基于神经网络的回转窑建模及其优化控制设计

实现水泥回转窑温度稳定性控制,水泥回转窑熟料煅烧是一个涉及传质、传热和物理化学反应的复杂多变量、多扰动非线性过程。为了稳定回转窑烧成温度以提高孰料烧成质量,降低能耗,传统的控制方法,存在干扰大,稳定时间长等问题。在分析水泥回转窑工艺的基础上,采用Elman神经网络建立回转窑系统的模型,提出BP神经网络的PID控制方法,根据系统的运行状态,调节PID控制器的参数,以达到性能指标,设计了回转窑温度优化控制器,具有超调量小、动态性好、收敛速度快和控制精度高等优点。进行仿真的结果表明,回转窑烧成带温度逐渐趋于稳定,实现了对水泥回转窑的优化控制。     

基于双启发动态规划的预分解窑控制器设计

针对水泥预分解窑熟料煅烧过程多变量、多扰动、非线性,难以建立精确的数学模型,实际生产中对工人的生产经验依赖性较强等问题,提出采用误差反向传播(BP)神经网络建立烧成系统数学模型,设计预分解窑双启发动态规划(DHP)控制器。DHP评价网络输出代价函数J关于状态量的偏导数,获得最优或者次优的控制信号,然后由动作网络输出控制信号,使系统得到期望的控制轨迹。仿真结果表明控制器响应时间较快,各参量超调量均不大,有助于实际系统的稳定运行。     

Model predictive control of a rotary cement kiln

A first principles model of a cement kiln is used to control and optimize the burning of clinker in the cement production process. The model considers heat transfer between a gas and a feed state via convection and radiation. Furthermore, it contains effects such as chemical reactions, feed transport, energy losses and energy input. A model predictive controller is used to stabilize a temperature profile along the rotary kiln, guarantee good combustion conditions and maximize production. Moving horizon estimation was used for online estimation of selected model parameters and unmeasured states. Results from the pilot site are presented.     

利用工控组态软件实现动态模拟屏的控制

以日产2000t熟料预分解窑水泥厂为例,详细分析了动态模拟屏控制系统的结构设计及动态控制器线路的原理。介绍了利用工控组态软件“组态王”开发动态工艺模拟屏控制程序的基本过程。     

新型干法水泥生产控制系统中的参数优化

新型干法水泥回转窑生产技术正处于快速发展期,但是与之相配套的信息技术和控制技术却还有很多需要改进和优化的地方。本文介绍利用信息化技术对水泥熟料生产过程各种数据（DCS、生产、质量、设备、计量等）进行监控分析,在特定的生产线上运用数据挖掘技术实现的参数优化。     

水泥回转窑的计算机控制

本文针对我国水泥回转窑的现状,提出了一套可行的模糊控制方案.文中给出了辨识水 泥回转窑模糊模型以及量测数据模糊滤波的方法.经两个月的运行实践表明,该控制方案明 显地提高了水泥熟料的产量与质量.     

回转窑烧成带状态混合智能识别方法

针对氧化铝回转窑过程复杂、长期依赖人工看火操作而造成的生产过程不稳定、 产品质量一致性差、能源消耗大等问题,提出了基于烧成带火焰图像特征与关键过程数据融合的烧成带状态自动识别方法, 该方法由烧成带火焰图像的分割、特征提取、 关键过程数据的融合以及二叉树支持向量机分类器模型组成.工业实验表明, 该方法能够较准确地识别烧成带状态,为基于产品质量指标优化的窑温控制器提供决策依据.     

