Rational Use Of Energy

RATIONAL ENERGY USE AND WASTE MINIMIZATION GOALS BASED ON THE

USE OF PRODUCTION DATA

ENRIQUE POSADA

INDISA S.A. Medellín, Colombiawww.indisa.com.co

[email protected]

R'07 World Congress

Recovery of Materials and Energy for Resource Efficiency

Contents• Bases for this study.• How energy and resources use are

typically reported in Colombia.• Methodology for rational use of

appropriate specific energy consumption indicators for targeting and monitoring.

• Generalization of this methodology based on results obtained in a study involving 21 industrial plants in Colombia.

• Conclusions.

Bases for this study• The author works for INDISA, an

engineering consulting company.• In 2006 the author visited over 30

companies in Colombia and acquired information on their energy and resources use.

• This work was a part of energy audits sponsored by a utilities company to benefit its customers.

Bases for this study• Plenty and variety of data was

collected.• It offered a unique opportunity for

the generalization of ideas previously developed and tested by the author.

• The potential for savings is large.• This potential offers a clear

opportunity to help reduce global warming, at a very low cost.

How energy and resources use are typically reported in

Colombia• All visited companies gather enough

information useful to establish meaningful resources use indicators.

• All the companies use the data to calculate resources costs for their general processes.

• A minority of the companies have the means to measure resources consumption for individual operations


Colombia• About 2/3 of the companies import the

data to M.S. Excel sheets and calculate specific energy use indicators.

• About 35 % of the companies plot the indicators vs. time.

• These companies usually set goals associated with the indicators, based on fixed numbers, but without any clear attempts to correlate them with production levels or scientific/technological basis.


Colombia• The typical use the companies make of

the data is to tabulate energy consumptions and costs vs. production for certain periods of time.

• The data is usually plotted using bar graphs.

• Only in few cases dispersion graphs are used.


ColombiaProduction

020.00040.00060.00080.000

100.000120.000140.000

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

Prod

uctio

n, K

gs

Natural Gas

0200400600800

1.0001.2001.4001.6001.800

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

Nat

ural

gas

use

, MB

TU


Colombia• Only a couple of companies correlate

energy data with production.

• In no case the specific energy consumption indicators are used to establish rational energy use or energy saving goals.

• The dispersion of the data was not deliberately studied or used for any purpose.


Colombia

Example with a plot of energy data correlated with production

KWh vs TON SEPTIEMBRE - OCTUBRE 2005

y = 24,332x + 23443R2 = 0,822

55.000

57.000

59.000

61.000

63.000

65.000

67.000

69.000

1.400 1.500 1.600 1.700 1.800 1.900TON

KWh

Methodology for rational use of specific energy consumption

indicators • Calculation of specific consumptions,

e.g. electricity in kWh/Ton produced and fuel consumption per ton produced.

• They allow us to be objective and draw useful conclusions and establish goals for savings and rational energy use.


indicators

Plot of the indicator vs. production rate.

KWh/TON vs TON SEPTIEMBRE - OCTUBRE 2005

y = -0,0088x + 53,2R2 = 0,633

343536373839404142

1.400 1.500 1.600 1.700 1.800 1.900

Daily Production, TON

Indi

cato

r, Sp

ecifi

c el

ectr

ic

cons

umpt

ion,

KW

h/TO

N


indicators Specific consumption usually lowers as production rate increases.

Condition

At average producti

on

At maximu

m producti

on

At minimu

m producti

onProduction tons 1.680 1.825 1.422Specific consumption KWh/ton 38,3 36,0 41,1Cost of electricity US $/ton 3,37 3,16 3,61Savings at high production rates % 6,13Extra cost at low production rates % 7,26


indicators • Three data regions can be identified.• The first set is the near average data.

An average data line can be drawn using averaging techniques.

• The second set consists of the data with lower specific consumptions.

