基于IGA的漂白过程温度优化控制

针对工艺复杂的造纸过程漂白工段的温度控制优化问题,分析了漂白过程温度控制优化策略,提出了标准遗传算法的改进方法,采用基于改进型遗传算法(IGA)整定PID控制器的方法实现了对漂白塔温度的优化控制,并介绍了基于DCS的优化控制系统配置.仿真结果和实践证明了基于IGA的漂白过程温度控制优化方案的正确性和有效性.     

基于动态BP神经网络的漂白过程辨识研究

针对纸浆漂白过程的大惯性、大时滞、非线性,时变和多干扰的难控特点,本文提出利用动态BP神经网络来建立纸浆漂白过程的模型,仿真结果表明,该网络具有较好的准确性,能够较为真实的反应漂白过程.然后利用训练好的网络模型来模拟实际生产过程,在此基础上通过阶跃响应法来辨识对象模型的参数,得出了过程的具体数学模型,为纸浆漂白的优化控制提供了可能.     

基于西门子S7-400的造纸漂白工段PLC控制系统

介绍了漂白工段在制浆造纸工业中的重要性,针对漂白工段复杂的工艺特性,给出了基于西门子S7-400控制系统的结构及硬件配置.同时采用多模态控制算法实现对塔温的优化控制.     

制浆氯漂过程运行优化控制系统

制浆漂白过程中,漂白药品的加入量应随着运行工况的变化而不断调整,以保证产品漂白质量(白度)并节约药品用量。针对漂白过程常规DCS过程控制的不足,提出了氯漂过程的运行优化控制方法,对漂白化学药品加入量进行优化控制。通过对专家知识和生产数据的分析,集成案例推理、模糊推理、专家系统等智能技术和统计过程控制(SPC)技术,建立了氯漂前卡伯值的在线计算模型和漂后白度的预测模型,并实现了漂白化学药品加入量的优化。基于所提出方法,以WinCC为平台设计开发了氯漂过程运行优化控制系统。在某制浆厂氯漂过程工业试验表明,所提出方法和系统能够对氯气加入量进行及时有效地调整。对试验结果进行统计分析,得到优化后每分钟可以节约0.4 kg的氯气量,并且漂后白度和用药波动更小。     

改进遗传算法PID参数整定及在漂白温度控制中的应用

遗传算法（GA）是一种基于群智能的全局随机优化算法。针对简单遗传算法（SGA）收敛速度慢、易于早熟等缺点,采用改进的自适应交叉算子和自适应变异算子。结合兼顾性能指标和响应过程平衡的适配函数,以多种改进方式相结合的遗传算法对PID参数进行寻优整定。并将该控制器应用于纸浆漂白温度控制中,仿真结果表明：改进遗传算法能够明显改善收敛速度和寻优效果,当被控对象存在较大纯滞后、时间常数特性较大时,采用本方法优化PID控制器参数可获得比较满意的控制效果。     

IGA在漂白工段碱化塔温度优化控制的应用

针对工艺复杂的漂白工段碱化塔温度控制优化问题,分析了碱化塔温度优化控制策略,提出了标准遗传算法的改进方法IGA (即改进型遗传算法),有效避免了标准遗传算法寻优过早陷入局部最优解问题;采用基于改进型遗传算法PID整定寻优实现了对碱化塔温度的优化控制,并介绍了基于DCS的优化控制系统配置;仿真结果和实践证明了基于IGA的漂白过程温度优化方案具有操作方便、响应速度快和鲁棒性强等优点。     

基于混合神经网络与遗传算法方法的注塑参数优化

建立了基于混合神经网络与遗传算法方法的注塑工艺参数优化系统，用Matlab语言编制了应用程序，对神经网络的参数预测与遗传算法的优化过程进行求解。将网络预测结果与CAE模拟结果进行比较和误差分析，显示出BP网络的稳定性和可靠性；优化结果经CAE模拟和实验验证，证明是正确的，表明基于混合神经网络与遗传算法方法的注塑工艺参数优化方法是可行的。     

基于BP神经网络PID的漂白温度控制算法的研究

本文讨论了使用BP神经网络PID控制算法,并且将这种控制算法应用在漂白工段的控制当中。利用神经网络自学习能力,在线整定PID控制参数。实践证明BP神经网络PID控制器具有实现简单,适应性强,具有较高的控制精度等特点。     

