Audits to BenchmarkingIndustrial Energy Efficiency in India An academic perspective
Rangan BanerjeeDepartment of Energy Science and Engineering
IIT Bombay
Presentation at Ultrecht University on 24th May 2012
Timeline of important energy conservation initiatives in India
2
19
90
19
91
19
92
19
93
on
wa
rds
20
08
20
12
IIT Bombay
19
90
Working Group
to formulate
legislation on
Energy
Conservation
20
00
Energy
Conservation
Building Code
NMEE
approved
20
10
Energy
Conservation
Bill proposed
Energy
Conservation Act
passed
BEE formed
Benchmarking
Mining Railways
PCRA
Glass
Benchmarking
Cogen
Energy
Efficiency
ICMA
Chemical
Industry
Energy
Strategies
IGIDR
DSM
HT Industry
EIL
Combustion
Test Facility
Energy
Audits
Industrial
Energy
TERI
DEFINE AUDIT OBJECTIVES
QUESTIONNAIRE
REVIEW PAST RECORDS
WALK THROUGH / PLANT FAMILIARISATION
DATA REQUIREMENTS
MEASUREMENTS / TESTS
COMPUTE MASS / ENERGY BALANCES
ENUMERATE ENERGY CONSERVATION OPPORTUNITIES
EVALUATE ECOs
PRIORITISE RECOMMENDATIONS
DATA ANALYSIS
INSTALL MEASURES
4
Indian Examples
Energy Audit options Optimal response to time of use tariff
- process scheduling- cool storage
-cogeneration Benchmarking of glass furnace
Ceramic Tile Kiln Plant
Block Diagram for a Cement Plant
Sankey Diagram for a Cement Plant
Typical Audit Summary
OPTION ANNUAL SAVINGS
ENERGY MONEY(Rs.)
VIABILITY
WASTE HEAT RECOVERY
(FLUE GASES)
3.4 MW
19.3 Mus
- 105,500Nm3
2.9 CR
I : 8.9 CR
SPP 3.1 YR
IRR 40 %
NPV 15.8 CR
TOP GAS HEAT RECOVERY 2.7 MW
16.2 MUS
2.4 CR
I : 6 CR
SPP 2.5 YR
IRR 40 %
NPV 14.7 CR
AUTO
Y-- Y
97,000 kWh 1.45 LAKHS
I : 1.26 LAKHS
SPP < 1 YR
SPEED CONTROL
F.D. FAN
1,704,000 kWh 26 LAKHS -
FRP BLADES – COLLING
TOWER
11,100 kWh 0.17 LAKHS
I : 0.53 LAKHS
SPP 3.2 YRS
COOLING WATER –
OPERATIONS
1,125,000 kWh 17 LAKHS -
10
Practical Difficulties
Data Insufficiency
Data Inconsistency
Incomplete Evaluation of Options
Changes in External Environment
Schematic of HBI Plant
AIR TILES
OIL
EXHAUST
AIR
BURNER AIR
OIL
RAPID COOLING
COOLING
EXHAUST
BLAST
Schematic of
Glost Kiln in
Tile Factory
IT/hr
400 kg/hr 400 kg/hr
DG1
DG2
DG3
1000kVA
1000kVA
WASTE
HEAT
BOILER1
WASTE
HEAT
BOILER2
VARS
12oC
400TR
QR 7oC
1.8 T/hr
8ATA
BOILER 1 BOILER 2
1000kVA
Exhaust gases
15
DSM
OPTION
DEMAND
(MW)
ENERGY
(GWh)
PROG. COST
(MILLION Rs)
UTILITY
Rs/kW
CSE
p/kWh
PF 40.2 - 41 1000 -
TOD 110.4 - 190 1700 -
EAF 17.8 94.9 36 2000 20
CFL 1.1 4.7 3 2900 61
GHK 55.2 228.8 208 3800 86
HPSV 1.4 7.2 9 6500 -10
PUMPF 16.1 80.5 140 8700 77
EEM 9.3 46.4 83 9000 63
VSD 37.4 333.1 381 10200 105
VARS 11.2 79.1 119 10600 64
ELB 2.2 9.8 28 12400 100
TOTAL 302.3 885.3 1238 4100 82
COGEN 242.3 1358.0 1162 4800 76
TOTAL 544.6 2243.3 2400 4400 78
DSM HT Industrial Plan Results Maharashtra
0 50 100 150 200 250 3000
2000
4000
6000
8000
10000
12000
14000
Pf
Cost
of De
mand
Save
d (Rs
/kW)
Demand Saving (MW)
Least Cost Curve for DSM
T O D EAF
C F L
G H K
H P S V
P U M P F E E M
V S D
V A R S
E L B
17
Load curve of a typical day –MSEB(8/11/2000 source: WREB annual report-2001)
10260 MW9892 MW
6000
7000
8000
9000
10000
11000
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
De
ma
nd
, M
W
morning
peak Evening peak
18
SMEs in IndiaFuel Electricity (kWh) Water (m3)
Unit for Fuel
Average BestSaving
sAverage Best Savings Average Best
Saving
s
Breweries Fuel L/ kL Beer 58 44 24% 156 100 36% 9.1 7.9 13%
Beverage FuelL/ kL Beverage 9.35 5.29 43% - -
Tire Fuel Kg/t Finished
Tire210 162 23% 872 780 11% 8.4 4.8 43%
Textile Coal kg/1000 Mt 390 168 57% 195 44 77% 10.15 7.43 27%
SoyaCoal t / t Seed
Crushed63 47 25% 40 21 48% -
Rice branHusk t / t Seed
Crushed111 100 10% 27 25 7% -
Paper Coal kg/t Paper 360 259 28% - -
Specific energy consumption savings for SMEs in India
Source: CII and Forbes Marshall, 2005.
