Scilab-Tech- 26-June 2013-1

Official  publisher  &  professional  services  provider  of  Scilab,  Open  Source  So+ware  for  Numerical  Computa5on  &  Simula5on  

Jean-Pierre Bovée ScilabTec 26/6/2013Jean-pierre.bovee@sanofi.com

Our way to energy savings

How does Scilab contribute to this goal?

Agenda

• Understanding Energy Savings opportunities

• Focusing on Heating, Ventilation and Air-

Conditioning (HVAC):

– what it should cost (OPTICLIM: a simulation tool)

– What it actually costs

– How to cut related energy bill

• Next steps

• Conclusion

Understanding opportunities

A typical Air Handling Unit (AHU)

HVAC bill cutting schedule

Select an « easy » AHU

Site HVAC mapping

HVAC data collection

Easy: clear zone sharing with other AHUs, easy way to collect

real time data and further to change setpoints and working

schedule

Specifications of:• Room(s) • AHU Chillers and boilers � To populates simulation tool (OPTICLIM)

Populate OPTICLIM wih AHU collected data � get estimated

current energy consumption

Collect AHU real time data� Estimate energy

consumption

Based on this + site HVAC mapping, Identify energy � opportunities, using OPTICLIM

Matching± 20%

Question real data and actual AHU tuningNO

Validate with QA, HSE and Production

Implement and check actual energy bill reduction

A bad matching can originate from stuck valve, cabling error, poor control implementation, …

Monitor T°, RH, air flows and valves positions for heating and cooling valves

How Scilab contributes to energy savings

Estimate, Simulate, pre-select Savings opportunities

Check against current Data collected from the process

Confirm savings opportunities

Finalize , then run action plan

Check against current Data collected from the process

Scilab: Energy simulation (OPTICLIM)

Scilab: Energy monitoring

Scilab: Energy monitoring

Scilab: Energy simulation (OPTICLIM)

OBJECTIVES TOOLS

Air Handling Units

Real time Monitoring

o Atom module (Modbus over TCP/IP)

o We need to have a robust application: working with

Modbus over TCP/IP is a must.

o Modbus is the most common communication mode in

the industrial world

o Real time data collection: Scilab Team developped an

HVAC

what it should cost (OPTICLIM)

Objective

• Knowing this initial scenario

– AHU, boilers and chillers specifics

– Weather conditions

– Rooms specifics

– Working schedule

• Calculate expected consumption as for:

– Ventilation and auxiliaries

– Heating and cooling energy

• Simulate other scenario to reduce energy bill

• Select modifications versus payback

Some limits of the « Excel ware »

• Initially, OPTICLIM has been drafted using Excel.

• Lack of development good practicies from developpers

• With no seggregation between data and calculation, the application size

amounts to over 200 Mo (as of today)

• It could even increase with more to come weather data (10 years of data,

hour per hour)

• Maintenance might become a nightmare.

• Deployment across our organisation would no longer be sensible.

� Decision to migrate OPTICLIM as a Scilab application.

� « Exel ware », as an uncontrolled development, may turn a night mare …

| 10

Site HVAC initial Temperature mapping

20,5°C + 0,1°C

20°C + 0,1°C

21,7°C + 0,5°C19

,6°C

+ 0

,5°C

20,5

°C +

2°C

19,6°C + 0,1°C

19°C + 2°C

21°C + 0,1°C

19°C + 1°C

18°C + 2°C

20°C + 0,1°C

We notice:

• some quite unrealistic

setpoint tolerances

(0.1°C)

• setpoint discrepancies

that cannot be

explained by any

rationale related to

product considerations

HVAC

While checking OPTICLIM

Challenging rooms set points (T°, RH)

• First step: setpoints mapping

– Rooms

• T° and RH (if relevant) set points + control tolerance (paramount)

• Products manufactured in the room

– Question differences: why should identical product are manufactured

with different T° or RH setpoints

– Question Tolerances: ex � a tolerance of 0.1 °C is exactly

counterproductive

• Discuss with QA and Production

– Streamline the initial mapping (but do not change anything on the

AHU at this time)

| 12

HVAC site mapping: changing Temperature

setpoints at the weekend

19°C + 2°C

17°C + 6°C

19°C + 2°C

19°C + 3°C

19°C + 3°C

19°C + 2°C

19°C

+ 2

°C

19°C + 2°C

19°C

+ 3

°C

19°C + 3°C

19°C + 2°C

19°C + 2°C

19°C + 3°C

19°C + 3°C

19°C + 3°C

HVAC

speak with data

Make actual measurements on AHU(s)

• record these values over at least 3 days

• T°, HR, air flowrate(s) (current values and setpoints)

• setpoints applied to control valves (a source of big surprises…)

• understand ∆ between measured values and estimated values by OPTICLIM

• A control valve sometimes remain stuck

• Control systems can perform very poorly for various reasons

• tolerance is too small

• cabling errors on sensors or actuators

HVAC

speak with data

Start changing working conditions (AHU setpoints, reduced

mode, etc…)

• Estimate savings using OPTICLIM

• Apply the ones selected

• Measure actual savings

| 15

Identify energy � opportunities, using OPTICLIM

Step 1: changing Temperature setpoints at working hours

AHUInitial

T+∆t

New

T+∆t

Saving on

Air-heating

Saving on

Air-cooling

Total

savings (%)

total

savings(€)

