一种基于状态估计的间歇过程模型实时更新预测控制策略简

Nonlinear model predictive control （NMPC） is an appealing control technique for improving the per- formance of batch processes, but its implementation in industry is not always possible due to its heavy on-line computation. To facilitate the implementation of NMPC in batch processes, we propose a real-time updated model predictive control method based on state estimation. The method includes two strategies： a multiple model building strategy and a real-time model updated strategy. The multiple model building strategy is to produce a series of sim- plified models to reduce the on-line computational complexity of NMPC. The real-time model updated strategy is to update the simplified models to keep the accuracy of the models describing dynamic process behavior. The method is validated with a typical batch reactor. Simulation studies show that the new method is efficient and robust with respect to model mismatch and changes in process parameters.     

A self tuning controller for multicomponent batch distillation with soft sensor inference based on a neural network

Multicomponent batch distillation is an operation difficult to control not only for its nonlinear and transient behaviour, but because the product quality cannot be measured rapidly and with reliability. In the present work, a computational system for direct digital control is developed for a pilot plant batch distillation column. The development of a self tuning regulator and a soft sensor of composition based on a neural network is described. Top and reboiler temperature measurements are the basis for the on-line composition inference. The computational system was experimentally tested in a computer operated pilot column. It could be seen that the neural network soft sensor is a feasible and a reliable tool to solve on-line operational problems of the control engineering systems. The developed control system permits to operate the batch distillation column efficiently and is easy to be implemented and operated.     

DYNAMIC OPTIMAL CONTROL OF BATCH CRYSTALLIZATION PROCESSES

This work applies an on-line optimal control strategy developed by Zhang (2001) to two cooling batch crystallization processes. The algorithm initially finds the optimal crystallizer temperature and subsequently uses a feedback control system in order to achieve the desired final product quality of the crystals expressed in terms of the final crystal size distribution. In both batch processes, it is shown that the on-line optimal control approach provides better final product quality as compared with a simplified optimal cooling policy. The improvement is especially noticeable in the presence of plant/model mismatch or errors in the initial conditions.     

Batch-to-batch iterative learning control and within-batch on-line control for end-point qualities using MPLS-based dEWMA

A new feedback batch control strategy based on multiway partial least squares (MPLS) model and dEWMA (double exponentially weighted moving average) control for the end-point product quality system is proposed in this paper. It combines batch-to-batch (BtB) control with on-line tracking control within a batch. In the BtB operation, MPLS-based dEWMA control is done by applying feedback from the final output quality of the batch process. It utilizes the information from the current batch to improve quality for the next batch. The advantage of MPLS is to extract the strongest relationship between the input and the output variables in the reduced space of the latent variables model rather than in the real space of the highly dimensional manipulated variable trajectories. It is particularly useful for inherent noise suppression. Then the optimal manipulated variable trajectories in the score space without decoupler design can be directly and individually applied to each control loop under the MPLS modeling structure. Then the dEWMA controller can be applied to each SISO control loop respectively to address the model errors gradually reduced from model-plant mismatches and unmeasured disturbances. In on-line tracking control within a batch, the MPLS-based dEWMA control strategy is developed to explore the possible adjustments of the future input trajectories. It fixes up the disturbances just in time instead of until the next batch run and maintains the product specification when this batch is finished. To demonstrate the potential applications of the proposed design method, a typical batch reactor with processes of different dynamics is applied. Comparisons between MPLS-based dEWMA BtB control and MPLS-based dEWMA within-batch control are also made.     

On-line batch process monitoring using dynamic PCA and dynamic PLS models

Producing value-added products of high-quality is the common objective in industries. This objective is more difficult to achieve in batch processes whose key quality measurements are not available on-line. In order to reduce the variations of the product quality, an on-line batch monitoring scheme is developed based on the multivariate statistical process control. It suggests using the past measured process variables without real-time quality measurement at the end of the batch run. The method, referred to as BDPCA and BDPLS, integrates the time-lagged windows of process dynamic behavior with the principal component analysis and partial least square respectively for on-line batch monitoring. Like traditional MPCA and MPLS approaches, the only information needed to set up the control chart is the historical data collected from the past successful batches. This leads to simple monitoring charts, easy tracking of the progress in each batch run and monitoring the occurrence of the observable upsets. BDPCA and BDPLS models only collect the previous data during the batch run without expensive computations to anticipate the future measurements. Three examples are used to investigate the potential application of the proposed method and make a comparison with some traditional on-line MPCA and MPLS algorithms.     