基于双层复杂网络的城市交通网络协同优化方法

针对城市交通网络中旅客在公共交通出行路径选择时面临的地铁与公交双层网络在换乘衔接协同中存在的部分换乘站点之间距离过远、衔接导向不明确、局部换乘供需不平衡等问题，提出基于双层复杂网络的城市交通网络协同优化方法。首先，采用逻辑网络拓扑方法对城市交通网络进行拓扑，并基于复杂网络理论建立地铁-公交双层网络模型。然后，以换乘车站为研究对象，提出一种基于K-shell分解法和中心性权重分配的节点重要度评价方法，对大规模网络中的地铁、公交车站进行粗粒度和细粒度划分和识别，并在此基础上提出一种相互激励的双层城市交通网络协同优化方法，即在双层网络结构优化中引入复杂网络理论中对于网络拓扑中节点重要度的识别和筛选方法，通过对路径选择中高集聚效应的识别和有利节点的定位更新双层网络结构以优化现有网络的车站布局和衔接关系。最后，将提出的方法应用于成都市地铁-公交网络，优化了现有网络结构，得到了现有网络的最佳优化节点位置和优化数量，并且通过相关指标系统验证了该方法的有效性。实验结果表明，采用该方法优化32次后的网络全局效率达到最优，和平均最短路径的优化效果分别为15.89%、16.97%，旅客换乘行为提升57.44个百分点；优化方法对旅行成本在8000~12000 m的可达性影响最明显，优化效果平均达到23.44%；同时引入双层网络速度比和单位交通成本比，突出了不同运营状况下交通网络对协同优化过程的反应和敏感度的不同。     

两家iPhone修理商提供iPhone4“漂白”服务

由于种种原因,传说中的白色iPhone4还迟迟不见踪影。虽然颜色不是什么太要紧的事,不过也许有人就是喜欢白色。正应了那旬：“有需求就有市场。”为满足部分用户需求,iFixYouri和iResq两家iPhone修理商目前提供iPhone4“漂白”服务,可以把黑色的iPhone外壳换成白色。     

Neural networks in process life cycle profit modelling

Changes in operational environment of the process industry such as decreasing selling prices, increased competition between companies and new legislation, set requirements for performance and effectiveness of the industrial production lines and processes. For the basis of this study, a life cycle profit (LCP) model of a pulp process was constructed using different kind of process information including chemical consumptions and production levels of material and energy flows in unit processes. However, all the information needed in the creation of relevant LCP model was not directly provided by information systems of the plant. In this study, neural networks was used to model pulp bleaching process and fill out missing information and furthermore to create estimators for the alkaline chemical consumption. A data-based modelling approach was applied using an example, where factors affecting the sodium hydroxide consumption in the bleaching stage were solved. The results showed that raw process data can be refined into new valuable information using computational methods and moreover to improve the accuracy of life cycle profit models.     

Online prediction of pulp brightness using fuzzy logic models

The quality of thermomechanical pulp (TMP) is influenced by a large number of variables. To control the pulp and paper process, the operator has to manually choose the influencing variables, which can change significantly depending on the quality of the raw material (wood chips). Very little knowledge exists about the relationships between the quality of the pulp obtained by the TMP process and wood chip properties. The research proposed in this paper uses genetically generated knowledge bases to model these relationships while using measurements of wood chip quality, process parameter data and properties of raw material such as bleaching agents. The rule base of the knowledge bases will provide a better understanding of the relationships between the different influencing variables (input and outputs).     

Artificial neural networks for optimization of gold-bearing slime smelting

Pyrometallurgy is often used in the industrial process for treating gold-bearing slime. Slag compositions have remarkable influences on the recovery of gold and the gold content in slag. A method for determining optimum flux compounding with neural networks is studied in this paper, and the neural network model for estimating the gold contents with different slag compositions is presented. On the basis of the neural network model, an algorithm for searching the optimum flux compounding in the gold-slime smelting process is proposed, and the optimum flux compositions are obtained accordingly.     

基于AIGA-BP神经网络的粮食产量预测研究

为了提高预测粮食产量的准确度,针对BP神经网络进行粮食产量预测时易陷入局部最优的缺陷,主要借鉴免疫系统的浓度调节机制和遗传算法的全局寻优特性,用自适应免疫遗传算法（AIGA）来优化BP神经网络的权值和阈值,并给出了具体的优化过程。用优化的神经网络对粮食产量进行了仿真预测,通过仿真实验表明,与BP神经网络预测法和遗传神经网络预测法对比,优化的网络模型在粮食产量预测中取得了更精确的结果。     

PREDICTIVE MODELS OF THE SPRING-BACK IN THE BENDING PROCESS

Spring-back is one of the most sensitive features of sheet metal forming processes because the elastic recovery during unloading leads to some geometric changes in the product. Three parameters that are most influential on spring-back in the V-die bending process are sheet thickness, sheet orientation, and punch tip radius. In this research, radial basis function (RBF) neural network and back propagation (BP) neural network approaches are used to predict the spring-back using the data generated from experimental observations. The performances of the models in training and testing sets are compared with those observations. Furthermore, output obtained through regression analysis is used to compare with the two developed model outputs. The results indicate that both networks can be applied successfully for prediction of spring-back against the input parameters of the V-die bending process. Also, the BP model shows better performance for prediction of spring-back with respect to the RBF model.     