19
0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
0 20 40 60 80 100 120
Cumulative Electricity Savings (GWh)
Cost
of
Saved E
lectr
icit
y (
US
2005¢ /
kW
h)
12
34
56
7
8
9
101. Automation
2. Additives
3. Optimization
4. Energy Efficient Lighting
5. Energy Efficient Motor
6. Sizing
7. Variable Spped Drives
8. New Equipment
9. Equipment Modificiation Retrofits
10. Waste Heat Recovery
Conservation supply curve for electricity savings in the Indian cement industry
Source: Rane, 2009
20
Sample Industrial Load Profile (Mumbai)
21
Time of Use Tariff (MSEB-HT Ind., Jan 2002)
0
50
100
150
200
250
300
350
400
450
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hours
Pais
e/k
Wh
Off-peak
Peak
Partial Peak
Peak
22
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
23
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
24
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
25
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
26
Flour Mill
27
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
28
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
29
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
30
Cool Storage of Commercial Complex -underTOU 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)
31
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.
32
Willans Line
33
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
34
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
po
wer M
W
flat tariff TOU tariff
peak
period
demand
35
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 approachIndustrial 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
37
Glass furnace
Classification of furnace Type of firing (Cross fired / end
fired)
Raw material Batch material (like silica, soda
ash etc.)
Cullet (recycled glass)
Heat source Flame direct contact with glass
Minimum energy requirement Heating of raw material up to
reaction temperature
Endothermic heat of reaction for batch material
Doghouse (raw material
feeding section)
Throat (processed
glass outlet)
Melting end
Regenerator
Checker work
Working end
38
Modeling practices for glass furnace
Continuum Process model Commonly used Glass furnace process in
continuum equation
Three dimensional Navier-Stokes Equation and Hottel’s zone method for radiation
Process models used mainly troubleshooting and screening variables
Limitations of process models
Data intensive inputs
Needs specialized skills and computational facilities to use
Energy performance not studied
Not easy to link operating parameters and impact on energy performance
39
Approach for study
Overall energy and mass balance
Study of operating glass furnaces
Identifying key operating variables
Analyzing time series data of key operating
variables present in existing instrumentation
Conducting measurements for operating parameters
not captured in existing instrumentation
Literature search for furnace modelling
Refining assumptions and empirical relationships
with experimental measurements
Developing mathematical furnace models with
simplified assumptions for sub-processes
Solving these models for operating variables
Coupling models for understanding overall performance
Establishing relationship between dependent and
independent variables empirically and analytically
Comparing measured parameters and model result
Conducting parametric of variables using validated models
Identifying areas for energy performance improvement and
optimal operating strategy
40
Control volume
Combustion Space
Molten glass
Fuel
Batch Glass
Regenerator
Exhaust Gas Combustion Air
Control
Volume 1
Control
Volume 2
Control
Volume 3
, , , , ,,100
, , ,
( )obh bh g l wall g g g g rk bh f bh f w w latw C
g sensi g rk bh f w
m h Q Q m h m h m h m h h
Q Q Q Q
, , , , , , , , , , , ,
, , ,,
fu comb air nonreg noncomb air nonreg air air comb reg air comb reg l wall comb g tot f f
fu air reg l reg fair nonreg
m CV m m h m h Q Q m h
Q Q QQ
, , , , , , , , , , , , , , , , , , ,f tot in f in air leak reg air leak f tot out f out l wall reg air comb reg air comb reg out air comb reg inm h m h m h Q m h h
Eq. 1
Eq. 2
Eq. 3
41
Mass balance of furnace
Input streams
Batch material
Cullet (recycled glass)
Raw material
Moisture
Fuel
Combustion air (from regenerator)
Air leakage (Any air other than inlet from regenerator)
Output streams
Molten glass
Cullet
Glass from raw material
Flue gas to regenerator
Combustion products
Glass reaction products
