Evidence map›Paper›PMID 36790941›Full record

ArticleESCAPE. European Symposium on Computer Aided Process Engineering2022

A Hierarchical Approach to Monitoring Control Performance and Plant-Model Mismatch.

M Ziyan Sheriff, Yan-Shu Huang, Sunidhi Bachawala, Marcial Gonzelez, Zoltan K Nagy, Gintaras V Reklaitis

Abstract read
In one paragraph

Article in ESCAPE. European Symposium on Computer Aided Process Engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

M Ziyan SheriffDavidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA.
Yan-Shu HuangDavidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA.
Sunidhi BachawalaSchool of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA.
Marcial GonzelezSchool of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA.
Zoltan K NagyDavidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA.
Gintaras V ReklaitisDavidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA.

Funding

Industry 4.0 Implementation in Continuous Pharmaceutical ManufacturingU01FD006487 · FDA · UNIVERSITY OF DELAWARE · PI IERAPETRITOU, MARIANTHI · 2018 to 2020
$4.0M
FDA HHS U01 FD006487
6 · The paper itself

Abstract

Controllers are often tuned during plant commissioning, with a fixed process model. However, over time degradation can occur in the process, the process model and the controller, making it necessary to either re-tune the controller or re-identify the process model. Authors have proposed a variety of approaches to identify plant-model mismatch (PMM) and control performance degradation (CPD). While each approach may have its own advantages and disadvantages, they are generally designed to function on different timescales. The differing timescales result in the need for a multi-level hierarchical approach to monitor, detect, and manage PMM and CPD, as illustrated through a continuous pharmaceutical manufacturing application, i.e., a direct compression tablet manufacturing process. This work also highlights the requirement for index-based metrics, that enable the impact of PMM and CPD to be quantified and assessed from a control performance monitoring perspective, to aid fault diagnosis through root cause analysis to guide maintenance decisions for continuous manufacturing applications.

Indexed as

control performance monitoringnonlinear model predictive controlplant-model mismatch

Identifiers

PMID36790941
PMCPMC9923505

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.