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


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


AHU versus energy consump&on driving factors versus change frequency

•  Weather •  Room dimensions •  Room insula@on


•  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???


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?


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


And now… the OPTICLIM tool 1/9

•  Room features: –  Dimensions –  Setpoints…



•  Energy features: –  Dimensions –  Setpoints…



•  Everything about AHU itself…

Blowing fan

Exhaust fan

Controlled room(s)

Energy recovery



•  Everything about energy recovery, if any…

Blowing fan

Exhaust fan

Controlled room(s)

Energy recovery



•  Everything about energy recovery, if any…



•  Introducing a change



•  Assessing the effects of the change



•  Assessing the effects of simultaneous changes



•  But some&mes only one change is worth many others…


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


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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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


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!


Who did what?

1100 plus lines as for sole calcula&on

Louis Blavier built en&rely the code for all calcula&on related topics


Next step

•  Weather •  Room dimensions •  Room insula@on


•  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


REMEMBER

Today: most of these items are constant. Could we beaer op&mize the control?

Next step as an example


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


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

Thank you for your aaen&on

Time for ques&ons awer a good snap?


Presentations & Public Speaking

ScilabTEC 2015 - Sanofi