Energy Systems Modelling

Rangan Banerjee

Forbes Marshall Chair Professor

Department of Energy Science and Engineering

I

IIT Bombay

Invited Talk at National Research Workshop on Energy Technologies - 12 Jan 2017 IIEST, Shibpur, West Bengal, India

What is an Energy System?

Energy Flow Diagram

PRIMARY ENERGY

ENERGY CONVERSION FACILITY

SECONDARY ENERGY

TRANSMISSION & DISTRN. SYSTEM

FINAL ENERGY

ENERGY UTILISATION EQUIPMENT & SYSTEMS

USEFUL ENERGY

END USE ACTIVITIES

(ENERGY SERVICES)

COAL, OIL, SOLAR, GAS

POWER PLANT, REFINERIES

REFINED OIL, ELECTRICITY

RAILWAYS, TRUCKS, PIPELINES

WHAT CONSUMERS BUY DELIVERED ENERGY

AUTOMOBILE, LAMP, MOTOR, STOVE

MOTIVE POWER RADIANT ENERGY

DISTANCE TRAVELLED, ILLUMINATION,COOKED FOOD etc..

Decision Types / Perspectives

System selection Yes/No Best possible amongst options

System / Component Design

Decide Operating Strategy

Decide Policies

End Users

Manufacturers

Utility

Society / Government

Others

Model

What is a model?

What models are you familiar with?

Why do we need a model?

Model - Definition

n – a replica of somethinga representation of something to be

constructed (e.g. model of building)

v –to produce a representation or simulation of, to construct or fashion in imitation of

A model is a representation of reality

Steps in Model DevelopmentObjectives

Analyse Problem Situation

Decide Evaluation Criteria

Establish Relationships Problem Synthesis

Testing and Validation

Make InferencesPrescribe Actions

Judging the model

Ability to give correct inputs for the decision it is intended for

Reliability

Accuracy of prediction

Computational effort/ cost

Computational time

Duckworth- Lewis Method

A Fair Method for Resetting the Target in Interrupted One-Day Cricket Matches

Author(s): F. C. Duckworth and A. J. Lewis

Source: The Journal of the Operational Research Society, Vol. 49, No. 3 (Mar., 1998), pp.220-227

Duckworth Lewis Method

Two resources – overs and wickets

Z0(w) = asymptotic average total score from last 10-w wickets, b(w) exponential decay constant

Duckworth Lewis Method

What is a system?

System – collection of components whose performance parameters are inter-related

System simulation – means observing a synthetic system that imitates the performance of a real system.

Inputs

Performance characteristics of components

Properties of working substances

Conditions imposed by surroundings / environment

Optimisation

The aim of princes and philosophers is to improve.

Leibniz 1702

Man’s longing for perfection led to the theory of optimization

Beightler and Wilder

General characteristics of optimisation

1. Necessary condition – undetermined system

2. Objective

3. Competing Influences

4. Restrictions

Competing Influences

Criteria

Cost - Initial Cost, Operating Cost,

Life Cycle Cost

Reliability-Availability, Unmet Energy

Emissions - Local, Global

Sustainability

Equity

Satisficing

RATIONAL DECISION-MAKING IN BUSINESSORGANIZATIONSNobel Memorial Lecture, 8 December, 1978byHERBERT A. SIMONCarnegie-Mellon University *, Pittsburgh, Pennsylvania, USA

On How to Decide What to DoAuthor(s): Herbert A. SimonSource: The Bell Journal of Economics, Vol. 9, No. 2 (Autumn, 1978), pp. 494-507

A Behavioral Model of Rational ChoiceHerbert A. SimonThe Quarterly Journal of Economics, Vol. 69, No. 1. (Feb., 1955), pp. 99-118.

MODELLING & ANALYSIS

EQUIPMENT DESIGN

SYSTEM DESIGN / ANALYSIS

SYSTEM INTEGRATION

POTENTIAL ESTIMATION

ENERGY ANALYSIS

POLICY MODELLING

LFR

WIND

SWH Solar PV

ENERGY EFF.

SOLAR POWER

GLASS MINING

ISOL. SYST.

