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HMI³Production Planning
Workshop FUDIPOVienna, 19.10.2017
© Tieto Corporation
Agenda
Time Task
09:00 – 10:15Production Planning & Optimization (Site
Balance Optimization)
10:15 – 10:45Data Quality Assurance & Control (R2W,
Outlier Removal MHC)
10:45 – 11:00Data Preparation
(SOC, KPI,...)
11:00 – 11:30 Model integration
2
© Tieto Corporation
Tieto is the leading Nordic software
and services company
1968
Projects annually
Employing
3000
1.5
Serving Nordic clients since
1500Around
clients
Turnover of approximately
13000experts globally,in close to
20 countries
Servingcustomers in over
85
€ billion
countries worldwide
Investments in technology
and services more than
100€ million*per year
*incl. capital expenditure and operational costs
© Tieto Corporation
What is Production Planning/HMI³?
4
• Software and consultancy solution
• Based on data analytics
• Simulation, optimization predictive
production planning
• Production set point calculation tool for
each asset
• Tank level management
• Visualization and KPI Reporting
© Tieto Corporation
Goal of Production Planning/HMI³?
5
• Increase pulp production
• Decrease energy cost and chemical
consumption
• Increase planning efficiency and
effectiveness in case of
disturbances (What-If Cases)
• Increase production stabilization
HMI³the approach
© Tieto Corporation
Inte
rnal
HMI³ the approach
7 Recovery Line
Pulp ConsumerFiber Line
Utility Line
© Tieto Corporation
Inte
rnal
8
Cooking 2 Pulp Dryer
~ 400 t
Paper maschine 1~ 10 t/h
Paper maschine 2 ~ 20 t/h
Paper maschine 3 ~ 15 t/h
Pape rmaschine 4 ~ 10 t/h
Biomass Boiler
Turbine 1+2
Cooking 1
O2 DelignificationBleaching
Evaporation
Recovery Boiler
Causticizing
Storage Tower
5000m³
White Liquor T
2000m³
Customer situation
© Tieto Corporation
Inte
rnal
9
Customer situation
© Tieto Corporation
Inte
rnal
10
Customer situation
© Tieto Corporation
Inte
rnal
11
Customer situation
© Tieto Corporation
Inte
rnal
12
Customer situation
© Tieto Corporation
Inte
rnal
13
HMI³ optimized
© Tieto Corporation
Inte
rnal
Tank Level
Management
Pulp to
Paper
Set Point
Optimization
Prediction
HMI³ solutions
© Tieto Corporation15
Cooking 2 Pulp Dryer
~ 400 t
Paper machine 1~ 10 t/h
Paper machine 2 ~ 20 t/h
Paper machine 3 ~ 15 t/h
Paper machine 4 ~ 10 t/h
Biomass Boiler
Turbine 1+2
Cooking 1
O2 DelignificationBleaching
Evaporation
Recovery Boiler
Causticizing
Storage Tower
5000m³
White Liquor T
2000m³
HMI³ prediction principles
Inte
rnal
© Tieto Corporation
Mathematical modelling of tank levels
Modelling of tank levels based on historical process data
appyling phyiscal correlalations. Additional data driven
correction factor to ensure self-tuning of models.
Example: Pulp tank level = function(production rate O2
delignification, production rate bleaching, correction factor)
Mathematical
model of tank
HMI³ prediction principles
Inte
rnal
© Tieto Corporation
Asset Planning
Future production rates and planned shut downs are defined
via the Asset Planning module.
Tank Level
ForecastAsset
PlanningAsset
Planning
HMI³ modules for prediction
future80%
20%
future
Based on the defined production rates the Tank Level
Forecast module predicts the future tank level behaviour.
+ Tank Level Forecast
Planned
Actual
HMI³ predicted
Inte
rnal
© Tieto Corporation
HMI³ modules for predictionAsset
PlanningAsset
Planning
future
HMI³ optimized
Asset Planning
Production limits and planned shutdowns are defined via the
Asset Planning module.
Fiber Line
Set Point Optimization
80%
20%
future
HMI³ optimized
The Fiber Line Set Point Optimization calcultes the
optimized set points for O2 delignification and bleaching.
The integrated Tank Level Management considers the
operational limits of the Buffer Tank and facilitates an optimal
utilization of available capacities.
+ Fiber Line Set Point Optimization
Planned
Actual
HMI³ predicted
HMI³Live Demo & Use Cases
Data Quality & Assurance Control
© Tieto Corporation
HMI³ - Outlier removal procedure
• First steps
Definition of physical limits
(e.g. tank level limits 0 - 100%)
Within data (PI) extraction
values outside physical limits
are not considered
Additional operational limits can
defined for further treatment
© Tieto Corporation
HMI³ - Outlier removal procedure
• After the applied data extraction - two cases can occur
Limit violation – “values outside limits”
Missing values (e.g. due measuring system failure)
• Definition of data preparation time horizon
Two different time horizons, where the data preparation method is applied,
can be defined via parameters (short-, long-term)
© Tieto Corporation
• The following mathematical methods can be selected:
Substitution value - a substitution value can be defined
Reference value - reference value is applied
Violated limit - limit value (“operational minimum”, ”operational maximum”) is
applied depending on min limit or max limit violation
No substitution - raw data is used
Last string - last available string is used
HMI³ - Outlier removal methods
© Tieto Corporation
Con
fide
ntial
How to measure the goodness of fit?• R² or coefficient of determination is a well-known statistical measure of how close the data
are to a fitted regression line
• R² = percentage of the response variable variation that is explained by a linear model, i.e.
