model predictive control of batch processes based on two

Model predictive control of batch processes based on two ...

May 01, 2018  Chen et al. combined the model predictive control (MPC) with the ILC based on 2-D theory for linear batch processes . To guarantee robust convergence along both time and batch directions, a 2D ILC scheme which integrates feedback control with feedforward control was developed by Gao et al. for robust tracking of desired trajectory [ 17 ].

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A two-dimensional design of model predictive control for ...

Sep 01, 2019  A modified two-dimensional (2D) structure based control strategy that incorporates model predictive control (MPC) and iterative learning control (ILC) in 2D sense is developed for batch processes in this article.

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Model predictive control for batch processes: Ensuring ...

Jan 01, 2014  5.3. Control results. The target is to define the trajectory of the MV, [u 1 u 100], so that the predicted quality at the end of the batch, y ˆ 1, reaches a desired set point.The determination of the MV trajectory is taken given the initial state of the batch, [m 1 m 4], and with the model identified in the previous section. Three different set points for quality are evaluated: 9.5 ...

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Two-Dimensional Iterative Learning Model Predictive ...

Dec 13, 2018  Abstract: To achieve improved control performance of batch processes under uncertainty, a novel two-dimensional model predictive iterative learning control (2D-MPILC) scheme is proposed. First, a new two-dimensional (2-D) extended nonminimal state space model is formulated where more degrees of freedom are offered for further controller design ...

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Apply Model Predictive Control to Reduce Batch Cycle Time ...

Model Predictive Control for Batch Processes MPC is an optimization-based multivariable control strategy that uses a mathematical model, incorporated into a control system, to predict in real-time the control action to be taken on the process. The predictive model

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Robust Nonlinear Model Predictive Control of Batch Processes

NMPC is an optimization-based multivariable constrained control technique using a nonlinear dynamic process model for the prediction of the process outputs Allgower et al.,Ž ¨ 1999; Bequette, 1991 . At each sampling time, the model is. 1776 July 2003 Vol. 49, No. 7 AIChE Journal

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Design and Analysis of Integrated Predictive Iterative ...

Jul 01, 2014  Based on the two-dimensional (2D) system theory, an integrated predictive iterative learning control (2D-IPILC) strategy for batch processes is presen

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Robust nonlinear model predictive control of batch processes

Apr 16, 2004  The control and analysis approaches are applied to a simulated batch crystallization process with a realistic uncertainty description. The proposed robust NMPC algorithm improves the robust performance by a factor of six compared to open loop optimal control, and a factor of two compared to nominal NMPC.

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Two-Dimensional Iterative Learning Model Predictive ...

Dec 13, 2018  Abstract: To achieve improved control performance of batch processes under uncertainty, a novel two-dimensional model predictive iterative learning control (2D-MPILC) scheme is proposed. First, a new two-dimensional (2-D) extended nonminimal state space model is formulated where more degrees of freedom are offered for further controller design ...

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Robust Nonlinear Model Predictive Control of Batch

NMPC is an optimization-based multivariable constrained control technique using a nonlinear dynamic process model for the prediction of the process outputs Allgower et al.,Ž ¨ 1999; Bequette, 1991 . At each sampling time, the model is. 1776 July 2003 Vol. 49, No. 7 AIChE Journal

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Batch reactor control using a multiple model‐based ...

This work presents the development of a model‐based controller design called Multiple Model Predictive Control (MMPC) based on a set of linear, time‐varying, state space models to regulate batch processes according to multiple, pre‐specified reference profiles.

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Robust nonlinear model predictive control of batch processes

Apr 16, 2004  The control and analysis approaches are applied to a simulated batch crystallization process with a realistic uncertainty description. The proposed robust NMPC algorithm improves the robust performance by a factor of six compared to open loop optimal control, and a factor of two compared to nominal NMPC.

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Model Learning Predictive Control for Batch Processes: A ...

Dec 30, 2018  capture nonlinearities in the control of batch processes. Therefore, in this work we propose a novel method combining MPC and ILC based on LPV models, and we call this method model learning predictive control (ML-MPC). Basically, the idea behind the method is to update the LPV model of the MPC iteratively, by using the repetitive behavior of ...

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NONLINEAR MODEL PREDICTIVE CONTROL OF BATCH

Abstract: Batch processes play a significant role in the production of most modern high-value added products. The paper illustrates the benefits of nonlinear model predictive control (NMPC) for the setpoint tracking control of an industrial batch polymerization reactor.

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Adaptive Model Predictive Batch Process Monitoring and Control

Oct 09, 2018  The present work addresses the problem of loss of model validity in batch process control via online monitoring and adaptation based model predictive control. To this end, a state space subspace-based model identification method suitable for batch processes is utilized and then a model predictive controller is designed. To monitor model performance, a model validity index is

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Model Predictive Monitoring for Batch Processes ...

Jul 21, 2004  In the procedure to monitor a new batch using the method proposed by Nomikos and MacGregor [AIChE J. 1994, 40 (8), 1361−1375], an assumption about the unknown future samples in the batch has to be taken. This work demonstrates that using the missing data option and solving the score estimation problem with an appropriate method are equivalent to the use of an accurate adaptive forecast model ...

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Data‐driven model predictive quality control of batch ...

Feb 11, 2013  The problem of driving a batch process to a specified product quality using data-driven model predictive control (MPC) is described. To address the problem of unavailability of online quality measurements, an inferential quality model, which relates the process conditions over the entire batch duration to the final quality, is required.

