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.     

Closed-loop step response identification of integrating and unstable processes

Motivated by the fact that integrating and unstable processes are usually operated in a closed-loop manner for safety and economic reasons, this paper proposes a systematic closed-loop identification method based on step response test to facilitate closed-loop system operation and on-line optimization. To avoid jeopardizing the closed-loop system stability of such a process, guidelines are given for proper implementation of a closed-loop step test for model identification. By introducing a damping factor to the closed-loop step response for realization of the Laplace transform in frequency domain, a frequency response estimation algorithm is developed in terms of the closed-loop control structure used for identification. Accordingly, three model identification algorithms are derived analytically in frequency domain to obtain the widely used low-order process models of first-order-plus-dead-time (FOPDT) and second-order-plus-dead-time (SOPDT). To enhance fitting accuracy for a higher order process, in particular for a specified frequency range interested to control design and on-line tuning, a weighted least-squares fitting algorithm is also given based on the estimated process frequency response points. Illustrative examples from the recent literature are used to demonstrate the effectiveness and merits of the proposed identification algorithms.     

On-line optimal control of a seeded batch cooling crystallizer

In this paper, an on-line optimal control methodology is developed for the optimal quality control of a seeded batch cooling crystallizer process. An extended Kalman filter is successfully implemented to predict seven unmeasured state variables based on three measurements in the batch process. A PI controller is used in a feedback control system to implement the optimal path. It is found that the PI controller can ensure tracking of the optimal path. The simulation results show that on-line optimal control strategy leads to a substantial improvement of the end product quality expressed in terms of the mean size and the width of the distribution. The effects of the plant/model mismatch and disturbances are also tested and discussed.     

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.     

Nonlinear state estimation and control of a batch reactive distillation

This paper deals with the advanced adaptive control of a batch reactive distillation (RD) column for the production of ethyl acetate. The nonlinear adaptive control law consists of the generic model controller (GMC) and an adaptive state estimator (ASE). In the first part of the present work, the design approach of the ASE scheme in two different forms, namely ASE1 and ASE2, has been addressed for a batch reactive rectifier. The predictor model of both the ASE estimators includes only a component mole balance equation around the condenser-reflux drum system and an extra state equation having no dynamics, and therefore, there is a large process/predictor mismatch. In presence of this structural discrepancy, the adaptive estimation schemes compute the imprecisely known parameters quite accurately based on the measured distillate composition under initialization error, disturbance and uncertainty. In the subsequent part, the adaptive GMC–ASE1 control structure has been formulated for the sample reactive column. This nonlinear control strategy shows comparatively better closed-loop performance than the gain-scheduled proportional integral (GSPI) controller due to the exponential error convergence capability of the estimation scheme and the high-quality control of the GMC law.     

Simultaneous optimization and control for polypropylene grade transition with two-layer hierarchical structure

In this paper, a two-layer hierarchical structure of optimization and control for polypropylene grade transition was raised to overcome process uncertain disturbances that led to the large deviation between the open-loop reference trajectory and the actual process. In the upper layer, the variant time scale based control vector parametric methods (VTS-CVP) was used for dynamic optimization of transition reference trajectory, while nonlinear model predictive controller (NMPC) based on closed-loop subspace and piece-wise linear (SSARX-PWL) model in the lower layer was tracking to the reference trajectory from the upper layer for overcoming high-frequency disturbances. Besides, mechanism about trajectory deviation detection and optimal trajectory updating onlinewere introduced to ensure a smooth transition for the entire process. The proposed method was validated with the real data from an industrial double-loop propylene polymerization reaction process with developed dynamic mechanismmathematicalmodel.     

Optimization and nonlinear control of a batch crystallization process

Crystallization process has been widely used for separation in many chemical industries due to its capability to provide high purity product. To obtain the desired quality of crystal product, an optimal cooling control strategy is studied in the present work. Within the proposed control strategy, a dynamic optimization is first preformed with the objective to obtain the optimal cooling temperature policy of a batch crystallizer, maximizing the total volume of seeded crystals. Two different optimization problems are formulated and solved by using a sequential optimization approach. Owing to the complex and nonlinear behavior of the batch crystallizer, the nonlinear control strategy which is based on a generic model control (GMC) algorithm is implemented to track the resulting optimal temperature profile. The optimization integrated with nonlinear control strategy is demonstrated on a seeded batch crystallizer for the production of potassium sulfate.     

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.     

