基于上位机的先进控制与实时优化系统及其应用

本文给出了先进控制与实时优化系统的要求，重点介绍了基于DEC Alpha-TDC3000－PC环境的先进控制与实时优化系统体系结构的设计思想与软件开发。最后，给出了该体系结构的应用实例－原油蒸馏装置的先进控制与实时优化系统。实际应用表明，该系统软件为实施先进控制与实时优化技术提供了一个方便实用的平台。     

“自动控制先进理论和应用技术”研讨会特邀专辑——前言

2010年12月8日，由华南理工大学一香港中文大学自动化科学与工程研究中心主办，华南理工大学自动化科学与工程学院控制与优化中心、香港中文大学机械与自动化工程学系、华南理工大学研究生院协办的“自动控制先进理论和应用技术”研讨会在华南理工大学召开，来自清华大学、北京大学和中国科学院等院校的7位专家学者在研讨会上作了精彩的学术报告．     

造纸企业综合自动化研究与应用

介绍了基于工业过程控制、优化、管理集成开发平台,对制浆造纸过程的典型关键装置,进行建模、控制与优化策略的研究,系统成套技术和工程应用方案的开发,形成基于先进软测量技术、控制技术、过程优化技术、网络技术的造纸过程典型关键设备的成套专用控制装置,具有重大应用、推广和产业化价值。     

基于现场总线的铅锌烟气治理信息化管理系统

为解决铅、锌冶炼中烟气治理与生产之间的协调问题，采用FF和Profibus现场总线和先进的系统软件开发了基于DeltaV搭建的信息化管理系统。详细分析丁信息化管理系统的控制策略及其功能模块，并介绍了生产优化调度技术和系统软件设计及DeltaV系统的开发应用经验。经烟气治理工程的运行实践表明，该信息化管理系统的检测和控制达到了国际先进水平。     

工业过程先进控制及优化软件产业

简述了先进控制与优化技术在过程工业中应用的重要意义，介绍了作者近年来把现代控制理论应用于过程工业的成功实例，同时给出应用中所需解决的一些问题。     

金融工程：信息与控制领域的挑战

这是一篇综述性文章，探讨了信息与控制理论技术在金融工程中的应用问题，分析了 金融预测、金融优化、金融控制和金融系统复杂性问题研究的进展，提出了信息控制理论技 术与金融工程交叉领域进一步研究的课题．对于金融工程与信息控制理论技术的研究应用有 一定意义．     

石油化工流程模拟、先进控制与过程优化技术的现状与展望

流程模拟、先进控制和过程优化技术的研究与应用是石油化工过程的一个重要方面.本文论述了该技术的发展现状和趋势,分析了我国在该研究中存在的主要问题、面临的机遇与挑战,指出了关键的技术问题.此外,对流程模拟、先进控制和过程优化技术的经济效益与应用前景也做了分析.最后,对于如何在我国开展石油化工流程模拟、先进控制与过程优化技术的研究与应用提出了几点建议和对策.     

乙苯催化脱氢制苯乙烯生产过程的机理建模及优化

为针对乙苯脱氢过程实施先进控制及优化技术, 基于反应动力学、物料衡算、能量守恒原理及Ergun方程, 通过求解微分方程组建立乙苯脱氢绝热负压径向反应器的机理模型, 可有效预测未来系统的生产状况. 在所建立的机理模型基础上, 根据实际生产状况建立了所需的约束条件及优化条件, 利用工程应用较多的多变量复合形约束算法实现了过程生产的在线优化. 通过改变各个操作条件明确了它们对脱氢生产的影响规律, 为后续先进控制与优化项目的实施奠定了基础.     

