RATIONAL ENERGY USE AND WASTE MINIMIZATION GOALS BASED ON THE
USE OF PRODUCTION DATA
ENRIQUE POSADA
INDISA S.A. Medellín, Colombiawww.indisa.com.co
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
How energy and resources use are typically reported in
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.
How energy and resources use are typically reported in
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.
How energy and resources use are typically reported in
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
How energy and resources use are typically reported in
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.
How energy and resources use are typically reported in
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.
Methodology for rational use of specific energy consumption
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
Methodology for rational use of specific energy consumption
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
Methodology for rational use of specific energy consumption
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.
Methodology for rational use of specific energy consumption
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
Methodology for rational use of specific energy 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.
Methodology for rational use of specific energy consumption
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
Methodology for rational use of specific energy consumption
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.
Methodology for rational use of specific energy consumption
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.
Methodology for rational use of specific energy consumption
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.
Methodology for rational use of specific energy consumption
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.
Generalization of this methodology
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
Datos Bajos Altos Ajuste
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
Datos Bajos Altos Ajuste
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
Datos Bajos Altos Ajuste
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
Datos Bajos Altos Ajuste
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
Datos Bajos Altos Ajuste
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
Datos Bajos Altos Ajuste
0,6
0,8
1,0
1,2
1,4
0,6 0,8 1,0 1,2 1,4
Datos Bajos Altos Ajuste
0,0
0,5
1,0
1,5
2,0
2,5
0,0 0,5 1,0 1,5 2,0
Datos Bajos Altos Ajuste
Generalization of this methodology
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
Generalization of this methodology
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
Generalization of this methodology
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
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