RENEWABLE ENERGY

DSM + REN

BIO H2 JATROPHA

ENERGY ECONOMY

MODEL

BUILDING

Equipment Design/Analysis Solar Thermal-LFR Modelling and analysis of receiver –heat loss, steady state hydrothermal analysis of absorber tubes

Experimental validation

System Design/Analysis Energy Efficiency: Model Based Benchmarking

Glass Furnace -Model flow diagram

Mass of air

Flue gas leakage

Oxygen % at

regenerator outlet

De

sig

n

va

riab

les

Guess for total

heat added

Fuel

stoichiometric

calculation

Glass reaction

calculation

Furnace air / flue gas

leakage calculations

Gap in flux line Gap near burner

Furnace operating pressure

Cooling air velocity

Number of burner

Burner air nozzle

diameter

Furnace design capacity

Melting area

Furnace design details

Color of glass

Furnace geometry

Air leakage

Regenerator

calculationFlue gas outlet

temperature

Heat loss from flue

gas

Heat loss from

regenerator wall

Oxygen % at furnace

outlet

Combustion

zone

stoichiometric

calculation

Furnace

wall lossesFurnace operating

characteristics

Heat of

reaction and

heat carried

by glass

Mass of flue gas

Heat loss from

furnace area wall

Gas from glass

reaction

Raw material composition

Furnace

geometry

calculation

Furnace design

characteristics

Heat carried with glass

Heat of reaction for glass

Heat loss batch gas

Heat loss from batch

moisture

Total

heat

added

in

furnace

Fuel calculationFuel calorific

value

Fuel composition

Glass composition

Moisture in batch and

cullet

Cullet %

Glass draw

Fuel consumptionCombustion species

Heat loss from flue

gas leakage

Heat loss from air

leakage

Ambient conditions

Glass outlet

temperature

Port neck

Checkers

packing

Glass level

1

2

5

Manual damper for

airflow selection and

control

6

7

Diverter

damper

3

4

8

Measurement locations

Combustion air

Furnace measurement

Measurementlocation

Type of measurement

1Oxygen % , Pyrometer checkers surface temperature

2Oxygen %, Flue gas temperature

3Oxygen %, Flue gas temperature

4Oxygen %, Skin temperature

5Pyrometer checkers surface temperature

6Velocity of air at the suction of blower

7Outside wall temperature for crown and side wall

8Pyrometer glass surface temperature

25

Model results: Actual SEC

2.8%(118)

0.7%(30)69

1%(45)

9.7%(414)

38.2 % (1628)

2%(84)6.1%

(261)5% (212)

4.6% (198)

29.4%(1256)

33.8%(1485)

69% (2939)

Heat carried in glass

Furnace wall losses

Heat lost in moistureHeat of glass

reaction

Batch gas losses

Heat loss from furnace opening

Heat lost steel superstructure

Regenerator wall losses

Heat loss from flue gas

Heat lost in cold air ingress

Heat recovery in air heating

100%(4267)

Energy introduced in furnace

From fuel 134% (5752)

Heat carried in regenerator from flue gas

0

2000

4000

6000

8000

10000

12000

14000

16000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Furnace number

SE

C (

kJ

/kg

)

Target SEC Actual SEC

26

Input Output Flow Diagram-Mining

Drilling/Blasting

Excavation

Transportation

Crushing/Finishing

Storage yard/dispatch

MINING UNIT BOUNDARY

INPUTS OUTPUTS

Unexcavated ore

Water

Energy requirements

Electricity

Diesel

Others

Engine oil

Lubricating oil

Finished ore

Gas emissionsCO, CO2, NOx

Dusts

Dewatering/pumping

Explosives

Waste/overburden

Shovel

(2.42%)

Dragline

(14.57%) Pumping

(17.85%)

Lighting

(3.01%)

Light vehicle(3.78%)

Coal handling(5.72%)

Input

152 MJ/ton (100%)

Dump trucks(32.52%)

Excavators(20.43%)

