Model algorithmic control of grade change operations in paper mills

In this work, the Model Algorithmic Control (MAC) method is applied to control the grade change operations in paper mills. The neural network model for the grade change operations is identified first and the impulse model is extracted from the neural network model. Results of simulations for MAC control of grade change operations are compared with plant operation data. The major contribution of the present work is the application of MAC in the industrial plants based on the identification of neural network models. We can confirm that the proposed MAC method exhibits faster responses and less oscillatory behavior compared to the plant operation data in the grade change operations.     

A study on the adaptive predictive control method for multivariable bilinear processes

A predictive control method for multivariable bilinear processes is derived based on ARMA model. To identify bilinear process models, we use simple equation error method extended to multivariable system. We can obtain the adaptive predictive controller for multivariable bilinear processes by incorporation of the identification algorithm. Offset compensator is provided to correct for the effects of unmeasured disturbances and model inaccuracies. A filter with a singled parameter is used to correct for the effects of an incorrect model. Results of simulation on multivariable bilinear processes show that the proposed control method has satisfactory performance.     

基于扰动观测器的模型预测控制在磨矿分级过程中的应用

磨矿分级过程（GCP）是冶金选矿行业的关键流程,其产品粒度指标必须严格控制,以保证精矿产品品位和金属回收率。GCP本质上是一个多变量强耦合过程,具有时滞和逆向特性,且存在强扰动。扰动的存在造成系统控制性能变差,甚至不稳定。以两输入两输出GCP为研究对象,提出了一种基于扰动观测器（DOB）的模型预测控制（MPC）复合控制方案DOB-MPC。仿真研究表明DOB-MPC不仅可以有效抑制GCP的外部扰动,而且可以抑制由模型失配和变量之间的耦合而导致的内部扰动;在获得良好的解耦控制能力的同时,取得了满意的抗扰动性能。     

ADAPTIVE MODEL PREDICTIVE CONTROL FOR SINGLE INPUT-SINGLE OUTPUT BILINEAR SYSTEMS WITH STABLE INVERSES

An adaptive control system for bilinear processes with stable inverses and without time delay is developed from a bilinear model predictive control algorithm and a projection identification algorithm. If the disturbance is bounded, the control error is bounded and the identification converges. If the disturbance is constant, the control error often converges to zero.     

Prediction and quality control of the melt index during production of high-density polyethylene

In polyolefin processes the melt index (MI) is the most important control variable indicating product quality. Because of the difficulty in the on-line measurement of MI, a lot of MI estimation and correlation methods have been proposed. In this work a new dynamic MI estimation scheme is developed based on system identification techniques. The empirical MI estimation equation proposed in the present study is derived from the 1 ^st -order dynamic models. Effectiveness of the present estimation scheme was illustrated by numerical simulations based on plant operation data including grade change operations in high density polyethylene (HDPE) processes. From the comparisons with other estimation methods it was found that the proposed estimation scheme showed better performance in MI predictions. The virtual sensor model developed based on the estimation scheme was combined with the virtual on-line analyzer (VOA) to give a quality control system to be implemented in the actual HDPE plant. From the application of the present control system, significant reduction of transition time and the amount of off-spec during grade changes was achieved     

Dynamic Process Operation Under Demand Response – A Review of Methods and Tools

Participating in electricity markets through demand response causes new requirements for optimizing process control of chemical plants. The last ten years have brought great advances in the formulation and solution of economic nonlinear model predictive control and state estimation to support operation of processes under dynamic constraints. However, gaps remain regarding the availabilities of suitable plant models capable of describing processes active in demand response as well as of robust schemes for state estimation and economic nonlinear model predictive control in commercial tools.     

A multiple model,state feedback strategy for robust control of non-linear processes

The major limitation of reported multiple model approaches is that robustness against process/controller disturbances cannot be addressed for processes consisting of hybrid stable/unstable regimes, or with chaotic dynamics. In this paper, a significantly modified multiple model approach is developed to achieve robust control with global stability. The new advances include: (1) stabilization of open-loop unstable plants using a state feedback strategy, (2) incorporation of an adjustable pre-filter to achieve offset-free control, (3) implementation of a Kalman filter for state estimation, and (4) connection of the multiple model approach with non-linear model predictive control to achieve a precise control objective. The improved controller design method is successfully applied to two non-linear processes with different chaotic behaviour. Compared with conventional methods without model modifications, the new approach has achieved significant improvement in control performance and robustness with a dramatically reduced number of local models.     

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.     

