遗传算法优化BP神经网络的水泥窑温度预测模型研究

水泥窑内温度的控制效果直接决定水泥熟料的质量。由于回转窑煅烧具有大滞后、非线性、多变量等复杂特性,并且各个因素之间存在相互联系和不确定性,这给回转窑温度的控制带来了困难。本文利用BP神经网络建立回转窑温度预测模型,并利用遗传算法对模型进行优化,将优化前后的仿真结果对比后发现,使用遗传算法优化后的BP神经网络回转窑温度预测模型的预测精度高于单独使用BP神经网络搭建的回转窑温度预测模型精度,为回转窑温度的预测提供了一种新的方法。     

基于微元法预测水泥回转窑出料量的研究

回转窑出料量是水泥生产过程关键变量之一,但无法直接测量。以微元法分析物料在回转窑内运动机理,建立水泥回转窑出料量的软仪表。通过MATLAB仿真和现场实际应用证明,软仪表能良好匹配工业现场的动态变化过程,预测精度高,能满足水泥厂实际需求,对保证水泥熟料质量和实现水泥烧成系统的优化控制具有重要意义。     

预测函数控制及其应用

在介绍了预测函数控制的基本原理及发展现状的基础上,针对间歇过程提出了一种在于预测函数控制的先进控制算法。仿真结果表明,该算法具有较好的跟踪效果,抗干扰能力及鲁棒性。     

回转窑铸锻件部件制造质量控制及分析

在水泥项目的实施中，回转窑是制造工程以散件或者部件形式交货的，如大齿圈、轮带、托轮、小齿轮、挡轮等，这些都是回转窑的铸锻部件，对这些部件的制造质量控制，是回转窑制造质量控制的关键。加强回转窑制造过程的质量控制和管理，是水泥工程项目实施成败的关键因素之一。     

预测函数控制在陶瓷烧成窑炉温度控制中的应用

针对陶瓷烧成生产过程的复杂性,提出一种陶瓷烧成温度预测函数控制方法。首先分析跟踪设定温度的陶瓷烧成温度预测函数控制方法的控制量结构,并给出预测函数控制方案;然后进行闭环系统性能分析;最后对模型进行仿真。     

模型预测控制在水泥生产线自动化的应用

介绍模型预测控制（MPC）在某5?000 t/d水泥生产线的应用,MPC系统用于分解炉、回转窑和篦冷机的控制。生产线动态模型由多变量系统辨识（动态数据建模）获得。该MPC系统的实施时间为3周,2018年7月投运后一直稳定运行。相对手动控制,MPC控制系统大大降低了分解炉出口温度等关键参数的波动性。与手动控制比较,可节煤15%。最新运行数据表明,分解炉出口温度波动进一步降低,熟料游离氧化钙合格率接近100%,而另一条手动控制生产线熟料游离氧化钙合格率约为80%。本文还提出一个MPC项目经济效益的简单估算公式。     

纸机模型的预测函数控制

造纸过程是一个多变量、强耦合、大时滞的过程,采用传统的PID控制要达到很好的控制效果是很困难的。采用三种预测控制方法分别对纸机模型进行控制,并分别进行了仿真研究。从仿真结果可以看出,动态矩阵的跟踪效果不如广义预测控制,广义预测控制算法的跟踪性能较好,但是计算量较大,预测函数控制的响应速度较快,计算简单,控制效果也较好。     

株化200kt/a PVC自控系统设计

针对电石法生产PVC控制对象的特性,在JX-300XP DCS系统的基础上,通过比值控制、串级控制、分程控制、预测函数控制及自适应控制等,成功设计200kt/a PVC生产装置自控系统。     

先进控制技术及应用

系统地阐述了预测函数控制的基本原理，并针对一阶过程和一阶加纯滞后过程给出了相应的控制算法。仿真和工业应用结果表明：预测函数控制具有良好的抗干扰能力和跟踪效果。     

氯乙酸生产结晶过程的预测函数控制仿真研究

针对氯乙酸生产结晶过程被控对象大滞后、大惯性的特点,设计了基于PFC—PID的串级控制系统：该系统将串级控制结构和预测函数控制算法相结合,内回路采用常规PID控制器,外回路采用预测函数控制(PFC)。结合了2者的优点,仿真结果表明,该串级控制系统在控制品质、鲁棒性等方面明显优于常规PID控制系统。     

A Composite Model Predictive Control Strategy for Furnaces

Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimization of furnaces could not only help to improve product quality but also benefit to reduce energy consumption and exhaust emission. Inspired by this idea, this paper presents a composite model predictive control (CMPC) strategy, which, taking advantage of distributed model predictive control architectures, combines tracking nonlinear model predictive control and economic nonlinear model predictive control metrics to keep process running smoothly and optimize operational conditions. The controllers connected with two kinds of communication networks are easy to organize and maintain, and stable to process interferences. A fast solution algorithm combining interior point solvers and Newton's method is accommodated to the CMPC realization, with reasonable CPU computing time and suitable online applications. Simulation for industrial case demonstrates that the proposed approach can ensure stable operations of furnaces, improve heat efficiency, and reduce the emission effectively.     

