AMINE GAS TREATMENT CASE - online.stat.tamu.eduonline.stat.tamu.edu/dist/analytics/sasday2014/presentations/shaw.pdf · •Reduce collapse of Amine processing contactor due to foaming

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AMINE GAS TREATMENT CASE

PROACTIVELY PREDICT COLLAPSE OF AN AMINE PROCESSING CONTACTOR


2

AMINE GAS CASE PREDICT COLLAPSE OF PROCESSING CONTACTOR

Save

$200M USD

per year

Foaming / Flooding events:

• 2 to 4 events per contactor per year

• Costing $250,000 per event

• 200 contactors worldwide

• Impact:

• Lost revenue

• High unplanned maintenance costs

• Poor amine utilization


AMINE GAS CASE SAS DAY AT TEXAS A&M:

PREDICT COLLAPSE OF PROCESSING CONTACTOR

• Project Objectives

• Data Overview

• Analytical Approach

• Summary of Analytical Findings

• Lessons Learned

• Requirements for Big Data Predictive Analytics

Agenda


AMINE GAS CASE AMINE GAS TREATMENT CASE OBJECTIVES

Business Challenge

• Reduce collapse of Amine processing contactor due to foaming and flooding

Business Impact

• Proactively predict foaming and flooding events

• Predict and monitor processing liquid levels

• Optimize Amine utilization

Value Driver

• Improve up-time and processing efficiency


AMINE GAS CASE GOAL: PROACTIVELY PREDICT PROCESSING FAULTS

Low Amine Gas Pickup

Foaming/Flooding Event

Excess Amine Pickup

Lean Amine to Contactor


AMINE GAS CASE PROCESSING CONTACTOR SYSTEM OVERVIEW

CO2 treated

CO2 feed gas

Storage Tanks

41, 42 & 43

C-101 C-102

Contactor

C101


AMINE GAS CASE ANALYTICAL APPROACH

SAMPLE EXPLORE MODIFY MODEL ASSESS

• Consolidate

multiple data

sources

• Quality Check

• Cleanse

• Visualize sensor

and fault data

• Collaborate with

subject matter

experts

• Identify

candidate

anomalies

• Transform

variables

• Filter outliers

• Cluster similar

acting groups

• Variable

selection

• Identify early

fault indicators

Root cause analysis

• Generate

predictive

models

Decision trees

Logistic regression

Neural networks

• Review results

with subject

matter experts

• Score new data

• Automate

ongoing

assessment of

prediction

accuracy

• Flag model

needing tuning

• Alert when

operation

becomes

unstable


AMINE GAS CASE DATA OVERVIEW

• Source: PI System

• Systems:

• Contactor C-101: 22 sensor variables & 18 calculated variables

• Contactor C-201: 22 sensor variables & 18 calculated variables

• Contactor Feed Flow: 1 sensor variable

• Storage tanks: 3 sensor variables (1 per tank)

• Time range:

• January 1, 2010 to April 12, 2014


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Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d . © Copyr i g h t 2014 OS Iso f t , LLC .







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ANALYTICAL APPROACH

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AMINE GAS CASE ANALYTICAL WORKFLOW

Identify

• Time periods to investigate

• Events to investigate

Explore

• See potential trends/areas to investigate

• Leading cause of failures

• Identify potential explanatory variables

Analyze

• Perform root cause analysis

• Statistical signatures for events

• Stability modeling and monitoring


AMINE GAS CASE EXPLORE SENSOR DATA AND FAILURE TIMES

February 10, 2013 to April 12, 2014


AMINE GAS CASE ANALYTICAL METHODS EMPLOYED

Pareto Analysis Correlation Analysis Variable Cluster

Analysis Principal Component

Analysis

Association Analysis Root Cause

Analysis Decision Trees Logistic Regression Stability Monitoring


AMINE GAS CASE CORRELATION ANALYSIS

Purpose:

• Measures strength of a linear relationship between numerical variables

Benefits:

Improves accuracy and performance of predictive models


AMINE GAS CASE VARIABLE CLUSTER ANALYSIS AND

PRINCIPAL COMPONENT ANALYSIS

Purpose:

• Identifies similar acting variables or groups

• Variable reduction technique

Benefits:

