一种适用于配料控制的累积量PID算法

配料是材料生产过程的重要环节，在一些连续称量配料工艺过程中，以往大多通过控制每种物料的瞬时流量来满足工艺要求，但往往误差较大，给生产带不良影响，为提高控制精度，本文提出了一种累积量控制PID算法，并在水泥生料配料控制中进行了实施，取得了理想的效果，并有很好的推广前景。     

自动配料系统在生阳极生产中的应用

本文简要介绍了生阳极车间配料系统的工艺要求、控制硬件组成和软件功能，它实现了配料过程的全自动化，并具有操作简便、维护量小、抗干扰能力强等特点。同时在配料控制系统中实现外部故障自动检测与诊断，提高设备运转率及劳动生产率，具有良好的推广应用前景。     

自动称量配料控制系统设计

目的为了实现配料称量过程的自动化控制,数据的实时监控,提高配料环节的生产效率以及称量包装精度。方法设计基于现场总线DeviceNET的自动称量配料控制系统,控制系统核心采用欧姆龙CJ1M-CPU13PLC以及电源、 I/O等模块相组合的方式,实现对称量配料过程的自动控制。通过DeviceNET现场总线对远程称量仪表数据进行采集,并将数据通过总线传送到下位机PLC中进行处理,并由PLC发出各种控制命令,管理级PC与PLC通过以太网实现通信。结果实验结果表明,所提出的控制方法能够有效提高称量配料精度,称量误差能控制在±3 g以内。结论该控制系统具有稳定性强、成本低、可编程能力强等优点,大大提高了配料称量包装精度,并实现了对数据进行实时监控。     

烧结自动化智能控制技术探讨

钢铁行业需要大量铁矿石,然而,天然富矿越来越少,高炉不得不使用贫矿,但贫矿直接人炉,不论在经济还是在操作上都是不合理的。本文主要针对烧结厂配料系统的现场工艺、控制要求和过程特点,采用先进的自动控制技术完成了烧结配料自动化控制系统的设计,达到了对配料过程变量的实时检测,工艺流程的实时监控,过程参数的实时控制。     

一种硅钙包芯线配料系统数字化改造方案

针对某包芯线机组生产过程中,易出现芯料不均和空料现象,且无法及时发现的问题,文章提出了一种配料系统数字化改造方案,通过在配料系统中加装料斗秤及自动控制和报警装置,实现均匀配料,并防止空心现象,实现高质量、高效率的包芯线生产。同时,为适应在恶劣的工作环境中进行大规模生产,降低包芯线机组在生产中产生的振动,提高抗干扰能力,保证工作环境对料斗秤高精度传感器的干扰减少,能够转换成准确的数据信号,通过PLC的集成控制来确保整个包芯线机组数字化系统的稳定性,促使包芯线的生产配料更加稳定可控。     

基于S7—300PLC的集成一体化烧结配料系统

在冶金行业生产中,配料系统是必不可少的工艺环节,包括烧结配料、竖炉配料、焦化配煤等,由于上述配料系统中普遍使用电子皮带秤、拖料秤、螺旋秤等计量设备,目前常用的结构有：一种是采用皮带秤变送器仪表＋PLC控制的结构,利用PLC和变送器仪表的通讯进行配料设定或手工在变送器仪表上设定,利用变送器仪表本身的单回路调节功能去进行流量控制,PLC完成设备之间的联锁;另一种是的硬件结构相同,只是流量控制由PLC去控制,变送器仪表只起采集流量信号的功能,PLCN时完成设备之间的联锁;本方案讨论的是取消变送器仪表的PLC集成一体化解决方案,以烧结配料为例说明。     

铁合金生产的配料自动化

铁合金生产过程中的配料称重及其过程自动控制，直接关系到产品经济技术指标的好坏，对产品质量控制，提质降耗有着不可替代的作用。我们自行设计、研制了3套自动配料控制系统以完全替代原来的38台机械配料秤，实现了配料准确可靠和过程自动控制，实现了对铁合金产品质量的有效控制。系统的配料数据自动打印为产品质量分析、成本控制提供了有力的手段，自动配料系统的正常运行大大提高了劳动生产率。1自动配料系统的设置及特点1·1系统设置整个配料系统由我们自行设计、研制、安装调试及检定（配料控制仪表及称重传感器选用浙江余姚太平洋自…     

