铅锌烧结过程综合透气性的集成预测模型

针对铅锌烧结过程透气性的预测具有模型不确定性和输入变量不确定性等特点,建立了综合透气性智能集成预测模型.首先建立了基于满意聚类的T-S综合透气性预测模型,针对聚类后各子模型结论参数的辨识工作计算复杂、容易陷入局部极值的问题,将混合粒子群优化算法用于这些结论参数的辨识;然后利用灰色理论建立了时间序列综合透气性预测模型;最后利用信息熵技术将2个预测模型进行集成,以获得集成预测模型.选取实际生产过程中100组合格的数据,分别用以上3种预测模型来预测相应的综合透气性,其相对误差的平均值分别为2.1%.3.2%,1.8%.实验结果表明,本文提出的集成预测方法能够有效地克服不确定性带来的影响、提高综合透气性的预测精度.     

基于烟气温度场分布的烧穿点智能集成预测方法

铅锌密闭鼓风烧结过程具有强非线性、时变和时滞等特性. 本文在分析过程热状态的基础上, 通过研究烧结机烟气温度梯度分布, 建立烟气温度分布烧穿点软测量判断模型, 结合烧穿点的动态特性, 运用智能集成建模的思想, 提出采用神经网络方法建立工艺参数预测模型, 采用灰色理论建立烟气温度分布时间序列预测模型, 通过模糊组合器综合与协调两个模型来预测烧穿点位置. 实际运行结果表明, 智能集成预测方法为铅锌烧结过程烧穿点的判断和预测提供了一种可行、有效的解决思路, 为实现过程的状态优化奠定了基础.     

铅锌烧结配料过程的智能集成建模与综合优化方法

以铅锌烧结配料过程为背景, 针对传统配料方法中存在的成本高和准确率低的问题, 提出一种智能集成建模与综合优化方法. 首先, 在建立过程神经网络模型和改进灰色系统预测模型的基础上, 利用信息论中熵值的概念, 提出一种既可保证预测精度又能满足配料计算对数据完备性要求的烧结块成分集成预测模型; 其次, 以成本最小为目标建立烧结配料优化模型, 采用基于专家推理策略和改进免疫遗传算法的定性定量综合集成方法, 实现烧结配料的优化. 仿真结果验证了该方法的有效性.     

铅锌烧结过程的集成建模方法及智能优化算法

以铅锌烧结过程的集成建模和优化控制为背景 ,首先集成主元分析、神经网络、模糊专家系统等多种方法 ,建立了铅锌烧结过程的综合工况模型 ,然后根据综合工况模型预测的结果 ,采用聚类搜索混沌遗传算法获得最优的操作参数 ,给出操作优化指导 .本文提出的集成建模方法与操作优化算法 ,很好地解决了多输入多输出复杂工业过程的建模和优化控制问题 ,在铅锌烧结过程的生产中产生了显著的效益     

铅锌烧结过程质量产量的智能集成优化控制

针对铅锌烧结过程具有大滞后、多约束的特点,建立烧结块质量产量神经网络预测模型和优化控制模型,提出一种融合聚类搜索粗优化和混沌遗传细优化的智能集成优化控制方法.首先采用模糊聚类算法进行优化样本查询,所得结果作为问题的次优解;然后采用最优保存对简单遗传混沌算法进行二次优化,求取问题的最优解;最后对智能集成方法进行实际验证,系统运行结果表明.该方法较好地实现了高产、优质的生产目标.并且具有全局收敛性和工业有效性,为解决复杂工业过程的优化控制问题提供了一种有效、实用的新思路.     

铅锌烧结过程烧穿点的集成预测模型

针对影响铅锌烧结过程烧穿点的因素具有不确定性的特点, 提出一种基于信息熵技术的烧穿点集成预测模型. 首先利用软测量技术获得烧穿点. 然后建立基于满意聚类的T-S预测模型以降低不确性因素所带来的影响,并将共轭梯度法和粒子群优化算法有机结合起来进行T-S模型中各个子模型的参数辨识, 以提高辨识精度. 接着建立基于工艺参数的神经网络预测模型. 最后考虑到信息熵技术具有信息融合和降低不确定性的能力, 利用其将以上预测模型进行集成. 实验结果表明所提出的集成预测模型具有较高的预测精度和较强的适应性.     

