共查询到20条相似文献,搜索用时 31 毫秒
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Won Hyun Kwon Kyung Hwan Ryu Jung-A Hwang Kyeong Hoon Kim Jay H. Lee Su Whan Sung 《Korean Journal of Chemical Engineering》2018,35(6):1240-1246
Previous batch control methods, such as iterative learning control (ILC) or run-to-run (R2R) control, can significantly improve the control performance of the batch process. However, to guarantee the expected good control performance, a fairly accurate process model is required for these controllers. Also, the implementation is numerically complicated so that it is difficult to be applied to real manufacturing processes. To overcome these problems, a new batch proportional-integral-derivative (PID) control method is proposed, which borrows the concept of the conventional PID control method. Simulation studies confirm that the proposed method shows acceptable performance in tracking a setpoint trajectory, rejecting disturbances, and robustness to noises and variation of process dynamics. The application to the commercial batch process of a single crystal grower verifies that the proposed method can significantly contribute to improving the control performances of real batch processes. 相似文献
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A batch-to-batch optimal iterative learning control (ILC) strategy for the tracking control of product quality in batch processes is presented. The linear time-varying perturbation (LTVP) model is built for product quality around the nominal trajectories. To address problems of model-plant mismatches, model prediction errors in the previous batch run are added to the model predictions for the current batch run. Then tracking error transition models can be built, and the ILC law with direct error feedback is explicitly obtained. A rigorous theorem is proposed, to prove the convergence of tracking error under ILC. The proposed methodology is illustrated on a typical batch reactor and the results show that the performance of trajectory tracking is gradually improved by the ILC. 相似文献
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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. 相似文献
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Gonzalo Guillén‐Gosálbez Ignacio E. Grossmann 《American Institute of Chemical Engineers》2009,55(1):99-121
This article addresses the design of sustainable chemical supply chains in the presence of uncertainty in the life cycle inventory associated with the network operation. The design task is mathematically formulated as a bi‐criterion stochastic mixed‐integer nonlinear program (MINLP) that simultaneously accounts for the maximization of the net present value and the minimization of the environmental impact for a given probability level. The environmental performance is measured through the Eco‐indicator 99, which incorporates the recent advances made in Life Cycle Assessment. The stochastic model is converted into its deterministic equivalent by reformulating the probabilistic constraint required to calculate the environmental impact in the space of uncertain parameters. The resulting deterministic bi‐criterion MINLP problem is further reformulated as a parametric MINLP, which is solved by decomposing it into two sub‐problems and iterating between them. The capabilities of the proposed model and solution procedure are illustrated through two case studies for which the set of Pareto optimal, or efficient solutions that trade‐off environmental impact and profit, are calculated. These solutions provide valuable insights into the design problem and are intended to guide the decision maker towards the adoption of more sustainable design alternatives. © 2008 American Institute of Chemical Engineers AIChE J, 2009 相似文献
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This paper proposes the simultaneous integration of environmentally benign solvent selection (product design), solvent recycling (process design) and optimal control for the separation of azeotropic systems using batch distillation. The previous work performed by Kim et al. (2004. Entrainer selection and solvent recycling in complex batch distillation. Chemical Engineering Communications 191(12), 1606-1633) combines the chemical synthesis and process synthesis under uncertainty. For batch distillation, optimal operation is also important due to the unsteady state nature of the process and high operating costs. Optimal control allows us to optimize the column operating policy by selecting a trajectory for the reflux ratio. However, there are time-dependent uncertainties in thermodynamic models of batch distillation due to the assumption of constant relative volatility. In this paper, the uncertainties in relative volatility were modeled using Ito processes and the stochastic optimal control problem was solved by combined maximum principle and non-linear programming (NLP) techniques. Then the previous work of optimal solvent selection and recycling was coupled with optimal control. As a real world example for this integrated approach, a waste stream containing acetonitrile-water was studied. The optimal design parameters obtained by Kim et al. (2004. Entrainer selection and solvent recycling in complex batch distillation. Chemical Engineering Communications 191(12), 1606-1633), for this separation were used and the optimal control policy is computed first without considering uncertainties by variable transformation technique. The deterministic optimal control policy improves the product yield by 4.0% as compared to the base case, verified using a rigorous simulator for batch distillation. When the stochastic optimal control policy was computed representing the relative volatility as an Ito process, a similar recovery rate was obtained from simulations, but the batch time was reduced significantly, producing the most profitable operation. 相似文献
