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• Dedicated team

Project managers

Process engineers

Development team

• Dedicated tools

PEPITo© data mining platform

Technological partnerships

• Focus on industry

pulp and paper, steel, aluminium, cement, energy production, food and beverage, chemicals

WHO ARE WE?

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OUR EXPERIENCE WITH EMIS IN PULP AND PAPER

(OR MT&R, M&V, ISO50001…)

+ Several ongoing projects in N-A and Europe

Energy audit in

20+ millswith focus on data

availability & quality, monitoring capability and

performance gap

Participation in Energy Blitz at

15+ millswith significant and

sustainable cost reductions

2 EMIS implementation in

mills with different

situation, motivation,

and culture

High level

monitoring models

implemented in

5+ mills

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4 KEY DRIVERS FOR ENERGY PERFORMANCE

What prevent us to take

action and sustain the

gain?

Best practice we have seen

Operation is

production-oriented

Address impact on production and quality

• Leverage process data to bring facts

• Set flexible and gradual rules

Problem solving culture

is CAPEX-oriented

Adopt a continuous improvement vision

• Optimization projects (OPEX)

• Secure ROI with energy management

Lots of data but lack of

relevant information

Cascade of KPI with adaptive targets

• Different KPI for each level of decision

• Multivariate analysis to set relevant target

Operators are not

empowered

Top-down approach, bottom up implementation

• Give operators practical tools for decision

support & troubleshooting tools

• Involve them at every step of the project

MANAGING

CHANGE

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Operation

Staff

ManagementMill manager – kWh/t total

Pulp plant manager –kWh/t pulp

plant

Papermachine manager –

kWh/t PM

Papermachine surintendent

– kWh/t PM

PM operator –kWh/t

Forming section

PM operator –kWh/t Press

section

PM operator –kWh/t Drying & finishing

Utility manager –

kWh/t power plant

NO ACCOUNTABILITY NO RESULTS

NO ACTIONABLE PARAMETERS NO ACCOUNTABILITY

Classical

approaches

do not bring

decision tools

in control

room

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MISSING LINK BETWEEN ENERGY STUDY AND

ENERGY MANAGEMENT

ENERGY

OPTIMIZATION

PROJECTSAre

recommendations

really applied and

maintained?

ENERGY

MANAGEMENT

SYSTEMSHow to sustain the

gains and take

actions to continue

to improve

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SUCCESSFUL IMPLEMENTATION IS A MIX OF PROCESS

EXPERTISE, TECHNOLOGY AND PEOPLE ENGAGEMENT

The right tool to the right person at the right time

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Continuous improvement

Integration in the performance system of the mill

Performance management

KPI and reporting structure, workshop

with operators and management,

communication plan (before, during, after)

of the mill

MORE THAN A TYPICAL OPTIMIZATION PROJECT

Optimization

projectof the mill

Optimization project

on high potential area

of the mill

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SUCCESS FACTORS

① You’re richer than you think

Meters, historian, display, analysis capabilities…

② Top-down approach, bottom-up

implementation

No accountability without actionable parameters

③ Start implementation with an energy

optimization project

Pilot: people readiness, potential, data available

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RULE #1: LEVERAGE EXISTING INFORMATION SYSTEMS

AND CONTINUOUS IMPROVEMENT STRUCTURE

Op

era

tors

S

up

erv

iso

rs M

an

ag

ers

Imp

ac

t o

f d

ec

isio

n o

n d

ay-t

o-d

ay e

ne

rgy c

os

t

Level of

decisionERP

Exce-

basedIntranet

Historia

n (e.g.