铅锌烧结过程透气性状态及热状态优化控制

本文针对具有非线性、强耦合、不确定、多约束特性的复杂铅锌密闭鼓风烧结过程，采用神经网络模型实现了透气性状态和烧穿点位置预测，集成惩罚函数法、基于聚类点的并行变步长网格法和最优保存简单遗传算法(OMSGA)进行状态整体优化．算法具有全局收敛性和工业有效性，解决了复杂铅锌烧结过程实时智能优化控制问题，取得了较好的工业应用效果．     

铅锌烧结过程智能集成优化控制技术

针对复杂的铅锌烧结过程。提出了基于神经网络和模糊专家规则模型的自学习模糊专家控制方法进行状态参数优化，基于神经网络模型的模糊C均值聚类搜索混沌遗传算法进行综合指标参数优化．智能集成优化控制技术具有高效性和实用性，有效地抑制了工况波动，提高了烧结矿产量和质量，取得了较好的工业控制效果．     

基于PNN和IGS的铅锌烧结块成分智能集成预测模型

针对复杂的烧结块成分预测问题, 提出一种基于过程神经网络和改进灰色系统的铅锌烧结块成分智能集成预测模型. 首先利用过程神经网络可充分表达时间序列中时间累积效应、灰色系统可弱化数据序列波动性的特点, 分别对烧结块成分进行预测, 然后从信息论的观点出发, 提出一种确定各预测模型加权系数的熵值递推算法, 通过对两个预测模型的预测结果进行加权集成, 获得更加准确的铅锌烧结块成分预测结果. 结果表明, 智能集成模型 的预测精度高于单一预测模型, 能有效地对烧结块成分进行预测, 满足了配料计算对预测精度和数据完备性的     

基于仿真的烧结生产建模与振动分析

在烧结生产中,风机的振动是一个严蕈的问题,利用仿真方法分析烧结生产线上主要振动部位,是一个重要且可行的方法.建立了烧结生产中气体流动的动力学模型,探讨了有限元分析方法在求解模型中应用.在ANSYS软件里建立了烧结主抽风系统的二维分析模型,在仿真过程中,根据料层透气性的不同,综合考虑流量、负压和温度等因素的影响,探讨烧结过程中主抽风系统的气动特性,给出了不同透气性条件下的烧结管道内的流速分布图和压力分布图.分析了烧结生产中发生喘振的原因,给出了烧结主抽风系统中最容易发生故障的区域.仿真结果表明:为保证烧结过程正常运作,料层透气性应保持在一合理范围内.研究成果可为优化烧结生产过程及提高烧结产量提供理论参考.     

Intelligent integrated optimization and control system for lead–zinc sintering process

The lead–zinc sintering process (LZSP) is an important step in imperial smelting. This paper presents an intelligent integrated optimization and control system (IIOCS) for the LZSP. The optimization and control scheme has a hierarchical configuration. First, the requirements of process control and the configuration of the IIOCS are described. Then, models for predicting quantity and quality (Q&Q) are established using correlation and mechanism analysis, and are implemented by improved back-propagation neural networks. Based on the models, an integrated algorithm combining c-means clustering, genetic, and chaos approaches is employed to optimize the operating parameters of the process. Finally, the control of the process state is carried out by a distributed control system to control the Q&Q of the product.     

Neural-network-based integrated model for predicting burn-through point in lead–zinc sintering process

This paper presents an integrated neural-network-based model for predicting the burn-through point (BTP) of a lead–zinc sintering process. This process features strong nonlinearity and time-varying parameters. First, experiments were carried out to establish a model of the gas temperature distribution (GTD) in the sintering machine; and based on the GTD model, a surface temperature model of the material (STMM) was established. Second, based on the STMM, a method of estimating the BTP that uses a soft-sensing technique was devised. In order to improve the estimation precision, a time-sequence-based model for predicting the BTP was built using grey system theory. Since the BTP is also affected by process parameters, a technological-parameter-based model for predicting the BTP was then built using a neural network. Finally, an integrated model for predicting the BTP was constructed by combining the time-sequence-based and the technological-parameter-based models using a fuzzy classifier. The result of actual runs shows that, compared to the manual control, the integrated prediction model reduced the variation in BTP by about 50%. This guarantees the improvement of the quality and quantity of the sinter.     