Recursive data-based prediction and control of product quality for a PMMA batch process

In many batch processes, frequent process/feedstock disturbances and unavailability of direct on-line quality measurements make it very difficult to achieve tight control of product quality. Motivated by this, we present a simple data-based method in which measurements of other process variables are related to end product quality using a historical data base. The developed correlation model is used to make on-line predictions of end quality, which can serve as a basis for adjusting the batch condition/time so that desired product quality may be achieved. This strategy is applied to a methyl methacrylate (MMA) polymerization process. Important end quality variables, the weight average molecular weight and the polydispersity, are predicted recursively based on the measurements of reactor cooling rate. Subsequently, a shrinking-horizon model predictive control approach is used to manipulate the reaction temperature. The results in this study show promise for the proposed inferential control method.     

带有中间储罐的间歇精馏工艺动态控制模拟优化

甲酸甲酯-甲醇-水是化工生产过程中最常见的三元混合物之一。目前,间歇精馏工艺分离该三元混合物的研究较少,在动态控制方面也少有报道。本文研究了分离甲酸甲酯-甲醇-水的带有中间储罐的间歇精馏工艺动态控制模拟优化。利用Aspen Plus和Aspen Plus Dynamics软件,在稳态模拟的基础上,分别考察了液位控制结构和组分控制结构两种控制方案。结果表明,液位控制结构控制性能较差,达到稳定后甲醇和水的纯度较低。组分控制结构虽能提高产品纯度,但出现了较为严重的振荡现象。根据对组分控制结构的动态响应分析,本文提出了一种改进的组分控制结构,该控制结构能实现带有中间储罐的间歇精馏工艺的稳健控制,使各产品纯度得到提高。     

Real-time monitoring of an industrial batch process

This paper describes the development of a real-time monitoring system for a batch process operated by Aroma and Fine Chemicals Limited. The process shares many similarities with other batch processes in that cycle times can vary considerably, instrumentation is limited and inefficient laboratory assays are required to determine the end-point of each batch. The aim of the work conducted in this study was to develop a data driven system to accurately identify the end-point of the batch. This information can then be used to reduce the overall cycle time of the process. Novel approaches based upon multivariate statistical techniques are shown to provide a soft sensor that can estimate the product quality throughout the batch and provide a long-term estimate of the likely cycle time. This system has been implemented on-line and initial results indicate that it offers potential to significantly reduce operating costs.     

考虑初始操作温度影响的间歇放热反应系统动态柔性分析

It is shown in this article that by changing the initial operation condition of the batch processes, the dynamic performance of the system can be varied largely, especially for the initial operational temperature of the exothermic reaction. The initial operation condition is often ignored in the designing batch processes for flexibility against disturbances or parameter variations. When the initial condition is not rigid as in the case of a batch reactor, where the initial reaction temperature is quite arbitrary, optimization can also be applied to determine the ＂best＂ initial condition to use. Problems for dynamic flexibility analysis of exothermic reaction including initial temperature and process operation can be formulated as dynamic optimization problems. Formulations are derived when the initial conditions are considered or not. When the initial conditions are considered, the initial condition can be transferred into control variables in the first optimal step. The solution of the dynamic optimization is on the basis of Rugge-Kutta integration algorithm and decomposition search algorithm. This method, as illustrated and tested with two highly nonlinear process problems, enables the determination of the optimal level. The dynamic performance is improved by the proposed method in the two exothermic reaction examples.     

Iterative learning controller synthesis using FIR models for batch processes

Adaptive iterative learning control based on the measured input-output data is proposed to solve the traditional iterative learning control problem in the batch process. It produces a control law with self-tuning capability by combining a batch-to-batch model estimation procedure with the control design technique. To build the unknown batch operation system, the finite impulse response (FIR) model with the lifted system is constructed for easy construction of a recursive least squares algorithm. It can identify the pattern of the current operation batch. The proposed model reference control method is applied to feedback control of the lifted system. It finds an appropriate control input so that the desired performance of the batch output can track the prescribed finite-time trajectory by iterative trials. Furthermore, on-line tracking control is developed to explore the possible adjustments of the future input trajectories within a batch. This can remove the disturbances in the current batch rather than the next batch trial and keep the product specifications consistent at the end of each batch. To validate the theoretical findings of the proposed strategies, two simulation problems are investigated.     

