生料浆配料过程的优化设定软件

在生料浆配料过程中,磨机负荷是与生产效率和能源消耗密切相关的重要指标,生料浆的钙比、碱比和水分是重要的质量指标,实现磨机负荷及生料浆各项质量指标的优化控制的关键是确定出合理的赤泥浆、碱粉、各台磨机的混矿、石灰石、碱赤泥浆流量控制回路的设定值。设计并开发了生料浆配料过程的优化设定软件,详细介绍了该软件的结构、功能、程序流程图及实现画面等。优化设定软件可以根据工况的变化自动调整各种流量控制回路的设定值,从而提高了配料过程的产品质量和生产效率,并节能降耗,取得了显著的应用效果。     

基于PSO算法的生料浆配料过程的优化控制

针对烧结法氧化铝生产流程中的生料浆配料过程的工艺特点,将PSO算法应用在配料过程中的原料分配优化问题中,并构造了惩罚函数来处理配料过程中的各种约束条件.基于PSO算法的优化控制不仅可以保证生料浆的各项工艺指标达到设定值,同时可以保证配料成本最低.在某氧化铝厂的应用结果表明,该优化控制可以取代传统的人工配料生产方式,提高了生料浆各项工艺指标的合格率,降低了配料成本,实现了优化配料,取得了显著的应用效益.     

生料浆配料过程磨机负荷的混合智能控制

针对在氧化铝生料浆配料过程中难以采用常规方法来控制磨机负荷状态的问题,提出了由负荷状态估 计模型和负荷调整模型组成的磨机负荷混合智能控制方法．负荷状态估计模型根据磨机的振动与电流信号,采用规 则推理方法估计出磨机的负荷状态．负荷调整模型采用案例推理方法自动调节磨机给料量,将负荷控制在合适范围 内．该方法成功应用于某氧化铝厂生料浆配料过程中,长期运行结果表明,提高了磨机台时产能,减少了“堵磨”故 障发生次数,提高了生产效率并节能降耗．     

氧化铝生料浆制备过程的智能优化控制方法

生料浆制备过程由配料和调槽两个子过程组成.针对该过程的工艺特点以及不确定性大、原料成分波动等过程特性,提出一种智能优化控制方法.将生料浆制备过程的优化目标分解为两个子过程的优化目标,采用模型预设定、指标在线预报、基于模糊规则的前馈和反馈补偿方法实现了配料子过程的优化控制,采用粒子群算法实现了调槽子过程的优化控制,从而最终实现了生料浆制备过程的优化控制.工业应用的结果表明了所提出方法的有效性.     

基本模糊控制器控制规则的提取

0 引言 模糊控制系统是以模糊数学、模糊语言形式的知识表示和模糊逻辑的规则推理为基础,采用计算机控制技术的数字控制系统.其组成核心是具有智能型的模糊控制器.其方框图如图1所示.     

水泥生料配料过程优化控制系统

介绍以基于生料率值控制为目的的非线性水泥生料优化配料过程控制,运用智能机理建模的方法,建立配料过程的非线性规划数学模型.针对模型中的多目标函数以及非线性约束特点,提出一种采用优化软件LINGO编制算法程序并通过应用程序接口(API)嵌入到ABB France2000的DCS系统中.工业实验表明该系统能有效地对配料过程进行优化控制,是解决非线性多目标优化问题的一种可行方法.     