Identifies most meaningful sensor data and events to further analyze

Cluster Analysis

Principal Component Analysis


AMINE GAS CASE MULTIPLE LINEAR REGRESSION

Dependent Var: Gas Treating Area Pickup Ratio

Explanatory Var: Flash Drum Level

Feed Filter Separator Level

CO2 in treated gas

Tray 21 Level

Feed Gas Pressure

DSG Stripper Temp

D101 NGL Train Separator

Feed gas - Lean DGA temp

Contactor Level

Lean DGA % Strength

NGL Train Separator Level

Feed Gas Temp

Dependent Var: Contactor Level

Explanatory Var: DSG Stripper Temp

Feed Gas Temp

Feed gas – Lean DGA temp



Lean DGA % Strength

Feed Filter Separator Level

Tray 21 Level

Flash Drum Level

Dependent Var: Feed Gas Flow

Explanatory Var: Lean DGA % Strength

DGA Antifoam Tank Level

Contactor Level

Flash Drum Level

Lower Tray #5 Temp

NGL Train Separator Level

Purpose: • Predicts contactor processing liquid levels

• Explains variables or combination of

variables affecting contactor processing

liquid levels

Benefits:

Analyzes more information generating

greater predictive power

Generates better understanding of

relationships between variables


AMINE GAS CASE CONTACTOR FOAMING/FLOODING DECISION TREE

Purpose:

• Illustrates all possible outcomes and highlights typical path

Benefits:

Visually identifies variables considered relevant to target event occurrence or non-occurrence


AMINE GAS CASE ROOT CAUSE ANALYSIS & ASSOCIATION ANALYSIS

Foaming/Flooding Event

Period: Jan 1/2013

to Dec 31/2013

Analytical rule Lift PDI205_v_gt95p & PDI022_v_gt95p ==> TARGET_EVENT 17.86

PDI205_v_gt95p & PDI006_v_gt95p ==> TARGET_EVENT 16.76

TI028_v_gt95p & PDI205_v_gt95p ==> TARGET_EVENT 15.94

TI028_v_gt95p & FIC004_v_gt95p ==> TARGET_EVENT 15.59

PDI022_v_gt95p & FIC004_v_gt95p ==> TARGET_EVENT 15.52

TI026_v_gt95p & FIC004_v_gt95p ==> TARGET_EVENT 15.45

TI026_v_gt95p & PDI022_v_gt95p ==> TARGET_EVENT 15.12

Purpose:

• Helps understand why processing fault occurred

• Identifies variables potentially contributing to fault/failure event

Benefits:

Identifies association rules to predict occurrence of future faults/failures


AMINE GAS CASE ALERT FOAMING/FLOODING ROOT CAUSE PATTERN

RECOGNITION WARNING


AMINE GAS CASE CONTACTOR LEVEL STABILITY MONITORING MODELS

• Provides better forecasting accuracy

• Detects and accounts for daily and seasonal operational differences

ARIMA

• Provides better explanatory power

• Traditional approach used by engineers

REGRESSION

Purpose:

• Provides predictive

capability to show

current state and alert

of potential problems

Benefits:

Automatically alerts

when operation

becomes unstable


AMINE GAS CASE SCORE NEW DATA AGAINST PREDICTION MODELS


AMINE GAS CASE ALERT UNSTABLE OPERATION TO PREVENT FUTURE

FOAMING/FLOODING EVENTS


AMINE GAS CASE SUMMARY OF ANALYTICAL FINDINGS

Proactively predict foaming and

flooding events

Predict and monitor processing liquid

levels

Optimize Amine utilization

Discovered

foaming/flooding

fault signatures

Developed prediction

models and stability

monitoring forecast

models

Developed stability

model to feed

optimization model


AMINE GAS CASE LESSONS LEARNED

Sensor and fault data can easily be analyzed

Wide variety of analytical methods available

Tremendous business value discovering root

cause of faults and failure initiating variables


AMINE GAS CASE REQUIREMENTS FOR BIG DATA PREDICTIVE ANALYTICS

Data:

Scalable Performance Data Server

High-Performance Architecture:

Analysis:


32

AMINE GAS CASE PREDICT COLLAPSE OF PROCESSING CONTACTOR

Save

$200M USD

per year

Foaming / Flooding events:

• 2 to 4 events per contactor per year

• Costing $250,000 per event

• 200 contactors worldwide

• Impact:

• Lost revenue

• High unplanned maintenance costs

• Poor amine utilization

Documents

AMINE GAS TREATMENT CASE - online.stat.tamu.eduonline.stat.tamu.edu/dist/analytics/sasday2014/presentations/shaw.pdf · •Reduce collapse of Amine processing contactor due to foaming