Water vapors
Air (Not reacted in combustion)
Flue gas leakage from furnace
42
Mass balance estimation
Estimation of flue gas formation
Based on stoichiometric
Calculation of combustion
Products of combustion
Air leakage
No methodology for estimation in
literature
Moisture in batch
Based on % in batch
Products of glass reaction
Based on stoichiometric
Calculation of glass
Species in furnace flue gas
CO2
H2O
SO2
O2
N2
Oxygen % in
flue gas (v/v
dry basis)
Used as indicator for
excess air control
43
Air leakage estimation
Furnace operates positive pressure Air leakage in local
negative pressure area
Air leakage due to higher pressure on air side
Air supplied for atomization and flame length control
Air for fuel atomization
/ flame control during
firing and tip cooling
air during non firing
Air induced by jet
effect of burner
Combustion air from
regenerator
Air leakage from
furnace joints
Air leakage
from flux line
cooling
Glass melt
44
Energy balance for furnace
Input streams Energy from fuel
Energy from preheated combustion air
Energy from batch material
Energy from air leakage
Output streams Energy carried in glass
Heat of reaction
Sensible heat of glass
Energy carried in flue gas
Energy for air leakage
Energy for batch gases
Energy for moisture
Energy for combustion air
Energy loss from walls
Surface heat loss from walls
Radiation losses (due to opening)
45
Energy balance glass melt
Heat of reaction for glass
Heat carried by glass
Heat carried by batch gas
Heat carried away
by glass
Heat carried by
batch gases and
moisture
Endothermic heat of
reaction for glass
formation
46
Furnace wall losses
Glass flow
direction
Flux line
Molten Glass
Zones along
furnace
sidewall depth
Zones along
furnace melter
sidewall length
Zones along furnace crown
and superstructure side
wall length
47
Furnace model input parameters
Design parameter
Design capacity of furnace
Melting area
Length to width ratio
Height of combustion volume
Refractory and insulation details
Operating parameters
Furnace draw
Type of fuel
Batch to cullet ratio
Moisture in batch
Furnace pressure
Oxygen at furnace outlet
Atomization pressure
Reversal time
Flux-line and burner tip cooling air pressure
48
Model flow diagram
Mass of air
Flue gas leakage
Oxygen % at
regenerator outlet
Desig
n
varia
ble
s
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
49
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
50
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
51
Model results: Target SEC
1.7 %
(63)
1.2%
(45)
10.5%
(39042.7 %
(1628)
1.6 %
(60)7 %
(262)
5.3 %
(196)
5.6 % (211)
23.5 %
(876)
40.5%
(1510)
69.6 %
(2597)
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 recovery
in air heating
100 %
(3730)
Energy
introduced
in furnace
140 %
(5240)
Heat carried in regenerator
from flue gas
Conclusions
Target SEC estimated for 16 industrial furnaces
Effect of furnace draw on target SEC is demonstrated
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
0
2000
4000
6000
8000
10000
12000
0 20 40 60 80 100 120 140 160 180 200 220 240 260 280
Draw (TPD)
Targ
et
SE
C (
kJ/k
g)
Generalized approach for model based benchmarking
Survey of existing
models of process Developing
experimentation
protocols
Study of actual process
operation (process audit)
Operating procedure
and practices
Control strategy and
instrumentation
Process constraints
Logbook parameters
Understanding basics
Defining system
boundary
Writing fundamental
equations governing
process
Decide assumptions
Identifying empirical
correlations for process
Model development Divide process into sub-
models
Identify input / output
parameters for sub-models
Identification of design and
operating variables
Developing linkage between
process parameters and
energy consumption
Experimentation
Validation of model
Refinement of model
Data from industrial
process
Usage of model
Target energy
estimation
Parametric analysis
Energy
intensive
process
54
Input Output Flow Diagram
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 emissions
CO, CO2, NOx
Dusts
Dewatering/pumpi
ng
Explosives
Waste/overburden
55
Shovel
(2.42%)
Energy usage Profileofan opencast coal mine; CIMFR study 2.5 million ton capacity
Dragline
(14.57%)Pumping
(17.85%)
Lighting
(3.01%)
Excavators(20.43%)
Dump trucks(32.52%)
Light vehicle(3.78%)
Coal handling(5.72%)
Total energy
395000 GJ(100%)
Coal handling
13%
Pumping41%
Draglines33%
Drills & Shovel
6%
Others including .
