Cost Effective Analysis of the Design of Safety Instrumented Systems Using Manta-Ray Foraging Optimization Algorithm
Bibliographic record
Abstract
This study aims to develop a new cost effectiveness analysis framework in the context of safety instrumented systems (SIS) design and operation.The primary objective is to achieve an optimal equilibrium among safety integrity, operational integrity, and lifecycle cost of SIS.It is essential to note that these objectives may often be in conflict; for instance, enhancing safety integrity could potentially diminish operational integrity and escalate costs.Achieving this balance is crucial to ensure that the risk level being addressed aligns precisely with the desired objectives while minimizing any adverse effects.The novelty of this paper lies in the refined formulation of a multi-objective optimization problem and the application of a recently developed swarm-based Manta-Ray Foraging Optimization (MRFO) algorithm.The effectiveness of this approach is demonstrated through a typical SIS design challenge, which entails satisfying specific measures in terms of Safety Integrity Level (SIL), spurious trip activation rate, and lifecycle cost.These measures depend on variables such as the number and voting scheme of components, their types, and the intervals for potential proof tests.For validation and comparison, the problem was initially tackled using a conventional approach based on genetic algorithms.Subsequently, the MRFO algorithm was employed, yielding highly satisfactory results and confirming its proficiency in resolving real-world SIS optimization challenges.Notably, the MRFO algorithm produced a greater number of solutions compared to the genetic algorithm approach.This increase in solution options is advantageous, offering decision-makers a broader array of choices for optimal system design.This study contributes significantly to the field of SIS design, presenting an innovative, algorithm-driven approach to balancing safety, operational integrity, and cost in system development, it also contributes to understanding the life cycle costs of security barriers in general.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".