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Record W4283652598 · doi:10.1002/cjce.24520

Experimental methods in chemical engineering: Hazard and operability analysis— <scp>HAZOP</scp>

2022· article· en· W4283652598 on OpenAlexaffvenue
Paolo Mocellin, Jacopo De Tommaso, Chiara Vianello, Giuseppe Maschio, Thomas Saulnier‐Bellemare, Luis D. Virla, Gregory S. Patience

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsUniversity of CalgaryPolytechnique Montréal
Fundersnot available
KeywordsHazard and operability studyProcess safety managementRisk analysis (engineering)Process (computing)Computer scienceProcess safetyHazardOperabilityFault tree analysisReliability engineeringScope (computer science)EngineeringSystems engineeringWork in processHazardous wasteOperations managementChemistryWaste managementBusiness

Abstract

fetched live from OpenAlex

Abstract Hazards and operability analysis (HAZOP) is one of a dozen‐structured Process Hazards Analysis (PHA) methodologies that assess risks associated with operating processes to mitigate their consequences. HAZOP applies to all six stages of process design from discovery to decommissioning. Industry massively adopted PHA methodologies as a consequence of several industrial disasters in the 1970s that increased society's scrutiny of chemical operations. HAZOPs are conducted by multidisciplinary teams that rely on a set of guide words in combination with the system parameters to identify deviations from the design intent. The team discuss the causes and consequences of deviations, and the project owner modifies the process accordingly. It relies on heuristics rather than algorithms, so the formal structure gives practitioners the false sense that the analysis is comprehensive. Academic institutions increasingly apply PHAs to experimental work, but the scope of a HAZOP is often ill‐suited for this environment as it requires dedicated personnel with particular expertise. Here, we outline the essential features of a HAZOP analysis for early career researchers engaging in process development for conditions that include, for example, high temperature, high pressure, toxic compounds (Hg, phosgene, CO), and potentially explosive and flammable mixtures like organic peroxides. Web of Science indexed over 100 000 documents that mention safety in 2021 and assigned 1500 to chemical engineering. A bibliometric analysis grouped them into five clusters: (1) lithium ion batteries and nanoparticles, (2) fire, simulation, and combustion, (3) models, risk, systems, and techniques (including HAZOP), (4) water treatment, and (5) mechanisms and thermal runaway. [Correction added on 01 September 2022, after first online publication: The item number 3 in the preceding sentence was duplicated in the original published article and has been deleted in this version.]

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.030
metaresearch head score (Gemma)0.069
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.030
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.069
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.012
Science and technology studies0.0010.005
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.002

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 head0.037
GPT teacher head0.330
Teacher spread0.293 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations35
Published2022
Admission routes2
Has abstractyes

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