Infrared Drying Process of an Experimental Water Painting: Model Predictive Control

This article deals with the experimental control of an infrared drying process of a water based epoxy-amine painting. This approach is based on a unidirectional diffusional modeling of infrared drying phenomena where both heat and mass transfers under shrinkage conditions are accounted for. The control problem is concerned with the tracking of any given trajectory for one of the characteristics (i.e., the temperature or the mean water content) during the drying cycle. This is solved using the well-known model predictive control framework where the nonlinear diffusional model is directly used in the control formulation. Experimental results show the efficiency of the trajectory tracking. This method can be extended for more general constrained control problem.     

Infrared Drying: From Process Modeling to Advanced Process Control

The article surveys the drying of solids materials and polymer solutions when infrared radiation (IR) is employed as the main heating source. The study reviews the current research trends of IR drying of specific applications. A case study similar to an industrial setting is presented to illustrate a model development and control scheme of an IR drying unit. The design and online implementation of an internal model controller (IMC) is discussed. The study demonstrates the controller capabilities to suppress random variations of the moisture content in the material entering the dryer. Simulation results also showed the success of model predictive control (MPC) multivariable controller ability, while handling process interactions and process constraints, to track setpoint changes in the humidity and temperature of the material exiting the dryer and to reject unmeasured stochastic disturbances in the inlet humidity stream.     

Infrared Drying: From Process Modeling to Advanced Process Control

The article surveys the drying of solids materials and polymer solutions when infrared radiation (IR) is employed as the main heating source. The study reviews the current research trends of IR drying of specific applications. A case study similar to an industrial setting is presented to illustrate a model development and control scheme of an IR drying unit. The design and online implementation of an internal model controller (IMC) is discussed. The study demonstrates the controller capabilities to suppress random variations of the moisture content in the material entering the dryer. Simulation results also showed the success of model predictive control (MPC) multivariable controller ability, while handling process interactions and process constraints, to track setpoint changes in the humidity and temperature of the material exiting the dryer and to reject unmeasured stochastic disturbances in the inlet humidity stream.     

基于模型预测控制的反应再生工艺控制实现

介绍了先进控制系统中的模型预测控制技术。具体阐述了模型预测控制中的动态矩阵控制算法（DMC）,包括预测模型、滚动优化、反馈校正。以炼油厂催化裂化装氍的反应再生工艺为例,分析了反应再生工艺控制的多变量、多耦合特性,指出了控制的难点。最后,采用动态矩阵控制算法对反应再生工艺的一再、二再温度进行了优化控制,最终实现了控制目标。     

压缩机涂装质量的过程控制

根据国家有关涂装技术标准,结合企业涂装工艺守则,对压缩机涂装生产特殊过程进行了质量控制,并对影响漆膜质量的主要因素建立了指标要求,保证了压缩机表面涂装符合需方的技术质量要求。     

基于模型预测控制的反应再生工艺控制实现

介绍了先进控制系统中的模型预测控制技术。具体阐述了模型预测控制中的动态矩阵控制算法（DMC）,包括预测模型,滚动优化,反馈校正。并以炼油厂催化裂化装置的反应再生工艺为例,分析了反应再生工艺控制的多变量、多耦合特性,指出了控制的难点。最后,采用动态矩阵控制算法对反应再生工艺的一再、二再温度进行了优化控制,最终实现了控制目标。     

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.     

天然胶乳厚胶膜热风干燥与红外干燥对比研究

采用失重法研究了天然胶乳厚胶膜在热风与红外线两种干燥方式下的干燥过程。研究结果表明,在相同条件下,红外干燥的干燥速率常数远大于热风干燥,采用红外干燥可大大缩短干燥时间,干燥温度和厚度均显著影响干燥过程,红外干燥对温度更敏感。通过动力学拟合结果,得到了干燥动力学方程,可以预测干燥时间。     

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.     

Advanced Control Strategy for Impingement Drying

This article examines dynamic behavior of impingement drying. Dynamic relations between the drying variables and the paper moisture are determined and interactivity between the process variables is defined. According to the results, impingement drying is interactive and therefore an advanced control strategy is developed which is based on multivariable model predictive control. The performance of the proposed control strategy is studied by simulation and the simulation results are compared with the simulation results of the present control scheme.     

化工过程强非线性系统的变模型自适应预测控制

提出一种变模型自适应预测控制算法 ;基于非线性状态空间模型 ,通过每步在当前工作点 (非平衡点 )线性化获得线性化子模型 ,以此进行状态反馈预测控制 ,线性化子模型随工作点变化 ,且不限于平衡点。通过pH值控制的对比仿真实验 ,证明其对强非线性过程的控制效果优于传统的多模型预测控制。最后分析讨论了该控制算法存在的几个重要问题 ,并指出与之相关的未来研究方向     

Advanced Control Strategy for Impingement Drying

Abstract

This article examines dynamic behavior of impingement drying. Dynamic relations between the drying variables and the paper moisture are determined and interactivity between the process variables is defined. According to the results, impingement drying is interactive and therefore an advanced control strategy is developed which is based on multivariable model predictive control. The performance of the proposed control strategy is studied by simulation and the simulation results are compared with the simulation results of the present control scheme.     

