Control of batch product quality by trajectory manipulation using latent variable models

Flores‐Cerrillo, Jesus; MacGregor, John F.

doi:10.1016/j.jprocont.2003.09.008

Cited by 109 publications

(97 citation statements)

References 27 publications

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“…Still another variant of the use of PLS models involves controlling the process in the reduced space (scores) of a latent variable model rather than in the space of the discretized inputs 40 .…”

Section: )mentioning

confidence: 99%

Control and Optimization of Batch Chemical Processes

Bonvin

François

2017

Coulson and Richardson's Chemical Engineering

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A batch process is characterized by the repetition of time-varying operations of finite duration. Due to the repetition, there are two independent "time" variables, namely, the run time during a batch and the batch index. Accordingly, the control and optimization objectives can be defined for a given batch or over several batches. This chapter describes the various control and optimization strategies available for the operation of batch processes. These include online and run-to-run control on the one hand, and repeated numerical optimization and optimizing control on the other. Several case studies are presented to illustrate the various approaches Keywords Batch control, predictive control, iterative learning control, run-to-run control, batch process optimization, dynamic optimization, optimizing control, run-to-run optimization.

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Section: )mentioning

confidence: 99%

Control and Optimization of Batch Chemical Processes

Bonvin

François

2017

Coulson and Richardson's Chemical Engineering

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“…When latent variable techniques are employed, main design choices are the data arrangement and the selection of the number of latent variables. The problem of optimum data arrangement for multivariate modeling is treated in a considerable number of references, covering applications such as statistical monitoring [23], process control [24], or image processing [25]. For instance, Dynamic PCA [26], which has raised a great interest in the scientific community, is simply a data rearrangement process followed by a traditional PCA modeling.…”

Section: Related Workmentioning

confidence: 99%

Multivariate Statistical Approach for Anomaly Detection and Lost Data Recovery in Wireless Sensor Networks

Magán-Carrión

Camacho

García-Teodoro

2015

International Journal of Distributed Sensor Networks

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Data loss due to integrity attacks or malfunction constitutes a principal concern in wireless sensor networks (WSNs). The present paper introduces a novel data loss/modification detection and recovery scheme in this context. Both elements, detection and data recovery, rely on a multivariate statistical analysis approach that exploits spatial density, a common feature in network environments such as WSNs. To evaluate the proposal, we consider WSN scenarios based on temperature sensors, both simulated and real. Furthermore, we consider three different routing algorithms, showing the strong interplay among (a) the routing strategy, (b) the negative effect of data loss on the network performance, and (c) the data recovering capability of the approach. We also introduce a novel data arrangement method to exploit the spatial correlation among the sensors in a more efficient manner. In this data arrangement, we only consider the nearest nodes to a given affected sensor, improving the data recovery performance up to 99%. According to the results, the proposed mechanisms based on multivariate techniques improve the robustness of WSNs against data loss.

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“…If, at the mid-way point of the batch, the estimate is significantly di↵erent from the target, then suitable corrective action is calculated and then implemented. This method was extended in [20] with the inclusion of several decision points during a batch progression at which prediction of batch endproduct quality is made and the required adjustments are computed and then implemented. The key drawback of the batch end-point control scheme is the fact that the controlled variables are assumed to be measured only at the very end of a batch at which point it is no longer possible to perform any corrective action.…”

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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Control of batch product quality by trajectory manipulation using latent variable models

Cited by 109 publications

References 27 publications

Control and Optimization of Batch Chemical Processes

Control and Optimization of Batch Chemical Processes

Multivariate Statistical Approach for Anomaly Detection and Lost Data Recovery in Wireless Sensor Networks

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

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