1. Slide | 1

2. WHO ARE WE?
• 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
Slide | 2

3. OUR EXPERIENCE WITH EMIS IN PULP AND PAPER
(OR MT&R, M&V, ISO50001…)
Participation in 2 EMIS
Energy Blitz at implementation in
mills with different
15+ mills situation, motivation,
and culture
with significant and
Energy audit in sustainable cost
reductions
20+ mills High level
with focus on data monitoring models
availability & quality,
monitoring capability and
implemented in
performance gap
5+ mills
+ Several ongoing projects in N-A and Europe Slide | 3

4. MANAGING
CHANGE
4 KEY DRIVERS FOR ENERGY PERFORMANCE
What prevent us to take
action and sustain the Best practice we have seen
gain?
Operation is Address impact on production and quality
production-oriented • Leverage process data to bring facts
• Set flexible and gradual rules
Problem solving culture Adopt a continuous improvement vision
is CAPEX-oriented • Optimization projects (OPEX)
• Secure ROI with energy management
Lots of data but lack of Cascade of KPI with adaptive targets
relevant information • Different KPI for each level of decision
• Multivariate analysis to set relevant target
Operators are not Top-down approach, bottom up implementation
empowered • Give operators practical tools for decision
support & troubleshooting tools
• Involve them at every step of the projectSlide | 4

5. NO ACCOUNTABILITY  NO RESULTS
NO ACTIONABLE PARAMETERS  NO ACCOUNTABILITY
Mill manager
Management – kWh/t total
Pulp plant Utility
Papermachin
manager – manager –
Staff e manager –
kWh/t pulp kWh/t power
kWh/t PM
plant plant
Classical Papermachin
approaches Operation e surintendent
– kWh/t PM
do not bring
decision tools
in control PM operator –
PM operator – PM operator –
room kWh/t
kWh/t Press kWh/t Drying
Forming
section & finishing
section
Slide | 5

6. MISSING LINK BETWEEN ENERGY STUDY AND
ENERGY MANAGEMENT
ENERGY ENERGY
OPTIMIZATION MANAGEMENT
PROJECTS SYSTEMS
Are How to sustain the
recommendations gains and take
really applied and actions to continue
maintained? to improve
Slide | 6

7. SUCCESSFUL IMPLEMENTATION IS A MIX OF PROCESS
EXPERTISE, TECHNOLOGY AND PEOPLE ENGAGEMENT
The right tool to the right person at the right time
Slide | 7

8. MORE THAN A TYPICAL OPTIMIZATION PROJECT
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)
Optimization project
Optimization
on high potential area
of the mill
projectof the mill
of the mill
Slide | 8

9. 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
Slide | 9

10. RULE #1: LEVERAGE EXISTING INFORMATION SYSTEMS
AND CONTINUOUS IMPROVEMENT STRUCTURE
Impact of decision on day-to-day energy cost
Historia
Level of Exce-
Managers
ERP Intranet n (e.g. DCS
decision based
PI)
Management X X X
Supervisors
Staff X X X
Operators
Operators X X X
Slide | 10

11. RULES #2: TOP DOWN APPROACH,
BOTTOM UP IMPLEMENTATION
Top
management:
Impact of decision on day-to-day energy cost
global view on
Management Gain in $$$ cost control
Managers
(month) GJ saved
Operation: Maintenance:
Staff (weekly) GJ saved HEX efficiency
Supervisors
Average GJ/T Screen uptime
Operation GJ / ton Control room:
vs. target focus on
(daily– hour)
Operators
actionable
parameters
Pressure Fresh water
Kraft pulp
setpoint per valve to WW
temperature
grade chest
Slide | 11

12. BOTTOM UP: GIVE DECISION TOOLS TO OPERATORS
SO THEY CAN TAKE ACTIONS
Predicted regimes based on 3+ process variables
KPI>1.1 KPI<1.1
A: Performance is C: Performance is
> 1.1 good and we know good “but we do
Actua why not know why”
l
value
B: Performance is D: Performance is
of KPI
< 1.1 bad “but we do bad and we know
not know why” why
Previously unseen situation! Insight to solve the problem
Operator alerts energy team 1. CO pre-heater > 15%
for more investigations 2. Temp heating tower < 84,5°C
Slide | 12

13. RULE #3: CHOOSE YOUR BATTLE
Normandy, 6 June 1944
Slide | 13

14. CLASSICAL EMIS IMPLEMENTATION SCHEME…
cashflow
PRESSURE planned
ON
CASHFLOW HIGH RISK
AND OF
RESOURCES PUSHBACK reality
“let’s implement, the
system will do the rest…”
implementation
Upfront investment:
measurements, IT, software, services
+ cost of internal resources
Slide | 14

15. IMPLEMENTATION BY SUCCESSIVE PROJECTS PROVIDES
MORE BUY-IN WHILE USING LESS RESOURCES
cashflow
Kickoff project sub-project #2 sub-project #3
PROGRESSIVE
AND PLANNED
BETTER
IMPLEMEN-
CHANCE OF
TATION
OPERATOR
BUY-IN
implementation
Upfront investment minimized:
focus on area with high potential, local
resources, integration in existing
systems, gain controlled and monitored
Slide | 15

