Predicting crude unit failures and production impact using lagging maintenance indicators in oil refineries
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Sadiq Ibrahim Almogargesh
Abstract
Oil refineries operate in environments of high pressure and temperature. Therefore, maintaining the availability of these assets is a challenge to the maintenance teams. The paper deals with the development of maintenance key performance indicators (KPIs), considering both leading (post-failure) and lagging metrics for maintenance performance. Traditionally, these KPIs are looked upon in isolation; possible relationships between them are not considered. The present study investigated the relationships between various lagging maintenance KPIs relating production rate to equipment availability. The study was conducted under the refinery crude unit, which is generally considered one of the most important parts of any oil refinery. A regression model was developed that empirically relates equipment availability with the production rate. This was further extended to an empirical equation correlating equipment availability and downtime duration with various downtime factors. The results indicate that the availability of equipment has a high correlation with the production rate. This relationship is driven by factors of downtime, which have been sorted according to duration and type. These equations greatly support the management decisions in the development of maintenance strategies. In addition, the research quantifies the costs related to each factor of downtime, which can be applied in formulating future maintenance strategies.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The author states no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
A Tables of classified data
| 2015 | ||||
| Reference date | Downtime | Factors code | Equipment availability | Overall equipment availability |
| 1 | 2 | F6 | 98 % | 94 % |
| 0 | F9 | 100 % | ||
| 5 | F6 | 94 % | ||
| 16 | F6 | 84 % | ||
| 2 | 195 | F1 | 29 % | 55 % |
| 111 | F5 | 42 % | ||
| 4 | F6 | 95 % | ||
| 3 | 10 | F4 | 89 % | 55 % |
| 420 | F1 | 16 % | ||
| 4 | 2 | F6 | 98 % | 93 % |
| 11 | F4 | 89 % | ||
| 0 | F4 | 100 % | ||
| 1 | F11 | 99 % | ||
| 3 | F6 | 97 % | ||
| 31 | F9 | 74 % | ||
| 2 | F6 | 98 % | ||
| 5 | 2 | F6 | 98 % | 82 % |
| 5 | F6 | 94 % | ||
| 53 | F1 | 61 % | ||
| 40 | F2 | 68 % | ||
| 8 | F11 | 91 % | ||
| 6 | 202 | F4 | 29 % | 33 % |
| 219 | F4 | 27 % | ||
| 116 | F4 | 42 % | ||
| 7 | 27 | F6 | 74 % | 68 % |
| 49 | F6 | 61 % | ||
| 8 | 4 | F6 | 95 % | 58 % |
| 55 | F9 | 58 % | ||
| 12 | F6 | 86 % | ||
| 262 | F1 | 22 % | ||
| 176 | F4 | 30 % | ||
| 9 | 5 | F6 | 94 % | 51 % |
| 198 | F9 | 28 % | ||
| 156 | F1 | 33 % | ||
| 204 | F1 | 27 % | ||
| 31 | F6 | 71 % | ||
| 10 | 9 | F1 | 89 % | 82 % |
| 1 | F6 | 99 % | ||
| 1 | F6 | 99 % | ||
| 109 | F1 | 41 % | ||
| 11 | 288 | F1 | 21 % | 43 % |
| 42 | F1 | 65 % | ||
| 12 | 40 | F1 | 66 % | 57 % |
| 186 | F1 | 30 % | ||
| 139 | F1 | 36 % | ||
| 3 | F6 | 96 % | ||
| 2014 | ||||
| Reference date | Downtime | Factors code | Equipment availability | Overall equipment availability |
| 1 | 24 | F6 | 76 % | 71 % |
| 2 | F6 | 97 % | ||
| 108 | F1 | 41 % | ||
| 244 | F1 | 24 % | ||
| 5 | F6 | 94 % | ||
| 4 | F9 | 95 % | ||
| 2 | 20 | F1 | 78 % | 74 % |
| 110 | F4 | 40 % | ||
| 1 | F6 | 99 % | ||
| 23 | F1 | 76 % | ||
| 19 | F1 | 79 % | ||
| 3 | 6 | F6 | 93 % | 70 % |
| 185 | F1 | 29 % | ||
| 1 | F6 | 99 % | ||
| 12 | F7 | 86 % | ||
| 96 | F1 | 44 % | ||
| 30 | F9 | 71 % | ||
| 4 | 11 | F1 | 87 % | 61 % |
| 198 | F4 | 27 % | ||
| 44 | F5 | 63 % | ||
| 160 | F1 | 32 % | ||
| 43 | F1 | 63 % | ||
| 4 | F6 | 95 % | ||
| 5 | 23 | F9 | 76 % | 76 % |
| 6 | 8 | F6 | 90 % | 90 % |
| 7 | 174 | F8 | 30 % | 69 % |
| 0 | F9 | 100 % | ||
| 101 | F9 | 43 % | ||
| 7 | F4 | 91 % | ||
| 18 | F6 | 81 % | ||
| 8 | 106 | F11 | 42 % | 55 % |
| 3 | F6 | 96 % | ||
| 154 | F11 | 33 % | ||
| 79 | F11 | 49 % | ||
| 9 | 53 | F1 | 59 % | 60 % |
| 53 | F1 | 59 % | ||
| 26 | F6 | 74 % | ||
| 206 | F1 | 27 % | ||
| 7 | F10 | 91 % | ||
| 80 | F1 | 48 % | ||