lighting7%
Electrical energy distribution pattern in Mine
Transportation58%
Excavation36%
Light vehicle
6%
Diesel consumption patternin mine
SFC= 0.152 GJ/ton
56
Variables affecting Specific fuel consumption (SFC) of Dump trucksOperating parameters
Pay Load
Distance between crusher & excavator
Speed of truck
Material handling rateMine environment
Wind speed
Mine gradient
Mine topography
Monsoon
Engine characteristics
Brake specific fuel consumption
MODEL
Speed of
Loaded
Dump truck
Speed of
Empty Dump
truck
OPTIMIZATION
Distance
Minimum SFC
Specific fuel
consumption
Control
input
Pay Load
Fuel
consumed
in idling
Load,
Unload time
Waiting
time
Information Flow Diagram
57
Pce
qd
Vce
Vce
1
4,5
27
17
WG
mf,idle
WL
Vec
17
21
WE
18
ttravel
mf,ec
mf,ce
SFCdump truck
x
Mf,ij
26
BF,ec
tload,UL
2425
td,cycle
Pec
BF,ce
L
20
23
tec
tce
15
16
19
twait
Vec
58
Variation of SFC with pay load and material handling
Variation of diesel consumption and SFC with Payload
for 65t dump truck
Variation of SFC with handling due to increase in speed for case
of 65t dump truck
59
Variation of SFC with handling for Single and multiple dump trucks and also with distance
Effect of multiple dump trucks on overall SFC
Variation of SFC with distance for 65t dump truck
60
Arun at Mahanand Dairy, Latur, India
61
Thermal Applications
Steel Reheating Furnace Raipur
Investment 37.5 lakhs, Annual
savings 30 lakhs , Simple Payback
period 1.25 years, IRR 80% (IITB,
Cosmos) (Rice Husk, wood) 1.25
Mkcals/hr
NARI, Sugarcane Leaves,
Bagasse, Ceramic Tile furnace 0.25
Mkcals/hr
Silk Drying – TERI, payback period
2.5 years
Carbon Dioxide Manufacture
Silk Drying – TERI
Steel Rolling
Mill
Reheating
Furnace
Raipur
1.25 Mkcal/hr
62
Biomethanation Plant example
Cattle dung, urban waste
High rate
Biomethanation
2.4 acres land
1 MW grid connected +
cogeneration
13.4 crores
UNDP-GEF
63
Biomass Gasifier Example
Arashi HiTech Biopower, Coimbatore
1 MW grid connected
100% producer gas engines
Two gasifiers – coconut shells, modified to include other biomass
Chilling producer gas with VARS operated on waste heat
Diary Industry – Case Study
Case Study – Existing Utilities
Boiler Capacity 4 Tph @ 10.5 kg/cm2
Furnace Oil Consumption 1700 – 2500 litres daily
Flat plate Collectors 288 nos
Connected Power Load 2500 kW
Running Power Load 1200 kW
Lighting Load 210 kW
Maximum Allowable Load 1250 kW
Assumed daily Load Curve for Dairy Case study
Present Schematics of Dairy Case Study
Renewable based Cogeneration Systems
Model ParametersInput•Turbine Entry Pressure•Superheat Temperature•Extraction Pressure•Electricity Load Curve•Process Heat Load Curve•Calorific value of Fuel•Type of Biomass used•Discount Rate•Life of proposed system
Output•Turbine Exit•Extraction Temperature•Daily Requirement of Biomass•Reduction in Emissions of carbon dioxide•Ratings of system components•Initial investment•Simple payback period•Net Present Value•Internal Rate of Return
Biomass Briquettes based system
Methodology and Results
Component Rating
Boiler8 Tph @ 20bar,
436ºC
Extraction Steam Turbine
1.5 MW
Condenser 3.6 MW
Biomass Briquetting Machine
1.8 Tph
Daily Biomass Requirement
32.02 tonnes
Reduction in CO2
emissions10.32 tonnes
Simple Payback Period 5.64 years
Internal Rate of Return 16.97 %
NOTE : Similar calculations were done for a biomass gasifier based system.