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Batch Process Control Strategy

Batch Process Control Strategy Examining How Manufacturers Are . ... • Feeds optimized based on raw material and batch composition analysis • Enhanced PID used for batch composition control with at -line or offline ... • Model predictive control to optimize batch profiles

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Model Predictive Control Strategies for Batch Sugar ...

This work applies two kind of MPC: (i) Classical Model-Based Predictive Control and (ii) Neural Network Model Predictive Control (NNMPC). The classical MPC strategy uses a discrete mode l obtained from general phenomenological model of the feed-batch crystallization process, consisting of mass, energy and population balance.

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Two-Dimensional Iterative Learning Model Predictive ...

To achieve improved control performance of batch processes under uncertainty, a novel two-dimensional model predictive iterative learning control (2D-MPILC) scheme is proposed.

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A Real-time Updated Model Predictive Control Strategy for ...

Mar 01, 2014  PROCESS SYSTEMS ENGINEERING AND PROCESS SAFETY Chinese Journal of Chemical Engineering, 22(3) 318ü329 (2014) DOI: 10.1016/S1004-9541(14)60057-4 A Real-time Updated Model Predictive Control Strategy for Batch Processes Based on State Estimation* YANG Guojun (ᶞളߑ), LI Xiuxi (ᶄ⿶௒) and QIAN Yu (䫧ᆽ)** School of Chemical Engineering,

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Model-based control strategies for a chemical batch ...

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.

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Batch reactor control using a multiple model‐based ...

This work presents the development of a model‐based controller design called Multiple Model Predictive Control (MMPC) based on a set of linear, time‐varying, state space models to regulate batch processes according to multiple, pre‐specified reference profiles.

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Adaptive Model Predictive Batch Process Monitoring and Control

Oct 09, 2018  The present work addresses the problem of loss of model validity in batch process control via online monitoring and adaptation based model predictive control. To this end, a state space subspace-based model identification method suitable for batch processes is utilized and then a model predictive controller is designed. To monitor model performance, a model validity index is

get price

NONLINEAR MODEL PREDICTIVE CONTROL OF BATCH

Abstract: Batch processes play a significant role in the production of most modern high-value added products. The paper illustrates the benefits of nonlinear model predictive control (NMPC) for the setpoint tracking control of an industrial batch polymerization reactor.

get price

Model Predictive Control Strategies for Batch Sugar ...

This work applies two kind of MPC: (i) Classical Model-Based Predictive Control and (ii) Neural Network Model Predictive Control (NNMPC). The classical MPC strategy uses a discrete mode l obtained from general phenomenological model of the feed-batch crystallization process, consisting of mass, energy and population balance.

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Integrated Batch-to-Batch and Nonlinear Model Predictive ...

Keywords: pharmaceutical crystallization, batch-to-batch control, hybrid model, nonlinear model predictive control, extended predictive self-adaptive control Introduction Most drug manufacturing processes include a series of crystallizations in which the product crystals are character-ized in terms of the crystal size and shape distribution. The

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Model Predictive Monitoring for Batch Processes ...

Jul 21, 2004  In the procedure to monitor a new batch using the method proposed by Nomikos and MacGregor [AIChE J. 1994, 40 (8), 1361−1375], an assumption about the unknown future samples in the batch has to be taken. This work demonstrates that using the missing data option and solving the score estimation problem with an appropriate method are equivalent to the use of an accurate adaptive forecast model ...

get price

Data‐driven model predictive quality control of batch ...

Feb 11, 2013  The problem of driving a batch process to a specified product quality using data-driven model predictive control (MPC) is described. To address the problem of unavailability of online quality measurements, an inferential quality model, which relates the process conditions over the entire batch duration to the final quality, is required.

get price

Model-based control strategies for a chemical batch ...

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.

get price

Batch Process Control Strategy

Batch Process Control Strategy Examining How Manufacturers Are . ... • Feeds optimized based on raw material and batch composition analysis • Enhanced PID used for batch composition control with at -line or offline ... • Model predictive control to optimize batch profiles

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Model predictive control - Wikipedia

Model predictive control (MPC) is an advanced method of process control that is used to control a process while satisfying a set of constraints. It has been in use in the process industries in chemical plants and oil refineries since the 1980s. In recent years it has also been used in power system balancing models and in power electronics. Model predictive controllers rely on dynamic models of ...

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Two-Dimensional Iterative Learning Model Predictive ...

Abstract: To achieve improved control performance of batch processes under uncertainty, a novel two-dimensional model predictive iterative learning control (2D-MPILC) scheme is proposed. First, a new two-dimensional (2-D) extended nonminimal state space model is formulated where more degrees of freedom are offered for further controller design ...

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Handling sensor faults in economic model predictive ...

Oct 30, 2018  The problem of sensor fault detection and isolation (FDI) and fault-tolerant economic model predictive control (FT-EMPC) for batch processes is addressed. To this end, we first model batch processes using subspace-based system identification techniques. The analytical redundancy within the identified model is subsequently exploited to detect ...

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Run-to-Run-Based Model Predictive Control of Protein ...

ABSTRACT: In this work, we develop a novel run-to-run-based model predictive controller (R2R-based MPC) for a batch crystallization process with process drift and inherent variation in solubility and crystal growth rates. In order to achieve the

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2002EU Predictive Control of Batch Reactors

4-Model Based Predictive Control “MBPC is not new” since the first commissioned application was done in 1973 and is now applied in most industrial fields. It is the only method capable of handling constraints. Being model based, all the above features of batch reactor control can be taken into account with no specific efforts.

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