Enabling the Process Engineer with PI AF and Data ...€¦ · Test New KPIs and use ad hoc...
Transcript of Enabling the Process Engineer with PI AF and Data ...€¦ · Test New KPIs and use ad hoc...
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Enabling the Process Engineer with PI AF and Data Analytics
-A Journey-Aubyn Chavez
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Aubyn Chavez• Digitalization & Process Technology Engineer
• Cornerstone Chemical Co.
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A safe producer of intermediate chemicals
Acrylonitrile Melamine Sulfuric Acid
Leading Industry Position
#2 North America Producer
Sole North American Producer
Leading Producer of Non-Fuming & Regen
Acid in the USGC
End Market • Consumer Products• Water Treatment• Aerospace• Energy• Transport
• Building Products• Consumer Products• Transport• Industrial Products
• Consumer Products• Building Products• Industrial Products• Water Treatment• Transport
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They are the most intimate with the chemical process.
Why process engineers?
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PI AF & TrendMiner
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Analytical Tools
Examples
Application ∙ Approach ∙ Obstacles ∙ Synergy
Report Automation ∙ KPI Automation ∙ Discovery ∙ Diagnostics
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PI AF
PSE(PI AF UI)ProcessbookPI VisionTrendMiner
Cornerstone’s OT Architecture
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Tag Building/KPIs
Automating Reports
Time Calculations
Data Comparison(Tags & Periods)
Data QueryPatternSearch/Recognition
Model Building
Monitoring
Event FramesPI AF
TrendMiner
PI Vision
Synergy
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PI AFTrendMinerDiscover, Diagnose Create Actionable KPIs on
Operations Dashboards
Test New KPIs and use ad hoc dashboarding
PI AF & TrendMiner Synergy
Bring in ‘tried & true” for Operations
(PI Vision/Processbook)
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Define the problem
Measure – aligned baseline metric
Analyze – find root cause, influences
Improve – implement solutions, verify improvement with metric
Control – sustaining improvement
DMAIC and how the tools apply:
PI AF
TrendMiner
PI AF, PI Vision & TrendMiner
PI AF, PI Vision &TrendMiner
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‘Measure’ ObstaclesThe ‘right’ KPIs Data Wrangling
How do we measure performance?
Are all groups aligned on the calculation?
Does the data exist?
Is the data reliable?
Avoid:GarbageInGarbageOut
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ExampleThe ‘right’ KPIs Data Wrangling
Finance:𝑈𝑟𝑒𝑎𝑇𝑜𝑡𝑀𝑒𝑙𝑇𝑜𝑡 ∗
𝑁𝐻3𝑇𝑜𝑡𝑈𝑟𝑒𝑎𝑇𝑜𝑡
𝑁𝐻3𝐶𝑟𝑒𝑑𝑖𝑡𝑈𝑟𝑒𝑎𝑇𝑜𝑡 ∗
𝑁𝐻3𝐶𝑟𝑒𝑑𝑖𝑡𝑀𝑒𝑙𝑇𝑜𝑡
Engineers:𝑁𝐻3𝑇𝑜𝑡 𝑁𝐻3𝑆𝑜𝑙𝑑 𝑈𝑟𝑒𝑎𝐼𝑛𝑣
𝑀𝑒𝑙𝑇𝑜𝑡Unreliable Transmitter for “NH3Sold” variable
Values come from ERP System (not a PI Tag)
Not aligned
Raw Material Usage KPIs: NH3 in Melamine Unit
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Analytical Tools
Examples
Application ∙ Approach ∙ Obstacles ∙ Synergy
Report Automation ∙ KPI Automation ∙ Discovery ∙ Diagnostics
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Manual Reports
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Manual Reports
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• Time Consuming
• Engineers are part time ‘clerks’
• Messy, can be illegible
• Difficult visibility (“out of sight out of mind”)
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PI AF – automate excel reporting
Automate desired values and visualize in excel or a dashboard.