• The third set corresponds to the data with higher specific consumptions.


indicators

Three data regions

Zones and criteria lines for indicator -SEPT-OCT 2005

34

36

38

40

42

1.400 1.500 1.600 1.700 1.800 1.900Daily Production, TON

Indi

cato

r, K

Wh/

TON

Average line Goal lineAlarm line Data near averageData high consumption Data low consumption


indicators • The “Alarm line” indicates the extremes

of inefficiency under current operating procedures. Paying attention to current ways of operation will lead to identification of inefficiency causes.

• Even more useful, it is the line of lower

consumptions, i.e. the “goal line”, which indicates the points of more efficient operation that are currently occurring.


indicators Analysis of data to establish goals

Average production tons 1.680Specific consumption at average production KWh/ton 38,34Cost of electricity US $/ton 3,37Possible average savings % 3,5Electricity cost US $/KWh 0,088Possible savings US $/ton 0,118Possible yearly savings (300 days per year) US $/year 59.382


indicators • Try to operate at points of higher

efficiencies, based on logistics, programming, sales and a good knowledge of the best operating points for the process.

• Attain the energy consumption reductions set by the goal line, by internal auditing and paying attention to the operation.

• There is usually no need of investment in additional equipment.


indicators • Take into account that specific energy

consumptions goals depend on production rates.

• A table of production rate related goals should be prepared and used as a base for targeting and monitoring. Fixed goals will not give the real sense of what is happening.

• Complement with historical monitoring of production rates, costs with the monitoring of the historical behavior of the indicator.


indicators Practical ways of attaining goals

• Form a responsible team . • Train the team in techniques of

continuous improvement, group work, analysis of opportunities and strategies.

• Establish appropriate specific indicators for the plant as a whole and also for specific equipment and processes, supported by adequate monitoring of associated production and energy variables.


indicators Practical ways of attaining goals

• At least once a week, the behavior of the indicators should be analyzed.

• Propose actions and bring them to practice.

• Gather and stimulate ideas from the involved personnel.

• Let the results, targets and benefits be known by all parties.

Generalization of this methodology• General view of the results obtained

from energy consumption data from 21 companies in Colombia.

• Information was converted into dimensionless numbers, so that different ways of measuring rates and specific consumptions could be compared.

Generalization of this methodology

The dimensionless numbers are:

Indicator of production rate =Rate / Average rate

Indicator of specific consumption =

Specific consumption / Average specific consumption.


Examples with some of the 27 plots drawn with the dimensionless numbers

0,700

0,800

0,900

1,000

1,100

1,200

1,300

0,000 0,500 1,000 1,500

Datos Bajos Altos Ajuste

0,6

0,7

0,8

0,9

1,0

1,1

1,2

1,3

1,4

1,5

0,6 0,7 0,8 0,9 1,0 1,1 1,2 1,3 1,4 1,5


0,4

0,6

0,8

1,0

1,2

1,4

1,6

0,4 0,6 0,8 1,0 1,2 1,4 1,6


0,4

0,6

0,8

1,0

1,2

1,4

1,6

1,8

0,2 0,4 0,6 0,8 1,0 1,2 1,4 1,6 1,8


0,4

0,6

0,8

1,0

1,2

1,4

1,6

0,6 0,8 1,0 1,2 1,4 1,6


0,6

0,7

0,8

0,9

1,0

1,1

1,2

1,3

1,4

0,8 0,9 1,0 1,1 1,2 1,3


0,6

0,7

0,8

0,9

1,0

1,1

1,2

0,8 0,9 1,0 1,1 1,2 1,3


0,6

0,8

1,0

1,2

1,4

0,6 0,8 1,0 1,2 1,4


0,0

0,5

1,0

1,5

2,0

2,5

0,0 0,5 1,0 1,5 2,0



General view of all the results obtained (electricity)

0,00

0,50

1,00

1,50

2,00

0,00 0,25 0,50 0,75 1,00 1,25 1,50 1,75

Production indicator (non dimensional)

Spec

ific

Con

sum

ptio

n in

dica

tor

(non

di

men

siona

l)

Data of 27 processes which use electricitySpecific consumption Indicator, mediumSpecific consumption Indicator, lowSpecific consumption Indicator, high