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Based on the two-dimensional (2D) systemtheory, an integrated predictive iterative learning control (2D-IPILC) strategy for batch processes is presented. First, the output response and the error transition model predictions along the batch index can be calculated analytically due to the 2D Roesser model of the batch process. Then, an integrated framework of combining iterative learning control (ILC) andmodel predictive control(MPC) is formed reasonably. The output of feedforward ILC is estimated on the basis of the predefined process 2D model. By minimizing a quadratic objective function, the feedback MPC is introduced to obtain better control performance for tracking problem of batch processes. Simulations on a typical batch reactor demonstrate that the satisfactory tracking performance as well as faster convergence speed can be achieved than traditional proportion type (Ptype) ILC despite the model error and disturbances. 相似文献
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The paper presents an approach to improve the product quality from batch-to-batch by exploiting the repetitive nature of batch processes to update the operating trajectories using process knowledge obtained from previous runs. The data based methodology is focused on using the linear time varying (LTV) perturbation model in an iterative learning control (ILC) framework to provide a convergent batch-to-batch improvement of the process performance indicator. The major contribution of this work is the development of a novel hierarchical ILC (HILC) scheme for systematic design of the supersaturation controller (SSC) of seeded batch cooling crystallizers. The HILC is used to determine the required supersaturation setpoint for the SSC and the corresponding temperature trajectory required to produce crystals with desired end-point property. The performance and robustness of these approaches are evaluated through simulation case studies. These results demonstrate the potential of the ILC approaches for controlling batch processes without rigorous process models. 相似文献
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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. 相似文献
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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. 相似文献
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Performance assessment of model predictive controllers is a problem of significant industrial relevance. Model predictive controllers belong to a class of linear time‐varying controllers, which compute the future control actions by minimizing a constrained, time‐varying objective function. In this work we propose a performance statistic that takes into account the time‐varying and constrained nature of model predictive control. The proposed measure compares the achieved objective function with its design value, online. Analytical expressions are derived to calculate the expected value of the design objective function under closed loop conditions. Simulation and industrial case studies are used to illustrate the applicability of the proposed metric. 相似文献
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Enhanced Performance Assessment of Subspace Model‐Based Predictive Controller with Parameters Tuning
This study focuses on performance assessment of model predictive control. An MPC‐achievable benchmark for the unconstrained case is proposed based on closed‐loop subspace identification. Two performance measures can be constructed to evaluate the potential benefit to update the new identified model. Potential benefit by tuning the parameter can be found from trade‐off curves. Effect of constraints imposed on process variables can be evaluated by the installed controller benchmark. The MPC‐achievable benchmark for the constrained case can be estimated via closed‐loop simulation provided that constraints are known. Simulation of an industrial example was done using the proposed method. 相似文献
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Uncertainty‐conscious methodology for process performance assessment in biopharmaceutical drug product manufacturing 下载免费PDF全文
Gioele Casola Hirokazu Sugiyama Christian Siegmund Markus Mattern 《American Institute of Chemical Engineers》2018,64(4):1272-1284
