造纸过程的统计过程监视

根据现实过程测量数据的时序相关特点,提出了一种动志主元分析方法,通过对造纸过程模型的仿真计算,分析了过程滞后对统计过程监规中主元分析性能的影响;并给出了旨在消除这些影响的过程监视方案,对造纸过程异常检测的仿真研究．验证了该方案的有效性。     

造纸过程用能监测及在线仿真的关键技术

本文给出了造纸过程用能监测与在线仿真的关键技术,包括建立单元模型,开发功能模型库,实现在线仿真和实时计算,重点给出了在线仿真的体系结构。用集成的方法,即单元模型集成物流、能流和流分析,计算集成功能模型库,仿真集成实时/关系数据库,不仅实现了造纸复杂过程的用能批量、自动简化计算和综合分析,而且便于实现造纸过程的用能模拟和在线仿真,将离线、封闭、分散的仿真变成在线、开放、集成的仿真,为实现用于用能优化的过程模拟和更有效挖掘造纸过程的节能空间打下基础和提供方法。     

基于PC的控制系统在制浆造纸过程中的应用

基于PC的控制因具有开放性、强大的数据处理能力和网络通讯能力等因素,在过程控制中应用越来越广泛。本文分析了基于PC的控制发展现状、组成及其优缺点,并用实例说明在制浆造纸过程中的应用。     

造纸过程中水分的智能控制

针对造纸过程的复杂性、工艺特点及要求，本文对造纸过程的水分控制，提出模糊控制和专家系统结合的智能控制方法。实践证明该控制器满足了造纸过程对水分的工艺要求，且具有良好的鲁棒性。     

统计过程控制应用于水磨糯米粉微生物监控

研究将统计过程控制（SPC）技术应用于水磨糯米粉微生物监测,绘制 X-R控制图,进行测量系统的分析。研究结果表明,未实施SPC技术时,部分批次糯米粉的菌落总数处于失控状态,而实施SPC技术后,糯米粉菌落总数一直处于稳态;SPC技术对糯米粉的生产环境起到很好的监控作用,以达到控制水磨糯米粉质量、降低微生物污染的最佳生产条件,最终实现生产过程的优化。实施统计过程控制,有利于企业及时发现并有效预防和控制生产过程中产生的微生物污染,把由微生物污染引发的潜在危害减至最小,对降低食品企业的生产成本、不断提高食品的质量具有重要意义。     

造纸过程计算实例 之二

(四)收到一批浆板共4040件,发货重量为390.00吨(以风干计)。抽样84件,发现重量为13660公斤。测定水分的浆样湿重8075克,绝干重4365克。计算这批浆板短缺或盈余的百分率(风干浆水分含量以10％计)为多少? 解:测定水分浆样的风干浆含量为: (?) 每件浆板样品的风干浆量为: (?) 这批浆板实际的风干浆量为: (?) (五)收到一批浆板2700件,发货重量为543.00吨(以风干计)。为测定水分取样72件,称量结果为     

造纸过程计算实例 之三

(十三)某造纸厂每天木浆用量80吨(绝干)。造纸机成形部纤维保留率为60％,包含其余部分纤维的白水,除其中10％从网坑溢流损失外,其余循环使用。成     

造纸过程计算实例 之四

(十六)某长网造纸机的湿部浆水系统如图4。造纸机抄宽为3.5米,车速250米/分,生产66克/米~2的纸,成纸水分为10%。漏网率分别为0.9(以体积计)和0.5(以固体物计)。在良好操作条件下,进入流浆箱和离心净化器的纸料浓度分别为5克/升和9克/升。网坑白水溢流量500升/分。假设离心净化器的     

统计过程控制SPC在ERP质量管理系统中的应用

研究了统计过程控制SPC与企业ERP质量管理相结合的应用,用C＋＋Builder6．0和SQLServer2000完成系统的设计与开发,实现2者的有机结合,既给SPC分析提供了丰富的数据来源,也弥补了ERP质量管理数据分析的不足,最后给出了应用实例,实现SPC管制图的分析。     

基于统计过程控制(SPC)的挂面加工过程质量控制

和面工序是挂面生产的关键环节,充足的和面工序能力是保证挂面产品质量稳定合格的关键。为保证挂面产品质量,以某企业一条挂面生产线为例,对和面过程进行监测,通过控制图法分析和面后面团的质量波动,确定影响质量波动的因素,提出相应的控制方法和控制参数,并对控制效果进行评估。监测的20d,面团水分含量存在1个异常波动点,面团水分均匀度存在1个异常波动点且有连续10个点低于中心线;异常波动的可能原因主要包括湿面头添加较多,和面时间不足,加水量不合适以及加水速度较快。降低波动、提高工序能力的主要方法有：选择品质稳定、适合加工挂面的面粉,选择合适的和面方式和时间,限制干、湿面头的添加量以及规范人员操作。结果表明：统计过程控制(SPC)作为和面工序过程质量控制工具,面团含水量和水分均匀度的波动均明显降低,工序能力均提高到一级,加工质量显著提高。     