27

Variation of SFC with pay load

Optimal value

M

OP

Q

Variation of material handled and SFC with speed

Comparison of Model result with actual

data

Mine Transport Model

Fuel saving potential In transport =17%

MODEL

Speed of

Loaded

Dump truck

Speed of

Empty Dump

truck

OPTIMIZATION

Distanc

e

Minimum SFC

Specific

fuel

consumptio

n

Contr

ol

input

Pay

Load

Fuel

consumed in

idling

Load,

Unload

timeWaiting

time

28

)(

)(

tQ

tWSWR

ore

r

)(

)(

tQ

tESEC

ore

pump

Water and energy assessment

IFD for water

and energy assessment

Volume Area model

a

w

re (t)

E (t) P (t)

h w (t) =d

S (t)

If

Ri

Surface water (9)

Pit Area

(3)

A (t)

el

Ground water (10)

Hw (t)

Radius of

influence (11)

Qin (t)

Total Water Inflow in mine

(16)

Qg (t)

Qs (t)

Water removal rate (17)

Wr (t)

Water storage

(20)d

As (t)

Hmax

Qr (t)

K

Water storage

(18, 19)

Pumping rate

(20, 22)

th

Pumping

power (23)

Qp (t)

H (t)

Energy

consumption

(24)

H (t)

Cumulative

volume (1)

Energy assessment model

Water assessment model

np (t)

qp

Mine

depth (2)

Vc (t)

c

b

t

t

Equivalent

radius (13, 14)

SEC

(29)

Alternate

method (5-8)

R r h

Note: The numbers in the IFD shows the equation numbers of the model

SWR

(28)

Qore (t)

k2

k1

29

WATER AND ENERGY ASSESSMENT RESULTS

Variation of energy, water and excavation index Variation of SEC and SWR with coal production

Water inflows and removal rate with timeSeasonal variations 2009-10(1) and 2010-11(2)

ILM Research Objective

Determine optimal response of industry for a specified time varying tariff –develop a general model applicable for different industries

Process Scheduling- Continuous/ Batch

Cool Storage

Cogeneration

Process Scheduling

Variable electricity cost normally not included

Flexibility in scheduling

Optimisation problem – Min Annual operating costs

Constraints – Demand, Storage and equipment

Models developed for continuous and batch processes (Illustrated for flour mill and mini steel plant)

Viable for Industry

Process Scheduling

Batch processes- batch time, quantity, charging, discharging, power demand variation (load cycles)

Raw material constraints, Allocation constraints, Storage constraints, Sequential Constraints, maintenance downtime

30 T MeltingArc furnace

Bar mill

Wire mill

40 T Melting Arc

furnace

St. steel Scrap mix or

Alloy steel scrap mix

Alloy steel

scrap mix

Convertor (only for

St Steel)

Ladle Arc

furnace

VD or VOD

station

Bloom caster

Billet caster

Bloom mill

ooo

ooo

Reheat furnace

Reheat furnace

Reheat

furnace

Wire products

for final finish

Rods, Bars for final

finish

Open store

Open store

Open store

Open store

Steel Plant Flow Diagram

Flour Mill

0

10

20

30

40

50

60

Time hours

Lo

ad

MW

Optimal with TOU tariff

Optimal with flat tariff

2 4 6 8 10 12 14 16 18 20 22 24

Steel Plant Optimal Response to TOU tariff

Process Scheduling Summary

Example Structure Results Saving

Flour Mill

Continuous

Linear, IP

120 variables

46 constraints

Flat- 2 shift - 25%store

TOU-3 shift

1%

6.4%

75%peak

reduction

Mini Steel Plant

Batch

Linear, IP

432 variables

630 constraints

Flat

TOU

Diff loading

8%

10%

50% peak reduction

Cool Storage

Cool Storage – Chilled water operate compressor during off-peak

Commercial case study (BSES MDC), Industrial case study (German Remedies)

Part load characteristics compressor,pumps

Non- linear problem – 96 variables, Quasi Newton Method

MD reduces from 208 kVA to 129 kVA, 10% reduction in peak co-incident demand, 6% bill saving

Cool Storage of Commercial Complex -under TOU tariff

129 kVA

208 kVA

0

50

100

150

200

250

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Time hours

kV

A

with optimal cool storage

Load following (without storage)

Cogeneration

Process Steam, Electricity load vary with time

Optimal Strategy depends on grid interconnection(parallel- only buying, buying/selling) and electricity,fuel prices

For given equipment configuration, optimal operating strategy can be determined

GT/ST/Diesel Engine – Part load characteristics –Non Linear

Illustrative example for petrochemical plant- shows variation in flat/TOU optimal.