基于Matlab的窑温模糊控制系统的设计与仿真

回转窑煅烧过程中的窑温是影响窑产和质量的重要因素,严重时会引起塌窑,影响生产的正常进行。在分析影响窑温的几个重要因素后,提出了模糊控制方案在回转窑温度控制中的应用,利用Matlab中的模糊工具箱设计模糊控制器,方便有效地实现参数自调整模糊控制系统的设计与仿真。仿真结果表明了该方法的有效性和正确性。     

基于LS-SVM的水泥熟料煅烧过程f-CaO软测量研究

针对水泥厂熟料质量指标f-CaO含量难以在线测量的问题,提出一种基于最小二乘支持向量机(LS-SVM)的软测量建模方法。仿真实验研究表明,最小二乘支持向量机建模具有良好的学习能力和泛化性能,且对数据样本的依赖程度低,是一种有效的软测量建模方法。给出的以DCS系统和SQL数据库为基础,通过Visual C 6.0编程的软测量实现方案,符合国内水泥厂实际情况,易于实现。建立的软测量模型和给出的软仪表实现方案对于保证水泥熟料质量和实现回转窑烧成系统的优化控制具有重要意义。     

基于Bang-Bang模糊自适应PID的干燥窑温度控制

为解决产品干燥窑温度控制过程中温度参数不易及时、准确调整的问题,该文提出了一种Bang-Bang(时间最优控制)模糊自适应PID复合控制器,利用Bang-Bang控制能最大限度的提高干燥窑温度的快速性和模糊自适应PID能提高干燥窑温度控制的准确性,使系统静态、动态都有比较理想的性能。建立干燥窑温度模型,并对该模型进行Bang-Bang模糊自适应PID控制和PID控制的仿真,两者比较结果:表明:Bang-Bang模糊自适应PID的响应明显比PID控制的响应效果优越。     

基于回声状态网络的回转窑工况识别方法

回转窑烧成带图像蕴含了丰富的温度场和熟料烧结信息,是回转窑烧结工况识别的重要依据.现有的基于静态图像的识别方法易受各种噪声影响,工况识别准确率较低.而视频图像序列蕴含了更多的烧结工况,具有区分性和不变性时空特征,在反映烧结工况变化方面信息更全面,更鲁棒.鉴于循环神经网络(RNN)和回声状态网络(ESN)适合处理时间序列...     

回转窑煅烧带温度控制器的设计与仿真

针对传统的回转窑煅烧带温度PID控制系统存在温度稳定性差、无法在线调整PID参数等问题,文章提出了一种采用模糊自整定PID参数控制方式设计回转窑煅烧带温度控制器的方案,介绍了该控制器的结构、设计步骤及回转窑煅烧过程系统的建模等,并采用Matlab中的Simulink模糊工具箱对模糊自整定PID温度控制器进行了仿真。仿真结果表明,该控制方法无超调量、调节时间短,能够实现参数的在线自调整。实际应用也证明了该控制方法的优越性。     

Neuro-fuzzy MIMO nonlinear control for ceramic roller kiln

Artificial neural networks (ANNs) and neuro-fuzzy systems (NFSs) have been widely used in modeling and control of many practical industrial nonlinear processes. However, most of them have concentrated on single-output systems only. In this paper, we present a comparative study using ANNs and co-active neuro-fuzzy inference system (CANFIS) in modeling a real, complicated multi-input–multi-output (MIMO) nonlinear temperature process of roller kiln used in ceramic tile manufacturing line. Using this study, we prove that CANFIS is better suited for modeling the temperature process in control phase. After that, a neural network (NN) controller has been developed to control the above mentioned temperature process due to a feedback control diagram. The designed controller performance is tested by a Visual C++ project and the resulting numerical data shows that this controller can work accurately and reliably when the roller kiln set-point temperature set changes.     

A new control strategy for IKN-type coolers

This paper presents a new system for the optimized control of IKN-type cement coolers based on adaptive predictive methodology. The new system introduces the concepts of ’layer on grate’ and ’thickness of slope’, which allow a decoupled multivariable control of the layer of clinker over the grate, and the flow rate of the cooler fans, solving a fundamental control problem for this type of process and maximizing the heat transfer from the cooling process to the kiln. The experimental results obtained in the Gador mill of Holcim, in Almeria (Spain), are analyzed.     