石油磺酸盐生产过程中无机盐含量控制研究

本文阐述了石油磺酸盐生产过程中无机盐的影响因素及对石油磺酸盐产品无机盐含量的控制工艺路线。并结合生产数据对石油生产过程中切盐工艺改进进行系统阐述,同时对今后生产过程中无机盐含量的控制提出科学性建议。     

西门子称重给料机在梅钢4#烧结机的应用

烧结生产在冶金生产中起着相当重要的作用。配料是烧结的重要工艺环节之一，为了达到降低烧结矿成本，提高烧结矿产量和质量的目的，提高配料称重给料机稳定性及精度成为首要问题。文章从应用的角度，介绍了西门子称重给料机的工作原理、组成、技术特点、调试要点及在梅钢4#烧结机配料工艺中的应用。     

艾萨炉仓式配料的动态优化管理方法研究

对艾萨炉炼铜生产过程中与精矿配料有关的问题进行分析,指出运料效率和运料质量是衡量配料质量最关键的两个指标,在艾萨炉配料生产实践中提出改进两指标措施,经实施取得了较好的效果仓式配料运料效率运料质量。     

有限混合体模型上的自动模型选择:一种崭新的数据建模方式

在数据建模和分析中,有限混合体模型被广泛地使用着。然而,如何仅仅针对一组来自于某个有限混合体模型的数据选择出分量或聚类的个数则依然是一个非常困难的问题。由于分量个数是混合体模型的规模度量,其选择问题被称为有限混合体的模型选择问题。最近,针对有限混合体模型,特别是高斯混合模型,一种自动模型选择学习机制逐步发展成熟起来。这种新的机制能够在学习参数的过程中自动地完成模型选择,为数据的建模与分析提供了一种新的思路与途径。本文将对于高斯混合模型或一般有限混合体模型的自动模型选择学习算法及其典型应用进行综述与总结。首先,我们综述了基于贝叶斯阴阳机和谐学习原则的自动模型选择学习算法。然后,我们描述了另一种基于熵惩罚的自动模型选择学习算法。最后,我们给出了自动模型选择学习算法的一些典型的应用。     

冲天炉配料自动控制系统

本文介绍一种配料控制系统的构成、自动控制方法和应用特点。     

Using a Genetic Algorithm to Generate D‐optimal Designs for Mixture Experiments

We propose and develop a genetic algorithm (GA) for generating D‐optimal designs where the experimental region is an irregularly shaped polyhedral region. Our approach does not require selection of points from a user‐defined candidate set of mixtures and allows movement through a continuous region that includes highly constrained mixture regions. This approach is useful in situations where extreme vertices (EV) designs or conventional exchange algorithms fail to find a near‐optimal design. For illustration, examples with three and four components are presented with comparisons of our GA designs with those obtained using EV designs and exchange‐point algorithms over an irregularly shaped polyhedral region. The results show that the designs produced by the GA perform better than, if not as well as, the designs produced by the exchange‐point algorithms; however, the designs produced by the GA perform better than the designs produced by the EV. This suggests that GA is an alternative approach for constructing the D‐optimal designs in problems of mixture experiments when EV designs or exchange‐point algorithms are insufficient. Copyright © 2012 John Wiley & Sons, Ltd.     

Using geometric mean to compute robust mixture designs

Mixture experiments involve developing a dedicated formulation for specific applications. We propose the weighted optimality criterion using the geometric mean as the objective function for the genetic algorithms. We generate a robust mixture design using genetic algorithms (GAs) of which the region of interest is an irregularly shaped polyhedral region formed by constraints on proportions of the mixture component. When specific terms in the initial model display unimportant effects, it is assumed that they are removed. The design generation objective requires model robustness across the set of the reduced models of the design. Proposing an alternative way to tackle the problem, we find that the proposed GA designs based on G- or/and IV-efficiency are robust to model misspecification.     