| 10 | 8 | F6 | 90 % | 90 % |
| 11 | 3 | F1 | 95 % | 83 % |
| 25 | F1 | 70 % | ||
| 12 | 9 | F6 | 86 % | 71 % |
| 298 | F1 | 16 % | ||
| 3 | F6 | 95 % | ||
| 8 | F6 | 87 % | ||
B Anova: single factor
| Summary | ||||
|---|---|---|---|---|
| Groups | Count | Sum | Average | Variance |
| Column 1 | 33 | 3,733 | 113.1212 | 11,535.36 |
| Column 2 | 33 | 72 | 2.181818 | 46.09091 |
| Column 3 | 33 | 835 | 25.30303 | 665.5928 |
| Column 4 | 33 | 760 | 23.0303 | 3,623.155 |
| Column 5 | 33 | 143 | 4.333333 | 366.6667 |
| Column 6 | 33 | 300 | 9.090909 | 120.5227 |
| Column 7 | 33 | 44 | 1.333333 | 3.666667 |
| Column 8 | 33 | 206 | 6.242424 | 906.9394 |
| Column 9 | 33 | 467 | 14.15152 | 1,507.883 |
| Column 10 | 33 | 39 | 1.181818 | 1.090909 |
| Column 11 | 33 | 731 | 22.15152 | 2,449.32 |
| ANOVA | ||||||
|---|---|---|---|---|---|---|
| Source of variation | SS | df | MS | F | P-value | F crit |
| Between groups | 340,595.3 | 10 | 34,059.53 | 17.65051 | 4.74E-26 | 1.857637 |
| Within groups | 679,241.2 | 352 | 1,929.663 | |||
| Total | 1,019,837 | 362 | ||||
C Table of the downtime factors codes with the recoreded downtime duration and occurance. this table istbli required to obtain the correlation among the codes
| F1 | F2 | F3 | F4 | F5 | F6 | F7 | F8 | F9 | F10 | F11 |
|---|---|---|---|---|---|---|---|---|---|---|
| 195 | 40 | 10 | 10 | 111 | 2 | 12 | 174 | 1 | 7 | 1 |
| 420 | 1 | 7 | 11 | 1 | 5 | 1 | 1 | 31 | 1 | 8 |
| 53 | 1 | 8 | 0 | 1 | 16 | 1 | 1 | 55 | 1 | 106 |
| 262 | 1 | 7 | 202 | 1 | 4 | 1 | 1 | 198 | 1 | 154 |
| 156 | 1 | 9 | 219 | 1 | 2 | 1 | 1 | 4 | 1 | 79 |
| 204 | 1 | 7 | 116 | 1 | 3 | 1 | 1 | 30 | 1 | 110 |
| 9 | 1 | 8 | 176 | 1 | 2 | 1 | 1 | 23 | 1 | 198 |
| 109 | 1 | 11 | 1 | 1 | 2 | 1 | 1 | 0 | 1 | 7 |
| 288 | 1 | 7 | 1 | 1 | 5 | 1 | 1 | 101 | 1 | 44 |
| 42 | 1 | 7 | 1 | 1 | 27 | 1 | 1 | 1 | 1 | 1 |
| 40 | 1 | 7 | 1 | 1 | 49 | 1 | 1 | 1 | 1 | 1 |
| 186 | 1 | 7 | 1 | 1 | 4 | 1 | 1 | 1 | 1 | 1 |
| 139 | 1 | 14 | 1 | 1 | 12 | 1 | 1 | 1 | 1 | 1 |
| 108 | 1 | 7 | 1 | 1 | 5 | 1 | 1 | 1 | 1 | 1 |
| 244 | 1 | 7 | 1 | 1 | 31 | 1 | 1 | 1 | 1 | 1 |
| 20 | 1 | 7 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 23 | 1 | 35 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 19 | 1 | 7 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 |
| 185 | 1 | 67 | 1 | 1 | 24 | 1 | 1 | 1 | 1 | 1 |
| 96 | 1 | 89 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 |
| 11 | 1 | 90 | 1 | 1 | 5 | 1 | 1 | 1 | 1 | 1 |
| 160 | 1 | 76 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 43 | 1 | 54 | 1 | 1 | 6 | 1 | 1 | 1 | 1 | 1 |
| 53 | 1 | 32 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 53 | 1 | 25 | 1 | 1 | 4 | 1 | 1 | 1 | 1 | 1 |
| 206 | 1 | 7 | 1 | 1 | 8 | 1 | 1 | 1 | 1 | 1 |
| 80 | 1 | 45 | 1 | 1 | 18 | 1 | 1 | 1 | 1 | 1 |
| 3 | 1 | 7 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 |
| 25 | 1 | 54 | 1 | 1 | 26 | 1 | 1 | 1 | 1 | 1 |
| 298 | 1 | 32 | 1 | 1 | 8 | 1 | 1 | 1 | 1 | 1 |
| 1 | 1 | 35 | 1 | 1 | 9 | 1 | 1 | 1 | 1 | 1 |
| 1 | 1 | 43 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 |
| 1 | 1 | 7 | 1 | 1 | 8 | 1 | 1 | 1 | 1 | 1 |
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Articles in the same Issue
- Frontmatter
- Research Articles
- Data-driven support vector regression-based hybrid models for prediction of syngas production in the gasification process of biomass
- Determination of hydrogen production in power plant using predictive machine learning methods
- Technical Note
- Response surface methodology optimization of dye adsorption by palm fatty acid distillate adsorbent
- Research Articles
- Raising pros and cons of falling film and packed column for absorption of NH3 by a NH3–H2O solution
- Predicting crude unit failures and production impact using lagging maintenance indicators in oil refineries
- Exploring the anticancer potential of some azaflavanones derivatives through molecular docking studies
- Classification of water quality based on aesthetic and chemical parameters
- Development of an optimized fractional-order controller featuring dead-time and disturbance compensation
