PI System Infrastructure

Presented by

Create Value with Data Analysis from PI System Infrastructure Case of an Evaporation Plant

Thiago Raddatz

Conference Theme and Keywords

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

Location of industrial units

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

Products of OSIsoft at Klabin

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

PI System Infrastructure

Data Mining Decision Action

PI System and Braincube: 2 complementary systems

OPINIONS feeling based on experience

Structured Information, but

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

s’ va

PI System Architecture

Interaction infrastucture with Braincube

Relationship between Systems

• 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

Technical Levels

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

Analysis

Pilotage

Reporting Staff and Prod Infrastructure

Value raw data in context

Organization of the data / Asset Framework (AF)

data transformation map

Automatic continuous data preparation

Analysis Exemple – Evaporation Plant

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

Situation before Analysis

• Average for the period: 4.9 ton of water/ ton of steam • Variability: from 4.30 to 5.40

Variability per month

Efficiency Analysis Model

• Definition of the limit: 4.9 tw/ts • Defining the period of analysis: 22 months • Defining the perimeter analysis: 237 process variables were selected

Data Mining

RANKING OF THE MOST IMPACTFUL PARAMETERS

Analysis of Variable

Correlation analysis

Hyperlift Braincube

Selecting The Most Impactful Variables

Result of the Analysis and Rule Creation

Twater/Tvapor

Result of the Analysis and Rule Creation

Rule Monitoring through the PI System

Rule Monitoring through Braintouch

Results

• 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

Other Analysis

• 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

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

Key Factors

• 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

Thiago Raddatz

traddatz@klabin.com.br

Technical Assistant, Recovery and Utilities Department

Klabin, Brazil

www.ipleanware.com

contact@ipleanware.com

Questions

Please wait for the microphone before asking your questions State your name & company

PI System Infrastructure

Documents

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