石化先进控制与优化技术的应用

1石化先进控制技术应用现状先进控制（APC-Advanced Process Control）是采用多变量预测及优化技术、基于过程动态数学模型、与常规控制相结合的新型工业控制系统。实施先进控制，可实现装置被控变量偏差降低、抗干扰能力增强、操作更加平稳，发挥装置最大潜能，提高产品收率和质量，能耗及物耗降低。Exxon Mobil、Shell、BP Amoco等国外著名石油石化公司早在上世纪80~90年代就在生产装置上普及了先进控制技术的应用。     

基于预测控制方法的燃烧优化研究与应用

提出了采用基于先进控制的燃烧、协调和汽温优化思路和方法,利用模型预测控制实现对燃烧等复杂过程的优化控制,研究成果在某电厂进行了应用和实施。实际应用结果表明,采用先进控制方法的燃烧优化使系统的调节品质大大提升,取得了较好的应用效果。     

基于Solaris的DCS内嵌式先进控制与优化软件的设计和应用

为加快先进控制与优化在工业领域的应用,设计并完成了一套基于Solaris系统的大型DCS嵌入式先进控制与优化软件,其在数据的高速稳定读写、数据缓存和预处理、算法模块化多线程并行处理、参数高度可配置化、配置文件标准化、统一的日志系统、线程管理以及DCS无缝衔接等方面进行了先进的设计,系统具有很高的通用性和可移植性,可广泛应用于工业控制领域.软件系统在电站锅炉燃烧优化及汽温控制中的应用,获得了非常好的应用效果,显示了极高的稳定性,显著提高了电站自动化水平并收到了良好的经济效益.     

双层结构预测控制研究进展

现代工业大系统的优化控制采用递阶结构,其中以预测控制为代表的先进过程控制已经成为重要的一级.目前,主流的工业预测控制技术均采用双层结构,即包含稳态优化层和动态控制层.双层结构预测控制技术可以有效解决复杂工业过程常见的多目标优化、多变量控制的难点问题.本文简要总结了双层结构预测控制的算法,并从控制输入与被控输出稳态关系入手分析了多变量预测控制稳态解的相容性和唯一性,说明了稳态优化的重要性.针对双层结构预测控制与区间预测控制的性能比较、稳态模型的奇异性以及闭环系统动态特性等提出了一些见解,并指出了需要重点研究的主题.     

Advanced control and on-line process optimization in multilayer structures

The paper demonstrates the place, role and mutual interaction of advanced control algorithms and on-line set-point optimization in process control structures. First, a multilayer control structure resulting from a functional decomposition is briefly presented. The role and selected realizations of advanced control algorithms, in particular mostly applied now model predictive control (MPC) ones, at direct control and supervisory constraint control layers is discussed. Then possible solutions to on-line set-point optimization, depending of disturbance dynamics, are presented: dynamic set-point optimization including involved structures based on temporal decomposition, and steady-state set-point optimization for cases with disturbance dynamics both much slower than and comparable with the process dynamics. For the last case, important in industrial practice, different structures of interaction and even integration of MPC and steady-state optimization are discussed. The topics are illustrated by briefly presented examples, selected from given references.     

DMOS-基于多种数据挖掘算法的工业优化软件系列

基于我们多年从事炼油，化工，冶金工业优化工作的经验，参照国际上先进控制和优化工程公司的工作模式，开发了适用于生产过程优化，故障诊断，优化新产品研制和配方设计的软件系列DMOS。DMOS软件分开发软件和运用软件两大类，前者包括一个数据挖掘方法库，其中多种模式识别，支持向量机算法，线性和非线性回归以及人工神经网络组成一个信息处理的统一流程。可处理用户的数据，开发适合用户需要的DMOS运行软件。后者包括数据库，模型库和简易方法库，可直接对生产进行优化开环指导或在线控制，DMOS软件系列为化工，炼油，钢铁等行业生产过程优化的工程化运营创造了条件。     

Reinforcement learning for batch process control: Review and perspectives

Batch or semi-batch processing is becoming more prevalent in industrial chemical manufacturing but it has not benefited from advanced control technologies to a same degree as continuous processing. This is due to its several unique aspects which pose challenges to implementing model-based optimal control, such as its highly nonstationary operation and significant run-to-run variabilities. While existing advanced control methods like model predictive control (MPC) have been extended to address some of the challenges, they still suffer from certain limitations which have prevented their widespread industrial adoption. Reinforcement learning (RL) where the agent learns the optimal policy by interacting with the system offers an alternative to the existing model-based methods and has potential for bringing significant improvements to industrial batch process control practice. With such motivation, this paper examines the advantages that RL offers over the traditional model-based optimal control methods and how it can be tailored to better address the characteristics of industrial batch process control problems. After a brief review of the existing batch control methods, the basic concepts and algorithms of RL are introduced and issues for applying them to batch process control problems are discussed. The nascent literature on the use of RL in batch process control is briefly reviewed, both in recipe optimization and tracking control, and our perspectives on future research directions are shared.     