This work presents an uncertainty‐conscious methodology for the assessment of process performance—for example, run time—in the manufacturing of biopharmaceutical drug products. The methodology is presented as an activity model using the type 0 integrated definition (IDEF0) functional modeling method, which systematically interconnects information, tools, and activities. In executing the methodology, a hybrid stochastic–deterministic model that can reflect operational uncertainty in the assessment result is developed using Monte Carlo simulation. This model is used in a stochastic global sensitivity analysis to identify tasks that had large impacts on process performance under the existing operational uncertainty. Other factors are considered, such as the feasibility of process modification based on Good Manufacturing Practice, and tasks to be improved is identified as the overall output. In a case study on cleaning and sterilization processes, suggestions were produced that could reduce the mean total run time of the processes by up to 40%. © 2017 American Institute of Chemical Engineers AIChE J, 64: 1272–1284, 2018 相似文献
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基于广义预测控制的间歇生产迭代优化控制 总被引:2,自引:1,他引:1
针对间歇生产,提出了一种基于广义预测控制的批次迭代优化控制策略--BGPC,在间歇过程中引入批次间优化的思想,将迭代学习控制ILC和广义预测控制GPC相结合,在GPC实时结构参数辨识的基础上利用前面批次的模型预测误差修正当前批次的模型预测值.该算法能够有效地克服模型失配、扰动和系统参数变化等情况.文章最后以一个数值例子和间歇反应器为对象进行仿真试验,验证了该算法是有效的. 相似文献
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Jie Zhang 《Chemical engineering science》2008,63(5):1273-1281
A neural network based batch-to-batch optimal control strategy is proposed in this paper. In order to overcome the difficulty in developing mechanistic models for batch processes, stacked neural network models are developed from process operational data. Stacked neural networks have enhanced model generalisation capability and can also provide model prediction confidence bounds. However, the optimal control policy calculated based on a neural network model may not be optimal when applied to the true process due to model plant mismatches and the presence of unknown disturbances. Due to the repetitive nature of batch processes, it is possible to improve the operation of the next batch using the information of the current and previous batch runs. A batch-to-batch optimal control strategy based on the linearisation of stacked neural network model is proposed in this paper. Applications to a simulated batch polymerisation reactor demonstrate that the proposed method can improve process performance from batch to batch in the presence of model plant mismatches and unknown disturbances. 相似文献
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Optimal placement of gas detectors: A P‐median formulation considering dynamic nonuniform unavailabilities 下载免费PDF全文
A. J. Benavides‐Serrano M. S. Mannan C. D. Laird 《American Institute of Chemical Engineers》2016,62(8):2728-2739
A stochastic programming formulation (SPqt), based on the P‐median problem, is proposed for determining the optimal placement of detectors in mitigation systems while considering nonuniform dynamic detector unavailabilities. Unlike previously proposed formulations, SPqt explicitly considers backup detection levels. This allows the modeller to determine the maximum degree of the nonlinear products to be used based on the trade‐off between computational complexity and solution accuracy. We analyze this trade‐off on formulation SPqt results by using 4 real data sets for the gas detector placement problem while using unavailability values obtained from real industry gas detector data. For this data, our results show that two detection levels are sufficient to find objective values within 1% of the optimal solution. Using two detection levels reduces the nonlinear formulation to a quadratic formulation. Three solution strategies are proposed for this quadratic formulation and then compared from the computational efficiency perspective. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2728–2739, 2016 相似文献
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A batch-to-batch optimal control approach for batch processes based on batch-wise updated nonlinear partial least squares (NLPLS) models is presented in this article. To overcome the difficulty in developing mechanistic models for batch/semi-batch processes, a NLPLS model is developed to predict the final product quality from the batch control profile. Mismatch between the NLPLS model and the actual plant often exists due to low-quality training data or variations in process operating conditions. Thus, the optimal control profile calculated from a fixed NLPLS model may not be optimal when applied to the actual plant. To address this problem, a recursive nonlinear PLS (RNPLS) algorithm is proposed to update the NLPLS model using the information newly obtained after each batch run. The proposed algorithm is computationally efficient in that it updates the model using the current model parameters and data from the current batch. Then the new optimal control profile is recalculated from the updated model and implemented on the next batch. The procedure is repeated from batch to batch and, usually after several batches, the control profile will converge to the optimal one. The effectiveness of this method is demonstrated on a simulated batch polymerization process. Simulation results show that the proposed method achieves good performance, and the optimization with the proposed NLPLS model is more effective and stable than that with a batch-wise updated linear PLS model. 相似文献