Developing Blending Models for Gasoline and Other Mixtures

The construction of gasoline blending models is discussed to illustrate some of the practical problems encountered in mixture experimentation. Attention is focused on the use and modification of the simplex and extreme vertices designs in the development of blending models. The XVERT, WYNN, EXCHANGE, CONSIM, and CADEX algorithms are shown to be useful aids in constructing linear and quadratic model designs when the region of feasible blends is restricted by single-component and multiple-component constraints. The evaluation of competing models and the use of the quadratic blending model in conjunction with linear programming calculations are also discussed. The methodology is general and can be used in all types of mixture experiments and product formulation studies, Examples are included to illustrate the use of the design algorithms and models.     

Effect of additives on the pore structure of ceramics based on crystalline SiO<Subscript>2</Subscript>

We have studied a method of controlling the pore structure of ceramics based on crystalline silica by introducing different burnable additives into the starting mixture. We have developed mathematical models in the form of best fit equations, which adequately represent the relationships between the burnable additive content of the starting mixture, the porosity of the ceramic samples, and their mechanical strength.     

Column Generation Based Heuristic Algorithm for Multi-Item Scheduling

The column generation algorithm for the multi-item lot-size scheduling problem under resource constraints is examined and improved upon by augmenting simpler heuristic routines in place of the time-consuming Wagner-Whitin dynamic programming routine. The heuristic algorithms thus developed are tested by controlling problem size, setup time, demand variability, and capacity change costs in test problems. The empirical results indicate that the proposed heuristic algorithms reduce CPU time as well as the number of iterations with only a slight loss in optimality.     

混合高斯参数估计的两种EM算法比较

混合高斯模型是一种典型的非高斯概率密度模型,获得广泛应用。其参数的优效估计可以通过最大似然方法获得,但最大似然估计往往因其非线性而难以实现,故期望最大化(Expectation-Maximization,EM)迭代算法成为一种常用的替代方法。常规EM算法性能受迭代初值设置影响大,且不能对模型阶数做出估计。一种名为贪婪EM的改进算法可以克服这两个缺点,获得更为准确的模型参数估计,但其运算量一般会远大于前者。本文对这两种EM算法进行综合研究,深入挖掘两者之间的关系,并基于相同的数值仿真实例,直观地演示比较两者的性能差异。     

Controlled hidden markov models for dynamically adapting patch clamp experiment to estimate Nernst potential of single-ion channels

This paper presents novel kernel-based stochastic learning algorithms for controlling the kinetics of single-ion channels in a patch clamp experiment. The algorithms yield efficient estimates of the equilibrium (Nernst) potential of an ion channel. The equilibrium potential of an ion channel is the applied external potential difference required to maintain electrochemical equilibrium across the ion channel. The algorithm adaptively controls the exploration of the learning algorithm to achieve an optimal balance between exploration and exploitation. An important feature of the resulting algorithm is that it is guaranteed to minimize the experimental effort. We illustrate the efficiency of the algorithms for the experimentally determined current voltage curve of a bi-ionic single potassium ion channel.     

High-throughput identification of proteins and unanticipated sequence modifications using a mass-based alignment algorithm for MS/MS de novo sequencing results

With the increasing availability of de novo sequencing algorithms for interpreting high-mass accuracy tandem mass spectrometry (MS/MS) data, there is a growing need for programs that accurately identify proteins from de novo sequencing results. De novo sequences derived from tandem mass spectra of peptides often contain ambiguous regions where the exact amino acid order cannot be determined. One problem this poses for sequence alignment algorithms is the difficulty in distinguishing discrepancies due to de novo sequencing errors from actual genomic sequence variation and posttranslational modifications. We present a novel, mass-based approach to sequence alignment, implemented as a program called OpenSea, to resolve these problems. In this approach, de novo and database sequences are interpreted as masses of residues, and the masses, rather than the amino acid codes, are compared. To provide further flexibility, the masses can be aligned in groups, which can resolve many de novo sequencing errors. The performance of OpenSea was tested with three types of data: a mixture of known proteins, a mixture of unknown proteins that commonly contain sequence variations, and a mixture of posttranslationally modified known proteins. In all three cases, we demonstrate that OpenSea can identify more peptides and proteins than commonly used database-searching programs (SEQUEST and ProteinLynx) while accurately locating sequence variation sites and unanticipated posttranslational modifications in a high-throughput environment.