Non-metallic minerals
10%
Paper, pulp and print
6%
Food and tobacco
5%
Non-ferrous metals
3%
Machinery
4%
Textile and leather
2%
Mining and quarrying
2%
Construction
1%
Transport equipment
1%
Wood and wood products
1%
Iron and Steel
20%
Others
16%
Chemical and Petrochemical
29%
Industrial Energy Use Trend
Share of industrial final energy use by different sectors in 2005
WorldIndia
45%
0%
1%
1%
1%
1%
6%
7%
18%
20% Iron and Steel
Chemical and Petrochemical
Non-metallic minerals
Food and Tobacco
Paper, pulp an print
Textile and LeatherMining and Quarrying
Non-ferrous metals
Machinery
Others
Source: ETP, 2008
68
Sectors MnTons % share
Iron and Steel 103 31%
Cement 98 29%
Fertilizers (N+P2O5) 24 7%
Pulp and Paper 20 6%
Other Industries 89 27%
Total Industries 334 -
India – CO2 emission Industry – 2005
Production Estimates for 2030 - India
Industrial Sector Production (Million
Tons) 2005 2030 CAGR
Caustic soda 2.2 14.4 7.8%
Soda Ash 2.0 7.8 5.6%
Aluminium 0.9 5.9 7.8%
Finished Steel 44.5 358.4 8.7%
Cement 153 1158 8.4%
Fertilizers (N+P2O5) 16.0 25.3 1.8%
Pulp and Paper 7.0 48.6 8.1%
National Energy Map : Technology Vision for 2030, TERI, India
70
Summing Up
Examples – illustrate variety of optimisation/ simulation models for energy sector
Decision context and model formulation critical
Reality check - Applicability
Generalisation important but..
Data intensity and uncertainty
71
Acknowledgment
Balkrishna Surve
Project Assistant
Ashok S.
Ph.D - 2003
J. Raghuram, Shaleen Khurana, Mahendra Rane
Santanu B.
Faculty
Lalit K Sahoo
(Ph.D.)
Vishal S.
Ph.D. - 2008
U.N. Gaitonde
Faculty
Tejal Kanitkar
(Ph.D.)
Rahul Bhat
(M.Tech.)
Dr. Ajay Mathur
DG-BEE, India
72
References
Ashok.S and R. Banerjee , “An Optimisation model for Industrial Load management”, IEEE Trans on Power Systems, Vol.16, No. 4, Nov.2001, pp 879-884.
Ashok S and R. Banerjee , Optimal Operation of Industrial Cogeneration for Load Management, IEEE Trans on Power Systems, Vol. 18, No. 2, May, 2003.
Ashok.S and R.Banerjee, Optimal cool storage capacity for load management , Energy, Vol. 28, pp 115-126, 2003.
Vishal S, U.N.Gaitonde,R Banerjee., “Model based energy benchmarking for glass furnace”, Energy Conversion and Management, Vol.48, pp 2718-2738, 2007.
J.K. Parikh , B.S.Reddy and R.Banerjee, Planning for Demand Side Management in the Electricity sector, Tata McGraw Hill , New Delhi,1994.
Rane, M., 2009: Impact of Demand Side Management on Power Planning. M.Tech. Diss., Department of Energy Science and Engineering, Indian Institute of Technology, Bombay, India.
CII and Forbes Marshall Study, 2005: Energy Conservation-Time to Get Specific. Confederation of Indian Industries (CII), Forbes Marshall, Pune, India.
Raghu Ram, J., Banerjee, R., “Energy and cogeneration targeting for a sugar factory,” Applied Thermal Engineering, (23)12, 1567-1575, August 2003.
Khurana, S., Banerjee, R., Gaitonde, U. N., “Energy balance and cogeneration for a cement plant,” Applied Thermal Engineering, (22)5, 485-494, April 2002.
Sahoo, L. K., Bandyopadhyay, S., and Banerjee, R., “Energy Performance of Dump Trucks in Opencast Mine.” In Proceedings of ECOS 2010, Lausanne, Switzerland, June 14-17, 2010.
Kanitkar, T., and Banerjee, R., “Power Sector Planning in India,” Journal of Economic Policy and Research, 7(1), 1-23, October, 2011.
Email: [email protected] Thank you