神经网络的模糊规则提取

神经网络的“黑箱问题”为该技术的广泛应用带来了一定限制,由于神经网络在一定条件下可与模糊系统相互转换,从神经网络中提取模糊规则为“黑箱问题”的解决提供了有效手段。本文在阐述基本概念的同时,分析了把连续值网络转化为二值网络和从神经网络到模糊系统的转换进行模糊规则提取的两类方法,通过解决Iris问题的实验结果比较了两类方法的性能。     

从一类数据信息中产生模糊规则的有限元方法

产生模糊规划的传统方法，指出利用传统的模糊规则生成方法所得到的模糊规则及由模糊规则所得到的各种控制和预测结果值得怀疑。本文邮从一类数据信息中产生模糊规则的有限元方法，基于本方法所产生的模糊规则是由所研究对象的精度决定的。最后给出的模糊规则生成的实例表明了本方法的有效性和简捷性。     

基于克隆选择的模糊分类规则提取算法

分类是许多研究领域的关键问题，模糊规则的提取质量对分类器的性能又有着极大影响．所提取的规则不仅在分类能力上要达到最优，同时在规则数量上也不能太多，否则会影响规则搜索和匹配的速度．结合人工免疫的克隆选择原理，采用克隆选择算法，提取通过多精度模糊分割产生的大量模糊if—then规则中的少数精华规则，从而建立了模糊分类所需要的有效规则集合，同时还对优化目标函数进行了改进．经仿真实验证明，该方法所提取的模糊规则具有分类准确率高，规则数目较少等特点。     

基于不确定性信息的水泥生料配料过程智能优化系统

针对水泥生料配料过程原料质量不稳定和检测环节大滞后所引入的诸多信息不确定性,结合目前广泛应用的干法水泥生产工艺,提出了一种生料配料过程两级智能优化系统,即首先对生料配料过程进行率值的优化控制,一定程度地减弱不确定信息对生料质量的影响,然后采用改进性遗传算法求解生料最优调配方案,配制出优质水泥生料,以此达到生料质量的优化...     

The operation control and application of CFB boiler unit with high blending ratio of coal slurry

In China, a large amount of coal slurry is produced every year as the by-product of coal washing. If all these slurry were utilized, it could not only bring huge economic benefits but also solve the environmental pollution caused by coal slurry storage. As one of the most important devices to burn the coal slurry, circulating fluidized bed (CFB) boilers needs to improve the control level to consume more slurry. In this paper, the characteristics of CFB boiler with large blending ratio of coal slurry are investigated from both the operation and control aspects. The energy fluctuation caused by long-term coal slurry combustion in CFB boiler is analyzed, which would influence the operating stability of the unit and restrict the blending ratio of coal slurry. Based on the analysis and boiler energy storage theory, a novel control strategy is proposed to improve energy conversion and stabilize energy fluctuation caused by co-combustion of coal slurry. Finally, the control strategy is applied to an actual 300 MW CFB boiler unit. The long-term operation shows that the improved control system can reduce the energy fluctuations, raise the control stability and increase the coal slurry blending ratio, bringing huge economic benefits to power plant.     

Fuzzy control of a neutralization process

This paper studies the control of pH neutralization processes using fuzzy controllers. As the process to be controlled is highly nonlinear the usual PI-type fuzzy controller is not able to control these systems adequately. To solve this problem, based on prior knowledge of the process, the pH neutralization process is divided into several fuzzy regions such as high-gain, medium-gain and low-gain, with an auxiliary variable used to detect the process operation region. Then, a fuzzy logic controller can also be designed using this auxiliary variable as input, giving adequate performance in all regions. This controller has been tested in real-time on a laboratory plant. On-line results show that the designed control system operates the plant in a range of pH values, despite perturbations and variations of the plant parameters, obtaining good performance at the desired working points.     

Dynamic data mining technique for rules extraction in a process of battery charging

Battery charging controllers design and application is a growing industry direction. Fast and efficient charging of battery packs is a problem which is difficult and often expensive to solve using conventional techniques. The majority of existing works on intelligent charging systems are based on expert knowledge and heuristics. Not all features of the desired charging behavior can be attained by the hard-wired logic implemented by expert generated rules. Because the battery charging is a highly dynamic process and the chemical technology a battery uses varies significantly for different battery types, data mining technique can be of real importance for extracting the charging rules from the large databases, especially when the charging logic is to be continuously changed during the life of the battery dependent on the type and characteristics of the battery and utilization conditions. In this paper we use soft computing-based data mining technique for extraction of control rules for effective and fast battery charging process. The obtained rules were used for NiCd battery charging. The comparative performance evaluation was done among the existing charging control methods and the proposed system, which demonstrated a significant increase of performance (minimum charging time and minimum overheating) using the soft computing-based approach.     