实时优化技术在乙烯装置在线优化中的应用

乙烯产业是重要的石化基础产业,对国民经济、石油化工产业及工业社会起着举足轻重的作用,近年来 在我国经济增长过程中保持着蓬勃发展的态势,但是,由于我国乙烯生产总体技术水平的相对滞后以及乙烯工 业所供原料复杂等因素,导致我国目前乙烯装置与世界乙烯生产先进水平存在一定差距,为了减小这一差距, 达到世界先进水平,离不开工业过程自动化和运用实时优化技术。为此,本文介绍了实时优化技术的国内外发 展现状和该技术在乙烯装置的应用情况,并通过实时优化系统的实施,建立了乙烯装置的全流程严格机理模型, 可以实时跟踪乙烯装置的生产情况,持续不断对装置进行在线优化,使装置的操作达到最佳的经济效益操作点, 此外,在优化中以装置的原料、产品和公用工程等价格为导向,对全装置生产过程自动操作执行,减少了优化 计算和结果执行中的人为干预。分析表明实时优化技术有效实现了乙烯装置增产、节能和降耗的目标,可为乙 烯生产企业创造新增经济效益。     

SBR工艺除磷过程与种群结构在线监测

For efficient energy consumption and control of effluent quality, the cycle duration for a sequencing batch reactor (SBR) needs to be adjusted by real-time control according to the characteristics and loading of wastewater. In this study, an on-line information system for phosphorus removal processes was established. Based on the analysis for four systems with different ecological community structures and two operation modes, anaerobic-aerobic process and anaerobic-anaerobic process, the characteristic patterns of oxidation-reduction potential (ORP) and pH were related to phosphorous dynamics in the anaerobic, anoxic and aerobic phases, for determination of the end of phosphorous removal. In the operation mode of anaerobic-aerobic process, the pH profile in the anaerobic phase was used to estimate the relative amount of phosphorous accumulating organisms (PAOs) and glycogen accumulating organisms (GAOs), which is beneficial to early detection of ecology community shifts. The on-line sensor values of pH and ORP may be used as the parameters to adjust the duration for phosphorous removal and community shifts to cope with influent variations and maintain appropriate operation conditions.     

Toward Optimal Operation Conditions of Freeze-Drying Processes via a Multilevel Approach

Freeze drying (lyophilization) offers an attractive dehydration method for valuable food and biological products, because it is capable of preserving product quality and biological activity while extending their shelf life. However, despite these benefits in terms of product quality, freeze drying is also a notoriously energy-intensive and time-consuming process. This requires an expensive operation to construct an efficient optimal decision-making tool able to drive the operation through the most effective paths that minimize time and maximize product quality. Here we propose an integrated approach to operational design and control of the freeze-drying process that combines dynamic modeling with efficient optimized off-line and on-line control. The required mass and energy balance equations still contain inherent nonlinearity, even in their lumped parameter version. This results in a set of complex dynamic, computationally costly optimization problems solved by selected global stochastic optimization algorithms. Real-time disturbances and model uncertainties are addressed via the proposed hierarchical multilevel approach, allowing recalculation of the required control strategies. The framework developed has been revealed as a useful tool to systematically define off-line and on-line optimal operation policies for many food and biological processing units.     