R-squared = Explained variation / Total variation
• In general, the higher the R-squared, the better the model fits your data (values: 0-100%)
• In some cases a very high R² is misleading better use an adjusted version of R²
R² weighted (R²w):
24
Overestimated:
R² = 100% R²w = 50%
Offset:
R² = 100% R²w = 50%
Overestimated & Offset:
R² = 100% R²w = 25%
© Tieto Corporation
Con
fide
ntial
How to track the model performance
online?
25
Model Health Check – Report
• comparison of selected historical data against simulated data in order to alert on drifts of defined parameters or missed updates to the models versus reality
• Summarizes relative model error based on defined operating range
• Each shift a report is generated automatically
• Calculates actual values and percentage inside warning (8%) and error limits (20%), all monthly, weekly and shiftly aggregated
• Non-valid time points (e.g. standstills -> defined by conditional KPI‘s) are not included in the aggregation
© Tieto Corporation
Con
fide
ntial
MHC Value & Evaluation Calculation
• MHC value describes percentage error of operating range (=OR), i.e.
(measured - simulated) / OR * 100
• No calculation of MHC value during downtimes etc. multiply MHC with condition tags
MHC = (measured - simulated) / OR * 100 * cond
Evaluation criterion:
percentage of samples with MHC value inside defined limits
• Warning limit – 8%
• Error limit – 20%
Quality criteria = #within / #samples * 100
#within = number of samples with absolute MHC value < 8% / < 20%
#samples = number of all samples
26
© Tieto Corporation
Con
fide
ntial
MHC Example
27
Outlier Detection!
© Tieto Corporation
Con
fide
ntial
29
Performance Tracking: ReportPerformance Summary on different time
spans: month, week, shift
Models have to be within narrow quality
limits in 80% of time
Lower values are marked in red and
indicate a possible need for tuning
Data Preparation(SOC, KPI,...)
© Tieto Corporation31
Data Integration and Preparation
• Data Integration
Add data (tag) to system
Add KPI (Key Performance Indicator)
Defining limits, operating range etc.
• Data Preparation
Outlier removal incl. example
Filtering / Clustering
Standard operating condition (SOC)
© Tieto Corporation
HMI³ - Data Integration Tag
• Interface to data storage system like OSIsoft PI, etc. is created
• Adding data (tag) to the HMI³ system via application
• Login
• Select “Master data” icon in the menu bar
• Category “Master data”
• Sub category “Tags”
© Tieto Corporation
HMI³ - Data Integration Tag
• Interface to data storage system like OSIsoft PI etc. is created
• New tags will be detected automatically and suggested for import
• Adding data (tag) to the HMI³ system via application
• Login
• Select “Master data” icon in the menu bar
• Category “Master data”
• Sub category “Tags”
© Tieto Corporation
HMI³ - Data Integration Tag
• Import/ Export definitions
Add
Define Tag – Abbreviation
Define Resolution
Define Calculation method
Define Engineering unit
Exception substitution rule
© Tieto Corporation
HMI³ - Data Integration KPI• Adding KPI (Key Performance Indicator) to the HMI³ system via application
• KPI
Add
Abbreviation (Name)
Description
Engineering unit
Formula
Predefined functions
can be selected
Functions can
be extended
© Tieto Corporation
HMI³ - Data Integration Limits
• Defining limits, operating range etc.
• Limit lines configuration
Add
Define Tag/KPI
Limit line type
© Tieto Corporation
• “Standard operating condition” - SOC
Used for prediction
Based on historical values
Weighted average calculation
Predefined time horizons can be selected
The weighting factor depends on this time period
Stable condition: values have to fulfill specific criteria to be considered in
average calculation
Clustering is applicable
HMI³ - Filtering methods
HMI³Model Integration
© Tieto Corporation
Model Integration – Modelling Style in Matlab
• An INPUT-Matrices, an PLAN-Matrices and a Parameter-Vector are handed over to a main-function
• Inside this function persistent variables are defined, generic tags are assigned, initial conditions are set and pre calculations are conducted before the object functions (single models) are called.
• After the calculation the resulting tags are assigned to an OUTPUT-Matrices.
© Tieto Corporation
Generate Matlab dll
40
• Matlab Compiler Library
necessary
• Create Project
• Select .NET Assembly
• Add all functions needed
• Generate Package dll
© Tieto Corporation
Interface Design
41
• Interface Design via XML file
• Parameter, Input, Plan, Output are specified
• Type definition
• Position is defined Matlab modell based
on matrices
Inte
rnal