A machine learning methodology for reliability evaluation of complex chemical production systems.

Area of Science:

• Chemical Engineering
• Systems Engineering
• Data Science

Background:

• System reliability is crucial for safe and sustainable chemical production.
• Complex chemical processes require robust reliability evaluation methods.

Purpose of the Study:

• To propose a novel hybrid model for chemical production system reliability evaluation.
• To integrate machine learning for subsystem and overall system reliability prediction.

Main Methods:

• Categorization of reliability factors into Man, Machine, Material, Management, and Environment (4M1E).
• Development of Support Vector Machine (SVM) models optimized by Particle Swarm Optimization (PSO) for subsystem reliability.
• Correlation of subsystem reliability with system reliability using Random Forest (RF).
• Enhancement of predictive accuracy using Markov Chain Residual error Correction (MCRC).

Main Results:

• The hybrid model demonstrated satisfactory prediction performance in a case study.
• Integration of multiple algorithms (SVM, PSO, RF, MCRC) improved reliability prediction accuracy.

Conclusions:

• The proposed hybrid model offers a reliable approach for evaluating complex chemical production systems.
• The methodology provides a valuable tool for enhancing operational safety and sustainability in the chemical industry.