基于推断模型的工业气相聚乙烯装置多牌号质量指标在线控制

针对气相法聚乙烯装置生产中的缺乏质量指标在线测量、大时间滞后和频繁的牌号切换等特性,基于质量指标推断模型,提出一种基于质量指标推断模型的多牌号质量指标在线控制方法。所提方法已成功应用于中石化某气相聚乙烯装置,长周期的运行结果证实了所提方法可以有效地减少产品质量波动,减轻了操作人员的工作强度,取得了显著的经济效益。     

Real‐time control of a semi‐industrial fed‐batch evaporative crystallizer using different direct optimization strategies

This article presents a model‐based control approach for optimal operation of a seeded fed‐batch evaporative crystallizer. Various direct optimization strategies, namely, single shooting, multiple shooting, and simultaneous strategies, are used to examine real‐time implementation of the control approach on a semi‐industrial crystallizer. The dynamic optimizer utilizes a nonlinear moment model for on‐line computation of the optimal operating policy. An extended Luenberger‐type observer is designed to enable closed‐loop implementation of the dynamic optimizer. In addition, the observer estimates the unmeasured process variable, namely, the solute concentration, which is essential for the intended control application. The model‐based control approach aims to maximize the batch productivity, as satisfying the product quality requirements. Optimal control of crystal growth rate is the key to fulfill this objective. This is due to the close relation of the crystal growth rate to product attributes and batch productivity. The experimental results suggest that real‐time application of the control approach leads to a substantial increase, i.e., up to 30%, in the batch productivity. The reproducibility of batch runs with respect to the product crystal size distribution is achieved by thorough seeding. The simulation and experimental results indicate that the direct optimization strategies perform similarly in terms of optimal process operation. However, the single shooting strategy is computationally more expensive. © 2010 American Institute of Chemical Engineers AIChE J, 57: 1557–1569, 2011     

Akustische Planung von petrochemischen Freianlagen — Möglichkeiten,Grenzen, Erfolge

Acoustic planning of open-air petrochemical plants . Open-air petrochemical plants can no longer be built without taking adequate steps regarding noise reduction. This necessitates a knowledge of the state of acoustics technology and its realizable scope of development. Economically, and from a production point of view, noise reduction can optimally be achieved only by acoustic planning prior to construction, since experience has shown that modifications of existing plant are less effective and often present operational difficulties. Acousting planning often calls for harmonizing conflicting requirements. This problem is illustrated using the construction of an extraction plant and an olefin plant as examples. In particular the main sources determining the sound level are dealt with, such as furnaces, waste heat recovery compressor houses, air-coolers, piping and control valves, as well as pumps and electric motors. Successful efforts in acquiring a technique safe in operation and satisfactory in sound reduction are highlighted.     

Model based control of a liquid swelling constrained batch reactor subject to recipe uncertainties

This work presents the application of nonlinear model predictive control (NMPC) to a simulated industrial batch reactor subject to safety constraint due to reactor level swelling, which can occur with relatively fast dynamics. Uncertainties in the implementation of recipes in batch process operation are of significant industrial relevance. The paper describes a novel control-relevant formulation of the excessive liquid rise problem for a two-phase batch reactor subject to recipe uncertainties. The control simulations are carried out using a dedicated NMPC and optimization software toolbox OptCon which implements efficient numerical algorithms. The open-loop optimal control problem is computed using the multiple-shooting technique and the arising nonlinear programming problem is solved using a sequential quadratic programming (SQP) algorithm tailored for large-scale problems, based on the freeware optimization environment HQP. The fast response of the NMPC controller is guaranteed by the initial value embedding and real-time iteration technologies. It is concluded that the OptCon implementation allows small sampling times and the controller is able to maintain safe and optimal operation conditions, with good control performance despite significant uncertainties in the implementation of the batch recipe.     

Optimal averaging level control for the tennessee eastman problem

This paper focuses on an industrial test problem, the Tennessee Eastman problem. The Tennessee Eastman process is moved to three operation modes with 90/10, 50/50 and 10/90 product G/H ratios. The base control system proposed in an earlier paper can properly control the process at all the three operation modes. Optimal averaging level control is added to the base control to improve the control of product flow during random and step feed disturbances. A new implementation of the optimal feedback averaging control algorithms (McDonald et al., 1986) is presented in order to deal with random disturbances. Simulation results show that control of product flow rate is significantly improved by using the optimal feedback averaging level control at all three operation modes.     

一类连续/间歇过程的迭代学习模型预测控制

An iterative learning model predictive control (ILMPC) technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control (ILC) handling repetitive signal and the flexibility of model predictive control (MPC). By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial appli-cation show that the proposed ILMPC method is effective for a class of continuous/batch processes.