基于定量反馈理论的多变量解耦预测控制及其在CSTR化学过程中的应用

1 INTRODUCTION Many multi-input and multi-output (MIMO) sys- tems worldwide are regarded as linear invariants, but there are still some difficulties in controlling these systems. The challenges arise from the need to achieve both robust stability and control performance when the plants to be controlled are highly uncer- tain[1―3]. Quantitative feedback theory (QFT) is a fre- quency domain design technique[4], which is perhaps the only known method that deals with highly uncer- tain pla…     

MD-CD interaction modeling and control of paper machines

For better quality control of paper machines, variations in the machine direction (MD), cross-machine direction (CD) and their inherent interactions should be minimized. In this paper, a dynamic MD-CD interaction model is developed by relating the effect of MD dry basis weight to the CD profile. Based on this model, a combined MD-CD generalized predictive control strategy is proposed to handle the strong MD-CD interaction. A set of industrial data was used to identify the interaction model. Results from closed-loop simulation of the interaction model under the proposed control strategy show that a significant improvement in CD control during a grade change can be achieved.     

A hybrid system of heuristics and simulated annealing for large size scheduling problems of batch processes with pipeless intermediate storage

This paper describes a new scheduling solution for large number multi-product batch processes with complex intermediate storage system. Recently many batch chemical industries have turned their attention to a more efficient system known as a pipeless batch system. But existing plants need to change their systems to pipeless systems, piece by piece. In this case, current systems are changed to pipeless systems by way of non critical process operations such as through the use of intermediate storage. We have taken the conventional batch plant with a pipeless storage system as an objective process. Although the operation of a pipeless storage system becomes more complex, its efficiency is very high. With this system, all of the storage should be commonly used by any batch unit. For this reason, solving the optimal scheduling problem of this system with a mathematical method is very difficult. Despite the attempts of many previous researches, there has been no contribution which solves the scheduling of intermediate storage for complex batch processes. In this paper, we have developed a hybrid system of heuristics and Simulated Annealing (SA) for large multi-product processes using a pipeless storage system. The results of this study show that the performance and computational time of this method are superior to that of SA and Rapid Access Extensive Search (RAES) methods.     

Novel soft sensor method for detecting completion of transition in industrial polymer processes

Soft sensors are widely used to estimate process variables that are difficult to measure online. In polymer plants that produce various grades of polymers, the quality of products must be estimated using soft sensors in order to reduce the amount of off-grade material. However, during grade transition, the predictive accuracy deteriorates because the state in polymer reactors is unsteady, causing the values of process variables to differ from the steady-state values used to construct regression models. Therefore, we have proposed to construct models that detect the completion of transition to ensure that the polymer quality evaluated after transition conforms to the predicted one. By using these models and regression models constructed for each product grade, the polymer quality can be predicted with high accuracy, selecting a regression model appropriately. The proposed method was applied to industrial plant data and was found to exhibit higher predictive performance than traditional methods.     

基于动态过程划分的熔融指数软测量建模

牌号切换过程是聚合反应过程节能降耗的关键环节,已有的模型往往因为质量样本较少而弱化甚至避开这一过程。因此,在之前专门针对牌号切换过程提出的三阶段分解方法的基础上,进一步面向整个生产过程,针对聚丙烯反应过程存在的同一牌号的稳定生产过程以及不同牌号间的切换过程具有不同动态过程的特性,按照不同的生产模式和生产牌号划分不同动态过程的样本,采用多模型的方法在各自的样本集上建立子模型,有针对性地把握相应的动态变化规律。为了实现多个子模型之间的切换,进一步基于反应条件和反应结果估计值构建了综合判断模型。最后,通过实际数据验证,三阶段多模型相对Kim多模型、单一模型来说,具有更好的预测结果。     

Adaptive Linear Dynamic Matrix Control applied to an integrating process

Dynamic Matrix Control (DMC) has proven to be a powerful tool for optimal regulation of chemical processes under constrained conditions. It is based on a linear convolution model derived from step-response measurements. A model predictive control algorithm optimises closed-loop performance for a nominal operating point. However, as the process moves away from this point, control usually becomes sub-optimal due to process non-linearity. As seen in this work, the DMC algorithm can be made adaptive, to establish a new local model, by recursive estimation of the local step response parameters from normal plant variations. However, when used for control of plants containing integrating process units, steady-state offsets occur for sustained changes. Thus, a novel Adaptive Linear Dynamic Matrix Control (ALDMC) algorithm has been developed and used to control a 2-input/2-output system with an integrating behaviour. Comparisons of model control and plant control with and without these features demonstrated the importance of integral compensation for integrating processes, and model adaptation in the case of plant/model mismatch. Some cross-compensation of integration by the adaptive feature was also noted. An holistic technique is demonstrated which simultaneously recognises residual integration disturbances and matrix parameter variations, whereas previous techniques which recognise only one of these will fail in the presence of the other.     

