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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 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


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


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


Inte

rnal

HMI³ the approach

7 Recovery Line

Pulp ConsumerFiber Line

Utility Line


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


Inte

rnal

9

Customer situation


Inte

rnal

10

Customer situation


Inte

rnal

11

Customer situation


Inte

rnal

12

Customer situation


Inte

rnal

13

HMI³ optimized


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



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


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


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


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


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


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)


• 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


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%


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


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


Con

fide

ntial

MHC Example

27

Outlier Detection!


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)


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”



• 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”



• Import/ Export definitions

Add

Define Tag – Abbreviation

Define Resolution

Define Calculation method

Define Engineering unit

Exception substitution rule


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


HMI³ - Data Integration Limits

• Defining limits, operating range etc.

• Limit lines configuration

Add

Define Tag/KPI

Limit line type


• “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


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.


Generate Matlab dll

40

• Matlab Compiler Library

necessary

• Create Project

• Select .NET Assembly

• Add all functions needed

• Generate Package dll


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

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