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Updated: Oct 5, 2025
11:20
In Situ Visualization of the Phase Behavior of Oil Samples Under Refinery Process Conditions
Published on: February 21, 2017
6.8K
Validating an abnormal situation prediction model for smart manufacturing in the oil refining industry
1The Department of Systems Science and Industrial Engineering, Binghamton University, 4400 Vestal Parkway East, Binghamton, NY 13902, USA.
Applied Ergonomics
|February 1, 2022
Summary
A mathematical model accurately predicts refinery operators' detection of abnormal events across various conditions. Visual behaviors effectively indicate operator states like attention and workload in smart manufacturing settings.
Area of Science:
- Human-Computer Interaction
- Cognitive Engineering
- Industrial Safety
Background:
- Smart manufacturing relies on human operators for complex monitoring tasks.
- Predicting human performance is crucial for optimizing safety and efficiency in industries like oil refining.
- Understanding operator visual behavior is key to assessing cognitive states.
Purpose of the Study:
- To validate a mathematical model for predicting refinery operators' detection of abnormal events.
- To examine how age, task load, complexity, and input devices affect operator visual behavior.
- To assess the generalizability of a predictive model for human performance in smart manufacturing.
Main Methods:
- Mathematical modeling of human performance in oil refinery monitoring.
- Analysis of operator visual behaviors (fixations, saccades) under varying conditions.
- Validation of a statistical model across different task loads and age groups.
Main Results:
- Touchscreen devices led to shorter fixation durations and lower fixation/saccade ratios.
- Older adults exhibited longer saccade durations and amplitudes with touchscreen devices.
- The predictive model demonstrated generalizability across task loads and age groups.
Conclusions:
- Visual behavior is a real-time indicator of operator cognitive workload, attention, and situation awareness.
- The validated model supports using visual behavior to predict oil refinery operator responsiveness.
- Findings inform the development of advanced smart manufacturing monitoring systems.
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