水泥回转窑点火用双模控制器设计

在水泥生产中,回转窑因新建或停窑检修后,都要重新开窑点火。点火问题处理得好坏,对回转窑能否又快又好地投入正常生产关系很大。笔者在研究了点火问题的基础上,提出了一种用于点火的控制器,把常规控制和FUZZY控制结合起来,形成一种双模控制器,经试运行显示,其结果大大好于人工控制。     

自适应PID控制器实现熟料料层自动控制

本文针对传统PID控制器在冷却机应用中存在的问题,提出了一种自适应PID控制器实现冷却机熟料料层的自动控制。在给出冷却机高温熟料料层厚度的测量方法之后,提出了模糊控制策略和PID控制器参数整定表。     

水泥篦冷机出口熟料温度自适应辨识模型

为了优化控制系统,建立篦冷机温度熟料出口的识别模型,利用篦冷机内熟料换热机理,找出熟料冷却过程的关键影响因素;利用回声状态网络辨识篦冷机运行数据,基于递归最小二乘法推导网络的在线学习算法,实现权值自适应调整,从而建立了篦冷机出口熟料温度的自适应辨识模型.仿真实验可知,在系统发生变化时构建的模型能够自适应调整网络输出权值矩阵,使模型快速收敛.与其他离线方法相比,提出的熟料出口温度的自适应模型更加持久有效,可以作为辨识模型指导篦冷机的控制.     

立窑水泥生料质量控制装置

本文介绍了一种立窑水泥生料质量控制装置,本装置以ＩＰＣ总线工业控制机作为控制核心,由我元素分析仪在线检测ＣａＯ、Ｆｅ２Ｏ３含量,采用预测自校正和ＰＩＤ挖掘相结合的控制算法,并能与上位机进行通讯。实际运行结果表明本装置是目前技术上较为 较为合理的一种,可以明显地提高水泥的产量和质量、具有广阔的推广应用前景。     

Detection of model-plant mismatch in closed-loop control system

The performance of model-based control systems depends a lot on the process model quality, hence the process model-plant mismatch is an important factor degrading the control performance. In this paper, a new methodology based on a process model evaluation index is proposed for detecting process model mismatch in closed-loop control systems. The proposed index is the ratio between the variance of the disturbance innovation and that of the model quality variable. The disturbance innovations are estimated from the routine operation data by an orthogonal projection method. The model quality variable can be obtained using the closed-loop data and the disturbance model estimated by adaptive Least absolute shrinkage and selection operator (Lasso) method. When the order of the disturbance model is less than 2 or the process time delay is large enough, no external perturbations are required. Besides, the proposed index is independent of the controller tuning and insensitive to the changes in disturbance model, which indicates that the proposed method can isolate the process model-plant mismatch from other factors affecting the overall control performance. Three systems with proportional integral (PI) controller, linear quadratic (LQ) controller and unconstrained model predictive control (MPC) respectively are presented as examples to verify the effectiveness of the proposed technique. Besides, Tennessee Eastman process shows the proposed method is able to detect process model mismatch of nonlinear systems.     

Intelligent Setting Control for Clinker Calcination Process

In the process of clinker calcination, the target value of the raw meal decomposition rate (RMDR) is different in the easy and difficult calcination stages because the boundary conditions of raw meal (i.e., raw meal flow, raw meal ingredients, and particle size) change frequently, where RMDR cannot be guaranteed to be within its desirable range. To solve this problem, an intelligent setting control method is proposed in this paper for a clinker calcination process. The proposed approach is realized by on‐line adjustment of the setpoints of control loops in line with the changes of raw meal boundary conditions. This method consists of five modules, namely an RMDR target value setting model, a control loop pre‐setting model, a feedback compensation model based on the fuzzy rules, a feedforward compensation model based on the fuzzy rules, and a soft measurement model for RMDR. Successful application to the clinker calcination process of the Jiuganghongda Cement Plant in China has been made, where the efficiency of the proposed method has been validated by the results of the practical application.