A sophisticated approach to predicting and preventing catastrophic incidents in complex industrial environments
Imagine an industrial park where a single accident can trigger a catastrophic chain reaction—a leak becomes a fire, a fire triggers an explosion, and the explosion releases toxic gases that endanger entire communities.
This isn't theoretical; incidents like the "2.28" explosion at Zhao County Industrial Park in Hebei Province and the "8.26" explosion at Tuochuang Industrial Park in Wuhan demonstrate the devastating potential of chemical park emergencies 1 . These accidents resulted in significant property damage and casualties, revealing a critical truth: traditional risk assessment methods often fall short in evaluating the complex, interconnected risks present in modern chemical industrial parks.
Chemical industry parks concentrate numerous petrochemical enterprises, with raw materials, intermediates, and products often being dangerous chemicals typically handled under high-temperature or high-pressure conditions 2 .
This concentration generates development benefits but also creates high regional safety risks. The problem is compounded by what safety experts call the "domino effect"—where an accident in one enterprise can trigger disasters in neighboring facilities 2 .
The AHP-fuzzy evaluation method represents a hybrid approach that combines the strengths of two mathematical frameworks: the Analytic Hierarchy Process (AHP) and fuzzy logic theory.
Traditional risk assessment methods have significant limitations in modern chemical parks:
These methods often rely too heavily on static data, making them poorly suited to the dynamic risk evolution characteristic of today's chemical parks 1 .
Researchers create a hierarchical structure of evaluation criteria and determine relative weights through expert pairwise comparisons 3 .
Using fuzzy membership functions, qualitative judgments transform into quantitative values, capturing inherent uncertainty in assessments 4 .
A key strength is modeling uncertain reasoning patterns using triangular fuzzy numbers—represented by three points (l, m, u) where l ≤ m ≤ u 4 .
| Linguistic Term | Triangular Fuzzy Number | Risk Level |
|---|---|---|
| Very Low | (0, 0, 0.25) | Minimal concern |
| Low | (0, 0.25, 0.5) | Minor concern |
| Medium | (0.25, 0.5, 0.75) | Moderate concern |
| High | (0.5, 0.75, 1) | Significant concern |
| Very High | (0.75, 1, 1) | Critical concern |
A recent study demonstrates the practical application of the AHP-fuzzy method in assessing the Beijing Fangshan District Emergency Industrial Park 1 . This park contains a diverse mix of enterprises, each with distinct risk profiles.
Researchers classified enterprises into seven categories based on their professional focus and characteristics 1 :
| Enterprise Type | Characteristics | Primary Risks |
|---|---|---|
| Medical & Healthcare | Biological agents, pharmaceuticals | Chemical leaks, biohazards |
| New Energy Storage | Batteries, energy systems | Lithium battery fires, explosions |
| Composite Materials | Chemical processing, manufacturing | Fires, toxic releases |
| Intelligent Manufacturing | Electronics, automation | Electrical fires, equipment failure |
| Mechanical Manufacturing | Heavy machinery, fabrication | Industrial accidents, mechanical failures |
| Consulting & Technical Services | Office-based, laboratories | Minor chemical incidents |
| Construction & Installation | Temporary operations, varied sites | Worksite accidents, material hazards |
The assessment revealed that emergency prevention capability and emergency preparedness capability were the most critical factors in the evaluation system, receiving the highest weights in the AHP analysis 3 .
For the Fangshan District case study, researchers developed a practical 4-day working scheme to implement the AHP-fuzzy evaluation methodology 5 :
Analysis of emergency plans, safety procedures, and historical incident data
Using the established indicator system to assess current conditions
Weight calculation and fuzzy comprehensive evaluation
Preliminary reporting of findings and recommendations
Modern implementations incorporate big data fusion to monitor multiple sensor data streams in real-time and create comprehensive digital twins of chemical parks 2 .
Virtual simulation technologies allow safety managers to simulate accident progression and test emergency responses without real-world risks 2 .
The AHP-fuzzy method continues to evolve with emerging technologies and expanding applications.
Machine learning enhances predictive capabilities
Continuous data feeding from distributed sensors
Combining with neural networks for pattern recognition
Adaptable evaluation systems across industrial parks
The AHP-fuzzy evaluation method represents a significant advancement in how we approach the complex challenge of chemical park safety.
By combining the structured decision-making of AHP with the uncertainty-handling capabilities of fuzzy logic, this approach provides safety managers with a powerful tool to identify vulnerabilities, prioritize improvements, and prevent catastrophic accidents.
As chemical parks continue to evolve in complexity and scale, methodologies that can effectively evaluate and manage their interconnected risks become increasingly vital. The AHP-fuzzy method offers a scientifically rigorous yet practically implementable approach that bridges the gap between theoretical risk models and the messy reality of industrial operations.
Through continued refinement and integration with emerging technologies like big data analytics and virtual simulation, this methodology promises to play a crucial role in building safer, more resilient industrial parks that can harness the benefits of chemical concentration while minimizing the risks to people, property, and the environment.