ScilabTEC 2015 - Sanofi
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Transcript of ScilabTEC 2015 - Sanofi
Calcula&ng air condi&oning costs and savings opportuni&es
ScilabTEC 2015
Jean-‐Pierre Bovée (Sanofi), Louis Blavier (re@red from Sanofi) mailto:jean-‐[email protected]
Energy challenge
• It is unlikely that energy costs will not resume increasing shortly
• Evidences of quick and brutal raises from various countries • Pharmaceu&cal sites: 2 categories
– Chemical sites: reac&ons, fermenta&onè most energy comes from gas
– Manufacturing sites: table&ng, filling and packaging lines è Air-‐Condi&oning (HVAC) amounts to 50% ± 5 of total energy costs • Ven&la&on itself amounts to about 50% of HVAC costs è 25% of total energy costs
• Manufacturing sites by far more numerous than chemical sites èmost efforts focus on HVAC energy reduc&on
Jean-‐Pierre Bovée, Louis Blavier 2
What HVAC means • Usually HVAC is made of:
– Energy produc&on (boilers / chillers) + Circula&ng fluids (heat and cold)
– Air Handling Units (AHU)
Blowing fan
Extrac@on fan
Controlled room(s)
Energy recovery
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AHU versus energy consump&on: some driving factors
• Weather • Energy costs (Gaz, Electricity, Carbon tax) • Room dimensions • Room insula&on
– Roof – Walls – Windows
• Air changing rate • Temperature set point • Hygrometry set point • Working calendar (working days, shutdown periods) • Heat produc&on efficiency • Cooling produc&on efficiency • Temperature control efficiency • AHU features: motors, fans, gearing • Air Recycling (as a percentage of total blown air) • Energy recovery
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AHU versus energy consump&on driving factors versus change frequency
• Weather • Room dimensions • Room insula@on
– Roof – Walls – Windows
• Energy costs (Gaz, Electricity, Carbon tax) • Air changing rate (as per regula@on) • Working calendar (working days, shutdown periods) • Heat produc@on efficiency • Temperature control efficiency • AHU features: motors, fans, gearing • Temperature set point • Hygrometry set point • Air Recycling (as a percentage of total blown air) • Energy recovery • Cooling produc@on efficiency
Change frequency From decades to centuries From years to decades Year Hour
Quite a significant number of factors!
What should I change???
Jean-‐Pierre Bovée, Louis Blavier 5
Air Handling Unit usual challenge what should be changed? when should it be changed? what is worth a change?
èto change or not to change?
• Site people FAQ: – What should be changed to deliver the best payback? – How to provide evidence of the payback? – What are the right priori&es?
– Purchasing says: nego&ate beaer energy contract – Technical folks (many) say: motors, chillers, boilers, insula&on, … – Quality says: as per regula&on… – Finance says: is produc&on required any longer?
Jean-‐Pierre Bovée, Louis Blavier 6
All previous considera&ons lead to imagine a tool…
…A tool that would allow for: • Predic&ng AHU power consump&on • Simula&ng AHU power consump&on versus all the above
driving factors • Delivering results in Kw/h and €
• Examples of expected answers: What if I change – out of working hours
• The temperature set point The insula&on of the roof • The supplied air flow and the recycling air flow rate
– The motors, fans or mechanical transmission – The energy recovery – The energy costs
AHU model factors Poten&al
savings
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And now… the OPTICLIM tool 1/9
• Room features: – Dimensions – Setpoints…
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And now… the OPTICLIM tool 2/9
• Energy features: – Dimensions – Setpoints…
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And now… the OPTICLIM tool 3/9
• Everything about AHU itself…
Blowing fan
Exhaust fan
Controlled room(s)
Energy recovery
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And now… the OPTICLIM tool 4/9
• Everything about energy recovery, if any…
Blowing fan
Exhaust fan
Controlled room(s)
Energy recovery
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And now… the OPTICLIM tool 5/9
• Everything about energy recovery, if any…
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And now… the OPTICLIM tool 7/9
• Assessing the effects of the change
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And now… the OPTICLIM tool 8/9
• Assessing the effects of simultaneous changes
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And now… the OPTICLIM tool 9/9
• But some&mes only one change is worth many others…
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How did we build this?
• Star&ng from a previous, bugged version, hardly understandable Excel version (200 Mo)
• Wri&ng new Func&on Requirement Specifica&ons • Discussing them with Scilab • Genng an offer on the final document • Developing • Tes&ng • Rolling out
8 months
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Did we reach the target?
• Users can assess very easily what can be undertaken to cut their energy bill
• Learning curve is very fast – Worldwide deployed, from Japan to Latam and North America
• Quite user friendly applica&on • Calcula&ons are more accurate (compared with Excel) • Maintenance is quite easier
– even for prototyping, Excel is no way a solu&on when complex calcula&ons are at stake
– A professional team supports further developments
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Did we reach the target?
• The applica&on is light: zipped = 6 Mo (<< 200 Mo) • Quite faster than the previous one (Excel based) • Data and code are segregated
– Data can be exchanged between users quite easily – Weather data can be locally managed
• Code modularity è easier roadmap • Mul&lingual applica&on, locally manageable • The applica&on is protected
– Code encryp&on – User authen&ca&on
• Easy version management and deployment
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Jean-‐Pierre Bovée, Louis Blavier 19
A bit of history • First tool based on Excel (2011) • Very heavy applica&on (200 Mo!) • Very manual Deployment è updates extremely painful • Only 2 languages • Impossible to integrate local weather data • Beyond a limit (quickly reached), EXCEL is no way an easy to debug tool!
• è decision made by end of 2013 to move to SCILAB
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Who did what? SCILAB Dream Team: Adeline Carnis, Charloae Hecquet, Dominique Callens and Antoine Elias developed all other coding features… quite a work! Jocelyn Lanusse and Claude Gomez brought their key support all along the project
Everything very carefully documented!
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Who did what?
1100 plus lines as for sole calcula&on
Louis Blavier built en&rely the code for all calcula&on related topics
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Next step
• Weather • Room dimensions • Room insula@on
– Roof – Walls – Windows
• Energy costs (Gaz, Electricity, Carbon tax) • Air changing rate (as per regula@on) • Working calendar (working days, shutdown periods) • Heat produc@on efficiency • Temperature control efficiency • AHU features: motors, fans, gearing • Temperature set point • Hygrometry set point • Air Recycling (as a percentage of total blown air) • Energy recovery • Cooling produc@on efficiency
Change frequency From decades to centuries From years to decades Year Hour
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REMEMBER
Today: most of these items are constant. Could we beaer op&mize the control?
Next step as an example
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Outside air Rela&ve humidity
Outside air temperature 20 °C
Enabling temperature setpoint to move with external temperature
Scilab assisted calcula&on and op&miza&on
Real &me, mul&variable control
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Hygrometry set point Air Recycling (as a percentage of total blown air) Energy recovery Cooling produc@on efficiency
Real @me outside air temperature and humidity Process
Model Energy
consump@on
Steepest descent with constraints
Best variable set to apply
AHU real @me
controller