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Record W2182630508 · doi:10.1016/j.ifacol.2015.09.009

Monitoring Safety of Process Operations Using Industrial Workflows

2015· article· en· W2182630508 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIFAC-PapersOnLine · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Process Modeling and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWorkflowComputer scienceProcess (computing)Process miningEvent (particle physics)Workflow technologyWorkflow management systemWork in processSoftware engineeringReliability engineeringSystems engineeringBusiness processEngineeringDatabaseBusiness process managementOperations managementOperating system

Abstract

fetched live from OpenAlex

An industrial workflow represents a sequence of tasks or actions that describes an operational procedure. In this paper, workflow strategies are developed that capture operational knowledge by analyzing event logs of how an operator has executed a procedure while controlling or monitoring a process. In this work, our prime focus is on infrequent process operations such as plant start-up and shutdown procedures. We propose a workflow conformance method to continuously monitor and compare operator actions with standard operating procedures (SOPs) and identify procedural violations that could compromise process safety and efficiency. An industrial case study is presented to illustrate applications of workflow conformance monitoring to identify operational problems associated with human factors, process and instrumentation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.857

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.125
GPT teacher head0.303
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it