Big Data Analytics in Process Safety Management (PSM)
Sudhakar Kabirdoss, PE
Global Process Safety, Micron Technology
Singapore.Source: IBM, https://strategylab.ca
CCPS Asia Pacific Regional Technical Steering Committee (TSC) Meeting 2nd Oct 2018, Singapore
Key Objectives for Industries
Manufacturing Operations
Business Growth
Productivity
Risk Management
R & D
Marketing
Big Data
- Size- Speed- Complexity- Uncertainty
1 KB = 103 byte1 MB = 106 byte1 GB = 109 byte1 TB = 1012 byte1 PB = 1015 byte1EB = 1018 byte1 ZB = 1021 byte1 YB = 1024 byte
Analytics
- Descriptive- Diagnostic - Predictive- Prescriptive
Co
mp
eti
tive A
dvan
tag
e
Basic Reporting What happened?
Ad Hoc Reporting How many, how often, where?
Dynamic Reporting Where exactly are the problems?
Reporting with Early Warning What actions are needed?
Basic Statistical Analysis Why is this happening?
Forecasting What if these trends continue?
Predictive Modeling What will happen next?
Decision Optimization What is the best decision?
Data Information Intelligence
Decision Support Decision Guidance
Reporting
Basic Analytics
Advanced Analytics
Descriptive
Diagnostic
Predictive
Prescriptive
Big Data Applications in other industries
Customer Service
• Sentiment analysis
• Customer category
• Brand Perception
• …
Supply chain
• Optimization
• Product distribution
• Forecasting
• …
Healthcare
• Drug delivery
• Personalized medications
• Disease Diagnosis
• …
Banking & Finance
• Risk Management
• Fraud Detection
• Forecasting
• …
Manufacturing
• Process Performance Optimization
• Yield Improvements
• Equipment Performance
• Asset utilization
• …
Database marketing
Financial risk management
Fraud detection
Process monitoring
Pattern detection
Typical approach in data analytics
Business Understanding [Process Safety policy, metrics,
standards, guidelines, eqpt.
spec ,etc]
Data Understanding
[parameters, type of
operations, mode, source,
etc.]
Data Preparation
[Impute, clean-up, formatting etc]
Modeling
[Classification, Regression,
Neural]
Evaluation
[Assessment, Validation,
Testing]
Deployment
Data
Data Type
Incident database
Equipment Inspection data
• ITPM data (SAP, CMMS)
Historian Data
• Process Parameters
• Alarms
• Event logs• Equipment
monitoring data
Design Data
• SOPs, P&IDs, PFD, HMB, Plot Plan, Layout
and so on…
Str
uct
ure
dIncident
Investigation Reports
PSM Audit Reports
Equipment Inspection
Reports
PHA Reports
Photos, images, videos
Shift communications
Un
stru
ctu
red
Supervised Learning
Unsupervised Learning
Process Safety Pyramid
API 754 Process Safety Pyramid
Standard/Adhocreports
Alerts, Hazard communications
Statistical analysis, extrapolation
Predictive modeling, Prevention
Benefits
Real Time Risk Evaluation
Optimal Maintenance Schedule
Asset Management
Resource Allocations
Visualization Dashboards
Case Study-Pump Failure Prediction
Variable Variable Name Data Type
x1 date NUM
x2 vibration NUM
x3 weather NOMINAL
x4 noise NOMINAL
x5 remote_start BINARY
x6 bearing_temp NUM
x7 seal_oil_pressure NUM
…
x29 operator_skill_level NOMINAL
Future works in Process Safety…
Incident Prediction
Equipment/Instrument Failure Prediction
Dynamic Risk matrix
Text analysis to complement with data analysis
Challenges
•Business requirements
•Data availability
•Data collection
•Data quality
•Discipline integration