Willans Line

LP Steam 5. 5 b, 180 oC

Gas turbine -1

Boiler

ST

PRDS-1

PRDS-3

Condenser

Deaerator

Process Load

Process Load

40 T/h

G

1

G

4

Process Load,

60 MW

BUS

Grid

7.52 MW

SHP Steam 100 bar,500o C

HP Steam 41b,400 oC

Fuel, LSHS

9.64 T/h

WHRB-1

Supp. Firing

LSHS 5.6 T/h

Stack

20 MW

Process Load,125 T/h

Process Load,150 T/h

MP Steam 20b, 300 oC

PRDS-2

Gas turbine -2

G

1

WHRB-2

Supp. Firing

LSHS 5.6 T/h

20 MW

Fuel, HSD

5.9 T/h

136 T/h

136 T/h

131.7 T/h12.5

MW

76.2 T/h60.6 T/h

117.1

T/h

40 T/h 49.5 T/h 16.2 T/h

20 T/h

40 T/h

53.4 T/h

Make up water,357 T/h

Cogeneration Example

Import Power from Grid with Cogeneration for a Petrochemical Plant

11 MW

17.6

21.6

00

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time hours

Imp

ort p

ow

er M

W

flat tariff TOU tariff

peak

period

demand

Export power to the grid with Cogeneration for a Petrochemical Plant

0

10

20

30

40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time hours

Exp

ort P

ow

er M

W

flat tariff TOU tariff

9.7 MW

Peak

period

demand

- Integrated approach

Industrial Load Management

Operating

cost

structure

Optimal

operating

strategy of

captive/

cogeneration

plant

Captive/Cogeneration

power model

Grid tariff, fuel costs,

Grid conditions

Modified process

demand profile

Process demand profile,

Cooling electric load

profile, Steam load profile

Process load

model Air conditioning

(cooling) load model

Optimal process load

schedule Optimal cool storage

Plant/

measured

input data

Modified cooling electric

load profile

Modified steam load

profile for process

related loads

TEAM SHUNYASOLAR DECATHLON EUROPE 2014

45

House in Versailles – 26th June, 2014

Team Shunya

70 students 13 disciplines 12 faculty

House assembly process

Team Shunya’s Solar House “H Naught”

Integration of traditional knowledge with modern simulations

2 bedrooms with modular furniture

Steel based prefab construction

Insulated wall panels for thermal comfort

Extensive daylighting provision

Synergy of Vastu Shastra and Passive Solar Architecture

Position 1st pref. 2nd pref.

Drawing and Dining Room

E N

Kitchen SE NW

Master Bedroom SW S

Kids Bedroom NW SW

Main Entrance NE E

Bathrooms NW W

House Architecture

Simulation and design

Usual Non AC for Mumbai (54 kWh/m2-year)Usual AC for Mumbai (68 kWh/m2-year)

Energy saving opportunities

Energy efficiency opportunities exist as thermal and lighting loads high

Use of simulation tools for window sizing, insulation sizing, overhangs and daylighting

Load reduction by 65% (AC case) & 63% (non AC case) for Mumbai

Electrical Energy Balance

Generation-Consumption Profile for Competition Day 1, June 30th 2014

52

0

500

1000

1500

2000

2500

3000

3500

4000

Demand (Wh) Supply (Wh)

-4

-3

-2

-1

0

1

2

3

4

Pow

er i

n k

W

Feed in Grid Load PV

Generation Consumption Profile for the competition duration

Performance:• PV Supply = 281 kWh, Demand = 146 kWh• Net Energy Positive, 135 kWh in 12 days• Energy payback analysis% for PV = 2.4 years

Contest Criteria: Load Consumption per unit area Positive Electrical Energy Balance Temporary Generation-