ADAPTIVE CONTROL OF A BATCH REACTOR

A globally stable adaptive predictive control system (APCS) is evaluated by application to a simulated PVC batch reactor. The reactor is run under APCS control with the objective of either temperature setpoint tracking or constant reaction rate. The batch nature of this system makes it possible to learn about the physical problem from successive runs. This knowledge is incorporated into the control strategy to improve the performance of the reactor. The problem of excessive manipulation of the control variable has been recognized and resolved by using control weighting. Performance of the adaptive technique is compared with previous results using self-tuning and PID control of the same reactor. APCS provides good, robust control despite the nonlinear dynamics of the system.     

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.     

A simple adaptive control strategy for temperature trajectory tracking in batch processes

In this paper, a simple adaptive control strategy is suggested for temperature tracking control of batch processes. A nonlinear controller, which is in structure very simple and consists of a single parameter, is proposed. To enable this controller to control a batch process adaptively, a simple parameter tuning algorithm is derived based on the Lyapunov stability theorem. The proposed adaptive control scheme is directly operational, which does not depend on process model and the only a priori process information required is the system response direction. To demonstrate the effectiveness and applicability of the proposed scheme, illustrative examples are provided. Extensive simulation results reveal that the proposed adaptive control strategy appears to be a simple and effective approach to batch process control, which provides robust control despite the wide range of operating conditions and nonlinear dynamics of the system.     

Pattern-based closed-loop quality control for the injection molding process

The basis for a novel pattern-based closed-loop control strategy for the injection molding process is presented. The strategy uses artificial neural networks (ANNs) embedded within a cascade design to analyze sensor patterns, identify process character and control part quality. The platform for this work, the injection molding process, is an industrially significant, cyclic manufacturing operation. Final part quality of this process is a nonlinear function of many machine and polymer variables. Part quality control of this process is currently attained via single input–single output machine controls supervised by human operators. Presented here is a method that employs ANN technology to improve upon this approach and provide the basis for closed-loop part quality control. In the cascade design, machine controller set-points of an inner loop are updated based on ANN analysis of mold cavity pressure patterns. The controller action maintains the desired pressure pattern set-point of the outer loop associated with desired part quality. Control strategy details are provided along with set-point tracking demonstrations that support feasibility of this pattern-based approach.     

Tracking the predefined optimal policies for multiple-fraction batch distillation by using adaptive control

Predefined optimal policies will be tracked with control systems to realize the optimum of multiple-fraction batch distillation. Adaptive control is proposed to carry out this task. Characteristics of batch distillation control are analyzed and a proper system is designed for controlling such processes. Besides tracking the optimal reflux ratio profile, the maximum vapor load will be maintained during the batch. In addition, a changing temperature profile of the condenser should be followed to reduce the operating energy with a possibly minimum subcooling. Recursive least square estimation (RLSE) with a variable forgetting factor is applied to the on-line identification of the plant to follow the changing dynamics of the process. Generalized predictive control (GPC) is used to track the predefined policies. The effectiveness of the control strategy is verified with a pilot batch column and the tracking performance is compared with that of PID controllers.     

Closed-loop optimal operation of batch distillation columns

Often, the main source of disturbance for a batch distillation system is an upset in the feed to the process. If the operation of a batch column is carried out on the basis of the nominal value of the feed composition, a high degree of uncertainty in the initial conditions to the batch may lead to run the column suboptimally, with a possibly large economic penalty. In this paper, a three-step strategy is proposed for the closed-loop implementation of optimal operating policies for batch rectifiers. First, the optimal reflux rate is calculated off-line for several feed compositions. Then, a correlation is developed off-line between the optimal reflux rate and the composition profile in the column at the end of the startup phase. Finally, the detection of the composition profile is performed on-line during the startup phase, so that the optimal reflux rate can be calculated and implemented in a closed-loop fashion. This allows operating the column optimally even though the actual feed composition is not known. Since the calculations to be performed on-line are straightforward, the computational demand is kept to a minimum. Results for binary and ternary systems indicate that, by using the proposed procedure, the column performance can be improved by as much as 30% with respect to a conventional open-loop optimal strategy.     