基于粗糙-模糊推理系统的化工过程建模研究

根据粗糙集方法所导出的规则构造模糊—神经网络,由规则的参数和离散化结果估计网络参数的初始值,使网络经训练能较快收敛并达到最优值。将其应用于PTA装置溶剂脱水塔精馏过程建模,所建模型的性能优于普通前馈神经网络,粗糙—模糊神经网络可以消除决策系统的冗余信息,降低模型复杂度。     

Degradation process prediction for rotational machinery based on hybrid intelligent model

Bearings are among the most critical and precise components in rotational machinery. The condition and health of bearings play an important role in the functionality and performance of rotational machinery. Since a neural network ensemble approach shows significantly improved generalization performance and outperforms those of a single neural network, one novel selective neural network ensemble model is developed for bearing degradation process prediction. An improved particle swarm optimization with simulated annealing is proposed to select the optimal subset formed by accurate and diverse networks and obtain a better ability to escape from the local optimum. An experimental setup to perform fatigue testing on ball bearings and several simulations are explored in order to validate the developed prediction model. Experimental results show that degradation process prediction based on the explored selective neural network ensemble model provides a means of enhancing the monitoring of ball bearings' condition, and the results of this model are superior in comparison with the results of a single neural network. This selective neural network ensemble model can be used as one excellent predictive maintenance tool in plants.     

A recurrent Elman network in conjunction with an electronic nose for fast prediction of optimum fermentation time of black tea

Tea industries enjoy a significant position in the socio-economic ladder for any demographics, especially in India who is the largest producer as well as consumer of the agro-product. While tea ranks only next to water in the pedigree of globally consumed beverages, the imperative fermentation stage in the processing of tea leaves is conventionally monitored through olfactory perception of tea tasters. Recent advances in the field of machine olfaction have witnessed the advent of electronic nose prototypes, which provide a scientific validation to the organoleptic estimations disseminated by the tasters. However, fermentation is a continuous process requiring constant monitoring whose successful completion relies heavily on identification of distinct aroma peaks emanated at optimum instants. Since the fermentation process is integral to the final quality, it is deemed beneficial if the optimum fermentation period can be predicted at an earlier stage. Such preemptive information can mitigate constant monitoring requirements and momentary concentration lapses. Recognizing the time series nature of the data generated during the fermentation process with an electronic nose prototype, we have implemented a recurrent Elman network to predict the optimum fermentation period for different black tea samples. The results showed that the prescribed network could predict the optimum period with confidence at the halfway of the process. The minimal error between the predicted and the actual fermentation period at the halfway point suggests that the proposed model can well be integrated with an electronic nose dedicated for monitoring the fermentation process.

     

Modelling of Pulp Flow Rate with Variable Consistency

Pulp and paper mills can be seen as big pumping plants, where mass is pumped from one step to another. The proper operation of the process in its different stages does not allow large deviations from given operation conditions, which makes it essential to monitor and control the flow rate and the consistency of the pulp. A structure is suggested for the modelling of pulp flow rate, and possibilities for using the pump-valve system as a flow meter are examined. The overall model structure consists of a Wiener model for pressure difference, a non-linear dynamic valve model, and a static mapping for flow rate. A full-scale pilot plant of pulp flow through a valve in a pipeline is used for experimentation. Linear dynamics and non-linearities due to the pump, valve and the pulp consistency are identified based on online measurements obtained from calibration tests. The results show that a simple model could be identified which, together with a valve model, can be further applied for the purposes of accurate flow control.     

一个基于偏序的定时投入关联网络作业调度算法

基于偏序的定时投入关联网络作业调度算法在一个大型作业管理系统中得到了应用，它的基本思想是对定时投入的关联网络作业找到一个最佳执行序列，以便减少互相关联的网络作业在执行时的等待时间，该算法首先将同时请求投入的多个有关联关系的网络作业按照偏序关系进行排序，形成关联作业，然后推算出网络作业的阶位值，最后产生一个最优的投入序列，从而大大提高关联作业执行时间，实际系统应用表明，此算法对作业管理系统中定时投入关联网络作业的快速执行有很强的优越性。