A novel system for on-farm fertility monitoring based on milk progesterone

Timely identification of a cow's reproduction status is essential to minimize fertility-related losses on dairy farms. This includes optimal estrus detection, pregnancy diagnosis, and the timely recognition of early embryonic death and ovarian problems. On-farm milk progesterone (P4) analysis can indicate all of these fertility events simultaneously. However, milk P4 measurements are subject to a large variability both in terms of measurement errors and absolute values between cycles. The objective of this paper is to present a newly developed methodology for detecting luteolysis preceding estrus and give an indication of its on-farm use. The innovative monitoring system presented is based on milk P4 using the principles of synergistic control. Instead of using filtering techniques and fixed thresholds, the present system employs an individually on-line updated model to describe the P4 profile, combined with a statistical process control chart to identify the cow's fertility status. The inputs for the latter are the residuals of the on-line updated model, corrected for the concentration-dependent variability that is typical for milk P4 measurements. To show its possible use, the system was validated on the P4 profiles of 38 dairy cows. The positive predictive value for luteolysis followed by estrus was 100%, meaning that the monitoring system picked up all estrous periods identified by the experts. Pregnancy or embryonic mortality was characterized by the absence or detection of luteolysis following an insemination, respectively. For 13 cows, no luteolysis was detected by the system within the 25 to 32 d after insemination, indicating pregnancy, which was confirmed later by rectal palpation. It was also shown that the system is able to cope with deviating P4 profiles having prolonged follicular or luteal phases, which may suggest the occurrence of cysts. Future research is recommended for optimizing sampling frequency, predicting the optimal insemination window, and establishing rules to detect problems based on deviating P4 patterns.     

A novel method of analyzing daily milk production and electrical conductivity to predict disease onset

This study evaluates the changes in milk production (yield; MY) and milk electrical conductivity (MEC) before and after disease diagnosis and proposes a cow health monitoring scheme based on observing individual daily MY and MEC. All reproductive and health events were recorded on occurrence, and MY and MEC were collected at each milking from January 2004 through November 2006 for 587 cows. The first 24 mo (January 2004 until December 2005) were used to investigate the effects of disease on MY and MEC, model MY and MEC of healthy animals, and develop a health monitoring scheme to detect disease based on changes in a cow's MY or MEC. The remaining 11 mo of data (January to November 2006) were used to compare the performance of the health monitoring schemes developed in this study to the disease detection system currently used on the farm. Mixed model was used to examine the effect of diseases on MY and MEC. Days in milk (DIM), DIM × DIM, and ambient temperature were entered as quantitative variables and number of calves, parity, calving difficulty, day relative to breeding, day of somatotropin treatment, and 25 health event categories were entered as categorical variables. Significant changes in MY and MEC were observed as early as 10 and 9 d before diagnosis. Greatest cumulative effect on MY over the 59-d evaluation period was estimated for miscellaneous digestive disorders (mainly diarrhea) and udder scald, at −304.42 and −304.17 kg, respectively. The greatest average daily effect was estimated for milk fever with a 10.36-kg decrease in MY and 8.3% increase in MEC. Milk yield and MEC was modeled by an autoregressive model using a subset of healthy cow records. Six different self-starting cumulative sum and Shewhart charting schemes were designed using 3 different specificities (98, 99, and 99.5%) and based on MY alone or MY and MEC. Monitoring schemes developed in this study issue alerts earlier relative to the day of diagnosis of udder, reproductive, or metabolic problems, are more sensitive, and give fewer false-positive alerts than the disease detection system currently used on the farm.     

造纸助剂和湿部化学及其进展

本文介绍了湿部化学和湿部化学助剂在造纸工业中的重要作用及其进展,以及国内外化学助剂的应用现状和前景     

造纸企业过程管控程序化的实践

从员工培训、技术开发、生产控制、客户与生产对接等几个方面的程序，举例说明程序化在过程管控中的作用，并研讨了程序化应用需要注意的事项。     

造纸生产自动控制系统的应用

叙述了造纸生产过程的电气传动和自动控制应用方面的情况及DCS集散控制系统的发展。介绍了保证造纸生产工艺和设备高效可靠运行重要手段的工业控制自动化技术。     

纸张定量横向分布控制系统的建模与预测控制

在造纸过程中，纸张定量的横向分布控制是一个复杂的工业控制系统，至今尚无满意的数学模型，用传统的控制方法难以得到好的控制效果，本文给出了定量横向分布的动态模型，采用多变量动态矩阵控制算法（ＤＭＣ）对其进行控制，并与ＰＩＤ控制的效果进行了比较。仿真结果达到了预期的控制目标。     

医用透析纸透气性能的研究

透气性能作为医用透析纸的重要指标,越来越得到使用者的重视。研究探讨了针叶木浆与阔叶木浆打浆度、浆料配比、干强剂用量、湿强剂、表面施胶剂种类及压光等造纸工艺对医用透析纸透气性能及其他物理性能的影响。