浅谈APC控制在聚丙烯装置的应用

随着计算机技术、控制技术、通讯技术、信息技术的迅速发展,集散控制系统在企业中得到了广泛的应用,进年来随着控制系统扁平化、数字化进程的加快以及现代控制理论的发展,高级计算机算法与DCS控制相互结合,先进控制系统得到了推广,APC系统的典型技术为基于多变量的模型控制和软测量技术,本文介绍了APC系统在聚丙烯装置的应用。     

多变量预测控制软件包APC-Hiecon在液化气回收装置上的应用

针对扬子石化公司芳烃厂液化气回收装置脱丁烷塔存在的进料波动大,操作变量多,关联性强等控制难点,使用多变量预测控制软件包APC－Hiecon设计多变量预测控制器来实现对脱丁烷塔的平稳控制并提高分离效果,取得了很好的控制效果和显著的经济效益。     

鲁棒多变量预估控制技术在合成氨装置中的应用

介绍了鲁棒多变量预估控制技术的工作原理及特点,阐述了合成氨装置先进控制系统的安装、测试以及运行效果。实际运行情况表明,先进控制系统提高了装置的控制平稳率和重要指标控制的稳定性,达到了预期效果。     

试论聚丙烯装置上的先进控制APC应用问题

本文以扩能改造后的聚丙烯装置生产过程为应用背景,介绍了APC(Advanced Process control)的功能设计和建模特点:说明了APC在聚丙烯生产上的实际应用情况以及存在的问题。     

先进控制技术在乙烯装置分离单元的应用

对过程控制中的软测量技术和多变量预测技术进行了讨论,并对软测量技术和多变量预测技术的基本概念进行了解释,综述了软测量技术和多变量预测技术在乙烯装置分离单元的丙烯精馏塔和碳二加氢反应器中的应用。     

多变量约束预估控制在FCC分馏塔上的应用研究

本文以某炼油厂FCC分馏塔为应用研究对象,介绍了多变量约束预估控制技术的思想和特点,重点论述了该技术在催化裂化装置分馏塔上应用的设计方案、数学模型的建立,并最终得出多变量约束预估控制系统具有优越性的结论。     

先进控制技术在轻烃分馏装置上的应用

针对轻烃连续分馏装置的操作难点,利用APC-Adcon高级多变量鲁棒预测控制软件,设计出一个先进控制系统,并在2×105t/a轻烃分馏装置上成功应用,取得了明显的经济效益.     

多变量预测控制在空分装置自动变负荷中的应用分析

针对空分装置变负荷过程的控制要求,提出一种实现自动变负荷功能的多变量预测控制策略。该策略以模型预测控制技术为核心解决了工艺参数间的强耦合问题,满足了大范围变负荷所引起的过程非线性控制要求,保证空分装置自动变负荷操作的品质与速度。详述了该策略的技术结构、工艺控制方案以及系统实施,实际运行情况表明了该策略的有效性。     

先进控制技术在稳定聚丙烯装置聚合温度上的应用

探究快速多变量预测和控制优化技术能否及时、有效地对聚丙烯聚合温度进行控制。对比PID反馈调节方法与Pavilion8快速多变量预测控制优化技术对聚丙烯聚合温度的控制效果。通过采用Pavilion8快速多变量预测控制优化技术,能够考虑多种干扰因素对温度的影响,及时预测温度的变化趋势,采用快速控制策略,及时、有效地对温度进行控制,大幅降低了温度的波动。     

APC在气相法流化床聚丙烯装置的实际应用

以国内引进的第一套DOW化学UNIPOL气相法流化床工艺聚丙烯装置为应用背景,介绍了APC(Advanced Process Control)的功能设计和建模特点,考察了APC(先进过程控制)在中国石油广西石化公司20万t·a-1气相法聚丙烯装置的工业实际应用优点以及存在的问题。结果表明,APC对反应器及产品质量的控制起到了很好的稳定和优化作用.     