ProOpter: An advanced platform for production analysis and optimization

This paper presents the prototype of an advanced platform for production analysis and optimization, referred to as ProOpter. The platform was developed to support the recently derived concept of holistic production control (HPC), which relies on model-based control. The prototype is comprised of a set of off-line and on-line modules. The off-line modules support the definition of key performance indicators (KPIs), the selection of the most influential input (manipulative) variables, and the identification of a simple production model from historical data. The on-line modules enable KPI prediction and suggest actions to the production manager, employing model-based production control and/or optimization techniques. In this way, a new decision-support reasoning based on historical production data can be introduced. ProOpter has a modular design and can be used as an add-on to existing production IT systems since it relies on established industrial communication standards. The use of the platform is validated on the well-known Tennessee Eastman benchmark simulation process and on two industrial case studies.     

Iterative learning control-based batch process control technique for integrated control of end product properties and transient profiles of process variables

Importance of batch processes has grown recently with the increasing economic competition that has pushed the manufacturing industries to pursue small quantity production of diverse high value-added products. Accordingly, systems engineering research on advanced control and optimization of batch processes has proliferated. In this paper, we examine the potentials of ‘iterative learning control (ILC)’ as a framework for industrial batch process control and optimization. First, various ILC rules are reviewed to provide a historical perspective. Next it is shown how the concept of ILC can be fused with model predictive control (MPC) to build an integrated end product and transient profile control technique for industrial chemical batch processes. Possible extensions and modifications of the technique are also presented along with some numerical illustrations. Finally, other related techniques are introduced to note the similarities and contemplate the opportunities for synergistic integration with the current ILC framework.     

基于支持向量机和粒子群算法的软测量建模

针对PX氧化过程中的4-CBA浓度的估计问题,提出了基于支持向量机和粒子群算法来估计机理模型参数的方法．用支持向量机回归来提取特征样本,这些少量的特征样本估计机理模型参数可以减少计算时间,同时避免了人工随机试凑法选择训练样本的盲目性．采用粒子群算法来估计非线性机理模型的参数,可以避免传统方法对初始点和样本的依赖．工业实例表明,本文提出的方法是有效的．     

Advanced control using decomposition and simple elements

The paper explores the standard advanced control elements commonly used in industry for designing advanced control systems. These elements include cascade, ratio, feedforward, decoupling, selectors, split range, and more, collectively referred to as “advanced regulatory control” (ARC). Numerous examples are provided, with a particular focus on process control. The paper emphasizes the shortcomings of model-based optimization methods, such as model predictive control (MPC), and challenges the view that MPC can solve all control problems, while ARC solutions are outdated, ad-hoc and difficult to understand. On the contrary, decomposing the control systems into simple ARC elements is very powerful and allows for designing control systems for complex processes with only limited information. With the knowledge of the control elements presented in the paper, readers should be able to understand most industrial ARC solutions and propose alternatives and improvements. Furthermore, the paper calls for the academic community to enhance the teaching of ARC methods and prioritize research efforts in developing theory and improving design method.     

Robust optimization-based multi-loop PID controller tuning: A new tool and its industrial application

Modern process plants are highly integrated and as a result, decentralized PID control loops are often strongly interactive. The iterative SISO tuning approach currently used in industry is not only time consuming, but does also not achieve optimal performance of the inherently multivariable control system. This paper describes a method and a software tool that allows control engineers/technicians to calculate optimal PID controller settings for multi-loop process systems. It requires the identification of a full dynamic model of the multivariable system, and uses constrained nonlinear optimization techniques to find the controller parameters. The solution is tailored to the specific control system and PID algorithm to be used. The methodology has been successfully applied in many industrial advanced control projects. The tuning results that have been achieved for interacting PID control loops in the stabilizing section of an industrial Gasoline Treatment Unit as well as a Diesel Desulfurization plant are presented.