A versatile software tool making best use of sparse data for closed loop process control

This paper presents the design of a software supported sliding mode controller for a biochemical process. The state of the process is characterized by cell mass and nutrient amount. The controller is designed for tracking of a desired profile in cell mass and it is shown that the nutrient amount in the controlled bioreactor evolves bounded. A smart software tool named Support Vector Machine (SVM), which minimizes the upper bound of an empirical risk function, is proposed to approximate the nonlinear function seen in the control law by using very limited number of numerical data. This removes the necessity of knowing the functional form of the nominal nonlinearity in the control law. It is shown that the controller is robust against noisy measurements, considerable amount of parameter variations, discontinuities in the command signal and large initial errors. The contribution of the present work is the achievement of robustness and tracking performance on a benchmarking process, under the presence of limited prior knowledge.     

基于集成学习的电力设备运行全流程数据共享方法

电力设备运行数据共享过程中存在数据安全系数过低问题,为此,本文引入集成学习技术,开展对电力设备运行全流程数据共享方法设计研究。通过构建电力设备运行全流程数据共享框架,根据集成学习提取电力设备运行全流程特征,通过半诚实模型和恶意模型实现电力设备运行全流程数据共享。通过实验证明,新的共享方法与区块链共享方法相比,能够有效扩大共享数据在电力设备运行全流程中的覆盖范围,并提高数据在共享时的安全性。     

Application of the generalized lambda distributions in a statistical process control methodology

In statistical process control (SPC) methodology, quantitative standard control charts are often based on the assumption that the observations are normally distributed. In practice, normality can fail and consequently the determination of assignable causes may result in error. After pointing out the limitations of hypothesis testing methodology commonly used for discriminating between Gaussian and non-Gaussian populations, a very flexible family of statistical distributions is presented in this paper and proposed to be introduced in SPC methodology: the generalized lambda distributions (GLD). It is shown that the control limits usually considered in SPC are accurately predicted when modelling usual statistical laws by means of these distributions. Besides, simulation results reveal that an acceptable accuracy is obtained even for a rather reduced number of initial observations (approximately a hundred). Finally, a specific user-friendly software have been used to process, using the SPC Western Electric rules, experimental data originating from an industrial production line. This example and the fact that it enables us to avoid choosing an a priori statistical law emphasize the relevance of using the GLD in SPC.     

A hybrid fuzzy-statistical clustering approach for estimating the time of changes in fixed and variable sampling control charts

Control charts are the most popular Statistical Process Control (SPC) tools used to monitor process changes. When a control chart produces an out-of-control signal, it means that the process has changed. However, control chart signals do not indicate the real time of the process changes, which is essential for identifying and removing assignable causes and ultimately improving the process. Identifying the real time of the process change is known as change-point estimation problem. Most of the traditional change-point methods are based on maximum likelihood estimators (MLE) which need strict statistical assumptions. In this paper, first, we introduce clustering as a potential tool for change-point estimation. Next, we discuss the challenges of employing clustering methods for change-point estimation. Afterwards, based on the concepts of fuzzy clustering and statistical methods, we develop a novel hybrid approach which is able to effectively estimate change-points in processes with either fixed or variable sample size. Using extensive simulation studies, we also show that the proposed approach performs considerably well in all considered conditions in comparison to powerful statistical methods and popular fuzzy clustering techniques. The proposed approach can be employed for processes with either normal or non-normal distributions. It is also applicable to both phase-I and phase-II. Finally, it can estimate the true values of both in- and out-of-control states’ parameters.