变增益的非线性预测控制算法

采用变增益策略,用输入与稳态输出的映射表示系统的静态非线性,用一个增益为1的ARX模型表示系统的动态模型,代替多数文献中常用的分段线性多模型方法进行非线性系统的预测控制.文中通过对连续搅拌釜反应器(CSTR)的仿真,验证了本算法的有效性.     

EXTRACTION OF NIOBIUM AND TANTALUM WITH BIS-2-ETHYLHEXYL ACETAMIDE

ABSTRACT The extraction of Nb and Ta from acid solutions with bis-2-ethylhexyl acetamide and the stripping of these metals with sulphuric acid solutions were investigated. The organic phase was a binary solution of bis-2-ethylhexyl acetamide and xylene, while the aqueous phase was composed of hydrofluoric acid solution or hydrofluoric-sulphuric acid solution containing 3.5-13 Kg/m³ Nb and 5-10 Kg/m³ Ta. Sulphuric acid, hydrofluoric acid and nitric acid were used as salting out agents to understand the effect on the extraction.

Niobium and tantalum were not sufficiently extracted from hydrofluoric acid solutions, whereas the extraction of both metals remarkably increased with an addition of sulphuric acid to the aqueous phase. Both metals were completely co-extracted under the aqueous condition of 6N hydrofluoric acid and 8N sulphuric acid. The stripping occurred for both metals with high efficiency when water or dilute sulphuric acid was used as a stripping agent. The increase in sulphuric acid concentrations caused less stripping of Ta, while the stripping of Nb was maintained at 80% up to 7N sulphuric acid.     

Multivariable Optimal Learning Control of Wafer Temperatures in a Commercial RTP Equipment

A multivariable optimal iterative learning control technique called BMPC (Batch Model Predictive Control) has been implemented and evaluated in a commercial RTP (Rapid Thermal Processing) system fabricating 200 mm silicon wafers. The wafer temperature was controlled at multiple points along the radial direction by manipulating multiple tungsten‐halogen lamp groups. The study has addressed the following two issues: feasibility of BMPC in a commercial RTP equipment and enhancement of temperature uniformity using redundant inputs. As a consequence, satisfactory tracking performance could be realized with BMPC with reduced efforts for design and implementation of the controller by the standardized identification and tuning procedure. Redundant inputs whose number is larger than that of the temperature measurements was attempted to relieve the directionality of the system. Experimental tests revealed that the approach can provide us with improved temperature uniformity as well as tracking performance.     

Characteristic equations and robust stability of a simplified predictive control algorithm

In predictive control, control calculations are done such that the difference between the desired and the predicted response of the process is minimized. The number of points on the prediction horizon at which the error is minimized and the number of future control moves considered affect the on-line computational effort involved in the solution of the constrained optimization problem. Earlier papers have shown that the control performance obtained using the DMC algorithm can also be obtained by using a simplified algorithm where the error is minimized at one point and one future control move is calculated. Because of its computational advantages, the simplified algorithm is analyzed further in this paper. Its transfer function is compared with the transfer function of the DMC algorithm. Characteristic equations to select tuning parameters are presented. The paper also compares the robust stability of the simplified and the DMC algorithms on SISO and MIMO process models. The results provide additional support to the viability of the simplified algorithm and thus indicate that it is possible for some processes to benefit from predictive control with only modest computational resources.     

基于Matlab/Simulink的气体分离装置多变量预测控制

依据丙烯精馏塔的工艺流程,介绍丙烯精馏塔机理模型和辨识模型的构建方法,并通过系统辨识方法得到了被控对象的多输入多输出模型。介绍预测控制及其算法的基本原理,通过编写基于状态空间模型的单步预测控制算法,对丙烯精馏塔进行多变量预测控制仿真,比较了预测控制算法对可测干扰,不可测干扰的抑制作用。     

苯乙烯装置先进控制技术的应用与研究

介绍了IMC-PID控制、多变量预测控制及分程控制在苯乙烯装置中的应用,叙述了先进控制的原理、优点,对影响控制器效果的因数、数学模型及算法进行了研究分析,并提出了优化改进的方法。     

Distributed dissipative model predictive control for process networks with imperfect communication

Results are developed to ensure stability of a dissipative distributed model predictive controller in the case of structured or arbitrary failure of the controller communication network; bounded errors in the communication may similarly be handled. Stability and minimum performance of the process network is ensured by placing a dissipative trajectory constraint on each controller. This allows for the interaction effects between units to be captured in the dissipativity properties of each process, and thus, accounted for by choosing suitable dissipativity constraints for each controller. This approach is enabled by the use of quadratic difference forms as supply rates, which capture detailed dynamic system information. A case study is presented to illustrate the results. © 2014 American Institute of Chemical Engineers AIChE J, 60: 1682–1699, 2014     

基于误差校正的预测控制算法综述

在分析了模型预测误差的来源后,对近20年来典型的基于误差校正的预测控制算法作了较全面的回顾,分析各种方法的研究现状及其成功应用的实例。