Application of state estimation based NMPC to an unstable nonlinear process

Model predictive control (MPC) has become very popular both in process industry and academia due to its effectiveness in dealing with nonlinear, multivariable and/or hard-constrained plants.Although linear MPC can be applied for controlling nonlinear processes by obtaining a linearized model of the plant, this is only valid in a limited region. Therefore, a substantial improvement can be achieved by using the whole knowledge of the process dynamics, specially in the presence of marked nonlinearities. This effect can be strong if the process to control is open-loop unstable.The purpose of this paper is to introduce a nonlinear model predictive controller (NMPC) based on nonlinear state estimation, in order to exploit the knowledge of the nonlinear dynamics and to avoid modeling simplifications or linearization.A state-space formulation is proposed to achieve the control objective. To update the optimization involved in NMPC strategy, state estimation based on the measured outputs is proposed.As a particular application, we consider an open-loop unstable jacketed exothermic chemical reactor. This CSTR is widely recognized as a difficult problem for the purpose of control. In order to achieve the control goal, a NMPController coupled with a state observer are designed. The observer is also used to estimate some unmeasured disturbances. Finally, computer simulations are developed for showing the performance of both the nonlinear observer and the control strategy.     

Investigation of a Model-Based Deep Reinforcement Learning Controller Applied to an Air Separation Unit in a Production Environment

The need for load flexibility and increased efficiency of energy-intensive processes has become more and more important in recent years. Control of the process variables plays a decisive role in maximizing the efficiency of a plant. The widely used control models of linear model predictive controllers (LMPC) are only partly suitable for nonlinear processes. One possibility for improvement is machine learning. In this work, one approach for a purely data-driven controller based on reinforcement learning is explored at an air separation plant (ASU) in productive use. The approach combines the model predictive controller with a data-generated nonlinear control model. The resulting controller and its control performance are examined in more detail on an ASU in real operation and compared with the previous LMPC solution. During the tests, stable behavior of the new control concept could be observed for several weeks in productive operation.     

Reference Trajectory Optimization Under Constrained Predictive Control

Chemical process systems often need to respond to frequently changing product demands. This motivates the determination of optimal transitions, subject to specification and operational constraints. However, direct implementation of optimal input trajectories would, in general, result in offset in the presence of disturbances and plant/model mismatch. This paper considers reference trajectory optimization of processes controlled by constrained model predictive control (MPC). Consideration of the closed‐loop dynamics of the MPC‐controlled process in the reference trajectory optimization results in a multi‐level optimization problem. A solution strategy is applied in which the MPC quadratic programming subproblems are replaced by their Karush‐Kuhn‐Tucker optimality conditions, resulting in a single‐level mathematical program with complementarity constraints (MPCC). The performance of the method is illustrated through application to two case studies, the second of which considers economically optimal grade transitions in a polymerization process.     

基于RBF神经网络陶瓷窑炉温度动态矩阵控制

陶瓷窑炉普遍具有纯滞后、大惯性、非线性、时变复杂等特点,其精确数学模型往往很难获取。针对这类系统,本文采用RBF神经网络建立被控对象模型,避免了常规控制算法建立对象精确数学模型的困难。应用动态矩阵预测算法实现对被控系统的预测控制。该控制方法具有很好的动、静态性能和强鲁棒性。以陶瓷窑炉温度为对象,与PID控制进行了比较;仿真结果证明了所提控制方法的有效性。     

Adaptive model predictive inventory controller for multiproduct batch plant

An inventory control system was developed for multiproduct batch plants with an arbitrary number of batch processes and storage units. Customer orders are received by the plant at order intervals and in order quantities that are subject to random fluctuations. The objective of the plant operation is to minimize the total cost while maintaining inventory levels within the storage or warehouse capacity by adjusting the startup times, the quantities of raw material orders, and production batch sizes. An adaptive model predictive control algorithm was developed that uses a periodic square wave model to represent the flows of the material between the processes and the storage units. The boundedness of the control output and the convergence of the estimated parameters in implementations of the proposed algorithm were mathematically proven under the assumption that disturbances in the orders are bounded. The effectiveness of this approach was demonstrated by performing simulations. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1867–1880, 2015     

Nonlinear model predictive control: current status and future directions

Linear model predictive control (LMPC) is well established as the industry standard for controlling constrained multivariable processes. A major limitation of LMPC is that plant behavior is described by linear dynamic models. As a result, LMPC is inadequate for highly nonlinear processes and moderately nonlinear processes which have large operating regimes. This shortcoming coupled with increasingly stringent demands on throughput and product quality has spurred the development of nonlinear model predictive control (NMPC). NMPC is conceptually similar to its linear counterpart except that nonlinear dynamic models are used for process prediction and optimization. The purpose of this paper is to provide an overview of current NMPC technology and applications, as well as to propose topics for future research and development. The review demonstrates that NMPC is well suited for controlling multivariable nonlinear processes with constraints, but several theoretical and practical issues must be resolved before widespread industrial acceptance is achieved.