PI)

DCS

Management X X X

Staff X X X

Operators X X X

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Operation

(daily– hour)

Staff (weekly)

Management (month)

Gain in $$$

GJ saved

Operation:

GJ saved

Average GJ/T

GJ / ton

vs. target

Pressure setpoint per

grade

Fresh water valve to WW

chest

Kraft pulptemperature

Maintenance:

HEX efficiency

Screen uptime

RULES #2: TOP DOWN APPROACH,

BOTTOM UP IMPLEMENTATION

Top

management:

global view on

cost control

Control room:

focus on

actionable

parameters

Op

era

tors

S

up

erv

iso

rs M

an

ag

ers

Imp

ac

t o

f d

ec

isio

n o

n d

ay-t

o-d

ay e

ne

rgy c

os

t

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BOTTOM UP: GIVE DECISION TOOLS TO OPERATORS

SO THEY CAN TAKE ACTIONS

Actua

l

value

of KPI

Predicted regimes based on 3+ process variables

> 1.1

< 1.1

KPI>1.1 KPI<1.1

A: Performance is good and we know

why

C: Performance is good “but we do not know why”

B: Performance is bad “but we do not know why”

D: Performance is bad and we know

why

Insight to solve the problem

1. CO pre-heater > 15%

2. Temp heating tower < 84,5°C

Previously unseen situation!

Operator alerts energy team

for more investigations

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RULE #3: CHOOSE YOUR BATTLE

Normandy, 6 June 1944

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CLASSICAL EMIS IMPLEMENTATION SCHEME…

Upfront investment:

measurements, IT, software, services

+ cost of internal resources

cashflow

implementation

HIGH RISK

OF

PUSHBACK

PRESSURE

ON

CASHFLOW

AND

RESOURCES

“let’s implement, the

system will do the rest…”

planned

reality

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IMPLEMENTATION BY SUCCESSIVE PROJECTS PROVIDES

MORE BUY-IN WHILE USING LESS RESOURCES

cashflow

implementation

PROGRESSIVE

AND PLANNED

IMPLEMEN-

TATION

BETTER

CHANCE OF

OPERATOR

BUY-IN

Kickoff project sub-project #2 sub-project #3

Upfront investment minimized:

focus on area with high potential, local

resources, integration in existing

systems, gain controlled and monitored

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TYPICAL ENERGY MAESTRO PROJECT

Kick off session

• KPI structure

• Workshops with operators and stakeholders

• Process understanding

• Data collection

Data analysis

• Exploration

• Rootcauseanalysis (multivariate data analysis)

• Modeling

Implementation preparation

• Test and validation of the model off line

• Programming of equations and dashboard

• Reporting structure

Implementation

• Operators training

• Stakeholder training

• Closing session

• Follow up plan

Immediate actions

taken based on

performance gap

analysis

•Awareness

•Capability building

of plant people

•First decisions,

first savings

• Better knowledge

of operation

• Optimization rules

of the process$$$$$$

$$$

NOT ONLY

ENERGY

PROJECT, IT’S

CHANGE

MANAGEMENT!

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ENERGYMAESTRO IN ACTION:

Energy management at a papermill – $600,000 / yr

• Implementation of a KPI monitoring structure

• Implementation of rules for optimal heat recovery operation

Paper machine energy optimization – $500,000 / yr

• Fast identification of the top causes for energy use variability

• Development of an action plan to close the gap

TMP heat recovery optimization – $800,000 / yr

• Multivariate analysis of reboiler low performance

• Development of an action plan to close the gap

Boiler optimization at a steel plant – $250,000 / yr

• Identification of operation rules that ensure high efficiency

• Implementation of preventive maintenance tool to reduce power use

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USER CASE #1

Chemicals – Steam network

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• Culture change in the way

steam network is managed

• Expected gains: 1,2 M$

• 3 month project, no CAPEX

① Kickoff with high management

② 5 workshops, 4 department,

60+ operators, 200+ ideas

③ Model development and

analysis of new setpoints

④ Implementation of new DCS

screen and Excel reports

⑤ Training of operators & staff

STEAM NETWORK OPTIMIZATION AT A

PHOSPHORIC ACID PLANT

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USER CASE #2

P&P - Heat recovery system

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HEAT RECOVERY SYSTEM OPTIMIZATION

0. BUILT KPI STRUCTRE AND CHOOSE PROJECTS

EAC # 4

P-machineSpecific KPIs:

GJ/t, exhaust

heat recovery,

kWh/t

Tactical level 2GJ/day recovered

EAC # 1

dirty steamt stm/MWh,

% valve opening

to preheater,

heating tower

temp, …

EAC # 2

TMP reboilerGJ/GJ

WW make-up

Preheater

efficiency

Pressure diff.