Iterative learning model predictive control for constrained multivariable control of batch processes

In this paper, we propose a model predictive control (MPC) technique combined with iterative learning control (ILC), called the iterative learning model predictive control (ILMPC), for constrained multivariable control of batch processes. Although the general ILC makes the outputs converge to reference trajectories under model uncertainty, it uses open-loop control within a batch; thus, it cannot reject real-time disturbances. The MPC algorithm shows identical performance for all batches, and it highly depends on model quality because it does not use previous batch information. We integrate the advantages of the two algorithms. The proposed ILMPC formulation is based on general MPC and incorporates an iterative learning function into MPC. Thus, it is easy to handle various issues for which the general MPC is suitable, such as constraints, time-varying systems, disturbances, and stochastic characteristics. Simulation examples are provided to show the effectiveness of the proposed ILMPC.     

一类化工过程多变量系统的自适应非线性预测控制

针对化工过程的一类多变量非线性系统,提出了一种自适应非线性预测控制(ANMPC)算法。在采用递归最小二乘法进行预测模型参数在线辨识的基础上,将系统的静态非线性关系用一个反向传播(BP)神经网络稳态模型来表示,通过稳态模型求得的动态增益来进一步校正预测模型的参数。详述了ANMPC控制器设计步骤,通过在一个多变量pH中和过程中的仿真验证了本算法的可行性和有效性。     

Multivariable model predictive control of a novel rapid pressure swing adsorption system

A multivariable model predictive control (MPC) algorithm is developed for the control and operation of a rapid pressure swing adsorption (RPSA)‐based medical oxygen concentrator. The novelty of the approach is the use of all four step durations in the RPSA cycle as independent manipulated variables in a truly multivariable context. The RPSA has a complex, cyclic, nonlinear multivariable operation that requires feedback control, and MPC provides a suitable framework for controlling such a multivariable system. The multivariable MPC presented here uses a quadratic optimization program with integral action and a linear model identified using subspace system identification techniques. The controller was designed and tested in simulation using a complex, highly coupled, nonlinear RPSA process model. The model was developed with the least restrictive assumptions compared to those reported in the literature, thereby providing a more realistic representation of the underlying physical phenomena. The resulting MPC effectively tracks set points, rejects realistic process disturbances and is shown to outperform conventional PID control. © 2017 American Institute of Chemical Engineers AIChE J, 64: 1234–1245, 2018     

应用先进控制实现环氧丙烷装置节能降耗

滨化集团东瑞化工有限公司的环氧丙烷装置原采用集散控制系统控制,为进一步对装置进行节能降耗,实施了先进控制。先进控制实施后取得了显著效果。先进控制投用率超过95%。环氧丙烷产量提高了1.03%,每吨环氧丙烷消耗蒸汽降低了0.0458t,同时操作平稳率大幅度提高。     

Encrypted model predictive control design for security to cyberattacks

In recent years, cyber-security of networked control systems has become crucial, as these systems are vulnerable to targeted cyberattacks that compromise the stability, integrity, and safety of these systems. In this work, secure and private communication links are established between sensor–controller and controller–actuator elements using semi-homomorphic encryption to ensure cyber-security in model predictive control (MPC) of nonlinear systems. Specifically, Paillier cryptosystem is implemented for encryption-decryption operations in the communication links. Cryptosystems, in general, work on a subset of integers. As a direct consequence of this nature of encryption algorithms, quantization errors arise in the closed-loop MPC of nonlinear systems. Thus, the closed-loop encrypted MPC is designed with a certain degree of robustness to the quantization errors. Furthermore, the trade-off between the accuracy of the encrypted MPC and the computational cost is discussed. Finally, two chemical process examples are employed to demonstrate the implementation of the proposed encrypted MPC design.     

从区间模型预测控制到双层结构模型预测控制

模型预测控制算法（MPC）存在设定点控制与区间控制两种策略,区间预测控制较之设定点控制在技术上具有先进性。目前,主流的预测控制软件技术均采用双层结构,即上层稳态优化计算最优设定点,下层动态控制负责动态跟踪最优设定点。从过程稳态的角度出发,分别对区间预测控制和双层结构预测控制进行了机理分析,从定性与定量两个方面比较了这两者的异同点,提出并证明了两者的一致性条件。论述了双层结构预测控制较之单层结构下的区间控制更具先进性。     

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     

先进过程控制技术在国内PP装置的应用

介绍了国内聚丙烯装置先进过程控制（APC）技术的应用现状,并结合装置分析各种APC技术的特点。聚丙烯装置采用APC技术可有效地提高生产装置的操作稳定性、提高装置产量和产品质量,并缩短产品牌号切换时间。