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

Data Growth in Industries (IoT)

Source: CISCO

Value

Domain Knowledge

Data

Meaningful Result

Application

Analytics

- Descriptive- Diagnostic - Predictive- Prescriptive

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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

Tools…

Logos, names and brands cited herein are the property of their respective owners

and more…

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

Case Study-Pump Failure Prediction (contd.)

screenshot

Case Study-Pump Failure Prediction (contd.)

ConfusionMatrix

Case Study-Pump Failure Prediction (contd.)

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

Questions?

Thank you!

How much data is being analyzed now?