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Related Concept Videos

Hazard Rate01:11

Hazard Rate

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The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Response Surface Methodology01:16

Response Surface Methodology

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Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
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Related Experiment Video

Updated: Jan 12, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Machine learning and bayesian network based on fuzzy AHP framework for risk assessment in process units.

Hassan Mandali1, Elham Keighobadi2, Hossein Ebrahimi3

  • 1Department of Occupational Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.

Scientific Reports
|November 7, 2025
PubMed
Summary

Artificial intelligence, including machine learning models like Random Forest and XGBoost, enhances process safety risk assessment. These AI techniques, combined with Bayesian networks and Multi-Criteria Decision Making (MCDM), effectively prioritize risks for mitigation.

Keywords:
Bayesian NetworkHAZOPMCDMMachine learningRisk assessment

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Area of Science:

  • Chemical Engineering
  • Process Safety
  • Artificial Intelligence

Background:

  • Risk assessment is vital for process unit safety.
  • Artificial intelligence (AI) offers advanced capabilities for risk prediction and assessment.
  • Integrating AI with traditional methods can improve risk assessment precision.

Purpose of the Study:

  • To evaluate the effectiveness of various machine learning algorithms in process risk assessment.
  • To compare conventional statistical methods with advanced AI techniques.
  • To explore the synergistic potential of AI with Bayesian networks and Multi-Criteria Decision Making (MCDM) for risk prioritization.

Main Methods:

  • Utilized a dataset of 160 deviations identified via Hazard and Operability (HAZOP) studies.
  • Employed a diverse range of algorithms: ensemble methods (Random Forest, Hist Gradient Boosting, XGBoost, CatBoost) and traditional methods (Logistic Regression, KNN, SVM, CNN).
  • Applied a fusion of Bayesian networks and MCDM for risk option prioritization.

Main Results:

  • Random Forest, XGBoost, and CatBoost demonstrated superior performance, achieving near-perfect AUC scores and accuracy.
  • The combined approach of Bayesian networks and MCDM identified "Corrosion in Electrolysis Cells" and "Damage and Explosion of Cells" as high-priority risks.
  • Machine learning models significantly outperformed traditional methods in accuracy and predictive power.

Conclusions:

  • Machine learning techniques are highly effective tools for process risk assessment.
  • The integration of Bayesian networks and MCDM provides a robust framework for prioritizing risks.
  • These methodologies enable the implementation of targeted control and preventive measures for enhanced industrial safety.