Post on 31-Dec-2016
transcript
© Copyright 2015 OSIsoft, LLC
Presented by
Create Value with Data Analysis from PI System Infrastructure Case of an Evaporation Plant
Thiago Raddatz
© Copyright 2015 OSIsoft, LLC
Conference Theme and Keywords
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Klabin
• Founded in 1899 • 100% Brazilian company • About 15,000 employees (direct and indirect) • Bovespa’s level 1 corporative management • Prodution of 2 million tons of corrugated paperboard, industrial sacks and logs • 16 mills in Brazil and 1 in Argentina
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Location of industrial units
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Real financial gains
The tools feature the best ways to work
Dissemination of knowledge through the results presented by the process monitoring tools
Solution(s) Results and Benefits
Data Analysis from PI System Infrastructure
Business Challenges
Large volume of data is difficult to analyze
Complex process are difficult to maintain optimized
Use of additional tools to the PI System to analyze the large volume of data.
Use of tools increases involvement of all toward the same goal of results.
PI System Insfrastructure can be enhanced with a data analysis tool for “big data” like IP Leanware’s tool called Braincube
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Products of OSIsoft at Klabin
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PI Batch
PI Batch View PI DataLink
PI Interface for OPC
PI Notifications
PI ODBC
PI ProcessBook
PI Profile
PI SDK
PI SMT
PI ACE
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PI System Infrastructure
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Data Mining Decision Action
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PI System and Braincube: 2 complementary systems
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OPINIONS feeling based on experience
DATA
Structured Information, but
gross
INDICATORS
ratio, count, trend, lead to
confusion
ANALYSIS
Can show correlations,
relations
INSIGHT
Influences decision-making
ACTION
Change process or practices
Data Infrastructure Decision and Action Infrastructure
Fact-based decision value chain
Sys
tem
s’ va
lue
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PI System Architecture
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Interaction infrastucture with Braincube
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Relationship between Systems
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• 2 sites: Otacílio Costa and Correia Pinto
• Dept: Pulp, PM, Recovery and Utilities
• Amount of tags: 24,549
• Data storage for 5 years
• Real time
• 2 sites: Otacílio Costa and Correia Pinto
• Dept: Pulp, PM, Recovery and Utilities
• 12,500 tags process
• Data storaged since 2009
• Contextualization
Data transmission frequency: 2 min
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Technical Levels
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ACQUISITION Create opportunities
CONTEXTUALIZATION Add value
DECISION & ACTION Generate results
High frequency
Big volumes
Large variety Multi sources and different forms of data
Technical transformation models
Historical information treatment Automatic continuous data preparation
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Analysis
Pilotage
Reporting Staff and Prod Infrastructure
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Value raw data in context
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Organization of the data / Asset Framework (AF)
data transformation map
Automatic continuous data preparation
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Analysis Exemple – Evaporation Plant
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Analysis Exemple – Evaporation Plant
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Challenge: to improve evaporation efficiency
• How to measure evaporation efficiency?
• Amount of steam required to evaporate 1 ton of water
ton of evaporated water / ton of steam
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Situation before Analysis
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• Average for the period: 4.9 ton of water/ ton of steam • Variability: from 4.30 to 5.40
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Analysis Exemple – Evaporation Plant
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Variability per month
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Efficiency Analysis Model
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• Definition of the limit: 4.9 tw/ts • Defining the period of analysis: 22 months • Defining the perimeter analysis: 237 process variables were selected
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Data Mining
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RANKING OF THE MOST IMPACTFUL PARAMETERS
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Analysis of Variable
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Correlation analysis
Hyperlift Braincube
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Selecting The Most Impactful Variables
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Result of the Analysis and Rule Creation
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Twater/Tvapor
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Result of the Analysis and Rule Creation
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Rule Monitoring through the PI System
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Rule Monitoring through Braintouch
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Results
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Results
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• Gains: Current Average Efficiency: 5.255 Tw/Ts (+7.1%) 65 tons of steam saved per day 23,000 tons/year • Process Stabilization After the rule no month under 5.04 tw/ts
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Other Analysis
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• Otacílio Costa – Pulp and PM – Speed Increase in PM – Average Moisture – Moisture Variation – Mullen – Steam Consumption – Washing Solids Content – Alkaline loss at Washing – Energy Consumption in the Refining – Recipes – Inputs
• Correia Pinto – Pulp and PM
– Pulp Production – Speed Increase in PM – Reduce Sliver on the Paper – Tear Test Improvement in the Pulp – Tear Test Improvement in the Paper
• Otacílio Costa – Recovery and Utilities – Recovery Boiler Efficiency – Evaporation Plant Efficiency – Causticizing Stabilization – Lime Kiln Production – Generators Production – Power Boiler Efficiency – Ash Leaching
• Correia Pinto – Recovery and Utilities
– Recovery Boiler Efficiency – Evaporation Plant Efficiency – Effective Alkali Stabilization – Generators Production
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Automated Secure File Transfer
On site Server
PI Server
Exchange Folder Flat files
Other Data
2 min data
NOW Automated Secure
Data Transfer
PI Server
Process data from the mill
PI Cloud Connect
Other Data
FUTURE
Future architecture
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Key Factors
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• Data Infrastructure – Various sources and types of data – Real time update
• Data Mining – Data Contextualization – Simple and powerful analysis tools to make decisions – Monitoring Tool
• People – Management: Vision and purpose – People: development and involvement
© Copyright 2015 OSIsoft, LLC
Thiago Raddatz
traddatz@klabin.com.br
Technical Assistant, Recovery and Utilities Department
Klabin, Brazil
www.ipleanware.com
contact@ipleanware.com
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Questions
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Please wait for the microphone before asking your questions State your name & company
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