利用状态空间模型和不敏卡尔曼滤波的分批补料发酵过程在线估计(英文)

On-line estimation of unmeasurable biological variables is important in fermentation processes, directly influencing the optimal control performance of the fermentation system as well as the quality and yield of the targeted product. In this study, a novel strategy for state estimation of fed-batch fermentation process is proposed. By combining a simple and reliable mechanistic dynamic model with the sample-based regressive measurement model, a state space model is developed. An improved algorithm, swarm energy conservation particle swarm optimization (SECPSO), is presented for the parameter identification in the mechanistic model, and the support vector machines (SVM) method is adopted to establish the nonlinear measurement model. The unscented Kalman filter (UKF) is designed for the state space model to reduce the disturbances of the noises in the fermentation process. The proposed on-line estimation method is demonstrated by the simulation experiments of a penicillin fed-batch fermentation process.     

基于自适应神经模糊推理系统的间歇反应精馏组分估算(英文)

Composition estimation plays very important role in plant operation and control. Extended Kalman filter (EKF) is one of the most common estimators, which has been used in composition estimation of reactive batch distillation, but its performance is heavily dependent on the thermodynamic modeling of vapor-liquid equilibrium, which is difficult to initialize and tune. In this paper an inferential state estimation scheme based on adaptive neuro-fuzzy inference system (ANFIS), which is a model base estimator, is employed for composition estimation by using temperature measurements in multicomponent reactive batch distillation. The state estimator is supported by data from a complete dynamic model that includes component and energy balance equations accompanied with thermodynamic relations and reaction kinetics. The mathematical model is verified by pilot plant data. The simulation results show that the ANFIS estimator provides reliable and accurate estimation for component concentrations in reactive batch distillation. The estimated states form a basis for improving the performance of reactive batch distillation either through decision making of an operator or through an automatic closed-loop control scheme.     

Model-based control strategies for a chemical batch reactor with exothermic reactions

Batch reactor control provides a very challenging problem for the process control engineer. This is because a characteristic of its dynamic behavior shows a high nonlinearity. Since applicability of the batch reactor is quite limited to the effectiveness of an applied control strategy, the use of advanced control techniques is often beneficial. This work presents the implementation and comparison of two advanced nonlinear control strategies, model predictive control (MPC) and generic model control (GMC), for controlling the temperature of a batch reactor involving a complex exothermic reaction scheme. An extended Kalman filter is incorporated in both controllers as an on-line estimator. Simulation studies demonstrate that the performance of the MPC is slightly better than that of the GMC control in nominal case. For model mismatch cases, the MPC still gives better control performance than the GMC does in the presence of plant/model mismatch in reaction rate and heat transfer coefficient.     

Dynamic On‐Line Reoptimization Control of a Batch MMA Polymerization Reactor Using Hybrid Neural Network Models

A hybrid neural network model based on‐line reoptimization control strategy is developed for a batch polymerization reactor. To address the difficulties in batch polymerization reactor modeling, the hybrid neural network model contains a simplified mechanistic model covering material balance assuming perfect temperature control, and recurrent neural networks modeling the residuals of the simplified mechanistic model due to imperfect temperature control. This hybrid neural network model is used to calculate the optimal control policy. A difficulty in the optimal control of batch polymerization reactors is that the optimization effort can be seriously hampered by unknown disturbances such as reactive impurities and reactor fouling. With the presence of an unknown amount of reactive impurities, the off‐line calculated optimal control profile will be no longer optimal. To address this issue, a strategy combining on‐line reactive impurity estimation and on‐line reoptimization is proposed in this paper. The amount of reactive impurities is estimated on‐line during the early stage of a batch by using a neural network based inverse model. Based on the estimated amount of reactive impurities, on‐line reoptimization is then applied to calculate the optimal reactor temperature profile for the remaining time period of the batch reactor operation. This approach is illustrated on the optimization control of a simulated batch methyl methacrylate polymerization process.     

自适应混合粒子群约束优化算法及其在软测量模型参数估计中的应用

对于软测量模型参数估计问题, 针对传统梯度法求解非线性最小二乘模型时依赖初值、需要追加趋势分析进行验证和无法直接求解复杂问题的缺陷, 提出将参数估计化为约束优化问题, 使用混合优化算法求解的新思路。为此提出一种自适应混合粒子群约束优化算法(AHPSO-C)。在AHPSO-C算法中, 为平衡全局搜索(混沌粒子群)和局部搜索(内点法), 引入自适应内点法最大函数评价次数更新策略。对12个经典测试函数的仿真结果表明, AHPSO-C是求解约束优化问题的一种有效算法。将算法用于淤浆法高密度聚乙烯(HDPE)串级反应过程中熔融指数软测量模型参数估计, 验证了方法的可行性与优越性。