16. NOT ONLY
ENERGY
PROJECT, IT’S
TYPICAL ENERGY MAESTRO PROJECT
CHANGE
MANAGEMENT!
Kick off session Data analysis Implementation Implementation
• KPI structure • Exploration preparation • Operators training
• Workshops with • Rootcause • Test and • Stakeholder
operators and analysis validation of the training
stakeholders (multivariate data model off line • Closing session
• Process analysis) • Programming of • Follow up plan
understanding • Modeling equations and
• Data collection dashboard
• Reporting
structure
Immediate actions
taken based on • Better knowledge •Awareness
performance gap of operation •Capability building
analysis • Optimization rules of plant people
of the process •First decisions,
$$$
$$$ first savings
$$$
Slide | 16

17. 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
Slide | 17

18. USER CASE #1
Chemicals – Steam network
Slide | 18

19. STEAM NETWORK OPTIMIZATION AT A
PHOSPHORIC ACID PLANT
• 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
Slide | 19

20. USER CASE #2
P&P - Heat recovery system
Slide | 20

21. HEAT RECOVERY SYSTEM OPTIMIZATION
0. BUILT KPI STRUCTRE AND CHOOSE PROJECTS
Tactical level 1
Total GJ/day consumed – Total energy cost in $/month
Tactical level 2
GJ/day recovered
Operational level
T dirty steam/MWH - % reboiler efficiency
Heat recovery EACs Users EACs
EAC # 1 EAC # 2 EAC # 3 EAC # 4
dirty steam TMP reboiler TMP P-machine
t stm/MWh, GJ/GJ Specific KPIs: Specific KPIs:
% valve opening WW make-up GJ/t, reject GJ/t, exhaust
to preheater, Preheater exhaust recov., heat recovery,
heating tower efficiency kWh/t kWh/t
temp, … Pressure diff.
Slide | 21

22. 1. DEFINE THE KPI AND SET THE TARGET
KPI: Ton of dirty Steam/MWH of refining energy
Slide | 22

23. 2. IDENTIFY POSSIBLE ROOTCAUSES THROUGH
BRAINSTORMING SESSION WITH OPERATORS
losses and
vent of dirty data
steam
circuit
Operation data
temperature
fouling data
header
data
pressure
HRS Users
performance
types of user data
capacity
Design safety valves
refiners
connected Slide | 23

24. 3. BUILD MODELS TO EXPLAIN AND TO IDENTIFY
OPTIMAL RULES OF OPERATION
1 Best performance
when dirty steam 2 Most of the bad
valve is open <15% performance
and heating tower occurs when dirty
outlet temp is >85 °C steam valve is open
more than 15%
3 Even when those
conditions are not met, 1 2
there’s alternatives
1
3
Slide | 24

25. 4. ADAPT AND IMPLEMENT THE MODELS AND RULES
IN OPERATORS ENVIRONMENT
Predicted regimes based on 3+ process variables
KPI>1.1 KPI<1.1
A: Performance is C: Performance is
> 1.1 good and we know good “but we do
Actua why not know why”
l
value
B: Performance is D: Performance is
of KPI
< 1.1 bad “but we do bad and we know
not know why” why
Previously unseen situation! Insight to solve the problem
Operator alerts energy team 1. CO pre-heater > 15%
for more investigations 2. Temp heating tower < 84,5°C
Slide | 25

26. IMMEDIATE AND SUSTAINABLE BENEFITS
$600,000/YR OF RECURRENT ENERGY COST SAVINGS
Sustainable gain
Unexpected end
of the drift
data analysis
Period of
“unexpected”
higher performance
Immediate results of data
analysis: new operation
rules for higher process
Cumulative efficiency
gain
Beginning of
unexpected
drift
Project duration = 3-4 months Slide | 26

27. USER CASE #3
P&P - Papermachine
Slide | 27

28. Paper machine – Consumption of steam per ton of paper
PAPER MACHINE ENERGY OPTIMIZATION
The causes for variability in
steam usage is not clear
Slide | 28

29. Paper machine – Consumption IMPACT OF THIS ON MY COSTS?
WHAT IS THE of steam per ton of paper
Step 1: Quantifying variability
Peaks of consumption
Medium consumption
≈ + 3.6 $/t
≈ + 3 $/t
Low consumption
Slide | 29

30. ISSUE TREE FOR PM VARIABILITY
Kraft
Step 2: Brainstorm rootacauses temperature
Groundwood
temperature
Furnish mix
temperature
Broke
Temperature temperature
setpoint
Steam consumption
at PM3 silo Furnish ratio
PM circuit
temperature
Steam consumption Make-up flows
at PM6
FW make-up
Make-up
temperature
Make-up flows
water make-up
temperature WW make-up
Make-up
temperature
Shower water Preheating
flows
Paper Showers
FW temperature
production Shower water
temperature
Paper Recirculation of
production Basis weight used water to
showers
Moisture
target at reel
Water to Stock
evaporate temperature
Drainage
Stock
freeness
Steam consumption
at PM6 dryers Press load
Pressing
Steam box
Dryer pressure
setpoints
Dryer pressure
differencials
Drying
efficiency
Dryer temperature
Number of can in
operation Slide | 30

31. SO WHAT… WHAT CAN WE DO ABOUT IT?
Step 3: Rootcause data analysis
Pareto chart
%
30
25
20
15
10
5
0
A B C D E F G H I J K L M N O P
Parameters
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32. Steam
CO silo
Speed
Slide | 32

33. Paper machine – Consumption of steam per ton of paper
NOW WE CAN TAKE CLEAR ACTIONS
+ stock temp
<140 °C
Speed < 2400 fpm
Step 4: Take actions
WW heating valve
opening > 44%
$500,000 recurrent savings
Slide | 33

34. THANK YOU!
Visit: www.myenergymaestro.com
Sebastien Lafourcade I slafourcade@pepite.ca I +1-5124-571-9118
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