Upload
philippe-mack
View
148
Download
5
Tags:
Embed Size (px)
Citation preview
Slide | 1
Slide | 2
• 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?
Slide | 3
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
Slide | 4
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
Slide | 5
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
Slide | 6
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
Slide | 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 | 8
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
Slide | 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 | 10
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
Slide | 11
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
Slide | 12
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
Slide | 13
RULE #3: CHOOSE YOUR BATTLE
Normandy, 6 June 1944
Slide | 14
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
Slide | 15
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
Slide | 16
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!
Slide | 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 – $250,000 / yr
• Identification of operation rules that ensure high efficiency
• Implementation of preventive maintenance tool to reduce power use
Slide | 18
USER CASE #1
Chemicals – Steam network
Slide | 19
• Culture change in the way
steam network is managed
• Expected gains: 1,2 M$
• 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
Slide | 20
USER CASE #2
P&P - Heat recovery system
Slide | 21
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
Slide | 22
KPI: Ton of dirty Steam/MWH of refining energy
1. DEFINE THE KPI AND SET THE TARGET
Slide | 23
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
Slide | 24
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
Slide | 25
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
Slide | 26
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
Slide | 27
USER CASE #3
P&P - Papermachine
Slide | 28
Paper machine – Consumption of steam per ton of paperPAPER MACHINE ENERGY OPTIMIZATION
The causes for variability in
steam usage is not clear
Slide | 29
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
Slide | 30
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
Slide | 31
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?
Slide | 32
Speed
CO silo
Steam
Slide | 33
+ 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
Slide | 34
THANK YOU!
Visit: www.myenergymaestro.com
Sebastien Lafourcade I [email protected] I +1-5124-571-9118