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How did we get from messy spreadsheet to simple & automated
dashboard?
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Real-Time KPIs•Raw Material Usage is a common KPI used in the industry to help drive optimization and lower costs.
•Our Melamine unit had a difficult year with Ammonia (NH3) usage and so it was requested that we automate an [NH3 consumed/Melamine produced] calculation to create more visibility and enable us to be more agile.
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𝑁𝐻3 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑑 𝑙𝑏𝑠𝑀𝑒𝑙𝑎𝑚𝑖𝑛𝑒 𝑃𝑟𝑜𝑑𝑢𝑐𝑒𝑑 𝑙𝑏𝑠
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Recall the example from earlier:The ‘right’ KPIs Data Wrangling
Finance:𝑈𝑟𝑒𝑎𝑇𝑜𝑡𝑀𝑒𝑙𝑇𝑜𝑡 ∗
𝑁𝐻3𝑇𝑜𝑡𝑈𝑟𝑒𝑎𝑇𝑜𝑡
𝑁𝐻3𝐶𝑟𝑒𝑑𝑖𝑡𝑈𝑟𝑒𝑎𝑇𝑜𝑡 ∗
𝑁𝐻3𝐶𝑟𝑒𝑑𝑖𝑡𝑀𝑒𝑙𝑇𝑜𝑡
Engineers:𝑁𝐻3𝑇𝑜𝑡 𝑁𝐻3𝑆𝑜𝑙𝑑 𝑈𝑟𝑒𝑎𝐼𝑛𝑣
𝑀𝑒𝑙𝑇𝑜𝑡Unreliable Transmitter for “NH3Sold” variable
Values come from ERP System (not a PI Tag)
Not aligned
Raw Material Usage KPIs: NH3 in Melamine Unit
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PI AF – Real-time KPIs
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PI ProcessBookPI DataLink
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PI AF – Real-time KPIs
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Discovery/Diagnostics - TrendMinerUnit: AcrylonitrileEquip: Reboiler on Waste Water Column
Problem: Reboiler is fouling more frequently as of late, causing it to have to be taken out of service for cleaning more often. This is very costly.
Hypothesis: Catalyst carry over from reactors is causing the fouling.
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WasteWaterColumn
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Reboiler Chest PressureUsed status PI Tag to filter out times Reboileris out of service
You can clearly see that after a certain point in time, the fouling frequency increased
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TrendMiner – Diagnostics – Catalyst Loss Hypothesis
Reboiler Chest Pressure
Total Catalyst Loss TPH
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Correlation coefficient: -0.009
Reboiler Chest Pressure
Total Catalyst Loss TPH
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01-31-2016 08-18-2016 03-06-2017 09-22-2017 04-10-2018 10-27-2018 05-15-2019 12-01-2019 06-18-2020
Total Ton Catalyst Lost per Online Period
By adding an integral calculation to TPH Catalyst Loss and exporting to Excel – we can disprove the catalyst hypothesis.
Tons
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Next Steps! Optimization
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D M A I C
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Recommendations
2) Align prioritized KPIs & Use Cases with Management
3) Get reliability/equipment specialists involved for a CBM & Predictive Maint initiative.
5) Don’t wait for others to run with it & don’t get discouraged.
4) Become a champion. Push for training. Schedule 1-on-1 time with engineers.
1) Establish relationship with PI Admin & IT based on
my journey
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CHALLENGES SOLUTION BENEFITS
PI AF & Data Analytics
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Using PI AF & TrendMiner, Cornerstone has progressively become more data driven.
Aligning with management & becoming a champion helps to overcome obstacles along the way.
Manual burdensome tasks
KPIs Discovery,
Diagnostics, Optimization
Technical Obstacles
Adaptation Obstacles
Process engineers utilizing their talents effectively and becoming more collaborative to start tackling problems that were previously too time-demanding or difficult.
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