Tactical level 1Total GJ/day consumed – Total energy cost in $/month

Heat recovery EACs Users EACs

EAC # 3

TMPSpecific KPIs:

GJ/t, reject

exhaust recov.,

kWh/t

Operational levelT dirty steam/MWH - % reboiler efficiency

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KPI: Ton of dirty Steam/MWH of refining energy

1. DEFINE THE KPI AND SET THE TARGET

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HRS performance

Operation

losses and vent of dirty

steamdata

circuit temperature

data

fouling data

Users

header pressure

data

types of user data

Design

capacity

safety valves

refiners connected

2. IDENTIFY POSSIBLE ROOTCAUSES THROUGH

BRAINSTORMING SESSION WITH OPERATORS

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3. BUILD MODELS TO EXPLAIN AND TO IDENTIFY

OPTIMAL RULES OF OPERATION

1

1

2

Best performance

when dirty steam

valve is open <15%

and heating tower

outlet temp is >85 °C

1Most of the bad

performance

occurs when dirty

steam valve is open

more than 15%

2

Even when those

conditions are not met,

there’s alternatives

3

3

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4. ADAPT AND IMPLEMENT THE MODELS AND RULES

IN OPERATORS ENVIRONMENT

Actua

l

value

of KPI

Predicted regimes based on 3+ process variables

> 1.1

< 1.1

KPI>1.1 KPI<1.1

A: Performance is good and we know

why

C: Performance is good “but we do not know why”

B: Performance is bad “but we do not know why”

D: Performance is bad and we know

why

Insight to solve the problem

1. CO pre-heater > 15%

2. Temp heating tower < 84,5°C

Previously unseen situation!

Operator alerts energy team

for more investigations

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IMMEDIATE AND SUSTAINABLE BENEFITS

$600,000/YR OF RECURRENT ENERGY COST SAVINGS

Project duration = 3-4 months

Cumulative gain

Beginning of unexpected

drift

Period of “unexpected”

higher performanceImmediate results of data analysis: new operation rules for higher process

efficiency

Unexpected end of the drift

data analysis

Sustainable gain

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USER CASE #3

P&P - Papermachine

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Paper machine – Consumption of steam per ton of paperPAPER MACHINE ENERGY OPTIMIZATION

The causes for variability in

steam usage is not clear

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Paper machine – Consumption of steam per ton of paperWHAT IS THE IMPACT OF THIS ON MY COSTS?

Low consumption

Medium consumption

Peaks of consumption

≈ + 3 $/t

≈ + 3.6 $/t

Step 1: Quantifying variability

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ISSUE TREE FOR PM VARIABILITY

Steam consumption at PM6

Steam consumption at PM3 silo

Steam consumption at PM6 dryers

Temperature setpoint

PM circuit temperature

Furnish mix temperature

Kraft temperature

Groundwood temperature

Broke temperature

Furnish ratio

water make-up temperature

FW make-up

WW make-up

Showers

Make-up flows

Make-up temperature

Make-up flows

Make-up temperature

Shower water flows

Shower water temperature

Preheating

FW temperature

Recirculation of used water to

showers

Paper production

Drying efficiency

Water to evaporate

Basis weight

Paper production

Moisture target at reel

Drainage

Stock temperature

Stock

freeness

Pressing

Press load

Steam box

Dryer pressure setpoints

Dryer pressure differencials

Number of can in operation

Dryer temperature

Step 2: Brainstorm rootacauses

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0

5

10

15

20

25

30

A B C D E F G H I J K L M N O P

%

Parameters

Pareto chartStep 3: Rootcause data analysis

SO WHAT… WHAT CAN WE DO ABOUT IT?

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Speed

CO silo

Steam

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+ stock temp <140 °C

WW heating valve opening > 44%

Speed < 2400 fpm

Paper machine – Consumption of steam per ton of paper

$500,000 recurrent savings

NOW WE CAN TAKE CLEAR ACTIONS

Step 4: Take actions

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THANK YOU!

Visit: www.myenergymaestro.com

Sebastien Lafourcade I slafourcade@pepite.ca I +1-5124-571-9118