E-Cube Energy as part of the GEF-UNIDO-BEE project was awarded assignment to demonstrate the use of Energy Information Management and Analytics System for Foundry Sector and explore how #EnergyAnalytics could be used to further enhance assessment and reporting of energy performance both at unit and cluster level. This presentation shows the normalization and rankings done for Induction Furnace type Foundry units using the data collected through the EIMAS deployed at participating units.

1. ENPI BENCHMARKING IN FOUNDRY
SECTOR UNITS AT COIMBATORE &
BELGAUM
ENERGY EFFICIENCY & RENEWABLE ENERGY IN SELECTED MSME
CLUSTERS IN INDIA.
www.foundry.en-view.com

2. About GEF-UNIDO-BEE Project
 UNIDO is currently implementing a GEF funded project titled „Energy Efficiency and
Renewable energy in selected MSME Clusters in India‟. Bureau of Energy Efficiency
(BEE) is the executing agency for the same.
 The project focuses on 12 clusters in 5 sectors, namely
Brass, Ceramics, dairy, Foundry and Hand tools.
 The three clusters in the foundry sector are Belgaum Coimbatore and Indore.
 The project proposed 29 demonstration projects across these 5 sectors and 12
clusters, including the demonstration project titled ‘Energy Information Management
and Analytics System for Foundries in Belgaum and Coimbatore.

3. Contents
 Factors considered for Benchmarking
 Methodology & Improvisations
 Findings &Adjustment Factors
 Rankings before and after Normalization
 Limitations
 Way Forward

4. Factors Considered For Benchmarking
Raw
Material
Mix
Product
Mix
Age of
Furnace
Capacity
of Furnace
Skilled
WorkForce

5. Methodology
• Forecasting production of
FG and SG grades based
on the Raw Material
(Universal) list.
Forecasting
Production
• Based on the ratio of FG
and SG type product
energy consumption is
forecasted and used for
SEC calculation.
Forecasting
Energy • Adjustment factors for
Capacity, Product
Mix, Age of the Furnace
are considered to find out
the Normalised SEC in
respect to cluster behavior.
Normalization

6. Improvisations/Hacks
Using independent
factors from the
considered factors for
normalization
Data
Manipulation
Cluster
Method
This was done to
overcome the lack of
data for all intervals
Statistical methodology
used to clean data
before analyzing.
Worked on inter-dependent
factors to create factors like
Capacity/Age, Skilled
Workforce / 500 kg/Hr
Capacity

7. Findings
Age Capacity
~8 Years ~750 Kg/Hr
SEC (Cluster Standard)
0.578 Gcal/Ton
53%
45%
2%
Product Mix %
FG SG Others
Note: Only for Induction Furnace Foundries

8. Adjustment Factor Product Mix
FG% SG% Other% Exp SEC
Baseline 53% 45% 2% 0.578
UNIT XYZ 21% 79% 0% 0.608
Adjustment
Factor
-5.19%
Adjustment Factor is the % change in the SEC expected if the unit was to have the
product mix similar to that of the cluster!

9. Adjustment Factor Age
Age Exp SEC
Baseline 8 Years 0.534
UNIT XYZ 10 0.556
Adjustment Factor -3.95%
Adjustment Factor is the % change in the SEC expected if the unit was to have the
age same as that of the cluster average.

10. Adjustment Factor Capacity
Installed Capacity Exp SEC
Baseline 750 Kg/Hr 0.533
UNIT XYZ 1000 Kg/Hr 0.520
Adjustment Factor 2.58%
Adjustment Factor is the % change in the SEC expected if the unit was to have the
installed capacity same as that of the cluster average.

11. Adjustment Factor Skilled Workforce
Skilled Workforce (Per 500
KG/Hr)
Exp SEC
Baseline 8 0.517
UNIT XYZ 7 0.547
Adjustment Factor -5.8%
Adjustment Factor is the % change in the SEC expected if the unit was to have the
same skilled workforce as that of the cluster average.

12. Total Adjustment Factor
-30.00%
-20.00%
-10.00%
0.00%
10.00%
20.00%
30.00%
40.00%
Unit IF1 Unit IF2 Unit IF3 Unit IF4 Unit IF5 Unit IF6 Unit IF7 Unit IF8 Unit IF9 Unit IF10Unit IF11Unit IF12Unit IF13Unit IF14Unit IF15Unit IF16
Adjustment Factor(Age) Adjustment Factor Skill Adjustment Factor Capacity Total Adjustment

13. Ranking Before & After Normalization
0
2
4
6
8
10
12
14
16
18
Unit IF14 Unit IF6 Unit IF9 Unit IF12 Unit IF16 Unit IF1 Unit IF7 Unit IF11 Unit IF3 Unit IF13 Unit IF4 Unit IF5 Unit IF10 Unit IF2 Unit IF15 Unit IF8
Rank After Normalisation Rank Normal

14. SEC vs Cluster Standard
0.35
0.45
0.55
0.65
0.75
0.85
0.95
Unit IF1 Unit IF2 Unit IF3 Unit IF4 Unit IF5 Unit IF6 Unit IF7 Unit IF8 Unit IF9 Unit IF10 Unit IF11 Unit IF12 Unit IF13 Unit IF14 Unit IF15 Unit IF16
Actual SEC Calculated SEC Normalised SEC Industry Standard

15. SEC Index Product Mix
0.86
1.00
0.96
0.88
0.88
0.85
0.87
0.89
0.91
0.93
0.95
0.97
0.99
1.01
FG 200 FG 150 FG 260 FG 250 FG 300
SEC Index (FG)

16. SEC Index Product Mix
0.64
0.66
0.71 0.72
1.00
0.71
0.58
0.50
0.60
0.70
0.80
0.90
1.00
1.10
SG 400_10 SG 400_15 SG 400_18 SG 450_10 SG 450_12 SG 500_7 SG 600_3
SEC Index (SG)

17. Limitations
 Limited “Volume” of data. Both in terms of horizontal and vertical spread.
 Inconsistency in “Data” especially the Raw Material Details.
 No major energy efficiency projects were implemented during the course of
EIMAS installation, hence couldn‟t be tracked in respect to changes in EnPIs.

18. Way Forward!
 Zeroed down on a considerably relevant methodology that can be
scaled in terms of making it dynamic and also including other
factors.
 Increased usage of EIMAs across sector will help in widening data
base and also the consistency of the same.
 Access to EnPIs will foster increased competitive spirit on Energy
Efficiency and also provide insights that can help foundries look into
operational improvements.

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