Control of product quality for batch nylon 6,6 autoclaves

Russell, S G; Robertson, Donald; Lee, JayHyung; Ogunnaike, B. A.

doi:10.1016/s0009-2509(98)00148-1

Cited by 50 publications

(39 citation statements)

References 7 publications

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“…Instead, trajectory tracking approaches rely on the assumption that batch product quality can be guaranteed if some key process variables that are readily measured, such as reactor temperature or pressure, follow their pre-determined trajectories. However, that assumption is not generally true [26], especially if the disturbance or plant model mismatch a↵ects the relationship between readily measured controlled variables and the product quality variables. Also, disturbances such as raw materials composition may significantly a↵ect product quality but have insignificant impact on other readily measured process variables, such as reactor temperature.…”

Section: Trajectory Tracking Controlmentioning

confidence: 99%

Trajectory tracking of batch product quality using intermittent measurements and moving window estimation

Lopez-Montero

Wan

Marjanović

2015

Journal of Process Control

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In order to meet tight product quality specifications for batch/semi-batch processes, it is vital to monitor and control product quality throughout the batch duration. The ideal strategy is to achieve end-product quality specifications through trajectory tracking control during a batch run. However, due to the lack of in-situ sensors for continuous monitoring of batch product quality, the measurements are usually implemented by laboratory assays and are inherently intermittent. Therefore, direct trajectory tracking of batch product quality is challenging in such applications. This paper proposes a practical approach to realise trajectory tracking control of batch product quality in those situations where only intermittent measurements are available. The first step of the approach consists in identifying a projection to latent structures (PLS) model to identify a relationship between readily measured process variable trajectories and intermittently measured batch product quality. Then the identified PLS-based prediction model is transformed into recursive formulation by utilising missing data imputation algorithms. Such recursive formulation allows identified PLS-based model to be readily incorporated as a predictor into standard model predictive control (MPC) framework. Case study employing simulated fed-batch fermentation process used to manufacture penicillin was employed to illustrate the principle and the e↵ectiveness of the proposed approach.

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Section: Trajectory Tracking Controlmentioning

confidence: 99%

Trajectory tracking of batch product quality using intermittent measurements and moving window estimation

Lopez-Montero

Wan

Marjanović

2015

Journal of Process Control

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“…When carboxyl is the limiting end-group, the imbalance ratio between carboxyl and amine end-groups (r), the conversion of the limiting end-group ( ), and the fraction of polymer molecules that are HMD (n 1 ) are given by Correlations for the molar volume of each species in the liquid phase as a function of temperature were taken from Russel et al 14 Pressure drop calculations for the two-phase flow accounts for the frictional term (due to friction at the pipe walls), the gravitational term (hydrostatic pressure, due to the change in elevations), and an additional accelerational term (due to the change of mixture density and gas expansion). The LockhartMartinelli correlation 26 was used in the two-phase frictional pressure drop calculations:…”

Section: Mathematical Modelmentioning

confidence: 99%

Simulation Model of Polyamide-6,6 Polymerization in a Continuous Two-Phase Flow Coiled Tubular Reactor

Pimentel

Giudici

2006

Ind. Eng. Chem. Res.

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A mathematical model was developed to simulate an industrial-scale continuous process of polyamide-6,6 polymerization composed by two reactors in series: a coiled tubular reactor operating partially under onephase flow and partially under two-phase flow conditions, followed by a continuous stirred tank reactor. The mass, heat, and momentum balances for the single and two-phase flows were considered in the tubular reactor model. Water and hexamethylenediamine evaporation rates were evaluated according to the thermodynamic vapor-liquid equilibrium equations. Frictional, gravitational, and accelerational terms, as well as fittings for tube expansions, were considered in the pressure drop calculations. The model developed is an improved version of a previous one (Giudici et al., Chem. Eng. Sci. 1999, 54, 3243-3252) and was tested and validated with industrial data under a wider range of conditions, including the effect of catalyst.

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“…The first process considered here is the batch condensation polymerization of nylon 6,6. A detailed theoretical model for this process was developed by Russell et al (1998a), and is used in this work. Details about the model and model parameters are described in the original publication.…”

Section: Off-line Analysis and On-line Monitoring Studiesmentioning

confidence: 99%

Multivariate monitoring of batch processes using batch‐to‐batch information

Flores‐Cerrillo

MacGregor

2004

AIChE Journal

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Multiway principal component analysis (MPCA) and multiway partial-least squares (MPLS) are well-established methods for the analysis of historical data from batch processes, and for monitoring the progress of new batches. Direct measurements made on prior batches can also be incorporated into the analysis by monitoring with multiblock methods. An extension of the multiblock MPCA/MPLS approach is introduced to explicitly incorporate batch-to-batch trajectory information summarized by the scores of previous batches, while keeping all the advantages and monitoring statistics of the traditional IntroductionBatch and semibatch processes are the main production devices in pharmaceuticals, paint-coating, adhesive, and in most of the specialty-chemical industries. However, such processes suffer from variations in initial charge, operating conditions, and impurity concentrations in raw materials. To allow for more consistent operation and final product quality, control and monitoring schemes have been implemented around such processes. The use of multivariate statistical process control methods based on multiway principal component analysis (MPCA), and multiway partial-least squares (MPLS), and their associated monitoring statistics have been shown to be successful with industrial data for both the analysis of completed batches, and for the on-line monitoring of new batches (MacGregor and Nomikos, 1992; MacGregor, 1994, 1995a,b;MacGregor and Kourti, 1995;. The success of such methods is largely because of the fact that the type of normal operating data necessary for building the required model is always readily available, and that the statistical control charts used for analysis and monitoring are easily developed from these data. One of the main characteristics of such methods is that the evolving within-batch measurement trajectories are projected into low dimensional latent variable spaces that summarize all the relevant information, and allow monitoring charts to be built in the reduced spaces where visually inspection and interpretation are easier.Multiway PCA and PLS for the analysis, monitoring, and prediction of final product quality in batch processes were first introduced by Nomikos and MacGregor (1992, 1995a. They illustrated the detection of abnormal batches with several criteria, such as the Q statistic (also known as square prediction error (SPE) or distance to the model in the X space (DMODX)), the instantaneous SPE (for on-line monitoring) and the PCA or PLS score plots or equivalently Hotelling's T 2 statistic. modes of operation, and prior processing conditions into the analysis and monitoring of batch processes. These multiblock methods allow one to incorporate any measured variable from prior batches into the analysis and monitoring schemes. In this article, we propose a variation of these multiblock methods to incorporate the PCA or PLS scores from prior batches.Recently, Dorsey and Lee (2001) proposed a monitoring framework based on state-space models that purported to consider batch-to-batch vari...

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Control of product quality for batch nylon 6,6 autoclaves

Cited by 50 publications

References 7 publications

Trajectory tracking of batch product quality using intermittent measurements and moving window estimation

Trajectory tracking of batch product quality using intermittent measurements and moving window estimation

Simulation Model of Polyamide-6,6 Polymerization in a Continuous Two-Phase Flow Coiled Tubular Reactor

Multivariate monitoring of batch processes using batch‐to‐batch information

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