系和动力关系,得到块体滑动垮落与节理面内摩擦角,及其与滑动垮落方向的关系式,为坚硬特厚煤层顶煤垮落分析提供新的研究角度。     

里必井田承压水带压开采评估

煤层底板承压水突出是发生水害事故的原因之一,要减少该类事故的发生,需对底板突水危险性进行准确评价。为保障里必井田承压水带压开采的安全进行,基于煤层底板突水研究理论分析,采用常规突水系数法和等效厚度突水系数法,分别对里必井田内3#煤和15#煤的奥灰水突水危险性进行了研究。研究发现,奥灰水对于3#煤的开采影响不大,但在局部构造及陷落柱发育地段及采区东北部区域仍存在一定的突水可能性;奥灰水对15#煤开采具有较大威胁,大部分区域具有较大突水危险性。等效岩层厚度法计算的突水系数考虑了不同性质岩层的岩石强度和阻水能力,计算更接近生产实际,结果更可靠;且在对突水系数法进一步优化后,其评价的结果可以更精确。     

地质构造及局部大倾角条件下综放工作面安全回采关键技术研究与应用

断层等地质构造是制约矿井安全生产的重要因素,尤其是在大倾角、大落差断层等条件下,片帮、漏冒顶等将会严重影响安全生产.针对大平矿3111工作面在最大倾角达30°条件下,通过三机防滑、注浆、爆破、调斜等措施,采取"硬过"的方式在大倾角条件下工作面顺利通过了大落差断层,取得了较好的实践效果,为矿井积累了宝贵的回采经验.     

老旧煤矿健康老龄化环境建设路径分析

被关闭的低效率老旧煤矿,作为很多退休煤矿职工的家庭所在地,依然担负着为其提供正常生活条件和养老功能的任务。为了营造煤炭企业退休职工适老环境,对老旧煤矿健康老龄化环境建设路径进行了探讨。在分析健康老龄化的普遍性问题后,从公共服务、卫生健康服务、认同感、原有养老模式等方面论述了老旧煤矿存在的具体问题;针对现状及问题,从理顺养老服务管理机制和合理确定具体任务目标两方面提出了对策与建议。     

Genomic studies of milk-related traits in water buffalo (Bubalus bubalis) based on single-step genomic best linear unbiased prediction and random regression models

Genomic selection has been widely implemented in many livestock breeding programs, but it remains incipient in buffalo. Therefore, this study aimed to (1) estimate variance components incorporating genomic information in Murrah buffalo; (2) evaluate the performance of genomic prediction for milk-related traits using single- and multitrait random regression models (RRM) and the single-step genomic best linear unbiased prediction approach; and (3) estimate longitudinal SNP effects and candidate genes potentially associated with time-dependent variation in milk, fat, and protein yields, as well as somatic cell score (SCS) in multiple parities. The data used to estimate the genetic parameters consisted of a total of 323,140 test-day records. The average daily heritability estimates were moderate (0.35 ± 0.02 for milk yield, 0.22 ± 0.03 for fat yield, 0.42 ± 0.03 for protein yield, and 0.16 ± 0.03 for SCS). The highest heritability estimates, considering all traits studied, were observed between 20 and 280 d in milk (DIM). The genetic correlation estimates at different DIM among the evaluated traits ranged from ?0.10 (156 to 185 DIM for SCS) to 0.61 (36 to 65 DIM for fat yield). In general, direct selection for any of the traits evaluated is expected to result in indirect genetic gains for milk yield, fat yield, and protein yield but also increase SCS at certain lactation stages, which is undesirable. The predicted RRM coefficients were used to derive the genomic estimated breeding values (GEBV) for each time point (from 5 to 305 DIM). In general, the tuning parameters evaluated when constructing the hybrid genomic relationship matrices had a small effect on the GEBV accuracy and a greater effect on the bias estimates. The SNP solutions were back-solved from the GEBV predicted from the Legendre random regression coefficients, which were then used to estimate the longitudinal SNP effects (from 5 to 305 DIM). The daily SNP effect for 3 different lactation stages were performed considering 3 different lactation stages for each trait and parity: from 5 to 70, from 71 to 150, and from 151 to 305 DIM. Important genomic regions related to the analyzed traits and parities that explain more than 0.50% of the total additive genetic variance were selected for further analyses of candidate genes. In general, similar potential candidate genes were found between traits, but our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the traits across parities. These results contribute to a better understanding of the genetic architecture of milk production traits in dairy buffalo and reinforce the relevance of incorporating genomic information to genetically evaluate longitudinal traits in dairy buffalo. Furthermore, the candidate genes identified can be used as target genes in future functional genomics studies.     

四川矿区典型煤层地应力场瓦斯渗透性规律实验研究

在深部开采或复杂地质条件下,地应力对煤岩渗透率的影响明显,进而影响煤矿瓦斯抽采的效果。对四川矿区不同典型煤层条件下的原煤进行了实验研究,分析矿区典型地应力特征条件下煤岩渗透率的变化规律,拟合了煤岩渗透率与有效应力的函数关系模型。研究发现:煤岩的渗透率与有效应力呈规律性的反比例关系,并存在拐点。这一发现为该矿区根据地应力条件进行煤层强化增透措施选取提供了依据,该研究成果在四川矿区白皎煤矿的保护层开采应用中取得了较好的效果,瓦斯抽采效果良好。     

重复采动覆岩裂隙率空间分布相似模拟研究——以陕北矿区为例

由于水资源短缺,陕北矿区利用采空区储存矿井水进行再利用,采空区储水量与采动裂隙空间分布密切相关,为准确预测采空区储水量,基于工作面上覆岩层断裂分带划分结果,确定煤矿地下水库的主要储水空间为垮落带与断裂带的裂隙空间,建立了采动覆岩裂隙率空间分布的计算方法,采用相似模拟试验得出了柠条塔矿1^-2煤和2^-2煤重复采动后上覆岩层的下沉曲线,计算出重复采动覆岩裂隙率在垂直和水平方向的空间分布情况。结果表明:沿垂直方向,随着高度增加,覆岩裂隙率逐渐减小,1^-2煤开采后岩层裂隙率由1.87%逐渐下降为1.18%,2^-2煤开采后,裂隙率由12.85%下降为4.11%;沿水平方向,垮落带、断裂带和弯曲下沉带的裂隙率在采空区边缘附近都会产生2个峰值,并且峰值位置逐渐向采空区中央移动,1^-2煤开采后,中间压实区的最大裂隙率为1.21%,两边离层区的最大裂隙率为2.19%,2^-2煤开采后,压实区和离层区最大裂隙率分别为16.1%和6.54%;重复开采后,上覆岩层的裂隙率显著增加,有...