Application of grey system theory to phosphorite sinter process: From modeling to control

The sintering process of phosphorite ore occurs with a large amount of return caused by untimely process control. The control task of phosphorite ore sintering is to regulate the parameters of the process to obtain a high quality sinter. The parameter clearly responsible for sinter quality is the temperature in the wind box. Therefore, in order to solve the control task, it is necessary to predict the highest temperature of the charge (also known as the burn through point (BTP)). In this paper, the theory of grey systems is used as a predictive model, which makes it possible to obtain an adequate model that uses a small number of initial samples of real temperature data. Based on the grey model GMC(1,n) a new optimal model is presented, which is constructed by using optimization algorithm. Optimal model predicts the BTP, and to establish an optimal regulation, a control synthesis is carried out through an optimization of the prediction according to the “particle swarm” algorithm.     

基于自适应进化算法的烧结配料优化

针对烧结配料系统的多样性、复杂性和相关性特点,基于广义回归神经网络建立了烧结配料预测模型,提出基于自适应加速机制的多种群进化算法的烧结配料优化算法。该算法在引入自适应加速机制和弹性缩放因子的前提下,充分运用了多种群进化算法的全局搜索能力寻找最优的工艺参数组合,将神经网络和自适应进化算法有机结合,实现了烧结配料的优化,增加了混合料中有用的化学成分,从而提高了产品质量。实际计算结果验证了该优化算法的正确性。     

Design and application of generalized predictive control strategy with closed-loop identification for burn-through point in sintering process

This paper presents a generalized predictive control (GPC) strategy with closed-loop model identification for burn-through point (BTP) control in the sintering process. First, the dynamic Auto-Regressive eXogenous (ARX) model structure is defined to describe sintering process. Considering the economy and security, a closed-loop identification method is adopted to update the parameters of the model. Then, BTP predictive control model is established based on GPC algorithm to predict BTP accurately and to calculate the strand velocity. Finally, a BTP control system is established and implemented in an iron and steel plant. The running results show that the system effectively guarantees the stability of sintering process and suppresses the fluctuation of BTP.     

Modeling and advanced control method of PVC polymerization process

Based on data driven modeling theory, PVC polymerization process modeling and intelligent optimization control algorithm is studied. Firstly, a multi-T–S fuzzy neural networks soft-sensing model combining the principal component analysis (PCA) and fuzzy c-means (FCM) clustering algorithm is proposed to predict the convention rate and velocity of Vinyle Chloride Monomer (VCM). The proposed hybrid learning algorithm utilizing the harmony search (HS) and least square method is used to adjust the model premise parameters and consequent parameters. Secondly, the generalized predictive control (GPC) algorithm of polymerizer temperature based on segmental affine is proposed. According to dynamic equation of polymerizer temperature deduced by heat balance mechanism, the segmental affine model is built by temperature and convention rate of the polymerizer. Then linear matrix inequality (LMI) method is used to design the controller. Finally, simulation results and industrial application show the validity and feasibility of the proposed control strategy.     

约束多目标粒子群算法在烧结配矿优化中的应用

针对钢铁生产的烧结配料过程中铁矿石价格变化大、矿石品位波动、烧结原料信息复杂、烧结配料约束繁多等对烧结配料成本的影响,提出了一种基于区域划分的约束多目标粒子群优化算法对烧结配矿进行优化.以国内某钢铁厂的实际烧结生产配料为例,构建烧结配料系统模型;为了协调全局探索和局部搜索的关系,将自适应角度划分策略融入约束评价准则,结合区域分布提取局部最优解信息,同时引入双外部存储集机制,维护种群多样性;通过标准函数集的测试,验证了所提算法的有效性.该算法应用于配矿过程中,能够兼顾成本与全铁含量,有效降低了烧结配料的成本,对烧结铁矿石资源的综合利用及质量保证具有重要意义.