Data-Based Modeling and Control of Nylon-6, 6 Batch Polymerization

Aumi, Siam; Corbett, Brandon; Mhaskar, Prashant; Clarke-Pringle, Tracy

doi:10.1109/tcst.2011.2175449

Cited by 29 publications

(16 citation statements)

References 47 publications

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“…These computed control actions over the prediction horizon are generated using the missing data algorithms and, in some sense, are influenced by the correlations in historical batches. Multimodel approaches − exist, which exploit the different features and qualities of respective data models involved in the framework, , successfully implementing such techniques where auto-regressive exogenous (ARX) and PLS models have been applied for modeling and control of batch polymerization processes.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control Embedding a Parallel Hybrid Modeling Strategy

Ghosh

Moreira

Mhaskar

2021

Ind. Eng. Chem. Res.

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This work addresses the problem of implementing a model predictive control (MPC) scheme that embeds a parallel hybrid subspace model as the predictive component of the control strategy. The hybrid model considered here is inspired by the framework proposed by Ghosh et al. (Hybrid Modeling Approach Integrating First-Principles Models with Subspace Identification. Ind. Eng. Chem. Res. 2019, 58, 13533− 13543), but it is adapted to make it amenable to online control. In particular, the framework uses a first-principles model and a subspace-based residual model (built with error between the process measurement data and the first-principles output of historical batches) in a parallel fashion. The present manuscript adapts this framework in a way that retains the linearity of the model utilized within the MPC. This is achieved by first building a subspace model (built with output data of the firstprinciples model) and then appending it with the residual model to have the same parallel hybrid model structure. This linear hybrid MPC is applied on a seeded batch crystallization process to reduce the volume of fines or crystals generated due to nucleation during the crystallization process, while maintaining a desired product quality at batch termination. The closed-loop results using the proposed control methodology are compared with a purely data-driven subspace-based model predictive controller.

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Section: Introductionmentioning

confidence: 99%

Model Predictive Control Embedding a Parallel Hybrid Modeling Strategy

Ghosh

Moreira

Mhaskar

2021

Ind. Eng. Chem. Res.

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“…As is often the case, the development of a realistic mechanistic model is a difficult task, and even if developed, might be very challenging to maintain, or to use for optimization and control. Thus the first principles model-based model predictive control (MPC) [3][4][5] implementations remain elusive for rotomolding control.…”

Section: Introductionmentioning

confidence: 99%

Handling Constraints and Raw Material Variability in Rotomolding through Data-Driven Model Predictive Control

et al. 2019

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This work addresses the problems of uniquely specifying and robustly achieving user-specified product quality in a complex industrial batch process, which has been demonstrated using a lab-scale uni-axial rotational molding process. In particular, a data-driven modeling and control framework is developed that is able to reject raw material variation and achieve product quality which is specified through constraints on quality variables. To this end, a subspace state-space model of the rotational molding process is first identified from historical data generated in the lab. This dynamic model predicts the evolution of the internal mold temperature for a given set of input move trajectory (heater and compressed air profiles). Further, this dynamic model is augmented with a linear least-squares based quality model, which relates its terminal (states) prediction with key quality variables. For the lab-scale process, the chosen quality variables are sinkhole area, ultrasonic spectra amplitude, impact energy and shear viscosity. The complete model is then deployed within a model-based control scheme that facilitates specifying on-spec products via limits on the quality variables. Further, this framework is demonstrated to be capable of rejecting raw material variability to achieve the desired specifications. To replicate raw material variability observed in practice, in this work, the raw material is obtained by blending the matrix resin with a resin of slightly different viscosity at varying weight fractions. Results obtained from experimental studies demonstrate the capability of the proposed model predictive control (MPC) in meeting process specifications and rejecting raw material variability.

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“…In Ref. 21, local linear ARX models are identified and combined using a weighting function to describe the nonlinear evolution of the process. In Refs.…”

Section: Introductionmentioning

confidence: 99%

“…28 The most commonly used modeling method however, is projection to latent spaces (PLS). 21,[29][30][31][32] In these contributions, trajectories of process measurements and inputs are related to quality in the model building step. Then, to control a new batch, the model is applied to available process measurements to determine an appropriate input.…”

Section: Introductionmentioning

confidence: 99%

Subspace identification for data‐driven modeling and quality control of batch processes

2016

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In this work, we present a novel, data-driven, quality modeling, and control approach for batch processes. Specifically, we adapt subspace identification methods for use with batch data to identify a state-space model from available process measurements and input moves. We demonstrate that the resulting linear time-invariant (LTI), dynamic, state-space model is able to describe the transient behavior of finite duration batch processes. Next, we relate the terminal quality to the terminal value of the identified states. Finally, we apply the resulting model in a shrinking-horizon, model predictive control scheme to directly control terminal product quality. The theoretical properties of the proposed approach are studied and compared to state-of-the-art latent variable control approaches. The efficacy of the proposed approach is demonstrated through a simulation study of a batch polymethyl methacrylate polymerization reactor. Results for both disturbance rejection and set-point changes (i.e., new quality grades) are demonstrated.

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Data-Based Modeling and Control of Nylon-6, 6 Batch Polymerization

Cited by 29 publications

References 47 publications

Model Predictive Control Embedding a Parallel Hybrid Modeling Strategy

Model Predictive Control Embedding a Parallel Hybrid Modeling Strategy

Handling Constraints and Raw Material Variability in Rotomolding through Data-Driven Model Predictive Control

Subspace identification for data‐driven modeling and quality control of batch processes

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