General view of all the results obtained (natural gas)

0,00

0,50

1,00

1,50

2,00

0,00 0,25 0,50 0,75 1,00 1,25 1,50 1,75

production indicator (non dimensional)

Spec

ific

cons

umpt

ion

indi

cato

r (n

on

dim

ensio

nal)

Data of 8 processes which use natural gasSpecific consumption Indicator, mediumSpecific consumption Indicator, lowSpecific consumption Indicator, high


Potential savings (%) vs. production indicator for the studied sample

0

4

8

12

16

20

0,00 0,25 0,50 0,75 1,00 1,25 1,50 1,75Production indicator (non dimensional)

Pote

ntia

l ave

rage

sav

ings

, %

Natural gas Electricity

Generalization of this methodologyResults of the correlations between indicators for the studied sample

Characteristic Minimum Maximum Average

Higher value for the production indicator 1,02 1,66 1,28

Lower value for the production indicator 0,25 0,92 0,67

Range of production indicator, % 16,04 135,20 60,82

Standard deviation of production data, % 6,71 42,38 17,50

Higher value for the specific consumption indicator 1,05 1,93 1,31

Lower value for the specific consumption indicator 0,41 0,96 0,81

Range of specific consumption indicator, % 8,45 133,02 49,63

Standard deviation of specific consumption indicator, % 3,80 45,65 12,48

Potential electric energy savings at average production, % 1,58 26,40 8,62

Electric energy savings at average production, US $/year 2.335 898.357 129.165

Slope of linear correlation between indicators -0,998 -0,026 -0,520

Intersection of line of correlation 1,026 2,00 1,52

Correlation factor R2 of line 0,007 0,96 0,52

Electricity in 27 processes

Generalization of this methodologyResults of the correlations between indicators for the studied sample Characteristic Minimum Maximum Average

Higher value for the production indicator 1,08 1,51 1,22

Lower value for the production indicator 0,34 0,92 0,73

Range of production indicator, % 16,04 117,31 48,90

Standard deviation of production data, % 6,98 42,38 15,45

Higher value for the specific consumption indicator 1,11 1,83 1,42

Lower value for the specific consumption indicator 0,28 0,90 0,72

Range of specific consumption indicator, % 29,54 154,81 69,83

Standard deviation of specific consumption indicator, % 8,80 53,05 20,86

Potential natural gas savings at average production, % 3,65 44,92 13,50

Natural gas savings at average production, US $/year 5.094 470.000 136.878

Slope of linear correlation between indicators -1,94 -0,50 -1,08

Intersection of line of correlation 1,50 2,94 2,08

Correlation factor R2 0,16 0,90 0,60

Natural gas in 8 processes

Generalization of this methodologyPrediction of potential savings at average production

y = 0,1426x + 1,5376R2 = 0,7368

y = 0,2685x - 5,2478R2 = 0,8039

05

101520253035404550

0 50 100 150 200

Range of specific consumptions, % of medium value

Perc

enta

ge o

f pos

sible

savi

ngs

at a

vera

ge p

rodu

ctio

n ra

te

Average electricity data for 27 processesAverage gas natural data for 8 processes

Conclusions • The proposed methodology for data

analysis offers a more rational approach to rational energy use, as consumption goals can be set in relation to production goals.

• It requires a more comprehensive and technical analysis, but it will result in larger savings and a better knowledge of the process.

• In principle, no investments need to be considered.

Conclusions • This study, involving 21 well established

companies in Colombia, found average potential savings of 8,6 % for electricity, and 13.5% for natural gas.

• These savings could be attained with operative practices, contributing to the national programs on global warming and sustainable development.

• It is possible to predict potential savings by converting data into dimensionless variables.

Conclusions • This methodology can be extended to

rationalization of other resources use, e.g. water, raw materials and to recycling and waste minimization programs.

• Although many companies in Colombia

apparently know the general principles behind this methodology, they do not use their data for the purposes stated on this presentation.

Thank you very much for your kind attention

Rational Use Of Energy

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