Consumption Correlation Maintaining Network Load State Managing Power Peaks

Thermal comfort modeling of naturally ventilated buildings

by increasing the air movement through ceiling fan

Typical arrangement of fan and house with a person

Fan speed m/s

Temperature ˚C Relative humidity Thermal comfort index

PMV-PPDSource: Son. H. Lou et al

Survey 10 Existing Kitchens

Analysis of Kitchen Designs

Problem to be addressed Inefficient and time consuming conventional retrofitting process

Oversizing and ineffective operation of building HVAC system

Aim of project

Integrate energy audit and building simulation in retrofitting process and quantify the benefits as compared to conventional process

Energy efficiency retrofit using building simulation

Modelling software used:

Simulation results

0

500

1000

1500

2000

2500

0 4 8 12 16 20 24

Hours

Po

wer g

en

erate

d in

MW January

June

September

Mean value

0

200

400

600

800

1000

1200

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Months

Win

d e

ne

rgy g

en

era

ted

(M

U)

Hourly variation of wind

power

Monthly variation of

wind energy generated

Installed wind power

Wind energy

generated

0

500

1000

1500

2000

2500

3000

3500

4000

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Year

Insta

lled

cap

acit

y (

MW

)

0

1000

2000

3000

4000

5000

6000

En

erg

y g

en

era

ted

(M

U)

System Integration – Wind – Tamil Nadu

Input n and

n discrete

wind

capacities

Select major sites

Extrapolated hourly wind

power generation

Effective load curve

Divide load curve into 100

MW bins

Record number of hours in

each bin

Calculate effective base and

peak load savings from

different LDCs obtained

Evaluate for n discrete wind

capacities

Sum up to obtain annual load

duration curve

Frequency distribution of load

over the year

Hourly

wind

speed

data

Wind

turbine

characteris

ticsInstalled

capacity of

wind power

Hourly

load

curve

Impacts on LDC

Target area

Weather data, area details

Identification and Classification of different end uses by sector (i)

Residential (1)Hospital (2) Nursing

Homes (3)Hotels

(4)Others (5)

POTENTIAL OF SWHS IN TARGET AREA

Technical Potential (m2 of collector area)

Economic Potential (m2 of collector area)

Market Potential (m2 of collector area)

Energy Savings Potential (kWh/year)

Load Shaving Potential (kWh/ hour for a monthly average day)

* Factors affecting the adoption/sizing of solar water heating systems

Sub-class (i, j)

Classification based on factors* (j)

Single end use point

Potential

Base load

for heating

Electricity/ fuel savings

Economic

viability

Price of

electricity

Investment

for SWHS

Technical

PotentialSWHS

capacity

Constraint: roof

area availability

Capacity of

SWHS

(Collector area)

Target

Auxiliary

heating

Single end use point

Micro simulation using

TRNSYS

Hot water

usage pattern

Weather

data

SIMULATION

Auxiliary heating requirement

No. of end

use points

Technical

Potential

Economic

Potential

Economic

Constraint

Market

Potential

Constraint: market

acceptance

Potential for end use sector (i = 1) Potential

for i = 2

Potential

for i = 3

Potential

for i = 4

Potential

for i = 5

Model for Potential Estimation of Target Area

Load Curve Representing Energy Requirement for Water Heating

0

100

200

300

400

500

600

700

800

900

1000

0 2 4 6 8 10 12 14 16 18 20 22 24Hour of day

En

erg

y C

on

sum

pti

on

(M

W)

Typical day of January

Typical day of May

Total Consumption =760 MWh/day

Total Consumption = 390 MWh/day

53%

Electricity Consumption for water heating of Pune

Total Consumption =14300 MWh/day

Total Consumption = 13900 MWh/day

Total Electricity Consumption of Pune

Diffusion of SWH

0

50

100

150

200

250

300

1990 2010 2030 2050 2070 2090

Year

So

lar W

ate

r H

ea

tin

g C

ap

acit

y (

co

llecto

r a

rea

in

mil

lio

n

sq. m

.)..

Actual installed (million sq. m.)Potential 140 million sq. m.Potential 60 million sq. m.Potential 200 million sq. m.Extrapolated Potential (million sq.m.)