501 20+0,1 20+2 -5,2% -62,1% -7,4% -953 €

505 20+0,1 20+2 -8,9% -53,0% -7,4% -3 378 €

506 20,5+2 20+2 -0,4% 18,1% 0,3% -459 €

508 20,5+0,1 20+2 -2,3% -54,2% -2,9% -563 €

509 21+0,1 20+2 -1,9% -37,4% -2,0% -1 338 €

510 21,7+0,5 20+2 -1,5% -23,5% -1,4% -1 033 €

511 19+2 20+2 -2,7% -33,5% -3,1% 213 €

517 19,6+0,5 20+2 -4,8% -57,4% -7,1% -1 073 €

518 19,6+0,1 20+2 -6,0% -68,8% -7,3% -868 €

519 21+0,1 20+2 -1,1% -35,7% -2,3% -445 €

525 19+1 20+2 -9,5% -32,2% -4,0% -2 161 €

526 19+2 20+2 -7,2% -60,8% -6,8% 256 €

527 20+2 20+2 0,0% 0,0% 0,0% 0 €

Σ of Expected annual savings from Temperature setpoint changes: - 11 802 €

Microsoft Excel Worksheet

| 16

Identify energy � opportunities, using OPTICLIM

Step 2: changing Temperature setpoints out of working hours

AHU

Over the

week

T+∆t

Weekend

T+∆t

Saving on

Air-heating

Saving on

Air-cooling

Total

savings (%)

total

savings(€)

501 20+2 17+6 -39,8% 10,7% -26,0% -2 472 €

505 20+2 17+6 -45,9% 9,6% -20,3% -5 671 €

506 20+2 17+6 -45,5% 10,6% -23,6% -4 891 €

508 20+2 17+6 -18,8% 7,5% -11,0% -1 336 €

509 20+2 17+6 -34,2% 8,7% -18,2% -3 655 €

510 20+2 17+6 -43,9% 9,9% -20,8% -2 166 €

511 20+2 17+6 -41,2% 8,5% -25,2% -3 396 €

517 20+2 17+6 -36,5% 12,5% -23,0% -4 540 €

518 20+2 17+6 -35,2% 10,2% -20,2% -2 336 €

519 20+2 17+6 -22,6% 17,1% -15,0% -812 €

525 20+2 17+6 -28,8% 9,3% -6,8% -1 153 €

526 20+2 17+6 -43,9% 10,5% -19,8% -2 901 €

527 20+2 17+6 -38,6% 11,2% -22,2% -3 328 €

Σ of Expected annual savings from Temperature changes out of working hours

-36,4% 10,4% -19,2% -38 657 €

| 17

Identify energy � opportunities, using OPTICLIM

Step 3: changing ventilation setpoints out of working hours

AHU over the

week Flow

rate

Weekend

Flow rate

Saving on

Air-

heating

Saving on

Air-cooling

Savings on

ventilation

Total

savings (%)

total

savings(€)

501 7255 5000 -19,3% -21,8% -24,9% -21,9% -1 467 €

505 23000 15000 -22,5% -5,8% -26,9% -24,3% -4 494 €

506 9370 5000 -37,7% -30,9% -37,6% -37,3% -5 275 €

508 6600 6600

509 11760 11760

510 8660 8660

511 10750 9500 -7,1% -5,0% -9,0% -7,9% -766 €

517 9900 7000 -22,6% -20,6% -23,5% -22,8% -3 380 €

518 6350 6350

519 1500 1500

525 16500 10000 -26,1% -6,7% -30,8% -28,3% -4 299 €

526 10500 7000 -24,1% -21,1% -26,5% -25,5% -2 843 €

527 9500 7000 -17,7% -18,7% -20,9% -19,5% -2 288 €

Σ of Expected annual savings from ventilation change: -24 812 €

• Temperature : 17°C.

• Tolerance : +6°CLe fichier excel

Σ of potentia

l savings: 70 k€ / year

So about 15% savings

| 18

Initial temperature setpoints at the week-end

19°C + 2°C

19°C + 2°C

19°C + 3°C

19°C + 3°C 19°C + 3°C

19°C + 2°C

19°C

+ 2

°C

19°C + 2°C

19°C

+ 3

°C

19°C + 3°C

19°C + 2°C

19°C + 2°C

19°C + 3°C

19°C + 3°C

19°C + 3°C

| 19

Speaking with data: Changing temperature

setpoints at the week-end � initial situation

Supplied air

extracted air

Cooling valve

| 20

Speaking with data: Changing temperature

setpoints at the week-end (17°C + 6°C)

Supplied air

extracted air

Cooling valve !

Beginning of the weekend

End of the weekend

Reason why it is so important to monitor control valves (heating and cooling)

What an unrealistic temperature tolerance (0.1 °C) can result in

Supplied air

extracted air

Cooling valve

heating valve

This situation is just TERRIBLE ���� heating and cooling valves oscillations result in an enormous energy wa ste

Reason why it is so important to monitor control valves (heating and cooling).Just enlarging

Supplied air

extracted air

Cooling valve heating

valve

Cutting Energy bill / Next steps

o At Sanofi global level

o Achieve migration of the simulation tool from Excel � Scilab

o Otherwise no effective deployment across our sites will ever happen

o Train all sites (> 100)

o At our site level (Lisieux)

o Check Excel migration with Scilab Team

o Deploy AHU monitoring

o Enhance AHU control using model based control (i.e. predictive control) � still 8 to 10% of potential savings

o Package this approach (simulation, monitoring, enhanced control) to pass it on to other sites

ConclusionHVAC: to conduct a wise energy bill reduction

o The right sequence appears as:

o Question HVAC in terms of

o Environment setpoints versus manufactured products requirements and

working schedule

o Actual control tuning (monitor setpoints AND actuators)

o Simulate changes (OPTICLIM)

o Speak with other business function: QA, Production, Energy management, …

o Implement changes having an acceptable payback, considering future energy

cost increase

o Do not stay dependant on equipment or services performing poorly

o Then and only then, think about changing boilers, chillers or energy production

equipment

And remember!

Scilab definitiv

ely helps achieve all of th

ese goals