Experimental Validation of the Heat and Mass Transfer Model for Convective Drying

The aim of this article is to present a self-consistent mathematical model describing the heat and mass transfer phenomena during the convective drying both in the constant and in the falling drying rate periods. This general model is developed on the basis of the theory of mixtures and the thermodynamics of irreversible processes. The boundary conditions are formulated and the numerical algorithm enabling calculation of the temperature and the drying curves in the two mentioned periods of drying is constructed. In this paper much effort is devoted to the experimental validation of the model. The convective drying of a cylindrical sample made of kaolin was examined both experimentally and numerically for comparison and the distribution of temperature and the drying curves were determined. A very good agreement of the experimental and theoretical results is stated.     

Applying Model Predictive Control to Dividing Wall Columns

The technology of dividing wall columns can offer enormous energy savings compared to common distillation columns and configurations. The technology of model predictive control is also advantageous since such a controller minimizes the future deviation of the predicted controlled variable from the reference point. The practical application of model predictive controllers for dividing wall columns is still limited due to limited experience with high interactions among the process variables. The scope of this work is the development and analysis of a method for the design of model predictive controllers for dividing wall columns. An experimental investigation verifies the practicability of the applied approach. The methods generated are transferable to other applications. Thus, the industrial acceptance of model predictive controllers for dividing wall columns is enhanced.     

Adaptive Nonlinear Model Predictive Control Using an On-line Support Vector Regression Updating Strategy

The performance of data-drivenmodels relies heavily on the amount and quality of training samples, so itmight deteriorate significantly in the regions where samples are scarce. The objective of this paper is to develop an online SVR model updating strategy to track the change in the process characteristics efficiently with affordable computational burden. This is achieved by adding a new sample that violates the Karush-Kuhn-Tucker conditions of the existing SVR model and by deleting the old sample that has the maximum distance with respect to the newly added sample in feature space. The benefits offered by such an updating strategy are exploited to develop an adaptive model-based control scheme, where model updating and control task perform alternately. The effectiveness of the adaptive controller is demonstrated by simulation study on a continuous stirred tank reactor. The results reveal that the adaptive MPC scheme outperforms its non-adaptive counterpart for largemagnitude set point changes and variations in process parameters.     

Nonlinear Model Algorithmic Control of a pH Neutralization Process

Control of pH neutralization processes is challenging in the chemical process industry because of their inherent strong nonlinearity. In this paper, the model algorithmic control （MAC） strategy is extended to nonlinear processes using Hammerstein model that consists of a static nonlinear polynomial function followed in series by a linear impulse response dynamic element. A new nonlinear Hammerstein MAC algorithm （named NLH-MAC） is presented in detail. The simulation control results of a pH neutralization process show that NLH-MAC gives better control performance than linear MAC and the commonly used industrial nonlinear propotional plus integral plus derivative （PID） controller. Further simulation experiment demonstrates that NLH-MAC not only gives good control response, but also possesses good stability and robustness even with large modeling errors.     

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。     

RBF网络模型在预测函数控制中的应用

对三容液位系统的非线性复杂特点,利用RBF网络对系统建立预测模型,着重分析了RBF网络结构的选取、模型参数辨识以及网络优化的问题.通过预测函数控制验证了RBF网络模型在非线性系统建模中的优越性.     

汽车涂装前处理槽液的工艺管理

在钢铁的制造、冲压成型中,会使用各种保护油、润滑油、冲压油、粘合剂、焊接助剂等,而要得到光滑平整、耐腐蚀好、附着力好的涂层取决于工件表面的清洁程度及磷化膜的质量。前处理就是对铁板、钢板、镀锌板等金属的表面进行清洗、化学处理而使底材易于电泳涂装,从而得到所需的防腐蚀涂层。本文针对汽车涂装前处理在实际生产过程中存在的工艺管理问题,结合本公司实际从技术角度阐述了其对涂装质量的影响。     

Optimal Control of Continuous Infrared Dryers

In this work, the optimal control policies of radiant temperature versus dryer length are determined for the continuous, steady-state operation of infrared dryers. The optimal control objective is to minimize the product humidity subject to a mathematical model of infrared dryers as well as a set of process inequality constraints. A robust optimal control method based on genetic algorithms is applied. Multiple air injections are also considered. The optimal results show considerable reduction in product humidity. Comparisons with corresponding base cases (using maximum possible uniform radiant temperatures) indicate improvements greater than 50% with the application of optimal control policies.