Potential = 60 million m 2

Potential = 140 million m 2

Potential = 200 million m 2

Estimated Potential in

2092 = 199 million m2

PLAN LAYOUT

64

65

A portion of the ELU map of Ward A of MCGM

Corresponding Satellite Imagery for the area from Google Earth

Analyzed in QGIS 1.8.0To determine-Building Footprint Ratios- Usable PV AreasFor Sample Buildings

66

0

0.5

1

1.5

2

2.5

0:0

1-

1:0

0

1:0

1-

2:0

0

2:0

1-

3:0

0

3:0

1-

4:0

0

4:0

1-

5:0

0

5:0

1-

6:0

0

6:0

1-

7:0

0

7:0

1-

8:0

0

8:0

1-

9:0

0

9:0

1-1

0:0

0

10

:01

-11:0

0

11

:01

-12:0

0

12

:01

-13:0

0

13

:01

-14:0

0

14

:01

-15:0

0

15

:01

-16:0

0

16

:01

-17:0

0

17

:01

-18:0

0

18

:01

-19:0

0

19

:01

-20:0

0

20

:01

-21:0

0

21

:01

-22:0

0

22

:01

-23:0

0

23

:01

-24:0

0

MU

s

Jan, 2014 Typical Load Profile vs PV Generation

1-AxisTracking @Highest eff.1-AxixTracking @Median eff.19 deg. FixedTilt @ Highesteff.19 deg. FixedTilt @ Medianeff.Typical HourlyDemand, Jan2014

0.115

0.125

0.135

0.145

0.155

0.165

0.175

0.185

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Capacity Factor for Mumbai

1-Axis Tracking

Fixed Tilt @ 19deg.

Annual Averagewith 1-AxisTrackingAnnual Averagewith Fised Tilt @19 deg.

Summing Up

Models – representation of reality

Can help in more efficient component, system design

Can help identify future sustainable routes, assess impacts

Blend of modelling, prototypes – sustainable systems of the future

Improved decision making, better choices

Value judgements- trade-offs between criteria

Optimising/ Satisficing

Acknowledgment

Balkrishna Surve

Project Assistant

Arun P.

Ph.D. - 2009

Santanu B.

Faculty

Lalit K Sahoo

(Ph.D.)

Vishal S.

Faculty

Doolla Suryanarayana

Faculty

Tejal Kanitkar

(Ph.D.) Rhythm Singh

(Ph.D)

Indu Pillai

Ph.D.-2008

Suneet Singh

Faculty

K. Aravind Kumar

(Ph.D) Jay Dhariwal

(Ph.D)

National Solar Thermal Power Project – Team Team Shunya – IITB / Rachana Sansad Thank you

Brijesh Pandey

(M.Sc-Ph.D)

Ramit Debnath

(M.Tech)

Rahul Katyal

(M.Tech)S.Ashok

Faculty-NIT Calicut

References

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Sahoo, S.S., Singh, S., Banerjee, R., “Analysis of heat losses from a trapezoidal cavity used for Linear Fresnel Reflector system,” Solar Energy, (86)5, 1313-1322, May 2012

Sardeshpande, V., Anthony, R., Gaitonde, U.N.,and Banerjee, R., “Performance analysis for glass furnace regenerator,” Applied Energy, 88(12), 4451-4458, December 2011

Vishal S, U.N.Gaitonde,R Banerjee., “Model based energy benchmarking for glass furnace”, Energy Conversion and Management, Vol.48, pp 2718-2738, 2007.

Arun P., Santanu Bandyopadhyay and R. Banerjee, ‘Sizing curve for design of isolated power systems’, Energy for Sustainable Development,Volume XI, No. 4, December 2007.

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Sahoo L. K., Bandyopadhyay S., Banerjee R. , Benchmarking energy consumption for dump trucks in mines, Applied Energy, 113, 1382-1396, 2014.

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P., Arun, Bandyopadhyay, S., and Banerjee, R., “Sizing curve for design of isolated power systems,” Energy for Sustainable Development, (11) 4, 21-28, December 2007.

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